def _predict(self, X=None): # Apply _predict_proba for each row predictions = np.apply_along_axis(self._predict_row, 1, X) # Normalize probabilities so that each row will sum up to 1.0 return softmax(predictions)
def _predict(self, X=None): # Apply _predict_proba for each row predictions = np.apply_along_axis(self._predict_row, 1, X) # Normalize probabilities so that each row will sum up to 1.0 return softmax(predictions)
def _predict(self, X=None): # Apply _predict_proba for each row predictions = np.apply_along_axis(self._predict_proba, 1, X) # Normalize probabilities return softmax(predictions)