def testing(self, x):
"""
Estimate the outputs corresponding to new input points
:param x: new input data (nb_data, dim_x)
:return: corresponding output predictions (nb_data, dim_y)
"""
nb_data = x.shape[0]
# Compute gate probabilities
inProdEst = np.zeros((nb_data, self.nb_class))
for n in range(0, nb_data):
for c in range(0, self.nb_class):
vTmp = khatriRaoProd(self.V[1][c], self.V[0][c])
for i in range(2, self.nb_dim_x):
vTmp = khatriRaoProd(self.V[i][c], vTmp)
vVec = np.dot(vTmp, np.ones((self.rank_g, 1)))
inProdEst[n, c] = self.beta[c] + np.dot(
vVec[:, None].T, x[n].flatten())
priors = softmax(inProdEst).T
ytmp = []
for c in range(0, self.nb_class):
# Compute experts predictions
alpha = self.alpha[c][:]
wmsTmp = self.W[c][0]
# Compute vec(W)
wVec = []
for j in range(self.nb_dim):
wTmp = khatriRaoProd(wmsTmp[1], wmsTmp[0])
for k in range(2, self.nb_dim_x):
wTmp = khatriRaoProd(wmsTmp[k], wTmp)
wVec.append(np.dot(wTmp, np.ones((self.rank_e[c], 1))))
yhat_tmp = np.zeros((nb_data, self.nb_dim))
for n in range(0, nb_data):
for dim in range(0, self.nb_dim):
yhat_tmp[n, dim] = alpha[dim] + np.dot(
wVec[dim][:, None].T, x[n].flatten())
# Append expert predictions weighted by the gate
ytmp.append(yhat_tmp * priors[i][:, None])
# Compute final predictions
return np.sum(ytmp, axis=0)
def func(v):
# Recover parameters from vector v
# vector v is composed as [ xhi_1, phi_1, psi_1, ..., xhi_c, phi_c, psi_c ]
V = np.reshape(v, (nb_dim * rank + 1, nb_class))
xhi = V[0]
phi = [
np.reshape(V[1:nb_dim1 * rank + 1, i], (nb_dim1, rank))
for i in range(nb_class)
]
psi = [
np.reshape(V[nb_dim1 * rank + 1::, i], (nb_dim2, rank))
for i in range(nb_class)
]
# Compute probabilitites
inProdEst = np.zeros((nb_data, nb_class))
for n in range(0, nb_data):
for dim in range(0, nb_class):
phipsiVec = np.dot(khatriRaoProd(psi[dim], phi[dim]),
np.ones((rank, 1)))
inProdEst[n, dim] = xhi[dim] + np.dot(phipsiVec[:, None].T,
x[n].flatten())
est = softmax(inProdEst)
est += 1e-308
# Regularization term
reg_term = 0.0
for dim in range(0, nb_class):
reg_term += np.linalg.norm(psi[dim]) + np.linalg.norm(phi[dim])
return -np.sum(y * np.log(est)) + reg_fact * reg_term
def testing(self, x):
"""
Estimate the outputs corresponding to new input points
:param x: new input data (nb_data, dim_x)
:return: corresponding output predictions (nb_data, dim_y)
"""
nb_data = x.shape[0]
# Compute gate probabilities
inProdEst = np.zeros((nb_data, self.nb_class))
for n in range(0, nb_data):
for dim in range(0, self.nb_class):
phipsiVec = np.dot(khatriRaoProd(self.psi[dim], self.phi[dim]),
np.ones((self.rank_g, 1)))
inProdEst[n, dim] = self.xhi[dim] + np.dot(
phipsiVec[:, None].T, x[n].flatten())
priors = softmax(inProdEst).T
ytmp = []
for i in range(0, self.nb_class):
# Compute experts predictions
alpha = self.alpha[i][:]
b1tmp = self.b1[i][:]
b2tmp = self.b2[i][:]
bVec = [
np.dot(khatriRaoProd(b2tmp[j], b1tmp[j]),
np.ones((self.rank_e[i], 1)))
for j in range(self.nb_dim)
]
yhat_tmp = np.zeros((nb_data, self.nb_dim))
for n in range(0, nb_data):
for dim in range(0, self.nb_dim):
yhat_tmp[n, dim] = alpha[dim] + np.dot(
bVec[dim][:, None].T, x[n].flatten())
# Append expert predictions weighted by the gate
ytmp.append(yhat_tmp * priors[i][:, None])
# Compute final predictions
return np.sum(ytmp, axis=0)
def func(v):
# Recover parameters from vector v
# vector v is composed as [ beta_1, v1_1, v2_1, ..., beta_c, v1_c, v2_c, ... ]
Vall = np.reshape(v, (nb_dim * rank + 1, nb_class))
beta = Vall[0]
V = []
start = 1
end = dims[0] * rank + 1
for m in range(len(dims)):
Vtmp = [
np.reshape(Vall[start:end, i], (dims[m], rank))
for i in range(nb_class)
]
V.append(Vtmp)
start = end
if m < len(dims) - 1:
end += dims[m + 1] * rank
# Compute probabilitites
inProdEst = np.zeros((nb_data, nb_class))
for n in range(0, nb_data):
for c in range(0, nb_class):
vTmp = khatriRaoProd(V[1][c], V[0][c])
for i in range(2, len(dims)):
vTmp = khatriRaoProd(V[i][c], vTmp)
vVec = np.dot(vTmp, np.ones((rank, 1)))
inProdEst[n, c] = beta[c] + np.dot(vVec[:, None].T,
x[n].flatten())
est = softmax(inProdEst)
est += 1e-308
# Regularization term
reg_term = 0.0
for c in range(0, nb_class):
for m in range(len(dims)):
reg_term += np.linalg.norm(V[m][c])
return -np.sum(y * np.log(est)) + reg_fact * reg_term
def grad(v):
# Recover parameters from vector v
V = np.reshape(v, (nb_dim * rank + 1, nb_class))
xhi = V[0]
phi = [
np.reshape(V[1:nb_dim1 * rank + 1, i], (nb_dim1, rank))
for i in range(nb_class)
]
psi = [
np.reshape(V[nb_dim1 * rank + 1::, i], (nb_dim2, rank))
for i in range(nb_class)
]
# Compute probabilities
inProdEst = np.zeros((nb_data, nb_class))
for n in range(0, nb_data):
for dim in range(0, nb_class):
phipsiVec = np.dot(khatriRaoProd(psi[dim], phi[dim]),
np.ones((rank, 1)))
inProdEst[n, dim] = xhi[dim] + np.dot(phipsiVec[:, None].T,
x[n].flatten())
est = softmax(inProdEst)
# Compute gradients
grad_xhi = np.sum(est - y, axis=0).flatten()
grad_phi = np.zeros((nb_dim1 * rank, nb_class))
grad_psi = np.zeros((nb_dim2 * rank, nb_class))
for dim in range(0, nb_class):
xVecPsi = np.zeros((nb_data, nb_dim1 * rank))
for n in range(0, nb_data):
xVecPsi[n] = np.dot(x[n], psi[dim]).flatten()
xVecPhi = np.zeros((nb_data, nb_dim2 * rank))
for n in range(0, nb_data):
xVecPhi[n] = np.dot(x[n].T, phi[dim]).flatten()
grad_phi[:, dim] = np.dot(xVecPsi.T, (est[:, dim] - y[:, dim]))
grad_psi[:, dim] = np.dot(xVecPhi.T, (est[:, dim] - y[:, dim]))
# Regularization term
grad_phi[:, dim] += 2 * reg_fact * phi[dim].flatten()
grad_psi[:, dim] += 2 * reg_fact * psi[dim].flatten()
return np.hstack(
(grad_xhi.flatten(), grad_phi.flatten(), grad_psi.flatten()))
def training(self,
x,
y,
y_class,
reg_rr=1e-2,
reg_lr=1e-2,
maxiter=100,
max_diff_ll=1e-5,
optmethod='BFGS'):
"""
Training the MME model
:param x: input data (nb_data, dim_x)
:param y: output data (nb_data, dim_y)
:param y_class: classes labels of outputs (nb_data, nb_classes)
:param reg_rr: regularization term of the experts
:param reg_lr: regularization term of the gate
:param maxiter: maximum number of iterations for the EM algorithm
:param max_diff_ll: maximum difference of log-likelihood for the EM algorithm
:param optmethod: optimization method for the logistic regression (gate)
"""
nb_data = x.shape[0]
dX = x.shape[1:]
self.nb_dim_x = len(dX)
self.nb_class = y_class.shape[1]
self.nb_dim = y.shape[1]
if type(self.rank_e) is not list:
self.rank_e = [self.rank_e for i in range(self.nb_class)]
# Experts initialization
self.alpha = []
self.W = []
for rank_e in self.rank_e:
tensRR = TensorRidgeRegression(rank=rank_e)
tensRR.training(x, y)
self.alpha.append(tensRR.alpha[:])
self.W.append(tensRR.W)
self.sigma = [np.eye(self.nb_dim) for i in range(0, self.nb_class)]
# Gating initialization
tensRR = TensorRidgeRegression(rank=self.rank_g)
tensRR.training(x, y_class)
beta_init = np.array(tensRR.alpha).T
Vall = beta_init
for m in range(self.nb_dim_x):
wTmpRR = []
for c in range(self.nb_class):
wTmpRR.append(tensRR.W[c][m])
Vtmp = np.reshape(np.array(wTmpRR),
(self.nb_class, dX[m] * self.rank_g)).T
Vall = np.vstack((Vall, Vtmp))
vinit = Vall.flatten()
self.beta, self.V = optTensLogReg(x,
y_class,
rank=self.rank_g,
v_init=vinit,
optmethod=optmethod,
reg_fact=reg_lr)
# EM algorithm
nb_min_steps = 2 # min num iterations
nb_max_steps = maxiter # max iterations
LL = np.zeros(nb_max_steps)
for it in range(nb_max_steps):
# E - step
Ltmp = np.zeros((self.nb_class, nb_data))
# Compute gate probabilities
inProdEst = np.zeros((nb_data, self.nb_class))
for n in range(0, nb_data):
for c in range(0, self.nb_class):
vTmp = khatriRaoProd(self.V[1][c], self.V[0][c])
for i in range(2, self.nb_dim_x):
vTmp = khatriRaoProd(self.V[i][c], vTmp)
vVec = np.dot(vTmp, np.ones((self.rank_g, 1)))
inProdEst[n, c] = self.beta[c] + np.dot(
vVec[:, None].T, x[n].flatten())
priors = softmax(inProdEst).T
# Compute experts distributions weighted by gate probabilities
for c in range(0, self.nb_class):
alpha = self.alpha[c][:]
wmsTmp = self.W[c][0]
# Compute vec(W)
wVec = []
for j in range(self.nb_dim):
wTmp = khatriRaoProd(wmsTmp[1], wmsTmp[0])
for k in range(2, self.nb_dim_x):
wTmp = khatriRaoProd(wmsTmp[k], wTmp)
wVec.append(np.dot(wTmp, np.ones((self.rank_e[c], 1))))
# Compute predictions
yhat_tmp = np.zeros((nb_data, self.nb_dim))
for n in range(0, nb_data):
for dim in range(0, self.nb_dim):
yhat_tmp[n, dim] = alpha[dim] + np.dot(
wVec[dim][:, None].T, x[n].flatten())
# Likelihood
Ltmp[c] = priors[c] * multi_variate_normal(
y, yhat_tmp, self.sigma[c], log=False)
# Compute responsabilities
GAMMA = Ltmp / (np.sum(Ltmp, axis=0) + 1e-100)
LL[it] = np.sum(np.sum(GAMMA * np.log(Ltmp + 1e-100)))
# M-step
# Experts parameters update
yhat = []
for c in range(0, self.nb_class):
r = np.diag(GAMMA[c])
sqrGAMMA = np.sqrt(GAMMA[c])
weighted_y = sqrGAMMA[:, None] * y
for d in range(len(x.shape) - 1):
sqrGAMMA = np.expand_dims(sqrGAMMA, axis=-1)
weighted_x = sqrGAMMA * x
tensRR = None
tensRR = TensorRidgeRegression(rank=self.rank_e[c])
tensRR.training(weighted_x, weighted_y, reg=reg_rr)
self.alpha[c] = tensRR.alpha[:]
self.W[c] = tensRR.W
yhat_tmp = tensRR.testing_multiple(x)
yhat.append(yhat_tmp * priors[c][:, None])
self.sigma[c] = np.dot(np.dot(
(y - yhat_tmp).T, r), (y - yhat_tmp)) / sum(
GAMMA[c]) + 1e-6 * np.eye(self.nb_dim)
# Gate parameters update
beta_init = np.array(self.beta).T
Vall = beta_init
for m in range(self.nb_dim_x):
Vtmp = np.reshape(np.array(self.V[m]),
(self.nb_class, dX[m] * self.rank_g)).T
Vall = np.vstack((Vall, Vtmp))
vinit = Vall.flatten()
self.beta, self.V = optTensLogReg(x,
GAMMA.T,
rank=self.rank_g,
v_init=vinit,
optmethod=optmethod,
reg_fact=reg_lr)
print(it)
# Check for convergence
if it > nb_min_steps:
if LL[it] - LL[it - 1] < max_diff_ll:
print('Converged after %d iterations: %.3e' % (it, LL[it]),
'red', 'on_white')
print(LL)
return LL[it]
print(
"TME did not converge before reaching max iteration. Consider augmenting the number of max iterations."
)
print(LL)
return LL[-1]
def training(self, x, y, reg=1e-2, maxDiffCrit=1e-4, maxIter=200):
"""
Train the parameters of the MRR model
:param x: input matrices (nb_data, dim1, dim2)
:param y: output vectors (nb_data, dim_y)
:param reg: regularization term
:param maxDiffCrit: stopping criterion for the alternative least squares procedure
:param maxIter: maximum number of iterations for the alternative least squares procedure
"""
# Dimensions
N = x.shape[0]
d1 = x.shape[1]
d2 = x.shape[2]
self.dY = y.shape[1]
for dim in range(0, self.dY):
# Initialization
# self.b1.append(np.random.randn(d1, self.rank))
# self.b2.append(np.random.randn(d2, self.rank))
# self.alpha.append(np.random.randn(1))
self.b1.append(np.ones((d1, self.rank)))
self.b2.append(np.ones((d2, self.rank)))
self.alpha.append(np.zeros(1))
self.bVec.append(np.random.randn(d1 * d2, 1))
# Optimization of parameters (ALS procedure)
nbIter = 1
prevRes = 0
while nbIter < maxIter:
# Update b1
zVec1 = np.zeros((N, d1 * self.rank))
for n in range(0, N):
zVec1[n] = np.dot(x[n], self.b2[-1]).flatten()
b1 = np.linalg.solve(
zVec1.T.dot(zVec1) + np.eye(d1 * self.rank) * reg,
zVec1.T).dot(y[:, dim] - self.alpha[-1])
self.b1[-1] = np.reshape(b1, (d1, self.rank))
# Update b2
zVec2 = np.zeros((N, d2 * self.rank))
for n in range(0, N):
zVec2[n] = np.dot(x[n].T, self.b1[-1]).flatten()
b2 = np.linalg.solve(
zVec2.T.dot(zVec2) + np.eye(d2 * self.rank) * reg,
zVec2.T).dot(y[:, dim] - self.alpha[-1])
self.b2[-1] = np.reshape(b2, (d2, self.rank))
# Update alpha
self.bVec[-1] = np.dot(khatriRaoProd(self.b2[-1], self.b1[-1]),
np.ones((self.rank, 1)))
alpha = 0
for n in range(0, N):
alpha += y[n, dim] - np.dot(self.bVec[-1][:, None].T,
x[n].flatten())
self.alpha[-1] = alpha[0] / N
# Compute residuals
res = 0
for n in range(0, N):
res += (
y[n, dim] - self.alpha[-1] -
np.dot(self.bVec[-1][:, None].T, x[n].flatten()))**2
resDiff = prevRes - res
# Check convergence
if resDiff < maxDiffCrit and nbIter > 1:
print('MRR converged after %d iterations.' % nbIter)
break
nbIter += 1
prevRes = res
if resDiff > maxDiffCrit:
print('MRR did not converged after %d iterations.' % nbIter)
def training(self,
x,
y,
y_class,
reg_rr=1e-2,
reg_lr=1e-2,
maxiter=100,
max_diff_ll=1e-5,
optmethod='BFGS'):
"""
Training the MME model
:param x: input data (nb_data, dim_x)
:param y: output data (nb_data, dim_y)
:param y_class: classes labels of outputs (nb_data, nb_classes)
:param reg_rr: regularization term of the experts
:param reg_lr: regularization term of the gate
:param maxiter: maximum number of iterations for the EM algorithm
:param max_diff_ll: maximum difference of log-likelihood for the EM algorithm
:param optmethod: optimization method for the logistic regression (gate)
:return:
"""
nb_data = x.shape[0]
d1 = x.shape[1]
d2 = x.shape[2]
self.nb_class = y_class.shape[1]
self.nb_dim = y.shape[1]
if type(self.rank_e) is not list:
self.rank_e = [self.rank_e for i in range(self.nb_class)]
# Initialization with sklearn
# ysk = np.sum(y.T * np.array(range(0, self.nb_class))[:, None], axis=0)
# mul_lr = sk.linear_model.LogisticRegression(multi_class='multinomial', solver='newton-cg').fit(xvec, ysk)
# self.V = mul_lr.coef_.T
# self.V = optLogReg(xvec.T, y.T)
# matRR = MatrixRidgeRegression(rank=self.rank_e)
# matRR.training(x, y)
#
# self.alpha = [matRR.alpha[:] for i in range(0, self.nb_class)]
# self.b1 = [matRR.b1[:] for i in range(0, self.nb_class)]
# self.b2 = [matRR.b2[:] for i in range(0, self.nb_class)]
# Experts initialization
self.alpha = []
self.b1 = []
self.b2 = []
for rank_e in self.rank_e:
matRR = MatrixRidgeRegression(rank=rank_e)
matRR.training(x, y)
self.alpha.append(matRR.alpha[:])
self.b1.append(matRR.b1[:])
self.b2.append(matRR.b2[:])
self.sigma = [np.eye(self.nb_dim) for i in range(0, self.nb_class)]
# Gating initialization
matRR = MatrixRidgeRegression(rank=self.rank_g)
matRR.training(x, y_class)
xhi_init = np.array(matRR.alpha).T
phi_init = np.reshape(np.array(matRR.b1),
(self.nb_class, d1 * self.rank_g)).T
psi_init = np.reshape(np.array(matRR.b2),
(self.nb_class, d2 * self.rank_g)).T
vinit = np.vstack((xhi_init, phi_init, psi_init)).flatten()
self.xhi, self.phi, self.psi = optMatLogReg(x,
y_class,
rank=self.rank_g,
v_init=vinit,
optmethod=optmethod,
reg_fact=reg_lr)
# EM algorithm
nb_min_steps = 2 # min num iterations
nb_max_steps = maxiter # max iterations
LL = np.zeros(nb_max_steps)
for it in range(nb_max_steps):
# E - step
Ltmp = np.zeros((self.nb_class, nb_data))
# Compute gate probabilities
inProdEst = np.zeros((nb_data, self.nb_class))
for n in range(0, nb_data):
for dim in range(0, self.nb_class):
phipsiVec = np.dot(
khatriRaoProd(self.psi[dim], self.phi[dim]),
np.ones((self.rank_g, 1)))
inProdEst[n, dim] = self.xhi[dim] + np.dot(
phipsiVec[:, None].T, x[n].flatten())
priors = softmax(inProdEst).T
# Compute experts distributions weighted by gate probabilities
for i in range(0, self.nb_class):
alpha = self.alpha[i][:]
b1tmp = self.b1[i][:]
b2tmp = self.b2[i][:]
bVec = [
np.dot(khatriRaoProd(b2tmp[j], b1tmp[j]),
np.ones((self.rank_e[i], 1)))
for j in range(self.nb_dim)
]
yhat_tmp = np.zeros((nb_data, self.nb_dim))
for n in range(0, nb_data):
for dim in range(0, self.nb_dim):
yhat_tmp[n, dim] = alpha[dim] + np.dot(
bVec[dim][:, None].T, x[n].flatten())
Ltmp[i] = priors[i] * multi_variate_normal(
y, yhat_tmp, self.sigma[i], log=False)
# Compute responsabilities
GAMMA = Ltmp / (np.sum(Ltmp, axis=0) + 1e-100)
LL[it] = np.sum(np.sum(GAMMA * np.log(Ltmp + 1e-100)))
# M-step
# Experts parameters update
yhat = []
for i in range(0, self.nb_class):
r = np.diag(GAMMA[i])
sqrGAMMA = np.sqrt(GAMMA[i])
weighted_x = sqrGAMMA[:, None, None] * x
weighted_y = sqrGAMMA[:, None] * y
matRR = None
matRR = MatrixRidgeRegression(rank=self.rank_e[i])
matRR.training(weighted_x, weighted_y, reg=reg_rr)
self.alpha[i] = matRR.alpha[:]
self.b1[i] = matRR.b1[:]
self.b2[i] = matRR.b2[:]
yhat_tmp = matRR.testing_multiple(x)
yhat.append(yhat_tmp * priors[i][:, None])
self.sigma[i] = np.dot(np.dot(
(y - yhat_tmp).T, r), (y - yhat_tmp)) / sum(
GAMMA[i]) + 1e-6 * np.eye(self.nb_dim)
# Gate parameters update
xhi_init = np.array(self.xhi).T
phi_init = np.reshape(np.array(self.phi),
(self.nb_class, d1 * self.rank_g)).T
psi_init = np.reshape(np.array(self.psi),
(self.nb_class, d2 * self.rank_g)).T
vinit = np.vstack((xhi_init, phi_init, psi_init)).flatten()
self.xhi, self.phi, self.psi = optMatLogReg(x,
GAMMA.T,
rank=self.rank_g,
v_init=vinit,
optmethod=optmethod,
reg_fact=reg_lr)
print(it)
# Check for convergence
if it > nb_min_steps:
if LL[it] - LL[it - 1] < max_diff_ll:
print('Converged after %d iterations: %.3e' % (it, LL[it]),
'red', 'on_white')
print(LL)
return LL[it]
print(
"MME did not converge before reaching max iteration. Consider augmenting the number of max iterations."
)
print(LL)
return LL[-1]
def training(self, x, y, reg=1e-2, maxDiffCrit=1e-4, maxIter=200):
"""
Train the parameters of the MRR model
:param x: input matrices (nb_data, dim1, dim2, ...)
:param y: output vectors (nb_data, dim_y)
:param reg: regularization term
:param maxDiffCrit: stopping criterion for the alternative least squares procedure
:param maxIter: maximum number of iterations for the alternative least squares procedure
"""
# Dimensions
N = x.shape[0]
dX = x.shape[1:]
self.dY = y.shape[1]
for dim in range(0, self.dY):
# Initialization
wms = []
for m in range(len(dX)):
wms.append(np.ones((dX[m], self.rank)))
self.alpha.append(np.zeros(1))
self.wVec.append(np.reshape(np.zeros(dX), -1))
# Optimization of parameters (ALS procedure)
nbIter = 1
prevRes = 0
while nbIter < maxIter:
for m in range(len(dX)):
# Compute Wm complement (WM o ... o Wm+1 o Wm-1 o ... o W1)
if m is 0:
wmComplement = wms[1]
for i in range(2, len(dX)):
wmComplement = khatriRaoProd(wms[i], wmComplement)
else:
wmComplement = wms[0]
for i in range(1, len(dX)):
if i != m:
wmComplement = khatriRaoProd(
wms[i], wmComplement)
# Update Wm
zVec = np.zeros((N, dX[m] * self.rank))
for n in range(0, N):
zVec[n] = np.dot(tensor2mat(x[n], m),
wmComplement).flatten()
wm = np.linalg.solve(
zVec.T.dot(zVec) + np.eye(dX[m] * self.rank) * reg,
zVec.T).dot(y[:, dim] - self.alpha[-1])
wms[m] = np.reshape(wm, (dX[m], self.rank))
# Update alpha
wTmp = khatriRaoProd(wms[1], wms[0])
for i in range(2, len(dX)):
wTmp = khatriRaoProd(wms[i], wTmp)
self.wVec[-1] = np.dot(wTmp, np.ones((self.rank, 1)))
alpha = 0
for n in range(0, N):
alpha += y[n, dim] - np.dot(self.wVec[-1][:, None].T,
x[n].flatten())
self.alpha[-1] = alpha[0] / N
# Compute residuals
res = 0
for n in range(0, N):
res += (
y[n, dim] - self.alpha[-1] -
np.dot(self.wVec[-1][:, None].T, x[n].flatten()))**2
resDiff = prevRes - res
# Check convergence
if resDiff < maxDiffCrit and nbIter > 1:
print('TRR converged after %d iterations.' % nbIter)
break
nbIter += 1
prevRes = res
if resDiff > maxDiffCrit:
print('TRR did not converged after %d iterations.' % nbIter)
self.W.append(wms)
# Tensor-valued mixture of experts
print('Tensor-valued mixture of experts...')
tme = TensorMixtureLinearExperts([tme_rank_e, tme_rank_e], tme_rank_g)
tme_LL = tme.training(X, y, y_class, reg_rr=1e-1, reg_lr=1e-1, maxiter=20, max_diff_ll=5.0, optmethod='CG')
tme_coeffs = []
tme_coeffs_lr = []
for c in range(0, tme.nb_class):
# TRR part
alpha = tme.alpha[c][:]
wmsTmp = tme.W[c][0]
# Compute vec(W)
tme_bVec = []
for j in range(tme.nb_dim):
wTmp = khatriRaoProd(wmsTmp[1], wmsTmp[0])
for k in range(2, tme.nb_dim_x):
wTmp = khatriRaoProd(wmsTmp[k], wTmp)
tme_bVec.append(np.dot(wTmp, np.ones((tme.rank_e[c], 1))))
tme_coeffs.append(np.reshape(tme_bVec[0], tuple_dim))
# TLR part
vTmp = khatriRaoProd(tme.V[1][c], tme.V[0][c])
for j in range(2, tme.nb_dim_x):
vTmp = khatriRaoProd(tme.V[j][c], vTmp)
tme_vVec = np.dot(vTmp, np.ones((tme.rank_g, 1)))
tme_coeffs_lr.append(np.reshape(tme_vVec, tuple_dim))
rmse_tme_coeffs_rr = np.sqrt(np.sum([(np.sum((bVec[i] - tme_coeffs[i].flatten()) ** 2))
tot_rmse = np.sum([(np.sum((bVec[i] - me_coeffs[i].flatten()) ** 2))
for i in range(Nclass)]) + np.sum((np.sum((phipsiVec - me_coeffs_lr[0].flatten()) ** 2)))
tot_rmse /= Nclass * d1 * d2 + d1 * d2
rmse_me_coeffs = tot_rmse
# Matrix-valued mixture of experts
print('Matrix-valued mixture of experts...')
mme = MaxtrixMixtureLinearExperts([mme_rank_e, mme_rank_e], mme_rank_g)
mme_LL = mme.training(X, y, y_class, reg_rr=1e-1, reg_lr=1e-1, maxiter=20, max_diff_ll=5.0, optmethod='CG')
mme_coeffs = []
mme_coeffs_lr = []
for i in range(0, mme.nb_class):
alpha = mme.alpha[i][:]
b1tmp = mme.b1[i][:]
b2tmp = mme.b2[i][:]
mme_bVec = [np.dot(khatriRaoProd(b2tmp[j], b1tmp[j]), np.ones((mme.rank_e[i], 1))) for j in range(mme.nb_dim)]
mme_coeffs.append(np.reshape(mme_bVec[0], (d1, d2)))
mme_phipsiVec = np.dot(khatriRaoProd(mme.psi[i], mme.phi[i]), np.ones((mme.rank_g, 1)))
mme_coeffs_lr.append(np.reshape(mme_phipsiVec, (d1, d2)))
rmse_mme_coeffs_rr = np.sqrt(np.sum([(np.sum((bVec[i] - mme_coeffs[i].flatten()) ** 2))
for i in range(Nclass)]) / (Nclass * d1 * d2))
rmse_mme_coeffs_lr = np.sqrt(np.sum((np.sum((phipsiVec - mme_coeffs_lr[0].flatten()) ** 2))) / (d1 * d2))
tot_rmse = np.sum([(np.sum((bVec[i] - mme_coeffs[i].flatten()) ** 2)) for i in range(Nclass)]) + \
np.sum((np.sum((phipsiVec - mme_coeffs_lr[0].flatten()) ** 2)))
tot_rmse /= Nclass * d1 * d2 + d1 * d2
rmse_mme_coeffs = np.sqrt(tot_rmse)
# Show recovered coefficients
def grad(v):
# Recover parameters from vector v
Vall = np.reshape(v, (nb_dim * rank + 1, nb_class))
beta = Vall[0]
V = []
start = 1
end = dims[0] * rank + 1
for m in range(len(dims)):
Vtmp = [
np.reshape(Vall[start:end, i], (dims[m], rank))
for i in range(nb_class)
]
V.append(Vtmp)
start = end
if m < len(dims) - 1:
end += dims[m + 1] * rank
# Compute probabilities
inProdEst = np.zeros((nb_data, nb_class))
for n in range(0, nb_data):
for c in range(0, nb_class):
vTmp = khatriRaoProd(V[1][c], V[0][c])
for i in range(2, len(dims)):
vTmp = khatriRaoProd(V[i][c], vTmp)
vVec = np.dot(vTmp, np.ones((rank, 1)))
inProdEst[n, c] = beta[c] + np.dot(vVec[:, None].T,
x[n].flatten())
est = softmax(inProdEst)
# Compute gradients
grad_beta = np.sum(est - y, axis=0).flatten()
grad_vec = grad_beta.flatten()
gradV = [
np.zeros((dims[m] * rank, nb_class)) for m in range(len(dims))
]
for c in range(nb_class):
for m in range(len(dims)):
# Compute Vm complement (VM o ... o Vm+1 o Vm-1 o ... o V1)
if m is 0:
vmComplement = V[1][c]
for i in range(2, len(dims)):
vmComplement = khatriRaoProd(V[i][c], vmComplement)
else:
vmComplement = V[0][c]
for i in range(1, len(dims)):
if i != m:
vmComplement = khatriRaoProd(V[i][c], vmComplement)
# Gradient
zVec = np.zeros((nb_data, dims[m] * rank))
for n in range(0, nb_data):
zVec[n] = np.dot(tensor2mat(x[n], m),
vmComplement).flatten()
gradV[m][:, c] = np.dot(zVec.T, (est[:, c] - y[:, c]))
# Regularization term
gradV[m][:, c] += 2 * reg_fact * V[m][c].flatten()
for m in range(len(dims)):
grad_vec = np.hstack((grad_vec, gradV[m].flatten()))
return grad_vec
