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fix linear kernel with NxMxM psi2

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This commit is contained in:
Zhenwen Dai 2015-09-07 11:43:58 +01:00
parent e906da0309
commit 276330d1d1
2 changed files with 64 additions and 26 deletions
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42
GPy/testing/kernel_tests.py
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@ -452,6 +452,8 @@ class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
self.w2 = np.random.randn(N,M)
self.w3 = np.random.randn(M,M)
self.w3 = self.w3+self.w3.T
self.w3n = np.random.randn(N,M,M)
self.w3n = self.w3n+np.swapaxes(self.w3n, 1,2)
def test_kernels(self):
from GPy.kern import RBF,Linear
@ -463,54 +465,70 @@ class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
self._test_kernel_param(k)
self._test_Z(k)
self._test_qX(k)
self._test_kernel_param(k, psi2n=True)
self._test_Z(k, psi2n=True)
self._test_qX(k, psi2n=True)
def _test_kernel_param(self, kernel, psi2n=False):
def _test_kernel_param(self, kernel):
def f(p):
kernel.param_array[:] = p
psi0 = kernel.psi0(self.Z, self.qX)
psi1 = kernel.psi1(self.Z, self.qX)
psi2 = kernel.psi2(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
if not psi2n:
psi2 = kernel.psi2(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
else:
psi2 = kernel.psi2n(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum()
def df(p):
kernel.param_array[:] = p
kernel.update_gradients_expectations(self.w1, self.w2, self.w3, self.Z, self.qX)
kernel.update_gradients_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, self.Z, self.qX)
return kernel.gradient.copy()
from GPy.models import GradientChecker
m = GradientChecker(f, df, kernel.param_array.copy())
self.assertTrue(m.checkgrad())
def _test_Z(self, kernel):
def _test_Z(self, kernel, psi2n=False):
def f(p):
psi0 = kernel.psi0(p, self.qX)
psi1 = kernel.psi1(p, self.qX)
psi2 = kernel.psi2(p, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
if not psi2n:
psi2 = kernel.psi2(p, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
else:
psi2 = kernel.psi2n(p, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum()
def df(p):
return kernel.gradients_Z_expectations(self.w1, self.w2, self.w3, p, self.qX)
return kernel.gradients_Z_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, p, self.qX)
from GPy.models import GradientChecker
m = GradientChecker(f, df, self.Z.copy())
self.assertTrue(m.checkgrad())
def _test_qX(self, kernel):
def _test_qX(self, kernel, psi2n=False):
def f(p):
self.qX.param_array[:] = p
self.qX._trigger_params_changed()
psi0 = kernel.psi0(self.Z, self.qX)
psi1 = kernel.psi1(self.Z, self.qX)
psi2 = kernel.psi2(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
if not psi2n:
psi2 = kernel.psi2(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
else:
psi2 = kernel.psi2n(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum()
def df(p):
self.qX.param_array[:] = p
self.qX._trigger_params_changed()
grad = kernel.gradients_qX_expectations(self.w1, self.w2, self.w3, self.Z, self.qX)
grad = kernel.gradients_qX_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, self.Z, self.qX)
self.qX.set_gradients(grad)
return self.qX.gradient.copy()