def check_multidim_input(self):
kernels = [
LinearKernel(),
PolynomialKernel(degree=6, gamma=1.0, coef0=1.0),
SigmoidKernel(gamma=0.2, coef0=0.3),
RBFKernel(gamma=1.0),
CustomKernel(kernelfunc)
]
args = [
N.random.randn(10),
N.random.randn(1, 10),
N.random.randn(5, 10)
]
for kernel in kernels:
self.assert_(type(repr(kernel)) is str)
for i, x in enumerate(args):
zshape0 = N.atleast_2d(x).shape[0]
for y in args[i:]:
zshape1 = N.atleast_2d(y).shape[0]
z = kernel(x, y)
self.assertEqual(z.shape[0], zshape0)
self.assertEqual(z.shape[1], zshape1)
u = kernel(y, x)
assert_array_equal(u.squeeze(), z.squeeze())
def check_combine(self):
kernel = LinearKernel()
y1 = N.random.randn(10)
x1 = N.random.randn(len(y1), 10)
origdata = LibSvmRegressionDataSet(y1, x1)
pcdata = origdata.precompute(kernel)
y2 = N.random.randn(5)
x2 = N.random.randn(len(y2), x1.shape[1])
moredata = LibSvmRegressionDataSet(y2, x2)
morepcdata = pcdata.combine(moredata)
expt_grammat = N.empty((len(y1) + len(y2),)*2)
x = N.vstack([x1,x2])
for i, xi in enumerate(x):
for j, xj in enumerate(x):
expt_grammat[i, j] = kernel(xi, xj)
for i, row in enumerate(morepcdata.grammat):
valuerow = row[1:]['value']
assert_array_almost_equal(valuerow, expt_grammat[i])
def check_precompute(self):
degree, gamma, coef0 = 4, 3.0, 2.0
kernels = [
LinearKernel(),
PolynomialKernel(degree, gamma, coef0),
RBFKernel(gamma),
SigmoidKernel(gamma, coef0)
]
y = N.random.randn(10)
x = N.random.randn(len(y), 10)
origdata = LibSvmRegressionDataSet(y, x)
for kernel in kernels:
# calculate expected Gram matrix
expt_grammat = N.empty((len(y),)*2)
for i, xi in enumerate(x):
for j, xj in enumerate(x):
expt_grammat[i, j] = kernel(xi, xj)
# get a new dataset containing the precomputed data
pcdata = origdata.precompute(kernel)
for i, row in enumerate(pcdata.grammat):
valuerow = row[1:]['value']
assert_array_almost_equal(valuerow, expt_grammat[i])
def check_custom_kernel(self):
kernel = CustomKernel(kernelfunc)
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 32.0)
def check_rbf_kernel(self):
kernel = RBFKernel(gamma=1.0)
x, y = N.array([2.]), N.array([3.])
self.assertAlmostEqual(kernel(x, y), N.exp(-1.))
def check_sigmoid_kernel(self):
kernel = SigmoidKernel(gamma=0.2, coef0=0.3)
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 0.80049902)
def check_polynomial_kernel(self):
kernel = PolynomialKernel(degree=6, gamma=1.0, coef0=1.0)
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 15625.)
def check_linear_kernel(self):
kernel = LinearKernel()
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 4.)
