add test optim

1 files changed, 65 insertions, 0 deletions

diff --git a/test/test_optim.py b/test/test_optim.py
new file mode 100644
index 0000000..43cba7d
--- /dev/null
+++ b/test/test_optim.py
@@ -0,0 +1,65 @@
+
+
+import ot
+import numpy as np
+
+# import pytest
+
+
+def test_conditional_gradient():
+
+    n = 100  # nb bins
+
+    # bin positions
+    x = np.arange(n, dtype=np.float64)
+
+    # Gaussian distributions
+    a = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+    b = ot.datasets.get_1D_gauss(n, m=60, s=10)
+
+    # loss matrix
+    M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
+    M /= M.max()
+
+    def f(G):
+        return 0.5 * np.sum(G**2)
+
+    def df(G):
+        return G
+
+    reg = 1e-1
+
+    G, log = ot.optim.cg(a, b, M, reg, f, df, verbose=True, log=True)
+
+    assert np.allclose(a, G.sum(1))
+    assert np.allclose(b, G.sum(0))
+
+
+def test_generalized_conditional_gradient():
+
+    n = 100  # nb bins
+
+    # bin positions
+    x = np.arange(n, dtype=np.float64)
+
+    # Gaussian distributions
+    a = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+    b = ot.datasets.get_1D_gauss(n, m=60, s=10)
+
+    # loss matrix
+    M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
+    M /= M.max()
+
+    def f(G):
+        return 0.5 * np.sum(G**2)
+
+    def df(G):
+        return G
+
+    reg1 = 1e-3
+    reg2 = 1e-1
+
+    G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True)
+
+    assert np.allclose(a, G.sum(1), atol=1e-05)
+    assert np.allclose(b, G.sum(0), atol=1e-05)