已知稀疏矩阵A和B，编程实现基于三元组顺序表实现A+B的运算，编写十个python程序

程序1：定义三元组顺序表的数据结构

class Triple:
    def __init__(self, row, col, value):
        self.row = row
        self.col = col
        self.value = value

程序2：定义稀疏矩阵A和B

A = [Triple(0, 0, 1), Triple(0, 1, 2), Triple(0, 3, 4), Triple(1, 0, 3), Triple(2, 2, 5)]
B = [Triple(0, 1, 1), Triple(1, 0, 2), Triple(2, 2, 3)]

程序3：定义稀疏矩阵的加法运算

def matrix_addition(A, B):
    result = []
    i = 0
    j = 0
    while i < len(A) and j < len(B):
        if A[i].row < B[j].row or (A[i].row == B[j].row and A[i].col < B[j].col):
            result.append(A[i])
            i += 1
        elif A[i].row == B[j].row and A[i].col == B[j].col:
            if A[i].value + B[j].value != 0:
                result.append(Triple(A[i].row, A[i].col, A[i].value + B[j].value))
            i += 1
            j += 1
        else:
            result.append(B[j])
            j += 1
    while i < len(A):
        result.append(A[i])
        i += 1
    while j < len(B):
        result.append(B[j])
        j += 1
    return result

程序4：调用加法运算，输出结果

result = matrix_addition(A, B)
for triple in result:
    print(triple.row, triple.col, triple.value)

程序5：定义稀疏矩阵A和B的转置矩阵

def transpose(matrix):
    result = []
    col_counts = [0] * len(matrix[0])
    for triple in matrix:
        col_counts[triple.col] += 1
    col_starts = [0]
    for i in range(len(col_counts) - 1):
        col_starts.append(col_starts[-1] + col_counts[i])
    for triple in matrix:
        result.append(Triple(triple.col, triple.row, triple.value))
        col_starts[triple.col] += 1
    return result

程序6：调用转置矩阵，输出结果

AT = transpose(A)
for triple in AT:
    print(triple.row, triple.col, triple.value)

BT = transpose(B)
for triple in BT:
    print(triple.row, triple.col, triple.value)

程序7：定义稀疏矩阵A和B的乘法运算

def matrix_multiplication(A, B):
    BT = transpose(B)
    result = []
    for i in range(len(A)):
        row = [0] * len(BT)
        for j in range(len(BT)):
            k = 0
            l = 0
            while k < len(A) and l < len(BT):
                if A[k].row < i or BT[l].row < j:
                    k += 1
                elif A[k].row > i or BT[l].row > j:
                    l += 1
                else:
                    if A[k].col < BT[l].col:
                        k += 1
                    elif A[k].col > BT[l].col:
                        l += 1
                    else:
                        row[j] += A[k].value * BT[l].value
                        k += 1
                        l += 1
        for j in range(len(row)):
            if row[j] != 0:
                result.append(Triple(i, j, row[j]))
    return result

程序8：调用乘法运算，输出结果

result = matrix_multiplication(A, B)
for triple in result:
    print(triple.row, triple.col, triple.value)

程序9：定义稀疏矩阵A的转置矩阵的乘法运算

def transpose_matrix_multiplication(A, B):
    AT = transpose(A)
    result = []
    for i in range(len(AT)):
        row = [0] * len(B)
        for j in range(len(B)):
            k = 0
            l = 0
            while k < len(AT) and l < len(B):
                if AT[k].row < i or B[l].row < j:
                    k += 1
                elif AT[k].row > i or B[l].row > j:
                    l += 1
                else:
                    if AT[k].col < B[l].col:
                        k += 1
                    elif AT[k].col > B[l].col:
                        l += 1
                    else:
                        row[j] += AT[k].value * B[l].value
                        k += 1
                        l += 1
        for j in range(len(row)):
            if row[j] != 0:
                result.append(Triple(i, j, row[j]))
    return result

程序10：调用转置矩阵的乘法运算，输出结果

result = transpose_matrix_multiplication(A, B)
for triple in result:
    print(triple.row, triple.col, triple.value)