'''
Created on 4 Sep 2015
@author: maxz
'''
import unittest
import numpy as np, GPy
from GPy.core.parameterization.variational import NormalPosterior
[docs]class Test(unittest.TestCase):
[docs] def setUp(self):
np.random.seed(12345)
self.N = 20
self.N_new = 50
self.D = 1
self.X = np.random.uniform(-3., 3., (self.N, 1))
self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))
[docs] def test_setxy_bgplvm(self):
k = GPy.kern.RBF(1)
m = GPy.models.BayesianGPLVM(self.Y, 1, kernel=k)
mu, var = m.predict(m.X)
X = m.X
Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
m.set_XY(Xnew, m.Y[:10].copy())
assert(m.checkgrad())
assert(m.num_data == m.X.shape[0])
assert(m.input_dim == m.X.shape[1])
m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X)
np.testing.assert_allclose(mu, mu2)
np.testing.assert_allclose(var, var2)
[docs] def test_setxy_gplvm(self):
k = GPy.kern.RBF(1)
m = GPy.models.GPLVM(self.Y, 1, kernel=k)
mu, var = m.predict(m.X)
X = m.X.copy()
Xnew = X[:10].copy()
m.set_XY(Xnew, m.Y[:10].copy())
assert(m.checkgrad())
assert(m.num_data == m.X.shape[0])
assert(m.input_dim == m.X.shape[1])
m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X)
np.testing.assert_allclose(mu, mu2)
np.testing.assert_allclose(var, var2)
[docs] def test_setxy_gp(self):
k = GPy.kern.RBF(1)
m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
mu, var = m.predict(m.X)
X = m.X.copy()
m.set_XY(m.X[:10], m.Y[:10])
assert(m.checkgrad())
assert(m.num_data == m.X.shape[0])
assert(m.input_dim == m.X.shape[1])
m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X)
np.testing.assert_allclose(mu, mu2)
np.testing.assert_allclose(var, var2)
[docs] def test_mean_function(self):
from GPy.core.parameterization.param import Param
from GPy.core.mapping import Mapping
class Parabola(Mapping):
def __init__(self, variance, degree=2, name='parabola'):
super(Parabola, self).__init__(1, 1, name)
self.variance = Param('variance', np.ones(degree+1) * variance)
self.degree = degree
self.link_parameter(self.variance)
def f(self, X):
p = self.variance[0] * np.ones(X.shape)
for i in range(1, self.degree+1):
p += self.variance[i] * X**(i)
return p
def gradients_X(self, dL_dF, X):
grad = np.zeros(X.shape)
for i in range(1, self.degree+1):
grad += (i) * self.variance[i] * X**(i-1)
return grad
def update_gradients(self, dL_dF, X):
for i in range(self.degree+1):
self.variance.gradient[i] = (dL_dF * X**(i)).sum(0)
X = np.linspace(-2, 2, 100)[:, None]
k = GPy.kern.RBF(1)
k.randomize()
p = Parabola(.3)
p.randomize()
Y = p.f(X) + np.random.multivariate_normal(np.zeros(X.shape[0]), k.K(X)+np.eye(X.shape[0])*1e-8)[:,None] + np.random.normal(0, .1, (X.shape[0], 1))
m = GPy.models.GPRegression(X, Y, mean_function=p)
m.randomize()
assert(m.checkgrad())
_ = m.predict(m.X)
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()