def __init__(self, p0, T, O):
req = TypedPSP(UncollapsedHMMRequestPSP(),
SPType([t.CountType()], t.RequestType()))
output = TypedPSP(UncollapsedHMMOutputPSP(O),
SPType([t.CountType()], t.IntegerType()))
super(UncollapsedHMMSP, self).__init__(req, output)
self.p0 = p0
self.T = T
self.O = O
def simulate(self, _trace, args):
madeaux = args.madeSPAux()
[xsum, ctN] = madeaux.cts()
(alpha, beta) = args.operandValues()
new_alpha = alpha + xsum
new_beta = beta + ctN
new_mu = args.np_prng().gamma(shape=(alpha + xsum),
scale=1. / new_beta)
output = TypedPSP(UGammaPoissonOutputPSP(new_mu, new_alpha, new_beta),
SPType([], t.CountType()))
return VentureSPRecord(SuffPoissonSP(NullRequestPSP(), output),
madeaux)
(alpha, n) = args.operandValues()
return simulateDirichlet([float(alpha) for _ in range(int(n))],
args.np_prng())
def logDensity(self, val, args):
(alpha, n) = args.operandValues()
return logDensityDirichlet(val, [float(alpha) for _ in range(int(n))])
def description(self, name):
return " %s(alpha, n) samples a simplex point according to the " \
"symmetric Dirichlet distribution on n dimensions with " \
"concentration parameter alpha." % name
registerBuiltinSP("symmetric_dirichlet", \
typed_nr(SymmetricDirichletOutputPSP(),
[t.PositiveType(), t.CountType()], t.SimplexType()))
#### Common classes for AAA dirichlet distributions
class DirCatSPAux(SPAux):
def __init__(self, n=None, counts=None):
if counts is not None:
self.counts = counts
elif n is not None:
self.counts = Node([0] * n)
else:
raise Exception("Must pass 'n' or 'counts' to DirCatSPAux")
def copy(self):
return DirCatSPAux(counts=copy.deepcopy(self.counts))
deterministic_typed(np.arange,
[t.IntegerType(), t.IntegerType()],
t.ArrayUnboxedType(t.IntegerType()),
min_req_args=1,
descr="%s([start], stop) returns an array of n consecutive integers " \
"from start (inclusive) up to stop (exclusive)."))
registerBuiltinSP("fill",
deterministic_typed(np.full,
[t.IntegerType(), t.NumberType()],
t.ArrayUnboxedType(t.NumberType()),
descr="%s(n, x) returns an array with the number x repeated n times"))
registerBuiltinSP("linspace",
deterministic_typed(np.linspace,
[t.NumberType(), t.NumberType(), t.CountType()],
t.ArrayUnboxedType(t.NumberType()),
descr="%s(start, stop, n) returns an array of n evenly spaced numbers " \
"over the interval [start, stop]."))
registerBuiltinSP("zero_matrix",
deterministic_typed(lambda n, m: np.zeros((n, m)),
[t.CountType(), t.CountType()], t.MatrixType(),
descr="%s(n, m) returns a zero matrix of shape n by m."))
registerBuiltinSP("id_matrix",
deterministic_typed(np.identity, [t.CountType()], t.MatrixType(),
descr="%s(n) returns an identity matrix of dimension n."))
registerBuiltinSP("diag_matrix",
deterministic_typed(np.diag,
def logDensity(self, value, args):
n = args.operandValues()[0]
xs = args.spaux().xs
assert len(xs) > n
theta = np.dot(xs[n], self.O)
return math.log(theta[value])
def incorporate(self, value, args):
n = args.operandValues()[0]
os = args.spaux().os
if n not in os: os[n] = []
os[n].append(value)
def unincorporate(self, value, args):
n = args.operandValues()[0]
os = args.spaux().os
del os[n][os[n].index(value)]
if not os[n]: del os[n]
class UncollapsedHMMRequestPSP(DeterministicPSP):
def simulate(self, args):
return Request([], [args.operandValues()[0]])
registerBuiltinSP(
"make_lazy_hmm",
typed_nr(MakeUncollapsedHMMOutputPSP(),
[t.SimplexType(), t.MatrixType(),
t.MatrixType()], SPType([t.CountType()], t.IntegerType())))
def simulate(self, args):
mu = args.operandValues()[0]
output = TypedPSP(SuffPoissonOutputPSP(mu), SPType([], t.CountType()))
return VentureSPRecord(SuffPoissonSP(NullRequestPSP(), output))
def simulate(self, args):
(alpha, beta) = args.operandValues()
mu = args.np_prng().gamma(shape=alpha, scale=1. / beta)
output = TypedPSP(UGammaPoissonOutputPSP(mu, alpha, beta),
SPType([], t.CountType()))
return VentureSPRecord(SuffPoissonSP(NullRequestPSP(), output))
def simulate(self, args):
(alpha, beta) = args.operandValues()
output = TypedPSP(CGammaPoissonOutputPSP(alpha, beta),
SPType([], t.CountType()))
return VentureSPRecord(SuffPoissonSP(NullRequestPSP(), output))
if p == 1:
return [n]
elif p == 0:
return [0]
else:
return [i for i in range(int(n) + 1)]
def description(self, name):
return ' %s(n, p) simulates flipping n Bernoulli trials independently '\
'with probability p and returns the total number of successes.' % name
registerBuiltinSP(
"binomial",
typed_nr(BinomialOutputPSP(),
[t.CountType(), t.ProbabilityType()], t.CountType()))
class CategoricalOutputPSP(DiscretePSP):
# (categorical ps outputs)
def simulate(self, args):
vals = args.operandValues()
if len(vals) == 1: # Default values to choose from
return simulateCategorical(
vals[0], args.np_prng(),
[VentureInteger(i) for i in range(len(vals[0]))])
else:
if len(vals[0]) != len(vals[1]):
raise VentureValueError(
"Categorical passed different length arguments.")
ps, os = vals
