def computeGaussKernel(x):
    """Compute the gaussian kernel on a 1D vector."""
    xnorm = np.power(euclidean_distances(x, x), 2)
    return np.exp(-xnorm / (2.0))
def computeGaussKernel(x):
    
    xnorm = np.power(euclidean_distances(x, x), 2)
    return np.exp(-xnorm / (2.0))
def computeGaussKernel ( x ) : xnorm = np . power ( euclidean_distances ( x , x ) , 2 ) return np . exp ( - xnorm / ( 2.0 ) )
Compute the gaussian kernel on a 1D vector.
Compute the gaussian kernel on a 1D vector .