自己手动实现lstm多变量预测python源码

以下是一个使用Python手动实现LSTM多变量预测的基本源代码：

import numpy as np

# 定义LSTM类
class LSTM:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

        # 初始化权重和偏置
        self.Wf = np.random.randn(hidden_size, input_size + hidden_size)
        self.Wi = np.random.randn(hidden_size, input_size + hidden_size)
        self.Wo = np.random.randn(hidden_size, input_size + hidden_size)
        self.Wc = np.random.randn(hidden_size, input_size + hidden_size)
        self.Wy = np.random.randn(output_size, hidden_size)

        self.bf = np.zeros((hidden_size, 1))
        self.bi = np.zeros((hidden_size, 1))
        self.bo = np.zeros((hidden_size, 1))
        self.bc = np.zeros((hidden_size, 1))
        self.by = np.zeros((output_size, 1))

    # 定义激活函数
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def tanh(self, x):
        return np.tanh(x)

    # 定义前向传播函数
    def forward(self, xt, ht_minus_one, ct_minus_one):
        self.concat = np.vstack((ht_minus_one, xt))

        ft = self.sigmoid(np.dot(self.Wf, self.concat) + self.bf)
        it = self.sigmoid(np.dot(self.Wi, self.concat) + self.bi)
        ot = self.sigmoid(np.dot(self.Wo, self.concat) + self.bo)
        ctilde_t = self.tanh(np.dot(self.Wc, self.concat) + self.bc)
        
        ct = ft * ct_minus_one + it * ctilde_t
        ht = ot * self.tanh(ct)

        yt = np.dot(self.Wy, ht) + self.by

        return yt, ht, ct

    def train(self, X, y, lr=0.01, epochs=100):
        for epoch in range(epochs):
            for i in range(len(X)):
                xt = X[i]
                yt = y[i]
                yt_pred, ht, ct = self.forward(xt, ht_minus_one, ct_minus_one)

                # 计算梯度
                dWy = np.dot((yt_pred - yt), ht)
                dby = yt_pred - yt

                delta = np.dot(self.Wy.T, yt_pred - yt) * (1 - ht**2)
                dWo = delta * self.sigmoid(np.dot(self.Wo, self.concat) + self.bo) * (1 - self.sigmoid(np.dot(self.Wo, self.concat) + self.bo))
                dWi = delta * self.sigmoid(np.dot(self.Wi, self.concat) + self.bi) * (1 - self.sigmoid(np.dot(self.Wi, self.concat) + self.bi))
                dWf = delta * self.sigmoid(np.dot(self.Wf, self.concat) + self.bf) * (1 - self.sigmoid(np.dot(self.Wf, self.concat) + self.bf))
                dWc = delta * (1 - ctilde_t**2) * self.sigmoid(np.dot(self.Wc, self.concat) + self.bc) * (1 - self.sigmoid(np.dot(self.Wc, self.concat) + self.bc))

                # 更新权重和偏置
                self.Wy -= lr * dWy
                self.by -= lr * dby

                self.Wo -= lr * dWo
                self.bo -= lr * dby

                self.Wi -= lr * dWi
                self.bi -= lr * dby

                self.Wf -= lr * dWf
                self.bf -= lr * dby

                self.Wc -= lr * dWc
                self.bc -= lr * dby

# 定义输入和输出数据
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])

# 初始化LSTM模型
lstm = LSTM(2, 4, 1)

# 训练模型
lstm.train(X, y)

# 进行预测
for i in range(len(X)):
    xt = X[i]
    yt_pred, _, _ = lstm.forward(xt, ht_minus_one, ct_minus_one)
    print(f"Input: {xt}, Predicted Output: {yt_pred}")

这是一个简单的LSTM模型，用于进行逻辑异或运算的预测。输入数据`X`是一个4x2的数组，输出数据`y`是一个4x1的数组。模型使用一个4维的隐藏状态并输出一个预测值。以上代码只是一个基本的框架，可以根据需要进行进一步的优化和改进。