ML-LSSVM(regression:parameter optimization)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

frame = pd.read_csv('lssvm.csv')
arr = frame.values[0:10]
x,y = arr[:,0:-1],arr[:,-1]

x_asis = np.random.random((1,2))
y_asis = np.random.random((1,2))

sample_nums = 50

def kernel(x,y,sigma):
a = x-y
result = np.exp(-a.dot(a.T)/(2*sigma**2))
return result

def matrixOmega(x, y, sigma):
计算x的长度并赋值给变量length
初始化一个全零的二维数组omega
遍历i和j的所有组合
对于每一个i,j的位置
计算对应的核函数