fixed conflicts

Author: Kilian Fatras

test/test_da.py

@@ -484,66 +484,3 @@ def test_linear_mapping_class():
     Cst = np.cov(Xst.T)
 
     np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
-
-
-def test_otda():
-
-    n_samples = 150  # nb samples
-    np.random.seed(0)
-
-    xs, ys = ot.datasets.make_data_classif('3gauss', n_samples)
-    xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples)
-
-    a, b = ot.unif(n_samples), ot.unif(n_samples)
-
-    # LP problem
-    da_emd = ot.da.OTDA()     # init class
-    da_emd.fit(xs, xt)       # fit distributions
-    da_emd.interp()    # interpolation of source samples
-    da_emd.predict(xs)    # interpolation of source samples
-
-    np.testing.assert_allclose(a, np.sum(da_emd.G, 1))
-    np.testing.assert_allclose(b, np.sum(da_emd.G, 0))
-
-    # sinkhorn regularization
-    lambd = 1e-1
-    da_entrop = ot.da.OTDA_sinkhorn()
-    da_entrop.fit(xs, xt, reg=lambd)
-    da_entrop.interp()
-    da_entrop.predict(xs)
-
-    np.testing.assert_allclose(
-        a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3)
-    np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3)
-
-    # non-convex Group lasso regularization
-    reg = 1e-1
-    eta = 1e0
-    da_lpl1 = ot.da.OTDA_lpl1()
-    da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta)
-    da_lpl1.interp()
-    da_lpl1.predict(xs)
-
-    np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3)
-    np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3)
-
-    # True Group lasso regularization
-    reg = 1e-1
-    eta = 2e0
-    da_l1l2 = ot.da.OTDA_l1l2()
-    da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True)
-    da_l1l2.interp()
-    da_l1l2.predict(xs)
-
-    np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3)
-    np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3)
-
-    # linear mapping
-    da_emd = ot.da.OTDA_mapping_linear()     # init class
-    da_emd.fit(xs, xt, numItermax=10)       # fit distributions
-    da_emd.predict(xs)    # interpolation of source samples
-
-    # nonlinear mapping
-    da_emd = ot.da.OTDA_mapping_kernel()     # init class
-    da_emd.fit(xs, xt, numItermax=10)       # fit distributions
-    da_emd.predict(xs)    # interpolation of source samples