def simulate(self, args):
weight = args.operandValues()[0]
# The made SP is the same as in the conjugate case: flip coins
# based on an explicit weight, and maintain sufficient statistics.
output = TypedPSP(SuffBernoulliOutputPSP(weight),
SPType([], t.BoolType()))
return VentureSPRecord(SuffBernoulliSP(NullRequestPSP(), output))
def __init__(self, mean, covariance):
self.mean = mean
self.covariance = covariance
output = dispatching_psp(
[gpType, gp1Type],
[GPOutputPSP(mean, covariance),
GPOutputPSP1(mean, covariance)])
super(GPSP, self).__init__(NullRequestPSP(), output)
def simulate(self, args):
(m0, k0, v0, S0) = args.operandValues()
m0 = np.mat(m0).transpose()
d = np.size(m0)
output = TypedPSP(CMVNOutputPSP(d, m0, k0, v0, S0),
SPType([], t.HomogeneousArrayType(t.NumberType())))
return VentureSPRecord(CMVNSP(NullRequestPSP(), output, d))
def simulate(self, _trace, args):
(alpha, beta) = args.operandValues()
madeaux = args.madeSPAux()
[ctY, ctN] = madeaux.cts()
new_weight = args.np_prng().beta(a=(alpha + ctY), b=(beta + ctN))
output = TypedPSP(SuffBernoulliOutputPSP(new_weight),
SPType([], t.BoolType()))
return VentureSPRecord(SuffBernoulliSP(NullRequestPSP(), output),
madeaux)
def simulate(self, args):
vals = args.operandValues()
alpha = vals[0]
os = vals[1] if len(vals) > 1 \
else [VentureInteger(i) for i in range(len(alpha))]
if len(os) != len(alpha):
raise VentureValueError(
"Set of objects to choose from is the wrong length")
output = TypedPSP(CDirCatOutputPSP(alpha, os), SPType([], t.AnyType()))
return VentureSPRecord(
DirCatSP(NullRequestPSP(), output, alpha, len(alpha)))
def simulate(self, args):
vals = args.operandValues()
(alpha, n) = (float(vals[0]), int(vals[1]))
os = vals[2] if len(vals) > 2 else [
VentureInteger(i) for i in range(n)
]
if len(os) != n:
raise VentureValueError(
"Set of objects to choose from is the wrong length")
output = TypedPSP(CSymDirCatOutputPSP(alpha, n, os),
SPType([], t.AnyType()))
return VentureSPRecord(DirCatSP(NullRequestPSP(), output, alpha, n))
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)
def simulate(self, _trace, args):
vals = args.operandValues()
alpha = vals[0]
os = vals[1] if len(vals) > 1 \
else [VentureInteger(i) for i in range(len(alpha))]
madeaux = args.madeSPAux()
assert isinstance(madeaux, DirCatSPAux)
counts = [count + a for (count, a) in zip(madeaux.counts, alpha)]
newTheta = args.np_prng().dirichlet(counts)
output = TypedPSP(UDirCatOutputPSP(newTheta, os),
SPType([], t.AnyType()))
return VentureSPRecord(
DirCatSP(NullRequestPSP(), output, alpha, len(alpha)), madeaux)
def simulate(self, args):
vals = args.operandValues()
alpha = vals[0]
n = len(alpha)
os = vals[1] if len(vals) > 1 else [
VentureInteger(i) for i in range(n)
]
if len(os) != n:
raise VentureValueError(
"Set of objects to choose from is the wrong length")
theta = args.np_prng().dirichlet(alpha)
output = TypedPSP(UDirCatOutputPSP(theta, os), SPType([], t.AnyType()))
return VentureSPRecord(DirCatSP(NullRequestPSP(), output, alpha, n))
def simulate(self, _trace, args):
vals = args.operandValues()
(alpha, n) = (float(vals[0]), int(vals[1]))
os = vals[2] if len(vals) > 2 else [
VentureInteger(i) for i in range(n)
]
madeaux = args.madeSPAux()
assert isinstance(madeaux, DirCatSPAux)
counts = [count + alpha for count in madeaux.counts]
newTheta = args.np_prng().dirichlet(counts)
output = TypedPSP(USymDirCatOutputPSP(newTheta, os),
SPType([], t.AnyType()))
return VentureSPRecord(DirCatSP(NullRequestPSP(), output, alpha, n),
madeaux)
def simulate(self, args):
(alpha, beta) = args.operandValues()
weight = args.np_prng().beta(a=alpha, b=beta)
output = TypedPSP(SuffBernoulliOutputPSP(weight),
SPType([], t.BoolType()))
return VentureSPRecord(SuffBernoulliSP(NullRequestPSP(), output))
def simulate(self, args):
(alpha, beta) = args.operandValues()
output = TypedPSP(CBetaBernoulliOutputPSP(alpha, beta),
SPType([], t.BoolType()))
return VentureSPRecord(SuffBernoulliSP(NullRequestPSP(), output))
def simulate(self, args):
vals = args.operandValues()
alpha = vals[0]
d = vals[1] if len(vals) == 2 else 0
output = TypedPSP(CRPOutputPSP(alpha, d), SPType([], t.AtomType()))
return VentureSPRecord(CRPSP(NullRequestPSP(), output))
def no_request(output):
return SP(NullRequestPSP(), output)
else:
args.append(VentureArray(flat_args[i:i + s]))
i += s
assert i == len(flat_args)
return args
class ApplyFunctionOutputPSP(DeterministicPSP):
def simulate(self, args):
vals = args.operandValues()
function = vals[0]
arguments = vals[1:]
sp_type = function.sp_type
unwrapped_args = sp_type.unwrap_arg_list(arguments)
#print sp_type.name(), unwrapped_args
returned = function.f(*unwrapped_args)
wrapped_return = sp_type.wrap_return(returned)
return wrapped_return
def description(self, _name=None):
return "Apply a VentureFunction to arguments."
# TODO Add type signature. Look at signature of apply?
applyFunctionSP = SP(NullRequestPSP(), ApplyFunctionOutputPSP())
registerBuiltinSP("apply_function", applyFunctionSP)
def simulate(self, args):
(alpha, beta) = args.operandValues()
output = TypedPSP(CGammaPoissonOutputPSP(alpha, beta),
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):
mu = args.operandValues()[0]
output = TypedPSP(SuffPoissonOutputPSP(mu), SPType([], t.CountType()))
return VentureSPRecord(SuffPoissonSP(NullRequestPSP(), output))
def name(self):
return self._name or '<covariance>'
def distribution(self, base, **kwargs):
return None
def gradient_type(self):
return t.ArrayUnboxedType(t.NumericArrayType())
makeGPType = SPType(
[GPMeanType('mean function'),
GPCovarianceType('covariance kernel')], gpType)
makeGPSP = SP(NullRequestPSP(), TypedPSP(MakeGPOutputPSP(), makeGPType))
registerBuiltinSP('make_gp', makeGPSP)
xType = t.NumericArrayType('x')
oType = t.NumberType('o')
def _mean_sp(F, argtypes):
def mean_gradientOfSimulate(args, direction):
return parameter_nest(F(*args).parameters, direction.getArray())
return deterministic_typed(F,
argtypes,
GPMeanType(),
sim_grad=mean_gradientOfSimulate,
