def optimize_model(args):
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Found model: {}'.format(args.modelfile)
if args.multi_slack:
mm_model = MultiSlackMaxMarginModel.read(args.modelfile, actions, feature_fn, margin_fn)
else:
mm_model = MaxMarginModel.read(args.modelfile, actions, feature_fn, margin_fn)
mm_model.C = args.C
mm_model.optimize_model()
mm_model.save_weights_to_file(args.weightfile)
def build_model(args):
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Building model into {}.'.format(args.modelfile)
if args.multi_slack:
mm_model = MultiSlackMaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
else:
mm_model = MaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
mm_model.load_constraints_from_file(args.constraintfile)
mm_model.save_model(args.modelfile)
def build_model(args):
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Building model into {}.'.format(args.modelfile)
if args.model == 'multi':
mm_model = MultiSlackMaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
elif args.model == 'bellman':
mm_model = BellmanMaxMarginModel(actions, args.C, args.D, args.F, 1, num_features, feature_fn, margin_fn) # changed
else:
mm_model = MaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
if not args.goal_constraints and args.model == 'bellman':
demofile = h5py.File(args.demofile, 'r')
ignore_keys = [k for k in demofile if demofile[k]['knot'][()]]
demofile.close()
else:
ignore_keys = None
mm_model.load_constraints_from_file(args.constraintfile, ignore_keys)
mm_model.save_model(args.modelfile)
def optimize_model(args):
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Found model: {}'.format(args.modelfile)
if args.model == 'multi':
mm_model = MultiSlackMaxMarginModel.read(args.modelfile, actions, num_features, feature_fn, margin_fn)
elif args.model == 'bellman':
mm_model = BellmanMaxMarginModel.read(args.modelfile, actions, num_features, feature_fn, margin_fn)
mm_model.D = args.D
mm_model.F = args.F
else:
mm_model = MaxMarginModel.read(args.modelfile, actions, num_features, feature_fn, margin_fn)
if args.save_memory:
mm_model.model.setParam('threads', 1) # Use single thread instead of maximum
# barrier method (#2) is default for QP, but uses more memory and could lead to error
mm_model.model.setParam('method', 1) # Use dual simplex method to solve model
#mm_model.model.setParam('method', 0) # Use primal simplex method to solve model
mm_model.C = args.C
mm_model.optimize_model()
mm_model.save_weights_to_file(args.weightfile)
def build_constraints(args):
#test_features(args, "sc")
test_features(args, "rope_dist")
feature_fn, margin_fn, num_features, actions = select_feature_fn(args)
print 'Building constraints into {}.'.format(args.constraintfile)
if args.model == 'multi':
mm_model = MultiSlackMaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
elif args.model == 'bellman':
mm_model = BellmanMaxMarginModel(actions, args.C, args.D, args.F, .9, num_features, feature_fn, margin_fn)
else:
mm_model = MaxMarginModel(actions, args.C, num_features, feature_fn, margin_fn)
if args.model == 'bellman':
add_bellman_constraints_from_demo(mm_model,
args.demofile,
args.start, args.end,
outfile=args.constraintfile,
verbose=True)
else:
add_constraints_from_demo(mm_model,
args.demofile,
args.start, args.end,
outfile=args.constraintfile,
verbose=True)