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import numpy as N
import libsvm
__all__ = [
'LinearKernel',
'PolynomialKernel',
'RBFKernel',
'SigmoidKernel',
'CustomKernel'
]
class LinearKernel:
"""Linear kernel: u'*v."""
def __init__(self):
self.kernel_type = libsvm.LINEAR
def __call__(self, x, y):
x = N.atleast_2d(x)
y = N.atleast_2d(y)
return N.dot(x, y.T)
def compact(self, *args):
return self
class PolynomialKernel:
"""Polynomial kernel: (gamma*u'*v + coef0)^degree."""
def __init__(self, degree, gamma, coef0):
self.kernel_type = libsvm.POLY
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
def __call__(self, x, y):
x = N.atleast_2d(x)
y = N.atleast_2d(y)
base = self.gamma * N.dot(x, y.T) + self.coef0
tmp = base
ret = 1.0
t = self.degree
while t > 0:
if t % 2 == 1: ret *= tmp
tmp *= tmp
t /= 2
return ret
def __repr__(self):
return '<PolynomialKernel: degree=%d, gamma=%.4f, coef0=%.4f>' % \
(self.degree, self.gamma, self.coef0)
def compact(self, *args):
raise NotImplementedError, \
'model compaction for PolynomialKernel not implemented'
class RBFKernel:
"""Radial basis function kernel: exp(-gamma*|u-v|^2)"""
def __init__(self, gamma):
self.kernel_type = libsvm.RBF
self.gamma = gamma
self.__call__ = self.evaluate
def evaluate(self, x, y):
x = N.atleast_2d(x)
y = N.atleast_2d(y)
xnorm = N.atleast_2d(N.sum(x*x, axis=1))
ynorm = N.atleast_2d(N.sum(y*y, axis=1))
z = xnorm + ynorm - 2 * N.atleast_2d(N.dot(x, y.T).squeeze())
return N.exp(-self.gamma * z)
def evaluate_compact(self, x, y):
raise NotImplementedError
def __repr__(self):
return '<RBFKernel: gamma=%.4f>' % (self.gamma,)
def compact(self, *args):
raise NotImplementedError, \
'model compaction for RBFKernel not implemented'
class SigmoidKernel:
"""Sigmoid kernel: tanh(gamma*u'*v + coef0)"""
def __init__(self, gamma, coef0):
self.kernel_type = libsvm.SIGMOID
self.gamma = gamma
self.coef0 = coef0
def __call__(self, x, y):
x = N.atleast_2d(x)
y = N.atleast_2d(y)
return N.tanh(self.gamma * N.dot(x, y.T) + self.coef0)
def __repr__(self):
return '<SigmoidKernel: gamma=%.4f, coef0=%.4f>' % \
(self.gamma, self.coef0)
def compact(self, *args):
raise NotImplementedError, \
'model compaction for SigmoidKernel not implemented'
class CustomKernel:
"""Custom kernel: any callable"""
def __init__(self, f):
self.kernel_type = libsvm.PRECOMPUTED
self.f = f
def __call__(self, x, y):
x = N.atleast_2d(x)
y = N.atleast_2d(y)
return self.f(x, y)
def __repr__(self):
return '<CustomKernel: %s>' % str(self.f)
def compact(self, *args):
raise NotImplementedError, \
'model compaction for CustomKernel not implemented'
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