Revision

**b08f3062c96677de266af26767634fd7c6e6611d**authored by Alexander G. de G. Matthews on**09 September 2016, 10:59:46 UTC**, committed by James Hensman on**09 September 2016, 10:59:46 UTC*** Renaming tf_hacks to tf_wraps * Changing tf_hacks to tf_wraps in code. * adding a tf_hacks file that raises deprecationwarnings * release notes * bumpng version on docs * importing tf_hacks, tf_wraps

**1 parent**61b0659

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.
from __future__ import absolute_import
import tensorflow as tf
import numpy as np
from .param import Parameterized
from . import densities
class Prior(Parameterized):
def logp(self, x):
"""
The log density of the prior as x
All priors (for the moment) are univariate, so if x is a vector or an
array, this is the sum of the log densities.
"""
raise NotImplementedError
def __str__(self):
"""
A short string to describe the prior at print time
"""
raise NotImplementedError
class Gaussian(Prior):
def __init__(self, mu, var):
Prior.__init__(self)
self.mu = np.atleast_1d(np.array(mu, np.float64))
self.var = np.atleast_1d(np.array(var, np.float64))
def logp(self, x):
return tf.reduce_sum(densities.gaussian(x, self.mu, self.var))
def __str__(self):
return "N("+str(self.mu) + "," + str(self.var) + ")"
class LogNormal(Prior):
def __init__(self, mu, var):
Prior.__init__(self)
self.mu = np.atleast_1d(np.array(mu, np.float64))
self.var = np.atleast_1d(np.array(var, np.float64))
def logp(self, x):
return tf.reduce_sum(densities.lognormal(x, self.mu, self.var))
def __str__(self):
return "logN("+str(self.mu) + "," + str(self.var) + ")"
class Gamma(Prior):
def __init__(self, shape, scale):
Prior.__init__(self)
self.shape = np.atleast_1d(np.array(shape, np.float64))
self.scale = np.atleast_1d(np.array(scale, np.float64))
def logp(self, x):
return tf.reduce_sum(densities.gamma(self.shape, self.scale, x))
def __str__(self):
return "Ga("+str(self.shape) + "," + str(self.scale) + ")"
class Laplace(Prior):
def __init__(self, mu, sigma):
Prior.__init__(self)
self.mu = np.atleast_1d(np.array(mu, np.float64))
self.sigma = np.atleast_1d(np.array(sigma, np.float64))
def logp(self, x):
return tf.reduce_sum(densities.laplace(self.mu, self.sigma, x))
def __str__(self):
return "Lap.("+str(self.mu) + "," + str(self.sigma) + ")"
class Uniform(Prior):
def __init__(self, lower=0, upper=1):
self.log_height = - np.log(upper - lower)
self.lower, self.upper = lower, upper
def logp(self, x):
return self.log_height * tf.cast(tf.size(x), tf.float64)
def __str__(self):
return "U("+str(self.lower) + "," + str(self.upper) + ")"
```

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