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Mods to regression.py now that we have get to get parameters. Moved Youter to YYT.

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This commit is contained in:
Neil Lawrence 2013-01-18 15:31:20 +00:00
parent 11d088cf90
commit 99034d0fb0
6 changed files with 43 additions and 38 deletions
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31
GPy/examples/regression.py
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@ -17,10 +17,8 @@ def toy_rbf_1d():
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# contrain all parameters to be positive
m.constrain_positive('')
# optimize
m.ensure_default_constraints()
m.optimize()
# plot
@ -35,10 +33,8 @@ def rogers_girolami_olympics():
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# contrain all parameters to be positive
m.constrain_positive('')
# optimize
m.ensure_default_constraints()
m.optimize()
# plot
@ -57,10 +53,8 @@ def toy_rbf_1d_50():
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# contrain all parameters to be positive
m.constrain_positive('')
# optimize
m.ensure_default_constraints()
m.optimize()
# plot
@ -75,10 +69,8 @@ def silhouette():
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# contrain all parameters to be positive
m.constrain_positive('')
# optimize
m.ensure_default_constraints()
m.optimize()
print(m)
@ -118,20 +110,15 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.)) + GPy.kern.white(1,variance=np.random.exponential(1.))
m = GPy.models.GP_regression(data['X'],data['Y'], kernel=kern)
params = m._get_params()
optim_point_x[0] = params[1]
optim_point_y[0] = np.log10(params[0]) - np.log10(params[2]);
# contrain all parameters to be positive
m.constrain_positive('')
optim_point_x[0] = m.get('rbf_lengthscale')
optim_point_y[0] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
# optimize
m.ensure_default_constraints()
m.optimize(xtol=1e-6,ftol=1e-6)
params = m._get_params()
optim_point_x[1] = params[1]
optim_point_y[1] = np.log10(params[0]) - np.log10(params[2]);
print(m)
optim_point_x[1] = m.get('rbf_lengthscale')
optim_point_y[1] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
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')
models.append(m)