import matplotlib.pyplot as plt from six.moves import xrange # calulate kernel value def calcKernelValue(matrix_x, sample_x, kernelOption): kernelType = kernelOption[0] numSamples = matrix_x.shape[0] kernelValue = mat(zeros((numSamples, 1))) if kernelType == 'linear': kernelValue = matrix_x * sample_x.T elif kernelType == 'rbf': sigma = kernelOption[1] if sigma == 0: sigma = 1.0 for i in xrange(numSamples): diff = matrix_x[i, :] - sample_x kernelValue[i] = exp(diff * diff.T / (-2.0 * sigma**2)) else:raise NameError('Not support kernel type! You can use linear or rbf!') return kernelValue帮我整理下格式

import matplotlib.pyplot as plt
from six.moves import xrange 

def calcKernelValue(matrix_x, sample_x, kernelOption):
    kernelType = kernelOption[0]
    numSamples = matrix_x.shape[0]
    kernelValue = mat(zeros((numSamples, 1)))
    
    if kernelType == 'linear':
        kernelValue = matrix_x * sample_x.T
    elif kernelType == 'rbf':
        sigma = kernelOption[1]
        if sigma == 0:
            sigma = 1.0
        for i in xrange(numSamples):
            diff = matrix_x[i, :] - sample_x
            kernelValue[i] = exp(diff * diff.T / (-2.0 * sigma**2))
    else:
        raise NameError('Not support kernel type! You can use linear or rbf!')
    
    return kernelValue