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Added log predictive density, student t degrees of freedom gradients and plotting functionality

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Alan Saul 2015-04-27 18:56:20 +01:00
parent ac4972ff99
commit 75ebe4bf10
4 changed files with 125 additions and 13 deletions
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13
GPy/plotting/matplot_dep/models_plots.py
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@ -216,7 +216,7 @@ def plot_fit_f(model, *args, **kwargs):
kwargs['plot_raw'] = True
plot_fit(model,*args, **kwargs)
def fixed_inputs(model, non_fixed_inputs, fix_routine='median'):
def fixed_inputs(model, non_fixed_inputs, fix_routine='median', as_list=True):
"""
Convenience function for returning back fixed_inputs where the other inputs
are fixed using fix_routine
@ -226,6 +226,8 @@ def fixed_inputs(model, non_fixed_inputs, fix_routine='median'):
:type non_fixed_inputs: list
:param fix_routine: fixing routine to use, 'mean', 'median', 'zero'
:type fix_routine: string
:param as_list: if true, will return a list of tuples with (dimension, fixed_val) otherwise it will create the corresponding X matrix
:type as_list: boolean
"""
f_inputs = []
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
@ -238,6 +240,11 @@ def fixed_inputs(model, non_fixed_inputs, fix_routine='median'):
f_inputs.append( (i, np.mean(X[:,i])) )
if fix_routine == 'median':
f_inputs.append( (i, np.median(X[:,i])) )
elif fix_routine == 'zero':
else: # set to zero zero
f_inputs.append( (i, 0) )
return f_inputs
if not as_list:
X[:,i] = f_inputs[-1][1]
if as_list:
return f_inputs
else:
return X