def predict(self, X):
y_scores = logistic_sigmoid(self._decision_scores(X))
y_scores[y_scores >= 0.5] = 1
y_scores[y_scores < 0.5] = -1
return y_scores
def predict(self, X):
y_scores = logistic_sigmoid(self._decision_scores(X))
y_scores[y_scores >= 0.5] = 1
y_scores[y_scores < 0.5] = -1
return y_scores
def predict(self, X):
"""Predict using the multi-layer perceptron model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples)
Predicted target values per element in X.
"""
X = atleast2d_or_csr(X)
scores = self.decision_function(X)
if len(scores.shape) == 1 or self.multi_label is True:
scores = logistic_sigmoid(scores)
results = (scores > 0.5).astype(np.int)
if self.multi_label:
return self._lbin.inverse_transform(results)
else:
scores = _softmax(scores)
results = scores.argmax(axis=1)
return self.classes_[results]
def predict(self, X):
y_scores = logistic_sigmoid(self._decision_scores(X))
y_scores[y_scores >= 0.5] = self.pos_class
y_scores[y_scores < 0.5] = self.neg_class
return y_scores
def logistic(X):
"""Compute the logistic function inplace.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
X_new : {array-like, sparse matrix}, shape (n_samples, n_features)
The transformed data.
"""
return logistic_sigmoid(X, out=X)
def predict_proba(self, X):
"""Probability estimates.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples, n_outputs)
Returns the probability of the sample for each class in the model,
where classes are ordered as they are in `self.classes_`.
"""
scores = self.decision_function(X)
if len(scores.shape) == 1:
scores = logistic_sigmoid(scores)
return np.vstack([1 - scores, scores]).T
else:
return _softmax(scores)
