# dot_product_timeit.py

# dot_product_timeit.py

import timeit

# Simulate large image data for image enhancing (e.g., 5 million pixel values)... or even whatever training
vector_size = 5000000  # Adjust based on your system's capability
vector1 = [i % 256 for i in range(vector_size)]  # Pixel values 0-255
vector2 = [(i + 50) % 256 for i in range(vector_size)]  # Offset pixel values

# Dot product implementation using generator and zip
def dot_product(A, B):
    return sum(a * b for a, b in zip(A, B))

# Basic arithmetic implementation with indexed loop
def basic_arithmetic(A, B):
    result = 0
    for i in range(len(A)):
        result += A[i] * B[i]
    return result

result_dot = dot_product(vector1, vector2)
result_basic = basic_arithmetic(vector1, vector2)

if result_dot == result_basic:
    print("Results are equal. Waiting for timeit comparison...")
else:
    print("Results are NOT equal. Quitting...")
    0/0

def run_dot_product():
    dot_product(vector1, vector2)

def run_basic_arithmetic():
    basic_arithmetic(vector1, vector2)

# Performance comparison with reduced iterations for large data
test_runs = 10  # Reduced due to large vector size
dot_time = timeit.timeit(run_dot_product, number=test_runs)
basic_time = timeit.timeit(run_basic_arithmetic, number=test_runs)

print(f"Performance comparison for {vector_size} elements ({test_runs} runs):")
print(f"Dot Product (optimized): {dot_time:.3f} seconds")
print(f"Basic Arithmetic (loop): {basic_time:.3f} seconds")
print(f"Dot Product was {basic_time/dot_time:.3f}x faster")