Package mdp :: Package nodes :: Class RandomizedPCAScikitsLearnNode
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Class RandomizedPCAScikitsLearnNode



Principal component analysis (PCA) using randomized SVD

This node has been automatically generated by wrapping the ``sklearn.decomposition.pca.RandomizedPCA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Linear dimensionality reduction using approximated Singular Value
Decomposition of the data and keeping only the most significant
singular vectors to project the data to a lower dimensional space.

Read more in the :ref:`User Guide <RandomizedPCA>`.

**Parameters**

n_components : int, optional
    Maximum number of components to keep. When not given or None, this
    is set to n_features (the second dimension of the training data).

copy : bool
    If False, data passed to fit are overwritten and running
    fit(X).transform(X) will not yield the expected results,
    use fit_transform(X) instead.

iterated_power : int, optional
    Number of iterations for the power method. 3 by default.

whiten : bool, optional
    When True (False by default) the `components_` vectors are divided
    by the singular values to ensure uncorrelated outputs with unit
    component-wise variances.

    Whitening will remove some information from the transformed signal
    (the relative variance scales of the components) but can sometime
    improve the predictive accuracy of the downstream estimators by
    making their data respect some hard-wired assumptions.

random_state : int or RandomState instance or None (default)
    Pseudo Random Number generator seed control. If None, use the
    numpy.random singleton.

**Attributes**

``components_`` : array, [n_components, n_features]
    Components with maximum variance.

``explained_variance_ratio_`` : array, [n_components]
    Percentage of variance explained by each of the selected components.         k is not set then all components are stored and the sum of explained         variances is equal to 1.0

``mean_`` : array, [n_features]
    Per-feature empirical mean, estimated from the training set.

**Examples**

>>> import numpy as np
>>> from sklearn.decomposition import RandomizedPCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = RandomizedPCA(n_components=2)
>>> pca.fit(X)                 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
RandomizedPCA(copy=True, iterated_power=3, n_components=2,
       random_state=None, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244...  0.00755...]

See also

PCA
TruncatedSVD

**References**


.. [Halko2009] `Finding structure with randomness: Stochastic algorithms
  for constructing approximate matrix decompositions Halko, et al., 2009
  (arXiv:909)`

.. [MRT] `A randomized algorithm for the decomposition of matrices
  Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert`

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Principal component analysis (PCA) using randomized SVD
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
 
_stop_training(self, **kwargs)
Concatenate the collected data in a single array.
 
execute(self, x)
Apply dimensionality reduction on X.
 
stop_training(self, **kwargs)
Fit the model with X by extracting the first principal components.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

    Inherited from Cumulator
 
_train(self, *args)
Collect all input data in a list.
 
train(self, *args)
Collect all input data in a list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.
 
__repr__(self)
repr(x)
 
__str__(self)
str(x)
 
_check_input(self, x)
 
_check_output(self, y)
 
_check_train_args(self, x, *args, **kwargs)
 
_get_train_seq(self)
 
_if_training_stop_training(self)
 
_inverse(self, x)
 
_pre_execution_checks(self, x)
This method contains all pre-execution checks.
 
_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.
 
_refcast(self, x)
Helper function to cast arrays to the internal dtype.
 
_set_dtype(self, t)
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
copy(self, protocol=None)
Return a deep copy of the node.
 
get_current_train_phase(self)
Return the index of the current training phase.
 
get_dtype(self)
Return dtype.
 
get_input_dim(self)
Return input dimensions.
 
get_output_dim(self)
Return output dimensions.
 
get_remaining_train_phase(self)
Return the number of training phases still to accomplish.
 
get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.
 
has_multiple_training_phases(self)
Return True if the node has multiple training phases.
 
inverse(self, y, *args, **kwargs)
Invert y.
 
is_training(self)
Return True if the node is in the training phase, False otherwise.
 
save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.
 
set_dtype(self, t)
Set internal structures' dtype.
 
set_input_dim(self, n)
Set input dimensions.
 
set_output_dim(self, n)
Set output dimensions.
Static Methods [hide private]
 
is_invertible()
Return True if the node can be inverted, False otherwise.
 
is_trainable()
Return True if the node can be trained, False otherwise.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples:
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

 

Principal component analysis (PCA) using randomized SVD

This node has been automatically generated by wrapping the ``sklearn.decomposition.pca.RandomizedPCA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Linear dimensionality reduction using approximated Singular Value
Decomposition of the data and keeping only the most significant
singular vectors to project the data to a lower dimensional space.

Read more in the :ref:`User Guide <RandomizedPCA>`.

**Parameters**

n_components : int, optional
    Maximum number of components to keep. When not given or None, this
    is set to n_features (the second dimension of the training data).

copy : bool
    If False, data passed to fit are overwritten and running
    fit(X).transform(X) will not yield the expected results,
    use fit_transform(X) instead.

iterated_power : int, optional
    Number of iterations for the power method. 3 by default.

whiten : bool, optional
    When True (False by default) the `components_` vectors are divided
    by the singular values to ensure uncorrelated outputs with unit
    component-wise variances.

    Whitening will remove some information from the transformed signal
    (the relative variance scales of the components) but can sometime
    improve the predictive accuracy of the downstream estimators by
    making their data respect some hard-wired assumptions.

random_state : int or RandomState instance or None (default)
    Pseudo Random Number generator seed control. If None, use the
    numpy.random singleton.

**Attributes**

``components_`` : array, [n_components, n_features]
    Components with maximum variance.

``explained_variance_ratio_`` : array, [n_components]
    Percentage of variance explained by each of the selected components.         k is not set then all components are stored and the sum of explained         variances is equal to 1.0

``mean_`` : array, [n_features]
    Per-feature empirical mean, estimated from the training set.

**Examples**

>>> import numpy as np
>>> from sklearn.decomposition import RandomizedPCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = RandomizedPCA(n_components=2)
>>> pca.fit(X)                 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
RandomizedPCA(copy=True, iterated_power=3, n_components=2,
       random_state=None, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244...  0.00755...]

See also

PCA
TruncatedSVD

**References**


.. [Halko2009] `Finding structure with randomness: Stochastic algorithms
  for constructing approximate matrix decompositions Halko, et al., 2009
  (arXiv:909)`

.. [MRT] `A randomized algorithm for the decomposition of matrices
  Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert`

Overrides: object.__init__

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

 
Concatenate the collected data in a single array.
Overrides: Node._stop_training

execute(self, x)

 

Apply dimensionality reduction on X.

This node has been automatically generated by wrapping the sklearn.decomposition.pca.RandomizedPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

X is projected on the first principal components previous extracted from a training set.

Parameters

X : array-like, shape (n_samples, n_features)
New data, where n_samples in the number of samples and n_features is the number of features.

Returns

X_new : array-like, shape (n_samples, n_components)

Overrides: Node.execute

is_invertible()
Static Method

 
Return True if the node can be inverted, False otherwise.
Overrides: Node.is_invertible
(inherited documentation)

is_trainable()
Static Method

 
Return True if the node can be trained, False otherwise.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

Fit the model with X by extracting the first principal components.

This node has been automatically generated by wrapping the sklearn.decomposition.pca.RandomizedPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X: array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples and n_features is the number of features.

Returns

self : object
Returns the instance itself.
Overrides: Node.stop_training