#!/usr/bin/python3

import torch
import pandas as pd


data = pd.read_csv('iris.data',sep = ',', header = None)
data[5] = data[4].apply(lambda x: 1 if x == 'Iris-versicolor' else 0)

x = torch.tensor(data[[0,1]].values, dtype=torch.float)
y = torch.tensor(data[5], dtype=torch.float)

y = y.reshape(100,1)

class Network(torch.nn.Module):

    def __init__(self):
        super(Network, self).__init__()
        self.fc = torch.nn.Linear(2,1)

    def forward(self,x):
        x = self.fc(x)
        x = torch.nn.functional.sigmoid(x)

        return x

network = Network()
optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
criterion = torch.nn.BCELoss()

for _ in range(3000):
    optimizer.zero_grad()
    ypredicted = network(x)

    loss = criterion(ypredicted,y)
    accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
    print('{:.3}'.format(loss.item()), "\t => ",  accuracy, '% accuracy')

    loss.backward()
    optimizer.step()