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51 lines
1 KiB
Python
51 lines
1 KiB
Python
import GPy
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import numpy as np
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import matplotlib.pyplot as plt
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import GPy.models.state_space_new as SS_new
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X = np.linspace(0, 10, 4000)[:, None]
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Y = np.sin(X) + np.random.randn(*X.shape)*0.1
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# Need to run these lines when X and Y are imported ->
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#X.shape = (X.shape[0],1)
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#Y.shape = (Y.shape[0],1)
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# Need to run these lines when X and Y are imported <-
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## Generation of minimal example data ->
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#X = np.random.rand(3)
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#sort_index = np.argsort(X)
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#X = X[sort_index]; X.shape = (X.shape[0],1)
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#Y = np.sin(10*X) + np.random.randn(*X.shape)*0.1
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## Generation of minimal example data <-
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#plt.figure()
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#plt.plot( X, Y)
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#plt.show()
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#kernel = GPy.kern.Matern32(X.shape[1])
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#m = GPy.models.StateSpace(X,Y, kernel)
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#
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#print m
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##
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#m.optimize(optimizer='bfgs',messages=True)
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##
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#print m
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kernel1 = GPy.kern.Matern32(X.shape[1])
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m1 = GPy.models.GPRegression(X,Y, kernel1)
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print m1
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m1.optimize(optimizer='bfgs',messages=True)
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print m1
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kernel2 = GPy.kern.Matern32(X.shape[1])
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m2 = SS_new.StateSpace(X,Y, kernel2)
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print m2
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m2.optimize(optimizer='bfgs',messages=True)
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print m2
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