spatial_image_analysis.py 文件源码

def projection_matrix(point_set, subspace_rank = 2):
    """
    Compute the projection matrix of a set of point depending on the subspace rank.

    Args:
     - point_set (np.array): list of coordinates of shape (n_point, init_dim).
     - dimension_reduction (int) : the dimension reduction to apply
    """
    point_set = np.array(point_set)
    nb_coord = point_set.shape[0]
    init_dim = point_set.shape[1]
    assert init_dim > subspace_rank
    assert subspace_rank > 0

    centroid = point_set.mean(axis=0)
    if sum(centroid) != 0:
        # - Compute the centered matrix:
        centered_point_set = point_set - centroid
    else:
        centered_point_set = point_set

    # -- Compute the Singular Value Decomposition (SVD) of centered coordinates:
    U,D,V = svd(centered_point_set, full_matrices=False)
    V = V.T

    # -- Compute the projection matrix:
    H = np.dot(V[:,0:subspace_rank], V[:,0:subspace_rank].T)

    return H