import numpy as np
import matplotlib.pyplot as plt

# Initialize variables
ws = []
gamma = 0.01
R = 1

# Generate random points within the circle of radius R
points = []
while len(points) < 100:
    x, y = np.random.uniform(-R, R, 2)
    if x**2 + y**2 <= R**2:
        points.append((x, y))

# Remove points with x < gamma
points = [point for point in points if point[0] >= gamma]

# Initialize w
ws = []
w = np.array([-0.1, 0.0])
ws.append(w.copy())

# Perceptron learning algorithm
convergence_flag = False
while not convergence_flag:
    np.random.shuffle(points)
    convergence_flag = True
    for v in points:
        if np.dot(w, v) <= 0:
            w += v
            ws.append(w.copy())
            convergence_flag = False
# Plot the items
fig, ax = plt.subplots(figsize=(10,10))

# Vertical dotted line at x=gamma
ax.axvline(x=gamma, linestyle='--', color='r', label='x=gamma')

# Circle with radius R centered at (0, 0)
circle = plt.Circle((0, 0), R, fill=False, color='b', linestyle='-', label='Circle')
ax.add_artist(circle)

# Jagged line connecting entries in ws
points = np.array(points)
ax.scatter(points[:,0], points[:,1])
ws = np.array(ws)
for i in range(1, ws.shape[0]):
    ax.arrow(ws[i-1, 0], ws[i-1, 1], ws[i, 0] - ws[i-1, 0], ws[i, 1] - ws[i-1, 1], color='g', linewidth=0.5,
             head_width=0.03, head_length=0.04, length_includes_head=True)

# Set axis limits
# ax.set_xlim(-R, R)
# ax.set_ylim(-R, R)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.legend()

plt.gca().set_aspect('equal')
plt.savefig("perceptron convergence.svg")