Revision f3154bfca90196e06d42c6b27d44d35e27e58563 authored by Artem Artemev on 03 December 2018, 00:36:31 UTC, committed by GitHub on 03 December 2018, 00:36:31 UTC
priors.py
# Copyright 2016 Valentine Svensson, James Hensman, alexggmatthews
#
# 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 tensorflow as tf
import numpy as np
from . import logdensities
from . import settings
from .params import Parameterized
from .core.base import IPrior
class Prior(Parameterized, IPrior): # pylint: disable=W0223
pass
class Exponential(Prior):
"""
Exponential distribution.
Support: [0, inf)
"""
def __init__(self, rate):
"""
:param float rate: Rate parameter (inverse scale) (rate > 0)
"""
Prior.__init__(self)
self.rate = np.atleast_1d(np.array(rate, settings.float_type))
if any(self.rate <= 0): # pragma: no cover
raise ValueError("The rate parameter has to be positive.")
def logp(self, x):
scale = 1 / self.rate
return tf.reduce_sum(logdensities.exponential(x, scale))
def sample(self, shape=(1,)):
return np.random.exponential(scale=1 / self.rate, size=shape)
def __str__(self):
return "Exp({})".format(self.rate.squeeze())
class Gaussian(Prior):
def __init__(self, mu, var):
Prior.__init__(self)
self.mu = np.atleast_1d(np.array(mu, settings.float_type))
self.var = np.atleast_1d(np.array(var, settings.float_type))
if any(self.var <= 0): # pragma: no cover
raise ValueError("The var parameter has to be positive.")
def logp(self, x):
return tf.reduce_sum(logdensities.gaussian(x, self.mu, self.var))
def sample(self, shape=(1,)):
return self.mu + np.sqrt(self.var) * np.random.randn(*shape)
def __str__(self):
return "N({},{})".format(self.mu.squeeze(), self.var.squeeze())
class LogNormal(Prior):
def __init__(self, mu, var):
Prior.__init__(self)
self.mu = np.atleast_1d(np.array(mu, settings.float_type))
self.var = np.atleast_1d(np.array(var, settings.float_type))
if any(self.var <= 0): # pragma: no cover
raise ValueError("The var parameter has to be positive.")
def logp(self, x):
return tf.reduce_sum(logdensities.lognormal(x, self.mu, self.var))
def sample(self, shape=(1,)):
return np.exp(self.mu + np.sqrt(self.var) * np.random.randn(*shape))
def __str__(self):
return "logN({},{})".format(self.mu.squeeze(), self.var.squeeze())
class Gamma(Prior):
def __init__(self, shape, scale):
Prior.__init__(self)
self.shape = np.atleast_1d(np.array(shape, settings.float_type))
self.scale = np.atleast_1d(np.array(scale, settings.float_type))
if any(self.scale <= 0): # pragma: no cover
raise ValueError("The scale parameter has to be positive.")
def logp(self, x):
return tf.reduce_sum(logdensities.gamma(x, self.shape, self.scale))
def sample(self, shape=(1,)):
return np.random.gamma(self.shape, self.scale, size=shape)
def __str__(self):
return "Ga({},{})".format(self.shape.squeeze(), self.scale.squeeze())
class Laplace(Prior):
def __init__(self, mu, sigma):
Prior.__init__(self)
self.mu = np.atleast_1d(np.array(mu, settings.float_type))
self.sigma = np.atleast_1d(np.array(sigma, settings.float_type))
if any(self.sigma <= 0): # pragma: no cover
raise ValueError("The sigma parameter has to be positive.")
def logp(self, x):
return tf.reduce_sum(logdensities.laplace(x, self.mu, self.sigma))
def sample(self, shape=(1,)):
return np.random.laplace(self.mu, self.sigma, size=shape)
def __str__(self):
return "Lap.({},{})".format(self.mu.squeeze(), self.sigma.squeeze())
class Beta(Prior):
def __init__(self, a, b):
Prior.__init__(self)
self.a = np.atleast_1d(np.array(a, settings.float_type))
self.b = np.atleast_1d(np.array(b, settings.float_type))
if any(self.a <= 0) or any(self.b <= 0): # pragma: no cover
raise ValueError("The parameters have to be positive.")
def logp(self, x):
return tf.reduce_sum(logdensities.beta(x, self.a, self.b))
def sample(self, shape=(1,)):
return np.random.beta(self.a, self.b, size=shape)
def __str__(self):
return "Beta({},{})".format(self.a.squeeze(), self.b.squeeze())
class Uniform(Prior):
def __init__(self, lower=0., upper=1.):
Prior.__init__(self)
self.lower, self.upper = lower, upper
if lower >= upper: # pragma: no cover
raise ValueError("The lower bound has to be smaller than the upper bound.")
@property
def log_height(self):
return - np.log(self.upper - self.lower)
def logp(self, x):
return self.log_height * tf.cast(tf.size(x), settings.float_type)
def sample(self, shape=(1,)):
return (self.lower +
(self.upper - self.lower)*np.random.rand(*shape))
def __str__(self):
return "U({},{})".format(self.lower, self.upper)
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