# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import numpy as np def hard_sigmoid(x): y = 0.2 * x + 0.5 return np.clip(y, 0, 1) def softmax(x, axis=-1): y = np.exp(x - np.max(x, axis, keepdims=True)) return y / np.sum(y, axis, keepdims=True) def sigmoid(x): if x <= 0: a = np.exp(x) a /= (1. + a) else: a = 1. / (1. + np.exp(-x)) return a def softplus(x): return np.log(1. + np.exp(x)) def softsign(x): return x / (1 + np.abs(x)) def tanh(x): return np.tanh(x) def log_logistic(x): if x <= 0: a = x - np.log(1 + np.exp(x)) else: a = - np.log(1 + np.exp(-x)) return a