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minor edits to regression examples

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This commit is contained in:
James Hensman 2014-10-22 16:21:05 +01:00
parent fd4404a11a
commit 476d867f89
1 changed files with 6 additions and 6 deletions
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12
GPy/examples/regression.py
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@ -151,16 +151,16 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
kern = GPy.kern.RBF(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50)) kern = GPy.kern.RBF(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))
m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern) m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern)
m['noise_variance'] = np.random.uniform(1e-3, 1) m.likelihood.variance = np.random.uniform(1e-3, 1)
optim_point_x[0] = m['rbf_lengthscale'] optim_point_x[0] = m.rbf.lengthscale
optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']); optim_point_y[0] = np.log10(m.rbf.variance) - np.log10(m.likelihood.variance);
# optimize # optimize
if optimize: if optimize:
m.optimize('scg', xtol=1e-6, ftol=1e-6, max_iters=max_iters) m.optimize('scg', xtol=1e-6, ftol=1e-6, max_iters=max_iters)
optim_point_x[1] = m['rbf_lengthscale'] optim_point_x[1] = m.rbf.lengthscale
optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']); optim_point_y[1] = np.log10(m.rbf.variance) - np.log10(m.likelihood.variance);
if plot: if plot:
pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1] - optim_point_x[0], optim_point_y[1] - optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k') pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1] - optim_point_x[0], optim_point_y[1] - optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')
@ -191,7 +191,7 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.RBF):
noise_var = total_var / (1. + SNR) noise_var = total_var / (1. + SNR)
signal_var = total_var - noise_var signal_var = total_var - noise_var
model.kern['.*variance'] = signal_var model.kern['.*variance'] = signal_var
model['noise_variance'] = noise_var model.likelihood.variance = noise_var
length_scale_lls = [] length_scale_lls = []
for length_scale in length_scales: for length_scale in length_scales: