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48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# 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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"""
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Simple Gaussian Processes classification
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"""
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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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pb.ion()
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pb.close('all')
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default_seed=10000
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model_type='Full'
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inducing=4
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seed=default_seed
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"""Simple 1D classification example.
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:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
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:param seed : seed value for data generation (default is 4).
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:type seed: int
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:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
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:type inducing: int
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"""
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X = np.arange(0,100,5)[:,None]
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F = np.round(np.sin(X/18.) + .1*X) + np.arange(5,25)[:,None]
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E = np.random.randint(-5,5,20)[:,None]
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Y = F + E
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pb.figure()
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likelihood = GPy.inference.likelihoods.poisson(Y,scale=1.)
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m = GPy.models.GP(X,likelihood=likelihood)
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#m = GPy.models.GP(X,Y=likelihood.Y)
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m.constrain_positive('var')
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m.constrain_positive('len')
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m.tie_param('lengthscale')
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if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
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m.approximate_likelihood()
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print m.checkgrad()
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# Optimize and plot
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m.optimize()
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#m.em(plot_all=False) # EM algorithm
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m.plot(samples=4)
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print(m)
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