def kernel_matrix(X, kernel, n1, n2):
(n, d) = X.shape
assert n == n1 + n2
K = mat.zeros((n, n))
for i in xrange(n):
for j in xrange(i + 1):
K[i, j] = kernel(X[i, :], X[j, :])
K[j, i] = K[i, j]
U1 = mat.sum(K[0:n1, :], 0) / n1
U2 = mat.sum(K[n1:n, :], 0) / n2
U1m = mat.tile(U1, (n1, 1))
U2m = mat.tile(U2, (n2, 1))
U = mat.bmat('U1m; U2m')
m1m1 = mat.sum(K[0:n1, 0:n1]) / (n1 * n1)
m1m2 = mat.sum(K[0:n1, n1:n]) / (n1 * n2)
m2m2 = mat.sum(K[n1:n, n1:n]) / (n2 * n2)
mumu = mat.zeros((n, n))
mumu[0:n1, 0:n1] = m1m1
mumu[0:n1, n1:n] = m1m2
mumu[n1:n, 0:n1] = m1m2
mumu[n1:n, n1:n] = m2m2
Kcu = K - U
Kuc = Kcu.T
N = mat.ones((n, n)) / n
Kc = K - U - U.T + mumu
return (K, Kuc, Kc)
def kernel_matrix(X, kernel, n1, n2):
(n, d) = X.shape
assert n == n1 + n2
K = mat.zeros((n,n))
for i in xrange(n):
for j in xrange(i+1):
K[i,j] = kernel(X[i,:], X[j,:])
K[j,i] = K[i,j]
U1 = mat.sum(K[0:n1,:],0) / n1
U2 = mat.sum(K[n1:n,:],0) / n2
U1m = mat.tile(U1, (n1,1))
U2m = mat.tile(U2, (n2,1))
U = mat.bmat('U1m; U2m')
m1m1 = mat.sum(K[0:n1, 0:n1]) / (n1*n1)
m1m2 = mat.sum(K[0:n1, n1:n]) / (n1*n2)
m2m2 = mat.sum(K[n1:n, n1:n]) / (n2*n2)
mumu = mat.zeros((n,n))
mumu[0:n1, 0:n1] = m1m1
mumu[0:n1, n1:n] = m1m2
mumu[n1:n, 0:n1] = m1m2
mumu[n1:n, n1:n] = m2m2
Kcu = K - U
Kuc = Kcu.T
N = mat.ones((n,n))/n
Kc = K - U - U.T + mumu
return (K, Kuc, Kc)
def kernel_supermatrix(self, i, j):
kernel = self.kernel
D1 = self.datasets[i]
D2 = self.datasets[j]
X1 = D1.X
X2 = D2.X
(n1, d) = X1.shape
(n2, d) = X2.shape
n = n1 + n2
X = mat.bmat('X1; X2')
K1 = D1.K
K2 = D2.K
K = mat.zeros((n,n))
K[0:n1, 0:n1] = K1
K[n1:n, n1:n] = K2
for i in xrange(n1):
for j in xrange(n1, n):
K[i,j] = kernel(X[i,:], X[j,:])
K[j,i] = K[i,j]
# Inelegant - improve later
U1 = mat.sum(K[0:n1,:],0) / n1
U2 = mat.sum(K[n1:n,:],0) / n2
U1m = mat.tile(U1, (n1,1))
U2m = mat.tile(U2, (n2,1))
U = mat.bmat('U1m; U2m')
m1m1 = mat.sum(K[0:n1, 0:n1]) / (n1*n1)
m1m2 = mat.sum(K[0:n1, n1:n]) / (n1*n2)
m2m2 = mat.sum(K[n1:n, n1:n]) / (n2*n2)
mumu = mat.zeros((n,n))
mumu[0:n1, 0:n1] = m1m1
mumu[0:n1, n1:n] = m1m2
mumu[n1:n, 0:n1] = m1m2
mumu[n1:n, n1:n] = m2m2
Kcu = K - U
Kuc = Kcu.T
N = mat.ones((n,n))/n
Kc = K - U - U.T + mumu
return (K, Kuc, Kc)
def kernel_supermatrix(self, i, j):
kernel = self.kernel
D1 = self.datasets[i]
D2 = self.datasets[j]
X1 = D1.X
X2 = D2.X
(n1, d) = X1.shape
(n2, d) = X2.shape
n = n1 + n2
X = mat.bmat('X1; X2')
K1 = D1.K
K2 = D2.K
K = mat.zeros((n, n))
K[0:n1, 0:n1] = K1
K[n1:n, n1:n] = K2
for i in xrange(n1):
for j in xrange(n1, n):
K[i, j] = kernel(X[i, :], X[j, :])
K[j, i] = K[i, j]
# Inelegant - improve later
U1 = mat.sum(K[0:n1, :], 0) / n1
U2 = mat.sum(K[n1:n, :], 0) / n2
U1m = mat.tile(U1, (n1, 1))
U2m = mat.tile(U2, (n2, 1))
U = mat.bmat('U1m; U2m')
m1m1 = mat.sum(K[0:n1, 0:n1]) / (n1 * n1)
m1m2 = mat.sum(K[0:n1, n1:n]) / (n1 * n2)
m2m2 = mat.sum(K[n1:n, n1:n]) / (n2 * n2)
mumu = mat.zeros((n, n))
mumu[0:n1, 0:n1] = m1m1
mumu[0:n1, n1:n] = m1m2
mumu[n1:n, 0:n1] = m1m2
mumu[n1:n, n1:n] = m2m2
Kcu = K - U
Kuc = Kcu.T
N = mat.ones((n, n)) / n
Kc = K - U - U.T + mumu
return (K, Kuc, Kc)
def prepare_bhatta(X1, X2, kernel, eta, verbose=False):
(n1, d1) = X1.shape
(n2, d ) = X2.shape
assert d1 == d
n = n1 + n2
X = mat.bmat('X1;X2')
(K, Kuc, Kc) = kernel_matrix(X, kernel, n1, n2)
G = GS_basis(Kc, verbose)
(null, g_dim) = G.shape
mu1 = mat.sum(Kuc[0:n1,:] * G,0) / n1
mu2 = mat.sum(Kuc[n1:n,:] * G,0) / n2
return (Kc, G, mu1, mu2)
def prepare_bhatta(X1, X2, kernel, eta, verbose=False):
(n1, d1) = X1.shape
(n2, d) = X2.shape
assert d1 == d
n = n1 + n2
X = mat.bmat('X1;X2')
(K, Kuc, Kc) = kernel_matrix(X, kernel, n1, n2)
G = GS_basis(Kc, verbose)
(null, g_dim) = G.shape
mu1 = mat.sum(Kuc[0:n1, :] * G, 0) / n1
mu2 = mat.sum(Kuc[n1:n, :] * G, 0) / n2
return (Kc, G, mu1, mu2)
def eig_bhatta(X1, X2, kernel, eta, r):
# Tested. Verified:
# Poly-kernel RKHS representations of all objects are roughly equal to eigenbasis representations (slight differences for S3)
# Correctness for X1 ~= X2
# Close results to empirical bhatta in test_suite_1
# Remaining issues: Eigendecomposition of centered kernel matrices
# occasionally produces negative-value eigenvalues
(n1, d1) = X1.shape
(n2, d2) = X2.shape
assert d1==d2
n = n1+n2
X = mat.bmat("X1;X2")
(K, Kuc, Kc) = kernel_matrix(X, kernel, n1, n2)
Kc1 = Kc[0:n1, 0:n1]
Kc2 = Kc[n1:n, n1:n]
(Lam1, Alpha1) = eigsh(Kc1, r)
(Lam2, Alpha2) = eigsh(Kc2, r)
Alpha1 = matrix(Alpha1)
Alpha2 = matrix(Alpha2)
Lam1 = Lam1 / n1
Lam2 = Lam2 / n2
Beta1 = mat.zeros((n,r))
Beta2 = mat.zeros((n,r))
for i in xrange(r):
Beta1[0:n1, i] = Alpha1[:,i] / (n1 * Lam1[i])**.5
Beta2[n1:n, i] = Alpha2[:,i] / (n2 * Lam2[i])**.5
#Eta = mat.eye((gamma, gamma)) * eta
Beta = mat.bmat('Beta1, Beta2')
assert not(any(math.isnan(Beta)))
Omega = eig_ortho(Kc, Beta)
mu1_w = mat.sum(Kuc[0:n1, :] * Omega, 0) / n1
mu2_w = mat.sum(Kuc[n1:n, :] * Omega, 0) / n2
Eta_w = eta * mat.eye(2*r)
S1_w = Omega.T * Kc[:,0:n1] * Kc[0:n1,:] * Omega / n1
S2_w = Omega.T * Kc[:,n1:n] * Kc[n1:n,:] * Omega / n2
S1_w += Eta_w
S2_w += Eta_w
mu3_w = .5 * (S1_w.I * mu1_w.T + S2_w.I * mu2_w.T).T
S3_w = 2 * (S1_w.I + S2_w.I).I
d1 = la.det(S1_w) ** -.25
d2 = la.det(S2_w) ** -.25
e1 = exp(-mu1_w * S1_w.I * mu1_w.T / 4)
e2 = exp(-mu2_w * S2_w.I * mu2_w.T / 4)
d3 = la.det(S3_w) ** .5
e3 = exp(mu3_w * S3_w * mu3_w.T / 2)
dterm = d1*d2*d3
eterm = e1*e2*e3
rval = float(dterm*eterm)
if math.isnan(rval):
rval = -1
return rval
def FRAHST_M(streams, energyThresh, alpha):
""" Fast rank adaptive row-Householder Subspace Traking Algorithm
"""
#Initialise
N = streams.shape[1]
rr = [1]
hiddenV = npm.zeros((streams.shape[0], N))
# generate random orthonormal - N x r
qq,RR = qr(rand(N,1))
Q_t = [mat(qq)]
S_t = [mat([0.000001])]
E_t = [0]
E_dash_t = [0]
z_dash = npm.zeros(N)
RSRE = mat([0])
No_inp_count = 0
iter_streams = iter(streams)
for t in range(1, streams.shape[0] + 1):
z_vec = mat(iter_streams.next())
z_vec = z_vec.T # Now a column Vector
hh = Q_t[t-1].T * z_vec # 13a
Z = z_vec.T * z_vec - hh.T * hh # 13b
Z = float(Z) # cheak that Z is really scalar
if Z > 0.0000001:
X = alpha * S_t[t-1] + hh * hh.T # 13c
# X.T * b = sqrt(Z) * hh # 13d
b = multiply(inv(X.T), sqrt(Z)) * hh # inverse method
phi_sq_t = 0.5 + (1 / sqrt(4 *((b.T * b) + 1))) # 13e
phi_t = sqrt(phi_sq_t)
delta = phi_t / sqrt(Z) # 13f
gamma = (1 - 2 * phi_sq_t) / (2 * phi_t) #13 g
v = multiply(gamma, b)
S_t.append(X - multiply(1/delta , v * hh.T)) # 13 h S_t[t] =
e = multiply(delta, z_vec) - (Q_t[t-1] * (multiply(delta, hh) - v)) # 13 i
Q_t.append(Q_t[t-1] - 2 * (e * v.T)) # 13 j Q[t] =
# Record hidden variables
hiddenV[t-1,:hh.shape[0]] = hh.T
# Record reconstrunted z
new_z_dash = Q_t[t-1] * hh
z_dash = npm.vstack((z_dash, new_z_dash.T))
# Record RSRE
new_RSRE = RSRE[0,-1] + (((norm(new_z_dash - z_vec)) ** 2) /
(norm(z_vec) ** 2))
RSRE = npm.vstack((RSRE, mat(new_RSRE)))
E_t.append(alpha * E_t[-1] + norm(z_vec) ** 2) # 13 k
E_dash_t.append( alpha * E_dash_t[-1] + norm(hh) ** 2) # 13 l
if E_dash_t[-1] < energyThresh[0] * E_t[-1] and rr[-1] < N: # 13 m
z_dag_orthog = z_vec - Q_t[t] * Q_t[t].T * z_vec
# try Q[t], not Q[t + 1]
Q_t[t] = npm.bmat([Q_t[t], z_dag_orthog/norm(z_dag_orthog)])
TR = npm.zeros((S_t[t].shape[0], 1))
BL = npm.zeros((1 ,S_t[t].shape[1]))
BR = mat(norm(z_dag_orthog) ** 2 )
S_t[t] = npm.bmat([[S_t[t], TR],
[ BL , BR]])
rr.append(rr[-1] + 1)
elif E_dash_t[-1] > energyThresh[1] * E_t[-1] and rr[-1] > 1 :
Q_t[t] = Q_t[t][:, :-1] # delete the last column of Q_t
S_t[t] = S_t[t][:-1, :-1] # delete last row and colum of S_t
rr.append(rr[-1] - 1)
else:
# Record hidden variables
hiddenV[t-1,:hh.shape[0]] = hh.T
# Record reconstrunted z
new_z_dash = Q_t[t-1] * hh
z_dash = npm.vstack((z_dash, new_z_dash.T))
# Record RSRE
new_RSRE = RSRE[0,-1] + (((norm(new_z_dash - z_vec)) ** 2) /
(norm(z_vec) ** 2))
RSRE = npm.vstack((RSRE, mat(new_RSRE)))
# Repeat last entries
Q_t.append(Q_t[-1])
S_t.append(S_t[-1])
rr.append(rr[-1])
E_t.append(E_t[-1])
E_dash_t.append(E_dash_t[-1])
# increment count
No_inp_count += 1
return Q_t, S_t, rr, E_t, E_dash_t, hiddenV, z_dash, RSRE, No_inp_count
def eig_bhatta(X1, X2, kernel, eta, r):
# Tested. Verified:
# Poly-kernel RKHS representations of all objects are roughly equal to eigenbasis representations (slight differences for S3)
# Correctness for X1 ~= X2
# Close results to empirical bhatta in test_suite_1
# Remaining issues: Eigendecomposition of centered kernel matrices
# occasionally produces negative-value eigenvalues
(n1, d1) = X1.shape
(n2, d2) = X2.shape
assert d1 == d2
n = n1 + n2
X = mat.bmat("X1;X2")
(K, Kuc, Kc) = kernel_matrix(X, kernel, n1, n2)
Kc1 = Kc[0:n1, 0:n1]
Kc2 = Kc[n1:n, n1:n]
(Lam1, Alpha1) = eigsh(Kc1, r)
(Lam2, Alpha2) = eigsh(Kc2, r)
Alpha1 = matrix(Alpha1)
Alpha2 = matrix(Alpha2)
Lam1 = Lam1 / n1
Lam2 = Lam2 / n2
Beta1 = mat.zeros((n, r))
Beta2 = mat.zeros((n, r))
for i in xrange(r):
Beta1[0:n1, i] = Alpha1[:, i] / (n1 * Lam1[i])**.5
Beta2[n1:n, i] = Alpha2[:, i] / (n2 * Lam2[i])**.5
#Eta = mat.eye((gamma, gamma)) * eta
Beta = mat.bmat('Beta1, Beta2')
assert not (any(math.isnan(Beta)))
Omega = eig_ortho(Kc, Beta)
mu1_w = mat.sum(Kuc[0:n1, :] * Omega, 0) / n1
mu2_w = mat.sum(Kuc[n1:n, :] * Omega, 0) / n2
Eta_w = eta * mat.eye(2 * r)
S1_w = Omega.T * Kc[:, 0:n1] * Kc[0:n1, :] * Omega / n1
S2_w = Omega.T * Kc[:, n1:n] * Kc[n1:n, :] * Omega / n2
S1_w += Eta_w
S2_w += Eta_w
mu3_w = .5 * (S1_w.I * mu1_w.T + S2_w.I * mu2_w.T).T
S3_w = 2 * (S1_w.I + S2_w.I).I
d1 = la.det(S1_w)**-.25
d2 = la.det(S2_w)**-.25
e1 = exp(-mu1_w * S1_w.I * mu1_w.T / 4)
e2 = exp(-mu2_w * S2_w.I * mu2_w.T / 4)
d3 = la.det(S3_w)**.5
e3 = exp(mu3_w * S3_w * mu3_w.T / 2)
dterm = d1 * d2 * d3
eterm = e1 * e2 * e3
rval = float(dterm * eterm)
if math.isnan(rval):
rval = -1
return rval
def entangle(Q, k): # Q must be an isometry
D = mix(Q, k)
C = mix(Q, k)
return mat.bmat([[D, C], [C, -D]]) / math.sqrt(2.0)
def entangle_with_knives(Q): # Q must be an isometry
D = mix_with_knives(Q)
C = mix_with_knives(Q)
return mat.bmat([[D, C], [C, -D]]) / math.sqrt(2.0)
