Пример #1
0
	def use_kernel(self):
		""" kernel_option=0: gaussian kernel
			kernel_option=1: polynomial kernel
			Otherwise:       linear kernel
		"""
		n = self.data_.shape[0]
		K = np.empty([n,n])
		for i in xrange(n):
			for j in xrange(n):
				K[i,j] = kernels.ker(self.data_[i,0:-1], self.data_[j,0:-1], self.kernel_option_)
		
		# W, V = MDS.find_coordinates(K)
		# print "W"
		# print W
		# print "V"
		# print V
		return K
Пример #2
0
    def use_kernel(self):
        """ kernel_option=0: gaussian kernel
			kernel_option=1: polynomial kernel
			Otherwise:       linear kernel
		"""
        n = self.data_.shape[0]
        K = np.empty([n, n])
        for i in xrange(n):
            for j in xrange(n):
                K[i, j] = kernels.ker(self.data_[i, 0:-1], self.data_[j, 0:-1],
                                      self.kernel_option_)

        # W, V = MDS.find_coordinates(K)
        # print "W"
        # print W
        # print "V"
        # print V
        return K
Пример #3
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	def kernel(self, x1, x2):
		return kernels.ker(x1, x2, self.kernel_option_)
Пример #4
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 def kernel(self, x1, x2):
     return kernels.ker(x1, x2, self.kernel_option_)