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kern params adapted: Nparams > num_params and fixes of input_dim

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Max Zwiessele 2013-06-05 16:14:30 +01:00
parent aa7fd122ca
commit 0490861099
42 changed files with 480 additions and 502 deletions
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30
GPy/examples/regression.py
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@ -10,16 +10,16 @@ import numpy as np
import GPy
def toy_rbf_1d(max_nb_eval_optim=100):
def toy_rbf_1d(optimizer='tnc', max_nb_eval_optim=100):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
data = GPy.util.datasets.toy_rbf_1d()
# create simple GP model
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(max_f_eval=max_nb_eval_optim)
m.optimize(optimizer, max_f_eval=max_nb_eval_optim)
# plot
m.plot()
print(m)
@ -29,7 +29,7 @@ def rogers_girolami_olympics(optim_iters=100):
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
data = GPy.util.datasets.rogers_girolami_olympics()
# create simple GP model
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
#set the lengthscale to be something sensible (defaults to 1)
@ -48,7 +48,7 @@ def toy_rbf_1d_50(optim_iters=100):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
data = GPy.util.datasets.toy_rbf_1d_50()
# create simple GP model
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
@ -64,7 +64,7 @@ def silhouette(optim_iters=100):
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
data = GPy.util.datasets.silhouette()
# create simple GP model
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
@ -244,18 +244,18 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.rbf):
lls = []
total_var = np.var(data['Y'])
kernel = kernel_call(1, variance=1., lengthscale=1.)
model = GPy.models.GPRegression(data['X'], data['Y'], kernel=kernel)
Model = GPy.models.GPRegression(data['X'], data['Y'], kernel=kernel)
for log_SNR in log_SNRs:
SNR = 10.**log_SNR
noise_var = total_var/(1.+SNR)
signal_var = total_var - noise_var
model.kern['.*variance'] = signal_var
model['noise_variance'] = noise_var
Model.kern['.*variance'] = signal_var
Model['noise_variance'] = noise_var
length_scale_lls = []
for length_scale in length_scales:
model['.*lengthscale'] = length_scale
length_scale_lls.append(model.log_likelihood())
Model['.*lengthscale'] = length_scale
length_scale_lls.append(Model.log_likelihood())
lls.append(length_scale_lls)
@ -270,7 +270,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, optim_iters=100):
rbf = GPy.kern.rbf(1)
noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP model
# create simple GP Model
m = GPy.models.SparseGPRegression(X, Y, kernel, M=M)
m.ensure_default_constraints()
@ -290,7 +290,7 @@ def sparse_GP_regression_2D(N = 400, M = 50, optim_iters=100):
noise = GPy.kern.white(2)
kernel = rbf + noise
# create simple GP model
# create simple GP Model
m = GPy.models.SparseGPRegression(X,Y,kernel, M = M)
# contrain all parameters to be positive (but not inducing inputs)
@ -318,7 +318,7 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
k = GPy.kern.rbf(1) + GPy.kern.white(1)
# create simple GP model - no input uncertainty on this one
# create simple GP Model - no input uncertainty on this one
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=optim_iters)
@ -326,7 +326,7 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
axes[0].set_title('no input uncertainty')
#the same model with uncertainty
#the same Model with uncertainty
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z, X_variance=S)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=optim_iters)