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Added kronecker and variational gaussian approximation gp's, vargpapprox needs generalising to any factorizing likelihood

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Alan Saul 2014-08-15 18:22:09 +01:00
parent 051a8115a2
commit 0b3f8b3cc7
4 changed files with 268 additions and 0 deletions
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39
GPy/testing/model_tests.py
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@ -423,6 +423,45 @@ class GradientTests(np.testing.TestCase):
m = GPy.models.GPHeteroscedasticRegression(X,Y,kern)
self.assertTrue(m.checkgrad())
def test_gp_kronecker_gaussian(self):
N1, N2 = 30, 20
X1 = np.random.randn(N1, 1)
X2 = np.random.randn(N2, 1)
X1.sort(0); X2.sort(0)
k1 = GPy.kern.RBF(1) # + GPy.kern.White(1)
k2 = GPy.kern.RBF(1) # + GPy.kern.White(1)
Y = np.random.randn(N1, N2)
m = GPy.models.GPKroneckerGaussianRegression(X1, X2, Y, k1, k2)
# build the model the dumb way
assert (N1*N2<1000), "too much data for standard GPs!"
yy, xx = np.meshgrid(X2, X1)
Xgrid = np.vstack((xx.flatten(order='F'), yy.flatten(order='F'))).T
kg = GPy.kern.RBF(1, active_dims=[0]) * GPy.kern.RBF(1, active_dims=[1])
mm = GPy.models.GPRegression(Xgrid, Y.reshape(-1, 1, order='F'), kernel=kg)
m.randomize()
mm[:] = m[:]
assert np.allclose(m.log_likelihood(), mm.log_likelihood())
assert np.allclose(m.gradient, mm.gradient)
X1test = np.random.randn(100, 1)
X2test = np.random.randn(100, 1)
mean1, var1 = m.predict(X1test, X2test)
yy, xx = np.meshgrid(X2test, X1test)
Xgrid = np.vstack((xx.flatten(order='F'), yy.flatten(order='F'))).T
mean2, var2 = mm.predict(Xgrid)
assert np.allclose(mean1, mean2)
assert np.allclose(var1, var2)
def test_gp_VGPC(self):
num_obs = 25
X = np.random.randint(0,140,num_obs)
X = X[:,None]
Y = 25. + np.sin(X/20.) * 2. + np.random.rand(num_obs)[:,None]
kern = GPy.kern.Bias(1) + GPy.kern.RBF(1)
m = GPy.models.GPVariationalGaussianApproximation(X,Y,kern)
self.assertTrue(m.checkgrad())
if __name__ == "__main__":
print "Running unit tests, please be (very) patient..."