Cómo reducir la matriz en forma escalonada de filas en Python

Hola, hemos encontrado la solución a lo que estabas buscando, deslízate y la obtendrás más abajo.

Solución:

Dado que el proceso gaussiano es recursivo, lo utilizamos en el código.

import numpy as np

def row_echelon(A):
    """ Return Row Echelon Form of matrix A """

    # if matrix A has no columns or rows,
    # it is already in REF, so we return itself
    r, c = A.shape
    if r == 0 or c == 0:
        return A

    # we search for non-zero element in the first column
    for i in range(len(A)):
        if A[i,0] != 0:
            break
    else:
        # if all elements in the first column is zero,
        # we perform REF on matrix from second column
        B = row_echelon(A[:,1:])
        # and then add the first zero-column back
        return np.hstack([A[:,:1], B])

    # if non-zero element happens not in the first row,
    # we switch rows
    if i > 0:
        ith_row = A[i].copy()
        A[i] = A[0]
        A[0] = ith_row

    # we divide first row by first element in it
    A[0] = A[0] / A[0,0]
    # we subtract all subsequent rows with first row (it has 1 now as first element)
    # multiplied by the corresponding element in the first column
    A[1:] -= A[0] * A[1:,0:1]

    # we perform REF on matrix from second row, from second column
    B = row_echelon(A[1:,1:])

    # we add first row and first (zero) column, and return
    return np.vstack([A[:1], np.hstack([A[1:,:1], B]) ])

A = np.array([[4, 7, 3, 8],
              [8, 3, 8, 7],
              [2, 9, 5, 3]], dtype='float')

row_echelon(A)

Siéntase libre de agregar algo como print(A[1:,:1]) si no entiendes algunas de las construcciones

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