def merge(self, follower, m):
if follower != self.creator:
fdq = crane.mantisStore.load_one(
{
'name': self.name,
'creator': follower
}, {
'dq': True
}).get('dq', [])
fdq = FlexibleVector(generic=fdq)
else:
fdq = self.dq
rd = (fdq.norm() + EPS) / (self.panda.z.norm() + EPS)
if rd < self.eps:
logger.debug('Converged, no need to merge')
return False
else:
self.panda.z.add(fdq, 1.0 / (m + 1 / self.rho))
logger.debug('m = {0}'.format(m))
logger.debug('update z {0}'.format(self.panda.z))
logger.debug('relative difference of z {0}'.format(rd))
metricValue(self, 'rz', rd)
#self.panda.update_fields({self.panda.FCONSENSUS:self.panda.z.generic()})
if fdq is not self.dq:
del fdq
return True
def merge(self, follower, m):
if follower != self.creator:
fdq = crane.mantisStore.load_one({'name':self.name,
'creator':follower},
{'dq':True}).get('dq',[])
fdq = FlexibleVector(generic=fdq)
else:
fdq = self.dq
rd = (fdq.norm() + EPS) / (self.panda.z.norm() + EPS)
if rd < self.eps:
logger.debug('Converged, no need to merge')
return False
else:
self.panda.z.add(fdq, 1.0 / (m + 1 / self.rho))
logger.debug('m = {0}'.format(m))
logger.debug('update z {0}'.format(self.panda.z))
logger.debug('relative difference of z {0}'.format(rd))
metricValue(self, 'rz', rd)
#self.panda.update_fields({self.panda.FCONSENSUS:self.panda.z.generic()})
if fdq is not self.dq:
del fdq
return True
def train(self, leader):
if not self.data:
logger.debug('no data, skip training')
return
logger.debug('gamma in mantis {0}'.format(self.gamma))
# check out z
if leader:
z = crane.pandaStore.load_one(
{
'name': self.panda.name,
'creator': leader
}, {
'z': True
}).get('z', [])
z = FlexibleVector(generic=z)
else:
z = self.panda.z
if z is None:
logger.debug('no consensus checked out')
return
#metricAbs(metricLog, self, '|z|', z)
#metricAbs(metricLog, self, '|q|', self.q)
metricRelAbs(self, 'z~q', self.q, z)
# update mu
self.dq.clear()
self.dq.add(self.mu, -1)
self.mu.add(self.q, 1)
self.mu.add(z, -1)
self.dq.add(self.mu, 1)
metricAbs(self, 'mu', self.mu)
#metricAbs(metricLog, self, '|dmu|', self.dq)
#metricValue(metricLog, self, 'sup(mu)', 2 * self.solver.num_instances * self.solver.maxxnorm() * z.norm())
# update w
self.solver.setModel0(z, self.mu)
#loss = self.solver.status()
#metricValue(metricLog, self, 'loss', loss)
#metricRelAbs(metricLog, self, '|q~w|', self.q, self.panda.weights)
#logger.debug('q = {0}'.format(self.q))
#logger.debug('w = {0}'.format(self.panda.weights))
self.solver.trainModel()
loss = self.solver.status()
metricValue(self, 'loss', loss)
metricValue(self, 'x', self.solver.maxxnorm())
# update q
r = self.rho / float(self.rho + self.gamma)
self.dq.add(self.q, -1)
self.q.clear()
self.q.add(z, r)
self.q.add(self.panda.weights, 1 - r)
self.q.add(self.mu, -r)
self.dq.add(self.q, 1)
if z is not self.panda.z:
del z
logger.debug('q = {0}'.format(self.q))
logger.debug('w = {0}'.format(self.panda.weights))
# measure convergence
#metricAbs(self, '|dq|', self.dq)
#metricAbs(self, '|q|', self.q)
metricRelAbs(self, 'q~w', self.q, self.panda.weights)
# commit changes
self.panda.update_fields(
{self.panda.FWEIGHTS: self.panda.weights.generic()})
self.commit()
def train(self, leader):
if not self.data:
logger.debug('no data, skip training')
return
logger.debug('gamma in mantis {0}'.format(self.gamma))
# check out z
if leader:
z = crane.pandaStore.load_one({'name':self.panda.name,
'creator':leader},
{'z':True}).get('z',[])
z = FlexibleVector(generic=z)
else:
z = self.panda.z
if z is None:
logger.debug('no consensus checked out')
return
#metricAbs(metricLog, self, '|z|', z)
#metricAbs(metricLog, self, '|q|', self.q)
metricRelAbs(self, 'z~q', self.q, z)
# update mu
self.dq.clear()
self.dq.add(self.mu, -1)
self.mu.add(self.q, 1)
self.mu.add(z, -1)
self.dq.add(self.mu, 1)
metricAbs(self, 'mu', self.mu)
#metricAbs(metricLog, self, '|dmu|', self.dq)
#metricValue(metricLog, self, 'sup(mu)', 2 * self.solver.num_instances * self.solver.maxxnorm() * z.norm())
# update w
self.solver.setModel0(z, self.mu)
#loss = self.solver.status()
#metricValue(metricLog, self, 'loss', loss)
#metricRelAbs(metricLog, self, '|q~w|', self.q, self.panda.weights)
#logger.debug('q = {0}'.format(self.q))
#logger.debug('w = {0}'.format(self.panda.weights))
self.solver.trainModel()
loss = self.solver.status()
metricValue(self, 'loss', loss)
metricValue(self, 'x', self.solver.maxxnorm())
# update q
r = self.rho / float(self.rho + self.gamma)
self.dq.add(self.q, -1)
self.q.clear()
self.q.add(z, r)
self.q.add(self.panda.weights, 1 - r)
self.q.add(self.mu, -r)
self.dq.add(self.q, 1)
if z is not self.panda.z:
del z
logger.debug('q = {0}'.format(self.q))
logger.debug('w = {0}'.format(self.panda.weights))
# measure convergence
#metricAbs(self, '|dq|', self.dq)
#metricAbs(self, '|q|', self.q)
metricRelAbs(self, 'q~w', self.q, self.panda.weights)
# commit changes
self.panda.update_fields({self.panda.FWEIGHTS:self.panda.weights.generic()})
self.commit()