Example #1
0
def optimize(w, globalSPLVars, params, iter):
	if params.splParams.splMode == 'CCCP' or iter <= params.splParams.splInitIters:
		wOptstateBundle= SSVM.cuttingPlaneOptimize(w, params, iter)
	else:
		assert(0) # I don't understand
		globalSPLVars.fraction = 1
		SPLSelector.select(globalSPLVars, w, params)
		wOptstateBundle  = SSVM.cuttingPlaneOptimize(w, params, iter)



	return wOptstateBundle
Example #2
0
def optimize(w, params, spl_params):
	if spl_params.spl_mode == 0:
		w = SSVM.cuttingPlaneOptimize(w, params, spl_params)
	elif spl_params.spl_mode == 1:
		assert(0) #TODO: actually do stuff
	elif spl_params.spl_mode == 2:
		assert(0) #TODO: actually do stuff
	else:
		assert(0) #TODO: actually do stuff
	return w
Example #3
0
def checkConvergence(w, globalSPLVars, params, curBestObj, wBest,iter):
	if (params.splParams.splMode!='CCCP') and globalSPLVars.fraction < .9999: #NOT INCLUDING EVERYTHING, IGNORE RESULTS
		return ( False, numpy.inf, w)

	obj,margin,constraint,mostViolatedLatents= SSVM.computeObjective(w, params)

	if obj < curBestObj:
		wBest = w

	return (obj >= (curBestObj - params.maxDualityGap), min([obj, curBestObj]), wBest,obj)
Example #4
0
	def run( self ):
		try:
			while 1:
				(blob, mapper, reducer) = self.input_queue.get()
				if mapper == HImputation.imputeSingle:
					env,task = SSVM.initializeMosek(self.examples[0].params)
					blob.env = env
					blob.task = task

				mapped = [mapper(blob,example) for example in self.examples]
				if reducer is not None:
					A = reduce(reducer,mapped)
					self.output_queue.put(A)
				else:
					self.output_queue.put(mapped)

		except Exception, e:
			logging.debug('worker died')
			import traceback
			traceback.print_exc(file=sys.stderr)
			sys.stderr.flush()
Example #5
0
def reoptimizeW(optState,example):
	assert(example.params.splParams.splMode == 'CCCP')
	marginsNew = []
	margins = optState.margins
	FNewtranspose = numpy.asmatrix(numpy.zeros(optState.F.T.shape))
	Ftranspose = optState.F.T
	latents = optState.latents
	for iteration in range(len(margins)):
		ybar, hbar = latents[iteration][example.id]
		contributedMargin = CommonApp.delta(ybar, example.trueY)
		contributedConstraintGiven = CommonApp.padCanonicalPsi(example.psis()[example.h,:].T, example.trueY, example.params)
		contributedConstraintHBar = CommonApp.padCanonicalPsi(example.psis()[hbar,:].T, ybar, example.params)
		FNewtranspose[iteration,:] = (Ftranspose[iteration,:] - (contributedConstraintGiven - contributedConstraintHBar).T)
		marginsNew.append(margins[iteration]-contributedMargin)

	FNew = FNewtranspose.T
	FTF = FNew.T*FNew

	wNew, objective, gap = SSVM.solveDualQPV2(FTF, FNew,marginsNew,example.params, optState.env,optState.task)


	return wNew