forgot the test file in my last checkin...

scikits/learn/utils/tests/test_svd.py

+# Author: Olivier Grisel <olivier.grisel@ensta.org>
+# License: BSD
+
+import numpy as np
+from scipy import sparse
+from scipy import linalg
+
+from numpy.testing import assert_equal
+from numpy.testing import assert_almost_equal
+
+from ..extmath import fast_svd
+
+
+def test_fast_svd():
+    """Check that extmath.fast_svd is consistent with linalg.svd"""
+    n_samples = 100
+    n_features = 500
+    rank = 5
+    k = 100
+
+    # generate a matrix X of rank `rank`
+    np.random.seed(42)
+    X = np.dot(np.random.randn(n_samples, rank),
+               np.random.randn(rank, n_features))
+    assert_equal(X.shape, (n_samples, n_features))
+
+    # compute the singular values of X using the slow exact method
+    U, s, V = linalg.svd(X, full_matrices=False)
+
+    # compute the singular values of X using the fast approximate method
+    Ua, sa, Va = fast_svd(X, k)
+    assert_equal(Ua.shape, (n_samples, k))
+    assert_equal(sa.shape, (k,))
+    assert_equal(Va.shape, (k, n_features))
+
+    # ensure that the singular values of both methods are equal up to the real
+    # rank of the matrix
+    assert_almost_equal(s[:rank], sa[:rank])
+
+    # check the singular vectors too
+    # XXX: some vectors are not equal, while others perfectly match?
+    #assert_almost_equal(U[:, :rank], Ua[:, :rank])
+    #assert_almost_equal(V[:rank, :], Va[:rank, :])
+
+    # check the sparse matrix representation
+    X = sparse.csr_matrix(X)
+
+    # compute the singular values of X using the fast approximate method
+    Ua, sa, Va = fast_svd(X, k)
+    assert_almost_equal(s[:rank], sa[:rank])
+
+