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merging by hand...
This commit is contained in:
James Hensman
commit
850f2fb470
31 changed files with 597 additions and 96 deletions
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@ -5,3 +5,4 @@ import classification
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import regression
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import dimensionality_reduction
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import tutorials
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import stochastic
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@ -25,7 +25,6 @@ def crescent_data(seed=default_seed): # FIXME
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Y[Y.flatten()==-1] = 0
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m = GPy.models.GPClassification(data['X'], Y)
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m.ensure_default_constraints()
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#m.update_likelihood_approximation()
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#m.optimize()
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m.pseudo_EM()
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@ -75,7 +74,6 @@ def toy_linear_1d_classification(seed=default_seed):
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# Model definition
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m = GPy.models.GPClassification(data['X'], Y)
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m.ensure_default_constraints()
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# Optimize
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#m.update_likelihood_approximation()
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@ -106,7 +104,6 @@ def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
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m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
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m['.*len']= 4.
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m.ensure_default_constraints()
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# Optimize
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#m.update_likelihood_approximation()
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# Parameters optimization:
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@ -137,7 +134,6 @@ def sparse_crescent_data(num_inducing=10, seed=default_seed):
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Y[Y.flatten()==-1]=0
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m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
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m.ensure_default_constraints()
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m['.*len'] = 10.
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#m.update_likelihood_approximation()
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#m.optimize()
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@ -163,7 +159,6 @@ def FITC_crescent_data(num_inducing=10, seed=default_seed):
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m = GPy.models.FITCClassification(data['X'], Y,num_inducing=num_inducing)
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m.constrain_bounded('.*len',1.,1e3)
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m.ensure_default_constraints()
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m['.*len'] = 3.
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#m.update_likelihood_approximation()
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#m.optimize()
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@ -37,7 +37,6 @@ def BGPLVM(seed=default_seed):
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# m.optimize(messages = 1)
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# m.plot()
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# pb.title('After optimisation')
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m.ensure_default_constraints()
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m.randomize()
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m.checkgrad(verbose=1)
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@ -53,7 +52,6 @@ def GPLVM_oil_100(optimize=True):
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m.data_labels = data['Y'].argmax(axis=1)
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# optimize
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m.ensure_default_constraints()
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if optimize:
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m.optimize('scg', messages=1)
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@ -108,7 +106,6 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
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m.data_colors = c
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m.data_t = t
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m.ensure_default_constraints()
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m['rbf_lengthscale'] = 1. # X.var(0).max() / X.var(0)
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m['noise_variance'] = Y.var() / 100.
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m['bias_variance'] = 0.05
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@ -134,7 +131,6 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_f_eval=50, plot=
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m['.*lengt'] = 1. # m.X.var(0).max() / m.X.var(0)
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m['noise'] = Yn.var() / 100.
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m.ensure_default_constraints()
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# optimize
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if optimize:
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@ -159,7 +155,6 @@ def oil_100():
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m = GPy.models.GPLVM(data['X'], 2)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_iters=2)
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# plot
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@ -239,7 +234,6 @@ def bgplvm_simulation_matlab_compare():
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# X=mu,
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# X_variance=S,
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_debug=False)
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m.ensure_default_constraints()
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m.auto_scale_factor = True
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .01
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@ -263,7 +257,6 @@ def bgplvm_simulation(optimize='scg',
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m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True)
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# m.constrain('variance|noise', logexp_clipped())
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m.ensure_default_constraints()
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .01
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@ -292,7 +285,6 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
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for i, Y in enumerate(Ylist):
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m['{}_noise'.format(i + 1)] = Y.var() / 100.
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m.ensure_default_constraints()
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# DEBUG
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# np.seterr("raise")
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@ -320,7 +312,6 @@ def brendan_faces():
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# optimize
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m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=10000)
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ax = m.plot_latent(which_indices=(0, 1))
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@ -346,7 +337,6 @@ def stick():
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data = GPy.util.datasets.stick()
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# optimize
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m = GPy.models.GPLVM(data['Y'], 2)
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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m._set_params(m._get_params())
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plt.clf
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@ -388,7 +378,6 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
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m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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ax = m.plot_latent()
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@ -420,7 +409,6 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
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# m.set('iip', Z)
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# m.set('bias', 1e-4)
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# # optimize
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# # m.ensure_default_constraints()
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#
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# import pdb; pdb.set_trace()
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# m.optimize('tnc', messages=1)
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@ -18,7 +18,6 @@ def toy_rbf_1d(optimizer='tnc', max_nb_eval_optim=100):
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m = GPy.models.GPRegression(data['X'],data['Y'])
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# optimize
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m.ensure_default_constraints()
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m.optimize(optimizer, max_f_eval=max_nb_eval_optim)
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# plot
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m.plot()
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@ -36,7 +35,6 @@ def rogers_girolami_olympics(optim_iters=100):
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m['rbf_lengthscale'] = 10
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# optimize
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m.ensure_default_constraints()
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m.optimize(max_f_eval=optim_iters)
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# plot
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@ -52,7 +50,6 @@ def toy_rbf_1d_50(optim_iters=100):
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m = GPy.models.GPRegression(data['X'],data['Y'])
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# optimize
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m.ensure_default_constraints()
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m.optimize(max_f_eval=optim_iters)
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# plot
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@ -68,7 +65,6 @@ def silhouette(optim_iters=100):
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m = GPy.models.GPRegression(data['X'],data['Y'])
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=True,max_f_eval=optim_iters)
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print(m)
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@ -92,7 +88,6 @@ def coregionalisation_toy2(optim_iters=100):
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m = GPy.models.GPRegression(X,Y,kernel=k)
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m.constrain_fixed('.*rbf_var',1.)
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#m.constrain_positive('.*kappa')
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m.ensure_default_constraints()
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m.optimize('sim',messages=1,max_f_eval=optim_iters)
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pb.figure()
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@ -124,7 +119,6 @@ def coregionalisation_toy(optim_iters=100):
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m = GPy.models.GPRegression(X,Y,kernel=k)
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m.constrain_fixed('.*rbf_var',1.)
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#m.constrain_positive('kappa')
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m.ensure_default_constraints()
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m.optimize(max_f_eval=optim_iters)
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pb.figure()
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@ -162,7 +156,6 @@ def coregionalisation_sparse(optim_iters=100):
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m.constrain_fixed('.*rbf_var',1.)
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m.constrain_fixed('iip')
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m.constrain_bounded('noise_variance',1e-3,1e-1)
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m.ensure_default_constraints()
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m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
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#plotting:
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@ -189,11 +182,9 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
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log_SNRs = np.linspace(-3., 4., resolution)
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data = GPy.util.datasets.della_gatta_TRP63_gene_expression(gene_number)
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# Sub sample the data to ensure multiple optima
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#data['Y'] = data['Y'][0::2, :]
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#data['X'] = data['X'][0::2, :]
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# Remove the mean (no bias kernel to ensure signal/noise is in RBF/white)
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data['Y'] = data['Y'] - np.mean(data['Y'])
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lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
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@ -220,7 +211,6 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
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optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
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# optimize
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m.ensure_default_constraints()
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m.optimize('scg', xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
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optim_point_x[1] = m['rbf_lengthscale']
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@ -273,7 +263,6 @@ def sparse_GP_regression_1D(N = 400, num_inducing = 5, optim_iters=100):
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# create simple GP Model
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m = GPy.models.SparseGPRegression(X, Y, kernel, num_inducing=num_inducing)
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m.ensure_default_constraints()
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m.checkgrad(verbose=1)
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m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
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@ -294,7 +283,6 @@ def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
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m = GPy.models.SparseGPRegression(X,Y,kernel, num_inducing = num_inducing)
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# contrain all parameters to be positive (but not inducing inputs)
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m.ensure_default_constraints()
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m.set('.*len',2.)
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m.checkgrad()
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@ -320,7 +308,6 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
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# create simple GP Model - no input uncertainty on this one
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m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=optim_iters)
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m.plot(ax=axes[0])
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axes[0].set_title('no input uncertainty')
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@ -328,7 +315,6 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
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#the same Model with uncertainty
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m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z, X_variance=S)
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=optim_iters)
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m.plot(ax=axes[1])
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axes[1].set_title('with input uncertainty')
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40
GPy/examples/stochastic.py
Normal file
40
GPy/examples/stochastic.py
Normal file
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@ -0,0 +1,40 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import pylab as pb
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import numpy as np
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import GPy
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def toy_1d():
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N = 2000
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M = 20
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#create data
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X = np.linspace(0,32,N)[:,None]
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Z = np.linspace(0,32,M)[:,None]
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Y = np.sin(X) + np.cos(0.3*X) + np.random.randn(*X.shape)/np.sqrt(50.)
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m = GPy.models.SVIGPRegression(X,Y, batchsize=10, Z=Z)
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m.constrain_bounded('noise_variance',1e-3,1e-1)
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m.param_steplength = 1e-4
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fig = pb.figure()
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ax = fig.add_subplot(111)
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def cb():
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ax.cla()
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m.plot(ax=ax,Z_height=-3)
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ax.set_ylim(-3,3)
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fig.canvas.draw()
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m.optimize(500, callback=cb, callback_interval=1)
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m.plot_traces()
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return m
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@ -24,7 +24,6 @@ def tuto_GP_regression():
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print m
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m.plot()
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m.ensure_default_constraints()
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m.constrain_positive('')
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m.unconstrain('') # may be used to remove the previous constrains
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@ -135,7 +134,6 @@ def tuto_kernel_overview():
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pb.ylabel("+ ",rotation='horizontal',fontsize='30')
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m.plot(ax=axs, which_parts=[False,False,False,True])
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m.ensure_default_constraints()
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return(m)
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@ -144,6 +142,5 @@ def model_interaction():
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Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
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k = GPy.kern.rbf(1) + GPy.kern.bias(1)
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m = GPy.models.GPRegression(X, Y, kernel=k)
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m.ensure_default_constraints()
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return m
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