import numpy as np
import math

import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif']=['SimHei']

def showData(data):
    # 创建柱状图
    plt.figure(figsize=(10, 6))
    plt.bar(range(len(data)), data)

    # 添加标题和标签
    plt.title('基本柱状图示例')
    plt.xlabel('数据索引')
    plt.ylabel('数值')

    # 显示网格
    plt.grid(axis='y', alpha=0.75)

    plt.show()

def generate_matrices_and_compute(m, n):
    """
    生成m*n矩阵和n*m矩阵，计算点积并应用softmax
    """
    # 生成随机矩阵
    matrix_a = np.random.rand(m, n)  # m*n矩阵
    matrix_b = np.random.rand(n, m)  # n*m矩阵

    print("矩阵 A ({}x{}):".format(m, n))
    print(matrix_a)
    print("\n矩阵 B ({}x{}):".format(n, m))
    print(matrix_b)

    # 计算点积
    #dot_product = np.dot(matrix_a, matrix_b)
    dot_product = np.dot(matrix_a, matrix_b)/math.sqrt(m)
    #print("\n点积结果 ({}x{}):".format(m, m))
    #print(dot_product)

    # 应用softmax
    # 对每一行应用softmax
    softmax_result = np.exp(dot_product) / np.sum(np.exp(dot_product), axis=1, keepdims=True)

    print("\nSoftmax结果 ({}x{}):".format(m, m))
    print(softmax_result)
    showData(softmax_result[0].tolist())
    return matrix_a, matrix_b, dot_product, softmax_result


# 示例使用
if __name__ == "__main__":
    m, n = 300, 400  # 可以修改这些值

    print("=" * 50)
    print("矩阵运算示例: m={}, n={}".format(m, n))
    print("=" * 50)

    A, B, dot, softmax = generate_matrices_and_compute(m, n)

    # 验证结果
    print("\n验证:")
    print("点积矩阵形状:", dot.shape)
    print("Softmax矩阵形状:", softmax.shape)
    print("每行之和:", np.sum(softmax, axis=1))  # 每行之和应该为1