Python sklearn.decomposition.RandomizedPCA() Examples

The following are 4 code examples of sklearn.decomposition.RandomizedPCA(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.decomposition , or try the search function .
Example #1
Source File: preprocessing.py    From skl-groups with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def __init__(self, k=None, mle_components=False, varfrac=None,
                 randomize=False, whiten=False):
        n_specs = sum(1 for x in [k, mle_components, varfrac] if x)
        if n_specs > 1:
            msg = "can't specify number of components in more than one way"
            raise TypeError(msg)
        if n_specs == 0:
            varfrac = DEFAULT_VARFRAC

        if randomize:
            if k is None:
                raise TypeError("can't do random PCA without a specific k")
            pca = RandomizedPCA(k, whiten=whiten)
        else:
            if k is not None:
                n_components = k
            elif mle_components:
                n_components = 'mle'
            elif varfrac is not None:
                n_components = varfrac
            pca = PCA(n_components, whiten=whiten)
        super(BagPCA, self).__init__(pca)
Example #2
Source File: test_pca.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_deprecation_randomized_pca():
    rng = np.random.RandomState(0)
    X = rng.random_sample((5, 4))

    depr_message = ("Class RandomizedPCA is deprecated; RandomizedPCA was "
                    "deprecated in 0.18 and will be "
                    "removed in 0.20. Use PCA(svd_solver='randomized') "
                    "instead. The new implementation DOES NOT store "
                    "whiten ``components_``. Apply transform to get them.")

    def fit_deprecated(X):
        global Y
        rpca = RandomizedPCA(random_state=0)
        Y = rpca.fit_transform(X)

    assert_warns_message(DeprecationWarning, depr_message, fit_deprecated, X)
    Y_pca = PCA(svd_solver='randomized', random_state=0).fit_transform(X)
    assert_array_almost_equal(Y, Y_pca)
Example #3
Source File: reduce_randomizedPCA.py    From practicalDataAnalysisCookbook with GNU General Public License v2.0 5 votes vote down vote up 
def reduce_randomizedPCA(x):
    '''
        Reduce the dimensions using Randomized PCA algorithm
    '''
    # create the CCA object
    randomPCA = dc.RandomizedPCA(n_components=2, whiten=True,
        copy=False)

    # learn the principal components from all the features
    return randomPCA.fit(x)
Example #4
Source File: rotation_forest.py    From RotationForest with MIT License 5 votes vote down vote up 
def pca_algorithm(self):
        """ Deterimine PCA algorithm to use. """
        if self.rotation_algo == 'randomized':
            return RandomizedPCA(random_state=self.random_state)
        elif self.rotation_algo == 'pca':
            return PCA()
        else:
            raise ValueError("`rotation_algo` must be either "
                             "'pca' or 'randomized'.")