矩阵与状态转移方程

def get_row(matrix, row):
    return matrix[row]
def get_column(matrix, column_number):
    column = []
    for i in range(len(matrix)):
        column.append(matrix[i][column_number])
    return column

def dot_product(vector_one, vector_two):
    sum = 0
    for i in range(len(vector_one)):
        sum = sum + vector_one[i] * vector_two[i]
    return sum
def matrix_multiplication(matrixA, matrixB):
    A = len(matrixA)
    B = len(matrixB[0])
    ### HINT: The len function in Python will be helpful
    m_rows = A
    p_columns = B
    result = []
    row_result = []
    for i in range(m_rows):
        row_vector = get_row(matrixA, i)
        for j in range(p_columns):
            column_vector = get_column(matrixB,j)
            product = dot_product(row_vector, column_vector)
            row_result.append(product)
        result.append(row_result)
        row_result = []
    return result

def transpose(matrix):
    matrix_transpose = []
    row_result = []
    row = len(matrix)
    column = len(matrix[0])
    for j in range(column):
        for i in range(row):
            row_result.append(matrix[i][j])
        matrix_transpose.append(row_result)
        row_result = []
    return matrix_transpose

def dot_product(vector_one, vector_two):
    sum = 0
    for i in range(len(vector_one)):
        sum = sum + vector_one[i] * vector_two[i]
    return sum
def matrix_multiplication(matrixA, matrixB):
    product = []
    row_result = []
    
    ## Take the transpose of matrixB and store the result
    ##       in a new variable
    matrixB_transpose = transpose(matrixB)
    
    ## Use a nested for loop to iterate through the rows
    ## of matrix A and the rows of the tranpose of matrix B
    for i in range(len(matrixA)):
        for j in range(len(matrixB_transpose)):
            dot_result = dot_product(matrixA[i], matrixB_transpose[j])
            row_result.append(dot_result)
    ## TODO: Calculate the dot product between each row of matrix A
    ##         with each row in the transpose of matrix B
        product.append(row_result)
        row_result = []
    return product

def identity_matrix(n):
    
    identity = []
    row = []
    # Write a nested for loop to iterate over the rows and
    # columns of the identity matrix. Remember that identity
    # matrices are square so they have the same number of rows
    # and columns
    for i in range(n):
        for j in range(n):
            if i == j:
                row.append(1)
            else:
                row.append(0)
        identity.append(row)
        row = []
    # Make sure to assign 1 to the diagonal values and 0 everywhere
    # else
    
    return identity