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Fixing #8484 #8485

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deefdd8
my test script
Feb 23, 2017
a903ef5
r. commit
Feb 28, 2017
7f18df2
introduced pca_modifications and deleted test.py file
Mar 1, 2017
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42 changes: 26 additions & 16 deletions sklearn/decomposition/pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,8 +134,11 @@ class PCA(_BasePCA):
to guess the dimension
if ``0 < n_components < 1`` and svd_solver == 'full', select the number
of components such that the amount of variance that needs to be
explained is greater than the percentage specified by n_components
n_components cannot be equal to n_features for svd_solver == 'arpack'.
explained is greater than the percentage specified by n_components.
if svd_solver == 'arpack', the number of components must be strictly
less than the minimum of n_features and n_samples:

n_components == min(n_samples, n_features)

copy : bool (default True)
If False, data passed to fit are overwritten and running
Expand Down Expand Up @@ -166,7 +169,7 @@ class PCA(_BasePCA):
arpack :
run SVD truncated to n_components calling ARPACK solver via
`scipy.sparse.linalg.svds`. It requires strictly
0 < n_components < X.shape[1]
0 < n_components < min(X.shape)
randomized :
run randomized SVD by the method of Halko et al.

Expand Down Expand Up @@ -205,7 +208,7 @@ class PCA(_BasePCA):
Percentage of variance explained by each of the selected components.

If ``n_components`` is not set then all components are stored and the
sum of explained variances is equal to 1.0.
sum of the ratios is equal to 1.0.

singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
Expand All @@ -221,7 +224,8 @@ class PCA(_BasePCA):
The estimated number of components. When n_components is set
to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this
number is estimated from input data. Otherwise it equals the parameter
n_components, or n_features if n_components is None.
n_components, or the lesser value of n_features and n_samples
if n_components is None.

noise_variance_ : float
The estimated noise covariance following the Probabilistic PCA model
Expand Down Expand Up @@ -365,7 +369,7 @@ def _fit(self, X):

# Handle n_components==None
if self.n_components is None:
n_components = X.shape[1]
n_components = min(X.shape)
else:
n_components = self.n_components

Expand Down Expand Up @@ -395,10 +399,11 @@ def _fit_full(self, X, n_components):
if n_samples < n_features:
raise ValueError("n_components='mle' is only supported "
"if n_samples >= n_features")
elif not 0 <= n_components <= n_features:
elif not 0 <= n_components <= min(n_samples, n_features):
raise ValueError("n_components=%r must be between 0 and "
"n_features=%r with svd_solver='full'"
% (n_components, n_features))
"min(n_samples, n_features)=%r with "
"svd_solver='full'"
% (n_components, min(n_samples, n_features)))

# Center data
self.mean_ = np.mean(X, axis=0)
Expand Down Expand Up @@ -453,14 +458,19 @@ def _fit_truncated(self, X, n_components, svd_solver):
raise ValueError("n_components=%r cannot be a string "
"with svd_solver='%s'"
% (n_components, svd_solver))
elif not 1 <= n_components <= n_features:
elif not 1 <= n_components <= min(n_samples, n_features):
raise ValueError("n_components=%r must be between 1 and "
"n_features=%r with svd_solver='%s'"
% (n_components, n_features, svd_solver))
elif svd_solver == 'arpack' and n_components == n_features:
"min(n_samples, n_features)=%r with "
"svd_solver='%s'"
% (n_components, min(n_samples, n_features),
svd_solver))
elif svd_solver == 'arpack' and n_components == min(n_samples,
n_features):
raise ValueError("n_components=%r must be stricly less than "
"n_features=%r with svd_solver='%s'"
% (n_components, n_features, svd_solver))
"min(n_samples, n_features)=%r with "
"svd_solver='%s'"
% (n_components, min(n_samples, n_features),
svd_solver))

random_state = check_random_state(self.random_state)

Expand Down Expand Up @@ -495,7 +505,7 @@ def _fit_truncated(self, X, n_components, svd_solver):
self.explained_variance_ratio_ = \
self.explained_variance_ / total_var.sum()
self.singular_values_ = S.copy() # Store the singular values.
if self.n_components_ < n_features:
if self.n_components_ < min(n_samples, n_features):
self.noise_variance_ = (total_var.sum() -
self.explained_variance_.sum())
else:
Expand Down