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REFACTORING: model names, lowercase, classes uppercase

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This commit is contained in:
Max Zwiessele 2013-06-05 13:02:03 +01:00
parent 2a39440619
commit 2e5e8ac026
50 changed files with 436 additions and 3307 deletions
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32
GPy/examples/regression.py
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@ -15,7 +15,7 @@ def toy_rbf_1d(max_nb_eval_optim=100):
data = GPy.util.datasets.toy_rbf_1d()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
@ -30,7 +30,7 @@ def rogers_girolami_olympics(max_nb_eval_optim=100):
data = GPy.util.datasets.rogers_girolami_olympics()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
m = GPy.models.GPRegression(data['X'],data['Y'])
#set the lengthscale to be something sensible (defaults to 1)
m['rbf_lengthscale'] = 10
@ -49,7 +49,7 @@ def toy_rbf_1d_50(max_nb_eval_optim=100):
data = GPy.util.datasets.toy_rbf_1d_50()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
@ -65,7 +65,7 @@ def silhouette(max_nb_eval_optim=100):
data = GPy.util.datasets.silhouette()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
@ -87,9 +87,9 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
k2 = GPy.kern.coregionalise(2,1)
k2 = GPy.kern.Coregionalise(2,1)
k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k)
m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('.*kappa')
m.ensure_default_constraints()
@ -119,9 +119,9 @@ def coregionalisation_toy(max_nb_eval_optim=100):
Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2)
k2 = GPy.kern.Coregionalise(2,2)
k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k)
m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
m.ensure_default_constraints()
@ -155,10 +155,10 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
Z = np.hstack((np.random.rand(M,1)*8,np.random.randint(0,2,M)[:,None]))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2)
k2 = GPy.kern.Coregionalise(2,2)
k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001)
m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z)
m = GPy.models.SparseGPRegression(X,Y,kernel=k,Z=Z)
m.scale_factor = 10000.
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
@ -213,7 +213,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
for i in range(0, model_restarts):
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)
m = GPy.models.GPRegression(data['X'],data['Y'], kernel=kern)
optim_point_x[0] = m.get('rbf_lengthscale')
optim_point_y[0] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
@ -260,7 +260,7 @@ def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf
kernel = signal_kernel_call(1, variance=signal_var, lengthscale=length_scale) + GPy.kern.white(1, variance=noise_var)
model = GPy.models.GP_regression(data['X'], data['Y'], kernel=kernel)
model = GPy.models.GPRegression(data['X'], data['Y'], kernel=kernel)
model.constrain_positive('')
length_scale_lls.append(model.log_likelihood())
lls.append(length_scale_lls)
@ -276,7 +276,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP model
m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
m = GPy.models.SparseGPRegression(X, Y, kernel, M=M)
m.ensure_default_constraints()
@ -296,7 +296,7 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
kernel = rbf + noise
# create simple GP model
m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M)
m = GPy.models.SparseGPRegression(X,Y,kernel, M = M)
# contrain all parameters to be positive (but not inducing inputs)
m.ensure_default_constraints()
@ -325,7 +325,7 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
k = GPy.kern.rbf(1) + GPy.kern.white(1)
# create simple GP model - no input uncertainty on this one
m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z)
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
m.plot(ax=axes[0])
@ -333,7 +333,7 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
#the same model with uncertainty
m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S)
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=max_nb_eval_optim)
m.plot(ax=axes[1])