https://github.com/GPflow/GPflow
Revision 18bf145d49c38ebd531bd21bd8886dce7cd045a0 authored by James Hensman on 20 April 2016, 15:28:52 UTC, committed by James Hensman on 20 April 2016, 15:28:52 UTC
Tip revision: 18bf145d49c38ebd531bd21bd8886dce7cd045a0 authored by James Hensman on 20 April 2016, 15:28:52 UTC
Merge branch 'master' into q_sqrT_fixing_err
Merge branch 'master' into q_sqrT_fixing_err
Tip revision: 18bf145
priors.py
import densities
import tensorflow as tf
import numpy as np
from param import Parameterized
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, self.var = np.atleast_1d(np.array(mu, np.float64)), 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 Gamma(Prior):
def __init__(self, shape, scale):
Prior.__init__(self)
self.shape, self.scale = np.atleast_1d(np.array(shape, np.float64)), 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) + ")"
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