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TEST: Making test for the Standard Periodic Kernel similar to other kernel tests.
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Alexander Grigorievskiy
parent
0e2f235fc5
commit
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3 changed files with 14 additions and 229 deletions
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@ -156,11 +156,11 @@ class StdPeriodic(Kern):
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self.wavelengths.gradient = 0
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self.lengthscales.gradient = 0
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def gradients_X(self, dL_dK, X, X2=None):
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"""derivative of the covariance matrix with respect to X."""
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raise NotImplemented("Periodic kernel: dK_dX not implemented")
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def gradients_X_diag(self, dL_dKdiag, X):
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raise NotImplemented("Periodic kernel: dKdiag_dX not implemented")
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# def gradients_X(self, dL_dK, X, X2=None):
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# """derivative of the covariance matrix with respect to X."""
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#
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# raise NotImplemented("Periodic kernel: dK_dX not implemented")
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#
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# def gradients_X_diag(self, dL_dKdiag, X):
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#
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# raise NotImplemented("Periodic kernel: dKdiag_dX not implemented")
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@ -312,7 +312,12 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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k = GPy.kern.LinearFull(self.D, self.D-1)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_standard_periodic(self):
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k = GPy.kern.StdPeriodic(self.D, self.D-1)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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class KernelTestsMiscellaneous(unittest.TestCase):
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def setUp(self):
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N, D = 100, 10
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@ -1,220 +0,0 @@
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# -*- coding: utf-8 -*-
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"""
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Alexander Grigorevskiy, 2015
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"""
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import numpy as np
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import scipy as sp
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from scipy import linalg
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import GPy
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import matplotlib.pyplot as plt
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def normalize(D):
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"""
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Function which mormalizes the data to zero mean unit variance.
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D - column vector, or column-wise matrix
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"""
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n_rows = D.shape[0]
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means = np.nanmean(D, axis= 0)
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tmp = D - np.tile( means, (n_rows,1) ) # temporary result. Data with substracted mean
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stds = np.nanstd(tmp,axis=0, ddof=1 ) # one degree of freadom as matlab default
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result = np.divide( tmp, stds )
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return (result,means,stds)
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def generate_data(func_type_num, gd_plot=False):
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"""
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Function generates data.
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"""
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func_types = [ 'linear', 'sin' ]
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func_type = func_types[func_type_num]
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time_int = np.array((0, 20)) # time interval
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points_num = 300
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t_points = np.random.rand(points_num) * ( time_int[1] - time_int[0] ) + time_int[0]
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t_points = np.sort( t_points )
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t_regular_points = np.linspace(time_int[0], time_int[1], num=points_num )
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noise_sigma = 1
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# linear function ->
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def linear_function(tt):
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a = 2; b= 1
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ff = lambda tt: a*tt + b
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return ff(tt)
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# linear function <-
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# sin function ->
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def sin_function(tt):
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a = 2; b= 10; # a-period
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ff = lambda tt: b * np.sin( 2*np.pi/a * tt )
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return ff(tt)
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# sin function <-
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if func_type == 'linear':
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xx_func = linear_function( t_points ) + np.random.randn( len(t_points) ) * noise_sigma
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xx_reg_func = linear_function( t_regular_points )
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elif func_type == 'sin':
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xx_func = sin_function( t_points ) + np.random.randn( len(t_points) ) * noise_sigma
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xx_reg_func = sin_function( t_regular_points )
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if gd_plot:
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plt.figure(1)
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plt.plot( t_points, xx_func, 'b.' )
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plt.plot( t_regular_points, xx_reg_func, '-b' )
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plt.title('Generated Samples and Underlying Function')
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plt.show()
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#plt.close()
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return (t_points, xx_func)
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def generate_data2D(func_type_num, gd_plot=False):
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"""
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Function generates data.
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"""
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func_type = 'sin'
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time_int = np.array((0, 20)) # time interval
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points_num = 300
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t_points_x = np.random.rand(points_num) * ( time_int[1] - time_int[0] ) + time_int[0]
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t_points_y = np.random.rand(points_num) * ( time_int[1] - time_int[0] ) + time_int[0]
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t_regular_points_x = np.linspace(time_int[0], time_int[1], num=points_num )
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t_regular_points_y = np.linspace(time_int[0], time_int[1], num=points_num )
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noise_sigma = 1
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# sin function ->
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def sin_function(tt1,tt2):
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a1 = 2; a2=8; b= 10; # a - wavelengths
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ff = lambda tt1, tt2: b * np.sin( 2*np.pi/a1 * tt1 + 2*np.pi/a2 * tt2)
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return ff(tt1,tt2)
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# sin function <-
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if func_type == 'sin':
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xx_func = sin_function( t_points_x, t_points_y) + np.random.randn( len(t_points_x) ) * noise_sigma
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xx_reg_func = sin_function( t_regular_points_x, t_regular_points_y)
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# if gd_plot:
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# plt.figure(1)
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# xx,yy = np.meshgrid(t_regular_points_x, t_regular_points_x)
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#
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# plt.title('Generated Samples and Underlying Function')
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# plt.show()
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# #plt.close()
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return (t_points_x, t_points_y, xx_func)
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def test_simple():
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"""
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Simple test
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"""
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(xx, yy) = generate_data( 1, gd_plot=True )
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xx = xx[:, np.newaxis ];
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yy = yy[:, np.newaxis ];
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input_dim = 1
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xx1 = xx # np.hstack( (xx, xx**2)) # possible input data transformation
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(xx_norm, xx_mean, xx_std) = normalize( xx1) # normalize in place
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(yy_norm, yy_mean, yy_std) = normalize( yy ) # normalize in place
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#periodic_kernel = GPy.kern.Linear(input_dim)
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periodic_kernel = GPy.kern.StdPeriodic(input_dim)
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KK = periodic_kernel
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# Show sample paths ->
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Cov = KK.K( xx_norm, xx_norm); # KK.plot();GPy.kern.periodic_Matern52
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#plt.figure(1)
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plt.matshow(Cov); plt.colorbar()
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plt.title('Covariance BEFORE optimization')
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#plt.show()
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zz = np.random.multivariate_normal( np.zeros(xx.shape[0]), Cov, 5 )
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plt.figure(2)
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for i in range(5):
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plt.plot(xx_norm,zz[i,:])
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plt.title('Covariance sample paths')
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plt.show()
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# Show sample paths <-
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gpy_reg = GPy.models.GPRegression( xx_norm, yy, KK )
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print "Before Optimization ->\n"
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print gpy_reg
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gpy_reg.plot()
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gpy_reg.optimize_restarts(num_restarts = 3, robust=True, parallel=False, num_processes=4)
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print "After Optimization ->\n"
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print gpy_reg
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gpy_reg.plot()
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return gpy_reg
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def test_2D_sine():
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"""
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Test function of 2D argument.
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"""
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(xx, yy,zz) = generate_data2D( 1, gd_plot=True )
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xx = xx[:, np.newaxis ];
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yy = yy[:, np.newaxis ];
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zz = zz[:, np.newaxis ];
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input_dim = 1
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xx1 = np.hstack( (xx, yy)) # possible input data transformation
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(xx_norm, xx_mean, xx_std) = normalize( xx1) # normalize in place
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(yy_norm, yy_mean, yy_std) = normalize( yy ) # normalize in place
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#periodic_kernel = GPy.kern.Linear(input_dim)
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periodic_kernel = GPy.kern.StdPeriodic(input_dim,active_dims=[0,])
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KK = periodic_kernel
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# Show sample paths ->
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Cov = KK.K( xx_norm, xx_norm); # KK.plot();GPy.kern.periodic_Matern52
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#plt.figure(1)
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plt.matshow(Cov); plt.colorbar()
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plt.title('Covariance BEFORE optimization')
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#plt.show()
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zz = np.random.multivariate_normal( np.zeros(xx.shape[0]), Cov, 5 )
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plt.figure(2)
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for i in range(5):
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plt.plot(xx_norm,zz[i,:])
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plt.title('Covariance sample paths')
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plt.show()
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# Show sample paths <-
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gpy_reg = GPy.models.GPRegression( xx_norm, yy, KK )
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print "Before Optimization ->\n"
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print gpy_reg
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gpy_reg.plot()
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gpy_reg.optimize_restarts(num_restarts = 3, robust=True, parallel=False, num_processes=4)
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print "After Optimization ->\n"
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print gpy_reg
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gpy_reg.plot()
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return gpy_reg
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if __name__ == '__main__':
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reg = test_simple()
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#reg = test_2D_sine()
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