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So many changes

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This commit is contained in:
Ricardo Andrade 2013-02-01 13:17:17 +00:00
parent de53917039
commit 182c4c7d64
7 changed files with 118 additions and 139 deletions
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74
GPy/likelihoods/likelihood_functions.py
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@ -7,6 +7,7 @@ from scipy import stats
import scipy as sp
import pylab as pb
from ..util.plot import gpplot
#from . import EP
class likelihood:
"""
@ -19,7 +20,7 @@ class likelihood:
self.location = location
self.scale = scale
class probit(likelihood):
class Probit(likelihood):
"""
Probit likelihood
Y is expected to take values in {-1,1}
@ -28,8 +29,6 @@ class probit(likelihood):
L(x) = \\Phi (Y_i*f_i)
$$
"""
def __init__(self,location=0,scale=1):
likelihood.__init__(self,Y,location,scale)
def moments_match(self,data_i,tau_i,v_i):
"""
@ -47,24 +46,18 @@ class probit(likelihood):
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
return Z_hat, mu_hat, sigma2_hat
def predictive_values(self,mu,var,all=False):
def predictive_values(self,mu,var):
"""
Compute mean, and conficence interval (percentiles 5 and 95) of the prediction
"""
mu = mu.flatten()
var = var.flatten()
mean = stats.norm.cdf(mu/np.sqrt(1+var))
if all:
p_05 = np.zeros([mu.size])
p_95 = np.ones([mu.size])
return mean, p_05, p_95
else:
return mean
p_05 = np.zeros([mu.size])
p_95 = np.ones([mu.size])
return mean, p_05, p_95
def _log_likelihood_gradients():
return np.zeros(0) # there are no parameters of whcih to compute the gradients
class poisson(likelihood):
class Poisson(likelihood):
"""
Poisson likelihood
Y is expected to take values in {0,1,2,...}
@ -73,9 +66,6 @@ class poisson(likelihood):
L(x) = \exp(\lambda) * \lambda**Y_i / Y_i!
$$
"""
def __init__(self,Y,location=0,scale=1):
assert len(Y[Y<0]) == 0, "Output cannot have negative values"
likelihood.__init__(self,Y,location,scale)
def moments_match(self,i,tau_i,v_i):
"""
@ -134,52 +124,12 @@ class poisson(likelihood):
sigma2_hat = m2 - mu_hat**2 # Second central moment
return float(Z_hat), float(mu_hat), float(sigma2_hat)
def predictive_values(self,mu,var,all=False):
def predictive_values(self,mu,var):
"""
Compute mean, and conficence interval (percentiles 5 and 95) of the prediction
"""
mean = np.exp(mu*self.scale + self.location)
if all:
tmp = stats.poisson.ppf(np.array([.05,.95]),mu)
p_05 = tmp[:,0]
p_95 = tmp[:,1]
return mean,p_05,p_95
else:
return mean
def _log_likelihood_gradients():
raise NotImplementedError
def plot(self,X,mu,var,phi,X_obs,Z=None,samples=0):
assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X,phi,phi.flatten())
pb.plot(X_obs,self.Y,'kx',mew=1.5)
if samples:
phi_samples = np.vstack([np.random.poisson(phi.flatten(),phi.size) for s in range(samples)])
pb.plot(X,phi_samples.T, alpha = 0.4, c='#3465a4', linewidth = 0.8)
if Z is not None:
pb.plot(Z,Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
class gaussian(likelihood):
"""
Gaussian likelihood
Y is expected to take values in (-inf,inf)
"""
def moments_match(self,i,tau_i,v_i):
"""
Moments match of the marginal approximation in EP algorithm
:param i: number of observation (int)
:param tau_i: precision of the cavity distribution (float)
:param v_i: mean/variance of the cavity distribution (float)
"""
mu = v_i/tau_i
sigma = np.sqrt(1./tau_i)
s = 1. if self.Y[i] == 0 else 1./self.Y[i]
sigma2_hat = 1./(1./sigma**2 + 1./s**2)
mu_hat = sigma2_hat*(mu/sigma**2 + self.Y[i]/s**2)
Z_hat = 1./np.sqrt(2*np.pi) * 1./np.sqrt(sigma**2+s**2) * np.exp(-.5*(mu-self.Y[i])**2/(sigma**2 + s**2))
return Z_hat, mu_hat, sigma2_hat
def _log_likelihood_gradients():
raise NotImplementedError
tmp = stats.poisson.ppf(np.array([.05,.95]),mu)
p_05 = tmp[:,0]
p_95 = tmp[:,1]
return mean,p_05,p_95