class_docstr: Incremental principal components analysis (IPCA).
  Linear dimensionality reduction using Singular Value Decomposition of
  the data, keeping only the most significant singular vectors to
  project the data to a lower dimensional space. The input data is centered
  but not scaled for each feature before applying the SVD.
  Depending on the size of the input data, this algorithm can be much more
  memory efficient than a PCA, and allows sparse input.
  This algorithm has constant memory complexity, on the order
  of ``batch_size * n_features``, enabling use of np.memmap files without
  loading the entire file into memory. For sparse matrices, the input
  is converted to dense in batches (in order to be able to subtract the
  mean) which avoids storing the entire dense matrix at any one time.
  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>`.
  .. versionadded:: 0.16
  
  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)``.
  whiten : bool, optional
      When True (False by default) the ``components_`` vectors are divided
      by ``n_samples`` times ``components_`` 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 sometimes
      improve the predictive accuracy of the downstream estimators by
      making data respect some hard-wired assumptions.
  copy : bool, (default=True)
      If False, X will be overwritten. ``copy=False`` can be used to
      save memory but is unsafe for general use.
  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.
  svd_solver : string {'auto', 'full', 'tsqr', 'randomized'}
      auto :
          the solver is selected by a default policy based on `X.shape` and
          `n_components`: if the input data is larger than 500x500 and the
          number of components to extract is lower than 80% of the smallest
          dimension of the data, then the more efficient 'randomized'
          method is enabled. Otherwise the exact full SVD is computed and
          optionally truncated afterwards.
      full :
          run exact full SVD and select the components by postprocessing
      randomized :
          run randomized SVD by using ``da.linalg.svd_compressed``.
  iterated_power: integer
  random_state: None or integer
      Parameters used for randomized svd.
  
  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.
  singular_values_ : array, shape (n_components,)
      The singular values corresponding to each of the selected components.
      The singular values are equal to the 2-norms of the ``n_components``
      variables in the lower-dimensional space.
  mean_ : array, shape (n_features,)
      Per-feature empirical mean, aggregate over calls to ``partial_fit``.
  var_ : array, shape (n_features,)
      Per-feature 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/met-mppca.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.
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code: def __init__(
      self,
      n_components=None,
      whiten=False,
      copy=True,
      batch_size=None,
      svd_solver="auto",
      iterated_power=0,
      random_state=None,
  ):
      self.n_components = n_components
      self.whiten = whiten
      self.copy = copy
      self.batch_size = batch_size
      self.svd_solver = svd_solver
      self.iterated_power = iterated_power
      self.random_state = random_state
  
file_path: dask-ml/dask_ml/decomposition/incremental_pca.py
imports: import dask
  import numpy
  from dask import array, compute
  from dask.array import linalg
  from scipy import sparse
  from sklearn.utils import gen_batches
  from sklearn.utils.validation import check_random_state
  from .._compat import DASK_2_26_0, DASK_2_28_0
  from .._utils import draw_seed
  from ..utils import check_array, svd_flip
  from .None import pca
  from .extmath import _incremental_mean_and_var
label_desc: Use image node_id 1 to create a new IncrementalPCA object from inherited base classes: pca with example: obj = IncrementalPCA(n_components, whiten, copy, batch_size, svd_solver, iterated_power, random_state)