apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-06 02:24:17 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'devel' into gpy_one_fixes

Browse source
This commit is contained in:
Max Zwiessele 2016-04-01 11:45:10 +01:00
commit f4f3d8546e
32 changed files with 36241 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

353
GPy/testing/gpy_kernels_state_space_tests.py Normal file
View file

@ -0,0 +1,353 @@
# -*- coding: utf-8 -*-
# Copyright (c) 2015, Alex Grigorevskiy
# Licensed under the BSD 3-clause license (see LICENSE.txt)
"""
Testing state space related functions.
"""
import unittest
import numpy as np
import GPy
import GPy.models.state_space_model as SS_model
from .state_space_main_tests import generate_x_points, generate_sine_data, \
generate_linear_data, generate_brownian_data, generate_linear_plus_sin
#from state_space_main_tests import generate_x_points, generate_sine_data, \
# generate_linear_data, generate_brownian_data, generate_linear_plus_sin
class StateSpaceKernelsTests(np.testing.TestCase):
def setUp(self):
pass
def run_for_model(self, X, Y, ss_kernel, kalman_filter_type = 'regular',
use_cython=False, check_gradients=True,
optimize=True, optimize_max_iters=1000,predict_X=None,
compare_with_GP=True, gp_kernel=None,
mean_compare_decimal=10, var_compare_decimal=7):
m1 = SS_model.StateSpace(X,Y, ss_kernel,
kalman_filter_type=kalman_filter_type,
use_cython=use_cython)
if check_gradients:
self.assertTrue(m1.checkgrad())
if optimize:
m1.optimize(optimizer='lbfgsb',max_iters=optimize_max_iters)
if compare_with_GP and (predict_X is None):
predict_X = X
if (predict_X is not None):
x_pred_reg_1 = m1.predict(predict_X)
x_quant_reg_1 = m1.predict_quantiles(predict_X)
if compare_with_GP:
m2 = GPy.models.GPRegression(X,Y, gp_kernel)
m2.optimize(optimizer='lbfgsb', max_iters=optimize_max_iters)
#print(m2)
x_pred_reg_2 = m2.predict(predict_X)
x_quant_reg_2 = m2.predict_quantiles(predict_X)
# Test values
#print np.max(np.abs(x_pred_reg_1[0]-x_pred_reg_2[0]))
np.testing.assert_almost_equal(np.max(np.abs(x_pred_reg_1[0]- \
x_pred_reg_2[0])), 0, decimal=mean_compare_decimal)
# Test variances
#print np.max(np.abs(x_pred_reg_1[1]-x_pred_reg_2[1]))
np.testing.assert_almost_equal(np.max(np.abs(x_pred_reg_1[1]- \
x_pred_reg_2[1])), 0, decimal=var_compare_decimal)
def test_Matern32_kernel(self,):
np.random.seed(234) # seed the random number generator
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_Matern32(1,active_dims=[0,])
gp_kernel = GPy.kern.Matern32(1,active_dims=[0,])
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X,
compare_with_GP=True,
gp_kernel=gp_kernel,
mean_compare_decimal=10, var_compare_decimal=7)
def test_Matern52_kernel(self,):
np.random.seed(234) # seed the random number generator
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_Matern52(1,active_dims=[0,])
gp_kernel = GPy.kern.Matern52(1,active_dims=[0,])
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
optimize = True, predict_X=X,
compare_with_GP=True, gp_kernel=gp_kernel,
mean_compare_decimal=8, var_compare_decimal=7)
def test_RBF_kernel(self,):
np.random.seed(234) # seed the random number generator
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_RBF(1,active_dims=[0,])
gp_kernel = GPy.kern.RBF(1,active_dims=[0,])
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=1, var_compare_decimal=1)
def test_periodic_kernel(self,):
np.random.seed(322) # seed the random number generator
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel.lengthscale.constrain_bounded(0.27, 1000)
ss_kernel.period.constrain_bounded(0.17, 100)
gp_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel.lengthscale.constrain_bounded(0.27, 1000)
gp_kernel.period.constrain_bounded(0.17, 100)
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=3, var_compare_decimal=3)
def test_quasi_periodic_kernel(self,):
np.random.seed(329) # seed the random number generator
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_Matern32(1)*GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
gp_kernel = GPy.kern.Matern32(1)*GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=1, var_compare_decimal=2)
def test_linear_kernel(self,):
np.random.seed(234) # seed the random number generator
(X,Y) = generate_linear_data(x_points=None, tangent=2.0, add_term=20.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + GPy.kern.sde_Bias(1, active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,])
self.run_for_model(X, Y, ss_kernel, check_gradients= False,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=5, var_compare_decimal=5)
def test_brownian_kernel(self,):
np.random.seed(234) # seed the random number generator
(X,Y) = generate_brownian_data(x_points=None, kernel_var=2.0, noise_var = 0.1,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_Brownian()
gp_kernel = GPy.kern.Brownian()
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=10, var_compare_decimal=7)
def test_exponential_kernel(self,):
np.random.seed(234) # seed the random number generator
(X,Y) = generate_linear_data(x_points=None, tangent=1.0, add_term=20.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_Exponential(1, active_dims=[0,])
gp_kernel = GPy.kern.Exponential(1, active_dims=[0,])
self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=5, var_compare_decimal=6)
def test_kernel_addition(self,):
#np.random.seed(329) # seed the random number generator
np.random.seed(333)
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
(X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
# Sine data <-
Y = Y + Y1
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
def get_new_kernels():
ss_kernel = GPy.kern.sde_Linear(1,X) + GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(3, 8)
gp_kernel = GPy.kern.Linear(1) + GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(3, 8)
return ss_kernel, gp_kernel
# Cython is available only with svd.
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd',
use_cython=True, optimize_max_iters=10, check_gradients=False,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=5, var_compare_decimal=5)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'regular',
use_cython=False, optimize_max_iters=10, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=5, var_compare_decimal=5)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd',
use_cython=False, optimize_max_iters=10, check_gradients=False,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=5, var_compare_decimal=5)
def test_kernel_multiplication(self,):
np.random.seed(329) # seed the random number generator
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
def get_new_kernels():
ss_kernel = GPy.kern.sde_Matern32(1)*GPy.kern.sde_Matern52(1)
gp_kernel = GPy.kern.Matern32(1)*GPy.kern.sde_Matern52(1)
return ss_kernel, gp_kernel
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd',
use_cython=True, optimize_max_iters=10, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=-1, var_compare_decimal=-1)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'regular',
use_cython=False, optimize_max_iters=10, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=-1, var_compare_decimal=-1)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd',
use_cython=False, optimize_max_iters=10, check_gradients=True,
predict_X=X,
gp_kernel=gp_kernel,
mean_compare_decimal=-1, var_compare_decimal=0)
def test_forecast(self,):
"""
Test time-series forecasting.
"""
# Generate data ->
np.random.seed(339) # seed the random number generator
#import pdb; pdb.set_trace()
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
(X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
Y = Y + Y1
X_train = X[X <= 20]
Y_train = Y[X <= 20]
X_test = X[X > 20]
Y_test = Y[X > 20]
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
# Generate data <-
#import pdb; pdb.set_trace()
def get_new_kernels():
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
return ss_kernel, gp_kernel
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular',
use_cython=False, optimize_max_iters=30, check_gradients=True,
predict_X=X_test,
gp_kernel=gp_kernel,
mean_compare_decimal=0, var_compare_decimal=-1)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
use_cython=False, optimize_max_iters=30, check_gradients=False,
predict_X=X_test,
gp_kernel=gp_kernel,
mean_compare_decimal=-1, var_compare_decimal=-1)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
use_cython=True, optimize_max_iters=30, check_gradients=False,
predict_X=X_test,
gp_kernel=gp_kernel,
mean_compare_decimal=-1, var_compare_decimal=-1)
if __name__ == "__main__":
print("Running state-space inference tests...")
unittest.main()
#tt = StateSpaceKernelsTests('test_periodic_kernel')
#import pdb; pdb.set_trace()
#tt.test_Matern32_kernel()
#tt.test_Matern52_kernel()
#tt.test_RBF_kernel()
#tt.test_periodic_kernel()
#tt.test_quasi_periodic_kernel()
#tt.test_linear_kernel()
#tt.test_brownian_kernel()
#tt.test_exponential_kernel()
#tt.test_kernel_addition()
#tt.test_kernel_multiplication()
#tt.test_forecast()

22
GPy/testing/model_tests.py
View file

@ -148,6 +148,28 @@ class MiscTests(unittest.TestCase):
assert(gc.checkgrad())
assert(gc2.checkgrad())
def test_predict_uncertain_inputs(self):
""" Projection of Gaussian through a linear function is still gaussian, and moments are analytical to compute, so we can check this case for predictions easily """
X = np.linspace(-5,5, 10)[:, None]
Y = 2*X + np.random.randn(*X.shape)*1e-3
m = GPy.models.BayesianGPLVM(Y, 1, X=X, kernel=GPy.kern.Linear(1), num_inducing=1)
m.Gaussian_noise[:] = 1e-4
m.X.mean[:] = X[:]
m.X.variance[:] = 1e-5
m.X.fix()
m.optimize()
X_pred_mu = np.random.randn(5, 1)
X_pred_var = np.random.rand(5, 1) + 1e-5
from GPy.core.parameterization.variational import NormalPosterior
X_pred = NormalPosterior(X_pred_mu, X_pred_var)
# mu = \int f(x)q(x|mu,S) dx = \int 2x.q(x|mu,S) dx = 2.mu
# S = \int (f(x) - m)^2q(x|mu,S) dx = \int f(x)^2 q(x) dx - mu**2 = 4(mu^2 + S) - (2.mu)^2 = 4S
Y_mu_true = 2*X_pred_mu
Y_var_true = 4*X_pred_var
Y_mu_pred, Y_var_pred = m._raw_predict(X_pred)
np.testing.assert_allclose(Y_mu_true, Y_mu_pred, rtol=1e-4)
np.testing.assert_allclose(Y_var_true, Y_var_pred, rtol=1e-4)
def test_sparse_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k)

977
GPy/testing/state_space_main_tests.py Normal file
View file

@ -0,0 +1,977 @@
# -*- coding: utf-8 -*-
# Copyright (c) 2015, Alex Grigorevskiy
# Licensed under the BSD 3-clause license (see LICENSE.txt)
"""
Test module for state_space_main.py
"""
import unittest
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import GPy.models.state_space_setup as ss_setup
import GPy.models.state_space_main as ssm
def generate_x_points(points_num=100, x_interval = (0, 20), random=True):
"""
Function generates (sorted) points on the x axis.
Input:
---------------------------
points_num: int
How many points to generate
x_interval: tuple (a,b)
On which interval to generate points
random: bool
Regular points or random
Output:
---------------------------
x_points: np.array
Generated points
"""
x_interval = np.asarray( x_interval )
if random:
x_points = np.random.rand(points_num) * ( x_interval[1] - x_interval[0] ) + x_interval[0]
x_points = np.sort( x_points )
else:
x_points = np.linspace(x_interval[0], x_interval[1], num=points_num )
return x_points
def generate_sine_data(x_points=None, sin_period=2.0, sin_ampl=10.0, noise_var=2.0,
plot = False, points_num=100, x_interval = (0, 20), random=True):
"""
Function generates sinusoidal data.
Input:
--------------------------------
x_points: np.array
Previously generated X points
sin_period: float
Sine period
sin_ampl: float
Sine amplitude
noise_var: float
Gaussian noise variance added to the sine function
plot: bool
Whether to plot generated data
(if x_points is None, the the following parameters are used to generate
those. They are the same as in 'generate_x_points' function)
points_num: int
x_interval: tuple (a,b)
random: bool
"""
sin_function = lambda xx: sin_ampl * np.sin( 2*np.pi/sin_period * xx )
if x_points is None:
x_points = generate_x_points(points_num, x_interval, random)
y_points = sin_function( x_points ) + np.random.randn( len(x_points) ) * np.sqrt(noise_var)
if plot:
pass
return x_points, y_points
def generate_linear_data(x_points=None, tangent=2.0, add_term=1.0, noise_var=2.0,
plot = False, points_num=100, x_interval = (0, 20), random=True):
"""
Function generates linear data.
Input:
--------------------------------
x_points: np.array
Previously generated X points
tangent: float
Factor with which independent variable is multiplied in linear equation.
add_term: float
Additive term in linear equation.
noise_var: float
Gaussian noise variance added to the sine function
plot: bool
Whether to plot generated data
(if x_points is None, the the following parameters are used to generate
those. They are the same as in 'generate_x_points' function)
points_num: int
x_interval: tuple (a,b)
random: bool
"""
linear_function = lambda xx: tangent*xx + add_term
if x_points is None:
x_points = generate_x_points(points_num, x_interval, random)
y_points = linear_function( x_points ) + np.random.randn( len(x_points) ) * np.sqrt(noise_var)
if plot:
pass
return x_points, y_points
def generate_brownian_data(x_points=None, kernel_var = 2.0, noise_var = 2.0,
plot = False, points_num=100, x_interval = (0, 20), random=True):
"""
Generate brownian data - data from Brownian motion.
First point is always 0, and \Beta(0) = 0 - standard conditions for Brownian motion.
Input:
--------------------------------
x_points: np.array
Previously generated X points
variance: float
Gaussian noise variance added to the sine function
plot: bool
Whether to plot generated data
(if x_points is None, the the following parameters are used to generate
those. They are the same as in 'generate_x_points' function)
points_num: int
x_interval: tuple (a,b)
random: bool
"""
if x_points is None:
x_points = generate_x_points(points_num, x_interval, random)
if x_points[0] != 0:
x_points[0] = 0
y_points = np.zeros( (points_num,) )
for i in range(1, points_num):
noise = np.random.randn() * np.sqrt(kernel_var * (x_points[i] - x_points[i-1]))
y_points[i] = y_points[i-1] + noise
y_points += np.random.randn( len(x_points) ) * np.sqrt(noise_var)
return x_points, y_points
def generate_linear_plus_sin(x_points=None, tangent=2.0, add_term=1.0, noise_var=2.0,
sin_period=2.0, sin_ampl=10.0, plot = False,
points_num=100, x_interval = (0, 20), random=True):
"""
Generate the sum of linear trend and the sine function.
For parameters see the 'generate_linear' and 'generate_sine'.
Comment: Gaussian noise variance is added only once (for linear function).
"""
x_points, y_linear_points = generate_linear_data(x_points, tangent, add_term, noise_var,
False, points_num, x_interval, random)
x_points, y_sine_points = generate_sine_data(x_points, sin_period, sin_ampl, 0.0,
False, points_num, x_interval, random)
y_points = y_linear_points + y_sine_points
if plot:
pass
return x_points, y_points
def generate_random_y_data(samples, dim, ts_no):
"""
Generate data:
Input:
------------------
samples - how many samples
dim - dimensionality of the data
ts_no - number of time series
Output:
--------------------------
Y: np.array((samples, dim, ts_no))
"""
Y = np.empty((samples, dim, ts_no));
for i in range(0,samples):
for j in range(0,ts_no):
sample = np.random.randn(dim)
Y[i,:,j] = sample
if (Y.shape[2] == 1): # ts_no = 1
Y.shape=(Y.shape[0], Y.shape[1])
return Y
class StateSpaceKernelsTests(np.testing.TestCase):
def setUp(self):
pass
def run_descr_model(self, measurements, A,Q,H,R, true_states=None,
mean_compare_decimal=8,
m_init=None, P_init=None, dA=None,dQ=None,
dH=None,dR=None, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True):
#import pdb; pdb.set_trace()
state_dim = 1 if not isinstance(A,np.ndarray) else A.shape[0]
ts_no = 1 if (len(measurements.shape) < 3) else measurements.shape[2]
grad_params_no = None if dA is None else dA.shape[2]
ss_setup.use_cython = use_cython
global ssm
if (ssm.cython_code_available) and (ssm.use_cython != use_cython):
reload(ssm)
grad_calc_params = None
if calc_grad_log_likelihood:
grad_calc_params = {}
grad_calc_params['dA'] = dA
grad_calc_params['dQ'] = dQ
grad_calc_params['dH'] = dH
grad_calc_params['dR'] = dR
(f_mean, f_var, loglikelhood, g_loglikelhood, \
dynamic_callables_smoother) = ssm.DescreteStateSpace.kalman_filter(A, Q, H, R, measurements, index=None,
m_init=m_init, P_init=P_init, p_kalman_filter_type = kalman_filter_type,
calc_log_likelihood=calc_log_likelihood,
calc_grad_log_likelihood=calc_grad_log_likelihood,
grad_params_no=grad_params_no,
grad_calc_params=grad_calc_params)
f_mean_squeezed = np.squeeze(f_mean[1:,:]) # exclude initial value
f_var_squeezed = np.squeeze(f_var[1:,:]) # exclude initial value
if true_states is not None:
#print np.max(np.abs(f_mean_squeezed-true_states))
np.testing.assert_almost_equal(np.max(np.abs(f_mean_squeezed- \
true_states)), 0, decimal=mean_compare_decimal)
np.testing.assert_equal(f_mean.shape, (measurements.shape[0]+1,state_dim,ts_no) )
np.testing.assert_equal(f_var.shape, (measurements.shape[0]+1,state_dim,state_dim) )
(M_smooth, P_smooth) = ssm.DescreteStateSpace.rts_smoother(state_dim, dynamic_callables_smoother, f_mean,
f_var)
return f_mean, f_var
def run_continuous_model(self, F, L, Qc, p_H, p_R, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=None, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=0, grad_calc_params=None):
#import pdb; pdb.set_trace()
state_dim = 1 if not isinstance(F,np.ndarray) else F.shape[0]
ts_no = 1 if (len(Y_data.shape) < 3) else Y_data.shape[2]
ss_setup.use_cython = use_cython
global ssm
if (ssm.cython_code_available) and (ssm.use_cython != use_cython):
reload(ssm)
(f_mean, f_var, loglikelhood, g_loglikelhood, \
dynamic_callables_smoother) = ssm.ContDescrStateSpace.cont_discr_kalman_filter(F, L, Qc, p_H, p_R,
P_inf, X_data, Y_data, index = None,
m_init=None, P_init=None,
p_kalman_filter_type='regular',
calc_log_likelihood=False,
calc_grad_log_likelihood=False,
grad_params_no=0, grad_calc_params=grad_calc_params)
f_mean_squeezed = np.squeeze(f_mean[1:,:]) # exclude initial value
f_var_squeezed = np.squeeze(f_var[1:,:]) # exclude initial value
np.testing.assert_equal(f_mean.shape, (Y_data.shape[0]+1,state_dim,ts_no))
np.testing.assert_equal(f_var.shape, (Y_data.shape[0]+1,state_dim,state_dim))
(M_smooth, P_smooth) = ssm.ContDescrStateSpace.cont_discr_rts_smoother(state_dim, f_mean, \
f_var,dynamic_callables_smoother)
return f_mean, f_var
def test_discrete_ss_first(self,plot=False):
"""
Tests discrete State-Space model - first test.
"""
np.random.seed(235) # seed the random number generator
A = 1.0 # For cython code to run properly need float input
H = 1.0
Q = 1.0
R = 1.0
steps_num = 100
# generate data ->
true_states = np.zeros((steps_num,))
init_state = 0
measurements = np.zeros((steps_num,))
for s in range(0, steps_num):
if s== 0:
true_states[0] = init_state + np.sqrt(Q)*np.random.randn()
else:
true_states[s] = true_states[s-1] + np.sqrt(R)*np.random.randn()
measurements[s] = true_states[s] + np.sqrt(R)*np.random.randn()
# generate data <-
# descrete kalman filter ->
m_init = 0; P_init = 1
d_num = 1000
state_discr = np.linspace(-10,10,d_num)
state_trans_matrix = np.empty((d_num,d_num))
for i in range(d_num):
state_trans_matrix[:,i] = norm.pdf(state_discr, loc=A*state_discr[i], scale=np.sqrt(Q))
m_prev = norm.pdf(state_discr, loc = m_init, scale = np.sqrt(P_init)); #m_prev / np.sum(m_prev)
m = np.zeros((d_num, steps_num))
i_mean = np.zeros((steps_num,))
for s in range(0, steps_num):
# Prediction step:
if (s==0):
m[:,s] = np.dot(state_trans_matrix, m_prev)
else:
m[:,s] = np.dot(state_trans_matrix, m[:,s-1])
# Update step:
#meas_ind = np.argmin(np.abs(state_discr - measurements[s])
y_vec = np.zeros( (d_num,))
for i in range(d_num):
y_vec[i] = norm.pdf(measurements[s], loc=H*state_discr[i], scale=np.sqrt(R))
norm_const = np.dot( y_vec, m[:,s] )
m[:,s] = y_vec * m[:,s] / norm_const
i_mean[s] = state_discr[ np.argmax(m[:,s]) ]
# descrete kalman filter <-
(f_mean, f_var) = self.run_descr_model(measurements, A,Q,H,R, true_states=i_mean,
mean_compare_decimal=1,
m_init=m_init, P_init=P_init,use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=False)
(f_mean, f_var) = self.run_descr_model(measurements, A,Q,H,R, true_states=i_mean,
mean_compare_decimal=1,
m_init=m_init, P_init=P_init,use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=False)
(f_mean, f_var) = self.run_descr_model(measurements, A,Q,H,R, true_states=i_mean,
mean_compare_decimal=1,
m_init=m_init, P_init=P_init,use_cython=True,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=False)
if plot:
# plotting ->
plt.figure()
plt.plot( true_states, 'g.-',label='true states')
#plt.plot( measurements, 'b.-', label='measurements')
plt.plot( f_mean, 'r.-',label='Kalman filter estimates')
plt.plot( i_mean, 'k.-', label='Discretization')
plt.plot( f_mean + 2*np.sqrt(f_var), 'r.--')
plt.plot( f_mean - 2*np.sqrt(f_var), 'r.--')
plt.legend()
plt.show()
# plotting <-
return None
def test_discrete_ss_1D(self,plot=False):
"""
This function tests Kalman filter and smoothing when the state
dimensionality is one dimensional.
"""
np.random.seed(234) # seed the random number generator
# 1D ss model
state_dim = 1;
param_num = 2 # sigma_Q, sigma_R - parameters
measurement_dim = 1 # dimensionality od measurement
A = 1.0
Q = 2.0
dA= np.zeros((state_dim,state_dim,param_num))
dQ = np.zeros((state_dim,state_dim,param_num)); dQ[0,0,0] = 1.0
# measurement related parameters (subject to change) ->
H = np.ones((measurement_dim,state_dim ))
R = 0.5 * np.eye(measurement_dim)
dH = np.zeros((measurement_dim,state_dim,param_num))
dR = np.zeros((measurement_dim,measurement_dim,param_num)); dR[:,:,1] = np.eye(measurement_dim)
# measurement related parameters (subject to change) <-
# 1D measurement, 1 ts_no ->
data = generate_random_y_data(10, 1, 1) # np.array((samples, dim, ts_no))
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=True,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
if plot:
# plotting ->
plt.figure()
plt.plot( np.squeeze(data), 'g.-', label='measurements')
plt.plot( np.squeeze(f_mean[1:]), 'b.-',label='Kalman filter estimates')
plt.plot( np.squeeze(f_mean[1:]+H*f_var[1:]*H), 'b--')
plt.plot( np.squeeze(f_mean[1:]-H*f_var[1:]*H), 'b--')
# plt.plot( np.squeeze(M_sm[1:]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:]+H*P_sm[1:]*H), 'r--')
# plt.plot( np.squeeze(M_sm[1:]-H*P_sm[1:]*H), 'r--')
plt.legend()
plt.title("1D state-space, 1D measurements, 1 ts_no")
plt.show()
# plotting <-
# 1D measurement, 1 ts_no <-
# 1D measurement, 3 ts_no ->
data = generate_random_y_data(10, 1, 3) # np.array((samples, dim, ts_no))
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=True,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
#import pdb; pdb.set_trace()
if plot:
# plotting ->
plt.figure()
plt.plot( np.squeeze(data[:,:,1]), 'g.-', label='measurements')
plt.plot( np.squeeze(f_mean[1:,0,1]), 'b.-',label='Kalman filter estimates')
plt.plot( np.squeeze(f_mean[1:,0,1])+np.squeeze(H*f_var[1:]*H), 'b--')
plt.plot( np.squeeze(f_mean[1:,0,1])-np.squeeze(H*f_var[1:]*H), 'b--')
# plt.plot( np.squeeze(M_sm[1:,0,1]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,0,1])+H*np.squeeze(P_sm[1:])*H, 'r--')
# plt.plot( np.squeeze(M_sm[1:,0,1])-H*np.squeeze(P_sm[1:])*H, 'r--')
plt.legend()
plt.title("1D state-space, 1D measurements, 3 ts_no. 2-nd ts ploted")
plt.show()
# plotting <-
# 1D measurement, 3 ts_no <-
measurement_dim = 2 # dimensionality of measurement
H = np.ones((measurement_dim,state_dim))
R = 0.5 * np.eye(measurement_dim)
dH = np.zeros((measurement_dim,state_dim,param_num))
dR = np.zeros((measurement_dim,measurement_dim,param_num)); dR[:,:,1] = np.eye(measurement_dim)
# measurement related parameters (subject to change) <
data = generate_random_y_data(10, 2, 3) # np.array((samples, dim, ts_no))
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
# (f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
# mean_compare_decimal=16,
# m_init=None, P_init=None, dA=dA,dQ=dQ,
# dH=dH,dR=dR, use_cython=True,
# kalman_filter_type='svd',
# calc_log_likelihood=True,
# calc_grad_log_likelihood=True)
if plot:
# plotting ->
plt.figure()
plt.plot( np.squeeze(data[:,0,1]), 'g.-', label='measurements')
plt.plot( np.squeeze(f_mean[1:,0,1]), 'b.-',label='Kalman filter estimates')
plt.plot( np.squeeze(f_mean[1:,0,1])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot( np.squeeze(f_mean[1:,0,1])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,0,1]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,0,1])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,0,1])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("1D state-space, 2D measurements, 3 ts_no. 1-st measurement, 2-nd ts ploted")
plt.show()
# plotting <-
# 2D measurement, 3 ts_no <-
def test_discrete_ss_2D(self,plot=False):
"""
This function tests Kalman filter and smoothing when the state
dimensionality is two dimensional.
"""
np.random.seed(234) # seed the random number generator
# 1D ss model
state_dim = 2;
param_num = 3 # sigma_Q, sigma_R, one parameters in A - parameters
measurement_dim = 1 # dimensionality od measurement
A = np.eye(state_dim); A[0,0] = 0.5
Q = np.ones((state_dim,state_dim));
dA = np.zeros((state_dim,state_dim,param_num)); dA[1,1,2] = 1
dQ = np.zeros((state_dim,state_dim,param_num)); dQ[:,:,1] = np.eye(measurement_dim)
# measurement related parameters (subject to change) ->
H = np.ones((measurement_dim,state_dim))
R = 0.5 * np.eye(measurement_dim)
dH = np.zeros((measurement_dim,state_dim,param_num))
dR = np.zeros((measurement_dim,measurement_dim,param_num)); dR[:,:,1] = np.eye(measurement_dim)
# measurement related parameters (subject to change) <-
# 1D measurement, 1 ts_no ->
data = generate_random_y_data(10, 1, 1) # np.array((samples, dim, ts_no))
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=True,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
if plot:
# plotting ->
plt.figure()
plt.plot( np.squeeze(data), 'g.-', label='measurements')
plt.plot( np.squeeze(f_mean[1:,0]), 'b.-',label='Kalman filter estimates')
plt.plot( np.squeeze(f_mean[1:,0])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot( np.squeeze(f_mean[1:,0])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,0]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,0])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,0])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("2D state-space, 1D measurements, 1 ts_no")
plt.show()
# plotting <-
# 1D measurement, 1 ts_no <-
# 1D measurement, 3 ts_no ->
data = generate_random_y_data(10, 1, 3) # np.array((samples, dim, ts_no))
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=True,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
if plot:
# plotting ->
plt.figure()
plt.plot( np.squeeze(data[:,:,1]), 'g.-', label='measurements')
plt.plot( np.squeeze(f_mean[1:,0,1]), 'b.-',label='Kalman filter estimates')
plt.plot( np.squeeze(f_mean[1:,0,1])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot( np.squeeze(f_mean[1:,0,1])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,0,1]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,0,1])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,0,1])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("2D state-space, 1D measurements, 3 ts_no. 2-nd ts ploted")
plt.show()
# plotting <-
# 1D measurement, 3 ts_no <-
# 2D measurement, 3 ts_no ->
# measurement related parameters (subject to change) ->
measurement_dim = 2 # dimensionality od measurement
H = np.ones((measurement_dim,state_dim))
R = 0.5 * np.eye(measurement_dim)
dH = np.zeros((measurement_dim,state_dim,param_num))
dR = np.zeros((measurement_dim,measurement_dim,param_num)); dR[:,:,1] = np.eye(measurement_dim)
# measurement related parameters (subject to change) <
data = generate_random_y_data(10, 2, 3) # np.array((samples, dim, ts_no))
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
(f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
mean_compare_decimal=16,
m_init=None, P_init=None, dA=dA,dQ=dQ,
dH=dH,dR=dR, use_cython=False,
kalman_filter_type='svd',
calc_log_likelihood=True,
calc_grad_log_likelihood=True)
# (f_mean, f_var) = self.run_descr_model(data, A,Q,H,R, true_states=None,
# mean_compare_decimal=16,
# m_init=None, P_init=None, dA=dA,dQ=dQ,
# dH=dH,dR=dR, use_cython=True,
# kalman_filter_type='svd',
# calc_log_likelihood=True,
# calc_grad_log_likelihood=True)
if plot:
# plotting ->
plt.figure()
plt.plot( np.squeeze(data[:,0,1]), 'g.-', label='measurements')
plt.plot( np.squeeze(f_mean[1:,0,1]), 'b.-',label='Kalman filter estimates')
plt.plot( np.squeeze(f_mean[1:,0,1])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot( np.squeeze(f_mean[1:,0,1])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,0,1]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,0,1])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,0,1])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("2D state-space, 2D measurements, 3 ts_no. 1-st measurement, 2-nd ts ploted")
plt.show()
# plotting <-
# 2D measurement, 3 ts_no <-
def test_continuos_ss(self,plot=False):
"""
This function tests the continuos state-space model.
"""
# 1D measurements, 1 ts_no ->
measurement_dim = 1 # dimensionality of measurement
X_data = generate_x_points(points_num=10, x_interval = (0, 20), random=True)
Y_data = generate_random_y_data(10, 1, 1) # np.array((samples, dim, ts_no))
try:
import GPy
except ImportError as e:
return None
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
(F,L,Qc,H,P_inf,P0, dFt,dQct,dP_inft,dP0) = periodic_kernel.sde()
state_dim = dFt.shape[0];
param_num = dFt.shape[2]
grad_calc_params = {}
grad_calc_params['dP_inf'] = dP_inft
grad_calc_params['dF'] = dFt
grad_calc_params['dQc'] = dQct
grad_calc_params['dR'] = np.zeros((measurement_dim,measurement_dim,param_num))
grad_calc_params['dP_init'] = dP0
# dH matrix is None
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, 1.5, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, 1.5, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=False,
kalman_filter_type='rbc',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, 1.5, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=True,
kalman_filter_type='rbc',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
if plot:
# plotting ->
plt.figure()
plt.plot( X_data, np.squeeze(Y_data[:,0]), 'g.-', label='measurements')
plt.plot( X_data, np.squeeze(f_mean[1:,15]), 'b.-',label='Kalman filter estimates')
plt.plot( X_data, np.squeeze(f_mean[1:,15])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot( X_data, np.squeeze(f_mean[1:,15])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,15]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,15])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,15])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("1D measurements, 1 ts_no")
plt.show()
# plotting <-
# 1D measurements, 1 ts_no <-
# 1D measurements, 3 ts_no ->
measurement_dim = 1 # dimensionality od measurement
X_data = generate_x_points(points_num=10, x_interval = (0, 20), random=True)
Y_data = generate_random_y_data(10, 1, 3) # np.array((samples, dim, ts_no))
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
(F,L,Qc,H,P_inf,P0, dFt,dQct,dP_inft,dP0) = periodic_kernel.sde()
state_dim = dFt.shape[0];
param_num = dFt.shape[2]
grad_calc_params = {}
grad_calc_params['dP_inf'] = dP_inft
grad_calc_params['dF'] = dFt
grad_calc_params['dQc'] = dQct
grad_calc_params['dR'] = np.zeros((measurement_dim,measurement_dim,param_num))
grad_calc_params['dP_init'] = dP0
# dH matrix is None
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, 1.5, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, 1.5, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=False,
kalman_filter_type='rbc',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, 1.5, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=True,
kalman_filter_type='rbc',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
if plot:
# plotting ->
plt.figure()
plt.plot(X_data, np.squeeze(Y_data[:,0,1]), 'g.-', label='measurements')
plt.plot(X_data, np.squeeze(f_mean[1:,15,1]), 'b.-',label='Kalman filter estimates')
plt.plot(X_data, np.squeeze(f_mean[1:,15,1])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot(X_data, np.squeeze(f_mean[1:,15,1])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,15,1]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,15,1])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,15,1])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("1D measurements, 3 ts_no. 2-nd ts ploted")
plt.show()
# plotting <-
# 1D measurements, 3 ts_no <-
# 2D measurements, 3 ts_no ->
measurement_dim = 2 # dimensionality od measurement
X_data = generate_x_points(points_num=10, x_interval = (0, 20), random=True)
Y_data = generate_random_y_data(10, 2, 3) # np.array((samples, dim, ts_no))
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
(F,L,Qc,H,P_inf,P0, dFt,dQct,dP_inft,dP0) = periodic_kernel.sde()
H = np.vstack((H,H)) # make 2D measurements
R = 1.5 * np.eye(measurement_dim)
state_dim = dFt.shape[0];
param_num = dFt.shape[2]
grad_calc_params = {}
grad_calc_params['dP_inf'] = dP_inft
grad_calc_params['dF'] = dFt
grad_calc_params['dQc'] = dQct
grad_calc_params['dR'] = np.zeros((measurement_dim,measurement_dim,param_num))
grad_calc_params['dP_init'] = dP0
# dH matrix is None
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, R, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=False,
kalman_filter_type='regular',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
(f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, R, P_inf, X_data, Y_data, index = None,
m_init=None, P_init=P0, use_cython=False,
kalman_filter_type='rbc',
calc_log_likelihood=True,
calc_grad_log_likelihood=True,
grad_params_no=param_num, grad_calc_params=grad_calc_params)
# (f_mean, f_var) = self.run_continuous_model(F, L, Qc, H, R, P_inf, X_data, Y_data, index = None,
# m_init=None, P_init=P0, use_cython=True,
# kalman_filter_type='rbc',
# calc_log_likelihood=True,
# calc_grad_log_likelihood=True,
# grad_params_no=param_num, grad_calc_params=grad_calc_params)
if plot:
# plotting ->
plt.figure()
plt.plot(X_data, np.squeeze(Y_data[:,0,1]), 'g.-', label='measurements')
plt.plot(X_data, np.squeeze(f_mean[1:,15,1]), 'b.-',label='Kalman filter estimates')
plt.plot(X_data, np.squeeze(f_mean[1:,15,1])+np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
plt.plot(X_data, np.squeeze(f_mean[1:,15,1])-np.einsum('ij,ajk,kl', H, f_var[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_sm[1:,15,1]), 'r.-',label='Smoother Estimates')
# plt.plot( np.squeeze(M_sm[1:,15,1])+np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
# plt.plot( np.squeeze(M_sm[1:,15,1])-np.einsum('ij,ajk,kl', H, P_sm[1:], H.T)[:,0,0], 'r--')
plt.legend()
plt.title("1D measurements, 3 ts_no. 2-nd ts ploted")
plt.show()
# plotting <-
# 2D measurements, 3 ts_no <-
#def test_EM_gradient(plot=False):
# """
# Test EM gradient calculation. This method works (the formulas are such)
# that it works only for time invariant matrices A, Q, H, R. For the continuous
# model it means that time intervals are the same.
# """
#
# np.random.seed(234) # seed the random number generator
#
# # 1D measurements, 1 ts_no ->
# measurement_dim = 1 # dimensionality of measurement
#
# x_data = generate_x_points(points_num=10, x_interval = (0, 20), random=False)
# data = generate_random_y_data(10, 1, 1) # np.array((samples, dim, ts_no))
#
# import GPy
# #periodic_kernel = GPy.kern.sde_Matern32(1,active_dims=[0,])
# periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
# (F,L,Qc,H,P_inf,P0, dFt,dQct,dP_inft,dP0t) = periodic_kernel.sde()
#
# state_dim = dFt.shape[0];
# param_num = dFt.shape[2]
#
# grad_calc_params = {}
# grad_calc_params['dP_inf'] = dP_inft
# grad_calc_params['dF'] = dFt
# grad_calc_params['dQc'] = dQct
# grad_calc_params['dR'] = np.zeros((measurement_dim,measurement_dim,param_num))
# grad_calc_params['dP_init'] = dP0t
# # dH matrix is None
#
#
# #(F,L,Qc,H,P_inf,dF,dQc,dP_inf) = ssm.balance_ss_model(F,L,Qc,H,P_inf,dF,dQc,dP_inf)
# # Use the Kalman filter to evaluate the likelihood
#
# #import pdb; pdb.set_trace()
# (M_kf, P_kf, log_likelihood,
# grad_log_likelihood,SmootherMatrObject) = ss.ContDescrStateSpace.cont_discr_kalman_filter(F,
# L, Qc, H, 1.5, P_inf, x_data, data, m_init=None,
# P_init=P0, calc_log_likelihood=True,
# calc_grad_log_likelihood=True,
# grad_params_no=param_num,
# grad_calc_params=grad_calc_params)
#
# if plot:
# # plotting ->
# plt.figure()
# plt.plot( np.squeeze(data[:,0]), 'g.-', label='measurements')
# plt.plot( np.squeeze(M_kf[1:,15]), 'b.-',label='Kalman filter estimates')
# plt.plot( np.squeeze(M_kf[1:,15])+np.einsum('ij,ajk,kl', H, P_kf[1:], H.T)[:,0,0], 'b--')
# plt.plot( np.squeeze(M_kf[1:,15])-np.einsum('ij,ajk,kl', H, P_kf[1:], H.T)[:,0,0], 'b--')
# plt.title("1D measurements, 1 ts_no")
# plt.show()
# # plotting <-
# # 1D measurements, 1 ts_no <-
if __name__ == '__main__':
print("Running state-space inference tests...")
unittest.main()
#tt = StateSpaceKernelsTests('test_discrete_ss_first')
#res = tt.test_discrete_ss_first(plot=True)
#res = tt.test_discrete_ss_1D(plot=True)
#res = tt.test_discrete_ss_2D(plot=False)
#res = tt.test_continuos_ss(plot=True)

53
GPy/testing/util_tests.py
View file

@ -1,5 +1,5 @@
#===============================================================================
# Copyright (c) 2016, Max Zwiessele
# Copyright (c) 2016, Max Zwiessele, Alan Saul
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
@ -46,4 +46,53 @@ class TestDebug(unittest.TestCase):
self.assertFalse(checkFullRank(tdot(array), name='test'))
array = np.random.normal(0, 1, (25,25))
self.assertTrue(checkFullRank(tdot(array)))
self.assertTrue(checkFullRank(tdot(array)))
def test_fixed_inputs_median(self):
""" test fixed_inputs convenience function """
from GPy.plotting.matplot_dep.util import fixed_inputs
import GPy
X = np.random.randn(10, 3)
Y = np.sin(X) + np.random.randn(10, 3)*1e-3
m = GPy.models.GPRegression(X, Y)
fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False)
self.assertTrue((0, np.median(X[:,0])) in fixed)
self.assertTrue((2, np.median(X[:,2])) in fixed)
self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
def test_fixed_inputs_mean(self):
from GPy.plotting.matplot_dep.util import fixed_inputs
import GPy
X = np.random.randn(10, 3)
Y = np.sin(X) + np.random.randn(10, 3)*1e-3
m = GPy.models.GPRegression(X, Y)
fixed = fixed_inputs(m, [1], fix_routine='mean', as_list=True, X_all=False)
self.assertTrue((0, np.mean(X[:,0])) in fixed)
self.assertTrue((2, np.mean(X[:,2])) in fixed)
self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
def test_fixed_inputs_zero(self):
from GPy.plotting.matplot_dep.util import fixed_inputs
import GPy
X = np.random.randn(10, 3)
Y = np.sin(X) + np.random.randn(10, 3)*1e-3
m = GPy.models.GPRegression(X, Y)
fixed = fixed_inputs(m, [1], fix_routine='zero', as_list=True, X_all=False)
self.assertTrue((0, 0.0) in fixed)
self.assertTrue((2, 0.0) in fixed)
self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
def test_fixed_inputs_uncertain(self):
from GPy.plotting.matplot_dep.util import fixed_inputs
import GPy
from GPy.core.parameterization.variational import NormalPosterior
X_mu = np.random.randn(10, 3)
X_var = np.random.randn(10, 3)
X = NormalPosterior(X_mu, X_var)
Y = np.sin(X_mu) + np.random.randn(10, 3)*1e-3
m = GPy.models.BayesianGPLVM(Y, X=X_mu, X_variance=X_var, input_dim=3)
fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False)
self.assertTrue((0, np.median(X.mean.values[:,0])) in fixed)
self.assertTrue((2, np.median(X.mean.values[:,2])) in fixed)
self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed