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Incremental principal components analysis (IPCA). This node has been automatically generated by wrapping the ``sklearn.decomposition.incremental_pca.IncrementalPCA`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Linear dimensionality reduction using Singular Value Decomposition of centered data, keeping only the most significant singular vectors to project the data to a lower dimensional space. Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA. This algorithm has constant memory complexity, on the order of ``batch_size``, enabling use of np.memmap files without loading the entire file into memory. The computational overhead of each SVD is ``O(batch_size * n_features ** 2)``, but only 2 * batch_size samples remain in memory at a time. There will be ``n_samples / batch_size`` SVD computations to get the principal components, versus 1 large SVD of complexity ``O(n_samples * n_features ** 2)`` for PCA. Read more in the :ref:`User Guide <IncrementalPCA>`. **Parameters** n_components : int or None, (default=None) Number of components to keep. If ``n_components `` is ``None``, then ``n_components`` is set to ``min(n_samples, n_features)``. batch_size : int or None, (default=None) The number of samples to use for each batch. Only used when calling ``fit``. If ``batch_size`` is ``None``, then ``batch_size`` is inferred from the data and set to ``5 * n_features``, to provide a balance between approximation accuracy and memory consumption. copy : bool, (default=True) If False, X will be overwritten. ``copy=False`` can be used to save memory but is unsafe for general use. whiten : bool, optional When True (False by default) the ``components_`` vectors are divided by ``n_samples`` times ``components_`` 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 sometimes improve the predictive accuracy of the downstream estimators by making data respect some hardwired assumptions. **Attributes** ``components_`` : array, shape (n_components, n_features) Components with maximum variance. ``explained_variance_`` : array, shape (n_components,) Variance explained by each of the selected components. ``explained_variance_ratio_`` : array, shape (n_components,) Percentage of variance explained by each of the selected components. If all components are stored, the sum of explained variances is equal to 1.0 ``mean_`` : array, shape (n_features,) Perfeature empirical mean, aggregate over calls to ``partial_fit``. ``var_`` : array, shape (n_features,) Perfeature empirical variance, aggregate over calls to ``partial_fit``. ``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. ``n_components_`` : int The estimated number of components. Relevant when ``n_components=None``. ``n_samples_seen_`` : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across ``partial_fit`` calls. **Notes** Implements the incremental PCA model from: `D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, Volume 77, Issue 13, pp. 125141, May 2008.` See http://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf This model is an extension of the Sequential KarhunenLoeve Transform from: `A. Levy and M. Lindenbaum, Sequential KarhunenLoeve Basis Extraction and its Application to Images, IEEE Transactions on Image Processing, Volume 9, Number 8, pp. 13711374, August 2000.` See http://www.cs.technion.ac.il/~mic/doc/sklip.pdf We have specifically abstained from an optimization used by authors of both papers, a QR decomposition used in specific situations to reduce the algorithmic complexity of the SVD. The source for this technique is `Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5, section 5.4.4, pp 252253.`. This technique has been omitted because it is advantageous only when decomposing a matrix with ``n_samples`` (rows) >= 5/3 * ``n_features`` (columns), and hurts the readability of the implemented algorithm. This would be a good opportunity for future optimization, if it is deemed necessary. **References** D. Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, Volume 77, Issue 13, pp. 125141, May 2008. G. Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5, Section 5.4.4, pp. 252253. See also PCA RandomizedPCA KernelPCA SparsePCA TruncatedSVD














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supported_dtypes Supported dtypes 

Incremental principal components analysis (IPCA). This node has been automatically generated by wrapping the ``sklearn.decomposition.incremental_pca.IncrementalPCA`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Linear dimensionality reduction using Singular Value Decomposition of centered data, keeping only the most significant singular vectors to project the data to a lower dimensional space. Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA. This algorithm has constant memory complexity, on the order of ``batch_size``, enabling use of np.memmap files without loading the entire file into memory. The computational overhead of each SVD is ``O(batch_size * n_features ** 2)``, but only 2 * batch_size samples remain in memory at a time. There will be ``n_samples / batch_size`` SVD computations to get the principal components, versus 1 large SVD of complexity ``O(n_samples * n_features ** 2)`` for PCA. Read more in the :ref:`User Guide <IncrementalPCA>`. **Parameters** n_components : int or None, (default=None) Number of components to keep. If ``n_components `` is ``None``, then ``n_components`` is set to ``min(n_samples, n_features)``. batch_size : int or None, (default=None) The number of samples to use for each batch. Only used when calling ``fit``. If ``batch_size`` is ``None``, then ``batch_size`` is inferred from the data and set to ``5 * n_features``, to provide a balance between approximation accuracy and memory consumption. copy : bool, (default=True) If False, X will be overwritten. ``copy=False`` can be used to save memory but is unsafe for general use. whiten : bool, optional When True (False by default) the ``components_`` vectors are divided by ``n_samples`` times ``components_`` 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 sometimes improve the predictive accuracy of the downstream estimators by making data respect some hardwired assumptions. **Attributes** ``components_`` : array, shape (n_components, n_features) Components with maximum variance. ``explained_variance_`` : array, shape (n_components,) Variance explained by each of the selected components. ``explained_variance_ratio_`` : array, shape (n_components,) Percentage of variance explained by each of the selected components. If all components are stored, the sum of explained variances is equal to 1.0 ``mean_`` : array, shape (n_features,) Perfeature empirical mean, aggregate over calls to ``partial_fit``. ``var_`` : array, shape (n_features,) Perfeature empirical variance, aggregate over calls to ``partial_fit``. ``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. ``n_components_`` : int The estimated number of components. Relevant when ``n_components=None``. ``n_samples_seen_`` : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across ``partial_fit`` calls. **Notes** Implements the incremental PCA model from: `D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, Volume 77, Issue 13, pp. 125141, May 2008.` See http://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf This model is an extension of the Sequential KarhunenLoeve Transform from: `A. Levy and M. Lindenbaum, Sequential KarhunenLoeve Basis Extraction and its Application to Images, IEEE Transactions on Image Processing, Volume 9, Number 8, pp. 13711374, August 2000.` See http://www.cs.technion.ac.il/~mic/doc/sklip.pdf We have specifically abstained from an optimization used by authors of both papers, a QR decomposition used in specific situations to reduce the algorithmic complexity of the SVD. The source for this technique is `Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5, section 5.4.4, pp 252253.`. This technique has been omitted because it is advantageous only when decomposing a matrix with ``n_samples`` (rows) >= 5/3 * ``n_features`` (columns), and hurts the readability of the implemented algorithm. This would be a good opportunity for future optimization, if it is deemed necessary. **References** D. Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, Volume 77, Issue 13, pp. 125141, May 2008. G. Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5, Section 5.4.4, pp. 252253. See also PCA RandomizedPCA KernelPCA SparsePCA TruncatedSVD




Apply dimensionality reduction to X. This node has been automatically generated by wrapping the sklearn.decomposition.incremental_pca.IncrementalPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. X is projected on the first principal components previously extracted from a training set. Parameters
Returns X_new : arraylike, shape (n_samples, n_components) Examples >>> import numpy as np >>> from sklearn.decomposition import IncrementalPCA >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> ipca = IncrementalPCA(n_components=2, batch_size=3) >>> ipca.fit(X) IncrementalPCA(batch_size=3, copy=True, n_components=2, whiten=False) >>> ipca.transform(X) # doctest: +SKIP



Fit the model with X, using minibatches of size batch_size. This node has been automatically generated by wrapping the sklearn.decomposition.incremental_pca.IncrementalPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
y: Passthrough for Pipeline compatibility. Returns

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