Talk:Google Colab

AgroGuard

Real-Time Human and Animal Intrusion Detection System for Agricultural Farms with Adaptive Alarm and Notification Mechanisms Evangelistra (talk) 04:34, 26 July 2026 (UTC)

طلبوا منا تقرير بخصوص هذا الكود مع حل الشيفرة والسرك

import numpy as np import matplotlib.pyplot as plt import time

rng = np.random.default_rng(42)

x1 = np.linspace(-1, 1, 25) y1 = 2 * x1 + 1 + rng.normal(0, 0.15, len(x1))

x2 = rng.uniform(-1, 1, 5000) y2 = 2 * x2 + 1 + rng.normal(0, 1.0, len(x2))

results = []

for name, x, y in [('Small', x1, y1), ('Large', x2, y2)]: A = np.c_[np.ones(len(x)), x]

t = time.perf_counter()

theta_ne = np.linalg.solve(A.T @ A, A.T @ y)

time_ne = time.perf_counter() - t

theta = np.zeros(2)

epochs = 15

learning_rate = 0.001

t = time.perf_counter()

for epoch in range(epochs):

    for a, actual in zip(A, y):

        error = actual - a @ theta

        theta += learning_rate * error * a

time_lms = time.perf_counter() - t

mse_ne = np.mean((y - A @ theta_ne) ** 2)

mse_lms = np.mean((y - A @ theta) ** 2)

print(name, 'NE MSE/time:', mse_ne, time_ne)

print(name, 'LMS MSE/time:', mse_lms, time_lms)

if name == 'Large':

    results = [x, y, A, theta_ne, theta.copy()]

x, y, A, theta_ne, theta_lms = results order = np.argsort(x)

plt.figure(figsize=(8, 5)) plt.scatter(x, y, s=10, label='Data', alpha=0.5) plt.plot(x[order], (A @ theta_ne)[order], color='red', label='Normal Equation') plt.plot(x[order], (A @ theta_lms)[order], '--', color='green', label='LMS')

plt.xlabel('Input x') plt.ylabel('Output y') plt.title('LMS and Normal Equation (Modified)') plt.legend() plt.grid(True) plt.show() ~2026-57088-98 (talk) 20:40, 26 October 2026 (UTC)

ضعه في ملف pdf مع خط مرتب ورسم جيد لان استنسخه ~2026-57088-98 (talk) 20:41, 26 October 2026 (UTC)