import numpy as np


class LSTM:
    def __init__(self, input_dim, hidden_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.Wf = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
        self.Wi = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
        self.Wc = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
        self.Wo = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
        self.bf = np.zeros((hidden_dim, 1))
        self.bi = np.zeros((hidden_dim, 1))
        self.bc = np.zeros((hidden_dim, 1))
        self.bo = np.zeros((hidden_dim, 1))
        self.h = np.zeros((hidden_dim, 1))
        self.c = np.zeros((hidden_dim, 1))

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

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

    def forward(self, x_t):
        combined = np.vstack((self.h, x_t))
        f_t = self.sigmoid(np.dot(self.Wf, combined) + self.bf)
        i_t = self.sigmoid(np.dot(self.Wi, combined) + self.bi)
        C_tilde_t = self.tanh(np.dot(self.Wc, combined) + self.bc)
        self.c = f_t * self.c + i_t * C_tilde_t
        o_t = self.sigmoid(np.dot(self.Wo, combined) + self.bo)
        self.h = o_t * self.tanh(self.c)
        return self.h

    def backward(self, x_t, h_t, y_t, learning_rate):
        # Your backward pass implementation here
        pass

    def train(self, X, y, num_epochs, learning_rate):
        for epoch in range(num_epochs):
            for i in range(len(X)):
                x_t = X[i]
                y_t = y[i]

                # Forward pass
                h_t = self.forward(x_t)

                # Calculate loss and perform backward pass
                loss = np.mean((h_t - y_t) ** 2)  # Example loss
                self.backward(x_t, h_t, y_t, learning_rate)

                if i % 100 == 0:  # Print loss every 100 samples
                    print(f"Epoch {epoch}, Sample {i}, Loss: {loss}")