import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler


input_size = 1
hidden_size = 1
num_layers = 2
output_size = 1

class MLP(nn.Module):
    def __init__(self, input_size=1, hidden_size=1, num_layers=2, output_size=1):
        super(MLP, self).__init__()
        # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        # self.lstm=nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        self.fc0=nn.Linear(input_size,hidden_size,bias=False)
        self.relu = nn.ReLU()
        self.fc = nn.Linear(hidden_size, output_size)
        
        nn.init.constant_(self.fc0.weight,0.78)
        nn.init.xavier_uniform_(self.fc.weight)
        nn.init.zeros_(self.fc.bias)

    def forward(self, x):
        # out, _ = self.lstm(x)
        out0=self.fc0(x)
        # out1=self.relu(out0)
        # print(f"out{out[0]}")
        # print(f"out{out[-1]}")
        # out2 = self.fc(out1)
        return out0