Compute percentiles for dense matrices. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data used to scale along the features axis.
(self, X, random_state)
| 2807 | self.copy = copy |
| 2808 | |
| 2809 | def _dense_fit(self, X, random_state): |
| 2810 | """Compute percentiles for dense matrices. |
| 2811 | |
| 2812 | Parameters |
| 2813 | ---------- |
| 2814 | X : ndarray of shape (n_samples, n_features) |
| 2815 | The data used to scale along the features axis. |
| 2816 | """ |
| 2817 | if self.ignore_implicit_zeros: |
| 2818 | warnings.warn( |
| 2819 | "'ignore_implicit_zeros' takes effect only with" |
| 2820 | " sparse matrix. This parameter has no effect." |
| 2821 | ) |
| 2822 | |
| 2823 | n_samples, n_features = X.shape |
| 2824 | references = self.references_ * 100 |
| 2825 | |
| 2826 | if self.subsample is not None and self.subsample < n_samples: |
| 2827 | # Take a subsample of `X` |
| 2828 | X = resample( |
| 2829 | X, replace=False, n_samples=self.subsample, random_state=random_state |
| 2830 | ) |
| 2831 | |
| 2832 | self.quantiles_ = np.nanpercentile(X, references, axis=0) |
| 2833 | |
| 2834 | def _sparse_fit(self, X, random_state): |
| 2835 | """Compute percentiles for sparse matrices. |