Return log probability estimates for the test vectors X. Parameters ---------- X : {array-like, object with finite length or shape} Training data. Returns ------- P : ndarray of shape (n_samples, n_classes) or list of such arrays
(self, X)
| 400 | return P |
| 401 | |
| 402 | def predict_log_proba(self, X): |
| 403 | """ |
| 404 | Return log probability estimates for the test vectors X. |
| 405 | |
| 406 | Parameters |
| 407 | ---------- |
| 408 | X : {array-like, object with finite length or shape} |
| 409 | Training data. |
| 410 | |
| 411 | Returns |
| 412 | ------- |
| 413 | P : ndarray of shape (n_samples, n_classes) or list of such arrays |
| 414 | Returns the log probability of the sample for each class in |
| 415 | the model, where classes are ordered arithmetically for each |
| 416 | output. |
| 417 | """ |
| 418 | proba = self.predict_proba(X) |
| 419 | if self.n_outputs_ == 1: |
| 420 | return np.log(proba) |
| 421 | else: |
| 422 | return [np.log(p) for p in proba] |
| 423 | |
| 424 | def __sklearn_tags__(self): |
| 425 | tags = super().__sklearn_tags__() |
nothing calls this directly
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