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76 lines
1.8 KiB
Python
76 lines
1.8 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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import numpy as np
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"""
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Sparse Gaussian Processes regression with an RBF kernel
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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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np.random.seed(2)
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pb.ion()
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N = 500
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M = 5
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######################################
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## 1 dimensional example
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# sample inputs and outputs
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X = np.random.uniform(-3.,3.,(N,1))
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#Y = np.sin(X)+np.random.randn(N,1)*0.05
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F = np.sin(X)+np.random.randn(N,1)*0.05
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Y = np.ones([F.shape[0],1])
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Y[F<0] = -1
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likelihood = GPy.inference.likelihoods.probit(Y)
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# construct kernel
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rbf = GPy.kern.rbf(1)
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noise = GPy.kern.white(1)
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kernel = rbf + noise
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# create simple GP model
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#m1 = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
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m1 = GPy.models.sparse_GP(X, kernel, M=M,likelihood= likelihood)
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# contrain all parameters to be positive
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m1.constrain_positive('(variance|lengthscale|precision)')
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#m1.constrain_positive('(variance|lengthscale)')
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#m1.constrain_fixed('prec',10.)
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#check gradient FIXME unit test please
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m1.checkgrad()
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# optimize and plot
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m1.optimize('tnc', messages = 1)
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m1.plot()
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# print(m1)
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######################################
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## 2 dimensional example
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# # sample inputs and outputs
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# X = np.random.uniform(-3.,3.,(N,2))
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# Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(N,1)*0.05
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# # construct kernel
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# rbf = GPy.kern.rbf(2)
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# noise = GPy.kern.white(2)
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# kernel = rbf + noise
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# # create simple GP model
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# m2 = GPy.models.sparse_GP_regression(X,Y,kernel, M = 50)
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# create simple GP model
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# # contrain all parameters to be positive (but not inducing inputs)
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# m2.constrain_positive('(variance|lengthscale|precision)')
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# #check gradient FIXME unit test please
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# m2.checkgrad()
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# # optimize and plot
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# pb.figure()
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# m2.optimize('tnc', messages = 1)
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# m2.plot()
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# print(m2)
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