import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()

data = pd.DataFrame(iris.data, columns=iris.feature_names)
length = data.iloc[:,0]  #length = data["sepal length (cm)"]
width = data.iloc[:,1]
label = iris.target


def graph():
    #graph
    label = iris.target
    x = []
    y = []
    for i in range(2):
        index = np.where(label == i)[0]
        x.append(length[index])
        y.append(width[index])
        plt.scatter(x[i], y[i], label=iris.target_names[i])
    plt.legend()



index = np.where(label < 2)[0]
label = label[index]
x = np.matrix(data.iloc[index,:2])
one = np.matrix(np.ones((len(label)))).T
x = np.hstack((one,x))

w = np.matrix([0,0,0], dtype=np.float32).T
def sigmoid(x):
    return 1/(1+np.exp(-x))
eta = 0.1

for j in range(20):
    per = np.random.permutation(100)
    E = 0
    for i in range(100):
        da = x[per][i].T
        y = sigmoid(w.T * da)
        t = label[per][i]
        E += t * np.log(y) + (1-t) * np.log(1-y)

        dE = (y[0,0]-t) * da
        w += -eta * dE
    print("w is:", w)
    print("error:", E[0,0])
# w0 + w1*x + w2*y = 0
gridX = np.linspace(4,7.5,100)
gridY = np.linspace(1.5,4.5,100)

Y = (-w[0,0]-w[1,0]*gridX)/w[2,0]
plt.plot(gridX, Y)
graph()
plt.show()