Python Rosenblatt Perceptron

perceptron.py

import matplotlib.pyplot as plt
import numpy as np

X = np.array([
    [0.8, 0.4],
    [0.3, 0.1],
    [0.8, 0.8],
    [0.4, 0.6],
    [0.6, 0.8],
    [0.4, 0.2],
    [0.4, 0.5],
])
Y = np.array([0, 0, 1, 1, 1, 0, 1])

def plot_points(X, Y, ax):
    for i, x in enumerate(X):
        ax.scatter(x[0], x[1], s=120, 
                   marker=('_' if Y[i] <= 0 else '+'), linewidths=2, 
                   c=('r' if Y[i] <= 0 else 'b')
                  )
        
fig, ax = plt.subplots(figsize=(6, 6))
plot_points(X, Y, ax)

def update(X, Y):
    w = np.zeros(X.shape[1]+1)
    epochs = 100
    #fig, axes = plt.subplots(figsize=(5, 30), nrows=epochs, ncols=1)
    for e in range(epochs):  
        for x, y in zip(X, Y):
            pred = np.where((np.dot(w[:2], x)+w[2]) >= 0.0, 1, 0)
            w[:2] += eta*(y-pred) * x
            w[2] += eta*(y-pred)
    return w
  
def predict(w, x):
    return np.where((np.dot(w[:2], x)+w[2]) > 0.0, 1, 0)
  
w = update(X, Y)
for a in range(0, 10):
    for b in range(0, 10):
        i, j = a/10, b/10
        p = predict(w, [i, j])
        plt.scatter(i, j, s=120, marker=('_' if p <= 0 else '+'), linewidths=2,
                   c=('r' if p <= 0 else 'b')
                   )

(X, y):

hyperplane: