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TEST: Remove test file which is incompatible with other tests in GPy.
This commit is contained in:
Alexander Grigorievskiy
parent
951cae0d72
commit
abdce992ec
1 changed files with 0 additions and 701 deletions
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# -*- coding: utf-8 -*-
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"""
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Created on Sat May 16 22:02:25 2015
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@author: alex
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"""
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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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from test_periodic_kernel import generate_sine_data
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from test_periodic_kernel import generate_linear_data
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from test_periodic_kernel import generate_brownian_data
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def test_matern32(X=None,Y=None):
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"""
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Test Matern 32 Covariance Function
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"""
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np.random.seed(234) # seed the random number generator
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if (X is None) or (Y is None):
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# Sine data ->
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
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plot = False, points_num=300, x_interval = (0, 20), random=True)
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# Sine data <-
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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matern32_kernel = GPy.kern.Matern32(1,active_dims=[0,])
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kernel1 = matern32_kernel
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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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x_pred_reg = m1.predict(X)
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x_quant_reg = m1.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_reg[0], '-r' )
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plt.plot( X, x_quant_reg[0], '--r' )
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plt.plot( X, x_quant_reg[1], '--r' )
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plt.title('Regular Matern32 Kernel Model')
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plt.show()
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sde_Matern32_kernel = GPy.kern.sde_Matern32(1,active_dims=[0,])
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kernel2 = sde_Matern32_kernel
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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) # max_iters=5
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x_pred_ss = m2.predict(X)
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x_quant_ss = m2.predict_quantiles(X)
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plt.figure(2)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_ss[0], '-r' )
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plt.plot( X, x_quant_ss[0], '--r' )
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plt.plot( X, x_quant_ss[1], '--r' )
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plt.title('State-Space Matern32 Kernel Model')
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plt.show()
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print(m1)
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print(m1.objective_function_gradients())
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print(m2)
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print(m2.objective_function_gradients())
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print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
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print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
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return X,Y
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def test_matern52(X=None,Y=None):
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"""
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Test Matern 52 Covariance Function
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"""
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np.random.seed(234) # seed the random number generator
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if (X is None) or (Y is None):
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# Sine data ->
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
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plot = False, points_num=300, x_interval = (0, 20), random=True)
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# Sine data <-
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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matern52_kernel = GPy.kern.Matern52(1,active_dims=[0,])
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kernel1 = matern52_kernel
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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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x_pred_reg = m1.predict(X)
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x_quant_reg = m1.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X,x_pred_reg[0], '-r' )
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plt.plot( X, x_quant_reg[0], '--r' )
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plt.plot( X, x_quant_reg[1], '--r' )
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plt.title('Regular Matern52 Kernel Model')
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plt.show()
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sde_Matern52_kernel = GPy.kern.sde_Matern52(1,active_dims=[0,])
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kernel2 = sde_Matern52_kernel
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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) # max_iters=5
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x_pred_ss = m2.predict(X)
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x_quant_ss = m2.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_ss[0], '-r' )
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plt.plot( X, x_quant_ss[0], '--r' )
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plt.plot( X, x_quant_ss[1], '--r' )
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plt.title('State-Space Matern52 Kernel Model')
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plt.show()
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print(m1)
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print(m1.objective_function_gradients())
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print(m2)
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print(m2.objective_function_gradients())
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print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
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print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
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return X,Y
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def test_RBF(X=None,Y=None):
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"""
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Test RBF Covariance Function
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"""
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np.random.seed(234) # seed the random number generator
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if (X is None) or (Y is None):
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# Sine data ->
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
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plot = False, points_num=300, x_interval = (0, 20), random=True)
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# Sine data <-
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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Gaussian_kernel = GPy.kern.RBF(1,active_dims=[0,])
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kernel1 = Gaussian_kernel
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kernel1.lengthscale = 1.0
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kernel1.variance = 1.0
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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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x_pred_reg = m1.predict(X)
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x_quant_reg = m1.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_reg[0], '-r' )
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plt.plot( X, x_quant_reg[0], '--r' )
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plt.plot( X, x_quant_reg[1], '--r' )
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plt.title('Regular RBF Kernel Model')
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plt.show()
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sde_RBF_kernel = GPy.kern.sde_RBF(1,active_dims=[0,])
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kernel2 = sde_RBF_kernel
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kernel2.lengthscale = 1.0
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kernel2.variance = 1.0
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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) # max_iters=5
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x_pred_ss = m2.predict(X)
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x_quant_ss = m2.predict_quantiles(X)
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plt.figure(2)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_ss[0], '-r' )
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plt.plot( X, x_quant_ss[0], '--r' )
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plt.plot( X, x_quant_ss[1], '--r' )
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plt.title('State-Space RBF Kernel Model')
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plt.show()
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print(m1)
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print(m1.objective_function_gradients())
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print(m2)
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print(m2.objective_function_gradients())
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print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
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print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
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return X,Y
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def test_kernel_multiplication(X=None,Y=None):
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"""
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Test State-Space multiplication of kernels
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"""
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#np.random.seed(234) # seed the random number generator !!! Error occured
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np.random.seed(234)
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if (X is None) or (Y is None):
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# Sine data ->
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
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plot = False, points_num=300, x_interval = (0, 20), random=True)
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# Sine data <-
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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matern32_kernel = GPy.kern.Matern32(1,active_dims=[0,])
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matern52_kernel = GPy.kern.Matern52(1,active_dims=[0,])
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kernel1 = matern32_kernel*matern52_kernel
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m1 = GPy.models.GPRegression(X,Y, kernel1)
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print(m1)
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m1.mul.Mat32.variance.constrain_fixed(1.0)
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m1.optimize(optimizer='bfgs',messages=True)
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x_pred_reg = m1.predict(X)
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x_quant_reg = m1.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_reg[0], '-r' )
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plt.plot( X, x_quant_reg[0], '--r' )
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plt.plot( X, x_quant_reg[1], '--r' )
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plt.title('Regular Matern52*Matern32 multiplication Kernel Model')
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plt.show()
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sde_Matern32_kernel = GPy.kern.sde_Matern32(1,active_dims=[0,])
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sde_Matern52_kernel = GPy.kern.sde_Matern52(1,active_dims=[0,])
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kernel2 = sde_Matern32_kernel*sde_Matern52_kernel
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#kernel2.prod.
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m2 = SS_new.StateSpace(X,Y, kernel2)
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print(m2)
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m2.mul.Mat32.variance.constrain_fixed(1.0)
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m2.optimize(optimizer='bfgs',messages=True) # max_iters=5
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x_pred_ss = m2.predict(X)
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x_quant_ss = m2.predict_quantiles(X)
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plt.figure(2)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_ss[0], '-r' )
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plt.plot( X, x_quant_ss[0], '--r' )
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plt.plot( X, x_quant_ss[1], '--r' )
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plt.title('State-Space Matern52*Matern32 multiplication Kernel Model')
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plt.show()
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print(m1)
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print(m1.objective_function_gradients())
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print(m2)
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print(m2.objective_function_gradients())
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print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
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print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
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return X,Y
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def test_periodic(X=None,Y=None):
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"""
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Test regular periodic covariance and State-Space representation.
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"""
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np.random.seed(235) # seed the random number generator
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if (X is None) or (Y is None):
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# Sine data ->
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
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plot = False, points_num=300, x_interval = (0, 20), random=True)
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# Sine data <-
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
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#quasi_periodic_kernel = GPy.kern.Matern32(1) + periodic_kernel
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kernel1 = periodic_kernel
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kernel1.lengthscales.constrain_bounded(0.25, 1000)
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kernel1.wavelengths.constrain_bounded(0.15, 100)
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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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x_pred_reg = m1.predict(X)
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x_quant_reg = m1.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_reg[0], '-r' )
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plt.plot( X, x_quant_reg[0], '--r')
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plt.plot( X, x_quant_reg[1], '--r')
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plt.title('Regular Periodic Kernel Model')
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plt.show()
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periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
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kernel2 = periodic_kernel
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kernel2.lengthscales.constrain_bounded(0.25, 1000)
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kernel2.wavelengths.constrain_bounded(0.15, 100)
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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) # max_iters=5
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x_pred_ss = m2.predict(X)
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x_quant_ss = m2.predict_quantiles(X)
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plt.figure(2)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_ss[0], '-r' )
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plt.plot( X, x_quant_ss[0], '--r' )
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plt.plot( X, x_quant_ss[1], '--r' )
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plt.title('State-Space Periodic Kernel Model')
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plt.show()
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print(m1)
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print(m1.objective_function_gradients())
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print(m2)
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print(m2.objective_function_gradients())
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print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
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print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
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return X,Y
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def test_add_and_periodic(X=None,Y=None):
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"""
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Test regular periodic covariance and State-Space representation.
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"""
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np.random.seed(234) # seed the random number generator
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if (X is None) or (Y is None):
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# Sine data ->
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
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plot = False, points_num=300, x_interval = (0, 20), random=True)
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# Sine data <-
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
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add_periodic_kernel = GPy.kern.Matern32(1) + periodic_kernel
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kernel1 = add_periodic_kernel
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kernel1.std_periodic.lengthscales.constrain_bounded(0.25, 1000)
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kernel1.std_periodic.wavelengths.constrain_bounded(0.15, 100)
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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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x_pred_reg = m1.predict(X)
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x_quant_reg = m1.predict_quantiles(X)
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plt.figure(1)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_reg[0], '-r')
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plt.plot( X, x_quant_reg[0], '--r')
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plt.plot( X, x_quant_reg[1], '--r')
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plt.title('Sum of Matern32 and Regular Periodic Kernel Model')
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plt.show()
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periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
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add_periodic_kernel = GPy.kern.sde_Matern32(1) + periodic_kernel
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kernel2 = add_periodic_kernel
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kernel2.std_periodic.lengthscales.constrain_bounded(0.25, 1000)
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kernel2.std_periodic.wavelengths.constrain_bounded(0.15, 100)
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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) # max_iters=5
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x_pred_ss = m2.predict(X)
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x_quant_ss = m2.predict_quantiles(X)
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plt.figure(2)
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plt.plot( X, Y, 'b.' )
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plt.plot( X, x_pred_ss[0], '-r')
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plt.plot( X, x_quant_ss[0], '--r')
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plt.plot( X, x_quant_ss[1], '--r')
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plt.title('State-Space of Sum of Matern32 and Regular Periodic')
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plt.show()
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print(m1)
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print(m1.objective_function_gradients())
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print(m2)
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print(m2.objective_function_gradients())
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print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
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print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
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return X,Y
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def test_quasi_periodic(X=None,Y=None):
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"""
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Test regular periodic covariance and State-Space representation.
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"""
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np.random.seed(234) # seed the random number generator
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if (X is None) or (Y is None):
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# Sine data ->
|
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(X,Y) = generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
|
||||
plot = False, points_num=300, x_interval = (0, 20), random=True)
|
||||
# Sine data <-
|
||||
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
|
||||
|
||||
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
|
||||
quasi_periodic_kernel = GPy.kern.Matern32(1) * periodic_kernel
|
||||
kernel1 = quasi_periodic_kernel
|
||||
#kernel1.Mat32.variance = 2
|
||||
kernel1.std_periodic.lengthscales.constrain_bounded(0.25, 1000)
|
||||
kernel1.std_periodic.wavelengths.constrain_bounded(0.15, 100)
|
||||
|
||||
m1 = GPy.models.GPRegression(X,Y, kernel1)
|
||||
print(m1)
|
||||
|
||||
m1.optimize(optimizer='bfgs',messages=True )
|
||||
x_pred_reg = m1.predict(X)
|
||||
x_quant_reg = m1.predict_quantiles(X)
|
||||
|
||||
plt.figure(1)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_reg[0], '-r' )
|
||||
plt.plot( X, x_quant_reg[0], '--r' )
|
||||
plt.plot( X, x_quant_reg[1], '--r' )
|
||||
plt.title('Regular quasi-periodic (with Matern32)')
|
||||
plt.show()
|
||||
|
||||
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
|
||||
quasi_periodic_kernel = GPy.kern.sde_Matern32(1) * periodic_kernel
|
||||
|
||||
kernel2 = quasi_periodic_kernel
|
||||
#kernel2.Mat32.variance = 2
|
||||
kernel2.std_periodic.lengthscales.constrain_bounded(0.25, 1000)
|
||||
kernel2.std_periodic.wavelengths.constrain_bounded(0.15, 100)
|
||||
m2 = SS_new.StateSpace(X,Y, kernel2)
|
||||
print(m2)
|
||||
|
||||
m2.optimize(optimizer='bfgs',messages=True) # max_iters=5
|
||||
x_pred_ss = m2.predict(X)
|
||||
x_quant_ss = m2.predict_quantiles(X)
|
||||
|
||||
plt.figure(1)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_ss[0], '-r' )
|
||||
plt.plot( X, x_quant_ss[0], '--r' )
|
||||
plt.plot( X, x_quant_ss[1], '--r' )
|
||||
plt.title('State-Space quasi-periodic (with Matern32)')
|
||||
plt.show()
|
||||
|
||||
print(m1)
|
||||
print(m1.objective_function_gradients())
|
||||
print(m2)
|
||||
print(m2.objective_function_gradients())
|
||||
print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
|
||||
print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
|
||||
|
||||
return X,Y
|
||||
|
||||
def test_linear(X=None,Y=None):
|
||||
"""
|
||||
Test Linear Covariance Function (same as Bayesian linear regression)
|
||||
"""
|
||||
|
||||
np.random.seed(234) # seed the random number generator
|
||||
|
||||
if (X is None) or (Y is None):
|
||||
# Linear data ->
|
||||
(X,Y) = generate_linear_data(x_points=None, tangent=2.0, add_term=20.0, noise_var=2.0,
|
||||
plot = False, points_num=300, x_interval = (0, 20), random=True)
|
||||
|
||||
# Linear data <-
|
||||
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
|
||||
|
||||
linear_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) #+\
|
||||
#GPy.kern.White(1,active_dims=[0,])
|
||||
kernel1 = linear_kernel
|
||||
|
||||
m1 = GPy.models.GPRegression(X,Y, kernel1)
|
||||
print(m1)
|
||||
#import pdb; pdb.set_trace()
|
||||
m1.optimize(optimizer='bfgs',messages=True)
|
||||
x_pred_reg = m1.predict(X)
|
||||
x_quant_reg = m1.predict_quantiles(X)
|
||||
|
||||
plt.figure(1)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_reg[0], '-r' )
|
||||
plt.plot( X, x_quant_reg[0], '--r' )
|
||||
plt.plot( X, x_quant_reg[1], '--r' )
|
||||
plt.title('Regular Linear Kernel Model')
|
||||
plt.show()
|
||||
|
||||
sde_Linear_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + GPy.kern.sde_Bias(1, active_dims=[0,]) #+\
|
||||
#GPy.kern.sde_White(1,active_dims=[0,])
|
||||
|
||||
kernel2 = sde_Linear_kernel
|
||||
|
||||
m2 = SS_new.StateSpace(X,Y, kernel2)
|
||||
print(m2)
|
||||
|
||||
m2.optimize(optimizer='bfgs',messages=True) # max_iters=5
|
||||
x_pred_ss = m2.predict(X)
|
||||
x_quant_ss = m2.predict_quantiles(X)
|
||||
|
||||
plt.figure(2)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_ss[0], '-r' )
|
||||
plt.plot( X, x_quant_ss[0], '--r' )
|
||||
plt.plot( X, x_quant_ss[1], '--r' )
|
||||
plt.title('State-Space Linear Kernel Model')
|
||||
plt.show()
|
||||
|
||||
print(m1)
|
||||
print(m1.objective_function_gradients())
|
||||
print(m2)
|
||||
print(m2.objective_function_gradients())
|
||||
print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
|
||||
print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
|
||||
|
||||
return X,Y
|
||||
|
||||
def test_Brownian(X=None,Y=None):
|
||||
"""
|
||||
Test Brownian Covariance Function.
|
||||
"""
|
||||
|
||||
np.random.seed(234) # seed the random number generator
|
||||
|
||||
if (X is None) or (Y is None):
|
||||
# Brownian data ->
|
||||
(X,Y) = generate_brownian_data(x_points=None, kernel_var=2.0, noise_var = 0.1,
|
||||
plot = False, points_num=300, x_interval = (0, 20), random=True)
|
||||
|
||||
# Brownian data <-
|
||||
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
|
||||
|
||||
brownian_kernel = GPy.kern.Brownian()
|
||||
|
||||
kernel1 = brownian_kernel
|
||||
|
||||
m1 = GPy.models.GPRegression(X,Y, kernel1)
|
||||
print(m1)
|
||||
#import pdb; pdb.set_trace()
|
||||
m1.optimize(optimizer='bfgs',messages=True)
|
||||
x_pred_reg = m1.predict(X)
|
||||
x_quant_reg = m1.predict_quantiles(X)
|
||||
|
||||
plt.figure(1)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_reg[0], '-r' )
|
||||
plt.plot( X, x_quant_reg[0], '--r' )
|
||||
plt.plot( X, x_quant_reg[1], '--r' )
|
||||
plt.title('Regular Brownian Kernel Model')
|
||||
plt.show()
|
||||
|
||||
sde_brownian_kernel = GPy.kern.sde_Brownian()
|
||||
|
||||
kernel2 = sde_brownian_kernel
|
||||
|
||||
m2 = SS_new.StateSpace(X,Y, kernel2)
|
||||
print(m2)
|
||||
|
||||
m2.optimize(optimizer='bfgs',messages=True) # max_iters=5
|
||||
x_pred_ss = m2.predict(X)
|
||||
x_quant_ss = m2.predict_quantiles(X)
|
||||
|
||||
plt.figure(2)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_ss[0], '-r' )
|
||||
plt.plot( X, x_quant_ss[0], '--r' )
|
||||
plt.plot( X, x_quant_ss[1], '--r' )
|
||||
plt.title('State-Space Brownian Kernel Model')
|
||||
plt.show()
|
||||
|
||||
print(m1)
|
||||
print(m1.objective_function_gradients())
|
||||
print(m2)
|
||||
print(m2.objective_function_gradients())
|
||||
print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
|
||||
print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
|
||||
|
||||
return X,Y
|
||||
|
||||
def test_exponential(X=None,Y=None):
|
||||
"""
|
||||
Test Exponential Covariance Function.
|
||||
"""
|
||||
|
||||
np.random.seed(234) # seed the random number generator
|
||||
|
||||
if (X is None) or (Y is None):
|
||||
# Linear data ->
|
||||
(X,Y) = generate_linear_data(x_points=None, tangent=0.0, add_term=20.0, noise_var=2.0,
|
||||
plot = False, points_num=300, x_interval = (0, 20), random=True)
|
||||
|
||||
# Linear data <-
|
||||
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
|
||||
|
||||
exp_kernel = GPy.kern.Exponential(1, active_dims=[0,])
|
||||
kernel1 = exp_kernel
|
||||
|
||||
m1 = GPy.models.GPRegression(X,Y, kernel1)
|
||||
print(m1)
|
||||
#import pdb; pdb.set_trace()
|
||||
m1.optimize(optimizer='bfgs',messages=True)
|
||||
x_pred_reg = m1.predict(X)
|
||||
x_quant_reg = m1.predict_quantiles(X)
|
||||
|
||||
plt.figure(1)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_reg[0], '-r' )
|
||||
plt.plot( X, x_quant_reg[0], '--r' )
|
||||
plt.plot( X, x_quant_reg[1], '--r' )
|
||||
plt.title('Regular Exponential Kernel Model')
|
||||
plt.show()
|
||||
|
||||
sde_exp_kernel = GPy.kern.sde_Exponential(1, active_dims=[0,])
|
||||
|
||||
kernel2 = sde_exp_kernel
|
||||
|
||||
m2 = SS_new.StateSpace(X,Y, kernel2)
|
||||
print(m2)
|
||||
|
||||
m2.optimize(optimizer='bfgs',messages=True) # max_iters=5
|
||||
x_pred_ss = m2.predict(X)
|
||||
x_quant_ss = m2.predict_quantiles(X)
|
||||
|
||||
plt.figure(2)
|
||||
plt.plot( X, Y, 'b.' )
|
||||
plt.plot( X, x_pred_ss[0], '-r' )
|
||||
plt.plot( X, x_quant_ss[0], '--r' )
|
||||
plt.plot( X, x_quant_ss[1], '--r' )
|
||||
plt.title('State-Space Exponential Kernel Model')
|
||||
plt.show()
|
||||
|
||||
print(m1)
|
||||
print(m1.objective_function_gradients())
|
||||
print(m2)
|
||||
print(m2.objective_function_gradients())
|
||||
print("Maximum absolute diff in prediction: %s" % ( np.max(np.abs(x_pred_reg[0]-x_pred_ss[0])), ) )
|
||||
print("Maximum absolute diff in predicted variance: %s" % ( np.max(np.abs(x_pred_reg[1]-x_pred_ss[1])), ) )
|
||||
|
||||
return X,Y
|
||||
|
||||
#def compare_matlab_matrices():
|
||||
# """
|
||||
#
|
||||
# """
|
||||
# loading_folder = '/home/agrigori/Programming/python/my_utils/'
|
||||
#
|
||||
# import scipy as sp
|
||||
# matlab_F = sp.io.loadmat(loading_folder + 'matlab_F.mat')['F']
|
||||
# matlab_L = sp.io.loadmat(loading_folder + 'matlab_L.mat')['L']
|
||||
# matlab_Qc = sp.io.loadmat(loading_folder + 'matlab_Qc.mat')['Qc']
|
||||
# matlab_H = sp.io.loadmat(loading_folder + 'matlab_H.mat')['H']
|
||||
# matlab_Pinf = sp.io.loadmat(loading_folder + 'matlab_Pinf.mat')['Pinf']
|
||||
#
|
||||
# matlab_dF = sp.io.loadmat(loading_folder + 'matlab_dF.mat')['dF']
|
||||
# matlab_dQc = sp.io.loadmat(loading_folder + 'matlab_dQc.mat')['dQc']
|
||||
# matlab_dPinf = sp.io.loadmat(loading_folder + 'matlab_dPinf.mat')['dPinf']
|
||||
#
|
||||
# matlab_T = sp.io.loadmat(loading_folder + 'matlab_T.mat')['T']
|
||||
#
|
||||
# sde_RBF_kernel = GPy.kern.sde_RBF(1,active_dims=[0,])
|
||||
#
|
||||
# kernel2 = sde_RBF_kernel
|
||||
# kernel2.variance = 0016.0
|
||||
# kernel2.lengthscale = 133.0
|
||||
#
|
||||
#
|
||||
# (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0,T) = sde_RBF_kernel.sde()
|
||||
#
|
||||
# print('F max diff:', np.max(np.abs((F - matlab_F))) )
|
||||
# print('L max diff:', np.max(np.abs((L - matlab_L))) )
|
||||
# print('Qc max diff:', np.max(np.abs((Qc - matlab_Qc))) )
|
||||
# print('H max diff:', np.max(np.abs((H - matlab_H))) )
|
||||
# print('Pinf max diff:', np.max(np.abs((Pinf - matlab_Pinf))) )
|
||||
#
|
||||
# print('dF max diff:', np.max(np.abs((dF - matlab_dF))) )
|
||||
# print('dQc max diff:', np.max(np.abs((dQc - matlab_dQc))) )
|
||||
# print('dPinf max diff:', np.max(np.abs((dPinf - matlab_dPinf))) )
|
||||
#
|
||||
# print('T max diff:', np.max(np.abs((T - matlab_T))) )
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#X,Y = test_periodic() #(X,Y)
|
||||
#X,Y = test_add_and_periodic() #(X,Y) #(X=None,Y=None)
|
||||
#X,Y = test_quasi_periodic()
|
||||
#X,Y = test_matern32()
|
||||
#X,Y = test_matern52()
|
||||
#X,Y = test_kernel_multiplication() # badd
|
||||
#X,Y = test_linear()
|
||||
#test_Brownian()
|
||||
#test_exponential()
|
||||
X,Y = test_RBF()
|
||||
Loading…
Add table
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Reference in a new issue