class_docstr: Principal components analysis (PCA) algorithm. 
  It uses singular value decomposition to project the data into
  a lower dimensional space.
  
  :param n_components: number of components or the way it should be chosen.
      If it's an integer value, it's simply the number of components.
      If it's a float value and 0 < n_components < 1, set the number of components so that variance is greater than this value.
      If n_components = None, set the number of components as min(data.width, data.height).
  :type n_components: int, float, default=None
  :param svd_solver: full or randomized svd algorithm
  :type svd_solver: str, ['full', 'randomized'], default='full'
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docstr: Returns the percentage of variance explained by each of the selected principal axes.
          
docstr_tok: ["Returns","the","percentage","of","variance","explained","by","each","of","the","selected","principal","axes","."]
code: def explained_variance_ratio(self):
      """Returns the percentage of variance explained by each of the selected principal axes."""
      return super().explained_variance_ratio()
  
file_path: neoml/NeoML/Python/neoml/PCA.py
imports: import numpy
  from .Utils import convert_data, get_data
  from scipy.sparse import csr_matrix
  import neoml.PythonWrapper
label_desc: Use image node_id 9 for calling the PCA obj's underlying member method code with example usage: obj.explained_variance_ratio() and returns: super