def __init__(self,
outputs,
inputs,
hypers=None,
params=None,
distargs=None,
rng=None):
DistributionGpm.__init__(self, outputs, inputs, hypers, params,
distargs, rng)
# Distargs
self.l = distargs['l']
self.h = distargs['h']
# Sufficient statistics.
self.N = 0
self.sum_x = 0
self.sum_x_sq = 0
# Hyperparameters (fixed).
self.alpha = 2.
self.beta = 2.
# Uncollapsed mean and precision parameters.
if params is None: params = {}
self.mu = params.get('mu', None)
self.sigma = params.get('sigma', 1)
if not self.mu or not self.sigma:
self.mu, self.sigma = NormalTrunc.sample_parameters(
self.alpha, self.beta, self.l, self.h, self.rng)
def __init__(
self, outputs, inputs,
hypers=None, params=None, distargs=None, rng=None):
DistributionGpm.__init__(
self, outputs, inputs, hypers, params, distargs, rng)
# Distargs.
self.N = 0
self.data = OrderedDict()
self.counts = OrderedDict()
# Hyperparameters.
if hypers is None: hypers = {}
self.alpha = hypers.get('alpha', 1.)
def __init__(self,
outputs,
inputs,
hypers=None,
params=None,
distargs=None,
rng=None):
DistributionGpm.__init__(self, outputs, inputs, hypers, params,
distargs, rng)
# Sufficent statistics.
self.N = 0
self.x_sum = 0
# Hyperparameters.
if hypers is None: hypers = {}
self.alpha = hypers.get('alpha', 1.)
self.beta = hypers.get('beta', 1.)
assert self.alpha > 0
assert self.beta > 0
def __init__(self,
outputs,
inputs,
hypers=None,
params=None,
distargs=None,
rng=None):
DistributionGpm.__init__(self, outputs, inputs, hypers, params,
distargs, rng)
# Sufficient statistics.
self.N = 0
self.sum_x = 0
# Hyperparameters.
if hypers is None: hypers = {}
self.a = hypers.get('a', 1)
self.b = hypers.get('b', 1)
assert self.a > 0
assert self.b > 0
def __init__(self,
outputs,
inputs,
hypers=None,
params=None,
distargs=None,
rng=None):
DistributionGpm.__init__(self, outputs, inputs, hypers, params,
distargs, rng)
# Distargs.
k = distargs.get('k', None)
if k is None:
raise ValueError('Categorical requires distarg `k`.')
self.k = int(k)
# Sufficient statistics.
self.N = 0
self.counts = np.zeros(self.k)
# Hyperparameters.
if hypers is None: hypers = {}
self.alpha = hypers.get('alpha', 1.)
def __init__(self,
outputs,
inputs,
hypers=None,
params=None,
distargs=None,
rng=None):
DistributionGpm.__init__(self, outputs, inputs, hypers, params,
distargs, rng)
# Sufficient statistics.
self.N = 0
self.sum_sin_x = 0
self.sum_cos_x = 0
# Hyperparameters.
# Prior concentration of mean, mean of mean, and Vonmises kappa
if hypers is None: hypers = {}
self.a = hypers.get('a', 1.)
self.b = hypers.get('b', pi)
self.k = hypers.get('k', 1.5)
assert self.a > 0
assert 0 <= self.b <= 2 * pi
assert self.k > 0
def __init__(self, outputs, inputs, hypers=None, params=None,
distargs=None, rng=None):
DistributionGpm.__init__(
self, outputs, inputs, hypers, params, distargs, rng)
# Sufficient statistics.
self.N = 0
self.sum_log_x = 0
self.sum_minus_log_x = 0
# Hyperparameters (fixed).
self.mu = 5.
self.alpha = 1.
self.beta = 1.
# Parameters.
if params is None: params = {}
self.strength = params.get('strength', None)
self.balance = params.get('balance', 1)
if not self.strength or not self.balance:
self.strength, self.balance = Beta.sample_parameters(
self.mu, self.alpha, self.beta, self.rng)
assert self.mu > 0
assert self.alpha > 0
assert self.beta > 0
def __init__(self,
outputs,
inputs,
hypers=None,
params=None,
distargs=None,
rng=None):
DistributionGpm.__init__(self, outputs, inputs, hypers, params,
distargs, rng)
# Sufficient statistics.
self.N = 0
self.sum_x = 0
self.sum_x_sq = 0
# Hyper parameters.
if hypers is None: hypers = {}
self.m = hypers.get('m', 0.)
self.r = hypers.get('r', 1.)
self.s = hypers.get('s', 1.)
self.nu = hypers.get('nu', 1.)
assert self.s > 0.
assert self.r > 0.
assert self.nu > 0.