Gram-Schmidt Orthogonization using Numpy

gram_schmidt.py

import numpy as np

def gs_cofficient(v1, v2):
    return np.dot(v2, v1) / np.dot(v1, v1)

def multiply(cofficient, v):
    return map((lambda x : x * cofficient), v)

def proj(v1, v2):
    return multiply(gs_cofficient(v1, v2) , v1)

def gs(X):
    Y = []
    for i in range(len(X)):
        temp_vec = X[i]
        for inY in Y :
            proj_vec = proj(inY, X[i])
            temp_vec = map(lambda x, y : x - y, temp_vec, proj_vec)
        Y.append(temp_vec)
    return Y

#Test data
test = np.array([[3.0, 1.0], [2.0, 2.0]])
test2 = np.array([[1.0, 1.0, 0.0], [1.0, 3.0, 1.0], [2.0, -1.0, 1.0]])
print np.array(gs(test))
print np.array(gs(test2))

Pytorch version :

import torch    
def orthonormalize(vectors):    
    """    
        Orthonormalizes the vectors using gram schmidt procedure.    
    
        Parameters:    
            vectors: torch tensor, size (dimension, n_vectors)    
                    they must be linearly independant    
        Returns:    
            orthonormalized_vectors: torch tensor, size (dimension, n_vectors)    
    """    
    assert (vectors.size(1) <= vectors.size(0)), 'number of vectors must be smaller or equal to the dimension'    
    orthonormalized_vectors = torch.zeros_like(vectors)    
    orthonormalized_vectors[:, 0] = vectors[:, 0] / torch.norm(vectors[:, 0], p=2)    
    
    for i in range(1, orthonormalized_vectors.size(1)):    
        vector = vectors[:, i]    
        V = orthonormalized_vectors[:, :i]    
        PV_vector= torch.mv(V, torch.mv(V.t(), vector))    
        orthonormalized_vectors[:, i] = (vector - PV_vector) / torch.norm(vector - PV_vector, p=2)    
    
    return orthonormalized_vectors

Similar to @ingmarschuster 's vectorized version

def gram_schmidt(A,norm=True,row_vect=False):
    """Orthonormalizes vectors by gram-schmidt process
    
    Parameters
    -----------
    A : ndarray,
    Matrix having vectors in its columns
    
    norm : bool,
    Do you need Normalized vectors?
    
    row_vect: bool,
    Does Matrix A has vectors in its rows?
    
    Returns 
    -------
    G : ndarray,
    Matrix of orthogonal vectors 
    
    """
    if row_vect :
        # if true, transpose it to make column vector matrix
        A = A.T
    
    no_of_vectors = A.shape[1]
    G = A[:,0:1].copy() # copy the first vector in matrix
    # 0:1 is done to to be consistent with dimensions - [[1,2,3]]
    
    # iterate from 2nd vector to number of vectors
    for i in range(1,no_of_vectors):
        
        # calculates weights(coefficents) for every vector in G
        numerator = A[:,i].dot(G)
        denominator = np.diag(np.dot(G.T,G)) #to get elements in diagonal
        weights = np.squeeze(numerator/denominator)
        
        # projected vector onto subspace G 
        projected_vector = np.sum(weights * G,
                                  axis=1,
                                  keepdims=True)
        
        # orthogonal vector to subspace G
        orthogonalized_vector = A[:,i:i+1] - projected_vector
        
        # now add the orthogonal vector to our set 
        G = np.hstack((G,orthogonalized_vector))
        
    if norm :
        # to get orthoNORMAL vectors (unit orthogonal vectors)
        # replace zero to 1 to deal with division by 0 if matrix has 0 vector
        G = G/replace_zero(np.linalg.norm(G,axis=0))
    
    if row_vect:
        return G.T
    
    return G

def replace_zero(array): 
    
    for i in range(len(array)) :
        if array[i] == 0 : 
            array[i] = 1
    return array

G = np.array([[1,0,0],[1,1,0],[1,1,1],[1,1,1]])
gram_schmidt(G)
>
array([[ 0.5       , -0.8660254 ,  0.        ],
       [ 0.5       ,  0.28867513, -0.81649658],
       [ 0.5       ,  0.28867513,  0.40824829],
       [ 0.5       ,  0.28867513,  0.40824829]])

B = np.array([[1,0],[-2,0],[2,0]])
gram_schmidt(B)
>
array([[ 0.33333333,  0.        ],
       [-0.66666667,  0.        ],
       [ 0.66666667,  0.        ]])

Is there a 3D implementation of this procedure? I have n image in a non-orthogonal 3D system and I am trying to convert it to an orthogonal 3D image.

I simply implemented formula from a book (Maths for ML) and it is working fine with any matrix dimension (eg., 4x5).

import numpy as np
from numpy.linalg import inv

def _from_basis_to_project_matrix(B):
    """Formula page 87 book 'Maths For ML' """
    inv_Bt_B = inv(np.dot(B.T, B))
    proj_matrix = np.dot(B, np.dot(inv_Bt_B, B.T))
    return proj_matrix

def _projec(x, basis):
    """project `x` in the new vector space defined by `basis` """
    proj_matrix = _from_basis_to_project_matrix(basis)
    proj_x = np.dot(proj_matrix, x)
    return proj_x

def _get_col(x, col_id):
    """return column `col_id` from matrix `x` """
    raw_col_val = x[:, col_id]
    col_as_row = np.array([raw_col_val]).T
    return col_as_row

def gram_schmidt(B):
    nb_col = B.shape[1]
    first_col=_get_col(B, 0)
    
    U_vecs = [first_col]
    
    for i in range(1, nb_col):
        B_i = _get_col(B, i)
        U_i_1 = np.concatenate(U_vecs, axis=1)
        p = _projec(B_i, U_i_1)
        U_i = B_i - p
        U_vecs.append(U_i)

    return U_vecs

if __name__ == '__main__':
    # B=np.array([[2,1],[0,1]])
    # B=np.array([[1,0],[1,1],[1,2]])
    B = np.array([[0, 1, -3, -1],
                  [-1, -3, 4, -3],
                  [2, 1, 1, 5],
                  [0, -1, 2, 0],
                  [2, 2, 1, 7]
                  ])

    U = gram_schmidt(B)

    print(np.round(U, 2))

Is there a 3D implementation of this procedure? I have n image in a non-orthogonal 3D system and I am trying to convert it to an orthogonal 3D image.

If you are working solely with vectors, then there is no problem with any number of dimensions (assuming you have sufficiently many linearly independent vectors to form an orthonormal basis).

This should be fine even if you are using an MxNxP (matrix/tensor, whatever). In that case you'd be able to use vectors of length MP or NP. Just have some mapping from the interval [1,MP] to a "2D" slice of the "3D" object.
For instance, take phi: x -> (x // M, x % M) [or something like it] to map x, the index into the vector, to i.j the indices in the slice.
phi would then just fill rows or columns (depending on whether you're using a row-major convention or not).

#  this maps a vector of length 25 to a 5x5 matrix
for x in range(1,25):
    print(f'({x // 5},{x % 5})')

Linearity shouldn't be compromised by the method you use to traverse the "lower-dimensional" slice.