def build_laplacian_regularized(X,
laplacian_regularization,
var=1.0,
eps=0.0,
k=0,
laplacian_normalization=""):
"""
Function to construct a regularized Laplacian from data.
:param X: (n x m) matrix of m-dimensional samples
:param laplacian_regularization: regularization to add to the Laplacian (parameter gamma)
:param var: the sigma value for the exponential function, already squared
:param eps: threshold eps for epsilon graphs
:param k: number of neighbours k for k-nn. If zero, use epsilon-graph
:param laplacian_normalization: string selecting which version of the laplacian matrix to construct
'unn': unnormalized,
'sym': symmetric normalization
'rw': random-walk normalization
:return: Q (n x n ) matrix, the regularized Laplacian
"""
# build the similarity graph W
W = build_similarity_graph(X, var, eps, k)
"""
Build the Laplacian L and the regularized Laplacian Q.
Both are (n x n) matrices.
"""
L = build_laplacian(W, laplacian_normalization)
# compute Q
Q = L + laplacian_regularization * np.eye(W.shape[0])
return Q
def soft_hfs(X,
Y,
c_l,
c_u,
laplacian_regularization=0.000001,
var=1,
eps=0,
k=10,
laplacian_normalization=""):
# a skeleton function to perform soft (unconstrained) HFS,
# needs to be completed
#
# Input
# X:
# (n x m) matrix of m-dimensional samples
# Y:
# (n x 1) vector with nodes labels [1, ... , num_classes] (0 is unlabeled)
# c_l,c_u:
# coefficients for C matrix
#
# Output
# labels:
# class assignments for each (n) nodes
num_samples = np.size(X, 0)
Cl = np.unique(Y)
num_classes = len(Cl) - 1
# Indices of labelled and unlabelled data
l_idx = np.argwhere(Y != 0)[:, 0]
u_idx = np.argwhere(Y == 0)[:, 0]
# Build C matrix
diagC = np.zeros((num_samples, ))
diagC[l_idx] = c_l
diagC[u_idx] = c_u
C = np.diag(diagC)
C_inv = np.diag(1 / diagC)
# Target vector
y = one_hot_target(Y)[:, 1:]
# Build similarity graph and Laplacian
W = helper.build_similarity_graph(X, var, eps, k)
L = helper.build_laplacian(W, laplacian_normalization)
# Q matrix (regularized laplacian)
Q = L + laplacian_regularization * np.eye(num_samples)
# Computation of fstar
D = np.dot(C_inv, Q) + np.eye(num_samples)
D_inv = np.linalg.inv(D)
fstar = np.dot(D_inv, y)
# +1 : labels start at 1
labels = np.argmax(fstar, axis=1) + 1
return labels
def hard_hfs(X,
Y,
laplacian_regularization=0.000001,
var=1,
eps=0,
k=10,
laplacian_normalization=""):
# a skeleton function to perform hard (constrained) HFS,
# needs to be completed
#
# Input
# X:
# (n x m) matrix of m-dimensional samples
# Y:
# (n x 1) vector with nodes labels [1, ... , num_classes] (0 is unlabeled)
#
# Output
# labels:
# class assignments for each (n) nodes
num_samples = np.size(X, 0)
Cl = np.unique(Y)
num_classes = len(Cl) - 1
# Indices of labelled and unlabelled data
l_idx = np.argwhere(Y != 0)[:, 0]
u_idx = np.argwhere(Y == 0)[:, 0]
# Build similarity graph and Laplacian
W = helper.build_similarity_graph(X, var, eps, k)
L = helper.build_laplacian(W, laplacian_normalization)
# Extract blocks corresponding to unlabelled and labelled data
Luu = L[u_idx, :][:, u_idx]
Wul = W[u_idx, :][:, l_idx]
# fl with one hot encoding
fl = one_hot_target(Y[l_idx])
# Compute fu using regularized Laplacian
Q = Luu + laplacian_regularization * np.eye(u_idx.shape[0])
Q_inv = np.linalg.inv(Q)
fu = np.dot(Q_inv, np.dot(Wul, fl))
# Infer label from computed fu using thresholding
fu_lab = np.argmax(fu, axis=1)
# Consolidate labels from fu and fl
labels = np.zeros((num_samples, ), dtype=int)
# +1 because labels start at 1
labels[u_idx] = fu_lab + 1
labels[l_idx] = Y[l_idx]
return labels
def hard_hfs(X, Y, laplacian_regularization, var=1, eps=0, k=0, laplacian_normalization=""):
"""
TO BE COMPLETED
Function to perform hard (constrained) HFS.
:param X: (n x m) matrix of m-dimensional samples
:param Y: (n x 1) vector with nodes labels [0, 1, ... , num_classes] (0 is unlabeled)
:param laplacian_regularization: regularization to add to the Laplacian
:param var: the sigma value for the exponential function, already squared
:param eps: threshold eps for epsilon graphs
:param k: number of neighbours k for k-nn. If zero, use epsilon-graph
:param laplacian_normalization: string selecting which version of the laplacian matrix to construct
'unn': unnormalized,
'sym': symmetric normalization
'rw': random-walk normalization
:return: labels, class assignments for each of the n nodes
"""
num_samples = np.size(X, 0)
Cl = np.unique(Y)
num_classes = len(Cl)-1
"""
Build the vectors:
l_idx = (l x num_classes) vector with indices of labeled nodes
u_idx = (u x num_classes) vector with indices of unlabeled nodes
"""
l_idx = np.where(Y != 0)[0]
u_idx = np.where(Y == 0)[0]
"""
Compute the hfs solution, remember that you can use the functions build_laplacian_regularized and
build_similarity_graph
f_l = (l x num_classes) hfs solution for labeled data. It is the one-hot encoding of Y for labeled nodes.
example:
if Cl=[0,3,5] and Y=[0,0,0,3,0,0,0,5,5], then f_l is a 3x2 binary matrix where the first column codes
the class '3' and the second the class '5'.
In case of 2 classes, you can also use +-1 labels
f_u = array (u x num_classes) hfs solution for unlabeled data
f = array of shape(num_samples, num_classes)
"""
W = build_similarity_graph(X)
L = build_laplacian(W, laplacian_normalization="")
f_l = np.zeros((len(l_idx),num_classes))
for ind,c in enumerate(Y[l_idx]) :
if c != 0 :
f_l[ind][int(c)-1] = 1
L_uu = L[u_idx][:,u_idx]
L_ul = L[u_idx][:,l_idx]
f_u = np.linalg.pinv(L_uu).dot(- np.dot(L_ul, f_l))
# f = np.concatenate(f_l, f_u)
"""
compute the labels assignment from the hfs solution
labels: (n x 1) class assignments [1,2,...,num_classes]
"""
labels = np.zeros(len(X))
labels[l_idx] = np.argmax(f_l, axis =1) + 1
labels[u_idx] = np.argmax(f_u, axis =1) + 1
return labels