def extract_assessment(maker, params, data):
r = get_ripl()
r.assume("maker", maker)
r.assume("made",
v.app(v.sym("maker"),
*[v.app(v.sym("exactly"), p) for p in params]))
for item in data:
r.observe("(made)", item)
ans = r.infer("global_log_likelihood")
return ans
def extract_sample(maker, params, index, seed):
r = get_ripl(seed=seed)
r.assume("maker", maker)
expr = v.app(v.sym("list"), *[v.app(v.sym("made")) for _ in range(index+1)])
def one_sample():
r.assume("made", v.app(v.sym("maker"), *params))
ans = r.sample(expr)[-1]
r.forget("made")
return ans
results = [one_sample() for _ in range(default_num_samples(5))]
return results
def extract_cross_sample(maker, params, index1, index2, combiner, seed):
r = get_ripl(seed=seed)
r.assume("maker", maker)
index = max(index1, index2)
expr = v.app(v.sym("list"), *[v.app(v.sym("made")) for _ in range(index+1)])
def one_sample():
r.assume("made", v.app(v.sym("maker"), *params))
vec = r.sample(expr)
r.forget("made")
return combiner(vec[index1], vec[index2])
results = [one_sample() for _ in range(default_num_samples(5))]
return results
def _collectData(iid,ripl,address,num_samples=None,infer=None):
if num_samples is None:
num_samples = default_num_samples()
if infer is None:
infer = defaultInfer()
elif infer == "mixes_slowly":
# TODO Replace this awful hack with proper adjustment of tests for difficulty
infer = defaultInfer()
if infer is not "(rejection default all 1)":
infer = "(repeat 4 (do %s))" % infer
predictions = []
for _ in range(num_samples):
# TODO Consider going direct here to avoid the parser
ripl.infer(infer)
predictions.append(ripl.report(address))
if iid:
ripl.sivm.core_sivm.engine.reinit_inference_problem()
ripl.infer(v.app(v.sym("incorporate")))
return predictions
def observe(i, d):
r.observe(expr.app(expr.symbol("datum"), expr.integer(i), d), dataset[i][d])
def testBasicAnnotation():
sivm = get_ripl().sivm
expr = v.app(v.sym("add"), v.num(1), v.sym("foo"))
err.assert_sivm_annotation_succeeds(sivm.assume, v.sym("x"), expr)
def one_sample():
r.assume("made", v.app(v.sym("maker"), *params))
vec = r.sample(expr)
r.forget("made")
return combiner(vec[index1], vec[index2])
def one_sample():
r.assume("made", v.app(v.sym("maker"), *params))
ans = r.sample(expr)[-1]
r.forget("made")
return ans
},
'beta_bernoulli' : {
'native' : native_beta_bernoulli,
'optimized' : ['make_beta_bernoulli', 'make_uc_beta_bernoulli',
suff_stat_beta_bernoulli],
'gibbs' : 'make_uc_beta_bernoulli',
'param_sets' : [(1.0, 1.0),
(2.0, 3.0)],
'reporter' : reportSameDiscrete,
'combiner' : lambda x, y: (x,y)
},
'dir_cat' : {
'native' : native_dir_cat,
'optimized' : ['make_dir_cat', 'make_uc_dir_cat'],
'gibbs' : 'make_uc_dir_cat',
'param_sets' : [(v.app("array", 0.5, 0.5, 0.5),),
(v.app("array", 0.2, 0.2, 0.2, 0.2, 0.2),)],
'reporter' : reportSameDiscrete,
'combiner' : lambda x, y: (x,y),
},
'sym_dir_cat' : {
'native' : native_sym_dir_cat,
'optimized' : ['make_sym_dir_cat', 'make_uc_sym_dir_cat'],
'gibbs' : 'make_uc_sym_dir_cat',
'param_sets' : [(0.5, 4), (0.2, 8)],
'reporter' : reportSameDiscrete,
'combiner' : lambda x, y: (x,y),
},
'gamma_poisson' : {
'native' : native_gamma_poisson,
'optimized' : ['make_gamma_poisson', 'make_uc_gamma_poisson',