Python sklearn.decomposition.RandomizedPCA() Examples

The following are 4 code examples for showing how to use sklearn.decomposition.RandomizedPCA(). These examples are extracted from open source projects. 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.

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Example 1
Project: skl-groups   Author: djsutherland   File: preprocessing.py    License: 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
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pca.py    License: 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
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
Project: RotationForest   Author: joshloyal   File: rotation_forest.py    License: 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'.")