parameters: (self, X, random_state)
docstring: Compute percentiles for dense matrices.
  
          Parameters
          ----------
          X : ndarray, shape (n_samples, n_features)
              The data used to scale along the features axis.
docstring_summary: Compute percentiles for dense matrices.
docstring_tokens: Compute percentiles for dense matrices .
function: def _dense_fit(self, X, random_state):
          """Compute percentiles for dense matrices.
  
          Parameters
          ----------
          X : ndarray, shape (n_samples, n_features)
              The data used to scale along the features axis.
          """
          if self.ignore_implicit_zeros:
              warnings.warn("'ignore_implicit_zeros' takes effect only with"
                            " sparse matrix. This parameter has no effect.")
  
          n_samples, n_features = X.shape
          references = self.references_ * 100
  
          self.quantiles_ = []
          for col in X.T:
              if self.subsample < n_samples:
                  subsample_idx = random_state.choice(n_samples,
                                                      size=self.subsample,
                                                      replace=False)
                  col = col.take(subsample_idx, mode='clip')
              self.quantiles_.append(np.nanpercentile(col, references))
          self.quantiles_ = np.transpose(self.quantiles_)
          # Due to floating-point precision error in `np.nanpercentile`,
          # make sure that quantiles are monotonically increasing.
          # Upstream issue in numpy:
          # https://github.com/numpy/numpy/issues/14685
          self.quantiles_ = np.maximum.accumulate(self.quantiles_)
function_tokens: def _dense_fit ( self , X , random_state ) : if self . ignore_implicit_zeros : warnings . warn ( "'ignore_implicit_zeros' takes effect only with" " sparse matrix. This parameter has no effect." ) n_samples , n_features = X . shape references = self . references_ * 100 self . quantiles_ = [ ] for col in X . T : if self . subsample < n_samples : subsample_idx = random_state . choice ( n_samples , size = self . subsample , replace = False ) col = col . take ( subsample_idx , mode = 'clip' ) self . quantiles_ . append ( np . nanpercentile ( col , references ) ) self . quantiles_ = np . transpose ( self . quantiles_ ) # Due to floating-point precision error in `np.nanpercentile`, # make sure that quantiles are monotonically increasing. # Upstream issue in numpy: # https://github.com/numpy/numpy/issues/14685 self . quantiles_ = np . maximum . accumulate ( self . quantiles_ )