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BUG: restore numpy 1.3 compatbibility

This commit is contained in:
Gael Varoquaux 2012-10-27 23:03:40 +02:00
parent 8b6f28061d
commit 0a01292dbf
1 changed files with 3 additions and 3 deletions
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6
sklearn/cluster/spectral.py
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@ -303,7 +303,7 @@ def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,
for j in range(1, n_components):
# Accumulate c to ensure row is as orthogonal as possible to
# previous picks as well as current one
c += np.abs(vectors.dot(rotation[:, j - 1]))
c += np.abs(np.dot(vectors, rotation[:, j - 1]))
rotation[:, j] = vectors[c.argmin(), :].T
last_objective_value = 0.0
@ -312,7 +312,7 @@ def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,
while not has_converged:
n_iter += 1
t_discrete = vectors.dot(rotation)
t_discrete = np.dot(vectors, rotation)
labels = t_discrete.argmax(axis=1)
vectors_discrete = csc_matrix(
@ -335,7 +335,7 @@ def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,
else:
# otherwise calculate rotation and continue
last_objective_value = ncut_value
rotation = Vh.T.dot(U.T)
rotation = np.dot(Vh.T, U.T)
if not has_converged:
raise LinAlgError('SVD did not converge')