Fit the model with X. Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features)
(self, X, y=None)
| 146 | |
| 147 | @_fit_context(prefer_skip_nested_validation=True) |
| 148 | def fit(self, X, y=None): |
| 149 | """Fit the model with X. |
| 150 | |
| 151 | Initializes the internal variables. The method needs no information |
| 152 | about the distribution of data, so we only care about n_features in X. |
| 153 | |
| 154 | Parameters |
| 155 | ---------- |
| 156 | X : {array-like, sparse matrix} of shape (n_samples, n_features) |
| 157 | Training data, where `n_samples` is the number of samples |
| 158 | and `n_features` is the number of features. |
| 159 | |
| 160 | y : array-like of shape (n_samples,) or (n_samples, n_outputs), \ |
| 161 | default=None |
| 162 | Target values (None for unsupervised transformations). |
| 163 | |
| 164 | Returns |
| 165 | ------- |
| 166 | self : object |
| 167 | Returns the instance itself. |
| 168 | """ |
| 169 | X = validate_data(self, X, accept_sparse="csc") |
| 170 | random_state = check_random_state(self.random_state) |
| 171 | |
| 172 | n_features = X.shape[1] |
| 173 | if self.coef0 != 0: |
| 174 | n_features += 1 |
| 175 | |
| 176 | self.indexHash_ = random_state.randint( |
| 177 | 0, high=self.n_components, size=(self.degree, n_features) |
| 178 | ) |
| 179 | |
| 180 | self.bitHash_ = random_state.choice(a=[-1, 1], size=(self.degree, n_features)) |
| 181 | self._n_features_out = self.n_components |
| 182 | return self |
| 183 | |
| 184 | def transform(self, X): |
| 185 | """Generate the feature map approximation for X. |
nothing calls this directly
no test coverage detected