Store the feature-wise mean and standard deviation across the samples in `X` for future scaling. Parameters ---------- X : :py:class:`ndarray ` of shape `(N, C)` An array of N samples, each with dimensionality `C`
(self, X)
| 185 | return self.transform(X) |
| 186 | |
| 187 | def fit(self, X): |
| 188 | """ |
| 189 | Store the feature-wise mean and standard deviation across the samples |
| 190 | in `X` for future scaling. |
| 191 | |
| 192 | Parameters |
| 193 | ---------- |
| 194 | X : :py:class:`ndarray <numpy.ndarray>` of shape `(N, C)` |
| 195 | An array of N samples, each with dimensionality `C` |
| 196 | """ |
| 197 | if not isinstance(X, np.ndarray): |
| 198 | X = np.array(X) |
| 199 | |
| 200 | if X.shape[0] < 2: |
| 201 | raise ValueError("`X` must contain at least 2 samples") |
| 202 | |
| 203 | std = np.ones(X.shape[1]) |
| 204 | mean = np.zeros(X.shape[1]) |
| 205 | |
| 206 | if self.with_mean: |
| 207 | mean = np.mean(X, axis=0) |
| 208 | |
| 209 | if self.with_std: |
| 210 | std = np.std(X, axis=0, ddof=0) |
| 211 | |
| 212 | self._mean = mean |
| 213 | self._std = std |
| 214 | self._is_fit = True |
| 215 | |
| 216 | def transform(self, X): |
| 217 | """ |
no outgoing calls