|
| 1 | + |
| 2 | + |
| 3 | +import ot |
| 4 | +import numpy as np |
| 5 | + |
| 6 | +# import pytest |
| 7 | + |
| 8 | + |
| 9 | +def test_plot1D_mat(): |
| 10 | + |
| 11 | + n = 100 # nb bins |
| 12 | + |
| 13 | + # bin positions |
| 14 | + x = np.arange(n, dtype=np.float64) |
| 15 | + |
| 16 | + # Gaussian distributions |
| 17 | + a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std |
| 18 | + b = ot.datasets.get_1D_gauss(n, m=60, s=10) |
| 19 | + |
| 20 | + # loss matrix |
| 21 | + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) |
| 22 | + M /= M.max() |
| 23 | + |
| 24 | + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') |
| 25 | + |
| 26 | + |
| 27 | +def test_plot2D_samples_mat(): |
| 28 | + |
| 29 | + n = 50 # nb samples |
| 30 | + |
| 31 | + mu_s = np.array([0, 0]) |
| 32 | + cov_s = np.array([[1, 0], [0, 1]]) |
| 33 | + |
| 34 | + mu_t = np.array([4, 4]) |
| 35 | + cov_t = np.array([[1, -.8], [-.8, 1]]) |
| 36 | + |
| 37 | + xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) |
| 38 | + xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) |
| 39 | + |
| 40 | + G = 1.0 * (np.random.rand(n, n) < 0.01) |
| 41 | + |
| 42 | + ot.plot.plot2D_samples_mat(xs, xt, G, thr=1e-5) |
0 commit comments