def train_dc(self, max_iter=200):
""" Solve the LatentSVDD optimization problem with a
sequential convex programming/DC-programming
approach:
Iteratively, find the most likely configuration of
the latent variables and then, optimize for the
model parameter using fixed latent states.
"""
N = self.sobj.get_num_samples()
DIMS = self.sobj.get_num_dims()
# intermediate solutions
# latent variables
latent = [0] * N
#sol = 1.0*uniform(DIMS,1)-0.5
sol = matrix(0.0, (DIMS, 1))
psi = matrix(0.0, (DIMS, N)) # (dim x exm)
old_psi = matrix(0.0, (DIMS, N)) # (dim x exm)
threshold = 0
obj = -1
iter = 0
# terminate if objective function value doesn't change much
while iter < max_iter and (
iter < 3 or sum(sum(abs(np.array(psi - old_psi)))) >= 0.001):
print('Starting iteration {0}.'.format(iter))
print(sum(sum(abs(np.array(psi - old_psi)))))
iter += 1
old_psi = matrix(psi)
latent_old = list(latent)
# 1. linearize
# for the current solution compute the
# most likely latent variable configuration
for i in range(N):
# min_z ||sol - Psi(x,z)||^2 = ||sol||^2 + min_z -2<sol,Psi(x,z)> + ||Psi(x,z)||^2
# Hence => ||sol||^2 - max_z 2<sol,Psi(x,z)> - ||Psi(x,z)||^2
(foo, latent[i], psi[:, i]) = self.sobj.argmax(sol, i)
# 2. solve the intermediate convex optimization problem
kernel = Kernel.get_kernel(psi, psi)
svdd = SVDD(kernel, self.C)
svdd.train_dual()
threshold = svdd.get_threshold()
inds = svdd.get_support_dual()
alphas = svdd.get_support_dual_values()
sol = psi[:, inds] * alphas
#print alphas
self.sol = sol
self.latent = latent
return (sol, latent, threshold)
def train_dc(self, max_iter=200):
""" Solve the LatentSVDD optimization problem with a
sequential convex programming/DC-programming
approach:
Iteratively, find the most likely configuration of
the latent variables and then, optimize for the
model parameter using fixed latent states.
"""
N = self.sobj.get_num_samples()
DIMS = self.sobj.get_num_dims()
# intermediate solutions
# latent variables
latent = [0]*N
#sol = 1.0*uniform(DIMS,1)-0.5
sol = matrix(0.0, (DIMS,1))
psi = matrix(0.0, (DIMS,N)) # (dim x exm)
old_psi = matrix(0.0, (DIMS,N)) # (dim x exm)
threshold = 0
obj = -1
iter = 0
# terminate if objective function value doesn't change much
while iter<max_iter and (iter<3 or sum(sum(abs(np.array(psi-old_psi))))>=0.001):
print('Starting iteration {0}.'.format(iter))
print(sum(sum(abs(np.array(psi-old_psi)))))
iter += 1
old_psi = matrix(psi)
latent_old = list(latent)
# 1. linearize
# for the current solution compute the
# most likely latent variable configuration
for i in range(N):
# min_z ||sol - Psi(x,z)||^2 = ||sol||^2 + min_z -2<sol,Psi(x,z)> + ||Psi(x,z)||^2
# Hence => ||sol||^2 - max_z 2<sol,Psi(x,z)> - ||Psi(x,z)||^2
(foo, latent[i], psi[:,i]) = self.sobj.argmax(sol, i)
# 2. solve the intermediate convex optimization problem
kernel = Kernel.get_kernel(psi,psi)
svdd = SVDD(kernel, self.C)
svdd.train_dual()
threshold = svdd.get_threshold()
inds = svdd.get_support_dual()
alphas = svdd.get_support_dual_values()
sol = psi[:,inds]*alphas
#print alphas
self.sol = sol
self.latent = latent
return (sol, latent, threshold)
import matplotlib.pyplot as plt from svdd import SVDD from kernel import Kernel if __name__ == '__main__': # kernel parameter and type kparam = 0.1 ktype = 'rbf' # generate raw training data Dtrain = co.normal(2,100) # build kernel kernel = Kernel.get_kernel(Dtrain,Dtrain,ktype,kparam) # train svdd svdd = SVDD(kernel,0.9) svdd.train_dual() # generate test data grid delta = 0.1 x = np.arange(-4.0, 4.0, delta) y = np.arange(-4.0, 4.0, delta) X, Y = np.meshgrid(x, y) (sx,sy) = X.shape Xf = np.reshape(X,(1,sx*sy)) Yf = np.reshape(Y,(1,sx*sy)) Dtest = np.append(Xf,Yf,axis=0) print(Dtest.shape) # build test kernel kernel = Kernel.get_kernel(co.matrix(Dtest),Dtrain[:,svdd.get_support_dual()],ktype,kparam)
import cvxopt as co from svdd import SVDD from kernel import Kernel if __name__ == '__main__': # kernel parameter and type kparam = 0.1 ktype = 'rbf' # generate raw training data Dtrain = co.normal(2,100) # build kernel kernel = Kernel.get_kernel(Dtrain,Dtrain,ktype,kparam) # train svdd svdd = SVDD(kernel,0.9) svdd.train_dual() # generate test data grid delta = 0.1 x = np.arange(-4.0, 4.0, delta) y = np.arange(-4.0, 4.0, delta) X, Y = np.meshgrid(x, y) (sx,sy) = X.shape Xf = np.reshape(X,(1,sx*sy)) Yf = np.reshape(Y,(1,sx*sy)) Dtest = np.append(Xf,Yf,axis=0) print(Dtest.shape) # build test kernel kernel = Kernel.get_kernel(co.matrix(Dtest),Dtrain[:,svdd.get_support_dual()],ktype,kparam)
