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Changed quantile computation via sampling and added fallback for predictive mean and variance if conditional mean and variance are not implemented yet

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Alan Saul 2015-07-22 18:33:11 +01:00
parent af20bed747
commit 926b53bfce
2 changed files with 34 additions and 26 deletions
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34
GPy/likelihoods/likelihood.py
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@ -298,13 +298,8 @@ class Likelihood(Parameterized):
return self.conditional_mean(f)*p
scaled_mean = [quad(int_mean, fmin, fmax,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance))
return mean
def _conditional_mean(self, f):
"""Quadrature calculation of the conditional mean: E(Y_star|f)"""
raise NotImplementedError("implement this function to make predictions")
def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):
"""
Approximation to the predictive variance: V(Y_star)
@ -608,23 +603,30 @@ class Likelihood(Parameterized):
:param full_cov: whether to use the full covariance or just the diagonal
:type full_cov: Boolean
"""
pred_mean = self.predictive_mean(mu, var, Y_metadata)
pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata)
try:
pred_mean = self.predictive_mean(mu, var, Y_metadata=Y_metadata)
pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata=Y_metadata)
except NotImplementedError:
print "Finding predictive mean and variance via sampling rather than quadrature"
Nf_samp = 300
Ny_samp = 1
s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu
ss_y = self.samples(s, Y_metadata, samples=Ny_samp)
pred_mean = np.mean(ss_y, axis=1)[:, None]
pred_var = np.var(ss_y, axis=1)[:, None]
return pred_mean, pred_var
def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None):
#compute the quantiles by sampling!!!
N_samp = 500
s = np.random.randn(mu.shape[0], N_samp)*np.sqrt(var) + mu
#ss_f = s.flatten()
#ss_y = self.samples(ss_f, Y_metadata)
#ss_y = self.samples(s, Y_metadata, samples=100)
ss_y = self.samples(s, Y_metadata)
#ss_y = ss_y.reshape(mu.shape[0], N_samp)
Nf_samp = 300
Ny_samp = 1
s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu
ss_y = self.samples(s, Y_metadata, samples=Ny_samp)
#ss_y = ss_y.reshape(mu.shape[0], mu.shape[1], Nf_samp*Ny_samp)
return [np.percentile(ss_y ,q, axis=1)[:,None] for q in quantiles]
pred_quantiles = [np.percentile(ss_y, q, axis=1)[:,None] for q in quantiles]
return pred_quantiles
def samples(self, gp, Y_metadata=None, samples=1):
"""