From 71b17f4e956de4503cbdffc2a822bcea18ed85d1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Thu, 15 Feb 2018 16:48:29 +0100 Subject: [PATCH 01/13] large update gromov --- examples/plot_gromov.py | 16 ++- ot/gromov.py | 258 +++++++++++++++++++++++----------------- 2 files changed, 161 insertions(+), 113 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index d3f724c47..d42c21a3a 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -78,16 +78,26 @@ # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- - +#%% p = ot.unif(n_samples) q = ot.unif(n_samples) +ot.tic() +gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,verbose=True) +ot.toc() +ot.tic() +gw2,log2= ot.gromov.gromov_wasserstein0(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) +ot.toc() -gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) +ot.tic() +gw0,log0=ot.gromov.gw_lp(C1, C2, p, q, 'square_loss',log=True,verbose=True) +ot.toc() + + print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) pl.figure() -pl.imshow(gw, cmap='jet') +pl.imshow(gw2, cmap='jet') pl.colorbar() pl.show() diff --git a/ot/gromov.py b/ot/gromov.py index 20bf7ee42..a23303ab9 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -9,6 +9,7 @@ # Author: Erwan Vautier # Nicolas Courty +# Rémi Flamary # # License: MIT License @@ -16,40 +17,35 @@ from .bregman import sinkhorn from .utils import dist +from .optim import cg -def square_loss(a, b): - """ - Returns the value of L(a,b)=(1/2)*|a-b|^2 - """ - - return 0.5 * (a - b)**2 - - -def kl_loss(a, b): - """ - Returns the value of L(a,b)=a*log(a/b)-a+b - """ - - return a * np.log(a / b) - a + b +def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): + """ Return loss matrices and tensors for Gromov-Wasserstein fast computation - -def tensor_square_loss(C1, C2, T): - """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss + Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss function as the loss function of Gromow-Wasserstein discrepancy. + + The matrices are computed as described in Proposition 1 in [12] Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - T : A coupling between those two spaces + * C1 : Metric cost matrix in the source space + * C2 : Metric cost matrix in the target space + * T : A coupling between those two spaces The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : - f1(a)=(a^2)/2 - f2(b)=(b^2)/2 - h1(a)=a - h2(b)=b + * f1(a)=(a^2)/2 + * f2(b)=(b^2)/2 + * h1(a)=a + * h2(b)=b + + The kl-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : + * f1(a)=a*log(a)-a + * f2(b)=b + * h1(a)=a + * h2(b)=log(b) Parameters ---------- @@ -57,66 +53,77 @@ def tensor_square_loss(C1, C2, T): Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space - T : ndarray, shape (ns, nt) + T : ndarray, shape (ns, nt) Coupling between source and target spaces + p : ndarray, shape (ns,) + Returns ------- - tens : ndarray, shape (ns, nt) - \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - """ - - C1 = np.asarray(C1, dtype=np.float64) - C2 = np.asarray(C2, dtype=np.float64) - T = np.asarray(T, dtype=np.float64) - - def f1(a): - return (a**2) / 2 - - def f2(b): - return (b**2) / 2 - - def h1(a): - return a - - def h2(b): - return b - - tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens -= tens.min() - - return tens - + + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. -def tensor_kl_loss(C1, C2, T): """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss - function as the loss function of Gromow-Wasserstein discrepancy. - - Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - T : A coupling between those two spaces - The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : - L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : - f1(a)=a*log(a)-a - f2(b)=b - h1(a)=a - h2(b)=log(b) + if loss_fun == 'square_loss': + def f1(a): + return (a**2)/2 + def f2(b): + return (b**2)/2 + def h1(a): + return a + def h2(b): + return b + elif loss_fun == 'kl_loss': + def f1(a): + return a * np.log(a + 1e-15) - a + def f2(b): + return b + def h1(a): + return a + def h2(b): + return np.log(b + 1e-15) + + constC1=np.dot(np.dot(f1(C1),p.reshape(-1,1)), + np.ones(len(q)).reshape(1,-1)) + constC2=np.dot(np.ones(len(p)).reshape(-1,1), + np.dot(q.reshape(1,-1),f2(C2).T)) + constC=constC1+constC2 + hC1 = h1(C1) + hC2 = h2(C2) + + return constC,hC1,hC2 + +def tensor_product(constC,hC1,hC2,T): + """ Return the tensor for Gromov-Wasserstein fast computation + + The tensor is computed as described in Proposition 1 Eq. (6) in [12]. Parameters ---------- - C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space - C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space - T : ndarray, shape (ns, nt) - Coupling between source and target spaces + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + Returns ------- + tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result @@ -126,28 +133,43 @@ def tensor_kl_loss(C1, C2, T): "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - """ + """ + A=-np.dot(hC1, T).dot(hC2.T) + tens = constC+A + #tens -= tens.min() + return tens - C1 = np.asarray(C1, dtype=np.float64) - C2 = np.asarray(C2, dtype=np.float64) - T = np.asarray(T, dtype=np.float64) +def gwloss(constC,hC1,hC2,T): - def f1(a): - return a * np.log(a + 1e-15) - a + tens=tensor_product(constC,hC1,hC2,T) + + return np.sum(tens*T) - def f2(b): - return b +def gwggrad(constC,hC1,hC2,T): + + return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor - def h1(a): - return a +def gromov_wasserstein(C1,C2,p,q,loss_fun,alpha=1,log=False,**kwargs): - def h2(b): - return np.log(b + 1e-15) - tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens -= tens.min() + T = np.eye(len(p), len(q)) + + constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) + + G0=p[:,None]*q[None,:] + + def f(G): + return gwloss(constC,hC1,hC2,G) + def df(G): + return gwggrad(constC,hC1,hC2,G) + + if log: + res,log=cg(p,q,0,alpha,f,df,G0,log=True,**kwargs) + log['gw_dist']=gwloss(constC,hC1,hC2,res) + return res,log + else: + return cg(p,q,0,alpha,f,df,G0,**kwargs) - return tens def update_square_loss(p, lambdas, T, Cs): @@ -205,10 +227,10 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, +def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ - Returns the gromov-wasserstein coupling between the two measured similarity matrices + Returns the regularized gromov-wasserstein coupling between the two measured similarity matrices (C1,p) and (C2,q) @@ -259,25 +281,34 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, T : ndarray, shape (ns, nt) coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ C1 = np.asarray(C1, dtype=np.float64) C2 = np.asarray(C2, dtype=np.float64) T = np.outer(p, q) # Initialization + + constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) cpt = 0 err = 1 + + if log: + log={'err':[]} while (err > tol and cpt < max_iter): Tprev = T - if loss_fun == 'square_loss': - tens = tensor_square_loss(C1, C2, T) - - elif loss_fun == 'kl_loss': - tens = tensor_kl_loss(C1, C2, T) + # compute the gradient + tens=gwggrad(constC,hC1,hC2,T) T = sinkhorn(p, q, tens, epsilon) @@ -298,15 +329,15 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, cpt += 1 if log: + log['gw_dist']=gwloss(constC,hC1,hC2,T) return T, log else: return T - -def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, +def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ - Returns the gromov-wasserstein discrepancy between the two measured similarity matrices + Returns the entropic gromov-wasserstein discrepancy between the two measured similarity matrices (C1,p) and (C2,q) @@ -350,25 +381,25 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, ------- gw_dist : float Gromov-Wasserstein distance + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ - if log: - gw, logv = gromov_wasserstein( - C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log) - else: - gw = gromov_wasserstein(C1, C2, p, q, loss_fun, - epsilon, max_iter, tol, verbose, log) - - if loss_fun == 'square_loss': - gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) - elif loss_fun == 'kl_loss': - gw_dist = np.sum(gw * tensor_kl_loss(C1, C2, gw)) + gw, logv = entropic_gromov_wasserstein( + C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) + + log['T']=gw if log: - return gw_dist, logv + return logv['gw_dist'], logv else: - return gw_dist + return logv['gw_dist'] def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, @@ -421,6 +452,13 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ S = len(Cs) @@ -444,7 +482,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, while(err > tol and cpt < max_iter): Cprev = C - T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, + T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, max_iter, 1e-5, verbose, log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) From 341a6b55cde2c0826e4c5a558b7507828d01ae08 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Thu, 15 Feb 2018 16:54:23 +0100 Subject: [PATCH 02/13] add documentation --- README.md | 2 + ot/gromov.py | 110 ++++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 110 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 04994c000..c5e0575ab 100644 --- a/README.md +++ b/README.md @@ -200,3 +200,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. + +[13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. \ No newline at end of file diff --git a/ot/gromov.py b/ot/gromov.py index a23303ab9..c3ce41532 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -140,17 +140,123 @@ def tensor_product(constC,hC1,hC2,T): return tens def gwloss(constC,hC1,hC2,T): + """ Return the Loss for Gromov-Wasserstein + + The loss is computed as described in Proposition 1 Eq. (6) in [12]. + + Parameters + ---------- + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + T : ndarray, shape (ns, nt) + Current value of transport matrix T + + Returns + ------- + + loss : float + Gromov Wasserstein loss + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ tens=tensor_product(constC,hC1,hC2,T) return np.sum(tens*T) def gwggrad(constC,hC1,hC2,T): - + """ Return the gradient for Gromov-Wasserstein + + The gradient is computed as described in Proposition 2 in [12]. + + Parameters + ---------- + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + T : ndarray, shape (ns, nt) + Current value of transport matrix T + + Returns + ------- + + grad : ndarray, shape (ns, nt) + Gromov Wasserstein gradient + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor -def gromov_wasserstein(C1,C2,p,q,loss_fun,alpha=1,log=False,**kwargs): +def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): + """ + Returns the gromov-wasserstein discrepancy between the two measured similarity matrices + + (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + + Where : + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + p : distribution in the source space + q : distribution in the target space + L : loss function to account for the misfit between the similarity matrices + H : entropy + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + distribution in the source space + q : ndarray, shape (nt,) + distribution in the target space + loss_fun : string + loss function used for the solver either 'square_loss' or 'kl_loss' + + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + gw_dist : float + Gromov-Wasserstein distance + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ T = np.eye(len(p), len(q)) From ab2fc5486005732d60714b76eecf59d1104346f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Thu, 15 Feb 2018 17:09:28 +0100 Subject: [PATCH 03/13] awesome documentation --- ot/gromov.py | 188 ++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 135 insertions(+), 53 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index c3ce41532..8d0839725 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -204,11 +204,66 @@ def gwggrad(constC,hC1,hC2,T): """ return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor -def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): + + +def update_square_loss(p, lambdas, T, Cs): """ - Returns the gromov-wasserstein discrepancy between the two measured similarity matrices + Updates C according to the L2 Loss kernel with the S Ts couplings + calculated at each iteration - (C1,p) and (C2,q) + Parameters + ---------- + p : ndarray, shape (N,) + masses in the targeted barycenter + lambdas : list of float + list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : list of S ndarray, shape(ns,ns) + Metric cost matrices + + Returns + ---------- + C : ndarray, shape (nt,nt) + updated C matrix + """ + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.outer(p, p) + + return np.divide(tmpsum, ppt) + + +def update_kl_loss(p, lambdas, T, Cs): + """ + Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration + + + Parameters + ---------- + p : ndarray, shape (N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : list of S ndarray, shape(ns,ns) + Metric cost matrices + + Returns + ---------- + C : ndarray, shape (ns,ns) + updated C matrix + """ + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.outer(p, p) + + return np.exp(np.divide(tmpsum, ppt)) + + +def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): + """ + Returns the gromov-wasserstein transport between (C1,p) and (C2,q) The function solves the following optimization problem: @@ -247,14 +302,21 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): Returns ------- - gw_dist : float - Gromov-Wasserstein distance + T : ndarray, shape (ns, nt) + coupling between the two spaces that minimizes : + \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + log : dict + convergence information and loss References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational + mathematics 11.4 (2011): 417-487. """ @@ -270,73 +332,93 @@ def df(G): return gwggrad(constC,hC1,hC2,G) if log: - res,log=cg(p,q,0,alpha,f,df,G0,log=True,**kwargs) + res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) log['gw_dist']=gwloss(constC,hC1,hC2,res) return res,log else: - return cg(p,q,0,alpha,f,df,G0,**kwargs) - - - -def update_square_loss(p, lambdas, T, Cs): - """ - Updates C according to the L2 Loss kernel with the S Ts couplings - calculated at each iteration - - Parameters - ---------- - p : ndarray, shape (N,) - masses in the targeted barycenter - lambdas : list of float - list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration - Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + return cg(p,q,0,1,f,df,G0,**kwargs) - Returns - ---------- - C : ndarray, shape (nt,nt) - updated C matrix +def gromov_wasserstein2(C1,C2,p,q,loss_fun,log=False,**kwargs): """ - tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.outer(p, p) - - return np.divide(tmpsum, ppt) + Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) + The function solves the following optimization problem: -def update_kl_loss(p, lambdas, T, Cs): - """ - Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration + .. math:: + \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + Where : + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + p : distribution in the source space + q : distribution in the target space + L : loss function to account for the misfit between the similarity matrices + H : entropy Parameters ---------- - p : ndarray, shape (N,) - weights in the targeted barycenter - lambdas : list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration - Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + distribution in the source space + q : ndarray, shape (nt,) + distribution in the target space + loss_fun : string + loss function used for the solver either 'square_loss' or 'kl_loss' + + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True Returns + ------- + gw_dist : float + Gromov-Wasserstein distance + log : dict + convergence information and Coupling marix + + References ---------- - C : ndarray, shape (ns,ns) - updated C matrix + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational + mathematics 11.4 (2011): 417-487. + """ - tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.outer(p, p) - return np.exp(np.divide(tmpsum, ppt)) + T = np.eye(len(p), len(q)) + + constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) + + G0=p[:,None]*q[None,:] + + def f(G): + return gwloss(constC,hC1,hC2,G) + def df(G): + return gwggrad(constC,hC1,hC2,G) + res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) + log['gw_dist']=gwloss(constC,hC1,hC2,res) + log['T']=res + if log: + return log['gw_dist'],log + else: + return log['gw_dist'] def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ - Returns the regularized gromov-wasserstein coupling between the two measured similarity matrices + Returns the gromov-wasserstein transport between (C1,p) and (C2,q) (C1,p) and (C2,q) From 4a585de94109102c89bcd7ad43e35772e1027cd2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 11:58:59 +0100 Subject: [PATCH 04/13] update examples --- examples/plot_gromov.py | 29 ++++---- examples/plot_gromov_barycenter.py | 8 +-- ot/__init__.py | 3 +- ot/gromov.py | 109 ++++++++++++++++++++++++++++- 4 files changed, 129 insertions(+), 20 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index d42c21a3a..5f2d82653 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -81,23 +81,26 @@ #%% p = ot.unif(n_samples) q = ot.unif(n_samples) -ot.tic() -gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,verbose=True) -ot.toc() -ot.tic() -gw2,log2= ot.gromov.gromov_wasserstein0(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) -ot.toc() -gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw0,log0 = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True,log=True) -ot.tic() -gw0,log0=ot.gromov.gw_lp(C1, C2, p, q, 'square_loss',log=True,verbose=True) -ot.toc() +gw,log= ot.gromov.entropic_gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) -print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) +print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) +print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) -pl.figure() -pl.imshow(gw2, cmap='jet') + +pl.figure(1,(10,5)) + +pl.subplot(1,2,1) +pl.imshow(gw0, cmap='jet') pl.colorbar() +pl.title('Gromov Wasserstein') + +pl.subplot(1,2,2) +pl.imshow(gw0, cmap='jet') +pl.colorbar() +pl.title('Entropic Gromov Wasserstein') + pl.show() diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 180b0cf82..fde822bd5 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ def im2mat(I): for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', #5e-4, max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss',# 5e-4, max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss',# 5e-4, max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', #5e-4, max_iter=100, tol=1e-3) diff --git a/ot/__init__.py b/ot/__init__.py index a5df43d01..cee73792a 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -33,5 +33,4 @@ __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'gromov_wasserstein','gromov_wasserstein2'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/gromov.py b/ot/gromov.py index 8d0839725..e4dd112b4 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -590,7 +590,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, return logv['gw_dist'] -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, +def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -696,3 +696,110 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, cpt += 1 return C + + +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, + max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): + """ + Returns the gromov-wasserstein barycenters of S measured similarity matrices + + (Cs)_{s=1}^{s=S} + + The function solves the following optimization problem with block + coordinate descent: + + .. math:: + C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) + + + Where : + + Cs : metric cost matrix + ps : distribution + + Parameters + ---------- + N : Integer + Size of the targeted barycenter + Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + ps : list of S np.ndarray(ns,) + sample weights in the S spaces + p : ndarray, shape(N,) + weights in the targeted barycenter + lambdas : list of float + list of the S spaces' weights + loss_fun : tensor-matrix multiplication function based on specific loss function + update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel + with the S Ts couplings calculated at each iteration + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + init_C : bool, ndarray, shape(N,N) + random initial value for the C matrix provided by user + + Returns + ------- + C : ndarray, shape (N, N) + Similarity matrix in the barycenter space (permutated arbitrarily) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ + + S = len(Cs) + + Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] + lambdas = np.asarray(lambdas, dtype=np.float64) + + # Initialization of C : random SPD matrix (if not provided by user) + if init_C is None: + xalea = np.random.randn(N, 2) + C = dist(xalea, xalea) + C /= C.max() + else: + C = init_C + + cpt = 0 + err = 1 + + error = [] + + while(err > tol and cpt < max_iter): + Cprev = C + + T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, + numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=log) for s in range(S)] + if loss_fun == 'square_loss': + C = update_square_loss(p, lambdas, T, Cs) + + elif loss_fun == 'kl_loss': + C = update_kl_loss(p, lambdas, T, Cs) + + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = np.linalg.norm(C - Cprev) + error.append(err) + + if log: + log['err'].append(err) + + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt += 1 + + return C \ No newline at end of file From f089a3cbc27c30ba9416ea1659c2fdbac1857146 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 13:53:34 +0100 Subject: [PATCH 05/13] better pep8 but not solved --- examples/plot_barycenter_1D.py | 8 +- examples/plot_gromov.py | 25 ++-- ot/__init__.py | 4 +- ot/da.py | 6 + ot/gpu/__init__.py | 2 +- ot/gpu/da.py | 3 + ot/gromov.py | 235 +++++++++++++++++---------------- ot/utils.py | 2 + 8 files changed, 152 insertions(+), 133 deletions(-) diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 620936bda..ecf640ccd 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -############################################################################## +# # Generate data # ------------- @@ -48,7 +48,7 @@ M = ot.utils.dist0(n) M /= M.max() -############################################################################## +# # Plot data # --------- @@ -60,7 +60,7 @@ pl.title('Distributions') pl.tight_layout() -############################################################################## +# # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.title('Barycenters') pl.tight_layout() -############################################################################## +# # Barycentric interpolation # ------------------------- diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 5f2d82653..9188da914 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -20,7 +20,7 @@ import ot -############################################################################## +# # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- # @@ -43,7 +43,7 @@ xt = np.random.randn(n_samples, 3).dot(P) + mu_t -############################################################################## +# # Plotting the distributions # -------------------------- @@ -56,7 +56,7 @@ pl.show() -############################################################################## +# # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,33 +74,32 @@ pl.imshow(C2) pl.show() -############################################################################## +# # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- -#%% p = ot.unif(n_samples) q = ot.unif(n_samples) -gw0,log0 = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True,log=True) +gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) -gw,log= ot.gromov.entropic_gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) +gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) -pl.figure(1,(10,5)) +pl.figure(1, (10, 5)) -pl.subplot(1,2,1) +pl.subplot(1, 2, 1) pl.imshow(gw0, cmap='jet') -pl.colorbar() pl.title('Gromov Wasserstein') -pl.subplot(1,2,2) -pl.imshow(gw0, cmap='jet') -pl.colorbar() +pl.subplot(1, 2, 2) +pl.imshow(gw, cmap='jet') pl.title('Entropic Gromov Wasserstein') pl.show() diff --git a/ot/__init__.py b/ot/__init__.py index cee73792a..1500e594f 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -16,7 +16,6 @@ from . import optim from . import utils from . import datasets -from . import plot from . import da from . import gromov @@ -24,7 +23,6 @@ from .lp import emd, emd2 from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm -from .gromov import gromov_wasserstein, gromov_wasserstein2 # utils functions from .utils import dist, unif, tic, toc, toq @@ -32,5 +30,5 @@ __version__ = "0.4.0" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', - 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', + 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/da.py b/ot/da.py index 1d3d0bae5..ee73ec891 100644 --- a/ot/da.py +++ b/ot/da.py @@ -933,6 +933,7 @@ def distribution_estimation_uniform(X): class BaseTransport(BaseEstimator): + """Base class for OTDA objects Notes @@ -1180,6 +1181,7 @@ class label class SinkhornTransport(BaseTransport): + """Domain Adapatation OT method based on Sinkhorn Algorithm Parameters @@ -1289,6 +1291,7 @@ class label class EMDTransport(BaseTransport): + """Domain Adapatation OT method based on Earth Mover's Distance Parameters @@ -1377,6 +1380,7 @@ class label class SinkhornLpl1Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + LpL1 class regularization. @@ -1486,6 +1490,7 @@ class label class SinkhornL1l2Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + l1l2 class regularization. @@ -1608,6 +1613,7 @@ class label class MappingTransport(BaseEstimator): + """MappingTransport: DA methods that aims at jointly estimating a optimal transport coupling and the associated mapping diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index c8f94335a..a2fdd3d2e 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -5,7 +5,7 @@ from .bregman import sinkhorn # Author: Remi Flamary -# Leo Gautheron +# Leo Gautheron # # License: MIT License diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 05c580f3d..71a485a0b 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -188,6 +188,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, class OTDA_GPU(OTDA): + def normalizeM(self, norm): if norm == "median": self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) @@ -204,6 +205,7 @@ def normalizeM(self, norm): class OTDA_sinkhorn(OTDA_GPU): + def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): cudamat.init() xs = np.asarray(xs, dtype=np.float64) @@ -228,6 +230,7 @@ def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): class OTDA_lpl1(OTDA_GPU): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): cudamat.init() diff --git a/ot/gromov.py b/ot/gromov.py index e4dd112b4..b1e9ee0b4 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -20,12 +20,12 @@ from .optim import cg -def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): +def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): """ Return loss matrices and tensors for Gromov-Wasserstein fast computation Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss function as the loss function of Gromow-Wasserstein discrepancy. - + The matrices are computed as described in Proposition 1 in [12] Where : @@ -56,18 +56,18 @@ def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): T : ndarray, shape (ns, nt) Coupling between source and target spaces p : ndarray, shape (ns,) - + Returns ------- - + constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, @@ -76,39 +76,45 @@ def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): """ - if loss_fun == 'square_loss': def f1(a): - return (a**2)/2 + return (a**2) / 2 + def f2(b): - return (b**2)/2 + return (b**2) / 2 + def h1(a): - return a + return a + def h2(b): return b elif loss_fun == 'kl_loss': def f1(a): - return a * np.log(a + 1e-15) - a + return a * np.log(a + 1e-15) - a + def f2(b): - return b + return b + def h1(a): - return a + return a + def h2(b): return np.log(b + 1e-15) - constC1=np.dot(np.dot(f1(C1),p.reshape(-1,1)), - np.ones(len(q)).reshape(1,-1)) - constC2=np.dot(np.ones(len(p)).reshape(-1,1), - np.dot(q.reshape(1,-1),f2(C2).T)) - constC=constC1+constC2 + constC1 = np.dot(np.dot(f1(C1), p.reshape(-1, 1)), + np.ones(len(q)).reshape(1, -1)) + constC2 = np.dot(np.ones(len(p)).reshape(-1, 1), + np.dot(q.reshape(1, -1), f2(C2).T)) + constC = constC1 + constC2 hC1 = h1(C1) hC2 = h2(C2) - return constC,hC1,hC2 + return constC, hC1, hC2 + + +def tensor_product(constC, hC1, hC2, T): + """ Return the tensor for Gromov-Wasserstein fast computation -def tensor_product(constC,hC1,hC2,T): - """ Return the tensor for Gromov-Wasserstein fast computation - The tensor is computed as described in Proposition 1 Eq. (6) in [12]. Parameters @@ -116,14 +122,14 @@ def tensor_product(constC,hC1,hC2,T): constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) - + Returns ------- - + tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result @@ -133,15 +139,16 @@ def tensor_product(constC,hC1,hC2,T): "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - """ - A=-np.dot(hC1, T).dot(hC2.T) - tens = constC+A - #tens -= tens.min() + """ + A = -np.dot(hC1, T).dot(hC2.T) + tens = constC + A + # tens -= tens.min() return tens -def gwloss(constC,hC1,hC2,T): + +def gwloss(constC, hC1, hC2, T): """ Return the Loss for Gromov-Wasserstein - + The loss is computed as described in Proposition 1 Eq. (6) in [12]. Parameters @@ -149,15 +156,15 @@ def gwloss(constC,hC1,hC2,T): constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) T : ndarray, shape (ns, nt) - Current value of transport matrix T + Current value of transport matrix T Returns ------- - + loss : float Gromov Wasserstein loss @@ -166,16 +173,17 @@ def gwloss(constC,hC1,hC2,T): .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - + """ - tens=tensor_product(constC,hC1,hC2,T) - - return np.sum(tens*T) + tens = tensor_product(constC, hC1, hC2, T) + + return np.sum(tens * T) + + +def gwggrad(constC, hC1, hC2, T): + """ Return the gradient for Gromov-Wasserstein -def gwggrad(constC,hC1,hC2,T): - """ Return the gradient for Gromov-Wasserstein - The gradient is computed as described in Proposition 2 in [12]. Parameters @@ -183,15 +191,15 @@ def gwggrad(constC,hC1,hC2,T): constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) T : ndarray, shape (ns, nt) - Current value of transport matrix T + Current value of transport matrix T Returns ------- - + grad : ndarray, shape (ns, nt) Gromov Wasserstein gradient @@ -200,10 +208,10 @@ def gwggrad(constC,hC1,hC2,T): .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - - """ - return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor + """ + return 2 * tensor_product(constC, hC1, hC2, + T) # [12] Prop. 2 misses a 2 factor def update_square_loss(p, lambdas, T, Cs): @@ -261,7 +269,7 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): +def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -307,38 +315,40 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} log : dict convergence information and loss - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the - metric approach to object matching. Foundations of computational + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational mathematics 11.4 (2011): 417-487. - + """ T = np.eye(len(p), len(q)) - constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) - - G0=p[:,None]*q[None,:] - + constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + def f(G): - return gwloss(constC,hC1,hC2,G) + return gwloss(constC, hC1, hC2, G) + def df(G): - return gwggrad(constC,hC1,hC2,G) - + return gwggrad(constC, hC1, hC2, G) + if log: - res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) - log['gw_dist']=gwloss(constC,hC1,hC2,res) - return res,log + res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs) + log['gw_dist'] = gwloss(constC, hC1, hC2, res) + return res, log else: - return cg(p,q,0,1,f,df,G0,**kwargs) + return cg(p, q, 0, 1, f, df, G0, **kwargs) + -def gromov_wasserstein2(C1,C2,p,q,loss_fun,log=False,**kwargs): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): """ Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) @@ -383,40 +393,41 @@ def gromov_wasserstein2(C1,C2,p,q,loss_fun,log=False,**kwargs): Gromov-Wasserstein distance log : dict convergence information and Coupling marix - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the - metric approach to object matching. Foundations of computational + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational mathematics 11.4 (2011): 417-487. - + """ T = np.eye(len(p), len(q)) - constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) - - G0=p[:,None]*q[None,:] - + constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + def f(G): - return gwloss(constC,hC1,hC2,G) + return gwloss(constC, hC1, hC2, G) + def df(G): - return gwggrad(constC,hC1,hC2,G) - res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) - log['gw_dist']=gwloss(constC,hC1,hC2,res) - log['T']=res + return gwggrad(constC, hC1, hC2, G) + res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs) + log['gw_dist'] = gwloss(constC, hC1, hC2, res) + log['T'] = res if log: - return log['gw_dist'],log + return log['gw_dist'], log else: return log['gw_dist'] def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, - max_iter=1000, tol=1e-9, verbose=False, log=False): + max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -469,34 +480,34 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, T : ndarray, shape (ns, nt) coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - + International Conference on Machine Learning (ICML). 2016. + """ C1 = np.asarray(C1, dtype=np.float64) C2 = np.asarray(C2, dtype=np.float64) T = np.outer(p, q) # Initialization - - constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) + + constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) cpt = 0 err = 1 - + if log: - log={'err':[]} + log = {'err': []} while (err > tol and cpt < max_iter): Tprev = T # compute the gradient - tens=gwggrad(constC,hC1,hC2,T) + tens = gwggrad(constC, hC1, hC2, T) T = sinkhorn(p, q, tens, epsilon) @@ -517,13 +528,14 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, cpt += 1 if log: - log['gw_dist']=gwloss(constC,hC1,hC2,T) + log['gw_dist'] = gwloss(constC, hC1, hC2, T) return T, log else: return T + def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, - max_iter=1000, tol=1e-9, verbose=False, log=False): + max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the entropic gromov-wasserstein discrepancy between the two measured similarity matrices @@ -569,20 +581,19 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, ------- gw_dist : float Gromov-Wasserstein distance - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - """ + International Conference on Machine Learning (ICML). 2016. + """ gw, logv = entropic_gromov_wasserstein( - C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) - - log['T']=gw + C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) + + log['T'] = gw if log: return logv['gw_dist'], logv @@ -591,7 +602,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, - max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): + max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -640,13 +651,13 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - + International Conference on Machine Learning (ICML). 2016. + """ S = len(Cs) @@ -671,7 +682,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, Cprev = C T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, - max_iter, 1e-5, verbose, log) for s in range(S)] + max_iter, 1e-5, verbose, log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) @@ -698,14 +709,14 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, return C -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices (Cs)_{s=1}^{s=S} - The function solves the following optimization problem with block + The function solves the following optimization problem with block coordinate descent: .. math:: @@ -747,13 +758,13 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - + International Conference on Machine Learning (ICML). 2016. + """ S = len(Cs) @@ -777,7 +788,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, while(err > tol and cpt < max_iter): Cprev = C - T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, + T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) @@ -802,4 +813,4 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, cpt += 1 - return C \ No newline at end of file + return C diff --git a/ot/utils.py b/ot/utils.py index 31a002b0a..9eab3fcac 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -223,6 +223,7 @@ def check_params(**kwargs): class deprecated(object): + """Decorator to mark a function or class as deprecated. deprecated class from scikit-learn package @@ -320,6 +321,7 @@ def _is_deprecated(func): class BaseEstimator(object): + """Base class for most objects in POT adapted from sklearn BaseEstimator class From d41ffdb24de5cb234237482b3f332b06514b10f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 14:04:28 +0100 Subject: [PATCH 06/13] no more pep8 problem, need cleanup plots in test and examples --- examples/plot_gromov_barycenter.py | 8 ++++---- ot/da.py | 20 ++++++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index fde822bd5..58fc51af7 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ def im2mat(I): for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', #5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] - ], p, lambdast[i], 'square_loss',# 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] - ], p, lambdast[i], 'square_loss',# 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', #5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) diff --git a/ot/da.py b/ot/da.py index ee73ec891..c68865418 100644 --- a/ot/da.py +++ b/ot/da.py @@ -330,17 +330,17 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, if bias: xs1 = np.hstack((xs, np.ones((ns, 1)))) xstxs = xs1.T.dot(xs1) - I = np.eye(d + 1) - I[-1] = 0 - I0 = I[:, :-1] + Id = np.eye(d + 1) + Id[-1] = 0 + I0 = Id[:, :-1] def sel(x): return x[:-1, :] else: xs1 = xs xstxs = xs1.T.dot(xs1) - I = np.eye(d) - I0 = I + Id = np.eye(d) + I0 = Id def sel(x): return x @@ -361,7 +361,7 @@ def loss(L, G): def solve_L(G): """ solve L problem with fixed G (least square)""" xst = ns * G.dot(xt) - return np.linalg.solve(xstxs + eta * I, xs1.T.dot(xst) + eta * I0) + return np.linalg.solve(xstxs + eta * Id, xs1.T.dot(xst) + eta * I0) def solve_G(L, G0): """Update G with CG algorithm""" @@ -520,8 +520,8 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', K = kernel(xs, xs, method=kerneltype, sigma=sigma) if bias: K1 = np.hstack((K, np.ones((ns, 1)))) - I = np.eye(ns + 1) - I[-1] = 0 + Id = np.eye(ns + 1) + Id[-1] = 0 Kp = np.eye(ns + 1) Kp[:ns, :ns] = K @@ -535,14 +535,14 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', else: K1 = K - I = np.eye(ns) + Id = np.eye(ns) # ls regul # K0 = K1.T.dot(K1)+eta*I # Kreg=I # proper kernel ridge - K0 = K + eta * I + K0 = K + eta * Id Kreg = K if log: From b4665fe3c12780af4228cb1fb7dc8e1159c81f63 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 14:08:55 +0100 Subject: [PATCH 07/13] should pass tests now --- ot/gromov.py | 2 ++ test/test_gromov.py | 31 ++++++++++++++++++++++++++++++- test/test_plot.py | 2 ++ 3 files changed, 34 insertions(+), 1 deletion(-) diff --git a/ot/gromov.py b/ot/gromov.py index b1e9ee0b4..2a2387315 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -307,6 +307,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): Print information along iterations log : bool, optional record log if True + **kwargs : dict + parameters can be directly pased to the ot.optim.cg solver Returns ------- diff --git a/test/test_gromov.py b/test/test_gromov.py index e80829258..625e62a96 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -28,7 +28,36 @@ def test_gromov(): C1 /= C1.max() C2 /= C2.max() - G = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) + G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss') + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov + + +def test_entropic_gromov(): + n_samples = 50 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + + xt = xs[::-1].copy() + + p = ot.unif(n_samples) + q = ot.unif(n_samples) + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + + C1 /= C1.max() + C2 /= C2.max() + + G = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4) # check constratints np.testing.assert_allclose( diff --git a/test/test_plot.py b/test/test_plot.py index f7debee76..a50ed140e 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -12,6 +12,7 @@ def test_plot1D_mat(): import ot + import ot.plot n_bins = 100 # nb bins @@ -32,6 +33,7 @@ def test_plot1D_mat(): def test_plot2D_samples_mat(): import ot + import ot.plot n_bins = 50 # nb samples From 6355736a274a8e28c8909881b815a66f0a08d906 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 14:11:56 +0100 Subject: [PATCH 08/13] add proper import for ot.plot in examples --- examples/plot_OT_1D.py | 1 + examples/plot_OT_L1_vs_L2.py | 1 + examples/plot_optim_OTreg.py | 2 +- examples/plot_otda_d2.py | 2 +- 4 files changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 719058f88..90325c992 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -16,6 +16,7 @@ import numpy as np import matplotlib.pylab as pl import ot +import ot.plot from ot.datasets import get_1D_gauss as gauss ############################################################################## diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 090e809e8..c1ed22671 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -19,6 +19,7 @@ import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Dataset 1 : uniform sampling diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index e1a737ea0..92df0165e 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -28,7 +28,7 @@ import numpy as np import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # Generate data diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py index e53d7d6a2..70beb3585 100644 --- a/examples/plot_otda_d2.py +++ b/examples/plot_otda_d2.py @@ -20,7 +20,7 @@ import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # generate data From efdbf9e4fe9295fb1bec893e8aaa9102537cb7f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 14:14:08 +0100 Subject: [PATCH 09/13] update doc index --- docs/source/readme.rst | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 065093e28..514389fb3 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -172,6 +172,10 @@ want a quick look: images `__ - `Wasserstein Discriminant Analysis `__ +- `Gromov + Wasserstein `__ +- `Gromov Wasserstein + Barycenter `__ You can also see the notebooks with `Jupyter nbviewer `__. @@ -192,6 +196,7 @@ The contributors to this library are: Gayraud `__ - `Stanislas Chambon `__ - `Antoine Rolet `__ +- Erwan Vautier (Gromov-Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -204,6 +209,20 @@ languages): - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) +Using and citing the toolbox +---------------------------- + +If you use this toolbox in your research and find it useful, please cite +POT using the following bibtex reference: + +:: + + @article{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{\'e}mi and Courty, Nicolas}, + year={2017} + } + Contributions and code of conduct --------------------------------- @@ -286,6 +305,11 @@ arXiv:1608.08063. matrices `__ International Conference on Machine Learning (ICML). 2016. +[13] Mémoli, Facundo. `Gromov–Wasserstein distances and the metric +approach to object +matching `__. +Foundations of computational mathematics 11.4 (2011): 417-487. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master From ee19d423adc85a960c9a46e4f81c370196805dbf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 15:04:04 +0100 Subject: [PATCH 10/13] update notebooks --- docs/cache_nbrun | 2 +- .../auto_examples/auto_examples_jupyter.zip | Bin 89148 -> 85906 bytes .../auto_examples/auto_examples_python.zip | Bin 59370 -> 58682 bytes .../sphx_glr_plot_barycenter_1D_003.png | Bin 41555 -> 108687 bytes .../images/sphx_glr_plot_gromov_001.png | Bin 46633 -> 16548 bytes .../images/sphx_glr_plot_gromov_002.png | Bin 16945 -> 17330 bytes .../sphx_glr_plot_gromov_barycenter_001.png | Bin 47537 -> 48271 bytes .../images/sphx_glr_plot_otda_d2_001.png | Bin 131063 -> 130442 bytes .../images/sphx_glr_plot_otda_d2_003.png | Bin 213055 -> 216096 bytes .../images/sphx_glr_plot_otda_d2_006.png | Bin 99762 -> 102285 bytes .../thumb/sphx_glr_plot_OT_1D_thumb.png | Bin 18227 -> 14983 bytes .../thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png | Bin 10935 -> 9383 bytes .../sphx_glr_plot_barycenter_1D_thumb.png | Bin 16522 -> 13541 bytes .../sphx_glr_plot_gromov_barycenter_thumb.png | Bin 34183 -> 28787 bytes .../thumb/sphx_glr_plot_gromov_thumb.png | Bin 30843 -> 17804 bytes .../thumb/sphx_glr_plot_otda_d2_thumb.png | Bin 52743 -> 47553 bytes docs/source/auto_examples/index.rst | 67 ++- docs/source/auto_examples/plot_OT_1D.ipynb | 170 +++--- docs/source/auto_examples/plot_OT_1D.py | 1 + docs/source/auto_examples/plot_OT_1D.rst | 14 +- .../auto_examples/plot_OT_L1_vs_L2.ipynb | 170 +++--- docs/source/auto_examples/plot_OT_L1_vs_L2.py | 1 + .../source/auto_examples/plot_OT_L1_vs_L2.rst | 14 +- .../auto_examples/plot_barycenter_1D.ipynb | 144 ++--- .../auto_examples/plot_barycenter_1D.py | 8 +- .../auto_examples/plot_barycenter_1D.rst | 125 ++--- docs/source/auto_examples/plot_gromov.ipynb | 144 ++--- docs/source/auto_examples/plot_gromov.py | 32 +- docs/source/auto_examples/plot_gromov.rst | 206 +++---- .../plot_gromov_barycenter.ipynb | 178 +++--- .../auto_examples/plot_gromov_barycenter.py | 8 +- .../auto_examples/plot_gromov_barycenter.rst | 507 +++++++++--------- .../auto_examples/plot_optim_OTreg.ipynb | 194 +++---- docs/source/auto_examples/plot_optim_OTreg.py | 2 +- .../source/auto_examples/plot_optim_OTreg.rst | 15 +- docs/source/auto_examples/plot_otda_d2.ipynb | 194 +++---- docs/source/auto_examples/plot_otda_d2.py | 2 +- docs/source/auto_examples/plot_otda_d2.rst | 15 +- 38 files changed, 1047 insertions(+), 1166 deletions(-) diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 3f1e6ead1..ae0fe231f 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "71d3c106b3f395a6b1001078a6ca6f8d", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": 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zKYZdpzU=={h*6&|CLg~MfTkej>6P=3-<>GjKVJBcZw>_z5$7M@g#U06s66GQ(jC#z z{NpQB0r~W9ki5SB|Fn1SCIG-76h+YuvPj&2xtJnl+JN|S&jb$yKb0e5o?1@y6D0ceA%2%wD(U`z%ng&U@@00000NkvXXu0mjfbCSby diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index eb54ca896..227c40cbc 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -1,8 +1,12 @@ +:orphan: + POT Examples ============ This is a gallery of all the POT example files. + + .. raw:: html

@@ -45,13 +49,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov.py` + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` .. raw:: html @@ -61,17 +65,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_gromov + /auto_examples/plot_OT_2D_samples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` .. raw:: html @@ -81,17 +85,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_compute_emd .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + :ref:`sphx_glr_auto_examples_plot_WDA.py` .. raw:: html @@ -101,17 +105,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_compute_emd + /auto_examples/plot_WDA .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png - :ref:`sphx_glr_auto_examples_plot_WDA.py` + :ref:`sphx_glr_auto_examples_plot_gromov.py` .. raw:: html @@ -121,7 +125,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_WDA + /auto_examples/plot_gromov .. raw:: html @@ -145,13 +149,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` .. raw:: html @@ -161,17 +165,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_barycenter_1D + /auto_examples/plot_otda_mapping_colors_images .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` .. raw:: html @@ -181,7 +185,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_otda_mapping_colors_images + /auto_examples/plot_barycenter_1D .. raw:: html @@ -308,19 +312,24 @@ This is a gallery of all the POT example files. -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download - :download:`Download all examples in Python source code: auto_examples_python.zip ` + :download:`Download all examples in Python source code: auto_examples_python.zip ` .. container:: sphx-glr-download - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + + +.. only:: html -.. rst-class:: sphx-glr-signature + .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 26748c230..649efa602 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import get_1D_gauss as gauss" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Plot distributions and loss matrix\n----------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve Sinkhorn\n--------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 719058f88..90325c992 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -16,6 +16,7 @@ import numpy as np import matplotlib.pylab as pl import ot +import ot.plot from ot.datasets import get_1D_gauss as gauss ############################################################################## diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 975a9234e..5e4f73ee6 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -23,6 +23,7 @@ and their visualization. import numpy as np import matplotlib.pylab as pl import ot + import ot.plot from ot.datasets import get_1D_gauss as gauss @@ -171,11 +172,13 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 1.198 seconds) +**Total running time of the script:** ( 0 minutes 1.061 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -188,6 +191,9 @@ Solve Sinkhorn :download:`Download Jupyter notebook: plot_OT_1D.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index 2b9a3645f..aea1b3def 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 1 : uniform sampling\n----------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 2 : Partial circle\n--------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index 090e809e8..c1ed22671 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -19,6 +19,7 @@ import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Dataset 1 : uniform sampling diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index a569b5068..01d6ac294 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -26,6 +26,7 @@ https://arxiv.org/pdf/1706.07650.pdf import numpy as np import matplotlib.pylab as pl import ot + import ot.plot @@ -290,11 +291,13 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 1.976 seconds) +**Total running time of the script:** ( 0 minutes 3.750 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -307,6 +310,9 @@ Dataset 2 : Plot OT Matrices :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index a19e0fd02..01e759a05 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -1,126 +1,54 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ - "Barycenter computation\n----------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Barycentric interpolation\n-------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 620936bda..ecf640ccd 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -############################################################################## +# # Generate data # ------------- @@ -48,7 +48,7 @@ M = ot.utils.dist0(n) M /= M.max() -############################################################################## +# # Plot data # --------- @@ -60,7 +60,7 @@ pl.title('Distributions') pl.tight_layout() -############################################################################## +# # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.title('Barycenters') pl.tight_layout() -############################################################################## +# # Barycentric interpolation # ------------------------- diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index f17f2c23c..5b627cad8 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -18,34 +18,52 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. -.. code-block:: python +.. rst-class:: sphx-glr-horizontal - # Author: Remi Flamary - # - # License: MIT License - import numpy as np - import matplotlib.pylab as pl - import ot - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - from matplotlib.collections import PolyCollection + * + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png + :scale: 47 + * + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png + :scale: 47 + * + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png + :scale: 47 -Generate data -------------- .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection + + # + # Generate data + # ------------- + #%% parameters n = 100 # nb bins @@ -65,19 +83,9 @@ Generate data M = ot.utils.dist0(n) M /= M.max() - - - - - - -Plot data ---------- - - - -.. code-block:: python - + # + # Plot data + # --------- #%% plot the distributions @@ -87,22 +95,9 @@ Plot data pl.title('Distributions') pl.tight_layout() - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :align: center - - - - -Barycenter computation ----------------------- - - - -.. code-block:: python - + # + # Barycenter computation + # ---------------------- #%% barycenter computation @@ -130,22 +125,9 @@ Barycenter computation pl.title('Barycenters') pl.tight_layout() - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :align: center - - - - -Barycentric interpolation -------------------------- - - - -.. code-block:: python - + # + # Barycentric interpolation + # ------------------------- #%% barycenter interpolation @@ -212,29 +194,13 @@ Barycentric interpolation pl.show() - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png - :scale: 47 +**Total running time of the script:** ( 0 minutes 0.636 seconds) +.. only :: html -**Total running time of the script:** ( 0 minutes 0.814 seconds) - - - -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -247,6 +213,9 @@ Barycentric interpolation :download:`Download Jupyter notebook: plot_barycenter_1D.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index 865848e9c..6d6b52268 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -1,126 +1,54 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Erwan Vautier \r\n# Nicolas Courty \r\n#\r\n# License: MIT License\r\n\r\nimport scipy as sp\r\nimport numpy as np\r\nimport matplotlib.pylab as pl\r\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\r\nimport ot" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Sample two Gaussian distributions (2D and 3D)\r\n ---------------------------------------------\r\n\r\n The Gromov-Wasserstein distance allows to compute distances with samples that\r\n do not belong to the same metric space. For demonstration purpose, we sample\r\n two Gaussian distributions in 2- and 3-dimensional spaces.\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_samples = 30 # nb samples\r\n\r\nmu_s = np.array([0, 0])\r\ncov_s = np.array([[1, 0], [0, 1]])\r\n\r\nmu_t = np.array([4, 4, 4])\r\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n\r\n\r\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\nP = sp.linalg.sqrtm(cov_t)\r\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Plotting the distributions\r\n--------------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "fig = pl.figure()\r\nax1 = fig.add_subplot(121)\r\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\nax2 = fig.add_subplot(122, projection='3d')\r\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ - "Compute distance kernels, normalize them and then display\r\n---------------------------------------------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\r\nC2 = sp.spatial.distance.cdist(xt, xt)\r\n\r\nC1 /= C1.max()\r\nC2 /= C2.max()\r\n\r\npl.figure()\r\npl.subplot(121)\r\npl.imshow(C1)\r\npl.subplot(122)\r\npl.imshow(C2)\r\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Compute Gromov-Wasserstein plans and distance\r\n---------------------------------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "p = ot.unif(n_samples)\r\nq = ot.unif(n_samples)\r\n\r\ngw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\ngw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n\r\nprint('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n\r\npl.figure()\r\npl.imshow(gw, cmap='jet')\r\npl.colorbar()\r\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot\n\n\n#\n# Sample two Gaussian distributions (2D and 3D)\n# ---------------------------------------------\n#\n# The Gromov-Wasserstein distance allows to compute distances with samples that\n# do not belong to the same metric space. For demonstration purpose, we sample\n# two Gaussian distributions in 2- and 3-dimensional spaces.\n\n\nn_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\n\n\n#\n# Plotting the distributions\n# --------------------------\n\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()\n\n\n#\n# Compute distance kernels, normalize them and then display\n# ---------------------------------------------------------\n\n\nC1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()\n\n#\n# Compute Gromov-Wasserstein plans and distance\n# ---------------------------------------------\n\np = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py index d3f724c47..9188da914 100644 --- a/docs/source/auto_examples/plot_gromov.py +++ b/docs/source/auto_examples/plot_gromov.py @@ -20,7 +20,7 @@ import ot -############################################################################## +# # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- # @@ -43,7 +43,7 @@ xt = np.random.randn(n_samples, 3).dot(P) + mu_t -############################################################################## +# # Plotting the distributions # -------------------------- @@ -56,7 +56,7 @@ pl.show() -############################################################################## +# # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,20 +74,32 @@ pl.imshow(C2) pl.show() -############################################################################## +# # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- - p = ot.unif(n_samples) q = ot.unif(n_samples) -gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) -gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) -print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) +gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) -pl.figure() + +print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) +print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) + + +pl.figure(1, (10, 5)) + +pl.subplot(1, 2, 1) +pl.imshow(gw0, cmap='jet') +pl.title('Gromov Wasserstein') + +pl.subplot(1, 2, 2) pl.imshow(gw, cmap='jet') -pl.colorbar() +pl.title('Entropic Gromov Wasserstein') + pl.show() diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index 65cf4e48b..ad29f7a86 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -12,157 +12,160 @@ computation in POT. -.. code-block:: python - - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - from mpl_toolkits.mplot3d import Axes3D # noqa - import ot - - +.. rst-class:: sphx-glr-horizontal + * + .. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png + :scale: 47 + * -Sample two Gaussian distributions (2D and 3D) - --------------------------------------------- - - The Gromov-Wasserstein distance allows to compute distances with samples that - do not belong to the same metric space. For demonstration purpose, we sample - two Gaussian distributions in 2- and 3-dimensional spaces. + .. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png + :scale: 47 +.. rst-class:: sphx-glr-script-out -.. code-block:: python - - - - n_samples = 30 # nb samples - - mu_s = np.array([0, 0]) - cov_s = np.array([[1, 0], [0, 1]]) - - mu_t = np.array([4, 4, 4]) - cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) - P = sp.linalg.sqrtm(cov_t) - xt = np.random.randn(n_samples, 3).dot(P) + mu_t - - + Out:: + It. |Loss |Delta loss + -------------------------------- + 0|4.042674e-02|0.000000e+00 + 1|2.432476e-02|-6.619583e-01 + 2|2.170023e-02|-1.209448e-01 + 3|1.941223e-02|-1.178640e-01 + 4|1.823606e-02|-6.449667e-02 + 5|1.446641e-02|-2.605800e-01 + 6|1.184011e-02|-2.218140e-01 + 7|1.173274e-02|-9.150805e-03 + 8|1.173127e-02|-1.253458e-04 + 9|1.173126e-02|-1.256842e-06 + 10|1.173126e-02|-1.256876e-08 + 11|1.173126e-02|-1.256885e-10 + It. |Err + ------------------- + 0|7.034302e-02| + 10|1.044218e-03| + 20|5.426783e-08| + 30|3.532029e-12| + Gromov-Wasserstein distances: 0.0117312557987 + Entropic Gromov-Wasserstein distances: 0.0101639418389 +| -Plotting the distributions --------------------------- +.. code-block:: python -.. code-block:: python + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License - - - fig = pl.figure() - ax1 = fig.add_subplot(121) - ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - ax2 = fig.add_subplot(122, projection='3d') - ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') - pl.show() - - + import scipy as sp + import numpy as np + import matplotlib.pylab as pl + from mpl_toolkits.mplot3d import Axes3D # noqa + import ot + # + # Sample two Gaussian distributions (2D and 3D) + # --------------------------------------------- + # + # The Gromov-Wasserstein distance allows to compute distances with samples that + # do not belong to the same metric space. For demonstration purpose, we sample + # two Gaussian distributions in 2- and 3-dimensional spaces. -.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :align: center + n_samples = 30 # nb samples + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + mu_t = np.array([4, 4, 4]) + cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -Compute distance kernels, normalize them and then display ---------------------------------------------------------- + xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + P = sp.linalg.sqrtm(cov_t) + xt = np.random.randn(n_samples, 3).dot(P) + mu_t -.. code-block:: python + # + # Plotting the distributions + # -------------------------- - - - C1 = sp.spatial.distance.cdist(xs, xs) - C2 = sp.spatial.distance.cdist(xt, xt) - - C1 /= C1.max() - C2 /= C2.max() - - pl.figure() - pl.subplot(121) - pl.imshow(C1) - pl.subplot(122) - pl.imshow(C2) - pl.show() - + fig = pl.figure() + ax1 = fig.add_subplot(121) + ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + ax2 = fig.add_subplot(122, projection='3d') + ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') + pl.show() -.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :align: center + # + # Compute distance kernels, normalize them and then display + # --------------------------------------------------------- + C1 = sp.spatial.distance.cdist(xs, xs) + C2 = sp.spatial.distance.cdist(xt, xt) + C1 /= C1.max() + C2 /= C2.max() -Compute Gromov-Wasserstein plans and distance ---------------------------------------------- + pl.figure() + pl.subplot(121) + pl.imshow(C1) + pl.subplot(122) + pl.imshow(C2) + pl.show() + # + # Compute Gromov-Wasserstein plans and distance + # --------------------------------------------- + p = ot.unif(n_samples) + q = ot.unif(n_samples) -.. code-block:: python + gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) - - - p = ot.unif(n_samples) - q = ot.unif(n_samples) - - gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) - gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) - - print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) - - pl.figure() - pl.imshow(gw, cmap='jet') - pl.colorbar() - pl.show() + gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) + print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) + print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) -.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png - :align: center + pl.figure(1, (10, 5)) -.. rst-class:: sphx-glr-script-out + pl.subplot(1, 2, 1) + pl.imshow(gw0, cmap='jet') + pl.title('Gromov Wasserstein') - Out:: + pl.subplot(1, 2, 2) + pl.imshow(gw, cmap='jet') + pl.title('Entropic Gromov Wasserstein') - Gromov-Wasserstein distances between the distribution: 0.225058076974 + pl.show() +**Total running time of the script:** ( 0 minutes 1.465 seconds) -**Total running time of the script:** ( 0 minutes 4.070 seconds) +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -175,6 +178,9 @@ Compute Gromov-Wasserstein plans and distance :download:`Download Jupyter notebook: plot_gromov.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov_barycenter.ipynb b/docs/source/auto_examples/plot_gromov_barycenter.ipynb index d38dfbb44..4c2f28f26 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.ipynb +++ b/docs/source/auto_examples/plot_gromov_barycenter.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# Gromov-Wasserstein Barycenter example\n\n\nThis example is designed to show how to use the Gromov-Wasserstein distance\ncomputation in POT.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Erwan Vautier \r\n# Nicolas Courty \r\n#\r\n# License: MIT License\r\n\r\n\r\nimport numpy as np\r\nimport scipy as sp\r\n\r\nimport scipy.ndimage as spi\r\nimport matplotlib.pylab as pl\r\nfrom sklearn import manifold\r\nfrom sklearn.decomposition import PCA\r\n\r\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Smacof MDS\r\n ----------\r\n\r\n This function allows to find an embedding of points given a dissimilarity matrix\r\n that will be given by the output of the algorithm\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport scipy as sp\n\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\n\nimport ot" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\r\n \"\"\"\r\n Returns an interpolated point cloud following the dissimilarity matrix C\r\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\r\n target space\r\n\r\n Parameters\r\n ----------\r\n C : ndarray, shape (ns, ns)\r\n dissimilarity matrix\r\n dim : int\r\n dimension of the targeted space\r\n max_iter : int\r\n Maximum number of iterations of the SMACOF algorithm for a single run\r\n eps : float\r\n relative tolerance w.r.t stress to declare converge\r\n\r\n Returns\r\n -------\r\n npos : ndarray, shape (R, dim)\r\n Embedded coordinates of the interpolated point cloud (defined with\r\n one isometry)\r\n \"\"\"\r\n\r\n rng = np.random.RandomState(seed=3)\r\n\r\n mds = manifold.MDS(\r\n dim,\r\n max_iter=max_iter,\r\n eps=1e-9,\r\n dissimilarity='precomputed',\r\n n_init=1)\r\n pos = mds.fit(C).embedding_\r\n\r\n nmds = manifold.MDS(\r\n 2,\r\n max_iter=max_iter,\r\n eps=1e-9,\r\n dissimilarity=\"precomputed\",\r\n random_state=rng,\r\n n_init=1)\r\n npos = nmds.fit_transform(C, init=pos)\r\n\r\n return npos" - ], - "outputs": [], + "Smacof MDS\n----------\n\nThis function allows to find an embedding of points given a dissimilarity matrix\nthat will be given by the output of the algorithm\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Data preparation\r\n ----------------\r\n\r\n The four distributions are constructed from 4 simple images\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n \"\"\"\n Returns an interpolated point cloud following the dissimilarity matrix C\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\n target space\n\n Parameters\n ----------\n C : ndarray, shape (ns, ns)\n dissimilarity matrix\n dim : int\n dimension of the targeted space\n max_iter : int\n Maximum number of iterations of the SMACOF algorithm for a single run\n eps : float\n relative tolerance w.r.t stress to declare converge\n\n Returns\n -------\n npos : ndarray, shape (R, dim)\n Embedded coordinates of the interpolated point cloud (defined with\n one isometry)\n \"\"\"\n\n rng = np.random.RandomState(seed=3)\n\n mds = manifold.MDS(\n dim,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity='precomputed',\n n_init=1)\n pos = mds.fit(C).embedding_\n\n nmds = manifold.MDS(\n 2,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity=\"precomputed\",\n random_state=rng,\n n_init=1)\n npos = nmds.fit_transform(C, init=pos)\n\n return npos" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "def im2mat(I):\r\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\r\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\r\n\r\n\r\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\r\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\r\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\r\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\r\n\r\nshapes = [square, cross, triangle, star]\r\n\r\nS = 4\r\nxs = [[] for i in range(S)]\r\n\r\n\r\nfor nb in range(4):\r\n for i in range(8):\r\n for j in range(8):\r\n if shapes[nb][i, j] < 0.95:\r\n xs[nb].append([j, 8 - i])\r\n\r\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\r\n np.array(xs[2]), np.array(xs[3])])" - ], - "outputs": [], + "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Barycenter computation\r\n----------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "ns = [len(xs[s]) for s in range(S)]\r\nn_samples = 30\r\n\r\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\r\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\r\nCs = [cs / cs.max() for cs in Cs]\r\n\r\nps = [ot.unif(ns[s]) for s in range(S)]\r\np = ot.unif(n_samples)\r\n\r\n\r\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\r\n\r\nCt01 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\r\n [ps[0], ps[1]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt02 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\r\n [ps[0], ps[2]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt13 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\r\n [ps[1], ps[3]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt23 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\r\n [ps[2], ps[3]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)" - ], - "outputs": [], + "Barycenter computation\n----------------------\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Visualization\r\n -------------\r\n\r\n The PCA helps in getting consistency between the rotations\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "ns = [len(xs[s]) for s in range(S)]\nn_samples = 30\n\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\nCs = [cs / cs.max() for cs in Cs]\n\nps = [ot.unif(ns[s]) for s in range(S)]\np = ot.unif(n_samples)\n\n\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n\nCt01 = [0 for i in range(2)]\nfor i in range(2):\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n [ps[0], ps[1]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt02 = [0 for i in range(2)]\nfor i in range(2):\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n [ps[0], ps[2]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt13 = [0 for i in range(2)]\nfor i in range(2):\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n [ps[1], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt23 = [0 for i in range(2)]\nfor i in range(2):\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n [ps[2], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "clf = PCA(n_components=2)\r\nnpos = [0, 0, 0, 0]\r\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\r\n\r\nnpost01 = [0, 0]\r\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\r\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\r\n\r\nnpost02 = [0, 0]\r\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\r\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\r\n\r\nnpost13 = [0, 0]\r\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\r\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\r\n\r\nnpost23 = [0, 0]\r\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\r\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\r\n\r\n\r\nfig = pl.figure(figsize=(10, 10))\r\n\r\nax1 = pl.subplot2grid((4, 4), (0, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\r\n\r\nax2 = pl.subplot2grid((4, 4), (0, 1))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\r\n\r\nax3 = pl.subplot2grid((4, 4), (0, 2))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\r\n\r\nax4 = pl.subplot2grid((4, 4), (0, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\r\n\r\nax5 = pl.subplot2grid((4, 4), (1, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\r\n\r\nax6 = pl.subplot2grid((4, 4), (1, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\r\n\r\nax7 = pl.subplot2grid((4, 4), (2, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\r\n\r\nax8 = pl.subplot2grid((4, 4), (2, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\r\n\r\nax9 = pl.subplot2grid((4, 4), (3, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\r\n\r\nax10 = pl.subplot2grid((4, 4), (3, 1))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\r\n\r\nax11 = pl.subplot2grid((4, 4), (3, 2))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\r\n\r\nax12 = pl.subplot2grid((4, 4), (3, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" - ], - "outputs": [], + "Visualization\n-------------\n\nThe PCA helps in getting consistency between the rotations\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "clf = PCA(n_components=2)\nnpos = [0, 0, 0, 0]\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\n\nnpost01 = [0, 0]\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n\nnpost02 = [0, 0]\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n\nnpost13 = [0, 0]\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n\nnpost23 = [0, 0]\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n\n\nfig = pl.figure(figsize=(10, 10))\n\nax1 = pl.subplot2grid((4, 4), (0, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n\nax2 = pl.subplot2grid((4, 4), (0, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n\nax3 = pl.subplot2grid((4, 4), (0, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n\nax4 = pl.subplot2grid((4, 4), (0, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n\nax5 = pl.subplot2grid((4, 4), (1, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n\nax6 = pl.subplot2grid((4, 4), (1, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n\nax7 = pl.subplot2grid((4, 4), (2, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n\nax8 = pl.subplot2grid((4, 4), (2, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n\nax9 = pl.subplot2grid((4, 4), (3, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n\nax10 = pl.subplot2grid((4, 4), (3, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n\nax11 = pl.subplot2grid((4, 4), (3, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n\nax12 = pl.subplot2grid((4, 4), (3, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov_barycenter.py b/docs/source/auto_examples/plot_gromov_barycenter.py index 180b0cf82..58fc51af7 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.py +++ b/docs/source/auto_examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ def im2mat(I): for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) diff --git a/docs/source/auto_examples/plot_gromov_barycenter.rst b/docs/source/auto_examples/plot_gromov_barycenter.rst index ca2d4e920..531ee22b7 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.rst +++ b/docs/source/auto_examples/plot_gromov_barycenter.rst @@ -14,285 +14,285 @@ computation in POT. .. code-block:: python - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - - import numpy as np - import scipy as sp - - import scipy.ndimage as spi - import matplotlib.pylab as pl - from sklearn import manifold - from sklearn.decomposition import PCA - - import ot - + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License + import numpy as np + import scipy as sp + import scipy.ndimage as spi + import matplotlib.pylab as pl + from sklearn import manifold + from sklearn.decomposition import PCA + import ot -Smacof MDS - ---------- - - This function allows to find an embedding of points given a dissimilarity matrix - that will be given by the output of the algorithm + + + + + + +Smacof MDS +---------- + +This function allows to find an embedding of points given a dissimilarity matrix +that will be given by the output of the algorithm .. code-block:: python - - - def smacof_mds(C, dim, max_iter=3000, eps=1e-9): - """ - Returns an interpolated point cloud following the dissimilarity matrix C - using SMACOF multidimensional scaling (MDS) in specific dimensionned - target space - - Parameters - ---------- - C : ndarray, shape (ns, ns) - dissimilarity matrix - dim : int - dimension of the targeted space - max_iter : int - Maximum number of iterations of the SMACOF algorithm for a single run - eps : float - relative tolerance w.r.t stress to declare converge - - Returns - ------- - npos : ndarray, shape (R, dim) - Embedded coordinates of the interpolated point cloud (defined with - one isometry) - """ - - rng = np.random.RandomState(seed=3) - - mds = manifold.MDS( - dim, - max_iter=max_iter, - eps=1e-9, - dissimilarity='precomputed', - n_init=1) - pos = mds.fit(C).embedding_ - - nmds = manifold.MDS( - 2, - max_iter=max_iter, - eps=1e-9, - dissimilarity="precomputed", - random_state=rng, - n_init=1) - npos = nmds.fit_transform(C, init=pos) - - return npos - - - - - - - - -Data preparation - ---------------- - - The four distributions are constructed from 4 simple images + + + def smacof_mds(C, dim, max_iter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C + using SMACOF multidimensional scaling (MDS) in specific dimensionned + target space + + Parameters + ---------- + C : ndarray, shape (ns, ns) + dissimilarity matrix + dim : int + dimension of the targeted space + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run + eps : float + relative tolerance w.r.t stress to declare converge + + Returns + ------- + npos : ndarray, shape (R, dim) + Embedded coordinates of the interpolated point cloud (defined with + one isometry) + """ + + rng = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=max_iter, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=max_iter, + eps=1e-9, + dissimilarity="precomputed", + random_state=rng, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + + + + + + + +Data preparation +---------------- + +The four distributions are constructed from 4 simple images .. code-block:: python - - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - - square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 - cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 - triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 - star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 - - shapes = [square, cross, triangle, star] - - S = 4 - xs = [[] for i in range(S)] - - - for nb in range(4): - for i in range(8): - for j in range(8): - if shapes[nb][i, j] < 0.95: - xs[nb].append([j, 8 - i]) - - xs = np.array([np.array(xs[0]), np.array(xs[1]), - np.array(xs[2]), np.array(xs[3])]) - + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 + cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 + triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 + star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 + + shapes = [square, cross, triangle, star] + + S = 4 + xs = [[] for i in range(S)] + + for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) -Barycenter computation ----------------------- + + + + + +Barycenter computation +---------------------- .. code-block:: python - - - ns = [len(xs[s]) for s in range(S)] - n_samples = 30 - - """Compute all distances matrices for the four shapes""" - Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] - Cs = [cs / cs.max() for cs in Cs] - - ps = [ot.unif(ns[s]) for s in range(S)] - p = ot.unif(n_samples) - - - lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] - - Ct01 = [0 for i in range(2)] - for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], - [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - Ct02 = [0 for i in range(2)] - for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], - [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - Ct13 = [0 for i in range(2)] - for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], - [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - Ct23 = [0 for i in range(2)] - for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], - [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - - - - - - - -Visualization - ------------- - - The PCA helps in getting consistency between the rotations + + + ns = [len(xs[s]) for s in range(S)] + n_samples = 30 + + """Compute all distances matrices for the four shapes""" + Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] + Cs = [cs / cs.max() for cs in Cs] + + ps = [ot.unif(ns[s]) for s in range(S)] + p = ot.unif(n_samples) + + + lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + + Ct01 = [0 for i in range(2)] + for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], + [ps[0], ps[1] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + Ct02 = [0 for i in range(2)] + for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], + [ps[0], ps[2] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + Ct13 = [0 for i in range(2)] + for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], + [ps[1], ps[3] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + Ct23 = [0 for i in range(2)] + for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], + [ps[2], ps[3] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + + + + + + + +Visualization +------------- + +The PCA helps in getting consistency between the rotations .. code-block:: python - - - clf = PCA(n_components=2) - npos = [0, 0, 0, 0] - npos = [smacof_mds(Cs[s], 2) for s in range(S)] - - npost01 = [0, 0] - npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] - npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] - - npost02 = [0, 0] - npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] - npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] - - npost13 = [0, 0] - npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] - npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] - - npost23 = [0, 0] - npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] - npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] - - - fig = pl.figure(figsize=(10, 10)) - - ax1 = pl.subplot2grid((4, 4), (0, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') - - ax2 = pl.subplot2grid((4, 4), (0, 1)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') - - ax3 = pl.subplot2grid((4, 4), (0, 2)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') - - ax4 = pl.subplot2grid((4, 4), (0, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') - - ax5 = pl.subplot2grid((4, 4), (1, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') - - ax6 = pl.subplot2grid((4, 4), (1, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') - - ax7 = pl.subplot2grid((4, 4), (2, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') - - ax8 = pl.subplot2grid((4, 4), (2, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') - - ax9 = pl.subplot2grid((4, 4), (3, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') - - ax10 = pl.subplot2grid((4, 4), (3, 1)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') - - ax11 = pl.subplot2grid((4, 4), (3, 2)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') - - ax12 = pl.subplot2grid((4, 4), (3, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') + + + clf = PCA(n_components=2) + npos = [0, 0, 0, 0] + npos = [smacof_mds(Cs[s], 2) for s in range(S)] + + npost01 = [0, 0] + npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] + npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + + npost02 = [0, 0] + npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] + npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + + npost13 = [0, 0] + npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] + npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + + npost23 = [0, 0] + npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] + npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + + fig = pl.figure(figsize=(10, 10)) + + ax1 = pl.subplot2grid((4, 4), (0, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + + ax2 = pl.subplot2grid((4, 4), (0, 1)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + + ax3 = pl.subplot2grid((4, 4), (0, 2)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + + ax4 = pl.subplot2grid((4, 4), (0, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + + ax5 = pl.subplot2grid((4, 4), (1, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + + ax6 = pl.subplot2grid((4, 4), (1, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + + ax7 = pl.subplot2grid((4, 4), (2, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + + ax8 = pl.subplot2grid((4, 4), (2, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + + ax9 = pl.subplot2grid((4, 4), (3, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + + ax10 = pl.subplot2grid((4, 4), (3, 1)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + + ax11 = pl.subplot2grid((4, 4), (3, 2)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + + ax12 = pl.subplot2grid((4, 4), (3, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') @@ -302,11 +302,13 @@ Visualization -**Total running time of the script:** ( 8 minutes 43.875 seconds) +**Total running time of the script:** ( 0 minutes 5.906 seconds) + +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -319,6 +321,9 @@ Visualization :download:`Download Jupyter notebook: plot_gromov_barycenter.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 333331bca..02bf17555 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD with entropic regularization\n--------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index e1a737ea0..92df0165e 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -28,7 +28,7 @@ import numpy as np import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # Generate data diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 480149a15..5927428a6 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -35,7 +35,7 @@ arXiv preprint arXiv:1510.06567. import numpy as np import matplotlib.pylab as pl import ot - + import ot.plot @@ -636,11 +636,13 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 2.800 seconds) +**Total running time of the script:** ( 0 minutes 2.589 seconds) + +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -653,6 +655,9 @@ Solve EMD with Frobenius norm + entropic regularization :download:`Download Jupyter notebook: plot_optim_OTreg.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index 7bfcc9a8c..9c58e64c6 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Fig 3 : plot transported samples\n--------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py index e53d7d6a2..70beb3585 100644 --- a/docs/source/auto_examples/plot_otda_d2.py +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -20,7 +20,7 @@ import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # generate data diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 1bbe6d965..e5a60c401 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -27,7 +27,7 @@ of what the transport methods are doing. import matplotlib.pylab as pl import ot - + import ot.plot @@ -242,11 +242,13 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 47.000 seconds) +**Total running time of the script:** ( 0 minutes 39.829 seconds) + +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -259,6 +261,9 @@ Fig 3 : plot transported samples :download:`Download Jupyter notebook: plot_otda_d2.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ From fead9d6186020fdd37e167ddfa7a91c405188ce7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 15:04:22 +0100 Subject: [PATCH 11/13] update notebooks --- notebooks/plot_OT_1D.ipynb | 27 +- notebooks/plot_OT_L1_vs_L2.ipynb | 37 +- notebooks/plot_barycenter_1D.ipynb | 200 ++++------- notebooks/plot_gromov.ipynb | 274 +++++++------- notebooks/plot_gromov_barycenter.ipynb | 471 +++++++++++++------------ notebooks/plot_optim_OTreg.ipynb | 404 ++++++++++----------- notebooks/plot_otda_d2.ipynb | 25 +- 7 files changed, 678 insertions(+), 760 deletions(-) diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb index c4b2e6799..93d156d74 100644 --- a/notebooks/plot_OT_1D.ipynb +++ b/notebooks/plot_OT_1D.ipynb @@ -40,6 +40,7 @@ "import numpy as np\n", "import matplotlib.pylab as pl\n", "import ot\n", + "import ot.plot\n", "from ot.datasets import get_1D_gauss as gauss" ] }, @@ -94,9 +95,9 @@ "outputs": [ { "data": { - "image/png": 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TlpZG27ZtGT16NA8//HCx2wUaJ/w+XX1pU/OXR/VC61tU5vyNllAiyKJFeh2M2ajr19d2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\ncHFVKfBdXJU4CuLicVXicBK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FRHKkltK9J/Bl4CkzezJ675vAd4Cbzew44EXgiL40YPhwuOAC+Na34IEH4Lrr\nYMYMuOoqGDHCu4Xtvbf3wR071pOxJKyQQKN0pqSLkm4dSTeJsXTL2tRNW+JPuvWrpZfC76n+/Asm\nxNWQ1lbvFjZhAlx+OdxxB9x/vz9p9stf+j5Dhvjju4UHIHbbTd3HRCQ/GupJs4KhQ33WhKOO8vXX\nXvMn0ArjKJx/fvFhoo98xMdNGDu2OIaCJmCsU2VCVdJV0i1s7UPSTXLWiLI2ddOWRkq6DVlwK224\nIRx+uC8Ab78Nf/wjPPaY93L4/e/hxhuL+2++ebEAjx3rszJsvLEeBxaRbOWi4FYaPhwOPtiXgtdf\n9+JbWB5/3HtBFILMiBHFIRp32slfd9hBg950qaWQqqLf7Uq6SrrlR+1V0k1jfrSyNnXTlnqTbhxy\nWXC7MmqUdzHbf//ie++9B3/+sy9PPeVTlV9/vb9f0N5ePhD5jjv6YDlNfXOuUFxQ4VXhjaPw9v7h\niOKZG6nw1q/fFNyuDBvmvRz22qv4Xgjw4ovV0+386lewKvqHGjgQttuuuhDncgZeEWkY/brgdsXM\nU217u49GVrB8OTz/vKfgQhF+8EHvolYwfLhfhqi8NLHOOmn/VySr+Ghv4R0lXSXdepJu3x8DLp45\n+6Qbh6YruN0ZNMgL6E47lb//1lvw9NPlaXjGjOJYveCz+BamSy+8brih0rCIlFPB7cGIEd7v95Of\nLL4XArz0kqfhJ5/05fHHYebM4j7rr18swLvsAh/7mBfmXBThKD0V0pSSLkq6dSXd+ge8KZ4530lX\nBbcPzGCzzXw55JDi+++840X4iSe8CD/xBFx8sU8XBN4/ePfdfRk/3h/cGDEim/8GEUmfCm6M1l23\nOg2vWAHPPguzZ8Ojj8KsWT5+RCFAbbutF9/dd4dPfMIvabS0dH381BRSnZKukq6SbqxUcBM2cGDx\nssLxx/t7777rBXjWLC/C997r3dXAu7d96lOw337+mPOWW+bkMoSI9EgFNwPrrAP77usLFLuqPfQQ\n3HefL7/4hW9rby+OMXHQQSldgqis8Eq6Srp1JN0kp2D37flJuiq4DaC0q9rRR/vP9vPPF4vvLbfA\nNdf4wxgHHABHHOHjBq+7btYtF5HeUMFtQGb+4MV228FJJ0FHh48bccstPn383Xf7pYqDDvLie9hh\nMQ/aHqWdynSkpIuSbh+SbpLT9RTPnI+km/XtGalBa6vfWPvud2HhQnjkES/Ec+bAl77kg/V861vw\nxhtZt1Q4Q9TjAAAOdElEQVRE1kQFN2fMvPhefDEsWgS//a338T33XJ/1+OST/XpwXVpafGmNFrPy\npbUVWlsxM38qraWwtEZLtG4tvkTrxf2j4xeOF62v3r66HRXHKXwGLVZMkP5G2fZU7jKGUJ6kQ2cx\nzeJJd3USL93eGXxZfZjgabez05fCcaP14vZoqTxOZ0e0+Prq/Ts6fCkcr7B0dPoSHd86A9ZZcv7C\n9mh/6+z0tNsRoKOr9Wjp6IyWgEXbyrZ3BlpW+VL8nsJCtETrnZ7sWzoCLSXbK9cL+xW3+1I4Tssq\nT7HF41cshfNU7rfKfKk4bhxUcHOspcVvpt1zT3GKounTYfvt4dJL/edNRBqHCm4/scMOcO218MIL\nsM8+MGWK9+t95pneHyu0GKHF8p10C8dMmpJuj0m3NOXmOenGQQW3n9l8c7jrLrjhBpg/34vuH/6Q\ndatEBNRLoV8ygy98wYel3G8/70p2551++aEmLeV3sK3y93Ieei+k2XOh9PjqvRAp9l7o2whjftTS\n9ex7L9RPCbcf23RTf5hiyy19eqJXXsm6RSLNTQm3n9tgA7jtNh+j4YQTvA9vj5c2W8t/D+cy6WbR\nR7f0+Eq6kU76Nu5C6Z6NknTrp4TbBLbeGi64wGe1uP/+rFsj0rxUcJvEiSf68JCXX17Dzqt7IUS9\nE6LeA3nqvVCyUt1HV70X0u29UEMf3Tz0XoiDCm6TGDwYJk2CO+6ADz7IujUizUkFt4kccIBPlDlr\n1pr3Cy0tfo0wx0m3pqfRlHRTSbq9eRqtkZNuHFRwm8gee/jrn/6UbTtEmpV6KTSRddf1Xgt/+1sP\nO7ZGd8oLd5wLd8rz1HuhkUYYKz1+E/ZeSGYm4OrvTKv3Qj2UcJtMe7sPeiMi6VPCbTKjRsGrr655\nn9UpqbJvZZ6SbiOOpVt6/CZKusnMj1a6Z36SrhJukxkxAt56K+tWiDSnmhOumbUCs4FXQgiHmNkW\nwM+B9YA5wJdDCCuSaabEZcgQWLash50qr+HmMek28qwRpcdvgqSb7EzApXs2ftLtTcI9DXiuZP1/\ngUtCCFsDbwHHxdguScjgwTUUXBFJRE0J18w2AT4DfBs43bwT5L7AF6NdrgPOA65IoI0SowEDYOXK\nNe8TovRUzA35S7q5mB+t9Pj9OOkmMRMwpJ9041Brwr0U+AbFT2Q94O0QQuGBt5eBMV19o5lNNrPZ\nZjZ76dKldTVW6tfW5g8/iEj6eky4ZnYIsCSEMMfM9untCUII04HpAOPGjUs4LkhPWlqKgbA7obX4\nOx7ymXRzNRNw6fH7Y9JNYCZgSD/pxqGWSwp7Ap81s4OBwcA6wGXAcDNri1LuJoBGW82BWgquiCSj\nx4IbQjgbOBsgSrhfCyEcZWa/AA7HeypMAu5IsJ0SE7Oew1poLfzGz3HSjWPWiJLtSrpRM5o46cah\nnmOdid9Am4df070mniZJkmopuCKSjF49aRZCeAB4IPp6PrBb/E2SJNU0QNbqfrgFOUy6cc6PVrJd\nSTdqRi+Sbpzzo/l2KrZHWxNOunHQk2YiIinRWApNppaEW90PtyA/STeRmYBLtivpRs2oIekmMROw\nb6die7S1gZOuEq6ISEqUcKVKIeEW5DHpJjITsL9Rtl1JN2qGkm5NlHBFRFKihCtVOtui3/AVjwDn\nKunGMWsEKOnGkXRjmDWifL0g3aQbByVcEZGUKOFKlcI13EIiyGXSjXN+NFDSVdKNhRKuiEhKlHCl\nSnEsBZfPpJvATMCgpNuXpJvATMDl6wXJJt04KOGKiKRECVeqhNWBJM9JN4GZgEuPo6Qbbe856SYx\nE7Dvn27SjYMSrohISpRwpUrxGm7Xv+HzkHQTmQkYlHT7kHSTmAnYt6eddOunhCsikhIlXKlSTIf5\nTbpJzAQMSrp9S7rxzwRc2qb0km79lHBFRFKihCtVOlsr+x/mMOkmMBNwaZuUdHuTdBOYHw0ySLr1\nU8IVEUmJEq5UKfTDrR4tSUlXSbcPSTfJmYAhV0lXCVdEJCVKuFKl8hpuLpNuEjMBg5JuX5JuAjMB\nQ/pJNw5KuCIiKVHClSrdXcNV0lXS7UvSTWJ+NEg/6cZBCVdEJCVKuFIlVPwazmPSTWImYD9ndA4l\nXX+tIekmORMw5CvpKuGKiKRECVeqhNau389T0k1kJmBQ0u1L0k1ifjRIPenGQQlXRCQlSrhSpTMK\nEt39NlbSRUm3N0k3yZmAIbWkGwclXBGRlNSUcM1sOHA1sAMeQI4FngduAtqBhcARIYS3EmmlpKow\n40NnlDVzmXQTmQkYlHR7n3QTmQkYMki69as14V4G3BtC2A7YGXgOOAu4L4SwDXBftC4iIt3oMeGa\n2brA3sC/AYQQVgArzGwisE+023XAA8CZSTRS0rU6eZLfpJvMTMCgpNv7pJvETMCQRdKtXy0Jdwtg\nKfBjM3vCzK42s7WBDUIIr0b7vAZs0NU3m9lkM5ttZrOXLl0aT6tFRHKolmu4bcBY4JQQwqNmdhkV\nlw9CCMHMuvxVG0KYDkwHGDduXMK/jiUOxbEUXB6TbhIzAYOSbp+SbgIzAfv+URNSSrpxqOVILwMv\nhxAejdZn4gV4sZltBBC9LomtVSIi/VCPCTeE8JqZvWRmHwkhPA9MAJ6NlknAd6LXOxJtqaSm8kmz\nPCbdJGYCBiXdPiXdRGYChrSTbhxqffDhFGCGmQ0E5gPH4P+332xmxwEvAkfE1ioRkX6opoIbQngS\nGNfFpgnxNkcaQfdjKbg8JN1E5kcDJd2C3iTdRGYChrSTbhz0pJmISEo0loJUqRwPt1Iekm6ss0aA\nkm53aki6icwEXHqclJJuHJRwRURSooQrVbpLlFX7Ra+NmXR7fhoNlHRjs4akm8hMwJB60o2DEq6I\nSEqUcKVK1ZNmuUy6PT+NVraupBuPrpJuAjMB+/bonGkl3Rgo4YqIpEQJV6qEtvJkmcekm8RMwL6/\nkm5NSpNuAjMBQz6TrhKuiEhKlHClSvFJs/wm3VhmjQAl3XqF0LtxF6Bhk24clHBFRFKihCtVqmft\nVdJV0q1DEjMBQ+pJNw5KuCIiKVHClSqhtTJhrt4SvTZ+0k1kJmBQ0q2Hkq4SrohIWpRwpUqxH67L\nY9JNYiZgUNKNRV6TbgyUcEVEUqKEK1UqE26Bkq6SbqyaMOkq4YqIpEQJV6pFvRS6yzb5SLr1zxpR\nvl1JNzF5SboxUMIVEUmJEq5Uq7iGm8+kG9/8aOXblXQT0+BJNw4quFLFurmkkK/C2/ehHUu/S4VX\nhbf42ddPlxRERFKihCtVrK2YNSGfSTfJKdjLtyvpJqbhkm79lHBFRFKihCtVWlv9N3zx93r+km6y\nU7AXW6ek20xJt35KuCIiKVHClSqtbeUpK49JN9kp2EvPrqTr602QdGOghCsikpKaEq6ZTQGOx3/B\nPwUcA2wE/BxYD5gDfDmEsCKhdkqKBgwoxLvy/z3ylHSTnIK99LuUdJso6cagx4RrZmOAU4FxIYQd\n8H/ZI4H/BS4JIWwNvAUcF1+zRET6n1qv4bYBa5nZSmAI8CqwL/DFaPt1wHnAFXE3UNI3oLWy36GS\nrpKukm4cejxSCOEV4HvAIrzQvoNfQng7hFD43/BlYExX329mk81stpnNXrp0aTytFhHJoR4TrpmN\nACYCWwBvA78ADqr1BCGE6cB0gHHjxiX4607iMrCtuydrlHSVdPuQdJNMud7I4rnKzh1v0o1DLVl5\nP2BBCGFpCGElcCuwJzDczAo/gZsAr8TWKhGRfqiWa7iLgPFmNgRYBkwAZgP3A4fjPRUmAXck1UhJ\n16Cqa7iVGj/p9mbchdJ1Jd0Ekm4a13NLj59U0o1BLddwHwVmAo/jXcJa8EsEZwKnm9k8vGvYNbG1\nSkSkH6qpl0II4b+A/6p4ez6wW+wtkswNau0hUq7WuEk3yYkpQUm3V0k3zZ4LpcePO+nGQE+aiYik\nRGMpSJW12lb28jsaL+mmMQU7KOnWlHSz6KNbevyYkm4clHBFRFKihCtVBvc64RY0TtLtywhjvl1J\n188bnSeGpJvp02ilx68z6cZBCVdEJCVKuFJl7dZ6B31T0lXSLabZhhh3ofT4fU26MVDCFRFJiRKu\nVFmrta/XcCsp6SrpFvvh5j7pxkAJV0QkJUq4UmXttuUxH1FJt6mTbiOOpVt6/FqTbgyUcEVEUqKE\nK1WGtES9FGL/vyO9pJvETMC+XUnXzxudp5ak28izRpQev4ekGwclXBGRlCjhSpVhrf8ofyOHSbee\nsXTLj9ndOZV0/bzRedaQdHMxP1rp8btJunFQwhURSYkSrlQZWplwC3KVdOufNaL8mN2dU0nXzxud\np4ukm6uZgEuPX5l0Y6CEKyKSEiVcqbJOy7I175CLpFv/rBG+rqQLdSbdJGYC9gORqMqkGwMlXBGR\nlCjhSpUhLTU+adbASbeep9HK9lPS9f3qSbpJzAQM6SfdGCjhioikRAlXqqzT0k0vhe40YNJNYiZg\nX1fShd4l3URmAi7ZnlrSjYESrohISpRwpcqw3ibcAiXdLs6rpJvITMD+Rtn2PCRdJVwRkZQo4UqV\nYS11zvigpNvFeZs46SYwE7Cv5i/pKuGKiKRECVeqDLNCwshv0k1y1oiy/ZR0fb81Jd0EZgKGfCZd\nJVwRkZQo4UqVYS3R/xadUZxS0u2Wkm5kjUk3gZmAIZdJVwlXRCQlSrhSZWjL4OirqD9uDpNunPOj\ngZJufUk3gZmAS4+To6SrhNtkxo+H007LuhUizclCitXezJYCL6Z2QumNzUMIo7NuhEh/lmrBFRFp\nZrqkICKSEhVcEZGUqOCKiKREBVdEJCUquCIiKVHBFRFJiQquiEhKVHBFRFKigisikhIVXBGRlKjg\nioikRAVXRCQlKrgiIilRwRURSYkKrohISlRwRURSooIrIpISFVwRkZSo4IqIpEQFV0QkJSq4IiIp\nUcEVEUnJ/weCS3jrpPMW6wAAAABJRU5ErkJggg==\n", 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\n", 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yfTl0MwB7P90UqJg4PRvlTJdCbbVo4aMcjjyyYl8I8MknOy+389xzUFLixzRq\nBPvtt3MQawVeEamLrG7h1sTWrTB/vreCKwfx8uUVx7Ru7d0QO3ZNtGyZXN2pohau1MXm4T5pzsoB\nPrS/63RvvZQvjpkN1MJNocaNPUD79fv2/vXrvRuicghPnlwxVy/4Kr7ly6WX33bsqNawiHybWri1\nEAJ8+qm3ht95x7c5c3wu33Lt21cE8EEHwSGHeDCnawirhSupVDL4EADWHtiYDrO8n9femJtkSXWm\nFm5CzKB7d99OPLFi/5dfegjPmVMRwrfc4ssFgY8PPuww3wYM8As32rRJ5mcQkfgpcFOoVSu/Cu4H\nP6jYt20bfPABzJ4Ns2bBzJk+f0T5B4t99/XwPewwOPxw79JoUO053ETSU/5LxQB0fAnskO8BsHGE\nTxXZ+j1fGKZ03vxkikuQAreeNWpU0a1wzjm+b+NGD+CZMz2En3/elxcCX2n4hz+Eo4/2y5x79kzf\nbggRqRkFbgJatoTBg32DiqFqr74K06b59uij/lhhYcUcE8cdpy4IyTyheB4ALYqjHX16AlD6w/4A\nNF7s82SXfPJp7LXFTYGbBsw8WAsL4YwzPIDnz68I38ceg3vv9Ysxhg6FU0+FYcO8C0NEMocCNw2Z\n+YUX++0HF17oU1e+9ZYH7yOPwDPPeFfFccd5+A4fDk2aJF21SPWULvThPHkLox1dOgPQoN9+ANjn\n6/y4SivEZAudnskAeXl+Yu3GG2HpUpgxw4O4uBhOP90n6/ntb2HduqQrFZHdUeBmGDMP31tugWXL\n4O9/9zG+V1/tqx5fdJH3B4tkipIVn1Gy4jPK3v2Isnc/8mvsS0rI79GN/B7daNCiBQ2yZJYpBW4G\na9DAT6Y9+2zFEkUTJ8L++8Ntt3lXhIikDwVuljjgALjvPli4EI46CsaO9XG98+YlXZlIzZSuX0/p\n+vWUfPKpj1woK4OyMvLa7U1eu71p0KQJDTL0pIUCN8v06AFTp8IDD/ilxocf7gtyikjyFLhZyAx+\n+lOfhL1jRx9KNm1a0lWJ1E7Z5s2Ubd5M6dp1lK5dRwiBEAINmjWjQbNmWH4+lp8ZA64UuFmsWze/\nmKJnT1+eaMWKpCsSyW0K3CzXoQM88YTP93vuuRVzOIhkqrB1K2Hr1n+2fMtZ48ZY48b+ES9Nr4dX\n4OaA3r3h2mt9VYvp05OuRiR3KXBzxHnn+fSQEyYkXYlIaoWSEt+ilu8/NcjzLY0ocHNEkyYwahQ8\n9RRU+hQFYQepAAAGdElEQVQmIjFS4OaQoUP9Ip6Z2buwqoifqAgBykp9K+/TTYO+XQVuDhnoK1vz\n5pvJ1iGSqzJj8JqkRKtWPmrh44+TrkQkRmk0NEct3BxTWOiT3ohI/BS4OaZdO03jKJIUBW6OadMG\n1q9PugqR3FTtwDWzPDObY2ZTo/v7mNksM1tkZg+bWaP6K1NSpWlT2LIl6SpEclNNWriXAB9Wun8D\ncGsIoTewHjg7lYVJ/WjSRIErkpRqBa6ZdQV+BNwT3TdgMDAlOmQS8JP6KFBSq2FD2L496SpEclN1\nW7i3AT8HyqL7ewMbQggl0f3lQJeqvtHMRpvZbDObvSYLF4XLNPn5fvGDiMRvj4FrZicCq0MIxXs6\ntiohhIkhhKIQQlFBQUFtnkJSqEEDn0BfROJXnQsfjgB+bGYnAE2AlsDtQGszy49auV0BzbaaARS4\nIsnZYws3hHBVCKFrCKEQGAG8FEIYCUwHTokOGwU8VW9VSsqYpdWFNyI5pS7jcK8ALjWzRXif7r2p\nKUnqkwJXJDk1mkshhPAy8HL09WLg0NSXJPUpTSfCF8kJutJMRCQmCtwcoxauSHIUuCIiMVHgiojE\nRIErIhITBa6ISEwUuCIiMVHgiojERIErIhITBa6ISEwUuCIiMVHgiojERIErIhITBa6ISEwUuCIi\nMVHgiojERIErIhITBa6ISEwUuCIiMVHgiojERIErIhITBa6ISEwUuCIiMVHgiojERIErIhITBa6I\nSEwUuCIiMVHgiojERIErIhITBa6ISEwUuCIiMalW4JpZazObYmYfmdmHZjbQzNqa2YtmtjC6bVPf\nxYqIZLLqtnBvB54PIewHHAh8CFwJTAsh9AGmRfdFRGQX9hi4ZtYKGATcCxBC2BZC2AAMAyZFh00C\nflJfRYqIZIPqtHD3AdYAfzKzOWZ2j5k1AzqEED6PjlkJdKjqm81stJnNNrPZa9asSU3VIiIZqDqB\nmw/0B+4IIRwMbGaH7oMQQgBCVd8cQpgYQigKIRQVFBTUtV4RkYxVncBdDiwPIcyK7k/BA3iVmXUC\niG5X10+JIiLZYY+BG0JYCXxqZt+Jdg0BPgCeBkZF+0YBT9VLhSIiWSK/msddDEw2s0bAYuBMPKwf\nMbOzgU+AU+unRBGR7FCtwA0hvAMUVfHQkNSWIyKSvXSlmYhITBS4IiIxUeCKiMREgSsiEhMFrohI\nTBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCKiMREgSsi\nEhMFrohITBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCK\niMREgSsiEhMFrohITBS4IiIxqVbgmtlYM5tnZu+b2YNm1sTM9jGzWWa2yMweNrNG9V2siEgm22Pg\nmlkXYAxQFEI4AMgDRgA3ALeGEHoD64Gz67NQEZFMV90uhXxgLzPLB5oCnwODgSnR45OAn6S+PBGR\n7LHHwA0hrABuApbhQfslUAxsCCGURIctB7pU9f1mNtrMZpvZ7DVr1qSmahGRDFSdLoU2wDBgH6Az\n0Aw4rrovEEKYGEIoCiEUFRQU1LpQEZFMV50uhaOBJSGENSGE7cDjwBFA66iLAaArsKKeahQRyQrV\nCdxlwAAza2pmBgwBPgCmA6dEx4wCnqqfEkVEskN1+nBn4SfH3gbei75nInAFcKmZLQL2Bu6txzpF\nRDJe/p4PgRDCr4Ff77B7MXBoyisSEclSutJMRCQmClwRkZgocEVEYqLAFRGJiQJXRCQmClwRkZgo\ncEVEYqLAFRGJiQJXRCQmClwRkZgocEVEYqLAFRGJiQJXRCQmClwRkZgocEVEYqLAFRGJiQJXRCQm\nClwRkZgocEVEYqLAFRGJiQJXRCQmClwRkZgocEVEYqLAFRGJiQJXRCQmClwRkZgocEVEYqLAFRGJ\niQJXRCQmClwRkZgocEVEYqLAFRGJiQI3xwwYAJdcknQVIrnJQgjxvZjZGuCT2F5QaqJHCKEg6SJE\nslmsgSsiksvUpSAiEhMFrohITBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCKiMREgSsiEhMF\nrohITBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCKiMREgSsiEhMFrohITBS4IiIxUeCKiMRE\ngSsiEhMFrohITP4PrQ161dhEqnEAAAAASUVORK5CYII=\n", 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\n", 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+ "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -225,21 +226,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb index 5b49e82fd..9c3d24ae5 100644 --- a/notebooks/plot_OT_L1_vs_L2.ipynb +++ b/notebooks/plot_OT_L1_vs_L2.ipynb @@ -42,7 +42,8 @@ "\n", "import numpy as np\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -63,9 +64,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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Lc/F+Ly4U1y1WvGb2LATXTMn91Z6F4mtgsaD8+295W8GSU6WSfUa22TrggQVLw/KBO8KO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\nfeLcgsUAAAAASUVORK5CYII=\n", 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\n", 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UCfj6awFvb5BHwc12mtFphwnnDNEI3GjCsXeUaXMreP/4I21fWfDevt12cRNr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\nRU5ErkJggg==\n", 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\n", 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sK3JykiywbdzoUZJm9gR57y3p4GZm9eQfRiPJN062amt/vA04Edfmxmkk1oWDm5XDFZP1y2kk1oWDm5XDFZP1yWkk1o2Dm5XCFZP1y2kk1o2Dm5XCFZP1zWkk1oWDm5XDFZP1y/lu1oVTAawcHsZtZvPgPDczGznbfyhZYznPzarLOW42IKtXl10CqwoHNxs+57iZ2YA5uNnwOcfNCjQ5CVI2wY7X7p4cbQ5uNnTOcbMiTU5CRDbBjtf+Po02BzcbOue4mdmgObjZ8DnHzQZk1aqyS2BV4eBmw+fkWxsQt/5tRiF5bpKWAh8AdgIuiIj3tK3fDfgMcAiwFfjdiLi12z6d52ZmZu2GlucmaSfgI8CRwBLgeElL2jY7CfjfiDgAeB9wdr/HtRpznpuZDVgR3ZKHApsi4paIeARYBxzTts0xwIXp9SXAK6SZgbs2cpznZmYDVkRw2xe4vWX+jrRs1m0iYhtwH7BX+44knSJpWtL0li1bCiiaVZLz3MxswCo1oCQizo+I8YgYX7BgQdnFsQFxnpuZDVoRwW0zsLBlfr+0bNZtJO0M/CLZwBIbQc5zM7NBKyK4bQQOlPRsSbsCxwGXtW1zGXBCev06YCqq+jgCGzznuZnZgPUd3NI1tNOBy4EbgfURcYOkNZKOTpt9AthL0ibgz4Ez+z2u1Zjz3MxswPw8NzMzqw0/z82qy3luZjZgDm42fM5zM7MB27nsAtgIelye22mwbK3z3MysUG652dA5z83MBs3BzYbOeW5mNmgObjZ8znMzswFzcLPhc56bmQ2Y89zMzKw2nOdm1ed8NzMbEAc3K4/z3cxsQJznZuVxvpuZDYhbblYa57uZ2aA4uFlpnO9mZoPi4Gblcb6bmQ2Ig5uVx/luZjYgznMzM7PacJ6b1Yfz3cysYA5uVj7nu5lZwZznZuVzvpuZFcwtNyud893MrGgOblY657uZWdEc3Kx8znczs4I5uFn5nO9mZgVzcLPyLV++ffDI5O4pLWBigsmHlmfrnRZgZnPk4GbV4rQAMyuAUwGsWpwWYGYFcMvNKsVpAWZWBAc3qxSnBZhZEfoKbpKeIelKST9I/z69w3Y/l3Rtmi7r55jWcE4LMLMC9NtyOxP4ekQcCHw9zc/m4Yj4tTQd3ecxrcmcFmBmBeg3uB0DXJheXwi8us/92ahrTQuYZEcKwPLlfmKAmeXWb3DbOyLuTK/vAvbusN2TJE1L+pakjgFQ0ilpu+ktW7b0WTRrBKcGmNk89EwFkPQ14JmzrHpH60xEhKROTz7dPyI2S3oOMCXpexFxc/tGEXE+cD5kDyvtWXprPqcGmNk89Gy5RcQREfGCWaYvAndLehZA+vfHHfaxOf17C3AV8MLC3oE1mlMDzGw++u2WvAw4Ib0+Afhi+waSni5pt/R6DPh14L/6PK6NCKcGmNl89Bvc3gP8lqQfAEekeSSNS7ogbfN8YFrSdcAG4D0R4eBm+Tg1wMzmoa/bb0XEVuAVsyyfBk5Or/8d+JV+jmMjrFtqgK+7mVkHvkOJVVt7agBkgS2NlnR6gJnNxsHN6sfpAWbWg58KYPXj9AAz68EtN6sdpweYWS8OblY7Tg9ogHPOeeKIV183tQI5uFn9OD2g/lqumwK+bmqFc3Cz+pktPeDYY2HdOsAjKGuh5bopK1du/7Hi66ZWFEVU8xaO4+PjMT09XXYxrC5aWnM6fIKY2uAKsw5WroSzzoIVK2DNmrJLYzUg6ZqIGO+1nVtu1gyPG0HplkAtbNgAa9dmgW3tWncrW6Ec3KwRPIKyZlpa2qzxdVMrnoObNYJHUFZUy6jIx10LPffcx7es/cR1K5ivuVkz+JpbNflzsYL5mpuNlk43WD733NlbDh5FORy+FmolcXCzZpjtBssTE/C2t/k+lCXytVAri7slrflSQFtzz2msHPN9KIfO598K5G5JM9xyGJpOA0dOPdV3k7FSOLhZo3UcRbl7h8rY1+Lmp9NjiKDzw2bNBikiKjkdcsghYda3qamIsbGIqamAlvn3vnf25VNTZZe4vtI5XM0Kn0sbGGA6csQQt9ys2TqNoty2zaP4CuTuX6ucPBGwjMktNxukVasiILJWBsRqVgRky62Ds8/e3hrbfp6mprLlM6/dcrMBI2fLrfQg1mlycLOB61QZ96rER1WnLt6pqe7rzAqUN7i5W9JGU7dnwnUaHDHquXHdErI7df964IiVJU8ELGNyy80Gyl1sc+auXKsC3C1pNj8jXYk76FvFObiZ9aNbJd7ka3K+rmYVlze4+ZqbWbtu1+Og3tfkOt1JZCZ53dfVrCnyRMAyJrfcrDR5WmZ1HWnZo/U10l2yVgu4W9JsMLoGgLK77voJzHnXm5VoKMENeD1wA/AYMN5lu6XATcAm4Mw8+3Zws0rrFgD6uV7X7/p+W2ZlB2ezHoYV3J4PHARc1Sm4ATsBNwPPAXYFrgOW9Nq3g5tVVpcA0Hfw6Hd9yzbzaplVvVvVRt5QuyV7BLeXApe3zL8deHuvfTq4WWX1O1x+gOvdMrOmq1Jwex1wQcv8G4EPd9j2FGAamF60aNEgz4/ZYPTZLdjv+tYyuGVmTVRYcAO+Blw/y3RMyzaFBLfWyS03q6VhDOjIcb3PLTNrqiq13NwtaTZj0Nfc3DKzhssb3IaRxL0ROFDSsyXtChwHXDaE45pVT69E6H7XL1++/Zl025O0Jyay5WYjRFkgnOcfS68BPgQsAO4Fro2I35G0D1lX5FFpu6OA95ONnPxkRLyr177Hx8djenp63mUzM7PmkXRNRIz32m7nfg4SEV8AvjDL8h8BR7XMfwX4Sj/HMjMzy8v3ljQzs8ZxcDMzs8bp65rbIEnaAtxWdjn6MAbcU3YhasDnKR+fp3x8nvKp83naPyIW9NqossGt7iRN57noOep8nvLxecrH5ymfUThP7pY0M7PGcXAzM7PGcXAbnPPLLkBN+Dzl4/OUj89TPo0/T77mZmZmjeOWm5mZNY6D24BIOlfS9yV9V9IXJD2t7DJVlaTXS7pB0mOSGj2Ca64kLZV0k6RNks4suzxVJemTkn4s6fqyy1JlkhZK2iDpv9L/uT8pu0yD4uA2OFcCL4iIg4H/Jnsags3ueuBY4BtlF6RKJO0EfAQ4ElgCHC9pSbmlqqxPA0vLLkQNbAPOiIglwGHAHzX1O+XgNiARcUVEbEuz3wL2K7M8VRYRN0bETWWXo4IOBTZFxC0R8QiwDjim5DJVUkR8A/hJ2eWouoi4MyL+I71+ALgR2LfcUg2Gg9twvBn4atmFsNrZF7i9Zf4OGloR2fBJWgy8EPh2uSUZjL6eCjDqJH0NeOYsq94REV9M27yDrCvgs8MsW9XkOVdmNhyS9gT+AfjTiLi/7PIMgoNbHyLiiG7rJZ0IvAp4RYx4zkWvc2Wz2gwsbJnfLy0zmzdJu5AFts9GxKVll2dQ3C05IJKWAsuBoyPiobLLY7Xkp9hboSQJ+ARwY0ScV3Z5BsnBbXA+DDwFuFLStZI+WnaBqkrSayTdAbwU+LKky8suUxWkAUmnA5eTXfhfHxE3lFuqapL0OeBq4CBJd0g6qewyVdSvA28EDk/10rWSjur1R3XkO5SYmVnjuOVmZmaN4+BmZmaN4+BmZmaN4+BmZmaN4+BmZmaN4+BmZmaN4+BmZmaN4+BmZmaN8//yexB3lMqD9AAAAABJRU5ErkJggg==\n", 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\n", 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SDp5/3gzIX6m3j3lJeznH9fKy0fSSxA/jpT28+SbwxhvhxMvEVODsWWkP7e3m\nsaEHHBD3mliA66W4Xi47l14SC2C8tIYwR6tMzjXw1lYjYWsrFk0HUFuL5pOWYGZra//7l14a6ypW\nAjt2AKtXAx/9KNPnAAZ4efXxwIqaWrz22SU4j16SOGG8tIb2dmD//YHRo4NfdnLOwC+91HTAmDwZ\nMx80Lcmv3GJeo66OvSsj4oUXgJ4eps/78Hg5q7UOE15qwddup5ckZhgvreCdd4CNG8OLl8k5A0/D\n3pWx0t4OVFebDhnEQ20t9M4lOOOUOqyll8QWGC9jJeyHPSXnDNyFvSvjY+dO04HtiCOAIYkzJ1wa\nGoA9P1OLpi56SeyB8TJe2tvNrbZjx4az/MSFYfaujI8XXzSVeEUP3pKDtJffH8fe6MQeGC/jY8sW\nYN26cC83Jq4C7+tdefrpaIFJD2092e1d2dLCoQJDpL3dPEVnwoS418RCXC+HfMF4ec+X6CWxAMbL\n2Ag7fQ4ksQJP964880zcX1UHADi5yx3nl50zQmPXLjN86gc+AOyxR9xrYyEeL/8wog7d3fSSWADj\nZWy0twOpVLgPe0peJzbPrQ/V95vOGbW40Izzy84ZobFmDdDdzd7nOfF6+cASfPHzpjOb3nMPh1Yl\n8cF4GQtbtwJr1wLHHBPu95R0Bi4iY0VkuYi84P7dO8d8u0TkKbfcV8p3pmHnjGhpbweGDTMPo08C\ncbnZ0AAMmVaLa7s4tCrZnTi9ZLyMjueei+hhT6o66AJgIYDL3P8vA9CYY77OwSz/qKOO0rw4jmoq\npfMwRzWVMq9J4OzapdrYqHr33YNfBoA2LcG1YkuYbvrxstf1smtkSnuW00tbqTQvGS+j4dZbVX/6\nU9Xe3sF93q+XpV4DPwXAze7/NwM4tcTl+cczVGAnRgKzZwN1dWie0dL/PjtoBMLatcC2bYlLn8fj\npmdo1U6MxJ8+ORu9X6SXpI9YvWS8DJ9t24CXXormYU+lVuDvVdVX3f9fA/DeHPONEJE2EfmbiOQV\nVkTq3XnbOjo6cs+Y7pxRW4sjp08GFiwAZs/Gylta+2VlB41AWLkS2HNP4NBD416TogjUzcF4OfGc\nyZjylwV4/Bh6SfqI3UvGy3BZtco87CmS220LnaIDeATAs1nKKQDeyZj37RzL2N/9ezCAlwEc4ic9\nUDAl5IXpoVDo7VW99lrVO+8sbTkIIVUZl5vFeLnxt452VtFLW6lULxkvw+P221V/9KPBp89VA0yh\nq+pxqvoj9NXaAAAMbUlEQVQvWcq9AF4XkX0BwP37Ro5lbHD/rgHwRwAfKfS9xcAOGuGxbh3Q2Wln\n+tx2NxsagP3+g6OzVRpJ8JLxMhy6u82AV1Gkz4HSU+j3AZjh/j8DwL2ZM4jI3iIy3P0/BeD/AVhZ\n4vcOgKMNhcfKlea+78MPj3tNiiZ2NzOfFX5J9fXoXkovKxxrvGS8DJ4XXjBjZkQ1WmWpFfjVAD4j\nIi8AOM59DRGZJCI3uvNMBNAmIk8DaAFwtaoGWoGzg0Y4qJrbxw45BBg+PO61KZr43czo0Pb4MbOh\n9LLSscZLxsvgaW8HRo4EDjwwmu8raSAXVX0TwLQs09sAnOf+/xcA/1rK9xRkQAcNAAvqTAeNi1uB\nmeiTlRTHxo3Au+8mc6wHK9zM8LL23jq0HO12HJoJelmB2Ogl42Uw7NxpzsA//OFo0udAEodSzUb6\n2bcAZt7sPj5vwQKMRGe/jEmsheJg4ULTAodJnw8ZAkx8jS3yQZHh5R53L8Gn/my83HUGvSwKj5d9\n8ExxcDBeBovr5urVphKfOBGRuVkeFbgHdtAokcmTgbo6qNOC9nbg6G0tGH4ObzEplfTjRq/darz8\n4Vv0sihcL/sqcd76FAiMlwHgutmxpAV77QVMeClCN/10VY+rFHVbhBfvLRJVVapNTaqqOneu5/3G\nxsEtuxJwHN01NqV//PQc3TEmmFtMEPGIV2GWILzsHlqlfz6jSbu76aVv3BHuNp43R3sDuvWJXirj\nZQD0LHd0a3VKn6sL5rY8v17GLl2+MighXRnVcczWNTWpiqg2NZnXnvdJbl6ePkcV0O7vzglkeRUf\nKDO8fOOyJu2F6JNn0Uu/7Nyp+o/TjJfvXEQvMwvjZXy88ILqHz9t3NQ5pbtZuRV4Y2OfbH0tyKYm\n1epqDlqQjRz7q7e6WjdfFNz+qvhAmWU/v3xRk3YPNV4GdUZZNmTsr507VdvONvtrw3n0MlthvIyA\nbPvLcbT3a/XaMzalu77HM/DShMxg7lyzlfNgWkfzMEcBz86vdPK0wDPfL4WKD5QZ0MsCZHj55Fkm\nY7HuO/QyV6GXEZAZLx1HtaZGdfTofhcDcJMVuBde48lPjv0z4P0S9w8DZRYyron/7UtNum0bvezD\n81S37qFVuv479DJfYbyMiMxhaOvrd6+sS9xHrMDT8BpPXqJqcTNQZpDh5cZZ5gzz8dPppSq9HExh\nvAwf27yMXbp8JRAheY1nIDHtDwbKDLIchzdm918T7xlb2V6uXq36yOf69we9ZLyMBcvjZezS5SuB\npYQ8ZGtBzUKjLpq+e8eEskwTxdTCZqDMTy4vbzqnsrzsfdR4ufSzJiPReSW99FsYL0PA8ngZu3T5\nShhCquru1zCamnbvmFDOLc0YrnExUPrAc1y216R06WebdNuolHY9UBlevnuvuZd2HubozmFVurOR\nXhZTGC9DwuJ4Gbt0+UooQmbrReiRsqzSRFnSP4umOzoLjZH3MmWgLECGl72POrpjTEofPqGp73ni\nZXOrWYaXvb2q15xEL0stjJclksB4Gbt0+UooQvo8SGWRJrLox8dAWYAK9vKBWY52VqX0ybOadNc4\nejnYQi9LJIHxMnbp8pXQUkLZKNc0kSXbxUA5SDy3Um2tTunS45t0e03y0+pdDzjaNbJ/u9Zf3NSX\nYaCXyfDShrgSOJZsFyvwYvDb8vLc72ddK9PyljID5SDI8HL7Q452u9fG+9Lq45Ll5ebNqj+YRi/D\nKIyXRVAm8TJ26fKVyIT0eTCnwqT6Ym1l5hjKT+vrrUn/ZIOBchAU4eWWvSw4+8lzy82rt5t1u3mm\n+Q0985Xo0+XZoJeDIEnxMsf6pr1MeryMXbp8JdKUUDYy0ymOs/u0qFuZuVq/2dbNorQWA2WAeI7z\n9pqU3nauo80zHO2q9nR2i+PsJ8PNbQ+ajnh//VJ/xmB7TUq7fkAvwyg2eWlNvPSsV8HKOoHxsiRh\nAHwRwD8B9AKYlGe+EwCsArAawGV+lx+rkFkOemdVSqfC8dfKDELUAmfb3h+K7fdrRh0ow3TTOi/3\nyu7luyNSuu3BCL1sbNSt9zvaPdq42VmV0os+5NDLSvXSlniZnu6JmYuml4eXpco4EcARAP6YS0YA\newB4EcDBAIYBeBrAkX6WH6uQRVSe+VqeA0TNdQ/hiSf6Totn+1H03daQbT0sIYZAGZqbtnrZN274\n6JTe9XVzVu49833xRkff+Z2TvbNYtgBaX29Klu/yevnijY5ur0npZR/fPSgCagajoZcV6+Wg42WJ\nXqaXka2yBkzaP+leBiVlPhk/AeBhz+vZAGb7WW7sKaFMfLYyc1aouVI0+VI3ftJS+X4AlkgZV6oy\nDDeT7OWUKaq/Pa9/wJTu0Sltv87Rlxc52jM2pRt/a5bx9j2O7hpVo7tGjdZ1t5hpz11vzq4fa3D0\ngUv6GwedVSaNf+utqk/92KTNbUxLZoNehkip8TJXXPM8/ctXvNQsyy4TL6OQ8QwAN3penwPg536W\na52QJaa087b6soiXbRmhpqBCxNJAOSg3k+hlbyqlb97taH19di+nTNEBZ+ydVSltnuHsNu3mmY4e\nf3x+t31lneglvfQRLwtV7MXE3HL00o9ojwB4Nks5xTNPYDICqAfQBqBt/Pjx4e+pUsl35uvjuks6\nePqW1/KKOhdhBMoo3SxXL3tT5p7yiy/O7mBRQTXfNUhLoZcRU0S8zFcp00ufFbivhVRKCj0bQd3W\nlcC0eDFYeqZTHqnKbBTrZRldrikGehkx9NIXNlXgewJYA+D96O+Q8UE/y02EkLkYxL2HSUuLF4Ol\ngXJQbpadl7kCaK5rjfSSXgYNvRxAJBU4gNMArAfQDeD1dKsRwH4AHvTMdxKA52F6Vn7P7/ITLWQ2\nckmaqxd6AsXLRdSBMkw3y85L1exu5urtSy/pZVTQy7xFzLx2MmnSJG1ra4t7NUgAiMgKVZ0U93oE\nAb0sH+glsRG/Xg6JYmUIIYQQEiyswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAE\nwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkh\nhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEUlIFLiJfFJF/ikiviEzKM9/LIvKMiDwl\nIm2lfCchhaCXxFboJgmSPUv8/LMATgfwSx/z1qrqphK/jxA/0EtiK3STBEZJFbiqtgOAiASzNoQE\nAL0ktkI3SZBEdQ1cASwTkRUiUp9vRhGpF5E2EWnr6OiIaPVIhUIvia34cpNeVjYFz8BF5BEA78vy\n1vdU9V6f33OMqm4QkfcAWC4iz6nqn7LNqKq/AvArAJg0aZL6XD6pMOglsZUo3aSXlU3BClxVjyv1\nS1R1g/v3DRH5HYCPAcgaKL2sWLFik4iszZicAlBO14XKbXuA7Nt0UJBfQC9Dp9y2B4jASyA+NyvE\nS6D8tmnQXpbaia0gIlINYIiqbnH//yyA+X4+q6r7ZFlem6rm7L2ZNMpte4BkbBO9zE+5bQ+QnG0a\nrJuV4CVQfttUyvaUehvZaSKyHsAnADwgIg+70/cTkQfd2d4L4AkReRrAkwAeUNWlpXwvIfmgl8RW\n6CYJElFN1mUTtr7spxy3qRDlts3ltj1AeW5TIcpxm8ttm2I7A4+JX8W9AgFTbtsDlOc2FaLctrnc\ntgcoz20qRDluc7lt06C3J3Fn4IQQQghJ5hk4IYQQUvGwAieEEEISSCIrcL8PBLAdETlBRFaJyGoR\nuSzu9SkVEblJRN4QkWfjXpc4oJd2Qi/ppY0E4WUiK3D0PxCg4KAbtiIiewD4BYATARwJ4CwROTLe\ntSqZZgAnxL0SMUIv7aQZ9JJe2kczSvQykRW4qrar6qq416NEPgZgtaquUdUdABYDOCXmdSoJd6jH\nt+Jej7igl3ZCL+mljQThZSIr8DJhfwDrPK/Xu9MIiRN6SWyEXmYh9KFUB0tADwQgJFDoJbERelmZ\nWFuBB/FAAMvZAOBAz+sD3GnEYuglsRF6WZkwhR4frQAOE5H3i8gwAGcCuC/mdSKEXhIboZdZSGQF\nnuuBAElCVXsAfAPAwwDaASxR1X/Gu1alISJ3APgrgCNEZL2InBv3OkUJvbQTekkvbSQILzmUKiGE\nEJJAEnkGTgghhFQ6rMAJIYSQBMIKnBBCCEkgrMAJIYSQBMIKnBBCCEkgrMAJIYSQBMIKnBBCCEkg\n/wfPnU7C6ZZu4AAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -351,21 +352,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index 8acaeec86..bf83aeada 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -36,7 +36,48 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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gUcitYQKyhUmSJExOTmJychKrV6/G7t27YbPZqtpfPS7iZrMZq1atyhtxIUmSssYViUTg9/uVyIkQArvdDrPZrAiX4Sw0MCiOIVAGdaVQDRMACIKAiYkJTE9Po7u7G1dddVXWXKJStp8LTZ0t5sWaZVk0NTXlpSFlWYbX64UoiojH45iZmUEymQSQscTnGjTUppBKnYXGOpfBcsEQKIO6oLaKnzp1Clu3bs264KZSKYyNjSEUCuGyyy7Dvn37smzfpUCjk9yLLxWoRsBkMilOwdxBh7Q9UyKRwNzcHJLJZJYlXi1cpVriaZTK87whXAaXPIZAGdQUrRqmWCyWV8MUi8XQ39+Pyy+/PCtqKAeGYbJaHKkfbxSB0oNa4p1OJ1avXq08Ti3x1KARDoezLPG5tVzqaLMcZ2E8HkcqlcKaNWsM4TJoWAyBMqgJxWqYotEoPB4PeJ7H+vXrMTQ0VPVFUC+VdykIlB5qS7yaXEt8IBAAx3ElW+LVfwMZlyLHcQAMZ6FB42IIlEFVFKthohHA8PAwBgYG8lxx1aAnRJeyQOlRqSU+N+KilnitDh0Uw1lo0CgYAmVQEcVqmILBILxeL+x2O2w2G3bv3l3zY6BrULksR4EqhJ4lXhAExVkYCoUUSzzLsmAYBizLIhQKwel05lni1X+rKdVZSEXMEC6DajAEyqBkilnFZVnG9PQ0xsbG0NLSgm3btsHpdOKll16qy/EUiqAMAIvFomuJn5iYQCKRwMLCAqamphRLvMPhyDJoqC3xgOEsNFhcDIEyKEoxq3ita5hKhZokqHDmPm6gDcuySl1Wb2+v8rgkSUgmk8rgx1xLvHqdqxxLvOEsNKgUQ6AMdCk27oLWMAUCAXR3d+NNb3pTVqNVSj3rkmZmZhAIBCDLsuKM4zgO8/PzaGtry0pdGVxE6/fBsizcbjfcbnfW47IsI5lMKunCYDAIjuNACNGs5SrVEg9kOwuBTN9Cuj2z2ZyVjjR+jysPQ6AM8ig27iKdTmNsbAxzc3Po7e3F/v37C9YwmUwmyLJcdp2THrIsY2pqCqFQCGazGVdeeaUighzH4dy5c4jFYgiFQkrqKrd/3koXrnJuGNRtnPQs8YlEAuFwGIlEArIsl2SJV/9NmZ+fVyI89eePvtZwFq4sDIEyUChmFec4Dl6vF9FoFH19fdi4cWNJNUy1EihJkuDz+TA1NYW1a9eio6MD/f39sNls4Hle6ebgcDjQ29sLl8ul/JxeG6KVKlzUSl4Nakt8R0dH1rbT6bRyzgOBABKJBCRJyrLE0z/qqJt+TnKPzXAWrkwMgTIAIQSJREL5omvVMHm9XqRSKaxfvx5btmwp64tf7ZqQKIpKKrGrq0tph3Tq1CnN7eaaJ/TaEK1k4apnKyiGYWC322G327Ms8XQtikZcs7OzSCQSWZb4WCyGWCwGm81WtEu8erulOguNda5LC0OgVjBqq/iZM2ewfv16NDc3K8/TOUwAqqphohFUufA8j/HxcczOzqKnpyevHVK1dVArWbgWu1chkPm9FLPER6NRRKNRBINBJSrOXePKPeeGs3D5YgjUCkRr3AXLsorjSl3DtHHjxizRqoRyBSqdTsPr9WJ+fh6XXXYZ9u/fr5mOqlehbqnCFQgEkEwmL0nhWgqBKgS1xNtsNvT39yudNERRVM55riWeugnpOXc4HCULl+EsvDQwBGqFUKyGyWQyYWZmBmfOnEFzc7NSw1QL9Apqc0kmk/B6vVhYWCipT99id5IoJFzJZBLxeBzRaDRPuERRhN1uR0tLS8MIV6MJFIUQkhUlm81mNDc3590k5Vrip6enkUqlACCvlsvhcJRsiQfynYX0uXQ6jZaWFkW0DGdh/TEEaplTSg3T1NQUpqen0dbWhiuvvBJ2u72mx1AsgkokEvB4PEgkEli/fj2uuOKKkr74auHLvXNezE4ShezZHMdhYmICyWQSIyMjSKVSirnA5XLB7XbD6XTm3f3Xm0YVKEmSSjquYpZ4us5VriVe/TeFukO9Xi+uuOKKrOcMZ2F9MQRqmVLMKi4IAnw+H/x+P7q6urBu3TrlDr/W6AlULBbD6OgoeJ7HwMAA2tvba2K+aJRWRyaTCW63G83NzWBZVhm3QYUrkUhkRVwMw+SlCuslXI0qULSerVLUlng1uZb4+fl5cByXZYlXC1euJZ66C9WCZjgL648hUMsMLWFSf+HVNUw9PT1KDZPH46lb94VcIVlYWFD2NzAwkNf8tJztXoq9+Khw6UVc6rRVvYSrUQWqFvZ3LYpZ4mmzXb/fr1jirVarIlh6Y13Uf+e+D8NZWD2GQC0TSqlhGhsbU9Z3cmuYWJZVTBO1xmQyQZIkzM/Pw+PxgGVZDA4O5vWIKxe1EOUWdDayQOmxmMLVqAIFLG4vRbUlvr29XXk81xI/Pz+PeDyOo0ePKpb43PEmhrOw9hgCdYlTaNwFkEmjeTwepYZJb32nUit4KceXTCZx7tw5NDU1YdOmTXkmg0pRt1BayjWoelOOcFGjQDHhamSBagRyLfFWqxUcx6G/v18RLo7jEAqFMDExoWmJd7lcsNlshrOwCgyBukQpNO4CyPQ083g8IIQoNUyFPtAsyyKdTtfs+AghmJmZwdjYGGRZRm9vL/r6+mq2fcAYt1GNcHEch3Q6bQhViUiSpKxLWSwWtLS0oKWlJes1akt8OBzOs8Sro65yLPF027nOQmrQUK9x0T/LBUOgLiGKWcUJIZibm4PX64XVai2rhqlWEZR65EZrayt27NiB6enprAmvtaLRTRJLRTHhooMN/X4/fD4fgOIR10pHkqSiF/5ClvhcU0w5lnj13xS1QSOVSuGNN97A1q1bldc++OCD+Jd/+Zfq3nQDYAjUJQA1PkSjUaWAUS1Msiwr0UpTUxOGhobyXEzFqHYNijZwnZiYQEdHR9bIjWpbHelBhYiOQ6frACtdoPRQC9f8/Dy6u7vR3NycZc3OHbNBi2HdbrfmfKiVgiiKFdcF6tXPFbPEq9e4ClniaRRMi+0B4JlnnqnwnTYWhkA1MOpxF5Ik4fXXX8f+/fvzaph8Ph/a29urqmGqNILKbeCqNXKjXutbQMYR6PP5wDAMRFFUvqROp1NxYdUjervUUaf21NbsNWvWKK9RX0Dj8bjmfCh1HVcthKtRbyyqtb9rUcgSrx5vorbE2+32vHShKIpK+pFhGCSTyUWZx7YYGALVgBSyitML8cTEhFLDpDeHqRzKjaBEUcT4+DgCgQDWrVunNHDVgrr4agUhBIFAAF6vF06nEzt37lQWjQVBgNfrhSiKCAaDGBsbgyAIRbtorzRKWXvSu4CWIlx6Katqj2mpkCSpZuNiikHdmU6nU9cSn0gkMDU1BY7jwPM8ZFnG8PAwXn31VVgslrKMSL/85S9x7733QpIk3HXXXfj85z+f9Xw6ncYdd9yBV199Fe3t7XjssccUs8hdd92F1157DaIo4o477sAXvvCFmp0HwBCohqKYVVyWZZw/fx7BYBDr1q3Dvn37dEWhXEqNcnIbuBabBUW3nbvAWwmyLCMQCGB8fBxtbW3o6+tTbMK01sRisSjTXru7u7OOm36xZ2ZmkEgkIIqiEmWpo4FandNGphoxKEW4aLfycoSrHlFKrVhMgdJDzxIfDAYRDofR0dGBcDiM3/3udzh9+jR27tyJ1tZWbNu2Df/2b/+m+fuWJAkf+9jH8PTTT6Onpwd79+7FjTfeiC1btiiv+d73vofW1laMjIzg0Ucfxec+9zk89thjePzxx5FOp3Hy5ElwHIctW7bgAx/4APr7+2v2npf/N/ESoJhVnNYwcRwHl8uFDRs21PyLXCyCSqVSGBsbK9rAVYtqU3yyLMPv92NiYgLt7e3K+pbf79d1HuamirS6aNO1q9w7UkmSsroLUOFa6gtULalHtFKtcOVashuJRhAoPWivx9bWVtxzzz3YvXs3HnvsMXznO99BKBSCx+PRPa9Hjx7Fhg0bMDAwAAC45ZZbcOjQoSyBOnToEB544AEAwM0334yPf/zjyueH3uglk0lYrdaqG0vnYgjUElLMKh6LxeD1esFxHNavX49wOIzu7u66fIn1RIQ2cI1EIujv78emTZvK3n+pzWJzURsv1qxZgz179mStJ1XbSYJhGNhsNthstry5RepUis/ny1oDoKJF1wAa9a6/EIuZTitVuKanpxGLxfDyyy9XlSqsB40sUGoLPJBZl6UW+Pb29qxoK5epqSn09vYq/+7p6cGRI0d0X2M2m7Fq1SqEQiHcfPPNOHToELq6usBxHP71X/+14q4wehgCtQRojbtQXyzUrYDWr1+PtrY2MAwDr9db09HpanIjKHUD14GBgZIbuGpRrotPbf7QM17Q7dajDqpQdwHazy0ej2Nubk5xXWnZtBtduJY6WskVLtpQd2hoqKpUYT1oZIESBCFL/CORSF6NVj04evQoWJaF3+9HOBzGNddcg+uuu06JxmqBIVCLRDk1TBaLRbMVEBWRenxRaARVbQPXQtsuhtoR2NnZWdB4Qbe7mIW6ev3cZFlGKpVCPB7Pu6BSl5XD4cCqVasapr6oEQ0JdA2qWMTFcRzi8fiiClcjC1RuBFWOQK1bt06phQOAyclJrFu3TvM1PT09EEURkUgE7e3tOHjwIP74j/8YFosFa9aswR/8wR/glVdeMQTqUqLYuAtCiFLY2tTUhC1btuQVWFLq2S+Pjto+f/58VdNztSgmUKIoZnVWLyZMlEZpFqsenqeGFsb6fD6kUimMjo5qDjh0u92Lvv7SyAKlh1q4Vq9enfVz6siW1hMBUEZs0JRspcJVrya2tUAQhDyB2rRpU0k/u3fvXgwPD8Pr9WLdunV49NFHcfDgwazX3HjjjfjhD3+I/fv344knnsAf/dEfgWEYXHbZZXjuuedw++23I5FI4PDhw/jEJz5R0/dmCFSdKDbugq6v+Hy+kucw1VqgCCFKA1ez2QybzYbdu3fXbPsUPYGidvlSrOpaNHonCVoY29TUlDVuo9BIefX6llYT0lqh1Z17qanUxae+QdATLnUhLICstUT6s40qQMWoJoIym834xje+geuvvx6SJOEjH/kIhoaG8KUvfQl79uzBjTfeiDvvvBO33347NmzYgLa2Njz66KMAgI997GP48Ic/jKGhIRBC8OEPfxjbt2+v6XszBKrGFBt3QaMFmsbKXfgvRK0EiqYTPR4PHA4HrrjiCrjdbrz00ktVb1uLXIESBAETExOYnp5GT08P9u3bV1H6pFEiqHLR6yyg7uWW24RULVq1Kj5eLgKlR7nCpe7goC6EbXTh0oqgylmDuuGGG3DDDTdkPfaVr3xF+X+73Y7HH3887+fcbrfm47XEEKgaUayGSV0/VGkNE8uyeYPRyj3GmZkZeL1eNDU11XSseyHoWpEgCBgbG8Ps7Cx6e3vLsqprcakKlB56vdxEUcxKX9HiY7PZnCdcpRYfX4opvlqhJ1y0gwMVLrUJJpVKwePxNKRwqTtJAItnklgMDIGqEipMHMfh3Llz2L59e9YHN5lMYmxsDOFwuOz6oVzMZnNFEZS6wLW1tbUuY90LIYoiotEojh49WvU5UKMWouU8boNae3NNM4IgKMYMveJj+if3ZqgRz89SF+qqOzjkRlwvv/wympqa8oSrESKu3PWxSCRS0zXkpcQQqArJrWEym83KEDkAiMfj8Hg8ygyZzZs3V33HWm6KT5ZlTE5Owufz5TVwXQzo9N5gMAiTyVQzYaKs9HEbFosFra2tBYuPA4GAMiFWXXxMTTuN5ExbaoHSQ5ZlWCwWrF69uuSIaymFKxqNGhHUSoRaxbVqmOiCPa1hkiQJ69evr4lNm1KqQImiiMnJyYINXPWoReonlUrB6/UiHA6jv78ffX19OHnyZM2/oI1uklgKSi0+TqfTeP3117OKj9WmgaUQikYVKD2LuV7EtdTCJQiC0Sx2JVGKVTwUCiGRSMDr9WJgYKAudzDF1qDU5oPu7u6yXXHUzFDpXTXN0y8sLGD9+vVK1Kg+b7WECpEoiggEArDb7XC73StaoPTILT6emZnBnj178oqPQ6FQ1sVUvcZV76LYS02g9ChHuJLJJGRZrli4cudULbfPvSFQBVCPu6C23FxhoqYDt9uFuR0lAAAgAElEQVQNu92OK6+8sm7HYzabNXvPqQ0Yvb29FbviKi0ETiaT8Hg8iEajmmPl6zVuQ5IkxGIxHDlyBB0dHUprqHQ6rexPfYFtpHRWo1Cs+JgKV27xcT2GG8qy3JCNemtVpFtMuGgBMr1JoMJFz7fb7YbD4cg6llyLuXpfy4HG+zQ0AKXUMNHmpa2trdi5cyccDgdeeumlurqjclN81TRw1aJcIeE4Dh6PB/F4HAMDA9iyZYvme691RENHffj9fphMJuzbty8r5RqJRDA1NYX29nalCWwikVDSWVS06Be+Ee/al5pCFu3ccfJaxcd0uGE534VGrM0C6t9FotB4DbUdPncuFDW/0OsVy7JIpVLLJr0HGAKVRTGruLqGae3atXk1TNWmyIpBBYrjOHi9XkSj0YobuBbafjESiYTSFWFgYABDQ0MF91+ri466sLenpwe7du3C+fPnYTabIctylqPPZDKhra0tbx1Gq5cegKy71EourisFvXHyxYqP1edWr/i40UwblKVqc6QX3ao/x6FQCKlUCseOHcPXvvY1hMNhxONxHDx4EENDQ9i0aVNBx26ls6B+/OMfZ42UP3HiBF577TXs3LmzpufAECgUH3dBU2gzMzMFa5jMZrMy1bUe8DyPYDCopNL0IpZKKRZBxeNxjI6OIpVKYXBwsKYGkELkChNNYaZSqbJMEoXSWfTiGo1GlYsry7JZbXLcbrcxnVcHveJjSZKyIgB18XFu14zlsgZVb9SfYzrqfcOGDfjRj36E5557Dg899BAmJyfxq1/9Cj6fD88++2zNZ0HdeuutuPXWWwEAJ0+exE033VRzcQJWuEAVG3ehdqP19vbi6quvLvgFogJV6xA7Go3C4/EgmUzCbrdj7969dREGvQiKNpAVBAEDAwNKd/V6oydMlFoV6haKCtTmgfHx8bzpvPQC24hrJ40Ay7IFi49pJ4exsTHlPIdCoYaafNxoAqVGXaTLsixWrVqFyy+/HJ/97GeL/my1s6AojzzyCG655ZYavquLrMhvVbFxF/F4HF6vF/F4PMuNVgwqULViYWEBo6OjAICBgQHY7XacPXu2buKQG0FFo1GMjo5CkiRFmBaD3B59eqaPeneS0Lu4qgtkp6enEY/HlTojdbTVSN0GGg2t4uPz58+jvb0dJpOpouLjenGpCBSQPQuqGNXMglJnIB577DEcOnSomrehy4oRqGLjLoBMBbbH41EihXJTWLUQqNwGrhs2bFC+xIIg1FQAczGZTJAkCZFIBKOjoyCEYHBwcNGK/koVJvXx6glRPe22egWytM4oHo8rC9r0c2e322E2m2vqeltuSJIEq9WKpqamvHOrvinQKz6u1+RjSZKWPIrTIzdjs9htjo4cOQKn04mtW7fWZfvLXqBoDdP8/LySwsm1ilNBYFm2qhqmapq5EkIQDAbh9XqzGrjWavulwPM8hoeHYbfbNedR1Qv1uI1ShInSSL34Cg059Hq9ygU21/WWu761koVLz8XHMAysVqum6UVdfDw5OZnl1qxV8XGjR1CVDiusZhYU5dFHH8UHPvCBKt+FPstaoCRJgiAIIITg1KlT2L9/f14N09jYGJxOp6YglEslEZS6lqq5ublgA9dKR6cXY35+HqOjo0in0+jq6sLg4GDN96GF2hVZSVfzQp0kGgV6cXU4HMq4DeCi6y0ejyMcDmNychLpdFqJstTrW416915ryp25VMrkY+p0y+3kUM58qEYXKPXnY7FmQQGZG4r/+q//wm9/+9vavaEclrVAUegHkF7QaA1TS0sLduzYAYfDUZP9lCNQS93AlUaOo6OjsFqt2Lx5M+bn5+v6RaSLq7kR0/79+1fUuA2g8MgN9WTeeDyurMHkdi5v1ItmpdTKxVfInk07OZRTfNzoAqU+tsWaBQUAv/nNb9Db21vTCbp5x1i3LTcAJpMp627a6/XC7/dj9erVdWmcShvGFkKSJGVQ4erVq8uaB1ULaFum0dFROByOrAm+kUikbilEk8kEQRAwNTVVdipPD70O5peCQOlhNpvR0tKSdZFRN4CNx+NZhceVRASNSr1t5oW6lSeTScTjcc3i43g8DrfbDYvF0nD1cVoR1GLMggKAa6+9FocPHy7ziMtjWQsUkFlXmZiYUNxA5fanK4dCEZQ6aujs7CyrgWstUA8ppIua6tw1cFFEao0kSUin0zh69GhNhKkYl7JAaVGoAWwqlcqKuNQRgTriarQLqxZLVQelLiZWQ9OwZ8+eVeq4couPl3r9cDnPggKWuUARQnDs2DF0d3dj9erV6Orqqqs1VUugqm3gqkU57ZSo+cLj8cDtdhdd46plzzxJkjAxMaG0JNq9e3fN0qmFWG4CpYc6laXVjigej2d1daDFsXTcBs/zDVV43GiFujQNa7FYMDAwoNxQqouP1euH6vOr7ppRT3KbxS6nWVDAMhco2qeNEIJoNFpXizaQLVA8zyuzkKpp4JoLdfIVE7lc80Upa221cglKkqSYH6gonzhxYtHuMFeKQOmhV3hMB2vGYjFIkoTTp09nFR6rI66lKDxuxCm/QP4aVCnFx3NzczWZfFwK6nO2nGZBActcoNRYLJa6pK/U0G7jZ8+eRTgcRl9fHzZs2FDTu8JiAkUIwfT0NLxeL1paWsoyX9A6qEqhwkTtquposV4dzQkhmJ2dhcfjAcMwiqVYEISGXtxeCuhIeZfLhenpaaXzvnp9S11jtBRzohpRoEp1FxaafEzPr7r42GKx5AlXtTcGy2kWFLACBIreTVsslrpGUBzHYXR0FAsLC+jp6anJBF0t9KIcWZYxPT2NsbExtLW1YdeuXWW7AlmWrUhEciMmrV6FepbwSqFrahzHYXZ2FkNDQwCgdNnmeR7Hjh1TjAS5Hcwb8UK4WORGKlarFVarVbPwmK5vUas2AE1jxko+n8WwWCyaxpdSio8LOTZzf4/LMWuw7AWKYjab6xJB0dHuyWQS/f39iMViWfUutSZXoNS2+fb29qrcieWm+LRSeXp3gLWMoEKhEEZGRuB0OuFwOLB161alQ4jNZkNraytmZ2eVgXxqa/HMzIzi0FJfZFdSI9hSUmnqGqPcxrr0fOY63krtWm5QuPiY53lFuHJHxajPscVi0W0BtlxYMQJlsViQSCRqtj3ap04UxawGqrR3Xr2g61yyLGNqagoTExM1s6uXKiJqYerq6irJ+FELgZqfn8fIyAhsNpviQnzppZfyXpdrP9eyFhdqBKsWreVYb1TNWo9aiNasWaM8ri48Vnctp4XH6lTWSik8rgS1Y7NQ8fH8/Dzi8ThSqRROnjyJI0eOgGEYJVNUSqqw0lEbQGa8xl/8xV8gGo3CZDLh5Zdfrksd57IXKPpFrFUj13A4DI/HAyDTwHWxHTMmkwmBQABnzpzBmjVrampXLxZBVSJM6uOuVKAWFhYwMjICs9mcVbelJveCWyzdobfQrXf3upzShPUwI+gVHtP1F63mr7mNdRuRRkmbaRUfx2Ix+Hw+9Pf3Y2xsDC+88AJmZmawb98+AMDmzZvx8MMPa66fVTNqQxRF3HbbbfjRj36EHTt2IBQK1e2mY9kLFKUak4S6X5/FYsHGjRvzLmy1Yto7C0eTA6s68ufqTE5Owu/3o729vS51VHoiQvc9OTlZtjCpt13ulz0SiWBkZAQMw+Dyyy+v2zlXo5d2oYWc9EJbTpqwUS5ylMV0y+mtv6hvBHw+nzKP6+TJkw1VeNzIRhtqtHA6nXjXu96Fyy+/HJFIBI899hgEQcDY2Jjuuatm1Mavf/1rbN++HTt27ACArEiv1ix7gaJfxEoEqpQGrno/V8kFIL6QwKP/+FO0drbg9gfeB5PJlFXg29XVhb6+PjgcjrrcseQKVC2ESW/bhYjFYhgeHgYhJKubeznU8gKsThOqKTVNWA/3YjUstZ07K43V1gpAgkxYvPrqqxgcHNRsRZRbGGuz2RblPTS6QOUW6dLvCr2R1qOaURvnz58HwzC4/vrrEQwGccstt5Q0f6oSlr1AUcpJ8VGr9tjYWNEGrnr7qURA/vtff4FIKI5IKI7f/fQIeq5ci0AgkGVAmJiYqFs7Ipriq6UwUUoRqHg8jpGREQiCgA0bNpScPqURivrCuxhRS6lpwnA4rLgOGyFNuNQCBQCM7INFeAwMCQCwgmeuhsW8WrcVkbrweGpqKqswVr2+VWujy6UkUOXMgqp2v7/73e/w8ssvw+l04q1vfSt2796Nt771rTXf17IXKHUEVUyg1I64tra2ihq4VipQXCwJ7ykfyAWX1M/+z//gz//11rwCX5Zl61bPJcsyeJ7H4cOH0dnZWdO2UIVs5olEQhklT5tSlrPdRiM3Tejz+cCyLFpaWipOE9aSpRYoVnwJZvEnAOiNVhJm6Wlc1rEKILsBJvu7U6jwOHcqr1YE63Q6K/4cX0oCtVijNnp6evCWt7xFWQu74YYb8NprrxkCVQ2FugvUsoFrpWaM8TM+cIkE0uk07A4HVjWvQnpOzPtylNKQtlzUERMhpC79CrUiKFo7xnEcBgcHyx4QCVwaIzeAytOE6uigVhfKpRQok3QOZvFxADnfRQK4bFOwCAchWO4ASpxgrVUYq45g/X5/XuExPaelFB7LstzQAqW+gS5HoKoZtXH99dfjn//5n8FxHKxWK1588UV88pOfrOl7o6wYgdKiHg1cyxUonucxPj6OF5/6HUwmUyatdeHLOfKqFzv/MHtSZS2HFsqyjMnJSfh8PiViOnr0aF3a3KgFKplMwuPxIBaLYXBwEB0dHRVfMPVuPBrNmKBHpW7Caopkl0ygSAgW4YfIEycABJljMsnHYJK3QmZ3V7wbPaMLtWnH43GlyBtAnkNT3Vg3d5xFI6EVQZU6C6qaURutra341Kc+hb1794JhGNxwww14xzveUZf3uOwFSst+LIoixsfHMTMzk9eSp1pYli1JoHieh9frRSgUwmWXXQaH7II9p1fe6PExSKIE1pyd4qtWoLSEqd6910wmE9LpNM6cOYNIJIKBgQFs2bKl6gslFahGi5iqpZibMHc6bzlpwiU5X4TAIjwKgNN9nh6RWfx/4E1bAaZ2LXv0ZkQVGrVBu5vTrhqNVnhcbSfzakZt3HbbbbjtttvKPOLyWfYCpYZhGJw7dw6hUAi9vb3Yv39/zS2sZrO5oICk02l4vV7Mz8+jv78fGzduBCHA1PB03mtTSR4TZ6ewfttlymO5AkUIwTM/eRW7rrkc7WsL27CXQpiAzHuenp5GPB7H5s2bccUVV9Tsi04FKvf32EgXklpRyzThYp8fk3wCJnlY93kCKJkDhkTASs9CMt+g+/qaHVeBURs0ek2lUjh79qxSeJxbyL0UjXWB5T9qA1gBAsUwDFKpFLxeLxKJBDo7O+siTBS9FB89hnA4jPXr12PTpk3KRSLgmYHAa0dd518Z1RUoKk5Hnn8DnjcC+PBn/hhWW36KMleYiqUya3WHrY4SW1tb0drais7Ozqq3q4amDnPTMJdKiq8WFEsT5g45NJvNkGUZwWBwcXrpER5m8WeFX0IIGFw8BrP4AiT2DwGm/uNZtKDnNBaLobm5WTEQqBu/0puu3P551JhR79SgIVDLAEIIzpw5g+7ubgiCgI6OjroW/uX2/Esmk/B6vYhEIli/fr1mE9nJ8wHd7Q2/6sH1H/5D5d9qgZqbjuLI828AAIKBCH71X6/gXbfvV16rFqa1a9eWtMamd8EvB1okODs7i76+PmzcuBHBYBCxWKzibepBTRK0ZoZ26zbQTxNOT09jdnZWM6VVDzchK70IhoQLv4gAyPpa8GClw5DMf6jzA4uDJElZ56GcwuN6dyDRGve+nGZBAStAoBiGwe7du0EIQTgcXpSRG8lkEhzHwePxIB6PY/369QXTWuHpBd3thQILmJuaR8e6NmX7NELznssWtpMve3Dde3fBZreULUwUKoCVCBRd25uensZll12WFanWY9wGvTAcP34czc3NsNvt8Pv9iMfj4DgOJ0+eVFJcuYvfKxVaJOtyuZQuAkAd3YQkDbP4QvGXIT9qZ6XfQmLfAjBLZ1IQRbHoHLVC/fPUjYq1Co/pea2k8Dg3tb3cZkEBK0Cg1CzGTChRFDE9PY1QKISBgQEMDQ0V/eAtzEYKPj9+ZlIRKHUE5X0jW6BkUcZLzx2HvVUsW5golQiJKIrK5Fy9tb1aCxRtHJtKpbB161a0trZCEARlv0eOHMHAwEBe1211cSf9s1RrCEtJnhiUmSYs1U3ISr8HUEKTZpVJQjlGMg+TfBIyu7PMd1c7qskmFGpUTFs75U7kVd8I0I7lpbLcZkEBK0Sg6EJ6rRrGakHHbsRiMdjtduzevbvkO6LwTGGB8g9PY/fbtgO4eGGRRAnjwzOZF1yw0CaTSQyfnMKdf/2uiu3y5bgE1QMKe3p6sH//ft0vc60EKhKJYHh4GCzLYsuWLfB4PJrF1CaTCU6nM6/rNi3upKM3RkdH82pk6BrCco22ylljLOYm1EsTut1uuJw2uPFcaccEaNY+sdLhJRWoUqZXl4teY131Z5O2WMsdbEj/zr0BXK5rritCoCj1iKBisRhGR0fB8zwGBwdhs9mUBqelsjAbLfj81HD+GpV/PAQ+KSjCZLXZ0NLagoWZNEgRHSCEIBJJoqUlv31TKUJC17YmJiZ0BxRqbbeaL1E8Hsfw8DBkWcbGjRuV4ky9Oig9+7lWcWdujUwwGATHcfkX3Dq00lkKqr2YqSOD3JEb6jqjBfkIOldNwMQwYM1mmFkWrNkMlmU1yz+YvBgKMMnnABIBmPL7MdYCSZIWrVltKYXHNIqVJAnpdBoejwc+nw9utxsMw5R83al01MbY2BiuuOIKpd5q3759+Na3vlWbE6DBihAodbsjWpxXLep5UIODg8odZjqdLqtOKZVIIcWlC75m1jcHPsXDas9cHAkhePWl0wiHw4owMUzmSyQKEjxnAth85WWa2xJFCT/92WsYHw/hz+86gFWrsvPrhSIo9QyqtWvXliRMlEojKI7jlFTexo0b8xaBqUki94tZqHNILno1MrkXXNpKh46KUEdbS9lxu1zqVQeVlSYkBFb+p2BIC2RZhiRKECURQioFSRRBALAmU0a4LrgKTYzWOSRgpdeWzCzRCK2OtKLYVCqFM2fOoLm5GefOncMvf/lLjI+PY8+ePdi0aRO2bt2Kz3zmM5rfz2pGbQDA4OAgjh8/Xv83jhUiUJRapPgikQhGR0dBCNGcB1VqoS6lWPQEAIQAAc8sejd3K3dQU2ORLGFS88brPl2B+uWvT+HU6SkAwMFHD+Mjf3YNbLaLHwMtIZFlGYFAAGNjYxXPoCp35HsqlcLo6ChisRg2bNig2wapWARVDVrrMmrHFjUU0Jsep9OZJVyNVthJqZdAESJjQfgNIvxhSPIUXJhEO9sON+uGyWqCBarPDAEkWYIkihBFEXw6faEgNpUXbbHSKytaoLSg1vaOjg7cfffduPHGG/Hxj38cP//5zzE8PIwzZ87oHnc1ozYWmxUhUNWM3KAsLCwo03ILjYAot9NDKQIFQnDi8ClMhsexZs2azAgHPq4pTgAwcmoSkiSDZbOfl2WC06f9yr+npyN47dg49u8b1Dx+QogiTO3t7di7d2/FKa5SIyie5+HxeDA/P4/BwcGi3SbqKVB6+9NybKk7bofDYfh8PvA8D4vFAkIIHA5HzXvqVYpWYXO1SHIcU8n/i5Q0AQAwyX7EEUdcjqOTdKLdnDMziMl81liWhTXzT7AsC4vVCkmSMqLF85BEETI5jbG5Z2C29WVFrYtxHhtVoPRqoCwWC7Zs2ZIlNrlUM2oDALxeL6688ko0NzfjwQcfxDXXXFPLt5bFihAoSiUCFQ6HMTo6CpZlSxpUWO6daUEHn8r8MDUcwPW3/RGsVivmgiEshBK6+0olBQT9C+jsze4K7vOFwOWkE0+dmswSKJPJBEmSMD09DY/Hg7a2Nuzevbtqd1AxgVLXTuUWMhdisQVKD72O27RYWRTFLBecOtpyuVyLaoGv9XmRSRpTye8q4gQIAC7WvE2L0zDBhFazfo0ONUkwDAPzhbSfms0tKYS5dsTjcUxOTmadx9wBh7U8j7IsN2T6ttAsqHrS1dWFiYkJtLe349VXX8VNN92E06dP122Y6IoSqHJSfPPz8xgdHYXFYsGmTZvyHDe1QlOgVMJktdrQ0tKCdFhUohcuyoMQGUyB+pBJ71yeQJ19I99sMTkVxvx8HG1tbsWd5ff70dHRgV27dpU9bkQPPYGiFvVAIJBXO1XNdhdboPSwWq3KuIeuri4A2f3fIpEI/H4/UqmUYjNWC1c9LPC1TPERQjCd/BFS0rjyGEPmkdsQNiAG4DQ5YTPp3Oho2MzV2JgzaGt7Z2VuwirNLY2Ypq1mFlQ1ozZoBgEAdu/ejcHBQZw/fx579uypwbvKZ0UIFP2AFUu/0dHuo6OjsNlsJU/Q1dtWKR/shaAqxZclTFa0tLSAuXCxXghGkYhwcK1yIh7hi158p7xB7HnL5VnH88a5/H5/AHDi1CSGrmhXUpg9PT1ZRZy1INfFJ8syfD6f8iXInXtVKo0SQZWDuv/b2rVrlcfVbXQCgYDi1qLpQXrBrTZKqKVARYUjiIun1VsHQ/ILzwkI/KIf/ZZ+zX3r2cwpDPGDkYMgpov1RKW4CdVzoqxWa14pQSOm70qhmjZH1YzaCAaDaGtrA8uy8Hg8GB4ervm1Qs2KECiK3peSTjv1eDxwOBwYGhqqql1OOe2CIsFoQWFSMzUcwOV7BpGIpFHs2jvpncv692wwhnA4v2BSEAQ8++xraGvZiu3bt2N+fj5PxId9QTz7yghu++NdcDsqS/XRc0KHQo6Pj6Ozs7MsJ6AWl6JA6aHXRieVSimmDPWgQ3WEUE5RZ60ESpQjmEsfynqMIXFkUnz5cDKHsBRGm1ljIGWRCAoATPJJSKY/KnpcpRQd03ZE6vXBS6njSDWzoKoZtfGb3/wGX/rSl2CxWGAymfCtb32rrAGj5bIiBKqQMAWDQXg8Hrjd7rJGuxeCphKLCZQsy5j1BzN28QLCRJkansblewYRX0iDFCl2CgdjSMRScDVlPsR+f3YvNHq3Tu/m167tg9PpxMLCQtY63fHzfjz69DEQAnzv/x3F3Tftg0OjIW0ppNNpHD58GB0dHVUZLtSohUj9e74UBUoLtQVe3Y1Ar6jTZrNlpQkdDodmUWct1lVm0/8NiaSyj7dIz705aQ4tbEuepbxYBAUArPQ6JHNxgdKjkqJjnucRDofL7upQb6qZBQVUPmrjve99L9773vdWcMSVsSIESg3DMJAkSYmYmpubsWPHjqL9tsqBCpSesUCxbXvHEA8nigoTxT+aSdHFwqmSLr5T3iAu355x4kxPZ1KJoiginojDxJjQ1NSkiOj54RmsXbsqLw36vye8SrTmD0bx9JHzuPEtQ0X3TaE3AbRjw759+2rajkWvAHi5CJQeegXH6XRaiRKCwSCSyaSSCqOiJQhC1WuLSdGLuHAi59Fsc4QWAhGwIC3kRVGlRHUMGQdIDGBqtx5cKE0YjUaxsLDQkGnCldDJHFhhAkUIgSRJOHLkCFpaWrBz586aChNFz4xBhWl8fBwdHR3YsmkIz7gOl7zdmbEgCCGIzieLpvgAwOe5KFDj47OIRDKGDLcrv//c+HgI17w523QwG45jIqeR7bHzU7jhDzbDXMKXMhQKYWRkBC6XCzt37sSxY8dq3iuM1lcJgoBoNAq3261cMJazQGnBMAzsdjvsdntewTG1wIdCIczNzUGWZczMzBRtoaMFIQRz6Z/n758sQGtabi5z0hxa2dZsQSohxQcAJvkMZPaqEl5ZHbRno8PhwOWXX1zLbZQ0oSFQywy/34+xsTHIsoyhoaG6tqXPjULUwtTe3o49e/bAarViZjxY1nZj4QRmp+Yh8lJJEdekJzPi4vz58/B4AwVdYeMToQu1UxeP/dU3JvNex6UEnPbMYMfGbt39LiwsYHh4GFarFVu3bq3r+AtCCGZnZ5U0LR1zIAgCzGYz2traLpl1hXqR2/vNarXCbrejtbU162KbSGTWKIsVHHPSWSQlT85eSPGRGhcQiICIHEELq1prA4qm+ACAlU4vikAB2jVQemlC2vy1kJuwlmlCQ6CWGYIgYPfu3RgZGal7XQONoPSEiZJYKL/t0tipyZLSV5Io4o2THpw53YK1nb1wOHwFX8/zIqanI3C5qJmB4DUNgQKAl8/4NAWKiiHDMNi8eXPdrPnAxbZL4+PjaGlpwb59+yBJknJuTp06Bbvdjmg0ikAggFQqpUxDVZsLLlUXVzXQdJrWxTa34Jh22lYmybpdSDp+BmLKTskx4ADwJR9DWApnCVTpEdQbABEApv7rQaUW6TIMo7gyS3UTqiPXStKEWgK13GZBAStEoBiGQX9/v9LRvN4jN1iWRTAYxMjIiKYwUeILJYwhyGHiXABgGBCdoldJEpFIZKIIl8uJns5BxNKlvd/xiRC2bV2bKdQNRRFNaPcIHPbNIRzl0NqcMZQkEgkMDw9DEARs3LgRsykRL4xO4R3bN8Fkqm3UQgjBzMwMPB4POjo60N/fD/OFljg08mMYBhaLBa2trVlOLkEQNEdHqCOGpqamhm1RVCsKrffoFRzTcxdOnEQ06YEkSSCEXOgGYYbdMgczve8r4dRxMoeUnILdlFkLKzWCAniYZA9ktnRDQKVU20WiUKssKlzq4YbqouNiUX9uE9vlOAsKWCECBVxcNK/nTCjaGmhiYgIul0tXmCiVCFTAMwMTw0DKiaAy6wyZuhmX0wWL1QKAQWBiHvES12LGx0PYsb0LsixjbLpwuub4sB/7tqzDyMgIOI5T+uX9+tQIfnHiHABgLsbhtqt3wFKDKIUQoqxpNTc3K90tJicnSy7UpaKlvtPMLZqdmppCOp3OGtRXzvrMpUAlNnN67hK2k9tjrnQAACAASURBVHCLNDImkCQZksSDQRSiJClLULSzttJhW2N3YSmMLlPXxWMqKYYCTPLpS0KgtFC3yiolTUjXwtTCRdOE6t/hcpwFBawggaLUYyZUbs+6wcFBJZQvRLyCFF9wYg6mjlbl4itLEhIcB1EU4XI5L+zz4gd3diqMGFvaF398IqS0Ohqf1RcoWZbw0mtnYeeDGBwcxOrVq8EwDJK8gGfPjCqvO+4L4LJzq/DWLYO62yoFuqZls9mwffv2rFIAdRNa9YW3VBefXtFs7mK41voMjbYuNcoVqJTM4XziVcwLo4jwJ7HaYkab2ZwZo8GyMJtSYAgD5XJCMvsghECW5Yu/BybzezExDBjGhIgUwVrz2ouW8xIPySSfAfCeko+/UhazD1+paUJaTpBMJjEyMgK/3w+z2VzWzVOlozYoExMT2LJlCx544AH89V//ddXvvRArRqDUDWPp2OVqyRUmeldP7b3FSFQQQc0HwmjvaIUsy4jHYhBEEU6nE01Nbmh9w6d98+DcpV1Ek0kec6FM2mt8Or8Fk0wy6xMCz0OSXLhy9x7YrRfXAl4amUAqR/x/c34c125eD/bCF6ici6N6BpTemla96qBKWZ+ZmJhQxqI3NTVdMuM3yvkdjCVP41j0eYhEQFr2QZRFhEURbtaEKxwOWEym/M4RF4QoKyIiF/ctExmQRYhERCAdwCp2FQg1trBsUQMQQ+byukrUg0ZoFKuVJpQkCa+88gra2tpw5MgRPPnkkxgfH8fu3buxYcMGbNu2DZ/5zGc0SwmqHbUBAJ/61KfwJ3/yJ/V94xdYMQJFsVgsiEZL6CBeALUwaTVTLTVKqyTFl+J4xMIREJbJXBR1hIky618A3166i25ycgHJdBrh6EWBJUQGl0winU7D6XDA1doKBgzGA2Fs6svc7QmShBfPjeVtb4FL4oRvBlf2dSk1S8UujvTukOM4XH755QUXfxezk4Te+oy69kg9foOmZZLJZE0KwGtFqQI1wh3HsejzmZ8BD1G++L2JSzJOcUkMOVnYSxnpfmF3DMOAxcWLvsiIsLN2CDwPPs2Dk0SlkJhl2Uzj2Atdz9VrVCb5LKQVIFBayLKs3EDdeuuteM973oMbb7wR//u//4uRkRGcOHFCN7KvZtQGwzD42c9+hvXr19fVmatmxQlUNSm+YsJU7j7KcfGRC3fvSS4Jd0qEtcVZUs45leSRjrOwOkuLoianwhDYzHHRKvtUOgXHBVuy+q542DenCNQbgTlEkinNbb7whkcRqELdoXmex+joKBYWFrBhwwZ0dHQUL95sgFZHeuM31MMOw+EwAoFASZ0e6k0pAjWWPKOIEwAI8nzea5KyjOEkh632Ev0NGiRIAmABxmSCy33hokcy0bokihAlCRzPKwYYOidKNr0G2bGvrilWSZIaMoWr18mcZVls2rSpYEeJakZt2O12/NM//ROefvppfPWrX63xu9JmxQhUNTOhShUmSqkzoUqJoDLClATPp2G3O2AymUF4qeSLL8+L4OPpkgVqOhCB5E4hmeSRTCVht9nQ2tKqeUEb9l3s9/dGQL+mayy0gIlQRHNoYYLn8ZzXAzuXhJPjsH79emzevLnkFFSjdpIwmUxK7RG9oHR2doLnecRisbxOD/Wql9GimEBFxRCORZ9VPSJBlLXXJKOSiEnBjl6r9s1J0WMBQUSKZEVVYAATY4LJakXWWSAEopQZcigJ53DWewJpXs4ztNSqu0OjRlBLVQP1wAMP4JOf/GTFDbQrYcUIFKUcgSKEYHp6Gl6vt6y5SKVEULIsg4vpr1ORC+6ydDoNh8OB1tZWiEJG9Ph4CpaO0kJsQZCQTqThRmk1SZNTc5DbJMiyGS0t+T3T1EyHYoglUmhy2QsKFACcmJxGV85ojLlEAl98+peIJBJwOp340JW70d2tXwCshd6k3qUWKC3UDi690fLqhXC73Z4XbdXC/l5IoCQi4nDkfyCSi59fkSyAQKusQQRAMME70MYKcLGlD+pUE5EiaEMJDUdVs6JsAK7c5oLMXqFpaCGE5BUc22y2ss5fowoULUKnlDMLqppRG0eOHMETTzyBz372s1hYWIDJZILdbsfHP/7x2rwxDVaMQNEPZiniUakwUUrZRyqe0mxXpBYmWu1P8ycCn7kApONJuEpoKQNkBIpPFRfkdDoNjuMya3QRCW2dpd2RjUyG0NPdgrl44XTl6alZrOtyK66uqakpfPuVo0hJYiZ1yDD4rzdOY1NHB7rcpRf4NkKKr1r06mWKdTGnf8rtBl9IoN5IHEVEUHfCJ5rpPQBglK7lDMZ4J4Ychfvw6ZGUkxBRftrdJJ+FzF6ha2jRKh9QCo5VEaueCDWqQEmSVPEsqGpGbfz2t79VXvPAAw/A7XbXVZyAFSRQFL2UEJAtTK2trRVPki20D0oikhM90fWeVCpPmCg0ghJSPGSxtLtVnpfAp3jdixLP8+A4DqyZxapVqyATYCamfUHSYnRqDjGd8Qpq/AtRxNozDsepqSlwdhtmrWa4mIupR0KAn4+cx5/v3F3y/peDQGlRShfzmZkZpQmvw+HIEq1CRZ56n4W4uIBziVeyHpNIAjLR6hAhAypRWZAsCItmtJorW9/lTOWXXBSymxeauRWPx5FIJBAIBBCPxyHLsub5a1SB0oqgFmPUxlKwYgSqUGifK0y1nCSrh5LeUwmTzWYr2NlcHZUJXGltZQRBhCzKkHgRZtWYDDpug/Zpo1/EZIqHmCo9VTM+vYB5U/FjEXgBJyam0WS1YNeuXfj2yeOa6cPXAgFMDUaxrqm0EdKNugZVL/S6mKtHRtDWTrkTemm0oCVQhBAciz0PiWT/7kWd6AkaEc8470QLG63IMMEx5TtaK7GbaxVr643cSKUyUwNWrVpVcbRaD3IjqHLXoCodtaGGuvzqzdKf7SWCXrxo25zFEiZKIpJAKplEMpksKkwUUbi4DiAmtdsQZUPACzQtyMNssyh34AzDZAkTJc2LFwSKoJTKydn5GAIF7n5F4eL+gjYXBgYGkAJwLjSn+zP/MzKMP7+ytCiKChEhBIlEAg6HAyzLLluB0kJvZIQoikqKUB0tCIKAqakppZGuzWbDDD+G6fRY1nYJeIhEO23HaETNCdmMsGRBm7n8Ti1phgcv87CaynPN1cJurnf+jh07hrVr1yKdTmdFq3a7PW/C8WI6MXPHpZQ7C+pSYkUKlMlkUqa61lOYtO5UaQPZV4+8BkmSS54FBQCSKq2XiaAKi4goyiBy5iKdiiUhWTLuv0JdzdOCCEkgEAUJZkvxj0dalJCI8HA2ZadCJUlCIp5ZrHa5M/sLxBPg0jzOhEMFx4W8PjuDBM/DlWPxDXBRHJmbAC9L+P96h2BjzWAYBhzH4ciRI7BYLOB5XhEmu90Oq9V6yXZ8qBaz2aw5off48eNwOp3K2kwqncJo00vgrfELfQ0ztUeCbndyEdA0TQBTgr0igWIAROUoOkwdRV+rxiSfhYS3lL2/UpBlGa2trVk3cXRtUG1q4Tguq3M5/bten7lqI6hLiRUjUPSOemZmBvF4HPPz83WNmKhRgtqFc7tO9HZdBo/bX9Y26RoUAAhJHoQUrj8RBEmZgRWbj2Hdup6i9uU0NWJwPMyrin88OF5AikiKQMmSjASXgCRKcLloT8AMEiEYCc7jlWhhx58kyzg2E8Cbe/uUx4KpOP7l9ItIiZmL36nwND7cswMzo2NIp9PYs2cPzGaz4uobG8s8ru74QLtI064PTqdzWTeF1YK50J6oo6ND+ez7UucwHpYB0QZJEpFKJSFJImCdAZjMTZbJxAAXukNoRU+UqGRBTGLRVLajj0FUiqLDXK5ADQOEB5jai4FWzZ56bVDPiRkKhTA+Pg6e5/Pq3mrRZaSaNahLjRUjUIQQvPLKK3C73Whvb0d/f39d03lms1m506FpRLUjcPz3gbK3KYrqFJ++8QHI9MuLRmPK6HlWNpVUW5MWRDAMkE4IcJXgXE3yAlKClHFNcUnwPA+nywlbU765hAGDk/4gxsQFjS1l87LfrwgUL0v4v+ePKuIkSRI8swF8NxzDxzZfjbm5OTidTvB8Zi2M2l+tVit6enoAXOwiTdcZ1He+6t56haLL5YL6cyMTGafjL4FhTLBYLn5GRLKAtMReSJ9mxq8Qkkn9siYh68aIQXYz2Cnegc2OeOnHc8GRmiRJCESApaxRGiJM8jBktvQpz+VQ6g1Moc7lWl1Gis3cKsRKmQUFrCCBYhgGu3fvhslkwtmzZ2veMDYXlmUxMzMDv9+PVatW5UVrXLTMfoCEZEVQMi9B4gWY7NlCQPvFCQIPgL14wUkJkCUZJlb/7k0QJcgXUoJ8ojQTBscLSPI8FsIETpcTre4CM2kY4Lh/Fuya4ne7w+EQwskkWh0OPOsfxmRiATKRkUhwEEUBLqcLYasJYzIHl0YvPiB7oq66Bknd8UGSJOUCMj09jXg8rrjiaKS13EZwUIGKCAkcXTiKc4kg3KwJTWbThfdIIMghALQrOU1xsQDSYAijiAoIIIMA5IJGMUBItCAtMbCxJa4Bql4WlaJoN7frv1aDTHfz+ghUNeh95nJ7Ovp8PvA8rxQcq1OFWi7ClTILClhBAgVkohpZlus6E4oQgrm5OYRCIUiSpDtWvlCRrhaZO1jVN5nJ1ENZLggUIRc6TqTTcDidcLtdmJnJ7jnIczzsTfpRY1qgos0gzRU+PwSZ8QAxLgnWZILb2QyrvfDHiWEYTEVjWNfeCraAUGbeD/DKtB8H+vrxXGAECS6BdJpX7jypVPx06iz+1Lou7+dLNUmwLKvriovFYrojOJqamhq+KaweKZnHz+cO4xznwwzvg0gyv2s3a8Imtw1ONg2ZaHWGIJn0HoOLLa9o8EQy/yEX/jeQtqDHkokWGBOjdDFHTrSlcOGxqBxFO8oVqDMomu9uIPR6OtJoK5FI6M4rc7lceQK1XGdBAStMoCj1mAlFCMH8/DxGRkYUN1BnZ6emOAHlR1Dq6ImSTiThal+VKexNpZSOE0phb87PCInCAsWrXi8JEiRRBmvOvQATpC4U9cqmixFaKinA6ij8cSIAOEEExwlo0kgB5vJawI9ofA6+2WnYL7y33EtQVEzjrBTGbuSP26gUtatLbwTH+Pg4OI5b9DZF1RLkF/BrchKmhBVpmVPECcg0gD0eSWLQFUWz5q8y0zlCkwvhEz3rQeJEn5kHg4ujNyTV6A31rCii2mZSLj/Nx5AFMMQPwuTfqFTKUjhASyk49vv94DgOr732GoaHhxEIBP5/9t48SLKzPPf8fWfLtbKrunqTulst9aIFLUhYDQIvFww2Fr5wzcyEl7GxPQ7suDPjudh/TED4D4+XcJgwcWfudQC2I4xtjOeKxR7AXBuQHGaTAQkJZK0tdfXeVV37kutZvmX+OEuerMysyqruFoLy62iXqDqZJ/Pkye/53vd93ufBGNPH7BsW27XaePzxx/m1X/s1IL42v/M7v8M73/nOa3sBBsSOBKhr7QmVAlOhUOCuu+6iUqlw5syZDc+x1QxKrRvMFUBrpYk1tkKxWGR8ol8vT657TNjemJqeAlRc5IGwHVGqpUBiCMKQdquF63qMj4+z3Oq+B78dUds9GIzT6EiFwdDpbA5QQRDwnfPnuHSgwPjERLL7Hhzfbi/w87kFRRnFv7Rf4Fz7CtULVX50z32cKB+66hLdoAVkvUzR2bNnMypymmlFUfSKGPhcDNf42OVHaOJTw6Op+ll6Cs2pJtxedaitG7oVW7B0D43FsnKZdKKB1hsGg9Gx9YbRGmO6G4xlf5k93h5sy96CR9RzKOvaAdRGosYvZwwaOH788ce55557EEJw6dIllpaW+Imf+AlarRZHjx7lz//8z3vu0TSuxmrjrrvu4oknnojZuFeu8OpXv5q3v/3t171fu6MAKi8Y6/vbE7fMR2qk57our3rVq3pS9s1AsHMVGZRWCqU1YdsfCExxGKKolwq8WV8pK/ElCBV2Qkq1AlEU0kyGemu7dsULB9CJuu/P72yekbZlhDHQ3mDIOIxiO2zHcbArJZaUZGIDYBHAnOxwvrnCwUKVSEv+bvarvNA5j9KKMNL87ZWv8MbJ+3jDxLXvU2wmU5QSMqSUzM7O9hEyXq5FsKV8PjHzJXwdgIFAt4lM/2emTYQxgtOtKneN1SlY6T00nFo+LOaiApODKOdJiVAkRppGCLQx2FZMymjoBuV2GaUUArBTy43k56CxDFs9i3J+fEuvb6N4papIpCDuuvHA+3333cfnPvc5/uVf/iVjrw7T5bsaq428XYzv+y9bP3ZHAVQaV1viW1tbY2pqCiHEUCO9jfpcMpL4ndF3oxAz+LTWKKWwrJgqrPxo6I0ipe4rU4StYEPmX7fEFyOU3wygqCAZ6nXs3tvFD7vvLwrkprvOlopLREGgUEr39KG6A8RQG6th2zaX2qvQ1kxUN8i2kqb+l+fO8gtHXs0X57/F2XY/ff/LS09xQ2E3t5RvGP5c1yjWyxS5rovjOOzZs2egS+/6EuG1np/RRvPp2a+xJmNmncHQUv2GlGAwCWhJbTHVqvKqaqwMsZXsKY0V5RFqgWdtXC4zpssEFEIQElIql3CEk41JSCn7/KKcHHDZ1kUwayBGE03dLF6pABV//7vfm1SBBuJsKwWfQXE1Vht79uzhscce41d+5Ve4cOECH/vYx14WtuuOAqh8BrWdEl+j0eD06dMYYzh+/PiGCsKO4wx11e00tpC9JVTV+loDozWu44AQcRYVSmQocbz+j3F9eQ+IJY8iNfB4rQ1SpTtkg1KGVr3D7iMHcJ3+foA2mjB3DmMg6EhKlcGLq9SaMLMgMVkfSum4RKaVplKtZOfSxlAPfUQkNrWHMAaeWp7hB3ZP8q/1M/GCKujpbYDhM7OP8h+PvIOSvXV9xWsRg2R2UkZXo9HI5meiKMrmZ1Im4dUomX9z9QUuduay/61ERGh81tfPjOntMTWlw3xYYH+hDWxPqXxeFji0RSsOg6Gpm4zb44icgnnuALRWyAS4giBAac2VC58m5IFrMjLwSgWoYV5QL0e87nWv47nnnuOFF17gl37pl3jwwQevu/LOjgKoNLbK4ms2m0xNTRFFEcePHx+J0rlRiW/U/lMUhplenut6hE5/iSVsdXC8/gxuPUGie3w4EKCCSGZ+OybJhCxjYVuDbxE/kn3t8qATDQWo1rrr3e6ECBERRRGVSqUva6hHPjpuTNAOJJXi4Ka5SChkoZZ87MKjVDLsEX39/I4OeHTlWX5sz+hitNc7BjG6jDGZS2+j0ciUzPPaeukCvNkiOhss89Xlp3t+F1it3p5QfFb0gCzpUqfMbreOt81K5HxU4KDrb5lgV1d1xu0hzDQBlm3j2XbPfVMdb7Mc3DBwZGBUId00vpcAalQG39VYbeTjjjvuoFqt8uyzz3L//fdfxbvZPHYUQG3VtLDVajE1NUUQBJmy76ixIUBt0n+SiZCrELHpne041BfXqS8kNOqg6VOeGB2gonYAE73248YY6o0WkUya+ZYdN7INRJ2IwgDQyfef0vDbw7PSVhgCMWNLKcXqapNdtYmh5merYfcaNTrRUIBKgWgtarIQNbirsLG1+pNrL3Fy122Mu/F5jTEsRTMEusOYM0FtizM41yOEEBSLRYrFYo9aQV5bb2ZmJtPWK5fLWaaVautBnOX+w/w30TkB2EiHKBHi9FoBxkO4pn8DpAxc6lQ4Vtl6iQ+gY2wa2qFmb1KxWIcXTd1EGYUtRgcJhzPsqnns2tVddLcqpJvGKxmg8izRrWRQV2O1ce7cOQ4fPozjOFy4cIFTp05x8803X8u3NjB2FEClsZnjbbvd5syZM7Tb7QyYtlpe2egcwwAqBSaEiL8wuZ3SYMAxBM3BzxXJwQ3tPFHCYOi0Y+8pqcF1YyDSWmXJR9geDFD+AIAKNiBKtKIQrTVax198gYPnDS61KaNpyu7rbHQiDmyQtGoMq2EDZRSh0hQcK8us+o41ii8vPcVPHfghFsLLPNX4MqtRF/wPFG7m/tqPUbJfPtfQUWOQtl5KQ240Gj1Dn57ncbGwygV9Bcd2EgFdaKrBKh5maI9JsxiWubHYoLQZyAyJ+cjbBKBMX0ZniMkSQ7OogaGSod3urn6rQrrpzJFSKqPHv5IGtFNlmDS24gV1NVYbjz76KO9///txXRfLsvjwhz/cs3m6XrEjAWrYDdfpdDhz5gzNZpNjx46xZ8+ebd+cWynxqYQgkAm5rp+jMWYgzdyYuMQ3KAbNTUEXoDqJknqxVGR8YpzWQn6oNyWaQzgEdPIEiTSicPDsVLvj00iszS3LziwffD+iPMCKvh4FPQSPTiCRSuMMGO4VQIsAZWK5pflWyKFaERBDBWmfb17gRHOMU60vo9dlDbPBeb60/Al+eOKdjDmjZ8zfrcjTkPOx1F7l8xf+O1pp2mE7XnCFouWsxQuv1vG9LQQYlcgYrY947NYAl/wat1ZG9wnLx6IscNS0sYZ8lWLJ4/4/bljmGxK2+k4PQA2LYUK6qcLD3Nwc7XablZWVzOQwPyz73cquBmVQL4fVxrve9S7e9a53beMVX13sKIAaBja+73P27FnW1tY4evQod95551XvmkYp8elkhkapVFh1cP9Ga5NJEGUhRDyr1BrcgB5W4vObHZaXlykUCpmTLUAYrn+t8fkGKUoYYwiGGCYGnSgTjo2iiFazhY/BcVwMBq26gNDpyCEA1f+emn7EeKU/49IYmviYKFYSmG36jJkuwHU6fkxZd+xslqqjmnxu7vMcG/B8AC1V5ysrf8tbJn+BorVxyfCVGl9vPI9xoex2Z9NWogUsaSWZbMzyNICwAlKtomTMOXlE9zNeDks0Cy7VbSiVKwTLymOPs7Uy4XbKfJY+BaYDYuOZvEGRDl2nag2Tk5McPHgwMzlsNpuZwsMgS/mXQw5rfQb1/azDBzsMoPIhhMD3fc6fP8/y8jJHjx7ljjvuuGY32EYlvvpKg2ajQRRJKpVy3Ojd4Lzrs6d8yFAiwwjHy2ddpg+gtNZIpbCEoOyVKVZ62Td5Rp4Q3XZE1In6yhyBlDGBYUD4HUmh5NBsNRHE9PSO30ZEYSaHk0ZnQHamjaER9Q8UNzuyD6CkUsw3l9DGxLtKAT5QGqtgaUWnEzfngyBAtmScC9ialr3MaiQ4XHTwhuyEO6rFY6v/yI9M/A+IAcaKr+SYDZZ5pnGu53fSSHzdRFix3l5aPjZGxcOykAzQxnQJgem7JWeCMW51tpdFzUcbANQQ15jtl/meQduv3c7LzCKVRION2ZfD9PTy2da1nHWTUvbMJH0/e0HBDgOobrYQEgQBTzzxBEePHuW222675jufYfbq586d4/Tzp7MbeRR606ByXb7FEjZ9nN05WwvV1e1LZ6eEELiJHYXsRFDtAlQoVV85LP2fWhuiQOLlSAqDCBLxgwyN1SaWFzPz0lJEa0A5EMD3+8GvIf2BMjNNv/scWmuarRZKSTqOBJ3SyuMXvtCO2FewErHOIoVCfL0UioXgMuiY9n5utcF+m2Smxs68kOL5LMF8eInnW49xZ/X1g9/vKzCMMfzT4pOs77+11OpAkSJDQFZg68rrMWgodzks0fZsSonCxHoV841iRbkbz0QNeZ7tl/muDqCklBtSqDfT07tes27/lkF9H4cxhqmpKebm5vA8j1e/+tV9tfvrEVJKzp8/z9zcHEeOHGHPxF4axdFnQ+QgwkNuzidodSjv7jL5okihjUElJUbbcXqkgsJ1Sg5hHwD2rhZhO+oBqEEECaXiHpAVuT1fGKUNHZmCi1j3GE0YKgqFHG02HCzHFEYKP5SoKCAIQyqVMqHloPx4+DSSMn6PQjDfDKjIuOGdgp3BsCrn0SSDjgJWLItbd5UwJv6MlEpmapRGiLhM+53gn3HDy5QKC0RmDmM0jqhSdm5jzL2fon1o4Ov9bsTZ5jKfufwUX1o4g9YG1xbsKbnsL1u0db8zrjFySO9puGLETDjGMWelR8Ucuvss0YtyuRAsygI3DpmJGoZzTd1EGokjRl+qLP0imAaIfnbrqLFdqaNhenqDZt1Sf7KteEVdbQ/qey12FEAJIRgfH+fo0aM8//zz191ywxjDuXPnsuns17/+9ViWtbVBXWLh1v7Ildya3edTSrG2VkclO61BN3zUWg9Qg65Dd6e7nijRyWVEKTDFs1ouWpO48ca7vLbsPdf65KjTiTKA0hgaA/pPEJsYzi6usn+iwsRE/IWc68xjiEswxsT9La0UqwqigkjAJmYNBlaLQOcIJQYCpVkKJZOeg+e6kCuTah0SqHlCvcqTzSmONUxSGoszraZ9iWXnS9S817Cn8B9wre/eIhEqyd9Pv8BX5s5xsTOHTC5yoAzTzZDLzZC9VZtdxfx9ZNAM2gxohgrCAktRmcOmQcFSPVl8ulnKSr85+434h2BeekMBahhEGQx1VWf3lggrGls9gXLetIXH9MZ619qriWGzboO8olLWYX5sIA9IO8kLCnYYQAHs3bsXk/QsrhdAaa25fPlyJpf/+te/vict72xRKHaQKkQ+wlYn2aW1iCKJEPaGitphq3dhWp9BrV8qwhxRwmDwpczJLvUbIQadKAOonvLegDWo04kYH48b2s0o6OttZSVKywK7SLFUihmAKqCjYrXsNFsUloXrufHvCkWqpZio4ss2a3IRbQyCxP4BgbAEM75kj+cm5UGDQSP1IpFZAmGwbIsA6BQL3OC5SKlQUhKGAe22ZM18iRnxOGP6p9hdue9l945qy5A/Of0YF1or1GWL0PTe0yYZYr5cLxCpkD2V+O/GyAFzT4bN9PaMEcwFFW4qJazPdZlT9rZNTskjybYa0qYeQtXWmZJ5vhIwLNb0GrvZGqPSVo+j7Ddu24JDSnlddRI38idLs62FhQXOnTuHlDJTFmm1WoRhmCmLfD97QcEOBKjUJ+h6eEIZHfCn9gAAIABJREFUY5iZmeH8+fPs27eP8fFxDh8+3ANOxhja9a1lUDLaeNForTZZXVmhUo3r3PPz/eWcnucLZI95YbA+gxK9mU7YTntF0Gx3CIIwASaHQajjdySVRDu1FW3M3MoTJeo5ckQGOkLguDHotIIIndCjV6ImJlG+gP4y5pIvuaHqISyHVerYxsGGGISSf1ppFmTAnA6oeC6W08ZYyyBk8r66dPuLQcCkbePaFrZdoEAhe+tKaUL1aRba81yevpMwiJvl6S44DMPrQktuyZAPvvQNpttraGNYjvo/d2WibPmfa3nYFow54QDVCMOockbzQYWDxQa22ABYxGDPqCVdomq3YmKGSsqvxqBQCEtgpYSU3G3V1m1CE+JtwdZdmCsIcwkjbhr5MflIqwIvd9i2zdjYWI++Z15ZZHZ2lkuXLvFXf/VXfPzjH0drzUMPPcT999/PPffcs+HQ7natNh555BHe9773ZfN1H/jAB/jRH/3R63YN8vG9RU+6hnEtPaGMMczOzvKNb3yDZrPJyZMnOXHixMAsLQoiomEkgyExlMVnDFEYYaSiVhmjUIjnf4ZRzPORH9jdrAelpCbwA1ZXV2l0fFzHSb68g3enKTVdG9NDqEi0q3uOjSIVC9sSa+8ZY4ikzEosjuNkMCGVwQ8VgQpZC5tZGdNdB04Aq4EkUoZVuYDMZxVCICwLK1XHdl3qnoVVuIISsygdIKVERhEyyRSNMShjuBSm18xkdhGxcGmsLm2Pf4sbb5vi5Mn7ufPOO5mYmMD3fZaWljh37hxPPPEEL774ItPT09Tr9Q2HxTcLqTUfmfoW0+1Y9HU1aiLX9ZMMBrUuo7rS8GhJs67Wunnm1HNuY7EYbp3GjYAFVQDLwrZtHDf5fEWczWIMSsnk+kuUVGilMdqwKgcPGG8Utnps84OGxCtJSSJVFtmzZw+u63L33XfzG7/xGzzyyCNZZvXJT36Sd7zjHZw9e3bgc6RWG5///Od5/vnneeihh3j++ed7jslbbfzmb/4m733vewHYs2cPn/vc53jmmWf46Ec/+rLOQ+3IDAriBngQbOyPtFmk7rlnzpyhVqv12boPmoXastU7/Sw+rWI1cAA36ZsE7QCnGO8wo01KghB7QxVrxXUisfnI7W6VpL7cYPf+CYK2D/7GwB74seJ0K4o2Ld9AnEWZgiaQEUbrnmxo/aOX6y0it4mwBXZSWkyPyUOUMTDdXsV1W0POqjFGoom44ksOeCGW1bsgZZmWNhijmI4klTCk5rox6892chT0+NiV4GtEsske96cZHx9nYmIiA7C9e/fRarVoNBp98zR5e/nNDA+NMfztxWeYasa27MpoVqJm33FSR33XzxjDlWaZW9wIx0r9bzfuOw2K2WCMfV57yxW0yFisKpfd6TxV8nhLWL3bZZO//poFfwG35eLYsYJ5PNvmxI8b8hps9S2k8++3NRP1SgKoYVGtVhFC8O53v3vTkvLVWG3cd9992TF33nlnbJAaBJmk1vWMHQdQabiuS7PZ/6UeNVKTwmKxyD333NMzm5DGQIDaYv8Juj2ovN2G6zk9mVjY7FDZPcagGahBkWZQfeW9nvPKpBxq41nFWKF9CGU8H0pqZKg2Le+lsbbWolX0sYTAWgc6aWgdEyCavsEuG+zcatbl6XX/SxvFbLvNoV2JQndmSa4x9OrOSWOxFHrsLfS+3qxPAhAXCJm3LHY78YxbSsKwLCuWE0oJFHwHI0L2We9CSVhdXWNycnemFFKtVLjxhhuwLAudqBekDK/z589nFOe8mnle4PSbi5f4+uKF7HUuRw30ugxIG41i0GerkdrmSqPKoVodIbaXxXWUQ10W2OVufZM3FxW6ADUsEuuNvMKEV/YoUoytN6II1fHRRmMlc12Z/YZtJ72nEFt9C+X8yJZf4/cCQKUxSr/zaq020vi7v/s7XvOa17ws4AQ7EKCu1nJjbW2N06dPY9t2n0nh+hgIUGtbAyitNErG1gLpLNOgbWuqyadUXHraLKJ2ClDrFyiT6JCBbQusRM08ZvIZOiOWRf2OpKk3BqgUcP0AoqqFZXrzLUEvScJxXVoyYsz0XoKu+kE6waORRtIMXYzxEegcMA2+NnNhsQ+gBkVTa9aEzb5yN1PWWqOkRCpJp9NBSUVdfIPZ6BLB7JvZv+8Q+/btzySetNZJOSu+9sWCR6m4h/379mJZFgbRY3iYCpw6jkNYcvmblZcwSZlMGsVa1J8lSjPgvZhuptQKPRqhQ62w/TLjbFDZFkCtKJfICNx8D2uETGxVr3LQPYht2z0LZPf6K9ph2GN0iPV5WtbdVKtjW2blvZI0+KCf+p73gno54rnnnuO9730vDz/88Mt2zh0HUGlslSTRaDSYmppCa82tt97a46C60TnWA1RzbVjJqT9kFLG6UkclU+3rvzBx4zlerYNE8ijqkywaHKl5YV7iKKaMq0xYNF/yCtsRgVSoEcAPoNMO6QzZJWsdlw2FiEEniHQsgZQTa8sTINL3ro0h0ooohCE6sygUkQ5iRQQjaAYFakXVXf9ETAYwJgYsgwajaUmHprSpOpsv2OcDn92ug5N8HpZlYXkeLkmJNXEFtopXmDj2VeTa23n66aczMdJarZYNbjqOE2eHibWIUhJQOLZh90SVyd0VLEsALp1Q8YHnvkKoJDJQKKVYoUUkIiwhEMJCWAJtZF9GFQNz/DuRgNR8c4yKG2JvYig4LFajEr6yKdpbAzmDiG04tugTVVd1DjgH+qSPutc/d47M6HCFxvI3OHNmf4/1RpqZFgqFVxwQDYur8YK6WquNy5cv8853vpO//uu/5tixY9fg3YwWOxagRiVJtNttpqam8H2fEydObInSud0MKi8eW/CKOM6wx4hMJSZsdmLAGaG8B4l5YagIIonWKilVpfR00dfAjwJJszP6brnZDGDdeEa3p6BwHDfLgkKlEJHAKpBlFsaYvl5UmLi9RqHAK/QuqgZQRiJN2JOF1QObWs/8jwCcxCU2/VW8eC9FBSY8H23if8P6Z5E2nPd9jpd6extaKZqtuGw8lrgCwype9fMcr/wartiblfNWVla4ePEiYRhSLBZ75l5SlYG0/6U0YNr80+yLLMg6pYKAgoVvFNJX2MbCaJOUQTVKhJgekYfBfSapLRbbFfZXt1fqNsBcWOFIqb7psetjLipw4xZ9ojSaVbXK5AiWKHmjw2MHX+TwLQ9iILPeqNfrTE9PEwQBjuP0XP+XY3h/O3E1M1BXY7WxurrKT/7kT/L+97+fH/zBH7ym72mz2HEANaonlO/7nDlzhkajwfHjx5mcnNzyTmuQq25zrT30+EHisfXlDTKu3MtRUqHCaKT+UxqttRb1ZhtjyIBpo2hvYcC40wkxu+yE1k8GOunCAYnumzFIo7FDgXGISRK2HWvG5Z5PGY1K+kZRAOREAjQaqUPUACZaI3TQJhyqpB2HAGyWQpvj1QlKthU/qwnRpoMiB1oJU24ujNjnutQcB2NipYDUfDG1LUkj1ItcaP5nbii/i7Hq3VSrVW64Ibaej1XdfRqNBo1Gg9nZWTqdTg9NfWxsjCWt+KeFWcDN3uVC0ABjEGiEpRFoIqMQA+ebBseKX2ai1MHbYhaUxkJQ4dBmlPMB0TE2de2wa4sWHstqmd321uxvhLmApV9E27cPtN6IoohGo9EjT9RqtXj++eeHDsx+N+JqMqirsdr44Ac/yNTUFL/3e7+XKZ8//PDDPdfweoUYpHu2QWyvFvAKCq11Bkxf//rXecMb3tDz9zAMOXv2LCsrKxw9epR9+/ZtuwSwsLDA6uoqJ06cyH736f/6jzz91Rd6jjNa02q3icKoTzx2ea7O0vzgHaqSEsuyY4oucPg1J2hEhvomTMF0xqhyY401r790CHGZqm+hrVlE5dEmE5pRiLXPQdsmmymxLIsoirBTajEQKEWgIvDA3WP11Ni7GYzAV2GOBAHjeyVYGm3kQGDKx6FasE5FYXgcKXncMkTlPGbqhSjjo/EpCsVxS+P7i5RKpYTBufG9MlH4EfYW3461yUxPGIYZaK3W1/ir+edZVEFGBugQsqhy94UBjSLUiTI5EGdNm9PHxwoBB2trmx43LI6WV9hXGL7xGhZ7nIDbii1kJHHc0ffKR9wjVLfo16WtW4jc/zTS4K7WmieffJLbb7896wM2m80e8kq6cRjFnfdaxfLyMsvLyxw/fhyIQeKxxx7jj/7oj16W81/jGOmi7dgMan1EUcT58+eZn5/nlltuuSYCsoNKfK1cBmUSs7kgCGLp/kql7wu0mYqEoWv2FjQ7REMs2iHX10nKZ0gQhWHvMZ0+6v49akdQ3rwpq5PSlG5HWGNuz87TsuxkriuGm0CruBwlkx5K7nwiceD1dYhKMpcUpNp+hFsaDXTqgTMyQM34ETeVPeyBn71AiAKOKBBFJZZbLa64d/CjR34CKWYJ1DS+ukSgpgn10sDnXwm+Sit6nr2ln6Lq3DX0HvM8j8nJSSYnJ3l45iWkX2KXKSKlIpIhC+EqUsfvKWUbylS6SKST1oNlwuN7phuNoEAncii521NWmQ2q7N0G5XxJekSmParebPdxamnLAGXpc1j6WbR996bHpjN46cDs+mw3lSeam5uj0+lkw7XX2y9qp8kcwQ4EqPUhpeTixYtcuXKFm266KdPLuxYxEKBW25DYUHd8n1KxGPe1hny7ZbjBwrruMUGzQ1jqp7ubfF8np8/nNwOojFa2UFqjA73pZLfWGl/GjD9Lp8rg3aXStmOVcaVU3MhPeiVGG2RbIrxkwU1sIZSJhW8tsY5WHhZwyxKDTuwi9ND0vhnYKA0D/A77IjKGK37EodLgDEclZViI+0wNe5YlvcLB4p1U3Tu7x5lOD2D56jKhmk2khxaZbv05ZecYuwtvoeIMt3mZ7zT49PnnWesEaB0PBQd2iGVbeLaVlUljYohJSIq9/aae+bC+3yTnaY1x066VbSkDtZVLQ3nUtuj3ZBDMhQUOWFsbmG/qJr72KVrD1cYHhSM/S2jdDmLje34YxVwIQalUolQqsXfv3uz3qTvvoPm2PCFjuwrm+fP8G0DtkEhLfd/85jc5dOgQDzzwwDXf9di23QNQxhgWZhdZWVmJDQPHx2ONuQ1iswwqvyr7jQ7KLeb+FMv5qKTE5qw7V9QOMdpkJcJ8pJvwdMFS2oDUQ483OWkik5TvCLqgISDpRcmEwm4Tak2eJG5pC9u1MDpWlgiVJMopIWSLuBDIUGAnZIdYz40esMqDlgYagc34iBnXxU7IDUW3J4uK+0wdoijs6zM9tvqPvHnyf2aX250XsUWJsnOcsnM8+502EYGaIVCX8dU0gbrMTPsjOGKCmneSMffVeNb+7H2eWlrkdx/7EjONRu45NB0d4nqCSs1gO6CQaBFT14XoLemledRm0Yk82lGRipeChWH0R8OsX6FWSQBqCyB3RRbZ526dpLEgFzjsHd78wFwIs4itvoJy3rLhcVudgRrkzpv3i1peXs4UzFPlh7yC+aiVGillD8h9v3tBwQ4FqMuXL3PhwgWEENx7770bzjJdTaQZlDGG+fl5pqamaKw0GR8BmNIYZt0O/dJBfqONNTGeZShKa2zLipW6B4TSBhFKKG6eRSmdLHyhhmLuy7uODm4AnbxmIw3oGOWkiqndWS9Ka9R6YdjA4IwBloXUEiU0lrAyIdfsh9EowO8ovAIZvdrC6tFyi5dXgzGaVuiyvxIS6aCfgr0uQp3PouKyTqfjUyqVqFTGWb8CRzria8uf5i17fp6iPdyB1xIuJecIJedI7vIpQr2Ary6zFn4TZdoo7fH5Mzb/dKHFlY4PWFnBNUjUIcIQokVBYSzELvoMAxKR+0v6qofBzkK7TNmtJ5sSMeAok/vZ+7flqISv1vAsBQOUzIeBVqgtVnSB/SPqAKbR0A0CHVCwtjYH5MiH0darMdbeocdciyHdjRTM057WwsIC7XY7OzZfJhx0/kFmhf+WQX0fhjGG1772tX1aVNc6HMfB930ef/xxKpUKd5x4Ff9U/ubIjzdKZ5JGg6N3EdFSo4MQY1vYiZir2GA7q7XGCiRiIED19qBSBl0XoEyslZYI76a7wGgdPV11JMaLe0+242avJtT9/Q4dGqRWRPQ69saLXZJpJSufAZQEikm2FMXZUtqPsVLNPQQImyCyqVgTFF0LZSSRCYh0EP803R5XGhc7IXtsg99u47oe4+O7NnTWbak6X17+FG/c/T9RtEenKQthU7APULAPAPezFgT8yb9+izOr88z5ndizKSE7RMYgc7hggHbdwpMuXiXsK88NkoAa9L/TYzuRSzO0qXoRae1V9Dyqvy+ZB625qMaR0moi3NGrZL4RaF2RJfaztSzKYFiQCxzyturHFeJGf0Po/ScYYiV/vVQk8grmeXUGmYyVNJtNrly5QrPZzGbm8oSMKIp2XIlvx4nFCiG46aabcF33mgrGro96vc63v/1tgiDgrrvu4q677kIFowtywgjlPZFbHpKSpeoEmZjrhuBk4n6FCTbfuWqjuwIMQazsEEWJvYXr5koU8SAtxJmO0QYTxgBmWVY812M0vpJIoxNxIpNIE8VsvyCIhtrJr3vrREGsfu0kXlSu6yZDxvFQr4xiSZwoURm4vBbbktjCoWRXqLm7mfRu4EDhCAcKR5h0D1BzdlMQJfxAc7beyYZqR7F9X4sW+eelT9CS22PErfk+/+Vb3+RSvc6s72OMjSUK2KKEoIQyDkLYCGLH3/QqhW2XsNUt/eTpEaNW29JjlzpVwMrAziSaePE9kNwzPY+ysp/zwRjSVDCiBKIIwkNYNpaVbBhyLyYj0hhDQzusRdZWKorx9dJrdPTWpcOEuYCthqshvNwyR6ms0MGDB7n99tu5//77OXnyJLfccgulUom1tTVOnTrF3NwcU1NTPProo/zZn/0ZS0tLI89sfeELX+C2227j+PHjvP/97+/7exAE/MzP/AzHjx/nda97HefPnwdgaWmJN73pTVSrVX7913/9Wr7tkWJHZlCp5cb18IRqtVqcPn0aKSUnTpzgueeey26i5uroKhLASDNNxhhkFMWphbAQkRqppp0qQphg8PvP75WlSnfDBuVLBM7AmRBlkuc1pqulJrt6dgKBNhBplTH08rvtmPwAjNhL1kqgpMFxu69ZJK66ANjJc5oYZOfaEXttmei3Wdkgp5MAuidKyI7BlrC/EitHv2b8JIYmK9Ecq9ECDbm84RrakCs8vPgxXjv+ExwsHt/gyHWPCwP+yxOPMd9qUY986mF35swAvu66EndVB7tZTdj2sGyDU4qyv2wn/MihFblUvSiX8aRbnW6ZNe35ZR1EAcoIFoISB4pdqnt2rURKeVfx3JZQ2WdvjOBSWKIi1rIHpJlw6t017A3NyTmOuEe2PqMov4gRB9H2PX1/eyXo8AkhqFQqVCoV9u/fD8DTTz/NzTffzKVLl5iZmeGll17iF37hFyiVStx999381m/9ViYGm49UyfyRRx7h0KFDnDx5kne84x09QrF5JfOPf/zjvPe97+UTn/gExWKR3//93+fZZ5/l2Weffdnefxo7EqDSuJaeUPnB3hMnTvSYkKXR2mBId1BsxOBLmXkYE/slCUEUhehgNCaVTnpKJpDZAG1P5BBKKoXRGoTAUgzOJowhiMKMWZHtwEOTPb8BOkrmSk9pGSlXSArj3bsm2bVvsqUOA4HjDj8mXWTtpJclCyX2lNxYxV3GlhqtdoCMYuByHZdisYiVDBg/uTrNzx18c3Z9pA5ZlQusRPOsRvOsRHOsySV0bjg21AGPLn+Wm0q3cU/tR6jYG8tiBVLyJ99+gvlWC2U0M+3ufFO375T8nzEDrknMaPEbBUq2wCkkLL4UnbeYmiy2y1TctQGMvnyZNf+5mYRNCDOdArvtJrYluiAjLGKxXRtwekDLGIUxijXj4AtJ1Qnil57z7EpnNbvP1zU7bOkWdV1nlz3awGo+3OhvCMX/gbF6yRavBIAaFGkP6s477+R3f/d3efTRR/nqV7+KMYbnnnuux2Y+H1ejZF6pVPihH/ohpqamrvv7GxQ7EqBGVZMYJaIo4uzZsywtLXHs2DFe9apX9S326QK9VYAaqKuXAJM2BjtRw07Pp7UBf1SASpaJOKUBb/0XMs4yoyhCat1L6sgTJXKvRxn6GX7agDQYV+DLqNvLGhYhuHSHjw1x9hOrc/eDVuhDeQscl+lGyJ6Si2WJhBElCIJYbqhYLCb6bZJ2u4NSiifXViksa147cQe1Wo1qtcoe7yB7vK6OmTKSulxiJQGsGLwWuNh5kcv+aY6U7uBY+dXsdg/03RvaGP7ymae4sBaXBWc6dWTuGkVaIo0cAkzJFcraQgK/4VFx/fj65Zs++WM3AS0/cmhHbo7Rt1mI7FQRDnVTYdIKMEYnfloqeY0CKwdcxgiUFFi2h8HiYjjGbSUHIXyE8LHwAR8Iu9lWIumUB61pNU3B8fDcwhZHREK88E8Ivf8NY3V7WdfS7v1axnrgTFmBQgjuv//+oY+7Vkrm34145X0KL2NcjSeUlJILFy4wOzvLkSNHuPXWWweWGWzbzm74xvLWGsHrAUqp2MAtNumLezqotOcTLz46ij2VNmMJZqw84ixK5AAq1csDQNh91OUUoGJxWZ2pdPdbiMehfUMgop6Fd1gYYrKEXezSz21hYQsrEwPNQIt4h21p0FZiq7FJ1ENFPZCUbUGr1UIIQa1Wy+a1bNvC87rlS2MMz8ppjsqD1C/XaTZjJ99qtUqtVsuGOSfc/Uy4+4G7k8dpGnIlBiw5z9ONrxFqn0nvBva4Bxl391J1xvmH02d4Zn4egNXIZy30s2wp0hGhlkPeVQ6Y8nR4JfDrHsVd/aSJLuthAMnB9ILWYrtMeWAWtXlc8YtMuhv5a+lkg2QyKSytDUtG0tAeY/YYhtyuw2gQPgIfYafAFWTMTmUUV+Qsk/5krnxrZ6obtmVvUPNs44UfIvR+DWPdEj+fUi+rSvioka90bFEB6Hs2djRAbSeD0lpz+fJlLl26xMGDBzcd7E2p5o7jsLawNVHNKCnxpQaFtm1lBoWQfOeSGzXLiAzoIMIuDf+CpQSJNEwgYawAmAx0hLCwhEhmldY93pfoEjnbd3pU0defS3YiZHH0lc74wAYzmBloAQibMV1i71iRQEsCHRLoKP6p+g0TDXBmqcXN5bh8MYyCn51LCGzX5lHzIr9061upuRW01tlg5tzcHKdPn870E8fGxjLgqnmT1NxJjhCXUowxtNQaq9E8lzov8vjsZb7w4hoQC8LOdlRGvdfrGHs972AAMOVDBjbSt0dU2ujOluWjI218WabshaSK76NGWzmsSpcJt/e7FYNRDE6OYyelv64ppNaa0/U6x20L13Ezfy3LshGigjHl7qdpDIggBi3h08ZnsugybpXQRiOlRElJOwhQWscakHYOtBIyTfJu8cIPIp3/EeW84RVb4hsUo/TerlbJ/LsZOxKgtuMJZYzhypUrnDt3jv379/O6171upDJAXk1iSxmUMQR+QBTJzKCwbxsoukwunbPB0H64IUD1UdcD2WO14boeWsdA1eu2m3hN+WTvXZtYTy8uveWP7O7yREhW4hkldLC13eFKM2TvriJF26Vo57MfCHVEoCN8HdIKO3RkwJpt4ZbH8NzRF6E12eRvLj/CLx5+K1WnRK1W67FcMcZkbrmLi4ucO3eOKIoolUo9oFUp7KLqjCPkAZ652GSfV0MaydnmEoIQC400ahNw2vw6Bk0X21NY215nBYvtMkc8OxbaTRTfjUksSjKrksGPnvZLjDtRjg3YtVjpJdjE5b4UDwJAlYpUoMdfC0FiSBgbQ9q2gxAljCllm5CLsshB712UnADbmcEzMxSZRph6TCZKQMv3fZSMM9M8aDn641j6RYy+D9v+7i/O+VjfJ96KF9TVKJl/t2NHAlQao5AkUlv3qakpxsfHOXny5JYkS/IAVV9qbHJ0HFEY0mg0iSI11KBwfeQBSgUhG+UFvZ5OBtUJsRJWY8qOEMJC0/Up6rY5REzIUgaRlBmVVpn4q8mx8jI1CgUoRr7btDQYaRDOaF+QMFK0A0ll3TyXEFCwXYQC1Q7Z5+2itKuEROGqEq8b38dssMJssEywibkiwHJU5y8vfZ6fvvGN7C/0NqSFENnMSl67rdPp0Gg0WFtb49KlS3FJ2XH45PwVmlrh2A4LYZtQGWzhok2c9cUtuBwpImFGjgryRguChkdpfGvyQ/loRTatyKLqpYofdkJzz86SEB26gBWDlqElu1mUyoa0BwsTr4/zQch91VjNPztTAnBSSoIgQMoWGLBsG9eNMyLH9plX/8CN7v+ONPck3BGDJZpYZhq7cAXXm8Ez01gsgtFIpVBSEgYhbSUx5ivsK32D1uq/I4rezNjY+CvCM2p9VvdyKZkD3HzzzdTrdcIw5DOf+QwPP/xwD8HiesaOBKhRSRIrKyucPn2aUqnEvffeS2md/88okVeTqC9tnEGpRNNLCEGxUMJxRidV6FwpTm9ClEizoizD0WCb/CBmvA7mccxK6NtpH0G1QijbBLqry5AyvLvsLpESzChKC+WJmOwwQv1c+wa7OvqisNIM+wBKSjWwz+ThsBJE7HMP8WP7TmKMYU22uOIvMRssM+svcyVYoq367UXWoiZ/efELvHnPa3jN+K3YG8xHCSEye4eUKqy15sNPPk5Tx07JS+0mc1EbA0RCdUuSQmQtoVQpIw6TfW6bMRxlYCMDK2H1bS8WWgnpYuBH0QUt6A5hp+y86Y5NRa3gegJhj54V+1pzOQi5qdjNEGKbFhfHyX3GCUFHKkkURXQ6Hdb0CyzrDzBufp6x6gRjY2PY7i4Mu4jMHYTpvWcChJnBsWawvSu4ehqLK2AU9UadscrXCNV3mLt0F/OrJ7CdXl29crl8zTQ7R4mr1eF729vextve9rae36XWGQB0oiZOAAAgAElEQVTFYpFPfepTAx+bzkR9N2JHAlQag8RcIXbPfemll7Asa1Nb91HP0a53hqpC5H2gqtUqjutu6Bs18Dl6SnzBYOo4iehrAmb5vxs/QrjdL5yUilDKGGxyX8T0MY6yCIRIwClZMDOkyo7OQMsKwRvr+kDFrD/d8zO/hCnfsBXB6rVWyA27y9iWQGtDu91GRhGVajXrk62PT198ljtqe/Fsh3G3yrhb5Y6xI/FrNIaG7DAb5EFrmYZsIY3kiwuP8+21l/ihyXu4vXq4z+V1WDxy/hwvLC/jeR6RBUthiLZBGtUFniRr7V5FuvR9RO/nlvz/YaAVNFxsL9gW2QGgHVm0QovqVkDOCJSElvCIKkfZXfAwRJmnljKd5L+Hb6Smw4A9rkN5o16QENiOg+04dKtdBq1W0erzNBtvG+qvVSyWEOI4Uh8lMiZmwBuJxTwzK1/nyCGLijPPieqLnDj8PKG5h7p/B8t1h6WlJdrtdk/WnD739epdXY0X1Pdy7EiAygZH131rU/fcIAg4ceLENZERSQFqUHnPJIKS4QAfqGgjFfN1oXVKH06eV2lMJBHr2GhKKSI1eJA3JUpoHStFKDNk3imJqBMRVZ2cblv3h8lRmVPQMh2FkSLzr7KF6FnU+0ArNPFc54ibVK0Nq82QsmvwOx1K5XKiADH8MYtBi09deIafP3pf39+EENTcMjW3zK3VLkW3Jf0MtK74y/zz4rd5eP5b3Fo9zPHKQW4sTjLmDNbjO7W0yN+fPsVKvcNyo8Vyy4/npyzAFTExxOnSp7MaKV3QSj/mvHDuMNDCxKw+2XLxqnLTjGtYzLc8Kt5o7rcqmZmzExLEuXbI3oKLJVxs4WKLsRwbUyVA1UmAy8cYP7kX4KWOzz2VcuaqPFoILNvG2Bdw93+Ru2/5VWyr3OOvdeHCBVqtVp8Gnud5TJ1r4/u3c6PzKpRlgTEIVrCZoVaZYbxyGqwJjDhAZI7SbOmBEkV5MLxaFXPoB6jV1dXve5kj2KEAtT6CIODMmTPU6/Vtu+cOi7TP1VrMlYuS3oTvxwKkExP9PlADZ6CGhB6QmSk/xPJc1iuax3W7Acy8ToSOIoSwcByXMBxc+oxlb0ys+JDs6tfHQNDSgBRoR2HU+sHL2FpjPWjts6sUqw4dJemoiI6K8JUcSLHVWjO9sMbR/RXGE8HcUeIbixc4UdvDa/eMpoxdcYoccw5yrNJlQXVUwFywwhV/ieca52nIeOC2bBdxRQziC80On31smvqyRCmD1Hk1CCAwiIbAFEHUBCL9Zq7bTMXYn4JWfGGHgVY6BB21C5TLLpYTC+dqdPfnCKDVkRaNwKa2gaeW0XE/x7Ys7BwJwteGaT/i8AD7EoGNLSrYIi/Xo7NMKzQ+M2GBm4oB2mxd8aUjz3Kh+Z+5sfzLFL3Dmb9WGkqpzP797NmzrK6u4nkeu3btYn5+nkqlEmdFzl602YPUdyVvFtAtLHOJWtmiVinC/hrCPog2lazvuLKywsWLF7N5pd4MbmtGhzvRagN2OEBFUYTv+zz55JMcPXqUO+4Y7suz3UgFY+vLTTCpMnaH4iY+UKE/Ov29l/QQh/YDVLUY09MTRXODGVhmNMaALxPqrUWYKKGbdcf0nMWAiAzGG+16CQFWBFax1+69O3jZLTumoNVsBIzvKlG0XSYoJY8zBEolYBXRikJaoY8mZmRJ4W75M3zo3FPs8orcVhuucL1RlOwCN5cPcHP5QPa7QEfMBSvM+ks8Oz/Dp758iXY79v5VOn8tc9mvEIhAwCJQ0zAgEUuJKt0fohe0AHQ/aLUbgtoEIGxs4rmg9BHrLUoGOWvNt1zGCqr/djUGqeJS8DBCz/l2wF7PoTiKIRcWlihjiTIOsKTgVvcn2V8YT2xK4n+Buowym+vwhXqRC83/hz3FB9ldeBNCdJc827bxPI/FxUWKxSI//MM/jOM4tNvtHoAJw3iQOw8wrjsG3IYy3fIqUYhgmYLnUNxTZd/eXViWgyGet8yPJnQ6HRzH6cngKpXK0L7WvwHUDotz584xMzOD4zj8wA/8wHUbzEszqMvnpllZWcHzvM3tNowhHKKRNyj0ulklgyFsdbAna5miucEglc63Nnqm8TFgSQMuWRlQ5I8TCYEit55agUFtoXphfA1jcYYk0vPa6VnsLmglw5xr9Q6lZY3nxn0G13FxHJui7VCwLFqRomQcjuzaj7YFHRnhhTbH9k9yub1GMEAxfVBERvGnL36T/3jrA9y2a3sgtT4KlstNpX1cnvb5x68uIkIX17LwlcxdytxMU36EwABrFkgDY2aDIdM4ekAr+e/1oBV0DC1X4hW7mwBLWHEfRyQyRGm2S2wfb5JhaGM0oYIV32F3qXtNs3KebW94PysDZ1oBd9a2TjICeHzti7xp8qeZ9O6nRqyYYIxBmpUMrOKf00R6te/xBsWC/9+pR0+wt/gfqDh3AGRGpbfeemuPTNAgNqbv+1mJcH1fKwWYOCsqZfcvJh3p8HFswcT4GLsnxuMBZiGIoigDrUuXLmVGmOv7WvlZyjR2ghcU7GCAKhQKPPDAAzz99NOxpt11ik6nw5UrV7hyYZZdu3ZhjdBElZHaxGajN7TqLkTG6LgnEUbxBD3dxnkvey8lUfQSJULRO8QL5MBKdBcxYxAShGUlGcHmpSLjDzc8TJ6923tJ1jvbKlEqx/NqQRjQastMg9D1PMqlcjb0WfDi2/nf7T7OfXccYCFocbG1yqXWGpdaq1xqr9JRgzPTyCg+9OI3ePuhO3jLDcevOpM2xvD3T53ivz3xDKsdH19HRCk5JWHnWSIloHS17HpoDq0kzaltDlLrYxBohR2XUtnEdHBjkDrRYSQRZRXdf3GWZWdPZoBGp8jhikDqNp2wjbDpKedtFAuhZDGU7PG2vuQoI3l05TO8afKnqTlxiU4IgSt241q7GXO7gq9SNwjUTM7J+BKRXohBWs1yufVn2OpGli/fxO7KD3Dy5MlNiQ15J919+/Zlvx+lrxX3QUWsnpH0EVXuHqyNjbGrVsvWhdSxObWUP3PmTOaGnT5XGIY7pgcltiiZsb0u6yswwjDEGMMzzzzDkSNHeoYur0U0m01Onz5NFEVYlsXT/99pLr04M9Jj23Wf6QuLIx0bhhFhqLLFTQgrW8vKtx7GShlsBpqdIAEnMXABNrUCwfgWMklLIA6XSNXhlTEZjXwYaFl7XKzS6PTcQsHhppvGsy9mq93C8zwKXgGlZGylISVGx3b2juuwu1Tm//rhN1Ir99bIjDEsBW0utVcz4LrYXqUte9lkR6u7ecfhV3F8bHs6ZFJr/t9vPM0XT00x3awTqC593OgYbEYBwMzCvWwwNb1lkBoU5aqmtI4dGbeydNZfjEEr3nz0iLMamHQ1h8oW1UoVyxJEJoz/pd5aOhy6WfEswf3jZbxt0rOLdpk37f5pau7Whmi1CQjUNO3oItNz/0pHXqQ6EVF0d1PzTrLLPYln79v8iUaItK/VaDSo1+s9xInUuiXNitJqQX4NNsb0qFykNjWnTp3C8zzm5+f57d/+7UwN4k1vehP33nsvb37zm3ts6PPxhS98gfe85z0opXj3u9/N+973vp6/B0HAL/7iL/Lkk08yOTnJJz7xCW6++WYA/vAP/5CPfOQj2LbNH//xH/PWt771mlwnRrybdyxARVGE1ppTp06xd+/eaybr4fs+U1NTtFotTpw4Qblc5rnnnuNLH3yMVn0075qVhQaLs6N4Chk6nRApdZLl9H7mxZv244yV0dokvkiDqeeQlEw8G3XD1ij14sYSwhu84AwCLSoW9u6t7aJvPDiGUgGWEFQq1aF1+lToVUrJ3ZUqb6iNUyqVutJDtVpfKdcYw3LYiTOs1iqX2mtcbK3SlAFHq7s5OXmYeyZuYJe3gfZSLoJI8sdf/iZfPXOehXanC0zxBPPQ7HGzsMcs7JroE87dcggY36PZLJGPQSshVSS9QkMsUPzqySJjRS/JXHvfjzEgTdRrCKnDzMV4j+dw59jWCAL5KFhF3jDxDvYVtmb3ng7bHzx4kEOHDgGKQM8m5cFpjJG41m5Kzi2U7Jt7elVXG6n9e5ptNRqNgX0tz/MGghbA6dOnufHGG6nValiWxS//8i/znve8h1arxVNPPcWDDz7Ivffe23dupRS33nprj9XGQw891DNo++EPf5inn36aP/3TP+XjH/84n/70p/nEJz7B888/z8/93M/x+OOPMzMzw1ve8hZeeumla0WlH+kG2LElvjSulSdUFEWcO3eOxcVFjh07xp133okQIp6BanRGBicYjSCR6vNpbQaCE4DqBFCMqeva9BrG9RyXGBKKUA1l5g2NjoIhACWEwBGCPFfcNoLdtRq+lHSSf1IPLrEaE3/BFubrHDo8getsXE6ybRvbtikUCpwThn9/6wkOl8o0Gg1WV1d7Gt6pVNHY2BiTxTKThTL37r4xOa9hLfKzLOu/nX+KQEmqjsf+YpWaV6Rse1gipsa3ZMhq5HOpucZXvnOB+aU2Qerllegexpdh+ymQamgsx8Ipd0tvCZE/Aa2Y3qCS8u3QMDFhYmx8471mnEEJtLEwUmLZViYKfKEpOWZkVoa2UzWHRIrItVxcXNJBNmPIXIwDFdBWY0x6Af42zAYD7fOV5b/l3tobOV6+d1OgC4KAl156Ca019957L8ViutFwKNqHKNqHSKeJjDFEeoGWfBEhXCwKWKKAa+3GEtuniuft37fa1yoUCly+fJlWq0WhUEApRRiGvPDCC9x8883cdNNNPPjgg0PPfTVWG5/97Gf52Z/9WQqFArfccgvHjx/n8ccf5/Wvf/22r8VWY8cD1NV6QmmtuXjxItPT09x000088MADPTt827ZZm9+aSGywAUDpRIHAsi0c1yEYMi+ljUG2fdw94yilu0aCdJevfmaeQYQKUxj9tjC+QuwarQ8BoKShoC1qOSdQqXUMVlGUgFZEkGS4tm0jpYU14iBs7q3wkae/w//5ujewf//+TMkhXRjq9XqP/FC6m02Ba1ehyD0TN3DPxA3Zc66FPpfbq1xsrfHi2gIX26ushvEiq5Xh8lSTlZWAUCa9vrScdxXAlA+5qhGOwPK6vSWBiK9N7hSxl1YCXKRD0N1POvQFYWDwNqjmGkDJuDTpOE42iySEoKmgY5fYX3PjjElKpJIEfkBLxRJEtm33GEI6loND7GR8xRe8cfJBbijWMnuS1F+rpTb/rmij+fbaPzPjn+Xk+I9Ttsf6X78xzMzMcPHiRY4fPz60/JUPIQSeva+n3BdXAZpo06Er9SQQeBvOCY5yrs36WmfOnGFlZSVzXfjQhz7EwYMH+Yu/+Avuvvvuof5P+bgaq43p6WkeeOCBnsdOT09v+z1vJ3YsQOXljrZjuZEXjz1w4AAPPPDAwNRXCEFjaQtOukMYfOmciSVErIwgRGYJP8grSAgBYYRlCYJQ9zTNMypeMgiaf6Tw5ZYACl8NVa0YFs1Vn0KpC2qOZTHmeYx5HmEY0m4Z7GoRXJeOVHRkRNiSlGpb28W2wogPf/sJfuP+17Er2TnnF4Y8aAVBQL1ep16vMz09nYlx5suDtWKRO8cPcOd4l07eiAKmVhf5y689RWtVImW3PHOtgCkNYyBaVnj77A2f20Ik0lTdBdQkoKUSZl6nIXC9ftq4gWxY27ZtbMseWIs5u+YzUbTx7FjR3nWdTIE+zXyllPHn2W6jjca2uqD1qctf5n858iA3Fo9xY/FY9ryB7iRGkF1vraZcGZgTzgbn+fz8X3B79bXcVr0fR8T3VKvV4tSpU1SrVU6ePHlV3k5xFaAfALWJMEb1ynphXTW5xvM8JiYmWF1dJYoiTp48SblcZmpqioceeohPfvKTuK7L6dOn+dVf/VX+4A/+YKCL7vdL7FiASsNxnIzeOUpsRzy2uTj68wedqK9pqqQEBI6TF9s0ce8pITykbqvpgKYxBhVGdJodpLB6hme7Q5xkoJVSnkWgk5LgiO1GAwQ5A8MRorEWsPtAtefLrKSi2WpiWRa1XbUsCx1LdvkF4fCeNzzAcuBzqb7GxfoaF+t1Vjobl4nmWy0+8NjX+V9fcz8HxwYTYYQQmWFhfjeblmDq9TozMzP4vo/neT3lQdt2+Mw3XmTqygpGGQoILMeLM5Ck/Ka0GThbtJ0wCuSKxtm9tcVQkA5CJ6BlYEwW2VWzCVRsT9KRAZ0oQFgC13UHAlMaUhtOr/q8anep73UIAY5j4zg2EH+AxoDWMWhFMmLRX+K/PvVxfty9h4O79mebgGKxyP7CEfYXjuTOFbsYL0dzGXjVExdjaSTPNr7OVOs7HC/fhz2/i/pyk9tuu+26SgFZor9qMKh3FF+P0T+ntbU1Tp06xf79+7n//vtjgtXTT/Oe97yHt771rXz0ox+lUCggpeSll17aNDO8GquNUR57vWPHkiTSHd7KygpXrlwZSZ13bW2Nl156iUKhwPHjxymXB0varI//+zc+RONSv/DooFhdaLAwu5YMQMbZiWM7PWKh6Y9WO8y+FIP6UAaD2TeJVSnlvjjZ4E2v4kMSwhLYx2LCiNJxT0Nr0zuQuC7ELhcxsbXs5oabxylXPYw2tNotpJRUK1WcIbp5AD9y68288zV39PyuEQZcqte5WF/Lfi4PAC3PtnnbsRO86cjNOFch8plmWo1GgzNz8/z5v55mrp1oH1pxn2aQNE/ch4mzF5WQRkbeBAwIp2bhjF2dWKkQ/z97bx5fV13n/z/P3ZfsS5MmadKkWZoutE26IJtVFAQB/SKyiAMuIOgoRZxhGRV0BGTxJ6OiLCMjDIrKCA9B7MDIUlBEugCldEnTpmmz3aw3d1/O9vvj3HNyb3LT7E2h9/V45FFKbpNzb24+r/N+v1/v1wtqy3KxW0yEwmEkScTldiObVGJyPBFTIhJXRMb71a/Nd7DQPb35jKqC2+Tgk1lrsYRVAoGAMYdJDoPU5dXJkFUJnzjAsNSPV+ylJ3CEbl87DoeD+vxVLHavYIFtkbbrNY/QfzdH/+6MeT6yTFtbGz6fj8bGRtxuN7FYjHvuuYctW7bwwAMPpBVBTARJkqivr+ell16ivLycdevW8cQTT7B8+XLjMT//+c/ZtWuXIZJ4+umnefLJJ9m9ezef+9znDJHEmWeeSWtra0YkcSwxmdDCUChkhNI1NDRMWZIeHAyDKkxKfBAOxVKSalMVayPkIoqSFiyIkPaXUEVTzZmiMXAn3+WOEJ0uTU/+uigqakzC5LBgMZuT3iAqippo/4wiLTUiI+RP6SUh6I0imGWi0Sgul2tShrx/P3CE0+oqKc4emV9l2+wsKypmWdHInWQwHk9UWf5EpaWR1h/37+PvXR1srFzMhrJyHNNo/djtdqJ2O1s6O3nq3QOEwnG06tacqHZlZPQFaFNKxLnF+DmNiBxGDHNH1I6TgexXMNlH5lHTgaLCkV4/RS4V1yjvQqfZlvQ4NSlXSzTIC1TahmPk2My4p5CtpUMQIKxG+VNoGxeXf4TVTq3Vp89h/H4//f39hMNhzGazQVi6XLvAVkq2UEi03UxZtICPLL0EyRrBK/bRE22jLfwuLnM2xbYKFtgWYTUd+5Tc8Xw/k+H1emlpaaGsrIzm5mYEQWDHjh1885vf5MILL+S1114blaE1ecwkamP58uVcfPHFLFu2DIvFws9//vNjHuR4wlZQiqIYVke7d++mubl5zGOSPfrq6uqmJUVXVZWbz/sBTptzQveISDTK4ZZejXSS3wj6z0jfN5JkYnEJVU2n3SMxIE+07Jx2zOWT3fFIkFahEyHXDghJB6wpTcU1QlpZS3KJoRCJSylx8umgRX7LlNfm4Xa7ptQCqSzM4xtnbphyFRSKx+kIjBBWTzBIvsNBfUEhlTm5lLjd5NjsmEd9XUlR8EYjdAYCHBr28l5/H4eHhjlyeAhRlDGbLZgS1a0uJQcSP6uR3SL9Z6hHZ2hODmkqLUZMcyciLcHMhPOo8aCoiQBBBCqKsynOm5rLg6qqxBWJmCJiM8P6kly8kh9RnZ7gyCSYOLOoifV56e3GJEkyxAN+v59QKIQoioiiSHFxMRUVFYmW69iY+ZDswycNaHZMgh2LyYbbnIPNNLnVgbmCJEnGSsqyZctwOp1EIhHuvPNOtm7dykMPPXTMcpfmAZkKajJIV0FJksShQ4fo7++fsUeffzCIFJNQrCpp7z1UVVtADYU080xz8o9kNDFJRhy7qlvl6I9SdScJNeU2Qo3FpyBi0KTopriK2aoptPRDVpYl7ZskDladtEyCRqYuyUTpghxARZQVIqJINC4REbUPWUn+OgIWswU5JiBMIfMJ4MjgMP/33gHOPal+Sv/ObbOxtLCIpYUjy7fJpPV6ZwedAT8hMY7NpNkuiYpMaJRprnc4SE93QHNbsFgQ4ipKWEaNKahi0gtvEhBsAoJDk4YLFiHxemq7RXr7FFJJK51pbjJppcSTyJqyz5I/+XmUSrLjuKbO6x2OkuO2YZ9CFSQIAnazFXsiwTgey+ebdWfjl0N4okN4YoP0RIcmHQapqAp/6d9OW6iHc0o2kGdNragtFgv5+fnk5+cTiUTYt28fbrebhQsXEolE6O7uNpZi9b0iQ65tzSPLMuK6oKoqUSVMSPZjxpxYbhewCLZj1hIcHByktbWVRYsW0dDQgCAI/P3vf+fGG2/k85//PK+88sqMxB0fFJzwFRTA3//+d0455RQURaGjo4OOjg4qKyupqKiYcSjZ3jf288itv8HpdGIZVaZLCS8us9mM2+0mMByhr3uYpNtwQDN4VWQFs9mEyWwmGhWRdSlzourRSEo1yCoZ5ooSBMfk5wSC2YS5piDNoaemkJaWQaWRlsNto6SuAIvFOuY1UxQFfzBIOB5HsFiJyyoRUUQwC1TWT905XkDg8g+dRHNV2ZT+3WQQFsXEPEtrEXb4ffSHw4iSiKfHRyAgal5qEQXVL6Omz2YffcGYsswIOWOrnWTSSh6y6w7vBnGN+pLJpOUqtKE61XGd3nXo6jyT2aTFniR9zmm3ULMwe4rRFqlYV7iIK2qaUn6eqqoyLAbpiQ0amVqe2BCRNGGQOiyChdMKVrI+fyk208jvjP77mc4/L/kxoVDIqLQCgQCSJOF2u1NahOmETXKSY7pgiPjTu65MF6Io0traSiwWo7GxEYfDQSgU4vvf/z67d+/moYceor5+ajdf71NknCSOBjVRuQC8/vrr1NTU0NbWRklJCYsXL561u5eXfvNXnn/sJex2uxFhLcsyoWAQVVVTQs662gcIB6PoPztFHjlQzCbN2FOWFKITLPKOJi1TYR5C3lip7NFgqcpHmJTcXDUO2aK6XFRkFEXFbDZhsViQEzcCbpcr4eIwMgOLywobVlXizLXTOeSjY8hPZJI7aXNJUskQRZE3dr3H/7x7gAFRJRISCQ5EkOPTcHEwC5jyzJhcR69UxictIZW4Eo8XBIGqqnysNhMxWRoTT6IkVhRAc3wf78AtznVQWjA54c94OGNBDZ+tWnnUQ10LgwzTExtKSTEOyqkhnS6zg5Pzl7Emtw4xFGPfvn0UFBRQXV09pVmIqqqGk4NOWskL28kuI2OdMcZpr06DtPr7+zlw4ACLFy+mtFRbVXjttde45ZZbuPrqq7n22muP+YxnHpEhqKNBJ6jBwUHeeustysvLWbJkyay7mj/+/f9h19/3JnZFrIneuYQ7y40tUVFprS+F9n2elD0UQRCwmM0jUnBVszaaqsONJceNrbxYkzsrCdlzmoiOZJiL3JimeFgVLMolp9iNSiJWJBw2ZmnJXnkWswWL1YJJMLEg3831l55uKJ0Gg2E6vH46hnx0Dvnp8PqIiumdPgQEPtpYzSdW1s1ImZcOqqrS0dnJczveoyUgogpmBnoCBH0x4/O6uEGfFU1WlWfKMiPkjU8Uaa8HDOuhsaQl4HRaWVSZnzJDU9Fyx4LRCILdhiSoCeIav9KqKskixzWzgL2Tiyq5rHr1iKR9kghKEYOsPAny8sYDxMMxyuRczqzZQH1B1axUNMlODjppJa8R6NWWyzV2RpquZX60Nno8HqelpQVVVVm6dCk2mw2/3893vvMdjhw5wsMPP2x4351AyBDU0aAoCm+88QYWi4VgMMgpp5wy43beaKiqyj1X/Jyh/iEkSXPhdjpdOBx24/M6/N4wvV3exM4T2nxjVKskGhUN5/KpQLCacdUtSv0FUvVBeWKmMYq0BKcVy6KpuSU7sm0U1eQRCgYxmc24Xcn5NhoJS5KY2IUZMXg9e301p65aQk5Ozhi1kqqqDATDGmF5/RwZ9NHl9RNNsqcqy8vmnJV1LC9bMCuH1+CQlz/9YwfvDYWQTFZCgRgD3QHkCV77qZCWYDdhKrLMaJl3NGm53WZyc63aeweBuBjHYbfjdLlSVhBUIK5ojh06YUVlEUVVMZsFlizMmdI8Kh2W55VyZU0zrgnsqY6G/v5+dh/Yh6M0GyXHgifuJSLHKLHnUeuuYJGzOGVWNxuIxWIppBUOh7FYLCmVlsvlGnNWjEdafX19tLW1sWTJEhYsWICqqrz44ot897vf5brrruNLX/rSrJ877xNkCGoiDA8P43Q62b59OytXrpz16mmw28v/d/UvCAaDWKxWcpPk6aOXcdtbPUTD8TTSci3OPBabHjnpcNVVYLJNcFiojFRZqop1SSGTGbGAXgXK5FW7ycvPwTKpg0n7NyZB5eLTa1DEqNYOdLtTlmHTkVZ/IEyH12dUWp1eP/luB81VZaysKGFB9tjdmaNBUhQO9PTz4lu72NPnxWJ3AkJK1TQdqKq2pCsrY0lLsAqYiqwIltmbcZSWZqGqcRRFwWIxI8taaq7FnLAcsmp/ptN/xhStPWizmFhRUYgn5h83nmQyKLZncXXdespcU1vLiMVitLS0ANDQ0DDm9zImx+mNefGKQWwmCw6zDZfZQbEtd05EDqIoppCWHquRItpp6nAAACAASURBVMRIatXrz2Hfvn2YzWYaGhqwWq14vV5uueUWvF4vDzzwQMK09oRFhqAmgh65sXPnTpYsWTKpXZzJYnBwkBf/Zwvbn96FxWIxLPf111ufM4TDYULBCN7esJbflPixqfrOkaQgSdOYd4yCvaIYa+7Unl9xfQnOwiyicU2RF41pf4op15MQcSiaNU5eaTYFFVPPqVlRU8Lnzl4DQDgcNmyH/H4/siwbcQX6x+gZoaKq9AdCdCRmWcPhKCZBINdlJ9/lJNthw261YDGZkBSFuCTjj8TwhiN0DvnZ3+UhEArjcrmw2W2EAzH6uwKz8tqPhToSSWIG8wILojDzXy09N6iyKpcs90h7VkVbTdCd3iVJSiWtxEcyoS8tLOKaNU34xdiE8SRHg0Uwc255A2curJ2w5aeqKl1dXXR0dEzaP0+HqEgMi0HMggmLYMYsmLGazCkii9mEJElGXHwgECAYDALgdrtRVRWfz0ddXR0lJSWoqsqf//xnfvCDH3DjjTdy+eWXz3rV9KUvfYnnnnuOBQsW8N577435vKqqbNq0ic2bN+NyuXj00Udpamqa1WuYIjIENRF0gtq9ezfl5eWzEgAWCAQMS/q217rYtWWvISO32+1YrFYsZgvxeIxwJILDbsc/GCUwHNaWXxMVjCJPPCeaCiz5WTjKppYW6y7OZkFDyZj/L8sKkZhEMBQhGI4gKfoSsoDJLFCxogTTpOK9U3HuKUs5fXX1mP+vqiqhUMggrEAggCzLYyqtdKTV50+Qlnek0hKTAipFUSQU1DKmnC4niqIy6AkS8E7O+WM2YLGaKF2ch2RSiUqiJs2XRGKTDNJUlMROk2DCbDHjsFtYtCjP2M9KBzVRvUqiZJi9aq4lZiO9uGlhGVevaU6da6njx5McDWXOXC6sXM7S3PQ7ecFgkH379pGdnc2SJUtmRaQkqzKSqqAnpOmKvKnOxiaLUCjEnj17DL/HF154wbAmMplM3HrrrZx55pnk509xq30SeO2118jKyuKKK65IS1CbN2/mZz/7GZs3b+bNN99k06ZNY0xjjzEyBDUR9Eyo/fv3k5+fP6U7ttGIRqO0trYSiUSor68nJyeH+65+iMCQdmclyzKiJBGPxRBFEUHQ/M5URaD3iC/h6znauTOVsGRFy+WZDtLOoSaAyWKmcv3iMXMSWZINebzL5cJkNiFJilFlldUWIjtMhCJTbw9ddtZqTqpdOOHj9Iyd5EpL34FJnheMVkXJikKfP8RBTz9v7T9IXzBCxGRFVlVC/hiDniCSOBdV09FhtZkpq87DkjT7UVQ1EUuik5ZETB6ZvanqSNVktqRaLGVn2yktzZ6iEEMdydQSNdKqc7r5VOVi8nNzjdc0Xcs1OZ7kSHiYztAwPnEsyTfkLOCssjrqs4vQk2YPHTrE4OAgS5cunfXg0DHPMdFy1UXkOmYyu1RVlc7OTrq6ugz5u6qqPP3009x7771ceeWVFBcX88477/DWW2/x2GOPUVVVNfEXniLa29s577zz0hLUNddcw8aNG7nssssArXW6ZcsWI/5jHpBZ1J0sZpIJJUkSbW1tDAwMUFtbS1GRtgjaub/bICf9zR+PxRAEgfz8fEwmE5Ik0d0+iKwk9mkEDIcBbXkTzGYTZrMJEmeCvn+kyIpBXpNR9amijBoTp7QPpUgyUX8EZ57WLlIVrZLRq5dk3zyLxUSWxUaWy0YOVq75pzMIRuJ09vno6vfR2e+jq99PZAKJ/O9f3Ek4GmfD8sqjHhrJGTtlZZrUXN+B8fv9eDweWltbU0hLH3BHhvqxDPdx+alNFBYW0jPo5w9bdrEn0EuOzU5EkIiKIlO7d5sZxLhMd/sw5dX5mC3aHb5JEHBZrbisVkgYPeik5QuHCUSjKBYLUpr7xkAght1uoWAKSkwhsUBtMVt0j1f6gdciIT5dWMjAwACHDh1CFMWUlmt2djZ5Nid5Nue48SQdoWGOhIdp8ffR4u+jwpXLalcRzl4/VQvLDWPUuYYWZz9WzDBdk9dwOMzevXsN53Sz2YzH4+GGG27A7Xbz8ssvG2fCFVdcMTtPYhpIF7vR1dU1nwQ1KZzQBKW/AaeTCaUoCp2dnXR0dLBo0SJOPvlkBEEw7mhbd7Rpf1cUwsGgcagn330OD4SJx+SRdoY6ErmtJJGW4dyQiOA2mwXMZpPOWdrjZRVZUQzySne4SqEItikQFECwL4Az10UkEiEWi2kzGpvtqPc/A4MB3n6ng3VrF5OX7WTFklLjOof8Ebr6NdLq6tNIKxofuTlQFJVnXttDR+8w55yylCzn5IUryYNr3XVZURRjVtDW1obX68VqtVJQUEB33xAvbG3jvfYBVBXyXE70Jq+KSkyUiCacMCJxkagkzSlpiTGZnvZhFlbnaTclaaDKCmI4TLbZTGnxgkSooDoSAJnI1IrJEgMDIaxWM9nZMxP/7PcN83tUrl7dRIPDacxO/X4/g4ODKaSVkqllc5BrGxtPcsg3wLa2FrYOHkTJcVIh9rHaa2FZbsmMVH/TRToimqizpKoqR44cwePx0NDQQF5eHoqi8Jvf/Iaf/vSn3H777VxwwQWzuuR7IuKEJigd+n7SZKBLRw8ePEhxcTEbNmzAbDYbu0ugveFbth0kFAoRj8eNQ11/s6qqirc/wPBgMPWLp/NnSyYtSSO/EdIascgxWwTMScm1qpJMWNp/y8EIFE4tgiDQF8BcYMXpcmozukn+vm15tYWVK8pxOEYOHEEQKMx1UZjrMtp4qqoy4AslKi2/RloDft5q6WbPoT5OW13NusYKctzT800zJRzG+/r6sNvtnHrqqXT0B/jbOwfZdaCVuCimSPstFgtWi5YO67BacVitY0hLs29KCEdmmbRiUYneIz5Kq1JnSDopiKJIVlZWyowmtdLSSi2dtOIhmbqyfIKqiCcUnPa1Hvb5uOuN1/niSatZWliE2+02rIb064tEIvj9frxeL4cPHyYej+N0Og3Sys7OJjQ8TOjQET5ZvYySkhIEQSAkxekIDfOPgSNYBK0Sz7c7WeTKm/X9tsniaMQSDAbZu3cv+fn5rFu3DpPJRFdXF5s2baK0tJTXXnttTuZMM8HxEJ0xHZzQMyi93z4wMMDg4CANDQ1HfbzX62X//v243W5qa2ux2+0J49ORPSIxJtLy7n5+fdtTOBMZQwYxKSrhUIzBPj+xacxndOgtCVVVEnswmkZBIyuTsbyZDgvXLSUmysSiItGohDJOf1BVVGNZuLi+hNyyqQtIPrRhCWd9fPnEDxwFRVHpHw4apNUz4MfpsFJTVkB1WQElBVnaAvME0Nuvvf2DOHIX4BmOsbutF19w7GxEU7pp+1mSJKWQljVBXOY08uy5Ii13to2SylwEQSAeixMKh3A6nUmR5ZOHw2rhnz+6geJsF52BAB1+Hx0BzcqpJxiY8rVurFrMp+oasE3wM0hOLx4aGqK3txdVVcnJySEvL++oDg5hSaQ/GsRiMmEzmbEIZpwWKw7z/N1TK4rC4cOH6e/vN+ZliqLw2GOP8fDDD3P33Xdz9tlnz1vVdLQZ1J///Gfuv/9+QyRx3XXXsXXr1nm4SgMZkcRE0AlKj/5esWJF2seFQiH279+Poig0NDTgdrsNYoKRu62BgQEOHjzI9v/ZTc/efhRZJR4XiUVExLhEPCbNqjIvGaqqGkubhpN5kpeblrAqULG6FnfRyCA6HpeIRkWiMZFYVCIajSNJ+uDdgiCAPdtB2aqp72wICFx2yXrq6sYqAacKSVbo9wbp6vfRMxggEhOJxSVcDhtupw2rxYzZJCArKqIk0+3pp9PTjyzYiMvaSHyy0MIeZSRJRopLSIqMigKCouVl6aRltWI2m9OSlt4ajMY1gUNMTDcpOjrcOTacOQIms4ksd9aMlnrdNhvXfmQdFfmpIoS4LNMZ8Bv+gx1+Pz3B4ISuGPkOBxc2NLKmpPSoB7KiKGNaYckODn6/n1gsZqQX65VW8o2djpgsGao8kzDyMVeqvGQEAgH27t1LUVERixcvxmQy0d7ezje+8Q3q6+u55557yM6emp3YbOKyyy5jy5YtDAwMUFJSwve//31jbHHttdeiqipf//rXef7553G5XPzqV79i7dq183a9ZAhqYugEFQ6HaWlpYc2aNSmfj8fjHDhwwIjbKCgo0EQJalJIoCDg9/tpbW3FbrdTmFXMIzc+keoormox7tFInFhYJBqJE4+Kc/5iaqSVHPkAOeUFFNdXjNl90Vs0sVgMi8WOokA0ppFXPC5RtmYRtmlY4DjsVq7+8hkUFLgnfvAUIckynsFg0jzLR0evl0AwiMVsSUR5THx4qYpKNBgnGogRDcaIR6Rx1ZI2pwWr24Ity4pg0VzJBUi8nlYsVsvRSSuuCTAmIi09EyyvyE1JRe6s3JW7bFauPmMti4uOXg2LBmmNENd4pFWVm8s5S+pYUVQ85hr9fj/79u2jsLCQ6urqcUUQqqqmBEH6/X6i0Sh2uz1lppWOtGRVGekgGHLymanykqEoijG7bGxsJCsrC1mW+eUvf8ljjz3Gfffdx8aNGzOzpqkjQ1ATQXc0j8fj7Ny5k3Xr1gHa4dDe3o7H46G6utros+sCCJ2YotEoBw4cIBaLUVdXR05ODs/c/zy7Xt07ie+tEouKxCJxohHtz3hsekrCqcBsNVO+vt4gZ1QVwaSJO2w2e9r0UlVVqWwopeHkGrq7h+nuGWZgIGjEfUyEgnw3n//cyeTnzz5J6dBvJgLBEDlFZQyHJE052Oejzzt29qKqKrFgnOBQhPBwZFouHTanhZySLFx59oSNk6RZOclygrSshnPDeKSVTFgagWkVrD47A8grclFQMjVnjPFgNZu54pRVrCifWlWrk1ZHwM8Rn9Ye9IQCyAkiL83KYmNlFWsXlmFF4ODBgwQCAZYuXTrtBfjRpBWJRLDZbCmk5XSOjZyfLYPX4eFh9u3bx8KFC6ms1FSlra2tXHfddTQ1NXH77bfjds/de/oDjgxBTQSdoFRV5R//+Acnn3wyXV1dHD58mLKyMqqqqoxdjeR2niRJtLe3MzQ0RE1NDUVF2k7Hu6/u5dn7n5/29ciyos2GwnGDuCRxcsuaU8GipjpcBdnGNrzJJCSk9vJIXpMl2WVAMza9+t8+SVGpJrKIxSR6PD66u4fp6dFIa8g7vtDE7bJz2aUbKJ/GLOtoUBSFrq4uOjs7qa6uNgbvyYiJEj0Dfjr7fBw8Msju3V30HPEixWfntbXazeRX5OLKHZkPqaqitQgT3oOSYf47YjekzdESzvWKTDAUQlFULHYHcVk25loxUSa/2EX+gtkhKQGBT55Uz0cbq2f09URZpisY0EIgfT46An66vF6KZIXTqms4Y2kjtlnONIrH4ykL2+FwOCUiXl8lOJqZa7LRbjrIssyBAwcIBoM0NjbicrmQJImf//zn/OEPf+CnP/0pp5566qw+rxMQGYKaCMmRG3qscn5+vrHJPpqYdCuWzs5OFi1aRFlZmdG26DnYy2O3PokUn90qSJJkYhExpT0oyzNbJM1ZWEBWZSGKomj7TKMOET0cUUxUBLIkgyBQu2Ih519x8riHQDgcx+Px0dXtpadHIy9fIGJ83iQIbNhQw8YzGrDZZn5w6aIVvYU0XlSBKMq07Pewc2cHB9v6UdGUjdG4lPgQicYk4tLMCMuZa6dwUR4WW/rr0EgrSYiRIC0AJWHnpIkgUl9XRVWIijK1NUXkF7vp9Prp84cmXcGOhxXlJVy6fgVu+8zcy2HEP09SFLLLy/BEI/SGQritNvIdDuoKCih0zizKYzwkR8T7/f4Ug1eduNJ1BtJhaGiI/fv3U15eTkVFBYIgsGfPHjZt2sTpp5/O9773vWkJVTIYgwxBTQRVVRkYGGD//v0MDw9zyimn4HQ6U+Iu9Dd1f38/bW1tFBcXU1VVZRzqqqqy4//e5S+Pvqod5MfgmiVxhLT09uCkxBeqJjdHgJrTV+BwTj7mWz9cz71iDTa3tqCoRxMcbUYQDEbp7vHR1eU1Ki5VVWlaU8Wa1ZXTmk3prh2yLFNfX4/LNfbgkySZ9vZBdu/pYl+Lh2hsYtWkLKsT+A5ODJNZoKAiF3fB2NbTaIiSFlhpMZsxm81IsjwSs5KsHkyqtE5fXc05H2ogLst0eQOGy3vHkG9apJXjsHPxuhUsL09vQTQRJuOfJyoy3YEAEUnCbjZjN1twWCzkp3m/zBaSDV510jKbzSmSd7d7xG1fkiTDCaaxsRGn04koitx3331s3ryZX/ziF/MtKvigIUNQE0GSJLZv305NTQ27d+9m/fr1Kf1rWVIYGhhi354WBNlESVEJLrcLQYDAUBBPez+7X29huNc3j89COyTEuEQ0rM+04sSiqU4IKeGHZhNlK2vILpn6rkZRaQ5fvPETWK0W4vE4Pp/POAT0wXZubq5BWqOdqFVVxe+P0N3to7tnGEmScblsFBVls7A0l9zc8Q92Xebb29ub4toBWnu0r8/PkSNDHDo8wKFDA8THyZGaCmRZs3CKxEYqLWkSFaw730nholxMlrHCAEXVHC8UWRnjgg3azYBeZUliotIyCQZhrV9eyUVnrh4jtY+KEp1eP11eP0eGfHQO+egLTG6/b0V5CZ9a3UBR9uRvGGbinycqMsF4HIvJhFkwaWo8k4DVNHeBfTpp6cSlu5JbrVYCgQDl5eWUl5fjcDjYuXMnmzZt4pxzzuHb3/522gTe2cDzzz/Ppk2bkGWZq666iptvvjnl80eOHOHKK69keHgYWZa56667OPfcc+fkWo4xMgQ1GUSj2k7Mjh07sFgs5OXlkZubi8lkoq2tDVmWqaurIysri3g0jqetj64DHroP9NJ90IOvzz/PzyA91ERERzgYxe8LIsV1SyTtfeEuyqFide20vnbz6XWcffG6sd8zSY2lE5e+qJxcaaXzchsaCtHdM8zgYIhoVCQSjWO3WXG77djtZsLhEB5PD/n5+RQWFhGPy4RCMXy+CEPeEAMDQa06PAZI9h3UyEs0xALJsNjNLKguwOYyPD+0IMdIFJfbhd1mY7Lyd0VVEsauIpIkU5pn4+NrKigsyDtqBRuJi3R5/XR4/VpqsddP/zikZTGZ2FBTwceW1ZDnGr+6lmWZQ4cOMTQ0NKv+ebKSWJEQ9LD1EReVuYAoiuzdu5dYLEZBQQH9/f189atfRVEU/H4/1157LZ/+9KdZvnz5nBCU3gH4y1/+QkVFBevWreO3v/0ty5YtMx7zla98hTVr1vDVr36VPXv2cO6559Le3j7r1zIPyHjxTQSPx8Nzzz1HU1MTy5cvJxqN0tnZaRCTw+GgoKCAQCCAIAi4XC4ql1VQuWxkJyjkC9N9sJeeAx66D3joOtBLJGnuMl9QVAVRimGyKlQsXpCYqakpqkGH3UQ0NvVDfcdfWylbXMTK9anO44Ig4EgsJy9YoLWMdPm6z+djYGDAeG1dLpdRaWVnZ1NYmEVh4YjaS5YVBgeDtB3qZefOfQx5Y4iiBRUP4JnRazNTJPsOAqBqe1rRmKip8hLVlhST6Wnpp7AyD0eejWAwiNViIS8vd1Ly92SYBBM2m804KMMKvNEW4fyShYRCIXp6egyV2+i2a21JIbUlhcbXisRFoy2o/zkQDCMpCq8fOMKbbZ2srlzIGfVVLCpIdR7RZzQLFy6cdf88s8nE6PppJj55R4PuBlNTU8OCBVrQpdfrJSsri0996lNs3LiRnTt38pOf/IRTTz2Vq6++ekbfLx22bt1KbW0tNTU1AFx66aU888wzKQSlr7EA+Hw+w3fyRMEJXUH19vbyyCOPsGPHDlpaWpBlmXA4zLXXXstnP/tZiouLjXaAz+cz5i7JLazRA1NVVfH1B+hOEFb3Qa3amm3xxHjQLHEixOOxMRZLo7HyjEbO+vJH8BwZovvwID1Hhug5MkhgeGKCFQT45OUnc9KGmmldo27q6vP5CAQCKIqSIh92uVy0t7fj9Xqpq6sjPz8fWVbo7fNrUveE3L2/PzDpqPVjChVESSYSE/EFQthzLOQtKkBhdpdKnQ4rl3xsFQ2V2uxHr2BHt12TSSutc0NcTMSRaHlaHUM+BkNhyvNzWLe4nBULi/B0aPZFS5cuxTmF+eVsY6qR68mIx+Ps27cPQRBoaGjAZrMRiUS444472L59Ow8++GAKQcwl/vCHP/D888/zy1/+EoDHH3+cN998k/vvv994TE9PD2eddRZer5dQKMSLL75Ic3PzMbm+OUamxTdZvPrqq2zatIlzzz2X1atX884777Bt2zY8Hg81NTU0NzfT3NxMU1MTdrudQCBgtLB0A9WjtbBkWWGgc5Ceg710tWrE1d8xiDJDNV4ytPZanHAkrMV8p9kPGQ3BJPDPP/0ieSWpd8kBX5iewxpZ6cQVDacPqTv17OWc9okVmC0zmx3opq4+n4/e3l58Ph82m43CwkLjhiB5qK1DFCU8ngRpeYbp6hpmcCg4znc5hlA1sgiHtRBEu91OeVkeHz1rGf5IPOHu7qO73098FlYJTl21mLM31GNN83PQ7YaSl2AdDseYJdjRCMXidA752HXoCHsOd7KgqIj6ioUsK1tASc7sSN5nCxMRlKqqeDwe2tvbDTGHqqq88cYb3HjjjXz+85/nuuuum5UcqsliMgT14x//GFVV+da3vsUbb7zBl7/8Zd57770PQkx8hqAmi/b2dlwul9GW0qEoCq2trWzdupWtW7eyY8cOIpEIy5Yto7m5mbVr17JixQoURUkRC8iybEQ85CZydMYcrHGJ3kPaPKvnYC/drR6GPMPTun5RFAmFQpjN5rSH+NGw5mMr+eQ1HzvqY1RVZXggSPeRQYO4PB1DiIk9opKKfM78f2uoqhu7gzQV6GGPLpeLJUuWYDabU5RYegZVdna2QVrp5O7RqEiPx6ftZ3UP09U9zLAvPO3rmiqS87LcbneKRZHbZefC/9dETbVW8SiKysBwyCCszj4fPQP+KasHAYrz3Xz6wyuoKSs46uOSZ4XJdkMOhyPlRktRFPbu3YvD4aCurg6r1UowFqdryE8gFsNps2K3WCjKch51ZjXfiEaj7N27F7vdPvI8gkG+//3vs3fvXh566CHq6uqO+XW98cYbfO973+OFF14A4Ic//CEAt9xyi/GY5cuX8/zzzxtRGTU1NfzjH/8Yc1a9D5EhqLlAPB7n3Xff5c0332Tr1q3s2rULq9XKmjVraGpqYu3atSxZsoRoNGqQlj7DSj5Y0+1lRIJReto0stLmWR5Cw+MfrLIsEwqFUdX0+0yTggBfuP1SKuqnlgsjywqDHp/RFuw5MoTVZmHlhmoaTlqEYwq2SKIocvDgQYLBoBH2eLTHjm67Wq3WMW3XsTtaMUM52J0grkAa09gZQdXk9/F4XHMct6b/eQgIfGRjA6edWpeW0GVFoc8bNOJIOvt89Az6kSfpdtHUUMbHN9STlzWVNYIRY1efz0d/fz/RaJScnBwKCwuN9246sUAwGiMUF7GaNT9EsyDgsFnnzYlcR/LeYl1dHYWFhaiqyquvvsott9zCNddcw7XXXjtv1YgkSdTX1/PSSy9RXl7OunXreOKJJ1i+fMRg+ZxzzuGSSy7hC1/4Anv37uXMM8+kq6vruKpep4kMQR0LqKpKIBBgx44dvPnmm2zbto3W1laKioqM1uDatWtZsGBBysEaCoVSDtbc3NwxswFVVQkMBo0qq6vVQ89BD7FIfNJzpsmgsCyfq+79PNYZLs+KokR/1zB93cOYzSbsTit2p43SinzszrEHW/IOzeLFiyktPbrx6HjQ3QX0G4LkuYv++o6WuwMEAlFjlqX/GY6kb2VOeA2xOOFw2BCJTObXb0n1Aj79qTVkZU2c1yTJCr1DgaRYkmE8Q8Fx99+sFhMfWlnFaauqyXZNPg/K5/PR0tJCYWEhixcvTnFu0FWZTqczpdJKR1pRUQLUkQBONBHEsTpYI5EIe/fuxeVyUVtbi8Viwe/3853vfIeOjg4eeughFi9efEyu5WjYvHkz119/PbIs86UvfYlvf/vb3Hrrraxdu5YLLriAPXv2cPXVVxMMBhEEgXvuuYezzjprvi97NpAhqPmC3u/eunWrQVq6r59OWE1NTTgcjpR5VjQaNX759YM1eZ6lqio9PT3s2r4bU9SK6JPxtPXhOdQ343nWyec387ErzpjpUx+DWFSkt2OISFjzmItHJcwWEyarwoC3h4XlC1hSO7UdmmRIokw4GCUUiBIOxhIfUUKBCH5fgGAgRCgURlEVnE47ufnZFBbnUVpexIKyAnILRipZVVUZ9kVSRBg9PcPEjiJwUWTFODzcbjemcYIGx0OW286nzl9Dbe3UWzaiJOMZDGgmuQmz3L6hVN9Bq8VE89IKPrSyigX543viSZJk+Oc1NjaO6zGXnPukf4xO2E03hwWQEsa6gpDqSjibpKWqKh0dHXR3d9PQ0EB+fj6qqvKXv/yFW2+9lU2bNvHFL37xgzDDeb8jQ1DHExRF4cCBA0ZrMHmepbcG9bgPnbB8Pp+RxGuz2RgaGiIvL48lS5ak3LVKokTf4QG6D/ZqysFWDwPdQ1P+aX3iyx9h7SdWz+bTHoNYLMaunXvo7fTiMOXg7QvhGwqhquBw2XA4rVjtmv+ffthrS8aKFlkS1Vzho+E44WCM2AQR8snQDXJ1fzxFUXE4bZRVFVK9tIxlq6upqClOObxUVWVwMJggLK1F6PH4EEXZcH93u91YbTNLgl3btJiPnbkMu31mVWxMlPAMBFJmWgPD2utbU15A89IKlleXYE+qlvv7+zlw4ACVlZWUlZVNmTCSE3b1LoEoirjd7hQhRjrSmk0JeSgUYu/eveTm5lJTU4PZbGZoaIhbbrkFn8/HAw888L4I6TtBkCGo4x3J86xt27axa9cuLBZLyjzLZDLxpz/9iQ996EO43W5jsdiI1c7NTTvPioVj9LT10tXaS89BbZ4VMQDq9AAAHV5JREFUGJ3gmwbnfe0sVn9k6iGDEyE5F2jJkiWGwS4k3CW8YWOW1X14EM+RoSmRz3Qhy5pbg+7cYHOYqVleysp11dQtryQ7O3uM08PAwADbtu8GnEiSDY/HT2+ff8aLwrk5Ls4796RpVVNHQzQu0j0QMCJJeoeCFOW5qavIh8gQTruV+vr6tG3Q6SKZtPQPSZJwu90pHnnplranSlD6e6u3t5elS5eSm5uLqqo899xz3H777dx8881cdtllmarp+EKGoN5vSJ5n/fWvf+V3v/sd/f39rF69mlWrVtHc3My6detYsGCBIcnWLVt0c0y9NZjWF88bSuxleQziioZiY66j+ayT+NiVH57xTErH4OAgra2tLFiwgKqqqnFNXUe/FkN9gYTMXSOu3k7vnLi7j/rOhgu5O8dGeX0uixuLKCjKw+VyMTQ0hCAIY3aBRFGmr8+vVVndXrp7fPT3B6Zl6Nq4dCFnfWw5eXlzY66qqioH2trZte8Q9uxC8vNycTqsFOa6KS/OwTxHB3ny/ptebekdgmQ38qm0e/UgweTMqf7+fv71X/8VVVW5//77KSmZeWBmBrOODEG9XyHLMh/+8Ie55JJLuOaaaxgcHDSk7tu2baOnpydlnrVmzRqcTmeKCEPfdUkmrdHDbFVV8fb6NNVgYqHY09aHJEoULMznI5edwtKT0yvNJoNIJML+/fsRBIH6+voZu0DLkky/x0fP4RG5e3+Pb85SinVYbWYWLsmmsNJKcWk+kiSlGI/m5uamlbvH4xIej9YW7Ooapsfjm/SOltViZsP6Gk750BKcaQQm00UwGDTaYLqUX0coEmdgOITZLGCzWDCbBXLcjrS7VbMFRVHGVFq6y35ypTWatBRF4dChQwwODtLY2Eh2djaqqvLUU09x7733cuutt3LRRRd9ENRuH1RkCOr9DEmSxr2TTDfPCofDY/azAOOX3ufzIUmSYTGk72eNrmZkSaa/YzBRZXmQRImK+jKWnVKPO3dyd/R64OPAwICRRDxXEOMSvZ3ekUrr8BBD/YFZ+/qSJBIMhrBZrbiz3axcV82HPr6c3EJXyqGqqzInqmIjkbjh6q6pB334/OOvEjjsVtavq2bD+hpc00g01jFd/7xwNI6sqJhNmieeySRgS+SDzRUURRlTaSmKYuwWmkwmOjs7KS0tpbKyEpPJhMfj4YYbbiArK4v/+I//SDESnm1MZPAK8OSTT/K9730PQRBYtWoVTzzxxJxdz/sUGYI6kRCPx9m1a1fKfpbFYmH16tXGPKuuri5l1yUQCKCqakolkG7RNx6N09s+gMlswpXjwOaw4chyYB6lWFNVlb6+Ptra2ow8nfno+0fCMTxHhkZ2tA4P4T/KPlk6qKpCKBQ2lq6TiVwQoLGpitM+scIIcATGSLIjkUiKzZC+SjAaoVBsjNw9OKr1arWYWb1qEevX11BUOLWE2mT/vEWLFs3oZ6KqKpKsoPGTYEStm0xzW6noBq5tbW0EAgFsNhtvvfUWr776Kvn5+bzxxhv88Ic/nPOqaTIGr62trVx88cW8/PLL5Ofn09fX90FYrJ1tZAjqRMbo/azt27cb4X76fpY+zwqFQsY8S3dASK4E0tkmiXEJQcCwOAoGg7S2tmrmpLW1cxZPMF0k2zfpxBUJpd95ikWjhCMRXC4ndvv4bUlBgKWrKzn1EytYME5SsH5DkOzYMNEekaqq+PUdLUPu7iMS1a63qrKQNasrWdqw8Kiqv3g8Tmtr65z7581WxPrR4PV6aWlpoaysjEWLFiEIWqz8TTfdhCRJlJWVsXfvXhRF4cEHH5wzv7rJuD/ceOON1NfXc9VVV83JNXxAkCGoDFKhqiq9vb0p+1nd3d1j9rNcLlfKPEuvBJKXivVDVRRF2tra8Pv91C6pTWkdCSbhuFVOqarK8GBQI6uE32DnoT68Q8OYzRbcbteUHMeXrl7EKWcvp7RiYpuhdHtEyTOXdEIBVVXxesMpVdbAQJDFVYU0NpZRu2SBQVbJvnPJbt3HEsnnSrJac6rXIUkSBw4cIBQKsWzZMiNQ9NFHH+Xhhx/m3nvv5ayzzjK+biymVZ6zqUhMxmT88z796U9TX1/P66+/jizLfO973+MTn/jEnFzP+xgZghqNyfSOTzQkz7O2bdvG9u3bU+ZZzc3NnHTSSQBjcp5MJhPRaJSysjIWL16cVjI8eoHYZD52bgKThSRJtLW1Mewdpii/jMBQzCCuvq5h5CksQdcuL+NDH1/GoiWTb+mMVrfpQgF95qILBUbPCxVF29Hq6h6mr8+P1WrGahUQ40MsWJBDfX192t2j+UI60joadPXnokWLjP2s9vZ2vvGNb9DQ0MDdd99Ndnb2XF7yGEyGoM477zysVitPPvkknZ2dnHHGGezatYu8vPRV9gmKTB5UMmRZ5p//+Z9TescXXHDBMbPWP15hMpmor6+nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4emnn+bWW2+lrKyMUCjE22+/jaqqZGVlGZVWVlbWGJdzRVHGkNZMndCni+SZ2aJFi6irG1Esrjp5CaA5VfR1DydmWZq7+2Cvn/Hu6w7s7ubA7m7KFxey4cxG6lZWjJnVjYYgCGRlZZGVlWXk/eju7n6/n+7ubgIBTfiRvPialZVFcXE2xcXZRtpwT4+HsrJyBJOdri4fVpsFp8NKfv5YleGxxmS/vyiKtLa2EovFWL16NQ6HA1mW+c///E8ef/xxfvzjH7Nx48Z5eT7l5eV0dHQYf+/s7Byz/FtRUcGGDRuwWq1UV1dTX19Pa2sr69aNDfnM4Og4YSqoyfSOM0gPfZ710ksvceedd+LxeIxo7OR5VmlpqXGo+ny+lHmW3hpMN8+SZYWUE18QJjzUZ4pwOMy+ffsMh+upzMx0+6YeI0drkOHB9Cm1ufkumk6v46STl+DOnqHMXpbHuLubTCbsdjt+v5/i4mLq6urGVFqiKBMIRDFbTJgTbVet2pqfG4OjQXe1SPZm3L9/P5s2baKpqYk77rgDl2tu9sMmg8kYvD7//PP89re/5bHHHmNgYIA1a9bwzjvvUFhYeJSvfMIhU0Elo6ury7CsB+0u580335zHK3r/QBAEcnJyeOaZZ7j55pu58MILAVLmWY8++ig9PT0sXrw4ZZ7ldrsNv8G+vj7C4fCkjFxlSQadyFR11uZZyRL4hoaGabVd7A4rlXUlVNaNLICGg1GjLahnaIUCUXzeMK88u5PX/ryLhlUVnHRyDYsbSqf1XMxmM3l5ecY1S5LE/v378fv9lJSUEI1G2bp1qyF31z+cTicFBaneeqIoE094DAqCpsYzz2P7NR6P09LSgqqqNDc3Y7PZkCSJ+++/n6eeeoqf/vSnnHrqqfNybcmwWCzcf//9nH322YbB6/Lly1MMXs8++2z+7//+j2XLlmE2m7n33nsz5DRNnDAV1GR6xxnMDIqicPDgQUPqvn37dmO4rZNW8jxLr7Ti8XhKBPx4IoHRC7mmxG7OZDEwMMCBAwdmRW49EVRVJTAcTkkq9hwZIhoRyc510thUybKmKhZWFU6LFPTI8nT+eRPJ3ccLKEyetemkpf/3XCG5zbpkyRJDjr1nzx6uu+46PvzhD3PbbbfNeMk7g+MOGZFEMjItvvlB8jxr27ZtvPvuu5jNZtasWcOaNWtYu3Yt9fX1RoCeLsIYHQGfLvQxhbRUFRKLpGMDDKO0tLTMmqPFdKHbN6UkFYdi1Cwro25lOYtqiiecx+nPxWQyGZHlk8F05O76NesQBGFaSrzxEIvF2LdvHxaLxRB0xONx7rvvPv73f/+XX/ziF6xdu3ZWvlcGxx0yBJWMyfSOM5h7qKpKMBhMyc/av38/BQUFY+ZZyftZgUAAk8mUMs9KZy+kKIoxzlIUhc6ODnr7eo3AuuMNI/ZNQwz1+XE4bWTlOqmsW0B+0YhCTVVVOjs76erqora2dsZOCXpAYXIS9GTl7jpRJWMqpKXHxhw+fJi6ujrjuezcuZNNmzZx7rnn8m//9m/H3S5dBrOKDEGNRrpwsAzmH8n7WbrfYHd3N1VVVSnzrKysrDFpujabbYy9EGiLnfv376e4uDjRztMqE0HQ21fHl9Q9GaKo2TdFgjGsdguiGKfLc4SFFcXU1tZOymx3Opiu3H0qpBWJRNi3b58RI2+xWIjFYtx999289tprPPjgg0YbOIMPNDIEdbyho6ODK664gt7eXgRB4Ctf+QqbNm1iaGiISy65hPb2dhYvXsyTTz5Jfn7+fF/uvGL0PGvHjh2EQiEaGxtT5lmCIIxJ05VlGZPJRHV1NcXFxWl3gZSkaAxVnfo861gg2T+vZnEtgmrBYjFhspgwmUw4nNY5l+jrvnjJlSyMlbtPNM9LrgDr6+sNf8Zt27Zxww038NnPfpZvfetbx9XeVgZzigxBHW/o6emhp6eHpqYmAoEAzc3N/PGPf+TRRx+loKCAm2++mbvuuguv18vdd98935d73EEUxRS/wXfffReTycSaNWtYvXo1bW1tDAwMcMsttxgR336/3/DT0yut8eZZ2gcp4oD5Ii19STXZ2mc0YpG4Rq5mAcEkICBgOQbS8fHk7smtweSMsnA4zN69e8nKyjIqwHA4zJ133smOHTt48MEHaWxsnPPrzuC4Qoagjnd86lOf4utf/zpf//rX2bJlCwsXLqSnp4eNGzfS0tIy35d33EOfZ/32t7/lhz/8IYWFhciyTG5uLs3NzTQ1NRnzrEgkklIFCIJAdna20RpMF/qok1Yy5tq6KR6Ps3//fkRRnLJ/XopzR0KFd6xIVpKkMe7uFosFQRCIRqNUV1ezcOFCBEHg73//OzfeeCNXXHEF11133Zy1LHVM1kHmqaee4qKLLmLbtm0ZccbcI0NQxzPa29s544wzeO+996isrGR4eBjQDpn8/Hzj7xkcHZFIhMsvv5x///d/Z8WKFYZsOdlvsKura8x+VnZ2dso8S4/LSPYbtNvt45JW8v+fDQJIFg7Mpn/esTByTYdgMMju3btxOp243W62bdvGHXfcgc1mIxKJcNNNN3HBBRfMeQT7ZNzHQQs+/OQnP0k8Huf++++fFYLy+Xz09vZSX18/46/1AURmUfd4RTAY5DOf+Qz/8R//MSaX53gf4B9vcDqdPP3008bfBUGgpKSE888/n/PPPx9InWe9+OKL3HXXXQSDwZR51urVqzGbzUaV1d3dTTQaNaTYOnFZrda0pDUaU/kZ6q4WTqeTtWvXzuocJt11jL5e/e+z8b7TLZf6+/tpbGwkJycHVVXp6OggKyuLyy67jMbGRnbs2MFVV13FFVdcwWWXXTbj7zsetm7dSm1tLTU1NQBceumlPPPMM2MI6rvf/S433XQT995776x83+3bt/PEE0+wdOlS6uvrZ1WefyIhQ1DHGKIo8pnPfIbLL7/ccGQoKSmhp6fHaPFlsmNmFyaTibq6Ourq6vj85z8PpM6z/vu//ztlnqXnZ61cuRJRFPH5fAwODtLW1mZElOuElU7VBmNJIN3hpB/mfX1903a1mA5GX0s6Bd50oMevFxcXs3btWkwmEz6fj+9+97t0dnby7LPPUlVVBWjt7WOByTjIvPXWW3R0dPDJT35yxgQ1MDDApz/9aYqKitixYweXXnopMPcV6wcVGYI6hlBVlS9/+cs0NjZyww03GP//ggsu4LHHHuPmm2/mscceO2a/vCcyrFYrTU1NNDU18dWvfjVlP2vr1q3cfffdtLS0kJ+fP2Y/S4/LGG3iqicVp5tnQSppDQ8PGzL4devWzXssyXgH6GTu/BVFoa2tDa/Xy7Jly8jKykJVVV544QVuu+02rr/+er7whS/M+3NMB0VRuOGGG3j00Udn5esdPHiQj3/849x222187Wtf4+DBg8b3OR6f//GOzAzqGOJvf/sbp59+OitXrjTerHfeeScbNmzg4osv5siRI1RVVfHkk0/OaUx6BpPDRPMsXYiRk5NDMBg02oO6QCBd/HtyvlFDQwNud6pH3vvtTnt4eJiWlhYjfl0QBIaGhrj55pvx+/088MADcz5nOhomcpDx+XwsWbKErCwtpdjj8VBQUMCzzz47rTnUnXfeya5du/jtb39LJBKhqamJp556ymgpZlp9BjIiiQwmD1mWWbt2LeXl5Tz33HMcOnSISy+9lMHBQZqbm3n88cczm/2MVAvJfoOj51knnXQSZrM5xW8wGo0iCAKxWIzS0lIWL16c1iR3pvOsYwVZljlw4IDx3F0uF6qq8txzz3H77bdz8803c9lll8171TBVB5mNGzfyox/9aNoiibfffpuHH36Y6667jsbGRk466SQcDgennXYad9xxx5ylGr8PkRFJZDB5/OQnP6GxsRG/3w/ATTfdxDe/+U0uvfRSrr32Wh555BG++tWvzvNVzj9MJhO1tbXU1tZy+eWXA2PnWbt27UIQBFavXk1zczPl5eU8+OCD3HDDDdTW1hIOh9m1a5dhLZRskjvdedaxxNDQEPv376e8vJz6+noEQaC/v59/+Zd/QRAEXnzxRUpKSib+QscAk3Efn024XC6cTid//etfAfj4xz9OU1MTH/3oRzPkNA1kKqgM6Ozs5Morr+Tb3/42P/7xj/nTn/5EcXExHo8Hi8Uypk2SwdGhz7O2bt3Kz372M/72t7/R0NCAxWIxWoNr166lrKzMmGf5fD4CgQCqqhouDfo8K10VktwqOlZtI0mSaG1tJRKJ0NjYaMSvP/XUU/zoRz/itttu4zOf+cy8E+h84/e//z0vvPACf/rTn/jRj37ElVdeCWTae6OQqaAymByuv/567rnnHmPgPzg4SF5enmEUWlFRQVdX13xe4vsK+hLwW2+9RWNjI0888QROp5O+vj62bdvGm2++yeOPP05nZydVVVWsXbs2ZZ6lWwsdPnw4JfRRr7RGhz4ei0NvYGCA1tZWqqqqWLp0KYIg4PF4+OY3v0lOTg6vvPLKjA1sPyi45JJLOP/887nnnnuM1yRDTtNDhqBOcDz33HMsWLCA5uZmtmzZMt+X84GC3vLSUVJSwnnnncd5550HpM6zXnrpJe6++26CwSBLly41qqxVq1ZhNpuNpWKPx2PkOyUvFc/VfFAURVpaWpBlmaamJux2O4qi8Jvf/Ib777+fO+64g/POOy9z+I6Cy+XC5XIhyzJmsznz+kwTGYI6wfH666/z7LPPsnnzZiMzaNOmTQwPDyNJEhaLhc7OznlVYr1fMdGhNN4867333uPNN9/kN7/5Df/6r/+KyWQy5llr165lxYoVhrXQ8PAwR44cIR6PG1EZut/g6KiMZExmOVcPRUx2tujs7OS6666joqKC11577Zjtbr1fMdc2Th90ZGZQGRjYsmULP/rRj3juuef47Gc/y2c+8xlDJHHSSSfxta99bb4v8YTD6P2sbdu20dLSQl5enkFY+jxrdL6THvqoV1qTcR0HzQ9w3759CIJghCIqisKjjz7Kf/7nf3LPPfdw1llnZaqCDGaCjMw8g6khmaDa2tq49NJLGRoaYs2aNfz6179OK4vO4NhDVVX6+/tT9rOS51lNTU00NzeTm5tLMBg0RBjJ8yz9Izn0UVVVPB4P7e3t1NbWUlxcDMChQ4f4xje+QWNjI3fddRfZ2dlHu7wMMpgMMgSVwfsbw8PDXHXVVbz33nsIgsB//dd/0dDQkMnOSoPkeda2bdvYvn07gUBgzDzLYrEQCASMSiscDmO323G5XAwPD+N2u1m6dClWqxVZlnn44Yf59a9/zX333ceHP/zhOa+aJnIe//GPf8wvf/lLLBYLxcXF/Nd//Zdhn5TB+woZgsrg/Y0rr7yS008/nauuuop4PG5kCGWysyaH5HnWtm3beOeddzCZTKxatcogrbq6Oh555BFqa2spLS0lHo9z2223IYoig4ODrFixgp/97GfHZK9pMs7jr7zyChs2bMDlcvHAAw+wZcsWfv/738/5tWUw68gQVAbvX/h8PiOEMPmuvaGhIZOdNU0kz7O2bdvGK6+8wj/+8Q/q6urYsGEDGzZsYNWqVTzzzDNs3ryZM888E5/Px44dO7Barbz88stzWkFNZEs0Gm+//TZf//rXef311+fsmjKYM2T2oDJ4/+LQoUMUFxfzxS9+kZ07d9Lc3MxPfvITent7WbhwIQClpaX09vbO85W+f6DvZ23cuBFJkvjd737HM888Q0NDgzHPuvvuu2lsbOSll17C4XAY/1aW5Tlv703GeTwZjzzyCOecc86cXlMG84sMQWVwXEKSJN566y1+9rOfsWHDBjZt2sRdd92V8phMdtb0sX79ev72t78Z9jv6ftYPfvCDtI8/3uTSv/71r9m+fTuvvvrqfF9KBnOIjP97BsclKioqqKioYMOGDQBcdNFFvPXWW0Z2FpDJzpoBdEeK4wnl5eV0dHQYfx9v/+7FF1/kjjvu4Nlnn80oSz/gyBBUBsclSktLWbRokTFfeumll1i2bJmRnQVksrM+YFi3bh2tra0cOnSIeDzO7373uzFmrm+//TbXXHMNzz77bObm5ARARiSRwXGLd955x1Dw1dTU8Ktf/QpFUTLZWR9gbN68meuvv95wHv/2t7+d4jz+sY99jF27dhlzyMrKSp599tl5vuoMpoGMii+DucG2bdv48pe/zNatW5FlmfXr1/P73/+eFStWzPelHRPcd999/PKXv0QQBFauXMmvfvUrenp6MvlZGWQweWQIKoO5w3e+8x2i0SiRSISKiopxpcAfNHR1dXHaaaexZ88enE4nF198Meeeey6bN2/mwgsvNKyhVq1alcnPyiCD8TEpgsrMoDKYFm699Vb+8pe/sH37dm688cb5vpxjCkmSiEQiSJJEOBxm4cKFvPzyy1x00UWAtmD8xz/+cZ6vMoMM3v/IEFQG08Lg4CDBYJBAIEA0Gp3vyzlmKC8v51/+5V+orKxk4cKF5Obm0tzcnMnPyiCDOUCGoDKYFq655hp+8IMfcPnll3PTTTfN9+UcM3i9Xp555hkOHTpEd3c3oVCI559/fr4vK4MMPpDILOpmMGX893//N1arlc997nPIsswpp5zCyy+/zEc/+tH5vrQ5x4svvkh1dbXh9H3hhRfy+v/f3t28RBWFcRz/PiRtRShTeqHgipt04WZylwyD/QGWrdQU3BiCyzYlbdKNqzYJtmkxEAol4egm2khK4a6BuC6Mil5kRtpIgvq0mCluFuSV1HHm91mde3iYc1bzcM99zjnz87o/S2Qf6A1KYuvq6mJqagoonDCwuLhYEckJCmXNCwsLrK+v4+6/9me1tbUxOTkJVM7+rNnZWRobGwmC4I9TPgA2Njbo7OwkCAISiQQrKysHP0k50pSgRGJIJBJ0dHTQ0tJCU1MT29vb9Pf3Mzo6ytjYGEEQkMvl6OvrO+yp7qutrS0GBgbIZDJks1nS6TTZbPa3mImJCWpqalheXmZoaKiiloLl/1CZuYjEtpuTx9vb2xkeHqa1tZXNzU3q6upYXV3V+YkCKjMXqQy9vb3U1tb+tlE6n8+TSqVoaGgglUqxtrYGFK7cGBwcJAgCmpubWVpa2tOYfzt5fGflYjSmqqqK6upqcrncnsaTyqQEJXLE9fT0/FFJODIyQjKZJAxDksnkr29EmUyGMAwJw5Dx8XFtJpaSFneJT0RKkJmdB565+8Xi81vgsrt/MrN64IW7N5rZg2I7vTMu5nitwLC7txefbwG4+71IzFwx5qWZVQGfgZOuPx3ZJb1BiZSnU5Gk8xn4eWf7aeB9JO5DsS+uV0CDmV0ws+PAdWDnqa3TQHex3QE8V3KSOLQPSqTMubub2X9NDO6+aWY3gTngGPDQ3d+Y2V3gtbtPAxPAIzNbBvIUkpjIrilBiZSnL2ZWH1ni+1rs/wicjcSdKfbF5u4zwMyOvtuR9nfg6l5+WwS0xCdSrqLLa93A00h/lxVcAr7F/f4kclBUJCFyxJlZGrgMnAC+AHeAJ8Bj4BzwDrjm7nkrbEK6D1wB1oEb7v76MOYt8i9KUCIiUpK0xCciIiVJCUpEREqSEpSIiJQkJSgRESlJPwDFktDSR/aUIwAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Author: Remi Flamary \n", "#\n", @@ -47,26 +88,12 @@ "import ot\n", "# necessary for 3d plot even if not used\n", "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ + "from matplotlib.collections import PolyCollection\n", + "\n", + "#\n", + "# Generate data\n", + "# -------------\n", + "\n", "#%% parameters\n", "\n", "n = 100 # nb bins\n", @@ -84,74 +111,24 @@ "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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F3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8I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\nf17bI7LGndgbl1FhyON24XG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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "M /= M.max()\n", + "\n", + "#\n", + "# Plot data\n", + "# ---------\n", + "\n", "#%% plot the distributions\n", "\n", "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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0uQlqNS+5No3vSTvl/EGycTokzIX0UxBcCRpdDI27OY960c6SH6bISjRBiUgf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\n7lO97DJTnkA1LU11+nTVlStVS0vdsa0x+P2q2dm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycenter computation\n", + "# ----------------------\n", + "\n", "#%% barycenter computation\n", "\n", "alpha = 0.2 # 0<=alpha<=1\n", @@ -176,47 +153,12 @@ "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n", - "-------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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fh8/nQzqdhslkyou47HZ7lmAZzkIDg/IwBMpgUcitYQKyhUmSJExOTmJychKrV6/G7t27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\nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4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g448/hlarTcuI9Oqrr+LOO++EIAi4/fbbce+990bcHwgEcNttt+Hjjz9GdXU1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\nmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMs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TBk1q1bx0svvQREFQa2bdt2QgQniI41f/TR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\nsUcynvMIGwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycentric interpolation\n", + "# -------------------------\n", + "\n", "#%% barycenter interpolation\n", "\n", "n_alpha = 11\n", @@ -286,21 +228,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index 11c19d360..87937f09a 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -30,161 +30,58 @@ "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "# Author: Erwan Vautier \r\n", - "# Nicolas Courty \r\n", - "#\r\n", - "# License: MIT License\r\n", - "\r\n", - "import scipy as sp\r\n", - "import numpy as np\r\n", - "import matplotlib.pylab as pl\r\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\r\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample two Gaussian distributions (2D and 3D)\r\n", - " ---------------------------------------------\r\n", - "\r\n", - " The Gromov-Wasserstein distance allows to compute distances with samples that\r\n", - " do not belong to the same metric space. For demonstration purpose, we sample\r\n", - " two Gaussian distributions in 2- and 3-dimensional spaces.\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples = 30 # nb samples\r\n", - "\r\n", - "mu_s = np.array([0, 0])\r\n", - "cov_s = np.array([[1, 0], [0, 1]])\r\n", - "\r\n", - "mu_t = np.array([4, 4, 4])\r\n", - "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n", - "\r\n", - "\r\n", - "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\n", - "P = sp.linalg.sqrtm(cov_t)\r\n", - "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the distributions\r\n", - "--------------------------\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { - "image/png": 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7nZKSEq5du4bD4WB0dJQbN24wMjIStqF4rHL1kXJqhf0wcsOxjKyPay479PpH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\nmtfkErsZ71cQYrbMZRytoZj7aV6TS+xUfiHE8Pbs2QOlUon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+ "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "fig = pl.figure()\r\n", - "ax1 = fig.add_subplot(121)\r\n", - "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\n", - "ax2 = fig.add_subplot(122, projection='3d')\r\n", - "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute distance kernels, normalize them and then display\r\n", - "---------------------------------------------------------\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { - "image/png": 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hGD7BFnTDMAyfYAu6YRiGTzjggi4i1SLyrIhsE5GtIvJ3E/YiEXlKROon/tZe\n+hnGjMV82/AbUxFFEwC+6Zx7TUTyAWwSkacAfBbA086560VkPYD1AP7xXTsbAUq2eIWr5rM4GjJH\nqRVadP9mskkt1wHsb+A0mAAQP1EREvtZbCnbwoJe/3wWPcr/pER7HsuiXMFbrAa6DJ72/jpeM9x+\nkmVqNUCL32ShR4sA1QTQ0H0cYtm3jEWnO+v5s/Mf4wjeYFuEbJ2nzyUboEfAxULsE1o0XazQG3WZ\nFj3ozLnT5tvJDGCw1tt/IMbjGVrB1yQ8yhsDKp7sIFvzJ8vVvtOjnG64o5EFy2AJT/bpFbvJtnOA\no3fbj+f7qmYOR0Jua+Yo785VLDjWLm8lG6DXANVS4GoRoJoAmvXon8nW8i327dqf7yDbO9/g6Nho\nIV+r3LCe1jgwyGvOwHy2RVew4Bzr9G6ISOr7I4gDPqE759qdc69N/HsQwHYA8wCcB+CuiWZ3ATh/\nal0axszAfNvwGwf1Dl1E5gNYCeBlAHOdc+0T/9UBQH8EM4xZgPm24QemvKCLSB6A3wD4mnNuYN//\nc3vLHqmbgEXkKhF5VURejcf0xFmGcSSZDt9OjphvG0eeKS3oIhLEXoe/1zn34IS5U0QqJv6/AkBY\n+6xz7jbn3Crn3KpgJr/rM4wjyXT5diDHfNs48hxQFBURAXA7gO3OuRv2+a9HAFwO4PqJvx8+4LGS\nDll93kip8mUsGub+iqPNGv/+Q2RLZvCD0+J7BsgGALs+xWLN8h+zeBer5Hb5N7EiMVzBfZRvZHGk\ncy2LSWkcLIaaR3ke0nr0c9n5lVqyaTVAtRS4WgSoJoDWfpej7sLXcLvdH1ciZrtYvKv5HadkBYDR\nGo7WHSlltyx/ntPJIu6dyLa+KYbTTTCdvh0cdijb5O3fBVgUnfccC/FDNXy+DVfwW56FD+r+4NK4\nn1wl4rniPhYXn44cT7ZktlLX8wmuCbs7ZwH3q1yCec/xuPs7lBsIwOCxPO4ld/C9oaXA1SJANQG0\n6n+zb+/5J26XrvzSVfIGR+W2rWWhFADSEjyP4+l8rRb9jNvVXzbpXAJTi4Keyi6XkwD8LYA3RWTL\nhO1b2Ovs94vIlQAaAVw4pR4NY+Zgvm34igMu6M65FwDsbz/YR6Z3OIaROsy3Db9hkaKGYRg+wRZ0\nwzAMn5DamqJZgp7lXmHm9Q88SO0WXno12YJlLEbUlHA90sQzpWrfF657gWy/61hLtvQoiw/RIv6t\nPIOzaKKAyR2HAAAMxUlEQVTrYxwFOB5nocaN8/Gy+lgc7DmviDsB8PlznyLbrXNOJ5tWA1RLgatF\ngGoCaNktLCb138G1VYfz+Dlh94V6BG+0klW07CKe3EQOi2BZfd5rlezSU7Kmgni+oG2t93bK7uLr\n3HIR33I5b3BkrCaAtp7Ogj0AhBpZSBzuYNueddzP+WduJNsbfRyt2TRcQ7alZ9eTbcvrC/mzZ/G4\nK05XwsEBRB/h6O/G89h3tBqgWgpcLQJUE0Cr/5l9e/f1J5Kt8wQWQHM6dMEyzns7kOQhYveX2Sa9\nk+6h/YWNT8Ke0A3DMHyCLeiGYRg+wRZ0wzAMn2ALumEYhk9IqSgaSAC5nV6x5oztH6d2EmcBoPZG\ntkXqWEDpOVcXKN7ZzKk1gyey0Fr0KKcdzb2AU5mm3chCzeAa7rvyUZ5iLVqsdxl/t8YK9RqSG3aw\niJnXwP1k3MLnotUA1VLgahGgmgC65HOvki2wtI5sjRdwSlYAyGxnITMZ5nNJZPGc9S322hLPq12k\nBCfAeKb3+mvXGd0cdZzdpUQKfprFt6AeKIrIAvadh8/5KdkuePFLZMsJcOTqhZV8TW8eZFH0lGIW\nRctXs6C9cQNHee9q1jcvnHbpm2R75aFjyFb64NRqgGopcLUIUE0AXbCeBePwtSyo9i/h4wHAMafx\n/NT/mhsne9gnah733n89kalFitoTumEYhk+wBd0wDMMn2IJuGIbhE2xBNwzD8AkpFUUl7pAd9kYG\n7unkCMCcNv6eCTZx1GNuDqdpHazVi+8lY5w6NB7i08/qUVJwds8h24JBzoErLVx7NO8dVrJcUKkr\nqNQqTEvq37fDAc69XdjMImYixMJRdpiFOq0GqJYCV4sA1QTQ5NtcADS3XRfBxpXgTk3cyhjk84vG\nvQ21tMSpIrM3ibp7vIJg9yqOkAw18mcLNnNtznguz392jy6SF+xgH7vi5MvIVvQU+8PdAyeRTXJ5\nIpc9wALfTStYYA8M8cVb/Ku3yTY6V1cSX+w4mmx1G9if2i5ivyvfyIKsdl9pKXC1CFBNAC27mSNK\nR8/jDRcAsKmM0wsvekOpwTvMa1Z4pTeqN77JIkUNwzDeV9iCbhiG4RNsQTcMw/AJB1zQRaRaRJ4V\nkW0islVE/m7C/j0RaRWRLRN/znnvh2sY04f5tuE3piKKJgB80zn3mojkA9gkIn/J3/oT59y/TbUz\nGXdIH/GKookoi5XVv20nW+dZHKmmfR2VvKkrYwPVfKoVL7LIlDHA6VyLH2exM1bM4mnNExx1F5vL\n0ZppY9xv8TYWSzL26HU4m/+6mmxzFGFt5xc4OjOTyzOi83SuX6nVANVS4GoRoJoAWriBo+4AILBk\nEdnic1lMDHay8Jcs8orDu0d00fBdmDbfHs8KYGCpN19q33Iluk8TfCOcJrnkrVGyNZ6t5F4FAMfz\nlUiy0B05ij963NG7yJaexvO4ex1HXFYt5gjqlo5CsvWduZhs5av5HgeArucqydZ7BqfkjazkVNWh\nRr5Pc8N8n2o1QLUUuFoEqCaAZj/8Z24IIO+zK8gW5Izf6FnDTpG707suytQCRadUgq4dQPvEvwdF\nZDsATphsGLMM823DbxzUO3QRmQ9gJYCXJ0zXisgbInKHiPBXs2HMEsy3DT8w5QVdRPIA/AbA15xz\nAwB+BmARgGOx9ynnx/v53FUi8qqIvBqPK1lxDOMIMy2+HTXfNo48U1rQRSSIvQ5/r3PuQQBwznU6\n55LOuXEA/wlA3V3vnLvNObfKObcqGOSAGMM4kkybb2eZbxtHnqnschEAtwPY7py7YR97xT7NPgmA\ni1UaxgzGfNvwG1PZ5XISgL8F8KaIbJmwfQvAJSJyLAAHYA8Aruw8ifGgYLTcq9RntHHhWhllBVuU\nDQzBIZZ+B6v0U8pvYbV7tJjbxvN4PGMFHHabzOJ2JS0jZEvk8y4ebZdLZBEr9IXDXDgaAPKb+VzQ\n26+05B0o2WGeM01BH63hvrWCzlo+cy2cX9vNAgDJne+QLWOYdzqMHMNaZazAew3Gdxx0WMW0+Xa8\nYBzhj3v9Nv8lvqaRY3knVM+X+HXNYDc/8RfpmykQWaikyniGX/sXxPhCvz6PawpkvcY7s4bWKKku\n6tm/Kp/l8bV9gs/ZtXLKDwDIVVy78yS+X5bU8K6u+jPZRwKDPDdpCZ4HraCzls9cC+fXdrMAQOUn\nt5Et8TTvUJPt3Pl1n7vH8/P63ynb0xSmssvlBQBaIoHfT6kHw5ihmG8bfsMiRQ3DMHyCLeiGYRg+\nwRZ0wzAMn5DSfOguIIiFvEJWVg+/whwv5fzjaYpYkjHExr6l+ndU6WYWWgdqWfwJKtuJY0pYSXBI\nEUqzteTe6nCIaBEfL1rGohoAZPUoAlUei2iakJyj5HuffE0AYKSUXSO7iPNNawWdtXzmWjg/oAug\nidY2skVP5dQP0TnejsZT6s1egn2Cyvu9179tLV+AzFb2kap/ZbG5aw23i3AEPQCgaJsiGn6ZBbkX\nN7J4t7i8i2zVf8MC+9s/5LwBadewMNldyX5Y+3P2466rObUBAAzPZ1+sepLvjd0RFhdr/sj3xcB8\ndkateHdSyaqgFXRW85kr4fyALoCmfaSZG97E98D1P/y05+eO9p/onUw+/pRaGYZhGDMeW9ANwzB8\ngi3ohmEYPsEWdMMwDJ+QUhkpfXAMJX9s8di6bmE1IraVoxSTHHCJviUsHNU8yQVgAWD3J1isqfgT\niyh5r7MgF/4oixtFW7mfvhWcZzkzwoKVFqlW9RCLJclSPVK04SKOLKt9nHOQa6Jo28n8HR7iGrwo\nf76HbIkcju5LZLHApBV01vKZA3oEqCaAhn7xEtmKKryFlPf0s/CdKsaDgqFKr6BXyLokyp5pJVvj\nxRytGVjNSlv5nUo4I4DhckXUTih1Bp5WNhFs5rluqawlW0EGX9OBR1nMS3Jqd4yW8merv6PscgDQ\ndB3vShgP8nlrPtu1kteD6AoWXxf9jO+/3V/m4yV7uHizVtBZy2cO6BGgmgC6+Ksvk23gsUmR1f89\ntQro9oRuGIbhE2xBNwzD8Am2oBuGYfgEW9ANwzB8QmojRdMDSBZ5IwZrQhxt1pnLykpMiaQcy2dx\nI71DSyMLJAo4Wi19hIUZF2LxNDjC/SRzWYBJsl6C0WL+zhRFD8rpYhFyvFJRmAAkQyyQRIv4UiZC\nLEa5bCVStFBRnOPcR1Yfz0PfYiXCNc7nPLmg8//0XcCC3uQIUIAFUABItHuLFDs3NeHovSCZDfQf\nPWm+8zkCdORcFsqyH1MKFDco6Ysv5fTMAJBo5YjnXy/6A9nO/4ezyHZT7cNkWx5k3z7mv75Ctkc+\n+yOy7RhjcX79XZ8lW901XGAaAFZmc5rYB447iWxpnJEXOcohY518U9ZfxveA9LLP1TzO9094Jfvr\n5ILOf2FyClyAI0ABRQAFEFrnTSsdcFMT/O0J3TAMwyfYgm4YhuETbEE3DMPwCbagG4Zh+ARxbor5\nXaejM5EuAI0ASgB0p6zj9xY7l5lDrXOOVbkUYL4945nt5zIl307pgv4/nYq86pxblfKO3wPsXIx9\n8dMc2rnMPuyVi2EYhk+wBd0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cNyuRw06544Jc/xpFLWzTCjoDegrcL/3xw2TTBNC5H9tANi2t9LKLXiHbmhdO5POtZn/oOIU3C8w4XblAALunsS8iwgp9IMxzHdrBYce3r/wp2b74yifI1ncCn2/OQu7jzOV8/1xQpKeGvn39CrK5JN8HkVZ2iijX1c4Ke0I3DMPwCbagG4Zh+ARb0A3DMHzCIRd0EblLRDpEZNMBtgoReUxEto39bRVyjSmH+bbhN7IRRX8G4AcAfn6A7UYATzjnvi0iN479/MVDnUgGgpAnvPdHspqFnq73sRpYuZGF0rzjWHwr3aULY1oN0IJuToGrRYBqAmj5ao66C6ROJ1tIiTwdmM8RhO1n8jjMuYdFVgBIVLHIVLyBVZSuc+rItu55rhWaN5vbmLaRx7G3kUMDR6M8rtUPsogcinOKXgAIDfB4h9r7yRZbzmLZ6G5vHVXXd9ga/88wQb6dKgA6F3ufjyo28nGi6Jp5I2wsauFo22Sxfn1aDVAtBa4WAaoJoFpa6Zd6+B6Yt5bnac+FvNGgdg2ntW2qqyEbABx/N9/nHcv5nIUdSpiqErZ83bSryLbwMa6NKxleC5qG+f7paOZar89z9wAAUUXsDg/wvJRv5fs8r9dr2xHPTvA/5BO6c24NgO6DzBcBWD3279UALs6qNcOYRJhvG37jSN+hT3fOtY79uw37i+oahh8w3zamLEctirr9JY/GLXskIteKyDoRWZcaHifblGFMQg7Ht9Nx823j2HOkC3q7iNQAwNjfHeMd6Jy7wzm31Dm3NK+A33kbxiTjiHw7WGS+bRx7jjRS9GEAVwD49tjfD2XzoUASiLZ6X+6Xv58Fij2zK8iWLGIhMV7LD0/5XXoqy3eveIlsTxScRDYtBa4WAaoJoKV3v0C23Tex6DQ0k4XAVae+TLYdq+eRDQCKmrgGYf+px5Gt81yOsMvfxiJm48omsu0cZaW0YHkXtxvn+qgAi9pBJR0vAMRO4vmqeY7ndbCeo0dn3e/1nV39Si7mw+eIfDtaMoyzVnqjKdesWUTHCU89EOBr617I85Qs13NDBzrZZ/tn8jm1FLhaBKgmgE77T94E0PcRvgfOeC/78bNhHod0pT5XQ3VRtilRy+F+tsUWKWlx+bbHrr/h+sSJKh7bTCGPV9WjfJyMU5d558U8L+FefoYeqeQI12SR975K/EjLVcxks23xHgB/ArBARJpF5Grsd/aVIrINwF+N/WwYUwrzbcNvHPIJ3Tl3+Tj/pWSqMIypg/m24TcsUtQwDMMn2IJuGIbhE3KaPjdVCHSc6v0Oifyhlo6bvVaJnOrjCLLRaSwmaPU2AeCF+xeTbcHjvWTTaoBqKXC1CFBNAK27maPuAidzDcin3ncK2aILdbElUc7XOO0lHrNZ9/D39e6P8HH7fsUCaGlMEYn2VJLtuE5W+Yq2cNrRnuV6ZGD1OlZLExUsJkV6eCy2XePdIj5yqy6I54KBoXw8ucE7rwUDSh1bJfg3zfonap/m6Mrhah4XAGg5h8cmFOe2tRqgWgpcLQJUE0BLfsmbAJ48dynZ5jzJUa97P6mpw0DhHvaH6a6Yj9vJ9246XEW2Hr48zHhBqQt6Kt8r6Wncx46lvOaElOhPQK/BW71BSX1dyRNTtt17/7WyO+htZneYYRiGMdmxBd0wDMMn2IJuGIbhE2xBNwzD8Ak5FUVd0CFZ5hUkqjZm953SdzwLI8W7FeFouha5CJRvZzEiWcbHBgpZWNNqgGopcLUIUE0AzWx8jWzu/UpEaY0u8I6WKpGUtSyYVT7L4mQon/tdtk0RJsvYNcq2smCct+/gZIXA4GKOWu2dq89zfjdfY18Dt60JTJnagxTGsB5JmQuCw4KyTd5+JzjgGYlKvo5wL4/B7veyb4Z7dH/4zgW/INsX8i7jA5Xx0WqAailwtQhQTQCdf806su39Cvv26KAeOrzlc+zHZVUcGR0P8lj0bOGx/cUHf0i2T2+/nmx5iogsIb6f61ftItulNXzNAHDLAx8iW+dHec0KvMhrWybkvV8yWa7U9oRuGIbhE2xBNwzD8Am2oBuGYfgEW9ANwzB8Qk5F0ZklMXx35c89tptnX0jHjd7GEV/9Dfzd0/XXSorQKEeQAUBPJwsPMx5nASYZ5XOG4iy2aDVAtRS4WgSoJoDO/CZHlMqTHEULAI3FMbI90cgpazuU6MydZ9/O5xu+mmyz61hQ3drExXtCMa6xqJEJ6tF0LddwFKFzLJitaNhBtrNLX/f8/I1CjibOFekIMNDoFR2ju9hno1z6FX1zeGxqn2JBrvAl5cMA/qGWc4wVtnH0YbSZ2+k4he8BrQaolgJXjQBVBND6f2Lfbn1wIdkAoPBXLMgWdHJK3fgM3rxQUM337he2fphsxc08tolyPl958RDZtrw8k2zf2KrfpxGlDGjtj7idLh5aFBwUqR3QA2sJe0I3DMPwCbagG4Zh+ARb0A3DMHxCNhWL7hKRDhHZdIDt6yKyT0Q2jv3hF+GGMckx3zb8hrhx6uH97wEiKwAMAvi5c+6kMdvXAQw65/71cBorKalzS5d+2mPbcSnrssFh/p6p3KikA1W+joradfWg5SoWcAKvstgSUoq3D83gMZr9kBLxNcoqSM9CpUaiEgFad/5usrlz93FnAASnV7OxlEXfXZeyiFnQydfSvYyjBWsfZVGt9UwtHSzbjnuW5yA+XdffCzt4zIq2dJKtfzFfc0G7d07XbvgR+gf26eGUChPp28XzZ7glP/qYxxZ7nCNmh6dztGbdkzwG/ddxCtvCsF6H86pZLDp+b/UHyZYq5LmfcXor2ZpfYjFdqwEaLmLxenSQRdbiSr6pai7eTDYA2HYrp+m9bAVf37Mdc8i2dzdvppjVwL7U8QzPS34Xj03JHvbj2Iksalat1O/Tgf/mdk6/7kWyvXAHb5zoPyijdfMPvoeR5r2H9O1DPqE759YA4Phuw5jimG8bfuNo3qFfLyIvj/3ayhUgDGPqYr5tTEmOdEG/DcAcACcDaAXwb+MdKCLXisg6EVk3Oqq8zzCMycUR+Xayj/csG0auOaIF3TnX7pxLO+cyAH4MYNmbHHuHc26pc25pOFx0pP00jJxwpL4dKi3MXScNYxyOaEEXkQNVkw8A2DTesYYxlTDfNqYyhwz9F5F7ALwLQJWINAP4GoB3icjJAByAXQCuy6q1dAZ5fd6dCaI8tKcj/D2TKGP1XJQNOsm4/h2lhfF2lvFTlVZkOl2mhApXcX+Kmjhvs1bQWctnroXz79J2swBIt3eQzc1lRT1Zwu0U82YaRIp5B1CygHfnZIp5p4ML826Y4Qp2q2SRLtAnSnm+8iu47XRYScnQ7Z1TSR1ePvSJ9O3RRAh7tnp3FeUrNasjMb7egVq+tpLbS8jW16AXwb75jFVka3yW/T1Zwp/fPY197Pi7OYXCUB3PiVbQWctnroXza7tZAGDe57jw9COfPYts1ev49e3MSh7H2PF8X1S/xn7cvpTHZnAm+3HpVr6nhlfrBdD7uRQCnvnlqWQrb+H+RA/aONM++Oa7Ed/gkAu6c44TRQB3ZnV2w5jEmG8bfsMiRQ3DMHyCLeiGYRg+wRZ0wzAMn5DTfOiZSBD9870h6mElZ3SItUWUbWfhIJBWCu52ckg+AGzbymHB5ZsVoU0RH4a6WTAp3sC5qftPZQFm2ksjZNMKOmv5zOeU6vv2NQFUnttItrIFZ5AtfpySQmEbC17RfSx4xbdFyKalSqh4hXPS95zIIh8AlG3hyQ728EkjVVw0ufsUbxXmVGtO3dlDXiSF6kavsN2RZp/L72J/Hz6fx0Ae5VQO0RYlwTaA5KYCsm3/KAv5JVuV8YnwPHcsZxFzaAb7zXTHfdQKOmv5zC9b8RfuC3QBdPq/c+j/4KUsqraexffu9HntZOsMcUqMkZk8Dlef9izZ7qzm/gX6dL+TGbwW1d7KdQY2f62BbJEu72aD5IbsMlrYE7phGIZPsAXdMAzDJ9iCbhiG4RNsQTcMw/AJOVWRksVA29le4WLOvRzRNljPAljvXBYma9b0kG1YiWgDgMbfcDTkYC2LfMEkCyvlr7Mg23VOHdk6z2VhZdY9Sm73Z1kY0Qo677pUvxYtAlQTQCvu+hPZtq3m3Mvzv89jM1zLIbyz7mUh2MV5/mIXssDbdbIe6RZt4TloXsmiXFELf75nlbft9HOHFyk6kaRSQXR0eYVfF+H+JCrIBOzlea5uYVGzr1GPFI3PY7+r+LN2LI9hIMxCa2EHR/+G+1mUK9zJ4nc8yMdpBZ21fOaAHgGqCaDR+ziitKKY74H+VqWw+aDScIDH5t7tHNWZv4c3NFRs1v2u42K2da3i6442HXpzRkBPhU/YE7phGIZPsAXdMAzDJ9iCbhiG4RNsQTcMw/AJORVFI10ZzF/tFbK2Xs+CycqFL5HtmT0sJjRVcXWw/C697YZL9pCtACxGdA6xGFhXwuLruucXcNtKJOXuj3CkaCi/jGw7z76dbKd+41NkA/QUuFoEqCaAzruCi9T2PjKXbKMPc2RnxxXc77w8jhYcbGehraKWBTQAaFvG0ZQpDnxEMKGkAn7Km/44MHDsnk+CA4KKp73zH1vGwmamjNWtyudZaNOioEf1YFs1ovHpn7KQ2Hwep4sO7VAGW3j+YouUtNJhnrueLdzvgmrlPlMKOgN6ClwtAlQTQCvv5E0ATd/m40qb+Hx9o+w76ZdZnE9UsADatViP4kyn+Jydy3lsZ/6WPxvu8/pJ3nB2gr89oRuGYfgEW9ANwzB8gi3ohmEYPuGQC7qI1IvIH0XkNRF5VUQ+N2avEJHHRGTb2N/8QtswJjHm24bfyEYUTQH4vHPuRREpBrBeRB4DcCWAJ5xz3xaRGwHcCOCLb3aiZI3Dvpu8osDxN7Fo+MJ7lpAtMYtFgZqXWWCIz+AoNwAY/RALVHs/zsKmogdhRzfXXcybzcc1rmwi275f8YFl2ziyr3H4arK5ZXp4mFYDVEuBq0WAagJo6YXbydZ13TSyzf67Nm63ppJsTZfwHCTHmZf8GAtUsTk8VwNxFs8XXbTZ8/Pup9iXDsGE+XZJdRznXe8V5boSPCcBpRDukyGOrI0leLxOW/D6m3XBQ9sZLICOzOBxvX3lT8l23bSryFbB+xTQcyLbfvHBH5LtC1s/TLbKgC7yaTVAtRS4WgSoJoDOvlERSu9ZTLa/auR74HFwUdD8FvbD2qd1v5v/ndfItqOfxeBt6Vr+cLFXaE18dYLS5zrnWp1zL479ewDAZgC1AC4CsHrssNUAlEBXw5i8mG8bfuOw3qGLSAOAJQD+DGC6c6517L/aAPBXpmFMEcy3DT+Q9YIuIlEADwC4wTnXf+D/OecctMw/+z93rYisE5F16X5O5GQYx5qJ8O2hHn69ZRi5JqsFXURC2O/wdzvnfj1mbheRmrH/rwHQoX3WOXeHc26pc25psITf6xnGsWSifLuwnIPKDCPXyP4HkDc5QESw/z1it3PuhgPs3wEQO0A4qnDO/eObnauoqt6d8N6/99i0dLXdJyjpJJX0nTN/zWlo3YBeh3PvlfPIllEyjOYpv0RokYsVryvRdCewkFW6g8UfUfSggk/wtST+k1PqAkCygL+HtRqgqULuz0Ad2xSdDlW3s5jU/xGOPnSKVuMCbOxexMcBwPx/5wjernNncn8eY8G56eONnp933fldjLTszU49wsT6dkFNvWv4+P/x2Ep38kSnI9y9gk4WK9P5PMdunMevvr/lOp5lP+cIXiUwGoOKWF3zGIvfu/5mBtkqX+N7YKiKO1nczNfXcaqeCrj6Rd4I0LmYj9XqDhd2KlGcF3Fdz9mXs8Lbfzn7trYO5Q2xbbhOr/VasYHHYkSJhK17vI9sB9fVfb75v9A30nZI385ml8uZAD4G4BUReaMS8U0Avg3gPhG5GsBuAJdmcS7DmEyYbxu+4pALunPuWajf7QCA8ya2O4aRO8y3Db9hkaKGYRg+wRZ0wzAMn5DT9Lmpkgy6zvdGVS24hUXMRClHH8ZrWbkbaeDjBmt1IbH+QRZ6Ws9noSeQ4naK2tjW28hiUsFyzt2b2cN9LNvK17y1ibc6B87U3wZkilk4iiupe7UaoFoKXC0CtEcRQEt+yXUc8xpYwNxzCdeX/a71AAAMfElEQVRbzdSwOAUAvWfwsbF3KKljo41kq1rR6vm5+b+zLLz4FiDRNMKnd3tsZT+J0XHpOeyfPQs4ZXPXaSzwRTr1aNvkEM99cgk/q1W8qqSNPYEFPcmwLyaquD8dp3IbeXH22UQ5i5r5XfpmjPalfOzITBb8tRqgWgpcLQJ0kyKAltzDvt3xfT5Oq61aM6eT+weg9KucMnrL13hzRtcpnBc5GfWm7h29WxeRD8ae0A3DMHyCLeiGYRg+wRZ0wzAMn2ALumEYhk/IqSgKJ3AZr6gwchxHtBUpNSlT+dxVybAwoomaAJAuZ+FJa0dLnxvpYbFtNMpCVH88n2zHKVGAefu6yRaK1XPD4wTxujCLYyElQNbFOexVqwGqpcDVIkA1ATS1iyM987s5HehQTA+NDyZZbAsN8PU5RQ8cGPGeMzNeKGUOyGQEQyPe2qCbv9VAxxVUsjg8bTU7XbCChcDZJ6oZCPD9xvvJ9rHff4Fsbe/kz85ZyBHKTcOKqF3IfUxPY9+WENvKi9kPIz+o4M4AGJzJ97lWM/Xe7adyf5QaoFoK3LASAaoJoHNvYKF0z1d5EDt7OU0yALR9mdNuX3HOGrI9su4cssUPyiKcrWvbE7phGIZPsAXdMAzDJ9iCbhiG4RNsQTcMw/AJORVFAyOCgte9wmHzeaz8Va9noazuoWay9Z/CUXfhQb1W4c5LWLiY+TsWnkL9XKhgYDYLqtUPblVa4dqQRVtYdBpczHUTNY57lgUmABiu4GmreIWj0mIXcn8GFSFYqwFauo3b1SJANQG04i5OvVtyLotYAJAJ8zPFrP/hdKIdy5Rouue8wpob1CMpc0FJZAQrG701P5++j685s4sj/nq5zCtS/WzbkuLIZgB4z/YbyFaulB6Y9SjP/czlPWTraGaBvupRJVJ0KeeVrl+1i2xbXmYxvehE/VmydCuvB3dWn0W2/D1hsiUquI9aDdCAEniqRYBqAujMbz5PtrbPKWozdCH/Zy+cSbayEm47EzpoHLJMCm1P6IZhGD7BFnTDMAyfYAu6YRiGTzjkgi4i9SLyRxF5TUReFZHPjdm/LiL7RGTj2J8L3/ruGsbEYb5t+I1sRNEUgM87514UkWIA60XksbH/+55z7l+zbSwTAkaqvcLFtPmccnawlSOsiktZmOw4hb+PgiO6elC1qJ3b2cTtBEdZRBmYye2E4pzONaiILT3LWbjtncvnywRZDIpP16cnWcTX2HMii4ZdJ/M5K2pZPE0qdSW78znNrpYCV4sA1QTQvCfXkw0Amr/EglL1iyy29Z7Igldh81GLoBPm2xrhFezboyme04E+vt5QK4t+KGXBHgBCRWxPFXHUZMcS9u0Livi+eJ4/ClFqD4cG2HZpzTqyfWMrC+dVK1vJBgDDq/l+CfQpmwA2K/VDF/N9Ufv0CNl2fpjHQUuBq0WAagLojFtZKAWAxB8ayDawndecgQa+lkzEO7aawKqRTQm6VgCtY/8eEJHNAHiGDGOKYb5t+I3DeocuIg0AlgD485jpehF5WUTuEpHyCe6bYeQM823DD2S9oItIFMADAG5wzvUDuA3AHAAnY/9Tzr+N87lrRWSdiKxLDyoZpAzjGDMRvj3cw7/aG0auyWpBF5EQ9jv83c65XwOAc67dOZd2zmUA/BjAMu2zzrk7nHNLnXNLg1F+D24Yx5KJ8u2Ccs60aRi5JptdLgLgTgCbnXPfPcB+oHrxAQCbJr57hvHWYb5t+I1sdrmcCeBjAF4RkY1jtpsAXC4iJ2N/1u5dAK471ImCw0D5poOU6HVVdJxjE2JLeNfF3B/sJFtGyQEOAC3xRWTLK2CVXpTMAaF+TeHnsPzYSayeV6/jrS/53azGt1zDOxUKn+bdDwCQKOXv4bItA2SLtvAOlLZlPLj5Mb6++t9xnnOtoLOWz1wL59d2swBA3S28Q2D4Yn4gXvATvr49q9gnDpMJ8+3hdAiv9npD82d8luc0MYt3TvTO4R0tsTM5B3+wWYnnBzBay7uPpErZ4bSJbbevX0G26CC3sfNi7qNWe+CWBz5EtohSY2BgrZ7+op/Tl0Nm8PV1XMzHpVPsd/O/8xrZem9fQjatoLOWz1zbbaLtZgGAyPm72PjjaWQqbuJ+jx600yiQZf3zbHa5PAs9k8Aj2TVhGJMT823Db1ikqGEYhk+wBd0wDMMn2IJuGIbhE3KaD10yQN6IV0hpP0sJ6d7D3zNVL7O42HkBh9/n9ygKDID4UhZWip9j0TGjiR4V/Jo11M4Jq2ueY5EoUcFiUl8DD7tzfH1FWzgcGQDyK1hYC/bwHv/mlRzDnVJ01tgcFniDo5zDOvYOTRzmAdPymWvh/IAugBY8+BeydV59BtkqN3v7vXdknKraOcA5YDTtHYt9n5hOxxV0si8NL2cVsvxp3uJbskfPj98zl8XS/oV8bKJM8bsk32thJaQ/3MvHVW/gNjo/yvdZ7Y94s8Dx//Iq2QDgmV9y2ojaW7mmQNeqOdz2cr73d/TzJoCRSp6DLV+bRzatoLOWz1wL5wegCqDzP7GWbNv+YznZwt1eX7Ii0YZhGG8zbEE3DMPwCbagG4Zh+ARb0A3DMHxCjkVRR0WcK9cqubhPZnGjZQVHPdb/noXAvJgS5gYguJOFi8EGFn/yhlgwSRXycbHlyvnq+bORHv6sFmG3omEH2TYvPolsAJAOK+1UcS6RohZuJ5hg20CcRauqxzgKdzTKIrQWOacVdNbymQN6BKgqgN7Jhadb/tEbfZr+U5aVdN8CUoMh9DznjRSNdvJYJ3loMPer7McdZ7Mo2rWI5wkA5q5i30l8toJsPSexSB5p5SWgfCsnGhupZFF7qJInP/BiMdm6OEgbL9xxChsBlLdwSOTmrzWQLdrEcz3zt3y+bWnOhjz/cRbtu07hiXlk3Tlk0wo6a/nMAT0CVBNA533mz2QLnOwNmW0fZ7MHfS6rowzDMIxJjy3ohmEYPsEWdMMwDJ9gC7phGIZPyKkoGkg5RHq8okfkM1wsNjDCAl9/N0ddNX2AI+TyO/QiGhe/j9O0PrD5ZLKNaAJhDYsoo7s5Am3W/W1k23YNRwtmall0Orv0dbLtaufoNQAIdXOK4O5TFBFsFR9X/BSP2aKLNpNtY2gh2apW8FwNjLBYnXyO+zJeQWctBe7BEaAAC6AAcNy/eOd0rzt2FbECSaCw1SuCJqMsoDlFt43PryRbaRNHDoeGdVF0VAlvTkzn+yBVyI1H9/L58nrZP5NFfE+WbVdSJ4f4GbEgxsd1LtGfJaP72Bbp4usLDSrRrH1KjtlivmYtqjoZZcE4rmT4zYS43YMLOr/BwSlwAY4ABVgABYDMRm/aX+eyq4hlT+iGYRg+wRZ0wzAMn2ALumEYhk+wBd0wDMMniHO5SzkqIp0AdgOoAtCVs4bfWuxaJg+znHOsnucA8+1Jz1S/lqx8O6cL+v82KrLOObc05w2/Bdi1GAfipzG0a5l62CsXwzAMn2ALumEYhk84Vgv6Hceo3bcCuxbjQPw0hnYtU4xj8g7dMAzDmHjslYthGIZPyPmCLiIXiMjrIrJdRG7MdftHg4jcJSIdIrLpAFuFiDwmItvG/i4/ln3MBhGpF5E/ishrIvKqiHxuzD7lrmUyYb597Hm7+3ZOF3QRCQL4IYBVAE4AcLmIcGaaycvPAFxwkO1GAE845+YBeGLs58lOCsDnnXMnADgdwKfH5mEqXsukwHx70vC29u1cP6EvA7DdObfTOTcK4F4AF+W4D0eMc24NgO6DzBcBWD3279UALs5pp44A51yrc+7FsX8PANgMoBZT8FomEebbk4C3u2/nekGvBXBgws7mMdtUZrpz7o28sm0AOF/uJEZEGgAsAfBnTPFrOcaYb08y3o6+baLoBOL2bxmaMtuGRCQK4AEANzjn+g/8v6l2LcZby1Tzh7erb+d6Qd8HoP6An+vGbFOZdhGpAYCxvzuOcX+yQkRC2O/wdzvnfj1mnpLXMkkw354kvJ19O9cL+loA80RktoiEAVwG4OEc92GieRjAFWP/vgLAQ8ewL1khIgLgTgCbnXPfPeC/pty1TCLMtycBb3ffznlgkYhcCOD7AIIA7nLO/XNOO3AUiMg9AN6F/Znb2gF8DcCDAO4DMBP7s+1d6pw7WFyaVIjIWQCeAfAKgDfqg92E/e8ap9S1TCbMt489b3fftkhRwzAMn2CiqGEYhk+wBd0wDMMn2IJuGIbhE2xBNwzD8Am2oBuGYfgEW9ANwzB8gi3ohmEYPsEWdMMwDJ/w/wGyNoqewUkZPQAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\r\n", - "C2 = sp.spatial.distance.cdist(xt, xt)\r\n", - "\r\n", - "C1 /= C1.max()\r\n", - "C2 /= C2.max()\r\n", - "\r\n", - "pl.figure()\r\n", - "pl.subplot(121)\r\n", - "pl.imshow(C1)\r\n", - "pl.subplot(122)\r\n", - "pl.imshow(C2)\r\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Gromov-Wasserstein plans and distance\r\n", - "---------------------------------------------\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "Gromov-Wasserstein distances between the distribution: 0.201997813845\n" + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.767714e-02|0.000000e+00\n", + " 1|2.091125e-02|-8.017640e-01\n", + " 2|1.607458e-02|-3.008895e-01\n", + " 3|1.486883e-02|-8.109274e-02\n", + " 4|1.484811e-02|-1.394896e-03\n", + " 5|1.484791e-02|-1.400744e-05\n", + " 6|1.484790e-02|-1.400803e-07\n", + " 7|1.484790e-02|-1.400805e-09\n", + " 8|1.484790e-02|-1.400768e-11\n", + "It. |Err \n", + "-------------------\n", + " 0|7.522843e-02|\n", + " 10|2.233137e-04|\n", + " 20|5.767057e-07|\n", + " 30|2.315565e-09|\n", + " 40|9.789603e-12|\n", + "Gromov-Wasserstein distances: 0.014847904422308234\n", + "Entropic Gromov-Wasserstein distances: 0.011257422087381012\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -192,38 +89,121 @@ } ], "source": [ - "p = ot.unif(n_samples)\r\n", - "q = ot.unif(n_samples)\r\n", - "\r\n", - "gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n", - "gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n", - "\r\n", - "print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n", - "\r\n", - "pl.figure()\r\n", - "pl.imshow(gw, cmap='jet')\r\n", - "pl.colorbar()\r\n", + "# Author: Erwan Vautier \n", + "# Nicolas Courty \n", + "#\n", + "# License: MIT License\n", + "\n", + "import scipy as sp\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "import ot\n", + "\n", + "\n", + "#\n", + "# Sample two Gaussian distributions (2D and 3D)\n", + "# ---------------------------------------------\n", + "#\n", + "# The Gromov-Wasserstein distance allows to compute distances with samples that\n", + "# do not belong to the same metric space. For demonstration purpose, we sample\n", + "# two Gaussian distributions in 2- and 3-dimensional spaces.\n", + "\n", + "\n", + "n_samples = 30 # nb samples\n", + "\n", + "mu_s = np.array([0, 0])\n", + "cov_s = np.array([[1, 0], [0, 1]])\n", + "\n", + "mu_t = np.array([4, 4, 4])\n", + "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", + "\n", + "\n", + "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n", + "P = sp.linalg.sqrtm(cov_t)\n", + "xt = np.random.randn(n_samples, 3).dot(P) + mu_t\n", + "\n", + "\n", + "#\n", + "# Plotting the distributions\n", + "# --------------------------\n", + "\n", + "\n", + "fig = pl.figure()\n", + "ax1 = fig.add_subplot(121)\n", + "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "ax2 = fig.add_subplot(122, projection='3d')\n", + "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", + "pl.show()\n", + "\n", + "\n", + "#\n", + "# Compute distance kernels, normalize them and then display\n", + "# ---------------------------------------------------------\n", + "\n", + "\n", + "C1 = sp.spatial.distance.cdist(xs, xs)\n", + "C2 = sp.spatial.distance.cdist(xt, xt)\n", + "\n", + "C1 /= C1.max()\n", + "C2 /= C2.max()\n", + "\n", + "pl.figure()\n", + "pl.subplot(121)\n", + "pl.imshow(C1)\n", + "pl.subplot(122)\n", + "pl.imshow(C2)\n", + "pl.show()\n", + "\n", + "#\n", + "# Compute Gromov-Wasserstein plans and distance\n", + "# ---------------------------------------------\n", + "\n", + "p = ot.unif(n_samples)\n", + "q = ot.unif(n_samples)\n", + "\n", + "gw0, log0 = ot.gromov.gromov_wasserstein(\n", + " C1, C2, p, q, 'square_loss', verbose=True, log=True)\n", + "\n", + "gw, log = ot.gromov.entropic_gromov_wasserstein(\n", + " C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n", + "\n", + "\n", + "print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\n", + "print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n", + "\n", + "\n", + "pl.figure(1, (10, 5))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(gw0, cmap='jet')\n", + "pl.title('Gromov Wasserstein')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(gw, cmap='jet')\n", + "pl.title('Entropic Gromov Wasserstein')\n", + "\n", "pl.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb index 8102bcf5c..c931a6c89 100644 --- a/notebooks/plot_gromov_barycenter.ipynb +++ b/notebooks/plot_gromov_barycenter.ipynb @@ -32,20 +32,20 @@ }, "outputs": [], "source": [ - "# Author: Erwan Vautier \r\n", - "# Nicolas Courty \r\n", - "#\r\n", - "# License: MIT License\r\n", - "\r\n", - "\r\n", - "import numpy as np\r\n", - "import scipy as sp\r\n", - "\r\n", - "import scipy.ndimage as spi\r\n", - "import matplotlib.pylab as pl\r\n", - "from sklearn import manifold\r\n", - "from sklearn.decomposition import PCA\r\n", - "\r\n", + "# Author: Erwan Vautier \n", + "# Nicolas Courty \n", + "#\n", + "# License: MIT License\n", + "\n", + "\n", + "import numpy as np\n", + "import scipy as sp\n", + "\n", + "import scipy.ndimage as spi\n", + "import matplotlib.pylab as pl\n", + "from sklearn import manifold\n", + "from sklearn.decomposition import PCA\n", + "\n", "import ot" ] }, @@ -53,11 +53,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Smacof MDS\r\n", - " ----------\r\n", - "\r\n", - " This function allows to find an embedding of points given a dissimilarity matrix\r\n", - " that will be given by the output of the algorithm\r\n", + "Smacof MDS\n", + "----------\n", + "\n", + "This function allows to find an embedding of points given a dissimilarity matrix\n", + "that will be given by the output of the algorithm\n", "\n" ] }, @@ -69,49 +69,49 @@ }, "outputs": [], "source": [ - "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\r\n", - " \"\"\"\r\n", - " Returns an interpolated point cloud following the dissimilarity matrix C\r\n", - " using SMACOF multidimensional scaling (MDS) in specific dimensionned\r\n", - " target space\r\n", - "\r\n", - " Parameters\r\n", - " ----------\r\n", - " C : ndarray, shape (ns, ns)\r\n", - " dissimilarity matrix\r\n", - " dim : int\r\n", - " dimension of the targeted space\r\n", - " max_iter : int\r\n", - " Maximum number of iterations of the SMACOF algorithm for a single run\r\n", - " eps : float\r\n", - " relative tolerance w.r.t stress to declare converge\r\n", - "\r\n", - " Returns\r\n", - " -------\r\n", - " npos : ndarray, shape (R, dim)\r\n", - " Embedded coordinates of the interpolated point cloud (defined with\r\n", - " one isometry)\r\n", - " \"\"\"\r\n", - "\r\n", - " rng = np.random.RandomState(seed=3)\r\n", - "\r\n", - " mds = manifold.MDS(\r\n", - " dim,\r\n", - " max_iter=max_iter,\r\n", - " eps=1e-9,\r\n", - " dissimilarity='precomputed',\r\n", - " n_init=1)\r\n", - " pos = mds.fit(C).embedding_\r\n", - "\r\n", - " nmds = manifold.MDS(\r\n", - " 2,\r\n", - " max_iter=max_iter,\r\n", - " eps=1e-9,\r\n", - " dissimilarity=\"precomputed\",\r\n", - " random_state=rng,\r\n", - " n_init=1)\r\n", - " npos = nmds.fit_transform(C, init=pos)\r\n", - "\r\n", + "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n", + " \"\"\"\n", + " Returns an interpolated point cloud following the dissimilarity matrix C\n", + " using SMACOF multidimensional scaling (MDS) in specific dimensionned\n", + " target space\n", + "\n", + " Parameters\n", + " ----------\n", + " C : ndarray, shape (ns, ns)\n", + " dissimilarity matrix\n", + " dim : int\n", + " dimension of the targeted space\n", + " max_iter : int\n", + " Maximum number of iterations of the SMACOF algorithm for a single run\n", + " eps : float\n", + " relative tolerance w.r.t stress to declare converge\n", + "\n", + " Returns\n", + " -------\n", + " npos : ndarray, shape (R, dim)\n", + " Embedded coordinates of the interpolated point cloud (defined with\n", + " one isometry)\n", + " \"\"\"\n", + "\n", + " rng = np.random.RandomState(seed=3)\n", + "\n", + " mds = manifold.MDS(\n", + " dim,\n", + " max_iter=max_iter,\n", + " eps=1e-9,\n", + " dissimilarity='precomputed',\n", + " n_init=1)\n", + " pos = mds.fit(C).embedding_\n", + "\n", + " nmds = manifold.MDS(\n", + " 2,\n", + " max_iter=max_iter,\n", + " eps=1e-9,\n", + " dissimilarity=\"precomputed\",\n", + " random_state=rng,\n", + " n_init=1)\n", + " npos = nmds.fit_transform(C, init=pos)\n", + "\n", " return npos" ] }, @@ -119,10 +119,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Data preparation\r\n", - " ----------------\r\n", - "\r\n", - " The four distributions are constructed from 4 simple images\r\n", + "Data preparation\n", + "----------------\n", + "\n", + "The four distributions are constructed from 4 simple images\n", "\n" ] }, @@ -132,31 +132,54 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " import sys\n", + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " if __name__ == '__main__':\n" + ] + } + ], "source": [ - "def im2mat(I):\r\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\r\n", - " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\r\n", - "\r\n", - "\r\n", - "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\r\n", - "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\r\n", - "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\r\n", - "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\r\n", - "\r\n", - "shapes = [square, cross, triangle, star]\r\n", - "\r\n", - "S = 4\r\n", - "xs = [[] for i in range(S)]\r\n", - "\r\n", - "\r\n", - "for nb in range(4):\r\n", - " for i in range(8):\r\n", - " for j in range(8):\r\n", - " if shapes[nb][i, j] < 0.95:\r\n", - " xs[nb].append([j, 8 - i])\r\n", - "\r\n", - "xs = np.array([np.array(xs[0]), np.array(xs[1]),\r\n", + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", + "\n", + "\n", + "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\n", + "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\n", + "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\n", + "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n", + "\n", + "shapes = [square, cross, triangle, star]\n", + "\n", + "S = 4\n", + "xs = [[] for i in range(S)]\n", + "\n", + "\n", + "for nb in range(4):\n", + " for i in range(8):\n", + " for j in range(8):\n", + " if shapes[nb][i, j] < 0.95:\n", + " xs[nb].append([j, 8 - i])\n", + "\n", + "xs = np.array([np.array(xs[0]), np.array(xs[1]),\n", " np.array(xs[2]), np.array(xs[3])])" ] }, @@ -164,8 +187,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Barycenter computation\r\n", - "----------------------\r\n", + "Barycenter computation\n", + "----------------------\n", "\n" ] }, @@ -177,45 +200,45 @@ }, "outputs": [], "source": [ - "ns = [len(xs[s]) for s in range(S)]\r\n", - "n_samples = 30\r\n", - "\r\n", - "\"\"\"Compute all distances matrices for the four shapes\"\"\"\r\n", - "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\r\n", - "Cs = [cs / cs.max() for cs in Cs]\r\n", - "\r\n", - "ps = [ot.unif(ns[s]) for s in range(S)]\r\n", - "p = ot.unif(n_samples)\r\n", - "\r\n", - "\r\n", - "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\r\n", - "\r\n", - "Ct01 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\r\n", - " [ps[0], ps[1]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", - " max_iter=100, tol=1e-3)\r\n", - "\r\n", - "Ct02 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\r\n", - " [ps[0], ps[2]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", - " max_iter=100, tol=1e-3)\r\n", - "\r\n", - "Ct13 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\r\n", - " [ps[1], ps[3]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", - " max_iter=100, tol=1e-3)\r\n", - "\r\n", - "Ct23 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\r\n", - " [ps[2], ps[3]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", + "ns = [len(xs[s]) for s in range(S)]\n", + "n_samples = 30\n", + "\n", + "\"\"\"Compute all distances matrices for the four shapes\"\"\"\n", + "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\n", + "Cs = [cs / cs.max() for cs in Cs]\n", + "\n", + "ps = [ot.unif(ns[s]) for s in range(S)]\n", + "p = ot.unif(n_samples)\n", + "\n", + "\n", + "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n", + "\n", + "Ct01 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n", + " [ps[0], ps[1]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", + " max_iter=100, tol=1e-3)\n", + "\n", + "Ct02 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n", + " [ps[0], ps[2]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", + " max_iter=100, tol=1e-3)\n", + "\n", + "Ct13 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n", + " [ps[1], ps[3]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", + " max_iter=100, tol=1e-3)\n", + "\n", + "Ct23 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n", + " [ps[2], ps[3]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", " max_iter=100, tol=1e-3)" ] }, @@ -223,10 +246,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Visualization\r\n", - " -------------\r\n", - "\r\n", - " The PCA helps in getting consistency between the rotations\r\n", + "Visualization\n", + "-------------\n", + "\n", + "The PCA helps in getting consistency between the rotations\n", "\n" ] }, @@ -240,7 +263,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -249,9 +272,9 @@ }, { "data": { - "image/png": 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SZcmhT6vqV2L3O/PK+s+Vld/VnWMf1URIf2az38/P6uqqb25upj6MbXauGZNm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\nul4ztrXNSJOpUKNJxiStSdqUtLlv377O/sFqiz1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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -259,108 +282,108 @@ } ], "source": [ - "clf = PCA(n_components=2)\r\n", - "npos = [0, 0, 0, 0]\r\n", - "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\r\n", - "\r\n", - "npost01 = [0, 0]\r\n", - "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\r\n", - "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\r\n", - "\r\n", - "npost02 = [0, 0]\r\n", - "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\r\n", - "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\r\n", - "\r\n", - "npost13 = [0, 0]\r\n", - "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\r\n", - "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\r\n", - "\r\n", - "npost23 = [0, 0]\r\n", - "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\r\n", - "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\r\n", - "\r\n", - "\r\n", - "fig = pl.figure(figsize=(10, 10))\r\n", - "\r\n", - "ax1 = pl.subplot2grid((4, 4), (0, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\r\n", - "\r\n", - "ax2 = pl.subplot2grid((4, 4), (0, 1))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\r\n", - "\r\n", - "ax3 = pl.subplot2grid((4, 4), (0, 2))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\r\n", - "\r\n", - "ax4 = pl.subplot2grid((4, 4), (0, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\r\n", - "\r\n", - "ax5 = pl.subplot2grid((4, 4), (1, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\r\n", - "\r\n", - "ax6 = pl.subplot2grid((4, 4), (1, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\r\n", - "\r\n", - "ax7 = pl.subplot2grid((4, 4), (2, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\r\n", - "\r\n", - "ax8 = pl.subplot2grid((4, 4), (2, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\r\n", - "\r\n", - "ax9 = pl.subplot2grid((4, 4), (3, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\r\n", - "\r\n", - "ax10 = pl.subplot2grid((4, 4), (3, 1))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\r\n", - "\r\n", - "ax11 = pl.subplot2grid((4, 4), (3, 2))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\r\n", - "\r\n", - "ax12 = pl.subplot2grid((4, 4), (3, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", + "clf = PCA(n_components=2)\n", + "npos = [0, 0, 0, 0]\n", + "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\n", + "\n", + "npost01 = [0, 0]\n", + "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\n", + "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n", + "\n", + "npost02 = [0, 0]\n", + "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\n", + "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n", + "\n", + "npost13 = [0, 0]\n", + "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\n", + "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n", + "\n", + "npost23 = [0, 0]\n", + "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\n", + "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n", + "\n", + "\n", + "fig = pl.figure(figsize=(10, 10))\n", + "\n", + "ax1 = pl.subplot2grid((4, 4), (0, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n", + "\n", + "ax2 = pl.subplot2grid((4, 4), (0, 1))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n", + "\n", + "ax3 = pl.subplot2grid((4, 4), (0, 2))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n", + "\n", + "ax4 = pl.subplot2grid((4, 4), (0, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n", + "\n", + "ax5 = pl.subplot2grid((4, 4), (1, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n", + "\n", + "ax6 = pl.subplot2grid((4, 4), (1, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n", + "\n", + "ax7 = pl.subplot2grid((4, 4), (2, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n", + "\n", + "ax8 = pl.subplot2grid((4, 4), (2, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n", + "\n", + "ax9 = pl.subplot2grid((4, 4), (3, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n", + "\n", + "ax10 = pl.subplot2grid((4, 4), (3, 1))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n", + "\n", + "ax11 = pl.subplot2grid((4, 4), (3, 2))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n", + "\n", + "ax12 = pl.subplot2grid((4, 4), (3, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", "ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb index d36b0eed3..67165e97e 100644 --- a/notebooks/plot_optim_OTreg.ipynb +++ b/notebooks/plot_optim_OTreg.ipynb @@ -51,7 +51,8 @@ "source": [ "import numpy as np\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -105,9 +106,9 @@ "outputs": [ { "data": { - "image/png": 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yfTl0MwB7P90UqJg4PRvlTJdCbbVo4aMcjjyyYl8I8MknOy+389xzUFLixzRq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\ngSsiEhMFrohITP4PrQ161dhEqnEAAAAASUVORK5CYII=\n", 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\n", 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oq6JWraoPK41RJ5I5qVlfB70KOd8K1xe9cNz+FO+xCYA+XdcyvNcyABYc2YnZ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\nTkSuQmCC\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -423,232 +424,201 @@ "--------------------------------\n", " 0|1.692289e-01|0.000000e+00\n", " 1|1.617643e-01|-4.614437e-02\n", - " 2|1.612546e-01|-3.161037e-03\n", - " 3|1.611040e-01|-9.349544e-04\n", - " 4|1.610346e-01|-4.310179e-04\n", - " 5|1.610072e-01|-1.701719e-04\n", - " 6|1.609947e-01|-7.759814e-05\n", - " 7|1.609934e-01|-7.941439e-06\n", - " 8|1.609841e-01|-5.797180e-05\n", - " 9|1.609838e-01|-1.559407e-06\n", - " 10|1.609685e-01|-9.530282e-05\n", - " 11|1.609666e-01|-1.142129e-05\n", - " 12|1.609541e-01|-7.799970e-05\n", - " 13|1.609496e-01|-2.780416e-05\n", - " 14|1.609385e-01|-6.887105e-05\n", - " 15|1.609334e-01|-3.174241e-05\n", - " 16|1.609231e-01|-6.420777e-05\n", - " 17|1.609115e-01|-7.189949e-05\n", - " 18|1.608815e-01|-1.865331e-04\n", - " 19|1.608799e-01|-1.013039e-05\n", + " 2|1.612639e-01|-3.102965e-03\n", + " 3|1.611291e-01|-8.371098e-04\n", + " 4|1.610468e-01|-5.110558e-04\n", + " 5|1.610198e-01|-1.672927e-04\n", + " 6|1.610130e-01|-4.232417e-05\n", + " 7|1.610090e-01|-2.513455e-05\n", + " 8|1.610002e-01|-5.443507e-05\n", + " 9|1.609996e-01|-3.657071e-06\n", + " 10|1.609948e-01|-2.998735e-05\n", + " 11|1.609695e-01|-1.569217e-04\n", + " 12|1.609533e-01|-1.010779e-04\n", + " 13|1.609520e-01|-8.043897e-06\n", + " 14|1.609465e-01|-3.415246e-05\n", + " 15|1.609386e-01|-4.898605e-05\n", + " 16|1.609324e-01|-3.837052e-05\n", + " 17|1.609298e-01|-1.617826e-05\n", + " 18|1.609184e-01|-7.080015e-05\n", + " 19|1.609083e-01|-6.273206e-05\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 20|1.608695e-01|-6.468606e-05\n", - " 21|1.608686e-01|-5.738419e-06\n", - " 22|1.608661e-01|-1.495923e-05\n", - " 23|1.608657e-01|-2.784611e-06\n", - " 24|1.608633e-01|-1.512408e-05\n", - " 25|1.608624e-01|-5.397916e-06\n", - " 26|1.608617e-01|-4.115218e-06\n", - " 27|1.608561e-01|-3.503396e-05\n", - " 28|1.608479e-01|-5.098773e-05\n", - " 29|1.608452e-01|-1.659203e-05\n", - " 30|1.608399e-01|-3.298319e-05\n", - " 31|1.608330e-01|-4.302183e-05\n", - " 32|1.608310e-01|-1.273465e-05\n", - " 33|1.608280e-01|-1.827713e-05\n", - " 34|1.608231e-01|-3.039842e-05\n", - " 35|1.608212e-01|-1.229256e-05\n", - " 36|1.608200e-01|-6.900556e-06\n", - " 37|1.608159e-01|-2.554039e-05\n", - " 38|1.608103e-01|-3.521137e-05\n", - " 39|1.608058e-01|-2.795180e-05\n", + " 20|1.608988e-01|-5.940805e-05\n", + " 21|1.608853e-01|-8.380030e-05\n", + " 22|1.608844e-01|-5.185045e-06\n", + " 23|1.608824e-01|-1.279113e-05\n", + " 24|1.608819e-01|-3.156821e-06\n", + " 25|1.608783e-01|-2.205746e-05\n", + " 26|1.608764e-01|-1.189894e-05\n", + " 27|1.608755e-01|-5.474607e-06\n", + " 28|1.608737e-01|-1.144227e-05\n", + " 29|1.608676e-01|-3.775335e-05\n", + " 30|1.608638e-01|-2.348020e-05\n", + " 31|1.608627e-01|-6.863136e-06\n", + " 32|1.608529e-01|-6.110230e-05\n", + " 33|1.608487e-01|-2.641106e-05\n", + " 34|1.608409e-01|-4.823638e-05\n", + " 35|1.608373e-01|-2.256641e-05\n", + " 36|1.608338e-01|-2.132444e-05\n", + " 37|1.608310e-01|-1.786649e-05\n", + " 38|1.608260e-01|-3.103848e-05\n", + " 39|1.608206e-01|-3.321265e-05\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 40|1.608040e-01|-1.119118e-05\n", - " 41|1.608027e-01|-8.193369e-06\n", - " 42|1.607994e-01|-2.026719e-05\n", - " 43|1.607985e-01|-5.819902e-06\n", - " 44|1.607978e-01|-4.048170e-06\n", - " 45|1.607978e-01|-3.007470e-07\n", - " 46|1.607950e-01|-1.705375e-05\n", - " 47|1.607927e-01|-1.430186e-05\n", - " 48|1.607925e-01|-1.166526e-06\n", - " 49|1.607911e-01|-9.069406e-06\n", - " 50|1.607910e-01|-3.804209e-07\n", - " 51|1.607910e-01|-5.942399e-08\n", - " 52|1.607910e-01|-2.321380e-07\n", - " 53|1.607907e-01|-1.877655e-06\n", - " 54|1.607906e-01|-2.940224e-07\n", - " 55|1.607877e-01|-1.814208e-05\n", - " 56|1.607841e-01|-2.236496e-05\n", - " 57|1.607810e-01|-1.951355e-05\n", - " 58|1.607804e-01|-3.578228e-06\n", - " 59|1.607789e-01|-9.442277e-06\n", + " 40|1.608201e-01|-3.054747e-06\n", + " 41|1.608195e-01|-4.198335e-06\n", + " 42|1.608193e-01|-8.458736e-07\n", + " 43|1.608159e-01|-2.153759e-05\n", + " 44|1.608115e-01|-2.738314e-05\n", + " 45|1.608108e-01|-3.960032e-06\n", + " 46|1.608081e-01|-1.675447e-05\n", + " 47|1.608072e-01|-5.976340e-06\n", + " 48|1.608046e-01|-1.604130e-05\n", + " 49|1.608020e-01|-1.617036e-05\n", + " 50|1.608014e-01|-3.957795e-06\n", + " 51|1.608011e-01|-1.292411e-06\n", + " 52|1.607998e-01|-8.431795e-06\n", + " 53|1.607964e-01|-2.127054e-05\n", + " 54|1.607947e-01|-1.021878e-05\n", + " 55|1.607947e-01|-3.560621e-07\n", + " 56|1.607900e-01|-2.929781e-05\n", + " 57|1.607890e-01|-5.740229e-06\n", + " 58|1.607858e-01|-2.039550e-05\n", + " 59|1.607836e-01|-1.319545e-05\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 60|1.607779e-01|-5.997371e-06\n", - " 61|1.607754e-01|-1.564408e-05\n", - " 62|1.607742e-01|-7.693285e-06\n", - " 63|1.607727e-01|-9.030547e-06\n", - " 64|1.607719e-01|-5.103894e-06\n", - " 65|1.607693e-01|-1.605420e-05\n", - " 66|1.607676e-01|-1.047837e-05\n", - " 67|1.607675e-01|-6.026848e-07\n", - " 68|1.607655e-01|-1.240216e-05\n", - " 69|1.607632e-01|-1.434674e-05\n", - " 70|1.607618e-01|-8.829808e-06\n", - " 71|1.607606e-01|-7.581824e-06\n", - " 72|1.607590e-01|-1.009457e-05\n", - " 73|1.607586e-01|-2.222963e-06\n", - " 74|1.607577e-01|-5.564775e-06\n", - " 75|1.607574e-01|-1.932763e-06\n", - " 76|1.607573e-01|-8.148685e-07\n", - " 77|1.607554e-01|-1.187660e-05\n", - " 78|1.607546e-01|-4.557651e-06\n", - " 79|1.607537e-01|-5.911902e-06\n", + " 60|1.607826e-01|-6.378947e-06\n", + " 61|1.607808e-01|-1.145102e-05\n", + " 62|1.607776e-01|-1.941743e-05\n", + " 63|1.607743e-01|-2.087422e-05\n", + " 64|1.607741e-01|-1.310249e-06\n", + " 65|1.607738e-01|-1.682752e-06\n", + " 66|1.607691e-01|-2.913936e-05\n", + " 67|1.607671e-01|-1.288855e-05\n", + " 68|1.607654e-01|-1.002448e-05\n", + " 69|1.607641e-01|-8.209492e-06\n", + " 70|1.607632e-01|-5.588467e-06\n", + " 71|1.607619e-01|-8.050388e-06\n", + " 72|1.607618e-01|-9.417493e-07\n", + " 73|1.607598e-01|-1.210509e-05\n", + " 74|1.607591e-01|-4.392914e-06\n", + " 75|1.607579e-01|-7.759587e-06\n", + " 76|1.607574e-01|-2.760280e-06\n", + " 77|1.607556e-01|-1.146469e-05\n", + " 78|1.607550e-01|-3.689456e-06\n", + " 79|1.607550e-01|-4.065631e-08\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 80|1.607529e-01|-4.710187e-06\n", - " 81|1.607528e-01|-8.866080e-07\n", - " 82|1.607522e-01|-3.620627e-06\n", - " 83|1.607514e-01|-5.091281e-06\n", - " 84|1.607498e-01|-9.932095e-06\n", - " 85|1.607487e-01|-6.852804e-06\n", - " 86|1.607478e-01|-5.373596e-06\n", - " 87|1.607473e-01|-3.287295e-06\n", - " 88|1.607470e-01|-1.666655e-06\n", - " 89|1.607469e-01|-5.293790e-07\n", - " 90|1.607466e-01|-2.051914e-06\n", - " 91|1.607456e-01|-6.422797e-06\n", - " 92|1.607456e-01|-1.110433e-07\n", - " 93|1.607451e-01|-2.803849e-06\n", - " 94|1.607451e-01|-2.608066e-07\n", - " 95|1.607441e-01|-6.290352e-06\n", - " 96|1.607429e-01|-7.298455e-06\n", - " 97|1.607429e-01|-8.969905e-09\n", - " 98|1.607427e-01|-7.923968e-07\n", - " 99|1.607427e-01|-3.519286e-07\n", + " 80|1.607539e-01|-6.555681e-06\n", + " 81|1.607528e-01|-7.177470e-06\n", + " 82|1.607527e-01|-5.306068e-07\n", + " 83|1.607514e-01|-7.816045e-06\n", + " 84|1.607511e-01|-2.301970e-06\n", + " 85|1.607504e-01|-4.281072e-06\n", + " 86|1.607503e-01|-7.821886e-07\n", + " 87|1.607480e-01|-1.403013e-05\n", + " 88|1.607480e-01|-1.169298e-08\n", + " 89|1.607473e-01|-4.235982e-06\n", + " 90|1.607470e-01|-1.717105e-06\n", + " 91|1.607470e-01|-6.148402e-09\n", + " 92|1.607462e-01|-5.396481e-06\n", + " 93|1.607461e-01|-5.194954e-07\n", + " 94|1.607450e-01|-6.525707e-06\n", + " 95|1.607442e-01|-5.332060e-06\n", + " 96|1.607439e-01|-1.682093e-06\n", + " 97|1.607437e-01|-1.594796e-06\n", + " 98|1.607435e-01|-7.923812e-07\n", + " 99|1.607420e-01|-9.738552e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 100|1.607426e-01|-3.563804e-07\n", - " 101|1.607410e-01|-1.004042e-05\n", - " 102|1.607410e-01|-2.124801e-07\n", - " 103|1.607398e-01|-7.556935e-06\n", - " 104|1.607398e-01|-7.606853e-08\n", - " 105|1.607385e-01|-8.058684e-06\n", - " 106|1.607383e-01|-7.393061e-07\n", - " 107|1.607381e-01|-1.504958e-06\n", - " 108|1.607377e-01|-2.508807e-06\n", - " 109|1.607371e-01|-4.004631e-06\n", - " 110|1.607365e-01|-3.580156e-06\n", - " 111|1.607364e-01|-2.563573e-07\n", - " 112|1.607354e-01|-6.390137e-06\n", - " 113|1.607348e-01|-4.119553e-06\n", - " 114|1.607339e-01|-5.299475e-06\n", - " 115|1.607335e-01|-2.316767e-06\n", - " 116|1.607330e-01|-3.444737e-06\n", - " 117|1.607324e-01|-3.467980e-06\n", - " 118|1.607320e-01|-2.374632e-06\n", - " 119|1.607319e-01|-7.978255e-07\n", + " 100|1.607419e-01|-1.022448e-07\n", + " 101|1.607419e-01|-4.865999e-07\n", + " 102|1.607418e-01|-7.092012e-07\n", + " 103|1.607408e-01|-5.861815e-06\n", + " 104|1.607402e-01|-3.953266e-06\n", + " 105|1.607395e-01|-3.969572e-06\n", + " 106|1.607390e-01|-3.612075e-06\n", + " 107|1.607377e-01|-7.683735e-06\n", + " 108|1.607365e-01|-7.777599e-06\n", + " 109|1.607364e-01|-2.335096e-07\n", + " 110|1.607364e-01|-4.562036e-07\n", + " 111|1.607360e-01|-2.089538e-06\n", + " 112|1.607356e-01|-2.755355e-06\n", + " 113|1.607349e-01|-4.501960e-06\n", + " 114|1.607347e-01|-1.160544e-06\n", + " 115|1.607346e-01|-6.289450e-07\n", + " 116|1.607345e-01|-2.092146e-07\n", + " 117|1.607336e-01|-5.990866e-06\n", + " 118|1.607330e-01|-3.348498e-06\n", + " 119|1.607328e-01|-1.256222e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 120|1.607312e-01|-4.221434e-06\n", - " 121|1.607310e-01|-1.324597e-06\n", - " 122|1.607304e-01|-3.650359e-06\n", - " 123|1.607298e-01|-3.732712e-06\n", - " 124|1.607295e-01|-1.994082e-06\n", - " 125|1.607289e-01|-3.954139e-06\n", - " 126|1.607286e-01|-1.532372e-06\n", - " 127|1.607286e-01|-1.167223e-07\n", - " 128|1.607283e-01|-2.157376e-06\n", - " 129|1.607279e-01|-2.253077e-06\n", - " 130|1.607274e-01|-3.301532e-06\n", - " 131|1.607269e-01|-2.650754e-06\n", - " 132|1.607264e-01|-3.595551e-06\n", - " 133|1.607262e-01|-1.159425e-06\n", - " 134|1.607258e-01|-2.512411e-06\n", - " 135|1.607255e-01|-1.998792e-06\n", - " 136|1.607251e-01|-2.486536e-06\n", - " 137|1.607246e-01|-2.782996e-06\n", - " 138|1.607246e-01|-2.922470e-07\n", - " 139|1.607242e-01|-2.071131e-06\n", + " 120|1.607320e-01|-5.418353e-06\n", + " 121|1.607318e-01|-8.296189e-07\n", + " 122|1.607311e-01|-4.381608e-06\n", + " 123|1.607310e-01|-8.913901e-07\n", + " 124|1.607309e-01|-3.808821e-07\n", + " 125|1.607302e-01|-4.608994e-06\n", + " 126|1.607294e-01|-5.063777e-06\n", + " 127|1.607290e-01|-2.532835e-06\n", + " 128|1.607285e-01|-2.870049e-06\n", + " 129|1.607284e-01|-4.892812e-07\n", + " 130|1.607281e-01|-1.760452e-06\n", + " 131|1.607279e-01|-1.727139e-06\n", + " 132|1.607275e-01|-2.220706e-06\n", + " 133|1.607271e-01|-2.516930e-06\n", + " 134|1.607269e-01|-1.201434e-06\n", + " 135|1.607269e-01|-2.183459e-09\n", + " 136|1.607262e-01|-4.223011e-06\n", + " 137|1.607258e-01|-2.530202e-06\n", + " 138|1.607258e-01|-1.857260e-07\n", + " 139|1.607256e-01|-1.401957e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 140|1.607237e-01|-3.154193e-06\n", - " 141|1.607235e-01|-1.194962e-06\n", - " 142|1.607232e-01|-2.035251e-06\n", - " 143|1.607232e-01|-6.027855e-08\n", - " 144|1.607229e-01|-1.555696e-06\n", - " 145|1.607228e-01|-1.081740e-06\n", - " 146|1.607225e-01|-1.881070e-06\n", - " 147|1.607224e-01|-4.100096e-07\n", - " 148|1.607223e-01|-7.785200e-07\n", - " 149|1.607222e-01|-2.094072e-07\n", - " 150|1.607220e-01|-1.440814e-06\n", - " 151|1.607217e-01|-1.997794e-06\n", - " 152|1.607214e-01|-2.011022e-06\n", - " 153|1.607212e-01|-8.808854e-07\n", - " 154|1.607211e-01|-7.245877e-07\n", - " 155|1.607207e-01|-2.217159e-06\n", - " 156|1.607201e-01|-3.817891e-06\n", - " 157|1.607200e-01|-7.409600e-07\n", - " 158|1.607198e-01|-1.497698e-06\n", - " 159|1.607195e-01|-1.729666e-06\n", + " 140|1.607250e-01|-3.242751e-06\n", + " 141|1.607247e-01|-2.308071e-06\n", + " 142|1.607247e-01|-4.730700e-08\n", + " 143|1.607246e-01|-4.240229e-07\n", + " 144|1.607242e-01|-2.484810e-06\n", + " 145|1.607238e-01|-2.539206e-06\n", + " 146|1.607234e-01|-2.535574e-06\n", + " 147|1.607231e-01|-1.954802e-06\n", + " 148|1.607228e-01|-1.765447e-06\n", + " 149|1.607228e-01|-1.620007e-08\n", + " 150|1.607222e-01|-3.615783e-06\n", + " 151|1.607222e-01|-8.668516e-08\n", + " 152|1.607215e-01|-4.000673e-06\n", + " 153|1.607213e-01|-1.774103e-06\n", + " 154|1.607213e-01|-6.328834e-09\n", + " 155|1.607209e-01|-2.418783e-06\n", + " 156|1.607208e-01|-2.848492e-07\n", + " 157|1.607207e-01|-8.836043e-07\n", + " 158|1.607205e-01|-1.192836e-06\n", + " 159|1.607202e-01|-1.638022e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 160|1.607195e-01|-2.115187e-07\n", - " 161|1.607192e-01|-1.643727e-06\n", - " 162|1.607192e-01|-1.712969e-07\n", - " 163|1.607189e-01|-1.805877e-06\n", - " 164|1.607189e-01|-1.209827e-07\n", - " 165|1.607185e-01|-2.060002e-06\n", - " 166|1.607182e-01|-1.961341e-06\n", - " 167|1.607181e-01|-1.020366e-06\n", - " 168|1.607179e-01|-9.760982e-07\n", - " 169|1.607178e-01|-7.219236e-07\n", - " 170|1.607175e-01|-1.837718e-06\n", - " 171|1.607174e-01|-3.337578e-07\n", - " 172|1.607173e-01|-5.298564e-07\n", - " 173|1.607173e-01|-6.864278e-08\n", - " 174|1.607173e-01|-2.008419e-07\n", - " 175|1.607171e-01|-1.375630e-06\n", - " 176|1.607168e-01|-1.911257e-06\n", - " 177|1.607167e-01|-2.709815e-07\n", - " 178|1.607167e-01|-1.390953e-07\n", - " 179|1.607165e-01|-1.199675e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 180|1.607165e-01|-1.457259e-07\n", - " 181|1.607163e-01|-1.049154e-06\n", - " 182|1.607163e-01|-2.753577e-09\n", - " 183|1.607163e-01|-6.972814e-09\n", - " 184|1.607161e-01|-1.552100e-06\n", - " 185|1.607159e-01|-1.068596e-06\n", - " 186|1.607157e-01|-1.247724e-06\n", - " 187|1.607155e-01|-1.158164e-06\n", - " 188|1.607155e-01|-2.616199e-07\n", - " 189|1.607154e-01|-3.595874e-07\n", - " 190|1.607154e-01|-5.334527e-08\n", - " 191|1.607153e-01|-3.452744e-07\n", - " 192|1.607153e-01|-1.239593e-07\n", - " 193|1.607152e-01|-8.184984e-07\n", - " 194|1.607150e-01|-1.316308e-06\n", - " 195|1.607150e-01|-7.100882e-09\n", - " 196|1.607148e-01|-1.393958e-06\n", - " 197|1.607146e-01|-1.242735e-06\n", - " 198|1.607144e-01|-1.123993e-06\n", - " 199|1.607143e-01|-3.512071e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 200|1.607143e-01|-2.151971e-10\n" + " 160|1.607202e-01|-3.670914e-08\n", + " 161|1.607197e-01|-3.153709e-06\n", + " 162|1.607197e-01|-2.419565e-09\n", + " 163|1.607194e-01|-2.136882e-06\n", + " 164|1.607194e-01|-1.173754e-09\n", + " 165|1.607192e-01|-8.169238e-07\n", + " 166|1.607191e-01|-9.218755e-07\n", + " 167|1.607189e-01|-9.459255e-07\n", + " 168|1.607187e-01|-1.294835e-06\n", + " 169|1.607186e-01|-5.797668e-07\n", + " 170|1.607186e-01|-4.706272e-08\n", + " 171|1.607183e-01|-1.753383e-06\n", + " 172|1.607183e-01|-1.681573e-07\n", + " 173|1.607183e-01|-2.563971e-10\n" ] }, { "data": { - "image/png": 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Up1sTzaPNizrgomid3JIyP1Ot40of612/2sdmXyrzTnbh0H0AOKP3BwDcfdBj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\nsW1KbQQ9nkkAAAAASUVORK5CYII=\n", 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39uUWxIqzJI6rLVzr/50LN8bhJ8Hfimv9hDU29o59sf28Ql1nvv3Va3x87/LP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\n", 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Shmuu8ekMR49OuyVtq0MH+NGP4IUXfJ6IPKfAFUnatGk+jeG558IOO6TdmrY3ahT06QNXXJH3fbkKXJGkXXmlV36XXZZ2S5LRuTNcfjm89FLe9+UqcEWS9O678Je/wIUXetWXL0aN8su9X355Xle5ClyRJF1yCXTr5v2a+aRDB/jZz3zEwiOPpN2a1ChwRZLyzDO+/PSn7XtkwtaMHOmT81x8sc+MlocUuCJJqKjwM6923rn9jrttSFER3HADzJ7tp/zmIQWuSBL+8Ae/hPh110FJSdqtSc8RR/hMYr/8JSxYkHZrEqfAFWlrc+f6kKhjj4UTT0y7Nen73e98Qpvzzsu7A2gKXJG2FELNJc9vvdUndcl3Q4bAVVfBE0/4ZeHziAJXpC098AA8/bR/hB48OO3WZI/Ro2H//f2ik0uXpt2axChwRdrKrFle3R58sAeL1Cgq8uu4ffYZnH123nQtKHBF2kJlJZx2mn99//1QWJhue7LRfvv5nBKPPQa33ZZ2axKhwBVpC5ddBq++6kFSXp52a7LX6NFwzDF+O2VK2q1pcwpckdZ2991w7bXenXDqqWm3JrsVFPh1z7bf3oeLtfNLqytwRVrTP/7h8wYMHw433ph2a3LD9tv7lS+WL/fQXb067Ra1GQWuSGt57TW/8u6OO8JDD/l8t9I4++4L993n3Qpf/SqsW5d2i9qEAlekNUye7H2RZWU+BWE+zpXQUiNGeHfMiy/CSSfB2rVpt6jVKXBFWuqZZ+Dww6F7dw/b/v3TblHu+va34Y9/hOee826ZJUvSblGrUuCKNFcIPhnLiSfCrrv6qASNSGi5s86CcePgrbdg2DC/bScUuCLNsWiRfwQePRpOOMGvZpAPl8tJykknwd//7n25w4bBmDE+/0KOU+CKNEVVlX/k3X13ePZZH4nw2GPQtWvaLWt/Dj7Yq9sjjoALLoBDD4W33067VS2iwBVpjKoqH3mwzz4+7GufffzNf8EFmpCmLZWVwVNPwV13wfTpPv/Cf/0XfPBB2i1rFgWuyLbMmuUTzwwZAt/8pq976CG/7Pcee6Tbtnxh5leL+PBDv0TRk0/CnnvCUUf572LNmrRb2GgW8mTSCNm2oUOHhsmTJ6fdjPStXOnjaV94wbsM3njD1w8fDt//vvfbal6EdC1eDGPH+jJ3LnTsCEcfDUceCYcd5mFcVJRIU8xsSghhaKOfr8AVyKPA3bDBpwNcuBA+/dTfsLNmefU0dSpMm+bPKyqCAw+E//xPOPlkP5lBsktVlZ/ZN26cT4E5a5av79jRu3z22AN22cWnxRw40K+SXFbmw/da6Z+mAleaZbPAffxxGD++7Xe6tb+9zPrat1tbqqr86HVlpX9dUeHLhg2+rFvny6pVvtR3BlNRkV9rbI894IAD4AtfgC9+UQfCcs2sWfDKK/D66345+g8+qH9uBjP/3XbtCp07Q6dOUFrqVxYuKfEzBIuKfCks9PkeDjvMP+FssammBW4ydbfklhkz/ON0ErZ2wCmzvvZtfUvmDZF5cxQX+9KhA3Tp4tVOx441b7CePf0ssO2394pn0CDo10/dBO3Bjjv6kpkWE7x/d+5cv37awoV+IsXSpbBihf8DXrPGz2hbv97/Qa9a5f+wKyt9qa72pZUmj1eFK0AedSmItKKmVrgapSAikhAFrohIQtSlIACY2WJgTq1VvYFsnjkkm9uXzW2D7G5fNrcNtmzf4BBCWWO/WYEr9TKzyU3pm0paNrcvm9sG2d2+bG4btLx96lIQEUmIAldEJCEKXNmasWk3oAHZ3L5sbhtkd/uyuW3QwvapD1dEJCGqcEVEEqLAFRFJiAJXtmBmx5rZh2Y2w8wuTbktA83sBTN738ymmtmFcX0vM3vezKbH254ptrHQzN40s/Hx/o5mNim+fg+aWUmKbethZuPM7N9m9oGZHZxlr93o+Ht9z8weMLPSNF8/M7vTzBaZ2Xu11tX7epm7KbbzHTM7oKHtK3BlM2ZWCPwBOA7YE/iWme2ZYpMqgYtCCHsCw4DzYnsuBSaEEHYBJsT7abkQqH0Jgl8D14cQdgaWAWel0ip3I/BsCGF3YF+8nVnx2plZf+ACYGgIYS+gEDiVdF+/u4Bj66zb2ut1HLBLXEYBtzS49RCCFi2bFuBg4Lla9y8DLku7XbXa8wRwFPAhsENctwPwYUrtGRDfhEcA4wHDz0Qqqu/1TLht3YFZxIPjtdZny2vXH5gH9MJnLhwPHJP26weUA+819HoBtwHfqu95W1tU4UpdmTdBxvy4LnVmVg7sD0wC+oQQMpOdfgr0SalZNwA/BjKXlN0OWB5CqIz303z9dgQWA3+KXR63m1lnsuS1CyEsAK4F5gKfACuAKWTP65extderye8VBa7kBDPrAjwC/E8IYWXtx4KXF4mPbzSzE4BFIYQpSe+7kYqAA4BbQgj7A2uo032Q1msHEPtCR+D/GPoBndny43xWaenrpcCVuhYAA2vdHxDXpcbMivGwvS+E8GhcvdDMdoiP7wAsSqFphwBfNbPZwF/wboUbgR5mlpncP83Xbz4wP4QwKd4fhwdwNrx2AEcCs0IIi0MIFcCj+GuaLa9fxtZerya/VxS4UtfrwC7xSHEJfhDjybQaY2YG3AF8EEL4Xa2HngRGxq9H4n27iQohXBZCGBBCKMdfp7+HEE4DXgBOSbNtsX2fAvPMbLe4ajjwPlnw2kVzgWFm1in+njPty4rXr5atvV5PAqfH0QrDgBW1uh7ql0ZnuZbsXoDjgWnAR8DlKbflUPwj3DvAW3E5Hu8rnQBMB/4G9Eq5nYcD4+PXQ4DXgBnAw0CHFNu1HzA5vn6PAz2z6bUDrgL+DbwH/BnokObrBzyA9ydX4J8Qztra64UfIP1DfJ+8i4+22Ob2W3Rqr5kdi3+EKgRuDyH8qtkbExFp55oduHG85jR8iM58/KPot0II77de80RE2o+WXLX3QGBGCGEmgJn9BT/iuNXA7d27dygvL2/BLqWlli/3C5MOHLj5+ilTpiwJceb6owq+rhmNROp4vvrhrVxiuvFaErj1jUE7qO6TzGwUfhYGgwYNQleGTdePfwxjxkDdX4OZzan/O0SktbT5KIUQwtgQwtAQwtCyskZf+kfaSEUFFBen3QqR/NSSwM268ZrSsI0boSS1qVRE8ltLAjerxmtK46xdC507p90KkfzU7D7cEEKlmf0AeA4fFnZnCGFqq7VM2sTKldClS9qtEMlPLTloRgjhaeDpVmqLJOCzz2C77dJuhUh+0qm9eWbBAujbN+1WiOQnBW4eqaqC2bNhp53SbkkKzJq2iLQBBW4eee89qKyEvfZKuyUi+alFfbiSW156yW8POyzddmQFK6hzd/OqNlTXOdkuVNe5r5PxpOlU4eaRe++FPffc8rReEUmGKtw8MXEivP66n9ablzIVaZ3+2U2VbabijfetsM73ZyreWOmGOve32I9IPVTh5oGNG+F734M+feD009NujUj+UoXbzoUAl18Ob70Fjz8O3bql3aKUbapAM5Wq1xxWkKlUvbS1wljiZirigjp9vtXx+VVVvp2quL14f1Plq4pXalGF246F4LODXXutV7gjRqTdIpH8pgq3nVqxAi64AO65B847D266Ke0WZZmtVbrECjVT2cap1awovlUKY41Sd5RDdfy+Cr+6d6iMt5n7qnwFVbjt0uOP+2iEe++Fn/3MD5QV6DctkjpVuO3Iq6/CNdfAX/8K++zjwfuFL6TdqiyXqTRD7IsNdUYxbPoifhUrXSuJkwoXF23+eNyeVcbtVVT4+g0b/P7GirhelW8+Ut2T46qq4LHH4JBD4ItfhH/+E66+2q/ooLAVyS6qcHPUBx/Agw/CfffBjBlQXu79tGecoekXWyRWmJsqz02rfX2mQgmZ8bqxwg2lPqt7KI6jGzJ9OJmKd6NXtAXrN/r6dev94fWZ21gBV9atfFXxticK3BwybZqH7EMP+bwIZn6a7i9/CSedtOnTrohkKb1Fs9jq1fCPf8Df/gbPPw/vvushe+ihfiDs5JNhhx3SbmU7lal0Y8WZOdOsOt5ancrTYkUbSvwtVdXJ+3irO3jFu6kijt9fsMEr2MLVXtkWrFrnz1u91m/rVr6Zird688pbcosCN4tUVMCkSR6wEyb46biVlX4NskMOgRtugFNOgf79026piDSHAjdFixZ5wGaWV1+FNWu8iv385+Gii+DIIz1sO3ZMu7V5rjoziiGOJli3+ZlmmduCTGUcK95MhVvRxW8rS2MlnOnqrfRfbNFaPwWwZJWPYiha5hVv4Yo1/vyVq/12na+vjqMdNLohtyhwE7Jhg59eO3Gih+vEiTBrlj9WWAh77w0jR8Lw4XD44dCrV6rNFZE2oMBtA6tXwzvveMC++abfvvOOTyID3iUwbJifbnvQQV7N6kq6OaJO325m1jDLVLhxfWEcZ2tVXf155r/gqg5e4W7s7H26FZ38NsQz2Ao3+luyeHUpAKXLvfItXex/PEVLvNItXL7Sv291rIA31OnrVcWblRS4LbRw4ebB+uabMH16zd97r16w//5w4YUergcdBAMGpNtmEUmHAreR1q2D99/3kQK1l08/rXlOebmH62mnwX77+dcDBugSWe1apm93Y3W86/ct9rFaPNOsw4Y4DjdWvNABgKoO/sexvruvXdc50wfs9ws3eGdvyYpOAJQu8T7fzgt7+v1PfFRD4ZIV/n0rvPKtzozzrcz09arizQYK3DqqqmDmzC2DdcYMyMzIV1rqcxUcc0xNsO67L/TokW7bRSS75XXgLlq0ZbBOnQprvWjAzK9wu/fecOqpfrv33rDzzn6gS2STun27mUo39uUWxIqzJI6rLVzr/50LN8bhJ8Hfimv9hDU29o59sf28Ql1nvv3Va3x87/LP/PmdFnpp3Pljr5w7L/D9FH/qFS/L/LY609dbkRlXrPG8aciLwF271oO0brguWlTznLIyD9NzzqkJ1s99TgezRKT1tLvAXbrUD1y98UbN7bRpNV1YpaV+mfCvfKUmWPfe2y8/I9JqMhVvhY8uqMqMYohDVQrW+MeoLiu9L7Z4lVeqRbHEXV3hb831hb6dXn29Uu2zg49SYGe/+WSlj2L4ZKFXuKULvK+3yzyvnLvO7RXXe98uS5Z7u1atAmqN51XFm4icDdwQ4OOPtwzXuXNrnjNwIBxwwObdATvtpO4AEUlHzgRudbV3C7z8ss8v8PLL8MknNY/vuiscfLBf3WD//X3p3Tu99opsJlaQ1Rv8yKvFvl6LfbulcQ6F4uVx9MFyn/Jt5Urvs122ztdXD/bhC0P7zgPgy2UfArBhR3/eWyt8zOE7C/oBsHyO94l1mVMGQPfZ3nfcca5XuIWLlwIQVnnlXB37mFXxto2sDdyKCpgypSZc//UvWLbMH+vf38/GGjbMK9h994WuXbe5ORGR1GVV4IbgE2jfc49PQbgydjvtuqtPP/ilL/l0hOXlGtsqOWoroxkyV4IoiJVmt6Wx0l0cb+P425WfeZ/shJ28r3bZjr7+hLJ3ADh6wLsALN3BK+QJu+4JwN/m7QbAvFle4XaZ5dvpMdP7gDvFirdooVe81StjH68q3lbVYOCa2UDgHqAPEICxIYQbzawX8CBQDswGvhFCWNacRsycCX/+swftzJk+MuDkk+HEE30qwr59m7NVEZHsYqGBM1DMbAdghxDCG2bWFZgCfA34LrA0hPArM7sU6BlCuGRb2xo6dGiYPHnyZuuuuw5+9COvWIcPh9NP92pWw7GSZWZTQghDAY4q+LpOS0pL/OhmJT5aoaBLfCNs55Xu+kFeoa4Y4o+v3Mkf7rCLfxw8rvx9AE7tOQmA3Yq9z/jjWEk/tnI/v523LwCLZmwHQNeZfiS5x0deeXea49uzWPFuOoMtj0c1PF/9cIs/VzdY4YYQPgE+iV+vMrMPgP7ACODw+LS7gReBbQZuXWPHetiefDJcf72PKhARaa+a1IdrZuXA/sAkoE8MY4BP8S6HRps1C849F4YOhfvv90m2RfJepo83zv5VlTlTLY7bLV3mlWbpJ94H23WB98Eun+/jeB/d6SAAXtrFB+qePOgtAL7R7U0ALtlu+mb3Hxq4PwDjhvjt/PK43Y9iH+9HfjRmlkPGAAAPNElEQVQ6U/EWLsqMatA43uZo9FV7zawL8AjwPyGElbUfC94vUe/HUDMbZWaTzWzy4sWLN60fMAC+/GUfP/v0081rvIhILmmwDxfAzIqB8cBzIYTfxXUfAoeHED6J/bwvhhB229Z26vbhrloFRx0Fr7/uE8GMHAkjRvjZYJIs9eHmiALvay0o8XG31t0r3NDHK9K1g2PFu5N/eF01xCvP7Xf+DID/HPi233bzyrdfPAvowwqvvf6yzCvkZ2b76IYN03173T7y3Xef6WfKlc71M9b4zI+Tb5qrITPpczucnaw1+nAbrHDNzIA7gA8yYRs9CYyMX48Enmjqzrt2hWefhUsu8bkNTj3VRySMGuXjbjOzc4mItAeNGaVwKPAP4F0gE4E/wftxHwIGAXPwYWFLt7Wt+kYpZFRVwYsvwt13wyOP+IQzPXv6sLDDDvMxuAccAMXFTfnxpLFU4eaoTMVb6vPrFnTzPtfqTMU7yO8vH+IV7+od/S3cfYhXpsMHTPPbbj66oVehjwN+f4NfqXT84n0AeGPWIAA6fOQfP7vN9D+RbrP8GmslC3x7IVPx1p6Pt51Uu0mNUvgnsLUdDW9pAzIKC31Y2PDhcPPN8MQT8MILfqbZX//qz+nUyU/fzZwAceCBGj4mIrmjUX24rWVbFe62fPqpn4GWmUfh7bf9n6YZ7Labz5twwAE1cyjoAoxNpwq3nahT8VpXP+MslPmbYt1gr3hXDPaPiqsHx2uyDfY+2H36fwzAft3nA9ChwEchTFvjZx9N/tTHbq6Y46Mius7y/XWb7X3FnWfHa64tipXu8hXt5npriVS42aBvXzjlFF8Ali+HV17xg21vvulh/MADNc8fPLgmgA84wK/K0K+fTgcWkXTlRODW1aMHHH+8LxlLlnj4ZpY33oDHH6/5Z9qzZ80Ujfvs47d77aVJb6SdycxKlrlsSZwLoSCeKdYpzpvbaaafsba+v49CWDXI++amDtwVgCn9dwSgRx8fb7tDt5Wb3RbEvuClHb3S3djNK+b1PeKohjne19thQScs9utmrjCcz2N3czJw69O7tw8xO+qomnWrVnn3w9tv+yiId97x+RrimG3AJ8KpPRH53nv7ZDk6OCftQiaA18dwy1zcMs4MVbrIg7Z0tgdnj75+u6a/B+aaft4VMb2Pn1pcuV28DHxn305hJ7+/fgf/+Fhd7F0MlR29S6Nrt150WuAT7BQu8qFklrnsTx5e6LLdBG59unb1UQ6HHlqzLgSYM2fLy+088wzECZwoKYHdd98yiHUFXhFpiXYduPUx86q2vNxnI8vYsAE+/NCr4EwIv/QS3HdfzXN69PBuiLpdE926Jf1TiDRT5rLuG/y2Kp6oYCv9YFfxIj9Bouec2DWwvVe86/v4dJBr+sRL//T2j4Abu8eqtEPcfEyU9ZnJ/wuKqCrxKrpTJ3+wODOEbWm8tPuazU+ayPWDa9uSd4G7NR06eIDus8/m65ctg/fe27wavu++mrl6wa/im7lceua2b19VwyKyOQVuA3r29HG/X/pSzboQYN48r4bfesuXN96AceNqnrP99jUBvN9+8PnPezArhCWr1L3YZexPtXjQzZb6Aa/OC3x4WafuftCtoszvb+jts06t7+F9txVd/A+8Kk5GVV0IG7pn/ui9si0t9BNcSzp4/BQs9SeH1fEyP5m+3XZ4SXcFbjOYwaBBvpxwQs36FSs8hN9800P4zTfhd7/zywWBjw8+6CBfhg3zEzd69kznZxCR5ClwW1H37ltWwxs3wvvvw+TJMGkSTJzo80dkuqd23dXD96CD4Itf9C6NgkbP4SbSyrZyCaBM1Vmw3Ptdixf5yIOSbl7pdu7u/bSVPX39xq7ex1vZqWBTv26IhW5FV6+GLXi/cHH8gy8ojhVvkVe6Ya2fNtye+nYVuG2spKSmW+Hss33dypUewBMnegg/+6wPVwMf3vblL8ORR/ppzkOGqBtCpL1Q4KagWzc44ghfoGao2ssvw4QJvjz8sD9WXl4zx8Sxx6oLQhKWqSbDVsbzxpMZbKn3zxYvjJVvZ69eq7uUUtXFH6vu4JVtKNi8gqjqFAe9x4q3IFNhxKkjiZVu5iSOTZVuDvbtKnCzQO2haqef7n/jH35YE76PPAJ33OEnYxx9NHzjGz5vcPfuabdcRJpCgZuFzPzEi913h/PO86krX3/dg/ehh+Cpp7yr4thjPXxPPlmTtkvCMuN5M7eZ0Q3rvBq1eCqxlXagOPPH2dFvQ2kclVAS4ydT0Wb6jzvEydWrN/+jtvi86sxkOPFgNKF6s+/PZjo8kwMKC/3A2m9/C7Nnw6uvehBPmQLf/rZP1vPzn8Nnn6XdUhHZlpyYnlHqV13tcwZfd52fmtyxI5x5Jlx8sYdwU2h6RmlVmarVCrDYF2txFILFywPRIU4hmZm4pKhw8+/NZFMcjxsyk95kKtzM6IU6IyraqtJN5BI7kr0KCvxg2tNP11yiaOxY2HNPuOEG74oQkeyhwG0n9toL7rwTpk+Hww+H0aN9XO/UqWm3TPJSCL5UVxEqNhIqNlK9bh3V69ZRtXI1VStXU710eVyWUb10GWHpcl9WrvJlzTpfNlZ4dRuqfSks9KW4GIqLsaIiXwoLvZouiItZ1o2pVOC2M4MHw/jxcP/9MHOmh+6//pV2q0QENEqhXTKDb33Lp6U88kgfSvbkk979IJKaOmN6txjhkBl3awXxfqwHM+vrnoKZuax3webPMzLbzzw/e0YxqMJtxwYO9JMphgzxyxMtWJB2i0TymwK3nevTBx57zA/snnNOVvyTF9lc7O8NlZVxqSBUVlC9YYMv69ZTvW49Yd06X9Zv8CXz/KqqmhEK4BWyFWAFhhXYpvvZ0KerwM0DO+8MV1/tQ8deeCHt1ojkLwVunjj3XJ8e8uab026JSAMyIxxqjXSgumpTJZupgENF5WYLVVW+xNEMoToQquv5SJdipavAzROlpTByJDzxBMQrmohIwhS4eeToo/1CmRMnpt0SkWbYSuW7RQWc6dPNjNvNLFlAgZtHDj7Yb197Ld12iOQrjcPNI927+6iFjz5KuyUibSAHhuCows0z5eUwd27arRDJTwrcPNO7t6ZxFEmLAjfP9OwJy5al3QqR/NTowDWzQjN708zGx/s7mtkkM5thZg+aWUnbNVNaS6dOECflF5GENaXCvRD4oNb9XwPXhxB2BpYBZ7Vmw6RtlJYqcEXS0qjANbMBwFeA2+N9A44AxsWn3A18rS0aKK2ruBgqKhp+noi0vsZWuDcAP2bTPGdsBywPIVTG+/OB/vV9o5mNMrPJZjZ58eLFLWqstFxRkZ/8ICLJazBwzewEYFEIYUpzdhBCGBtCGBpCGFpWVtacTUgrKiiomUZURJLVmBMfDgG+ambHA6VAN+BGoIeZFcUqdwCg2VZzgAJXJD0NVrghhMtCCANCCOXAqcDfQwinAS8Ap8SnjQSeaLNWSqsxy4kTckTapZaMw70E+KGZzcD7dO9onSZJW1LgiqSnSXMphBBeBF6MX88EDmz9JklbyrKLmIrkFZ1pJiKSEAVunlGFK5IeBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIaFbhm1sPMxpnZv83sAzM72Mx6mdnzZjY93vZs68aKiOSyxla4NwLPhhB2B/YFPgAuBSaEEHYBJsT7IiKyFQ0Grpl1Bw4D7gAIIWwMISwHRgB3x6fdDXytrRopItIeNKbC3RFYDPzJzN40s9vNrDPQJ4TwSXzOp0Cf+r7ZzEaZ2WQzm7x48eLWabWISA5qTOAWAQcAt4QQ9gfWUKf7IIQQgFDfN4cQxoYQhoYQhpaVlbW0vSIiOasxgTsfmB9CmBTvj8MDeKGZ7QAQbxe1TRNFRNqHBgM3hPApMM/MdourhgPvA08CI+O6kcATbdJCEZF2oqiRzzsfuM/MSoCZwBl4WD9kZmcBc4BvtE0TRUTah0YFbgjhLWBoPQ8Nb93miIi0XzrTTEQkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGENCpwzWy0mU01s/fM7AEzKzWzHc1skpnNMLMHzaykrRsrIpLLGgxcM+sPXAAMDSHsBRQCpwK/Bq4PIewMLAPOasuGiojkusZ2KRQBHc2sCOgEfAIcAYyLj98NfK31myci0n40GLghhAXAtcBcPGhXAFOA5SGEyvi0+UD/+r7fzEaZ2WQzm7x48eLWabWISA5qTJdCT2AEsCPQD+gMHNvYHYQQxoYQhoYQhpaVlTW7oSIiua4xXQpHArNCCItDCBXAo8AhQI/YxQAwAFjQRm0UEWkXGhO4c4FhZtbJzAwYDrwPvACcEp8zEniibZooItI+NKYPdxJ+cOwN4N34PWOBS4AfmtkMYDvgjjZsp4hIzitq+CkQQvhf4H/rrJ4JHNjqLRIRaad0ppmISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBa6ISEIUuCIiCVHgiogkRIErIpIQBW6eGTYMLrww7VaI5CcLISS3M7PFwJzEdihNMTiEUJZ2I0Tas0QDV0Qkn6lLQUQkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGEKHBFRBKiwBURSYgCV0QkIQpcEZGE/H+ovX+5d2RUpwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -740,21 +710,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb index 038434adf..5862d4859 100644 --- a/notebooks/plot_otda_d2.ipynb +++ b/notebooks/plot_otda_d2.ipynb @@ -43,7 +43,8 @@ "# License: MIT License\n", "\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -126,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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gT4qzXkLy10DcG42+S5ImyEqpMhVWjV3rir22xC8IVkiqCcl1u3j2u5W8s2sH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\nxjrvokYGGT+CfUtQXHT2c+2vD3ut7BT2dYilLge+SM/ZunF9YIwjQcEdC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Avs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\nB/u4Lir5zTcvAAAAAElFTkSuQmCC\n", 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eNb6/H9bCZr+dtbVsiTSu1SQ979xK0o9RWQk4Niqflq1t1Wles64Ods+NSPUM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/fuNRPaq682k3jh3KN1CAKvoQPvgKsAlXEnyjd3oLs1NBXkGAcPGr/d5mYTbHDZZakpoOWnKvhV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K+OhleNxpZk1dQ75Yx9CgTMYUv6vHI4Bxc7MI4k7xn6K4Qb0cyXc5ti0WsU89JebpqVNFutyR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yFJExbR4iqIfK5yL4GzcOUYbvorYPdSLIV5Yfu7kWWLJXN/NX7M/RB875s5JP8n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\ny7K455cPk0lmkBSV2tO/ROHMxXS++hitrU+jXvGr0fcuOmUeT9/yI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\n", 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iTrVtt1q5cMRILhwxspMSazS9hx3l5QQiYdLc8YAxIJTuTqDYW8+RulpGDkoB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ADODCR5e5fG1V3pu4iIgUBnf781qEGr/82UQVkjwBoblkZkwCo0pzjWSJACJHVvm4oUIjEo+Bpa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\nx6aXdlB7up7oeAezVkxl4vxx/QLlg+8dpmTjUQ5vOU5jZbP+2oYjGIwKi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zMfojbTecZcE)3l(h2TBEB$Vp`o&~dF;kJMi<6pf3t+GF0RRH!;ds;jfUrPYK6U8+J zBQ-GKw!FN2G5;toUIXJA|G9Jye1*pA&gUnt@a-8ngUbK+RT}@x-z0LPps!>y?`Tn7 R1>drQ&{We=Em1}X|36|a1r7iJ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 227c40cbc..9d7c0f0ad 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -49,13 +49,13 @@ This is a gallery of all the POT example files. .. raw:: html -

+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_gromov.py` .. raw:: html @@ -65,17 +65,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_gromov .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` .. raw:: html @@ -85,17 +85,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_compute_emd + /auto_examples/plot_OT_2D_samples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - :ref:`sphx_glr_auto_examples_plot_WDA.py` + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` .. raw:: html @@ -105,17 +105,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_WDA + /auto_examples/plot_compute_emd .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov.py` + :ref:`sphx_glr_auto_examples_plot_WDA.py` .. raw:: html @@ -125,7 +125,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_gromov + /auto_examples/plot_WDA .. raw:: html diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index 6d6b52268..57d6a4a0f 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -1,54 +1,126 @@ { + "nbformat_minor": 0, + "nbformat": 4, + "metadata": { + "language_info": { + "file_extension": ".py", + "codemirror_mode": { + "version": 3, + "name": "ipython" + }, + "nbconvert_exporter": "python", + "mimetype": "text/x-python", + "version": "3.5.2", + "name": "python", + "pygments_lexer": "ipython3" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" + } + }, "cells": [ { + "outputs": [], + "source": [ + "%matplotlib inline" + ], "execution_count": null, "metadata": { "collapsed": false }, + "cell_type": "code" + }, + { + "source": [ + "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { "outputs": [], "source": [ - "%matplotlib inline" + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" ], + "execution_count": null, + "metadata": { + "collapsed": false + }, "cell_type": "code" }, { - "metadata": {}, "source": [ - "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + "Sample two Gaussian distributions (2D and 3D)\n---------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" ], + "metadata": {}, "cell_type": "markdown" }, { + "outputs": [], + "source": [ + "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ], "execution_count": null, "metadata": { "collapsed": false }, + "cell_type": "code" + }, + { + "source": [ + "Plotting the distributions\n--------------------------\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { "outputs": [], "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot\n\n\n#\n# Sample two Gaussian distributions (2D and 3D)\n# ---------------------------------------------\n#\n# The Gromov-Wasserstein distance allows to compute distances with samples that\n# do not belong to the same metric space. For demonstration purpose, we sample\n# two Gaussian distributions in 2- and 3-dimensional spaces.\n\n\nn_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\n\n\n#\n# Plotting the distributions\n# --------------------------\n\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()\n\n\n#\n# Compute distance kernels, normalize them and then display\n# ---------------------------------------------------------\n\n\nC1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()\n\n#\n# Compute Gromov-Wasserstein plans and distance\n# ---------------------------------------------\n\np = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" ], + "execution_count": null, + "metadata": { + "collapsed": false + }, "cell_type": "code" - } - ], - "metadata": { - "language_info": { - "name": "python", - "codemirror_mode": { - "name": "ipython", - "version": 3 + }, + { + "source": [ + "Compute distance kernels, normalize them and then display\n---------------------------------------------------------\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { + "outputs": [], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" + ], + "execution_count": null, + "metadata": { + "collapsed": false }, - "nbconvert_exporter": "python", - "version": "3.5.2", - "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" + "cell_type": "code" }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + { + "source": [ + "Compute Gromov-Wasserstein plans and distance\n---------------------------------------------\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { + "outputs": [], + "source": [ + "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "cell_type": "code" } - }, - "nbformat_minor": 0, - "nbformat": 4 + ] } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py index 9188da914..5cd40f651 100644 --- a/docs/source/auto_examples/plot_gromov.py +++ b/docs/source/auto_examples/plot_gromov.py @@ -19,7 +19,7 @@ from mpl_toolkits.mplot3d import Axes3D # noqa import ot - +############################################################################# # # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- @@ -42,7 +42,7 @@ P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t - +############################################################################# # # Plotting the distributions # -------------------------- @@ -55,7 +55,7 @@ ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() - +############################################################################# # # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,6 +74,7 @@ pl.imshow(C2) pl.show() +############################################################################# # # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index ad29f7a86..131861fe5 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -12,77 +12,38 @@ computation in POT. +.. code-block:: python -.. rst-class:: sphx-glr-horizontal - - - * - .. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :scale: 47 + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License - * + import scipy as sp + import numpy as np + import matplotlib.pylab as pl + from mpl_toolkits.mplot3d import Axes3D # noqa + import ot - .. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :scale: 47 -.. rst-class:: sphx-glr-script-out - Out:: - It. |Loss |Delta loss - -------------------------------- - 0|4.042674e-02|0.000000e+00 - 1|2.432476e-02|-6.619583e-01 - 2|2.170023e-02|-1.209448e-01 - 3|1.941223e-02|-1.178640e-01 - 4|1.823606e-02|-6.449667e-02 - 5|1.446641e-02|-2.605800e-01 - 6|1.184011e-02|-2.218140e-01 - 7|1.173274e-02|-9.150805e-03 - 8|1.173127e-02|-1.253458e-04 - 9|1.173126e-02|-1.256842e-06 - 10|1.173126e-02|-1.256876e-08 - 11|1.173126e-02|-1.256885e-10 - It. |Err - ------------------- - 0|7.034302e-02| - 10|1.044218e-03| - 20|5.426783e-08| - 30|3.532029e-12| - Gromov-Wasserstein distances: 0.0117312557987 - Entropic Gromov-Wasserstein distances: 0.0101639418389 +Sample two Gaussian distributions (2D and 3D) +--------------------------------------------- +The Gromov-Wasserstein distance allows to compute distances with samples that +do not belong to the same metric space. For demonstration purpose, we sample +two Gaussian distributions in 2- and 3-dimensional spaces. -| .. code-block:: python - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - from mpl_toolkits.mplot3d import Axes3D # noqa - import ot - - - # - # Sample two Gaussian distributions (2D and 3D) - # --------------------------------------------- - # - # The Gromov-Wasserstein distance allows to compute distances with samples that - # do not belong to the same metric space. For demonstration purpose, we sample - # two Gaussian distributions in 2- and 3-dimensional spaces. - n_samples = 30 # nb samples @@ -98,9 +59,18 @@ computation in POT. xt = np.random.randn(n_samples, 3).dot(P) + mu_t - # - # Plotting the distributions - # -------------------------- + + + + + +Plotting the distributions +-------------------------- + + + +.. code-block:: python + fig = pl.figure() @@ -111,9 +81,21 @@ computation in POT. pl.show() - # - # Compute distance kernels, normalize them and then display - # --------------------------------------------------------- + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png + :align: center + + + + +Compute distance kernels, normalize them and then display +--------------------------------------------------------- + + + +.. code-block:: python + C1 = sp.spatial.distance.cdist(xs, xs) @@ -129,9 +111,22 @@ computation in POT. pl.imshow(C2) pl.show() - # - # Compute Gromov-Wasserstein plans and distance - # --------------------------------------------- + + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png + :align: center + + + + +Compute Gromov-Wasserstein plans and distance +--------------------------------------------- + + + +.. code-block:: python + p = ot.unif(n_samples) q = ot.unif(n_samples) @@ -159,7 +154,40 @@ computation in POT. pl.show() -**Total running time of the script:** ( 0 minutes 1.465 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|4.517558e-02|0.000000e+00 + 1|2.563483e-02|-7.622736e-01 + 2|2.443903e-02|-4.892972e-02 + 3|2.231600e-02|-9.513496e-02 + 4|1.676188e-02|-3.313541e-01 + 5|1.464792e-02|-1.443180e-01 + 6|1.454315e-02|-7.204526e-03 + 7|1.454142e-02|-1.185811e-04 + 8|1.454141e-02|-1.190466e-06 + 9|1.454141e-02|-1.190512e-08 + 10|1.454141e-02|-1.190520e-10 + It. |Err + ------------------- + 0|6.743761e-02| + 10|5.477003e-04| + 20|2.461503e-08| + 30|1.205155e-11| + Gromov-Wasserstein distances: 0.014541405718693563 + Entropic Gromov-Wasserstein distances: 0.015800739725237274 + + +**Total running time of the script:** ( 0 minutes 1.448 seconds) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 9188da914..5cd40f651 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -19,7 +19,7 @@ from mpl_toolkits.mplot3d import Axes3D # noqa import ot - +############################################################################# # # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- @@ -42,7 +42,7 @@ P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t - +############################################################################# # # Plotting the distributions # -------------------------- @@ -55,7 +55,7 @@ ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() - +############################################################################# # # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,6 +74,7 @@ pl.imshow(C2) pl.show() +############################################################################# # # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index 87937f09a..6a237e62f 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -30,64 +30,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.767714e-02|0.000000e+00\n", - " 1|2.091125e-02|-8.017640e-01\n", - " 2|1.607458e-02|-3.008895e-01\n", - " 3|1.486883e-02|-8.109274e-02\n", - " 4|1.484811e-02|-1.394896e-03\n", - " 5|1.484791e-02|-1.400744e-05\n", - " 6|1.484790e-02|-1.400803e-07\n", - " 7|1.484790e-02|-1.400805e-09\n", - " 8|1.484790e-02|-1.400768e-11\n", - "It. |Err \n", - "-------------------\n", - " 0|7.522843e-02|\n", - " 10|2.233137e-04|\n", - " 20|5.767057e-07|\n", - " 30|2.315565e-09|\n", - " 40|9.789603e-12|\n", - "Gromov-Wasserstein distances: 0.014847904422308234\n", - "Entropic Gromov-Wasserstein distances: 0.011257422087381012\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Author: Erwan Vautier \n", "# Nicolas Courty \n", @@ -98,18 +41,30 @@ "import numpy as np\n", "import matplotlib.pylab as pl\n", "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "import ot\n", - "\n", - "\n", - "#\n", - "# Sample two Gaussian distributions (2D and 3D)\n", - "# ---------------------------------------------\n", - "#\n", - "# The Gromov-Wasserstein distance allows to compute distances with samples that\n", - "# do not belong to the same metric space. For demonstration purpose, we sample\n", - "# two Gaussian distributions in 2- and 3-dimensional spaces.\n", - "\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample two Gaussian distributions (2D and 3D)\n", + "---------------------------------------------\n", "\n", + "The Gromov-Wasserstein distance allows to compute distances with samples that\n", + "do not belong to the same metric space. For demonstration purpose, we sample\n", + "two Gaussian distributions in 2- and 3-dimensional spaces.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "n_samples = 30 # nb samples\n", "\n", "mu_s = np.array([0, 0])\n", @@ -121,27 +76,73 @@ "\n", "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n", "P = sp.linalg.sqrtm(cov_t)\n", - "xt = np.random.randn(n_samples, 3).dot(P) + mu_t\n", - "\n", - "\n", - "#\n", - "# Plotting the distributions\n", - "# --------------------------\n", - "\n", - "\n", + "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the distributions\n", + "--------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "fig = pl.figure()\n", "ax1 = fig.add_subplot(121)\n", "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", "ax2 = fig.add_subplot(122, projection='3d')\n", "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", - "pl.show()\n", - "\n", - "\n", - "#\n", - "# Compute distance kernels, normalize them and then display\n", - "# ---------------------------------------------------------\n", - "\n", - "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute distance kernels, normalize them and then display\n", + "---------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "C1 = sp.spatial.distance.cdist(xs, xs)\n", "C2 = sp.spatial.distance.cdist(xt, xt)\n", "\n", @@ -153,12 +154,68 @@ "pl.imshow(C1)\n", "pl.subplot(122)\n", "pl.imshow(C2)\n", - "pl.show()\n", - "\n", - "#\n", - "# Compute Gromov-Wasserstein plans and distance\n", - "# ---------------------------------------------\n", - "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Gromov-Wasserstein plans and distance\n", + "---------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.731009e-02|0.000000e+00\n", + " 1|1.846414e-02|-1.020678e+00\n", + " 2|1.752056e-02|-5.385587e-02\n", + " 3|1.470479e-02|-1.914863e-01\n", + " 4|1.371582e-02|-7.210441e-02\n", + " 5|1.263823e-02|-8.526431e-02\n", + " 6|1.190590e-02|-6.151022e-02\n", + " 7|1.014664e-02|-1.733837e-01\n", + " 8|1.011396e-02|-3.230516e-03\n", + " 9|1.011363e-02|-3.245532e-05\n", + " 10|1.011363e-02|-3.245682e-07\n", + " 11|1.011363e-02|-3.245683e-09\n", + " 12|1.011363e-02|-3.245598e-11\n", + "It. |Err \n", + "-------------------\n", + " 0|7.847531e-02|\n", + " 10|2.424218e-03|\n", + " 20|4.250670e-02|\n", + " 30|5.679949e-05|\n", + " 40|1.219762e-08|\n", + " 50|1.121975e-11|\n", + "Gromov-Wasserstein distances: 0.01011363084946804\n", + "Entropic Gromov-Wasserstein distances: 0.0063386519916289385\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "p = ot.unif(n_samples)\n", "q = ot.unif(n_samples)\n", "\n", From 8c84584ca49045c3a847a04b05d72931f2b73da7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Flamary?= Date: Fri, 16 Feb 2018 15:56:47 +0100 Subject: [PATCH 13/13] upate doc --- docs/source/all.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/all.rst b/docs/source/all.rst index 2348785c8..c84d968ed 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -20,6 +20,13 @@ ot.bregman .. automodule:: ot.bregman :members: +ot.gromov +---------- + +.. automodule:: ot.gromov + :members: + + ot.optim --------