Gram-Schmidt 算法的主要目的是将矩阵转换为Orthonormal Matrix
# GRADED FUNCTION
import numpy as np
import numpy.linalg as laverySmallNumber = 1e-14 # That's 1×10??? = 0.00000000000001# Our first function will perform the Gram-Schmidt procedure for 4 basis vectors.
# We'll take this list of vectors as the columns of a matrix, A.
# We'll then go through the vectors one at a time and set them to be orthogonal
# to all the vectors that came before it. Before normalising.
# Follow the instructions inside the function at each comment.
# You will be told where to add code to complete the function.
def gsBasis4(A) :B = np.array(A, dtype=np.float_) # Make B as a copy of A, since we're going to alter it's values.# The zeroth column is easy, since it has no other vectors to make it normal to.# All that needs to be done is to normalise it. I.e. divide by its modulus, or norm.B[:, 0] = B