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Nicolas CourtyNicolas Courty
Nicolas Courty
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Nicolas Courty
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docstrings and naming
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‎examples/plot_gromov.py

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@@ -26,7 +26,11 @@
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For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces.
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"""
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n_samples = 30 # nb samples
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=======
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n = 30 # nb samples
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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mu_s = np.array([0, 0])
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cov_s = np.array([[1, 0], [0, 1]])
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cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
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xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
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P = sp.linalg.sqrtm(cov_t)
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xt = np.random.randn(n_samples, 3).dot(P) + mu_t
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=======
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xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
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P = sp.linalg.sqrtm(cov_t)
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xt = np.random.randn(n, 3).dot(P) + mu_t
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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"""
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=============================================
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"""
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p = ot.unif(n_samples)
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q = ot.unif(n_samples)
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=======
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p = ot.unif(n)
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q = ot.unif(n)
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)
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gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)

‎examples/plot_gromov_barycenter.py

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@@ -91,12 +91,21 @@ def im2mat(I):
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return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
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square = spi.imread('../data/carre.png').astype(np.float64) / 256
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circle = spi.imread('../data/rond.png').astype(np.float64) / 256
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triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256
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arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256
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shapes = [square, circle, triangle, arrow]
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=======
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carre = spi.imread('../data/carre.png').astype(np.float64) / 256
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rond = spi.imread('../data/rond.png').astype(np.float64) / 256
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triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256
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fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256
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shapes = [carre, rond, triangle, fleche]
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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S = 4
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xs = [[] for i in range(S)]
@@ -118,36 +127,60 @@ def im2mat(I):
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The four distributions are constructed from 4 simple images
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"""
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ns = [len(xs[s]) for s in range(S)]
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n_samples = 30
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=======
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N = 30
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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"""Compute all distances matrices for the four shapes"""
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Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]
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Cs = [cs / cs.max() for cs in Cs]
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ps = [ot.unif(ns[s]) for s in range(S)]
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p = ot.unif(n_samples)
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=======
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p = ot.unif(N)
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]
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Ct01 = [0 for i in range(2)]
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for i in range(2):
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Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [
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=======
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Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [
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ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
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Ct02 = [0 for i in range(2)]
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for i in range(2):
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Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [
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=======
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Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
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Ct13 = [0 for i in range(2)]
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for i in range(2):
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Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [
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=======
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Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
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Ct23 = [0 for i in range(2)]
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for i in range(2):
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Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [
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=======
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Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
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"""

‎ot/gromov.py

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@@ -208,7 +208,11 @@ def update_kl_loss(p, lambdas, T, Cs):
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return(np.exp(np.divide(tmpsum, ppt)))
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def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):
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=======
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def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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"""
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Returns the gromov-wasserstein coupling between the two measured similarity matrices
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loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss'
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epsilon : float
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Regularization term >0
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<<<<<<< HEAD
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max_iter : int, optional
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=======
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numItermax : int, optional
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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Max number of iterations
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stopThr : float, optional
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Stop threshold on error (>0)
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cpt = 0
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err = 1
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while (err > stopThr and cpt < max_iter):
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=======
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while (err > stopThr and cpt < numItermax):
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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Tprev = T
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return T
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def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):
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=======
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def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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"""
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Returns the gromov-wasserstein discrepancy between the two measured similarity matrices
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if log:
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gw, logv = gromov_wasserstein(
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C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log)
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else:
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gw = gromov_wasserstein(C1, C2, p, q, loss_fun,
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epsilon, max_iter, stopThr, verbose, log)
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C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log)
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else:
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gw = gromov_wasserstein(C1, C2, p, q, loss_fun,
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epsilon, numItermax, stopThr, verbose, log)
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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if loss_fun == 'square_loss':
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gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw))
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return gw_dist
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def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):
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=======
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def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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"""
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Returns the gromov-wasserstein barycenters of S measured similarity matrices
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error = []
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while(err > stopThr and cpt < max_iter):
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=======
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while(err > stopThr and cpt < numItermax):
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Cprev = C
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T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,
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max_iter, 1e-5, verbose, log) for s in range(S)]
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=======
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numItermax, 1e-5, verbose, log) for s in range(S)]
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if loss_fun == 'square_loss':
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C = update_square_loss(p, lambdas, T, Cs)

‎test/test_gromov.py

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def test_gromov():
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n_samples = 50 # nb samples
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=======
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n = 50 # nb samples
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mu_s = np.array([0, 0])
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cov_s = np.array([[1, 0], [0, 1]])
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xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
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xt = [xs[n_samples - (i + 1)] for i in range(n_samples)]
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xt = np.array(xt)
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p = ot.unif(n_samples)
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q = ot.unif(n_samples)
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=======
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xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
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xt = [xs[n - (i + 1)] for i in range(n)]
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xt = np.array(xt)
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p = ot.unif(n)
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q = ot.unif(n)
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>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
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C1 = ot.dist(xs, xs)
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C2 = ot.dist(xt, xt)

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