#!/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)

x1 = torch.tensor(data[0], dtype=torch.float)
x0 = torch.ones(x1.size(0))
x = torch.stack((x0, x1)).transpose(0, 1)
y = torch.tensor(data[5], dtype=torch.float)

w = torch.rand(2, requires_grad=True)

optimizer = torch.optim.SGD([w], lr=0.005)

for _ in range(3000):
    optimizer.zero_grad()
    ypredicted = torch.nn.functional.sigmoid(x @ w)

    cost = - (torch.sum(y*torch.log(ypredicted) + (torch.ones_like(y) - y) * torch.log(1- ypredicted)))
    accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
    print(w, " => ", cost, " => ",  accuracy, '% accuracy')

    cost.backward()
    optimizer.step()