wassdistance/layers.py /Jump toCode definitionsSinkhornDistance Class __init__ Function forward Function M Function _cost_matrix Function ave Function. 93 lines (79 sloc) 3.4 KB. class SinkhornDistance ( nn. Module ): outputs an …
11.03.2019 · %matplotlib inline import matplotlib.pyplot as plt import numpy as np np.random.seed(42) n_points = 5 a = np.array([[i, 0] for i in range(n_points)]) b = np.array([[i, 1 ... import torch from layers import SinkhornDistance x = torch.tensor(a, dtype=torch.float) y = torch.tensor(b, dtype=torch.float) ...
Dec 04, 2020 · To do so we need to construct, for each line i, a (8x4) repeated version of tensor a [i]. This will do: a_i = torch.stack (8* [a [i]], dim=0) Then we calculate the distance between a [i] and each batch in b: dist (a_i.unsqueeze (1), b.unsqueeze (1)) Having a total of batch lines we can construct our final tensor stack. Here's the complete code:
26.02.2019 · We can easily see that the optimal transport corresponds to assigning each point in the support of p ( x) p ( x) to the point right above in the support of …
By adding an entropic regularization, Sinkhorn distance (Cuturi, 2013) has been widely computed since it is friendly to high dimensional cases (Altschuler ...
Feb 26, 2019 · We can easily see that the optimal transport corresponds to assigning each point in the support of p ( x) p ( x) to the point right above in the support of q ( x) q ( x). For all points, the distance is 1, and since the distributions are uniform, the mass moved per point is 1/5. Therefore, the Wasserstein distance is 5 × 1 5 = 1 5 × 1 5 = 1.
wassdistance/layers.py /Jump toCode definitionsSinkhornDistance Class __init__ Function forward Function M Function _cost_matrix Function ave Function. 93 lines (79 sloc) 3.4 KB. class SinkhornDistance ( nn. Module ): outputs an approximation of the regularized OT cost for point clouds. elements in the output, 'sum': the output will be summed.
jf(x) f(y)j d(x;y), dbeing the underlying metric on the space. import torch from layers import SinkhornDistance x = torch . The Wasserstein package computes Wasserstein distances and related quantities efficiently. The Wasserstein distance has also been used to measure similarity between word embeddings of documents or between signals or spectra. 1-Wasserstein …