1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
|
from numpy.testing import *
import numpy as N
set_local_path('../..')
from svm.kernel import *
restore_path()
def kernelfunc(x, y):
return 8 * N.dot(x, y.T)
class test_kernel(NumpyTestCase):
def check_linear_kernel(self):
kernel = LinearKernel()
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 4.)
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_sigmoid_kernel(self):
kernel = SigmoidKernel(gamma=0.2, coef0=0.3)
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 0.80049902)
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_custom_kernel(self):
kernel = CustomKernel(kernelfunc)
x = N.array([2.])
self.assertAlmostEqual(kernel(x, x), 32.0)
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())
if __name__ == '__main__':
NumpyTest().run()
|
