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[plotting] added samples plot

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mzwiessele 2015-10-03 21:14:32 +01:00
parent 57c4306d92
commit d7b5f45b71
7 changed files with 118 additions and 31 deletions
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21
GPy/plotting/gpy_plot/plot_util.py
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@ -31,7 +31,7 @@
import numpy as np
from scipy import sparse
def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, which_data_ycols, predict_kw):
def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, which_data_ycols, predict_kw, samples=0):
"""
Make the right decisions for prediction with a model
based on the standard arguments of plotting.
@ -46,12 +46,12 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
if plot_raw:
from ...likelihoods import Gaussian
from ...likelihoods.link_functions import Identity
lik = Gaussian(Identity(), 0) # Make the likelihood not add any noise
lik = Gaussian(Identity(), 1e-9) # Make the likelihood not add any noise
else:
lik = None
predict_kw['likelihood'] = lik
if 'Y_metadata' not in predict_kw:
predict_kw['Y_metadata'] = self.Y_metadata or {}
predict_kw['Y_metadata'] = {}
if 'output_index' not in predict_kw['Y_metadata']:
predict_kw['Y_metadata']['output_index'] = Xgrid[:,-1:].astype(np.int)
@ -61,6 +61,12 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
percentiles = self.predict_quantiles(Xgrid, quantiles=percentiles, **predict_kw)
else: percentiles = []
if samples > 0:
fsamples = self.posterior_samples(Xgrid, full_cov=True, size=samples, **predict_kw)
fsamples = fsamples[which_data_ycols] if fsamples.ndim == 3 else fsamples
else:
fsamples = None
# Filter out the ycolums which we want to plot:
retmu = mu[:, which_data_ycols]
percs = [p[:, which_data_ycols] for p in percentiles]
@ -70,8 +76,13 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
retmu[:, [i]] = self.likelihood.gp_link.transf(mu[:, [i]])
for perc in percs:
perc[:, [i]] = self.likelihood.gp_link.transf(perc[:, [i]])
return retmu, percs
if fsamples is not None and fsamples.ndim == 3:
for s in range(fsamples.shape[-1]):
fsamples[i, :, s] = self.likelihood.gp_link.transf(fsamples[i, :, s])
elif fsamples is not None:
for s in range(fsamples.shape[-1]):
fsamples[:, s] = self.likelihood.gp_link.transf(fsamples[:, s])
return retmu, percs, fsamples
def helper_for_plot_data(self, plot_limits, fixed_inputs, resolution):
"""