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)