<gh_stars>1-10
# Copyright 2019 Mycroft AI Inc.
#
# 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.
from math import exp, log, sqrt, pi
import numpy as np
from typing import *

def sigmoid(x):
    """Sigmoid squashing function for scalars"""
    return 1 / (1 + exp(-x))

def asigmoid(x):
    """Inverse sigmoid (logit) for scalars"""
    return -log(1 / x - 1)

def pdf(x, mu, std):
    """Probability density function (normal distribution)"""
    if std == 0:
        return 0
    return (1.0 / (std * sqrt(2 * pi))) * np.exp(-(x - mu) ** 2 / (2 * std ** 2))
# Copyright 2019 Mycroft AI Inc.
#
# 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.
from math import exp, log, sqrt, pi
import numpy as np
from typing import *

def sigmoid(x):
    """Sigmoid squashing function for scalars"""
    return 1 / (1 + exp(-x))

def asigmoid(x):
    """Inverse sigmoid (logit) for scalars"""
    return -log(1 / x - 1)

def pdf(x, mu, std):
    """Probability density function (normal distribution)"""
    if std == 0:
        return 0
    return (1.0 / (std * sqrt(2 * pi))) * np.exp(-(x - mu) ** 2 / (2 * std ** 2))
# Copyright 2019 Mycroft AI Inc. # # 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. Sigmoid squashing function for scalars Inverse sigmoid (logit) for scalars Probability density function (normal distribution)