def do_mh_kernel_update(trace, scope, block, extra):
(useDeltaKernels, deltaKernelArgs, updateValues) = extra
scaffolder = BlockScaffoldIndexer(scope,
block,
useDeltaKernels=useDeltaKernels,
deltaKernelArgs=deltaKernelArgs,
updateValues=updateValues)
return mixMH(trace, scaffolder, MHOperator())
def doit(scaffolder):
return mixMH(trace, scaffolder,
RejectionOperator(logBound, trials))
def doit(scaffolder):
return mixMH(trace, scaffolder, BogoPossibilizeOperator())
def doit(scaffolder):
return mixMH(trace, scaffolder,
GradientAscentOperator(rate, int(steps)))
def doit(scaffolder):
return mixMH(
trace, scaffolder,
NesterovAcceleratedGradientAscentOperator(rate, int(steps)))
def doit(scaffolder):
return mixMH(trace, scaffolder, StepOutSliceOperator(w, m))
def doit(scaffolder):
return mixMH(trace, scaffolder, DoublingSliceOperator(w, p))
def doit(scaffolder):
return mixMH(trace, scaffolder, EnumerativeMAPOperator())
def doit(scaffolder):
return mixMH(trace, scaffolder,
HamiltonianMonteCarloOperator(epsilon, int(L)))
def doit(scaffolder):
return mixMH(trace, scaffolder, MeanfieldOperator(steps, 0.0001))
def doit(scaffolder):
return mixMH(trace, scaffolder, FuncMHOperator())
def primitive_infer(trace, exp):
operator = exp[0]
if operator == "resimulation_mh":
(scaffolders, transitions, _) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, MHOperator())
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "func_mh":
(scaffolders, transitions, _) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, FuncMHOperator())
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "draw_scaffold":
(scaffolders, _transitions, _) = dispatch_arguments(trace, exp)
drawScaffold(trace, scaffolders[0])
elif operator == "mh_kernel_update":
(scope, block, transitions, extra) = parse_arguments(trace, exp)
return transloop(trace, transitions, lambda : \
do_mh_kernel_update(trace, scope, block, extra))
elif operator == "subsampled_mh":
(scope, block, transitions, extra) = parse_arguments(trace, exp)
return transloop(trace, transitions, lambda : \
do_subsampled_mh(trace, scope, block, extra))
elif operator == "subsampled_mh_check_applicability":
(scope, block, _transitions, _) = parse_arguments(trace, exp)
SubsampledBlockScaffoldIndexer(scope, block).checkApplicability(trace)
# Does not affect nodes
return 0.0
elif operator == "subsampled_mh_make_consistent":
(scope, block, transitions, _) = parse_arguments(trace, exp)
return transloop(trace, transitions, lambda : \
do_subsampled_mh_make_consistent(trace, scope, block, extra))
elif operator == "meanfield":
(scaffolders, transitions, extra) = dispatch_arguments(trace, exp)
steps = int(extra[0])
def doit(scaffolder):
return mixMH(trace, scaffolder, MeanfieldOperator(steps, 0.0001))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "hmc":
(scaffolders, transitions, (epsilon,
L)) = dispatch_arguments(trace, exp)
assert isinstance(L, numbers.Number)
assert isinstance(epsilon, numbers.Number)
def doit(scaffolder):
return mixMH(trace, scaffolder,
HamiltonianMonteCarloOperator(epsilon, int(L)))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "gibbs":
(scaffolders, transitions, _) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, EnumerativeGibbsOperator())
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "emap":
(scaffolders, transitions, _) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, EnumerativeMAPOperator())
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "gibbs_update":
(scope, block, transitions, _) = parse_arguments(trace, exp)
return transloop(trace, transitions, lambda : \
mixMH(trace, BlockScaffoldIndexer(scope, block, updateValues=True),
EnumerativeGibbsOperator()))
elif operator == "slice":
(scaffolders, transitions, (w, m)) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, StepOutSliceOperator(w, m))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "slice_doubling":
(scaffolders, transitions, (w, p)) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, DoublingSliceOperator(w, p))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "pgibbs":
(scope, block, transitions, extra) = parse_arguments(trace, exp)
particles = int(extra[0])
if isinstance(block, list): # Ordered range
(_, min_block, max_block) = block
scaffolder = BlockScaffoldIndexer(scope, "ordered_range",
(min_block, max_block))
return transloop(trace, transitions, lambda : \
mixMH(trace, scaffolder, PGibbsOperator(particles)))
else:
return transloop(trace, transitions, lambda : \
mixMH(trace, BlockScaffoldIndexer(scope, block),
PGibbsOperator(particles)))
elif operator == "pgibbs_update":
(scope, block, transitions, extra) = parse_arguments(trace, exp)
particles = int(extra[0])
if isinstance(block, list): # Ordered range
(_, min_block, max_block) = block
scaffolder = BlockScaffoldIndexer(scope,
"ordered_range",
(min_block, max_block),
updateValues=True)
return transloop(trace, transitions, lambda : \
mixMH(trace, scaffolder, PGibbsOperator(particles)))
else:
return transloop(trace, transitions, lambda : \
mixMH(trace, BlockScaffoldIndexer(scope, block, updateValues=True),
PGibbsOperator(particles)))
elif operator == "func_pgibbs":
(scope, block, transitions, extra) = parse_arguments(trace, exp)
particles = int(extra[0])
if isinstance(block, list): # Ordered range
(_, min_block, max_block) = block
scaffolder = BlockScaffoldIndexer(scope, "ordered_range",
(min_block, max_block))
return transloop(trace, transitions, lambda : \
mixMH(trace, scaffolder, ParticlePGibbsOperator(particles)))
else:
return transloop(trace, transitions, lambda : \
mixMH(trace, BlockScaffoldIndexer(scope, block),
ParticlePGibbsOperator(particles)))
elif operator == "func_pmap":
(scope, block, transitions, extra) = parse_arguments(trace, exp)
particles = int(extra[0])
if isinstance(block, list): # Ordered range
(_, min_block, max_block) = block
scaffolder = BlockScaffoldIndexer(scope, "ordered_range",
(min_block, max_block))
return transloop(trace, transitions, lambda : \
mixMH(trace, scaffolder, ParticlePMAPOperator(particles)))
else:
return transloop(trace, transitions, lambda : \
mixMH(trace, BlockScaffoldIndexer(scope, block),
ParticlePMAPOperator(particles)))
elif operator == "gradient_ascent":
# XXX, in order to mak default args work here, I need to check the lenght of
# the args to `gradient_ascent` conditioned on whether the second arg is a
# VentureSymbol.
if isinstance(exp[1], v.VentureSymbol):
expected_arg_length = 6
else:
expected_arg_length = 5
if len(exp) == expected_arg_length - 2:
exp += [v.VentureNumber(1.0), v.VentureNumber(1.0)]
elif len(exp) == expected_arg_length - 1:
exp += [v.VentureNumber(1.0)]
elif len(exp) < expected_arg_length - 2:
raise ValueError('Not enough args for gradient_ascent')
(scaffolders, transitions, extra) = dispatch_arguments(trace, exp)
(rate, steps) = extra
def doit(scaffolder):
return mixMH(trace, scaffolder,
GradientAscentOperator(rate, int(steps)))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "nesterov":
(scaffolders, transitions, (rate,
steps)) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(
trace, scaffolder,
NesterovAcceleratedGradientAscentOperator(rate, int(steps)))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "rejection":
(scaffolders, transitions, extra) = dispatch_arguments(trace, exp)
if len(extra) >= 1:
logBound = float(extra[0])
else:
logBound = None
if len(extra) == 2:
trials = int(extra[1])
else:
trials = None
def doit(scaffolder):
return mixMH(trace, scaffolder,
RejectionOperator(logBound, trials))
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "bogo_possibilize":
(scaffolders, transitions, _) = dispatch_arguments(trace, exp)
def doit(scaffolder):
return mixMH(trace, scaffolder, BogoPossibilizeOperator())
return transloop(trace, transitions,
scaffolder_loop(scaffolders, doit))
elif operator == "log_rejection_bound_at":
(scaffolders, _transitions, _) = dispatch_arguments(trace, exp)
assert len(
scaffolders) == 1, "log_rejection_bound_at doesn't support 'each'"
scaffold = scaffolders[0].sampleIndex(trace)
return computeRejectionBound(trace, scaffold, scaffold.border[0])
elif operator == "print_scaffold_stats":
(scaffolders, _transitions, _) = dispatch_arguments(trace, exp)
scaffold = scaffolders[0].sampleIndex(trace)
scaffold.show()
return scaffold.numAffectedNodes()
else:
raise Exception("INFER %s is not implemented" % operator)
