def ap_categorical_sampler(obs, prior_weight, upper,
size=(), rng=None, LF=DEFAULT_LF):
weights = scope.linear_forgetting_weights(scope.len(obs), LF=LF)
counts = scope.bincount(obs, minlength=upper, weights=weights)
# -- add in some prior pseudocounts
pseudocounts = counts + prior_weight
return scope.categorical(pseudocounts / scope.sum(pseudocounts),
upper=upper, size=size, rng=rng)
def hp_pchoice(label, p_options):
"""
label: string
p_options: list of (probability, option) pairs
"""
p, options = zip(*p_options)
n_options = len(options)
ch = scope.hyperopt_param(label, scope.categorical(p, upper=n_options))
return scope.switch(ch, *options)
def hp_pchoice(label, p_options):
"""
label: string
p_options: list of (probability, option) pairs
"""
p, options = zip(*p_options)
n_options = len(options)
ch = scope.hyperopt_param(label, scope.categorical(p, upper=n_options))
return scope.switch(ch, *options)
def hp_pchoice(label, p_options):
"""
label: string
p_options: list of (probability, option) pairs
"""
if not isinstance(label, basestring):
raise TypeError("require string label")
p, options = zip(*p_options)
n_options = len(options)
ch = scope.hyperopt_param(label, scope.categorical(p, upper=n_options))
return scope.switch(ch, *options)
def hp_pchoice(label, p_options):
"""
label: string
p_options: list of (probability, option) pairs
"""
if not isinstance(label, basestring):
raise TypeError('require string label')
p, options = zip(*p_options)
n_options = len(options)
ch = scope.hyperopt_param(label, scope.categorical(p, upper=n_options))
return scope.switch(ch, *options)
def ap_categorical_sampler(obs,
prior_weight,
p,
upper=None,
size=(),
rng=None,
LF=DEFAULT_LF):
weights = scope.linear_forgetting_weights(scope.len(obs), LF=LF)
counts = scope.bincount(obs, minlength=upper, weights=weights)
pseudocounts = scope.tpe_cat_pseudocounts(counts, upper, prior_weight, p,
size)
return scope.categorical(pseudocounts, upper=upper, size=size, rng=rng)
def ap_categorical_sampler(obs, prior_weight, p, upper=None,
size=(), rng=None, LF=DEFAULT_LF):
weights = scope.linear_forgetting_weights(scope.len(obs), LF=LF)
counts = scope.bincount(obs, minlength=upper, weights=weights)
pseudocounts = scope.tpe_cat_pseudocounts(counts, upper, prior_weight, p, size)
return scope.categorical(pseudocounts, upper=upper, size=size, rng=rng)
def ap_categorical_sampler(obs, prior_weight, upper, size=(), rng=None):
counts = scope.bincount(obs, minlength=upper)
# -- add in some prior pseudocounts
pseudocounts = counts + prior_weight
return scope.categorical(pseudocounts / scope.sum(pseudocounts),
size=size, rng=rng)