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39 lines
952 B
Python
39 lines
952 B
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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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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data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
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likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
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m = GPy.models.GP_EP2(data['X'],likelihood)
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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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m.approximate_likelihood()
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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()
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print(m)
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