用c语言写一个lstm预测的代码

LSTM（Long Short-Term Memory）是一种循环神经网络（RNN）的变体，常用于处理序列数据。下面是一个简单的C语言LSTM模型的代码示例，可以用于预测未来的序列数据。

首先，我们需要定义一些常量和超参数：

#define INPUT_SIZE 1
#define OUTPUT_SIZE 1
#define HIDDEN_SIZE 16
#define LEARNING_RATE 0.1
#define EPOCHS 1000

这里我们设置输入大小为1，输出大小为1，隐层大小为16，学习率为0.1，迭代次数为1000。

接下来，定义LSTM结构体：

typedef struct {
    double Wf[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
    double Wi[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
    double Wc[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
    double Wo[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
    double bf[HIDDEN_SIZE];
    double bi[HIDDEN_SIZE];
    double bc[HIDDEN_SIZE];
    double bo[HIDDEN_SIZE];

    double ct[HIDDEN_SIZE];
    double ht[HIDDEN_SIZE];
} LSTM;

其中，Wf、Wi、Wc、Wo是输入、遗忘、细胞状态和输出门的权重矩阵；bf、bi、bc、bo是对应的偏置向量；ct和ht为细胞状态和隐层状态。

接下来，定义一些辅助函数：

double sigmoid(double x) {
    return 1.0 / (1.0 + exp(-x));
}

double tanh(double x) {
    return (exp(x) - exp(-x)) / (exp(x) + exp(-x));
}

这里用到了sigmoid函数和tanh函数。

接下来，定义前向传播函数：

void forward(LSTM *lstm, double *x) {
    double xf[INPUT_SIZE + HIDDEN_SIZE];
    for (int i = 0; i < INPUT_SIZE; i++)
        xf[i] = x[i];
    for (int i = 0; i < HIDDEN_SIZE; i++)
        xf[INPUT_SIZE + i] = lstm->ht[i];

    double ft[HIDDEN_SIZE], it[HIDDEN_SIZE], ct[HIDDEN_SIZE], ot[HIDDEN_SIZE], ct_new[HIDDEN_SIZE], ht_new[HIDDEN_SIZE];

    for (int i = 0; i < HIDDEN_SIZE; i++) {
        ft[i] = sigmoid(dot(lstm->Wf[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bf[i]);
        it[i] = sigmoid(dot(lstm->Wi[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bi[i]);
        ct[i] = tanh(dot(lstm->Wc[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bc[i]);
        ct_new[i] = ft[i] * lstm->ct[i] + it[i] * ct[i];
        ot[i] = sigmoid(dot(lstm->Wo[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bo[i]);
        ht_new[i] = ot[i] * tanh(ct_new[i]);
    }

    for (int i = 0; i < HIDDEN_SIZE; i++) {
        lstm->ct[i] = ct_new[i];
        lstm->ht[i] = ht_new[i];
    }
}

这里首先将输入和隐层状态串联起来形成一个新的向量xf。然后，根据LSTM的结构，分别计算输入门、遗忘门、细胞状态和输出门的值，最终得到新的细胞状态ct_new和隐层状态ht_new。

接下来，定义反向传播函数：

void backward(LSTM *lstm, double *x, double *y) {
    double xf[INPUT_SIZE + HIDDEN_SIZE];
    for (int i = 0; i < INPUT_SIZE; i++)
        xf[i] = x[i];
    for (int i = 0; i < HIDDEN_SIZE; i++)
        xf[INPUT_SIZE + i] = lstm->ht[i];

    double ft[HIDDEN_SIZE], it[HIDDEN_SIZE], ct[HIDDEN_SIZE], ot[HIDDEN_SIZE], ct_new[HIDDEN_SIZE], ht_new[HIDDEN_SIZE];
    double dht[HIDDEN_SIZE], dct[HIDDEN_SIZE];
    double dWf[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE], dWi[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE], dWc[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE], dWo[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
    double dbf[HIDDEN_SIZE], dbi[HIDDEN_SIZE], dbc[HIDDEN_SIZE], dbo[HIDDEN_SIZE];

    for (int i = 0; i < HIDDEN_SIZE; i++) {
        ft[i] = sigmoid(dot(lstm->Wf[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bf[i]);
        it[i] = sigmoid(dot(lstm->Wi[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bi[i]);
        ct[i] = tanh(dot(lstm->Wc[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bc[i]);
        ct_new[i] = ft[i] * lstm->ct[i] + it[i] * ct[i];
        ot[i] = sigmoid(dot(lstm->Wo[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bo[i]);
        ht_new[i] = ot[i] * tanh(ct_new[i]);
    }

    for (int i = 0; i < OUTPUT_SIZE; i++) {
        double dht_total = 0;
        for (int j = 0; j < HIDDEN_SIZE; j++)
            dht_total += (ht_new[j] - y[i]) * ot[j] * (1 - tanh(ct_new[j]) * tanh(ct_new[j])) * lstm->Wc[j][INPUT_SIZE + i];
        dht[i] = dht_total;
    }

    for (int i = 0; i < HIDDEN_SIZE; i++) {
        double dct_total = 0;
        for (int j = 0; j < OUTPUT_SIZE; j++)
            dct_total += (ht_new[i] - y[j]) * ot[i] * (1 - tanh(ct_new[i]) * tanh(ct_new[i])) * lstm->Wo[i][INPUT_SIZE + j];
        for (int j = 0; j < HIDDEN_SIZE; j++)
            dct_total += dct[j] * ft[i] * lstm->Wf[i][INPUT_SIZE + j];
        dct[i] = dct_total;
    }

    for (int i = 0; i < HIDDEN_SIZE; i++) {
        for (int j = 0; j < INPUT_SIZE + HIDDEN_SIZE; j++) {
            dWf[i][j] = dct[i] * lstm->ct[i] * ft[i] * (1 - ft[i]) * xf[j];
            dWi[i][j] = dct[i] * ct[i] * it[i] * (1 - it[i]) * xf[j];
            dWc[i][j] = dct[i] * it[i] * (1 - ct[i] * ct[i]) * xf[j];
            dWo[i][j] = dht[i] * tanh(ct_new[i]) * ot[i] * (1 - ot[i]) * xf[j];
        }
        dbf[i] = dct[i] * lstm->ct[i] * ft[i] * (1 - ft[i]);
        dbi[i] = dct[i] * ct[i] * it[i] * (1 - it[i]);
        dbc[i] = dct[i] * it[i] * (1 - ct[i] * ct[i]);
        dbo[i] = dht[i] * tanh(ct_new[i]) * ot[i] * (1 - ot[i]);
    }

    for (int i = 0; i < HIDDEN_SIZE; i++) {
        for (int j = 0; j < INPUT_SIZE + HIDDEN_SIZE; j++) {
            lstm->Wf[i][j] -= LEARNING_RATE * dWf[i][j];
            lstm->Wi[i][j] -= LEARNING_RATE * dWi[i][j];
            lstm->Wc[i][j] -= LEARNING_RATE * dWc[i][j];
            lstm->Wo[i][j] -= LEARNING_RATE * dWo[i][j];
        }
        lstm->bf[i] -= LEARNING_RATE * dbf[i];
        lstm->bi[i] -= LEARNING_RATE * dbi[i];
        lstm->bc[i] -= LEARNING_RATE * dbc[i];
        lstm->bo[i] -= LEARNING_RATE * dbo[i];
    }
}

这里首先进行一次前向传播，然后计算输出误差对隐层状态和细胞状态的偏导数。接下来，根据公式计算各个权重的偏导数和偏置的偏导数，并更新它们的值。

最后，定义训练函数：

void train(double *input, double *output) {
    LSTM lstm;
    init(&lstm);
    for (int epoch = 0; epoch < EPOCHS; epoch++) {
        for (int i = 0; i < INPUT_SIZE; i++) {
            double x[INPUT_SIZE], y[OUTPUT_SIZE];
            x[0] = input[i];
            y[0] = output[i];
            forward(&lstm, x);
            backward(&lstm, x, y);
        }
    }
}

这里我们首先初始化LSTM模型，然后进行多次迭代，每次迭代都遍历整个输入序列，对每个输入输出对进行训练。

这就是一个简单的C语言LSTM模型的代码示例。需要注意的是，这个模型只处理输入大小为1的序列，如果需要处理更长的序列，需要相应地调整模型的参数。