access 网站开发,软件开发难度大吗,有没有做彩票直播的网站,长春高端网站制作文章目录 一、代码仓库二、矩阵的基本运算2.1 矩阵的加法2.2 矩阵的数量乘法2.3 矩阵和向量的乘法2.4 矩阵和矩阵的乘法2.5 矩阵的转置 三、手写Matrix代码Matrix.pymain_matrix.pymain_numpy_matrix.py 一、代码仓库 https://github.com/Chufeng-Jiang/Python-Linear-Algebra-… 文章目录 一、代码仓库二、矩阵的基本运算2.1 矩阵的加法2.2 矩阵的数量乘法2.3 矩阵和向量的乘法2.4 矩阵和矩阵的乘法2.5 矩阵的转置 三、手写Matrix代码Matrix.pymain_matrix.pymain_numpy_matrix.py 一、代码仓库 https://github.com/Chufeng-Jiang/Python-Linear-Algebra-for-Beginner/tree/main 二、矩阵的基本运算 2.1 矩阵的加法 2.2 矩阵的数量乘法 2.3 矩阵和向量的乘法 2.4 矩阵和矩阵的乘法 2.5 矩阵的转置 三、手写Matrix代码
Matrix.py
from .Vector import Vectorclass Matrix:参数二是一个二维列表def __init__(self, list2d):self._values [row[:] for row in list2d]classmethoddef zero(cls, r, c):返回一个r行c列的零矩阵return cls([[0] * c for _ in range(r)])def T(self):返回矩阵的转置矩阵return Matrix([[e for e in self.col_vector(i)]for i in range(self.col_num())])def __add__(self, another):返回两个矩阵的加法结果assert self.shape() another.shape(), \Error in adding. Shape of matrix must be same.return Matrix([[a b for a, b in zip(self.row_vector(i), another.row_vector(i))]for i in range(self.row_num())])def __sub__(self, another):返回两个矩阵的减法结果assert self.shape() another.shape(), \Error in subtracting. Shape of matrix must be same.return Matrix([[a - b for a, b in zip(self.row_vector(i), another.row_vector(i))]for i in range(self.row_num())])def dot(self, another):返回矩阵乘法的结果if isinstance(another, Vector):# 矩阵和向量的乘法assert self.col_num() len(another), \Error in Matrix-Vector Multiplication.return Vector([self.row_vector(i).dot(another) for i in range(self.row_num())])if isinstance(another, Matrix):# 矩阵和矩阵的乘法assert self.col_num() another.row_num(), \Error in Matrix-Matrix Multiplication.return Matrix([[self.row_vector(i).dot(another.col_vector(j)) for j in range(another.col_num())]for i in range(self.row_num())])def __mul__(self, k):返回矩阵的数量乘结果: self * kreturn Matrix([[e * k for e in self.row_vector(i)]for i in range(self.row_num())])def __rmul__(self, k):返回矩阵的数量乘结果: k * selfreturn self * kdef __truediv__(self, k):返回数量除法的结果矩阵self / kreturn (1 / k) * selfdef __pos__(self):返回矩阵取正的结果return 1 * selfdef __neg__(self):返回矩阵取负的结果return -1 * selfdef row_vector(self, index):返回矩阵的第index个行向量return Vector(self._values[index])def col_vector(self, index):返回矩阵的第index个列向量return Vector([row[index] for row in self._values])def __getitem__(self, pos):返回矩阵pos位置的元素r, c posreturn self._values[r][c]def size(self):返回矩阵的元素个数r, c self.shape()return r * cdef row_num(self):返回矩阵的行数return self.shape()[0]__len__ row_numdef col_num(self):返回矩阵的列数return self.shape()[1]def shape(self):返回矩阵的形状: (行数 列数)return len(self._values), len(self._values[0])def __repr__(self):return Matrix({}).format(self._values)__str__ __repr__
main_matrix.py
from playLA.Vector import Vector
from playLA.Matrix import Matrixif __name__ __main__:matrix Matrix([[1, 2], [3, 4]])print(matrix)print(matrix.shape {}.format(matrix.shape()))print(matrix.size {}.format(matrix.size()))print(len(matrix) {}.format(len(matrix)))print(matrix[0][0] {}.format(matrix[0, 0]))matrix2 Matrix([[5, 6], [7, 8]])print(matrix2)print(add: {}.format(matrix matrix2))print(subtract: {}.format(matrix - matrix2))print(scalar-mul: {}.format(2 * matrix))print(scalar-mul: {}.format(matrix * 2))print(zero_2_3: {}.format(Matrix.zero(2, 3)))T Matrix([[1.5, 0], [0, 2]])p Vector([5, 3])print(T.dot(p) {}.format(T.dot(p)))P Matrix([[0, 4, 5], [0, 0, 3]])print(T.dot(P) {}.format(T.dot(P)))print(A.dot(B) {}.format(matrix.dot(matrix2)))print(B.dot(A) {}.format(matrix2.dot(matrix)))print(P.T {}.format(P.T())) main_numpy_matrix.py
import numpy as npif __name__ __main__:# 矩阵的创建A np.array([[1, 2], [3, 4]])print(A)# 矩阵的属性print(A.shape)print(A.T)# 获取矩阵的元素print(A[1, 1])print(A[0])print(A[:, 0])print(A[1, :])# 矩阵的基本运算B np.array([[5, 6], [7, 8]])print(A B)print(A - B)print(10 * A)print(A * 10)print(A * B)print(A.dot(B))p np.array([10, 100])print(A p)print(A 1)print(A.dot(p))
