def get_mean_vector(A):
    mean_vector=[]
    for i in range(Feature_number):
        sum=0
        for value in A[:,i]:
            sum=sum+float(value)#accumulate all element in row i
        mean_vector.append(float(sum/len(A[:,i])))#add average value to MEAN_VECTOR
    return mean_vector

def get_covariance_matrix(A):
    if all_Feature == False:
        number=CUS_NUMBER
    else:
        number=Training_number
    A=numpy.reshape(A,(number,Feature_number))#transform One-dimensional matrix to matrix50*Feature_number matrix
    A=numpy.array(A,dtype='f')#set the values in the array are float
    mean_vector=get_mean_vector(A)#call MEAN_VECTOR()
    cov_matrix = numpy.reshape(numpy.zeros(Feature_number*Feature_number), (Feature_number,Feature_number))#matrix initialize
#original matrix minus MEAN_VECTOR
    for x in range(Feature_number):
        for y in range(len(A[:,x])):
            A[:,x][y]=float(A[:,x][y])-float(mean_vector[x])
#covariance(i,j)
#matrix multiply
    for x in range(Feature_number):
        for y in range(Feature_number):
            dot=0
            for z in range(len(A[:,x])):
                dot=float(A[:,x][z])*float(A[:,y][z])+dot#row_x＊row_Y
            cov_matrix[x][y]=dot/(number-1)#storage back to COV_MATRIX,them divide by N-1
    print(cov_matrix)