def rejection(model, condition, query, num_samples):
samples = []
for _ in range(int(num_samples / 100)):
samples += mapp(lambda _: single_rejection(model, condition, query),
range(100))
if num_samples % 100:
samples += mapp(lambda _: single_rejection(model, condition, query),
range(num_samples % 100))
return samples
def rejection(model, condition, query, num_samples):
samples = []
for _ in range(int(num_samples / 100)):
samples += mapp(lambda _: single_rejection(model, condition, query),
range(100))
if num_samples % 100:
samples += mapp(lambda _: single_rejection(model, condition, query),
range(num_samples % 100))
return samples
def test_hierarchical():
ELEMENTS_PER_BAG = 10
observed_samples = {
0: 1,
1: 1,
2: 1,
3: 1,
4: 1,
5: 10,
6: 10,
7: 10,
8: 10,
9: 10
}
def model(init_world, tick, data=observed_samples):
world = init_world.copy()
num_bag_types = church_geometric(world, "num_bag_types", tick, .4)
bag_ps = [church_uniform(world, "bag_type_p_%i" % i, tick, 0, 1)
for i in range(num_bag_types)]
bag_samples = {}
for bag in data.keys():
bag_p = church_listdraw(world, "bag_p_%i" % bag, tick, bag_ps)
bag_k = church_binomial_fixed(world, "bag_k_%i" % bag, tick,
ELEMENTS_PER_BAG, bag_p, fixed_val=data[bag])
bag_samples[bag] = bag_k
return (world, {"num_bag_types": num_bag_types,
"bag_samples": bag_samples})
def condition(val, data=observed_samples):
return val["bag_samples"] == data
def query(val, data=observed_samples):
return val["num_bag_types"]
def getdata(num_clusters):
data = {}
for i in range(10):
data[i] = ((i % num_clusters) + 1) * 20
return data
random_seed(10)
def runmcmc():
mcmc_results = mcmc(model, condition, query, num_steps=100, runtime=5 * 60)
mcmc_samples = mcmc_results["samples"]
return ("mcmc", len(mcmc_samples), histogram(mcmc_samples))
all_results = mapp(lambda f: f(), [runmcmc] * 5)
for result in all_results:
print result
def parallel(self): def parfor(i): self.values[i] = i mapp(parfor, range(self.values.size))
def test_hierarchical():
ELEMENTS_PER_BAG = 10
observed_samples = {
0: 1,
1: 1,
2: 1,
3: 1,
4: 1,
5: 10,
6: 10,
7: 10,
8: 10,
9: 10
}
def model(init_world, tick, data=observed_samples):
world = init_world.copy()
num_bag_types = church_geometric(world, "num_bag_types", tick, .4)
bag_ps = [
church_uniform(world, "bag_type_p_%i" % i, tick, 0, 1)
for i in range(num_bag_types)
]
bag_samples = {}
for bag in data.keys():
bag_p = church_listdraw(world, "bag_p_%i" % bag, tick, bag_ps)
bag_k = church_binomial_fixed(world,
"bag_k_%i" % bag,
tick,
ELEMENTS_PER_BAG,
bag_p,
fixed_val=data[bag])
bag_samples[bag] = bag_k
return (world, {
"num_bag_types": num_bag_types,
"bag_samples": bag_samples
})
def condition(val, data=observed_samples):
return val["bag_samples"] == data
def query(val, data=observed_samples):
return val["num_bag_types"]
def getdata(num_clusters):
data = {}
for i in range(10):
data[i] = ((i % num_clusters) + 1) * 20
return data
random_seed(10)
def runmcmc():
mcmc_results = mcmc(model,
condition,
query,
num_steps=100,
runtime=5 * 60)
mcmc_samples = mcmc_results["samples"]
return ("mcmc", len(mcmc_samples), histogram(mcmc_samples))
all_results = mapp(lambda f: f(), [runmcmc] * 5)
for result in all_results:
print result