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Fixed gp_base and svigp for sampling (doesn't use it but needs the arguments)

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This commit is contained in:
Alan Saul 2013-11-29 14:20:33 +00:00
parent b26c62f6af
commit 68ece19211
2 changed files with 8 additions and 9 deletions
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12
GPy/core/gp_base.py
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@ -16,7 +16,7 @@ class GPBase(Model):
def __init__(self, X, likelihood, kernel, normalize_X=False):
if len(X.shape)==1:
X = X.reshape(-1,1)
warning.warn("One dimension output (N,) being reshaped to (N,1)")
warnings.warn("One dimension output (N,) being reshaped to (N,1)")
self.X = X
assert len(self.X.shape) == 2, "too many dimensions for X input"
self.num_data, self.input_dim = self.X.shape
@ -76,7 +76,7 @@ class GPBase(Model):
:type noise_model: integer.
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
Ysim = self.posterior_samples_f(X, size, which_parts=which_parts, full_cov=True)
Ysim = self.posterior_samples_f(X, size, which_parts=which_parts)
if isinstance(self.likelihood,Gaussian):
noise_std = np.sqrt(self.likelihood._get_params())
Ysim += np.random.normal(0,noise_std,Ysim.shape)
@ -107,7 +107,7 @@ class GPBase(Model):
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
"""
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
- In two dimsensions, a contour-plot shows the mean predicted function
@ -176,8 +176,8 @@ class GPBase(Model):
upper = m + 2*np.sqrt(v)
Y = self.likelihood.Y
else:
m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts,sampling=False) #Compute the exact mean
m_, v_, lower, upper = self.predict(Xgrid, which_parts=which_parts,sampling=True,num_samples=15000) #Apporximate the percentiles
m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts, sampling=False) #Compute the exact mean
m_, v_, lower, upper = self.predict(Xgrid, which_parts=which_parts, sampling=True, num_samples=15000) #Apporximate the percentiles
Y = self.likelihood.data
for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
@ -185,7 +185,7 @@ class GPBase(Model):
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
Ysim = self.posterior_samples(Xgrid, samples, which_parts=which_parts, full_cov=True)
Ysim = self.posterior_samples(Xgrid, samples, which_parts=which_parts)
for yi in Ysim.T:
ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.

5
GPy/core/svigp.py
View file

@ -31,7 +31,6 @@ class SVIGP(GPBase):
"""
def __init__(self, X, likelihood, kernel, Z, q_u=None, batchsize=10, X_variance=None):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=False)
self.batchsize=batchsize
@ -433,7 +432,7 @@ class SVIGP(GPBase):
else:
return mu, diag_var[:,None]
def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False, sampling=False, num_samples=15000):
# normalize X values
Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
if X_variance_new is not None:
@ -443,7 +442,7 @@ class SVIGP(GPBase):
mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
# now push through likelihood
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, sampling=sampling, num_samples=num_samples)
return mean, var, _025pm, _975pm