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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
James Hensman
commit
e29e5624f5
7 changed files with 263 additions and 163 deletions
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@ -14,6 +14,7 @@ import priors
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import re
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import sys
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import pdb
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from GPy.core.domains import POSITIVE, REAL
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# import numdifftools as ndt
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class model(parameterised):
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@ -68,8 +69,9 @@ class model(parameterised):
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# check constraints are okay
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if isinstance(what, (priors.gamma, priors.inverse_gamma, priors.log_Gaussian)):
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constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain == 'positive']
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if what.domain is POSITIVE:
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constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain == POSITIVE]
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if len(constrained_positive_indices):
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constrained_positive_indices = np.hstack(constrained_positive_indices)
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else:
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@ -82,7 +84,7 @@ class model(parameterised):
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print '\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])
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print '\n'
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self.constrain_positive(unconst)
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elif isinstance(what, priors.Gaussian):
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elif what.domain is REAL:
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assert not np.any(which[:, None] == self.all_constrained_indices()), "constraint and prior incompatible"
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else:
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raise ValueError, "prior not recognised"
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@ -6,17 +6,20 @@ import numpy as np
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import pylab as pb
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from scipy.special import gammaln, digamma
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from ..util.linalg import pdinv
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from GPy.core.domains import REAL, POSITIVE
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import warnings
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class prior:
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def pdf(self,x):
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domain = None
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def pdf(self, x):
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return np.exp(self.lnpdf(x))
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def plot(self):
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rvs = self.rvs(1000)
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pb.hist(rvs,100,normed=True)
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xmin,xmax = pb.xlim()
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xx = np.linspace(xmin,xmax,1000)
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pb.plot(xx,self.pdf(xx),'r',linewidth=2)
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pb.hist(rvs, 100, normed=True)
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xmin, xmax = pb.xlim()
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xx = np.linspace(xmin, xmax, 1000)
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pb.plot(xx, self.pdf(xx), 'r', linewidth=2)
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class Gaussian(prior):
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@ -29,24 +32,24 @@ class Gaussian(prior):
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.. Note:: Bishop 2006 notation is used throughout the code
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"""
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def __init__(self,mu,sigma):
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domain = REAL
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def __init__(self, mu, sigma):
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self.mu = float(mu)
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self.sigma = float(sigma)
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self.sigma2 = np.square(self.sigma)
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self.constant = -0.5*np.log(2*np.pi*self.sigma2)
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self.constant = -0.5 * np.log(2 * np.pi * self.sigma2)
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def __str__(self):
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return "N("+str(np.round(self.mu))+', '+str(np.round(self.sigma2))+')'
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return "N(" + str(np.round(self.mu)) + ', ' + str(np.round(self.sigma2)) + ')'
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def lnpdf(self,x):
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return self.constant - 0.5*np.square(x-self.mu)/self.sigma2
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def lnpdf(self, x):
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return self.constant - 0.5 * np.square(x - self.mu) / self.sigma2
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def lnpdf_grad(self,x):
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return -(x-self.mu)/self.sigma2
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def lnpdf_grad(self, x):
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return -(x - self.mu) / self.sigma2
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def rvs(self,n):
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return np.random.randn(n)*self.sigma + self.mu
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def rvs(self, n):
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return np.random.randn(n) * self.sigma + self.mu
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class log_Gaussian(prior):
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@ -59,24 +62,24 @@ class log_Gaussian(prior):
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.. Note:: Bishop 2006 notation is used throughout the code
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"""
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def __init__(self,mu,sigma):
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domain = POSITIVE
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def __init__(self, mu, sigma):
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self.mu = float(mu)
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self.sigma = float(sigma)
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self.sigma2 = np.square(self.sigma)
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self.constant = -0.5*np.log(2*np.pi*self.sigma2)
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self.constant = -0.5 * np.log(2 * np.pi * self.sigma2)
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def __str__(self):
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return "lnN("+str(np.round(self.mu))+', '+str(np.round(self.sigma2))+')'
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return "lnN(" + str(np.round(self.mu)) + ', ' + str(np.round(self.sigma2)) + ')'
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def lnpdf(self,x):
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return self.constant - 0.5*np.square(np.log(x)-self.mu)/self.sigma2 -np.log(x)
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def lnpdf(self, x):
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return self.constant - 0.5 * np.square(np.log(x) - self.mu) / self.sigma2 - np.log(x)
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def lnpdf_grad(self,x):
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return -((np.log(x)-self.mu)/self.sigma2+1.)/x
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def lnpdf_grad(self, x):
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return -((np.log(x) - self.mu) / self.sigma2 + 1.) / x
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def rvs(self,n):
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return np.exp(np.random.randn(n)*self.sigma + self.mu)
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def rvs(self, n):
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return np.exp(np.random.randn(n) * self.sigma + self.mu)
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class multivariate_Gaussian:
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@ -89,47 +92,47 @@ class multivariate_Gaussian:
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.. Note:: Bishop 2006 notation is used throughout the code
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"""
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def __init__(self,mu,var):
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domain = REAL
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def __init__(self, mu, var):
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self.mu = np.array(mu).flatten()
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self.var = np.array(var)
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assert len(self.var.shape)==2
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assert self.var.shape[0]==self.var.shape[1]
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assert self.var.shape[0]==self.mu.size
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assert len(self.var.shape) == 2
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assert self.var.shape[0] == self.var.shape[1]
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assert self.var.shape[0] == self.mu.size
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self.D = self.mu.size
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self.inv, self.hld = pdinv(self.var)
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self.constant = -0.5*self.D*np.log(2*np.pi) - self.hld
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self.constant = -0.5 * self.D * np.log(2 * np.pi) - self.hld
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def summary(self):
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raise NotImplementedError
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def pdf(self,x):
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def pdf(self, x):
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return np.exp(self.lnpdf(x))
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def lnpdf(self,x):
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d = x-self.mu
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return self.constant - 0.5*np.sum(d*np.dot(d,self.inv),1)
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def lnpdf(self, x):
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d = x - self.mu
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return self.constant - 0.5 * np.sum(d * np.dot(d, self.inv), 1)
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def lnpdf_grad(self,x):
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d = x-self.mu
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return -np.dot(self.inv,d)
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def lnpdf_grad(self, x):
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d = x - self.mu
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return -np.dot(self.inv, d)
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def rvs(self,n):
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return np.random.multivariate_normal(self.mu, self.var,n)
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def rvs(self, n):
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return np.random.multivariate_normal(self.mu, self.var, n)
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def plot(self):
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if self.D==2:
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if self.D == 2:
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rvs = self.rvs(200)
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pb.plot(rvs[:,0],rvs[:,1], 'kx', mew=1.5)
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xmin,xmax = pb.xlim()
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ymin,ymax = pb.ylim()
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pb.plot(rvs[:, 0], rvs[:, 1], 'kx', mew=1.5)
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xmin, xmax = pb.xlim()
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ymin, ymax = pb.ylim()
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xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
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xflat = np.vstack((xx.flatten(),yy.flatten())).T
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zz = self.pdf(xflat).reshape(100,100)
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pb.contour(xx,yy,zz,linewidths=2)
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xflat = np.vstack((xx.flatten(), yy.flatten())).T
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zz = self.pdf(xflat).reshape(100, 100)
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pb.contour(xx, yy, zz, linewidths=2)
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def gamma_from_EV(E,V):
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def gamma_from_EV(E, V):
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"""
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Creates an instance of a gamma prior by specifying the Expected value(s)
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and Variance(s) of the distribution.
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@ -138,10 +141,10 @@ def gamma_from_EV(E,V):
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:param V: variance
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"""
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a = np.square(E)/V
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b = E/V
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return gamma(a,b)
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warnings.warn("use Gamma.from_EV to create Gamma Prior", FutureWarning)
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a = np.square(E) / V
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b = E / V
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return gamma(a, b)
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class gamma(prior):
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"""
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@ -153,33 +156,34 @@ class gamma(prior):
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.. Note:: Bishop 2006 notation is used throughout the code
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"""
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def __init__(self,a,b):
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domain = POSITIVE
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def __init__(self, a, b):
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self.a = float(a)
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self.b = float(b)
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self.constant = -gammaln(self.a) + a*np.log(b)
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self.constant = -gammaln(self.a) + a * np.log(b)
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def __str__(self):
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return "Ga("+str(np.round(self.a))+', '+str(np.round(self.b))+')'
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return "Ga(" + str(np.round(self.a)) + ', ' + str(np.round(self.b)) + ')'
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def summary(self):
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ret = {"E[x]": self.a/self.b,\
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"E[ln x]": digamma(self.a) - np.log(self.b),\
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"var[x]": self.a/self.b/self.b,\
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"Entropy": gammaln(self.a) - (self.a-1.)*digamma(self.a) - np.log(self.b) + self.a}
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if self.a >1:
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ret['Mode'] = (self.a-1.)/self.b
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ret = {"E[x]": self.a / self.b, \
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"E[ln x]": digamma(self.a) - np.log(self.b), \
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"var[x]": self.a / self.b / self.b, \
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"Entropy": gammaln(self.a) - (self.a - 1.) * digamma(self.a) - np.log(self.b) + self.a}
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if self.a > 1:
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ret['Mode'] = (self.a - 1.) / self.b
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else:
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ret['mode'] = np.nan
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return ret
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def lnpdf(self,x):
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return self.constant + (self.a-1)*np.log(x) - self.b*x
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def lnpdf(self, x):
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return self.constant + (self.a - 1) * np.log(x) - self.b * x
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def lnpdf_grad(self,x):
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return (self.a-1.)/x - self.b
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def lnpdf_grad(self, x):
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return (self.a - 1.) / x - self.b
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def rvs(self,n):
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return np.random.gamma(scale=1./self.b,shape=self.a,size=n)
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def rvs(self, n):
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return np.random.gamma(scale=1. / self.b, shape=self.a, size=n)
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class inverse_gamma(prior):
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"""
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@ -191,19 +195,20 @@ class inverse_gamma(prior):
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.. Note:: Bishop 2006 notation is used throughout the code
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"""
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def __init__(self,a,b):
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domain = POSITIVE
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def __init__(self, a, b):
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self.a = float(a)
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self.b = float(b)
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self.constant = -gammaln(self.a) + a*np.log(b)
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self.constant = -gammaln(self.a) + a * np.log(b)
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def __str__(self):
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return "iGa("+str(np.round(self.a))+', '+str(np.round(self.b))+')'
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return "iGa(" + str(np.round(self.a)) + ', ' + str(np.round(self.b)) + ')'
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def lnpdf(self,x):
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return self.constant - (self.a+1)*np.log(x) - self.b/x
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def lnpdf(self, x):
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return self.constant - (self.a + 1) * np.log(x) - self.b / x
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def lnpdf_grad(self,x):
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return -(self.a+1.)/x + self.b/x**2
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def lnpdf_grad(self, x):
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return -(self.a + 1.) / x + self.b / x ** 2
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def rvs(self,n):
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return 1./np.random.gamma(scale=1./self.b,shape=self.a,size=n)
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def rvs(self, n):
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return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n)
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@ -3,11 +3,10 @@
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import numpy as np
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from GPy.core.domains import POSITIVE, NEGATIVE, BOUNDED
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class transformation(object):
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def __init__(self):
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# set the domain. Suggest we use 'positive', 'bounded', etc
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self.domain = 'undefined'
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domain = None
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def f(self, x):
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raise NotImplementedError
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@ -24,8 +23,7 @@ class transformation(object):
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raise NotImplementedError
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class logexp(transformation):
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def __init__(self):
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self.domain = 'positive'
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domain = POSITIVE
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def f(self, x):
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return np.log(1. + np.exp(x))
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def finv(self, f):
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@ -43,8 +41,8 @@ class logexp_clipped(transformation):
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min_bound = 1e-10
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log_max_bound = np.log(max_bound)
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log_min_bound = np.log(min_bound)
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domain = POSITIVE
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def __init__(self, lower=1e-6):
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self.domain = 'positive'
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self.lower = lower
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def f(self, x):
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exp = np.exp(np.clip(x, self.log_min_bound, self.log_max_bound))
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@ -66,8 +64,7 @@ class logexp_clipped(transformation):
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return '(+ve_c)'
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class exponent(transformation):
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def __init__(self):
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self.domain = 'positive'
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domain = POSITIVE
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def f(self, x):
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return np.exp(x)
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def finv(self, x):
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@ -82,8 +79,7 @@ class exponent(transformation):
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return '(+ve)'
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class negative_exponent(transformation):
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def __init__(self):
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self.domain = 'negative'
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domain = NEGATIVE
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def f(self, x):
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return -np.exp(x)
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def finv(self, x):
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@ -98,8 +94,7 @@ class negative_exponent(transformation):
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return '(-ve)'
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class square(transformation):
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def __init__(self):
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self.domain = 'positive'
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domain = POSITIVE
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def f(self, x):
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return x ** 2
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def finv(self, x):
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@ -112,8 +107,8 @@ class square(transformation):
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return '(+sq)'
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class logistic(transformation):
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domain = BOUNDED
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def __init__(self, lower, upper):
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self.domain = 'bounded'
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assert lower < upper
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self.lower, self.upper = float(lower), float(upper)
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self.difference = self.upper - self.lower
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