def __init__(self, gamma):
Kernel.__init__(self)
if type(gamma) is not float:
raise TypeError("Gamma must be float")
self.gamma = gamma
def __init__(self, width, nu=1.5, sigma=1.0):
Kernel.__init__(self)
#GenericTests.check_type(width,'width',float)
GenericTests.check_type(nu, 'nu', float)
GenericTests.check_type(sigma, 'sigma', float)
self.width = width
self.nu = nu
self.sigma = sigma
def __init__(self, width=1.0, nu=1.5, sigma=1.0):
Kernel.__init__(self)
GenericTests.check_type(width,'width',float)
GenericTests.check_type(nu,'nu',float)
GenericTests.check_type(sigma,'sigma',float)
self.width = width
self.nu = nu
self.sigma = sigma
def get_spectrum_on_data(self, Mx, My):
'''Mx and My are Kx Ky when rff =False
Mx and My are phix, phiy when rff =True'''
if self.rff | self.induce_set:
Cx = np.cov(Mx.T)
Cy = np.cov(My.T)
lambdax = np.linalg.eigvalsh(Cx)
lambday = np.linalg.eigvalsh(Cy)
else:
Kxc = Kernel.center_kernel_matrix(Mx)
Kyc = Kernel.center_kernel_matrix(My)
lambdax = np.linalg.eigvalsh(Kxc)
lambday = np.linalg.eigvalsh(Kyc)
return lambdax, lambday
def get_spectrum_on_data(self, Mx, My):
'''Mx and My are Kx Ky when rff =False
Mx and My are phix, phiy when rff =True'''
if self.rff|self.induce_set:
Cx = np.cov(Mx.T)
Cy = np.cov(My.T)
lambdax=np.linalg.eigvalsh(Cx)
lambday=np.linalg.eigvalsh(Cy)
else:
Kxc = Kernel.center_kernel_matrix(Mx)
Kyc = Kernel.center_kernel_matrix(My)
lambdax=np.linalg.eigvalsh(Kxc)
lambday=np.linalg.eigvalsh(Kyc)
return lambdax,lambday
def __str__(self):
s = self.__class__.__name__ + "=["
s += "p=" + str(self.p)
s += "alpha=" + str(self.alpha)
s += ", " + Kernel.__str__(self)
s += "]"
return s
def __init__(self, sigma=1.0, is_sparse=False):
Kernel.__init__(self)
self.width = sigma
self.is_sparse = is_sparse
def __init__(self, is_sparse=False):
Kernel.__init__(self)
self.is_sparse = is_sparse
def __init__(self, degree, theta=1.0):
Kernel.__init__(self)
self.degree = degree
self.theta = theta
def __init__(self, p=1, alpha=0.5, sigma=1.0):
Kernel.__init__(self)
self.p = p
self.alpha = alpha
self.sigma = sigma
def __str__(self):
s=self.__class__.__name__+ "=["
s += "" + Kernel.__str__(self)
s += "]"
return s
def HSIC_V_statistic(Kx, Ky):
Kxc = Kernel.center_kernel_matrix(Kx)
Kyc = Kernel.center_kernel_matrix(Ky)
return np.sum(Kxc * Kyc)
def __init__(self, data_kernel):
Kernel.__init__(self)
self.data_kernel = data_kernel
def __init__(self, is_sparse = False):
Kernel.__init__(self)
self.is_sparse = is_sparse
def __init__(self, list_of_kernels):
Kernel.__init__(self)
self.list_of_kernels = list_of_kernels
def HSIC_V_statistic(Kx,Ky):
Kxc=Kernel.center_kernel_matrix(Kx)
Kyc=Kernel.center_kernel_matrix(Ky)
return np.sum(Kxc*Kyc)
def __init__(self, sigma):
Kernel.__init__(self)
GenericTests.check_type(sigma, 'sigma', float)
self.width = sigma
def __init__(self):
Kernel.__init__(self)
def __str__(self):
s=self.__class__.__name__+ "=["
s += "degree="+ str(self.degree)
s += ", " + Kernel.__str__(self)
s += "]"
return s
def __str__(self):
s = self.__class__.__name__ + "=["
s += "gamma=" + str(self.gamma)
s += ", " + Kernel.__str__(self)
s += "]"
return s
def __init__(self, degree,theta=1.0):
Kernel.__init__(self)
self.degree = degree
self.theta = theta
def __str__(self):
s = self.__class__.__name__ + "=["
s += ", " + Kernel.__str__(self)
s += "]"
return s
def __init__(self, list_of_kernels):
Kernel.__init__(self)
self.list_of_kernels = list_of_kernels
