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changing all parameterized objects to be compatible with the new parameterization
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e1bee4536a
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21 changed files with 645 additions and 529 deletions
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@ -255,7 +255,7 @@ def toy_rbf_1d(optimizer='tnc', max_nb_eval_optim=100):
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print(m)
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return m
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def toy_rbf_1d_50(max_iters=100):
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def toy_rbf_1d_50(max_iters=100, optimize=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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data = GPy.util.datasets.toy_rbf_1d_50()
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@ -263,14 +263,15 @@ def toy_rbf_1d_50(max_iters=100):
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m = GPy.models.GPRegression(data['X'], data['Y'])
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# optimize
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m.optimize(max_iters=max_iters)
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if optimize:
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m.optimize(max_iters=max_iters)
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# plot
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m.plot()
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print(m)
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return m
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def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4):
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def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize=True):
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# Create an artificial dataset where the values in the targets (Y)
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# only depend in dimensions 1 and 3 of the inputs (X). Run ARD to
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# see if this dependency can be recovered
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@ -300,7 +301,7 @@ def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4):
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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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m.optimize(optimizer='scg', max_iters=max_iters, messages=1)
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if optimize: m.optimize(optimizer='scg', max_iters=max_iters, messages=1)
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m.kern.plot_ARD()
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print(m)
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