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https://github.com/SheffieldML/GPy.git
synced 2026-05-21 14:05:14 +02:00
deleted kernpart, prod and add seem to work okay.
This commit is contained in:
James Hensman
parent
493506408c
commit
92d71384b7
16 changed files with 95 additions and 238 deletions
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@ -41,7 +41,7 @@ def coregionalization_toy2(optimize=True, plot=True):
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Y = np.vstack((Y1, Y2))
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#build the kernel
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k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
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k1 = GPy.kern.RBF(1) + GPy.kern.bias(1)
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k2 = GPy.kern.coregionalize(2,1)
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k = k1**k2
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m = GPy.models.GPRegression(X, Y, kernel=k)
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@ -68,7 +68,7 @@ def coregionalization_toy2(optimize=True, plot=True):
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# Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05
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# Y = np.vstack((Y1, Y2))
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#
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# k1 = GPy.kern.rbf(1)
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# k1 = GPy.kern.RBF(1)
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# m = GPy.models.GPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1])
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# m.constrain_fixed('.*rbf_var', 1.)
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# m.optimize(max_iters=100)
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@ -127,7 +127,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
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Z = np.hstack((np.linspace(t[:,0].min(), t[:, 0].max(), num_inducing)[:, None],
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np.random.randint(0, 4, num_inducing)[:, None]))
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k1 = GPy.kern.rbf(1)
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k1 = GPy.kern.RBF(1)
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k2 = GPy.kern.coregionalize(output_dim=5, rank=5)
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k = k1**k2
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@ -156,7 +156,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
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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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lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.RBF)
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if plot:
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pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)
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ax = pb.gca()
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@ -172,8 +172,8 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
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optim_point_y = np.empty(2)
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np.random.seed(seed=seed)
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for i in range(0, model_restarts):
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# kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))
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kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))
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# kern = GPy.kern.RBF(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))
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kern = GPy.kern.RBF(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))
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m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern)
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m['noise_variance'] = np.random.uniform(1e-3, 1)
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@ -196,7 +196,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
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ax.set_ylim(ylim)
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return m # (models, lls)
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def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.rbf):
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def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.RBF):
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"""
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Evaluate the GP objective function for a given data set for a range of
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signal to noise ratios and a range of lengthscales.
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@ -278,10 +278,10 @@ def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
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optimizer='scg'
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x_len = 30
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X = np.linspace(0, 10, x_len)[:, None]
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f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
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f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.RBF(1).K(X))
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Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
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kern = GPy.kern.rbf(1)
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kern = GPy.kern.RBF(1)
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poisson_lik = GPy.likelihoods.Poisson()
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laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
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@ -319,10 +319,10 @@ def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize
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if kernel_type == 'linear':
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kernel = GPy.kern.linear(X.shape[1], ARD=1)
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elif kernel_type == 'rbf_inv':
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kernel = GPy.kern.rbf_inv(X.shape[1], ARD=1)
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kernel = GPy.kern.RBF_inv(X.shape[1], ARD=1)
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else:
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kernel = GPy.kern.rbf(X.shape[1], ARD=1)
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kernel += GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
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kernel = GPy.kern.RBF(X.shape[1], ARD=1)
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kernel += GPy.kern.White(X.shape[1]) + GPy.kern.bias(X.shape[1])
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m = GPy.models.GPRegression(X, Y, kernel)
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# len_prior = GPy.priors.inverse_gamma(1,18) # 1, 25
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# m.set_prior('.*lengthscale',len_prior)
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@ -358,9 +358,9 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o
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if kernel_type == 'linear':
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kernel = GPy.kern.linear(X.shape[1], ARD=1)
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elif kernel_type == 'rbf_inv':
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kernel = GPy.kern.rbf_inv(X.shape[1], ARD=1)
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kernel = GPy.kern.RBF_inv(X.shape[1], ARD=1)
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else:
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kernel = GPy.kern.rbf(X.shape[1], ARD=1)
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kernel = GPy.kern.RBF(X.shape[1], ARD=1)
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#kernel += GPy.kern.bias(X.shape[1])
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X_variance = np.ones(X.shape) * 0.5
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m = GPy.models.SparseGPRegression(X, Y, kernel, X_variance=X_variance)
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@ -421,7 +421,7 @@ def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, opti
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X = np.random.uniform(-3., 3., (num_samples, 1))
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Y = np.sin(X) + np.random.randn(num_samples, 1) * 0.05
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# construct kernel
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rbf = GPy.kern.rbf(1)
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rbf = GPy.kern.RBF(1)
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# create simple GP Model
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m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)
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m.checkgrad(verbose=1)
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@ -444,7 +444,7 @@ def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, opt
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Y[inan] = np.nan
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# construct kernel
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rbf = GPy.kern.rbf(2)
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rbf = GPy.kern.RBF(2)
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# create simple GP Model
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m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)
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@ -476,9 +476,9 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
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# likelihood = GPy.likelihoods.Gaussian(Y)
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Z = np.random.uniform(-3., 3., (7, 1))
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k = GPy.kern.rbf(1)
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k = GPy.kern.RBF(1)
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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=GPy.kern.rbf(1), Z=Z)
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m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.RBF(1), Z=Z)
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if optimize:
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m.optimize('scg', messages=1, max_iters=max_iters)
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@ -489,7 +489,7 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
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print m
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# the same Model with uncertainty
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m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.rbf(1), Z=Z, X_variance=S)
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m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.RBF(1), Z=Z, X_variance=S)
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if optimize:
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m.optimize('scg', messages=1, max_iters=max_iters)
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if plot:
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