import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt


# Hyper Parameters
TIME_STEP = 10
INPUT_SIZE = 1
LR = 0.01


# data
steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)
x_np = np.sin(steps)   # float32 for converting torch FloatTensor
y_np = np.cos(steps)
plt.plot(steps, y_np, 'r-', label='target (cos)')
plt.plot(steps, x_np, 'b-', label='input (sin)')
plt.legend(loc='best')
#plt.show()


class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()
        self.rnn = nn.RNN(
            input_size=INPUT_SIZE,
            hidden_size=64,
            num_layers=1,
            batch_first=True
        )
        self.out = nn.Linear(64, 1)

    def forward(self, x, h_state):
        # x (batch, time_step, input_size)
        # h_state (n_layers, batch, hidden_size)
        # r_out (batch, time_step, hidden_size)
        r_out, h_state = self.rnn(x, h_state)

        outs = []  # save all prediction
        for time_step in range(r_out.size(1)):  # calculate output for each time step
            outs.append(self.out(r_out[:, time_step, :]))
        return torch.stack(outs, dim=1), h_state   # stack将list压成tensor,shape=(1, TIME_STEP, 1)

        # instead, for simplicity, you can replace above codes by follows
        # r_out = r_out.view(-1, 64)
        # outs = self.out(r_out)
        # return outs, h_state


rnn = RNN()
print(rnn)

optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
loss_func = nn.MSELoss()

h_state = None      # for initial hidden state

plt.figure(1, figsize=(12, 5))
plt.ion()           # continuously plot

for i in range(100):
    start, end = i * np.pi, (i+1) * np.pi   # time range
    # use sin predict cos
    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)
    x_np = np.sin(steps)
    y_np = np.cos(steps)

    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])  # shape(batch, time_step, input_size)
    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])

    prediction, h_state = rnn(x, h_state)
    # next step is important!!
    h_state = h_state.data  # repack the hidden state, break the connection from last iteration

    loss = loss_func(prediction, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # plotting
    plt.plot(steps, y_np.flatten(), 'r-')
    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')
    plt.draw()
    plt.pause(0.05)

plt.ioff()
plt.show()
