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author | Rémi Flamary <remi.flamary@gmail.com> | 2017-07-26 11:42:35 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2017-07-26 11:42:35 +0200 |
commit | 347e6288b87cbeef9b8fbc1a08cd130b96de1d61 (patch) | |
tree | 1d2dbc4faea4d74e1401bf2ff278e697ef214d01 /test | |
parent | 67b011a2a6a0cb8dffbb7a2619875f0e0d79588c (diff) |
n to n_samples
Diffstat (limited to 'test')
-rw-r--r-- | test/test_da.py | 11 | ||||
-rw-r--r-- | test/test_dr.py | 25 |
2 files changed, 14 insertions, 22 deletions
diff --git a/test/test_da.py b/test/test_da.py index 8df4795..a38390f 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -3,18 +3,15 @@ import numpy as np import ot -# import pytest - - def test_otda(): - n = 150 # nb samples + n_samples = 150 # nb samples np.random.seed(0) - xs, ys = ot.datasets.get_data_classif('3gauss', n) - xt, yt = ot.datasets.get_data_classif('3gauss2', n) + xs, ys = ot.datasets.get_data_classif('3gauss', n_samples) + xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples) - a, b = ot.unif(n), ot.unif(n) + a, b = ot.unif(n_samples), ot.unif(n_samples) # LP problem da_emd = ot.da.OTDA() # init class diff --git a/test/test_dr.py b/test/test_dr.py index 3faba48..e3d1e6b 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -13,15 +13,15 @@ except ImportError: @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_fda(): - n = 90 # nb samples in source and target datasets + n_samples = 90 # nb samples in source and target datasets np.random.seed(0) - # generate circle dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n) + # generate gaussian dataset + xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) - nbnoise = 8 + n_features_noise = 8 - xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise))) p = 1 @@ -35,20 +35,15 @@ def test_fda(): @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_wda(): - n = 100 # nb samples in source and target datasets - nz = 0.2 + n_samples = 100 # nb samples in source and target datasets np.random.seed(0) - # generate circle dataset - t = np.random.rand(n) * 2 * np.pi - ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 - xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) - xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + # generate gaussian dataset + xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) - nbnoise = 8 + n_features_noise = 8 - xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise))) p = 2 |