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Principal component analysis (PCA) This node has been automatically generated by wrapping the ``sklearn.decomposition.pca.PCA`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Linear dimensionality reduction using Singular Value Decomposition of the data and keeping only the most significant singular vectors to project the data to a lower dimensional space. This implementation uses the scipy.linalg implementation of the singular value decomposition. It only works for dense arrays and is not scalable to large dimensional data. The time complexity of this implementation is ``O(n ** 3)`` assuming n ~ n_samples ~ n_features. Read more in the :ref:`User Guide <PCA>`. **Parameters** n_components : int, None or string Number of components to keep. if n_components is not set all components are kept:: n_components == min(n_samples, n_features) if n_components == 'mle', Minka's MLE is used to guess the dimension if ``0 < n_components < 1``, 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 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. whiten : bool, optional When True (False by default) the `components_` vectors are divided by n_samples times singular values to ensure uncorrelated outputs with unit componentwise 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 there data respect some hardwired assumptions. **Attributes** ``components_`` : array, [n_components, n_features] Principal axes in feature space, representing the directions of maximum variance in the data. ``explained_variance_ratio_`` : array, [n_components] 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 ``mean_`` : array, [n_features] Perfeature empirical mean, estimated from the training set. ``n_components_`` : int The estimated number of components. Relevant when n_components is set to 'mle' or a number between 0 and 1 to select using explained variance. ``noise_variance_`` : float The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See "Pattern Recognition and Machine Learning" by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/metmppca.pdf. It is required to computed the estimated data covariance and score samples. **Notes** For n_components='mle', this class uses the method of `Thomas P. Minka: Automatic Choice of Dimensionality for PCA. NIPS 2000: 598604` Implements the probabilistic PCA model from: M. Tipping and C. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611622 via the score and score_samples methods. See http://www.miketipping.com/papers/metmppca.pdf Due to implementation subtleties of the Singular Value Decomposition (SVD), which is used in this implementation, running fit twice on the same matrix can lead to principal components with signs flipped (change in direction). For this reason, it is important to always use the same estimator object to transform data in a consistent fashion. **Examples** >>> import numpy as np >>> from sklearn.decomposition import PCA >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> pca = PCA(n_components=2) >>> pca.fit(X) PCA(copy=True, n_components=2, whiten=False) >>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS [ 0.99244... 0.00755...] See also RandomizedPCA KernelPCA SparsePCA TruncatedSVD














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Principal component analysis (PCA) This node has been automatically generated by wrapping the ``sklearn.decomposition.pca.PCA`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Linear dimensionality reduction using Singular Value Decomposition of the data and keeping only the most significant singular vectors to project the data to a lower dimensional space. This implementation uses the scipy.linalg implementation of the singular value decomposition. It only works for dense arrays and is not scalable to large dimensional data. The time complexity of this implementation is ``O(n ** 3)`` assuming n ~ n_samples ~ n_features. Read more in the :ref:`User Guide <PCA>`. **Parameters** n_components : int, None or string Number of components to keep. if n_components is not set all components are kept:: n_components == min(n_samples, n_features) if n_components == 'mle', Minka's MLE is used to guess the dimension if ``0 < n_components < 1``, 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 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. whiten : bool, optional When True (False by default) the `components_` vectors are divided by n_samples times singular values to ensure uncorrelated outputs with unit componentwise 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 there data respect some hardwired assumptions. **Attributes** ``components_`` : array, [n_components, n_features] Principal axes in feature space, representing the directions of maximum variance in the data. ``explained_variance_ratio_`` : array, [n_components] 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 ``mean_`` : array, [n_features] Perfeature empirical mean, estimated from the training set. ``n_components_`` : int The estimated number of components. Relevant when n_components is set to 'mle' or a number between 0 and 1 to select using explained variance. ``noise_variance_`` : float The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See "Pattern Recognition and Machine Learning" by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/metmppca.pdf. It is required to computed the estimated data covariance and score samples. **Notes** For n_components='mle', this class uses the method of `Thomas P. Minka: Automatic Choice of Dimensionality for PCA. NIPS 2000: 598604` Implements the probabilistic PCA model from: M. Tipping and C. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611622 via the score and score_samples methods. See http://www.miketipping.com/papers/metmppca.pdf Due to implementation subtleties of the Singular Value Decomposition (SVD), which is used in this implementation, running fit twice on the same matrix can lead to principal components with signs flipped (change in direction). For this reason, it is important to always use the same estimator object to transform data in a consistent fashion. **Examples** >>> import numpy as np >>> from sklearn.decomposition import PCA >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> pca = PCA(n_components=2) >>> pca.fit(X) PCA(copy=True, n_components=2, whiten=False) >>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS [ 0.99244... 0.00755...] See also RandomizedPCA KernelPCA SparsePCA TruncatedSVD




Apply the dimensionality reduction on X. This node has been automatically generated by wrapping the sklearn.decomposition.pca.PCA 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
Returns X_new : arraylike, shape (n_samples, n_components)



Fit the model with X. This node has been automatically generated by wrapping the sklearn.decomposition.pca.PCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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