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Added test generator (not quite finished yet)

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Alan Saul 2013-03-11 17:05:18 +00:00
parent 9b4cb78fdb
commit c39af496a6
3 changed files with 58 additions and 26 deletions
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4
GPy/examples/regression.py
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@ -194,7 +194,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
# Remove the mean (no bias kernel to ensure signal/noise is in RBF/white)
data['Y'] = data['Y'] - np.mean(data['Y'])
lls = GPy.examples.regression.contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
pb.contour(length_scales, log_SNRs, np.exp(lls), 20)
ax = pb.gca()
pb.xlabel('length scale')
@ -229,7 +229,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
ax.set_ylim(ylim)
return (models, lls)
def contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf):
def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf):
"""Evaluate the GP objective function for a given data set for a range of signal to noise ratios and a range of lengthscales.
:data_set: A data set from the utils.datasets director.

18
GPy/examples/tutorials.py
View file

@ -6,14 +6,14 @@
Code of Tutorials
"""
import pylab as pb
pb.ion()
import numpy as np
import GPy
def tuto_GP_regression():
"""The detailed explanations of the commands used in this file can be found in the tutorial section"""
import pylab as pb
pb.ion()
import numpy as np
import GPy
X = np.random.uniform(-3.,3.,(20,1))
Y = np.sin(X) + np.random.randn(20,1)*0.05
@ -39,11 +39,6 @@ def tuto_GP_regression():
# 2-dimensional example #
###########################
import pylab as pb
pb.ion()
import numpy as np
import GPy
# sample inputs and outputs
X = np.random.uniform(-3.,3.,(50,2))
Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(50,1)*0.05
@ -67,9 +62,6 @@ def tuto_GP_regression():
def tuto_kernel_overview():
"""The detailed explanations of the commands used in this file can be found in the tutorial section"""
import pylab as pb
import numpy as np
import GPy
pb.ion()
ker1 = GPy.kern.rbf(1) # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)