faster_arrays

matmul.py

import numpy as np
import timeit


def matrix_muliplication_with_np(matrix1, matrix2):
    return np.matmul(matrix1, matrix2)


def matrix_multiplication_with_for_loop(matrix1, matrix2):
    result = np.zeros((len(matrix1), len(matrix2[0])))

    for i in range(len(matrix1)):
        for k in range(len(matrix2)):
            for j in range(len(matrix2[0])):
                result[i][j] += matrix1[i][k] * matrix2[k][j]

    return result


if __name__ == "__main__":
    matrix1 = np.random.randint(1, 10, (1000, 1000))
    matrix2 = np.random.randint(1, 10, (1000, 1000))

    print(
        "Matrix multiplication with numpy: ",
        timeit.timeit(lambda: matrix_muliplication_with_np(matrix1, matrix2), number=1),
    )
    print(
        "Matrix multiplication with for loop: ",
        timeit.timeit(
            lambda: matrix_multiplication_with_for_loop(matrix1, matrix2), number=1
        ),
    )

sum.py

import numpy as np
import timeit


def sum_with_for_loop(array) -> int:
    sum = 0
    for i in array:
        sum += i
    return sum


def sum_with_np_sum(array) -> int:
    return np.sum(array)


if __name__ == "__main__":
    array = np.random.randint(0, 100, 1000000)

    # print time for for loop
    print(timeit.timeit(lambda: sum_with_for_loop(array), number=100))

    # print time for np.sum
    print(timeit.timeit(lambda: sum_with_np_sum(array), number=100))

sum_product.py

import numpy as np
import timeit


def sum_product_with_for_loop(array1, array2) -> int:
    sum = 0
    for i, j in zip(array1, array2):
        sum += i * j
    return sum


def sum_product_with_np_sum(array1, array2) -> int:
    return np.sum(array1 * array2)


if __name__ == "__main__":
    array1 = np.random.randint(0, 100, 1000000)
    array2 = np.random.randint(0, 100, 1000000)

    # Print the time taken to execute the function
    print(timeit.timeit(lambda: sum_product_with_for_loop(array1, array2), number=100))

    print(timeit.timeit(lambda: sum_product_with_np_sum(array1, array2), number=100))