sparse_coding.py 文件源码

def llc(X, D, knn=5):
    # the sparse coder introduced in
    # "Locality-constrained Linear Coding for Image Classification"

    n_samples = X.shape[1]
    n_atoms = D.shape[1]
    # has the distance of
    # each sample to each atom
    dist = np.zeros((n_samples, n_atoms))
    # calculate the distances
    for i in range(n_samples):
        for j in range(n_atoms):
            dist[i, j] = norm(X[:, i] - D[:, j])

    # has the indices of the atoms
    # that are nearest neighbour to each sample
    knn_idx = np.zeros((n_samples, knn)).astype(int)
    for i in xrange(n_samples):
        knn_idx[i, :] = np.argsort(dist[i, :])[:knn]
    # the sparse coding matrix
    Z = np.zeros((n_atoms, n_samples))
    II = np.eye(knn)
    beta = 1e-4
    b = np.ones(knn)
    for i in xrange(n_samples):
        idx = knn_idx[i, :]
        z = D.T[idx, :] - repmat(X.T[i, :], knn, 1)
        C = np.dot(z, z.T)
        C = C + II * beta * np.trace(C)
        # solve the linear system C*c=b
        c = solve(C, b)
        # enforce the constraint on the sparse codes
        # such that sum(c)=1
        c = c / float(np.sum(c))
        Z[idx, i] = c

    return Z