最小二乘法矩阵

#! /usr/bin/env python# -*- coding: utf-8 -*-import numpy as npdef calc_left_k_mat(k): """ 获得左侧k矩阵 :param k: :return: """ k_mat = [] for i in range(k + 1): now_line = [] for j in range(k + 1): now_line.append(j + i) k_mat.append(now_line) return k_matdef calc_right_k_mat(k): """ 计算右侧矩阵 :param k: :return: """ k_mat = [] for i in range(k + 1): k_mat.append([i, i + 1]) return k_matdef pow_k(x, k): """ 计算x列表中的k次方和 :param x: 点集合的x坐标 :param k: k值 :return: """ sum = 0 for i in x: sum += i ** k return sumdef get_left_mat_with_x(k_mat, k): """ 将 左侧k矩阵运算得到左侧新的矩阵 :param k_mat: :param k: :return: """ left_mat = [] for kl in k_mat: now_data = [] for k in kl: now_data.append(pow_k(x, k)) left_mat.append(now_data) return left_matdef get_right_mat_with(right_k_mat): """ 将 右侧k矩阵运算得到右侧新的矩阵 :param right_k_mat: :return: """ right_mat = [] for i in range(len(right_k_mat)): sum = 0 for xL, yL in zip(x, y): a = (xL ** right_k_mat[i][0]) * (yL ** right_k_mat[i][1]) sum += a right_mat.append(sum) return right_matdef fuse_mat(left, right): """ 融合两个矩阵 :param left: :param right: :return: """ new_mat = [] for i in range(len(left)): asd = np.append(left[i], right[i]) new_mat.append(list(asd)) return new_matif __name__ == '__main__': k = 3 x = [1, 2, 3] y = [1, 2, 3] # 计算原始左侧K矩阵 left_k_mat = calc_left_k_mat(k) print("原始左侧K矩阵") print(left_k_mat) # 计算原始右侧K矩阵 right_k_mat = calc_right_k_mat(k) print("原始右侧k矩阵") print(right_k_mat) # 计算左侧 k 矩阵 new_left_mat = get_left_mat_with_x(k_mat=left_k_mat, k=k) # 计算右侧 k 矩阵 new_right_mat = get_right_mat_with(right_k_mat=right_k_mat) print("计算后左侧K矩阵") print(new_left_mat) print("计算后右侧侧K矩阵") print(new_right_mat) print("-----" * 10) # 融合两个矩阵 左侧 矩阵每一行增加 右侧矩阵的对应行 new_all = fuse_mat(new_left_mat, new_right_mat) print("完整矩阵") print(new_all)

总结

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