def __init__(self, p=None, logit_p=None, *args, **kwargs):
super().__init__(*args, **kwargs)
if sum(int(var is None) for var in [p, logit_p]) != 1:
raise ValueError('Specify one of p and logit_p')
if p is not None:
self._is_logit = False
self.p = p = tt.as_tensor_variable(p)
self._logit_p = logit(p)
else:
self._is_logit = True
self.p = tt.nnet.sigmoid(logit_p)
self._logit_p = tt.as_tensor_variable(logit_p)
self.mode = tt.cast(tround(self.p), 'int8')
def __init__(self, p=None, logit_p=None, *args, **kwargs):
super().__init__(*args, **kwargs)
if sum(int(var is None) for var in [p, logit_p]) != 1:
raise ValueError('Specify one of p and logit_p')
if p is not None:
self._is_logit = False
self.p = p = tt.as_tensor_variable(floatX(p))
self._logit_p = logit(p)
else:
self._is_logit = True
self.p = tt.nnet.sigmoid(floatX(logit_p))
self._logit_p = tt.as_tensor_variable(logit_p)
self.mode = tt.cast(tround(self.p), 'int8')
def __init__(self, n, p, *args, **kwargs):
super(Multinomial, self).__init__(*args, **kwargs)
p = p / tt.sum(p, axis=-1, keepdims=True)
if len(self.shape) == 2:
try:
assert n.shape == (self.shape[0], )
except AttributeError:
# this occurs when n is a scalar Python int or float
n *= tt.ones(self.shape[0])
self.n = tt.shape_padright(n)
self.p = p if p.ndim == 2 else tt.shape_padleft(p)
else:
self.n = tt.as_tensor_variable(n)
self.p = tt.as_tensor_variable(p)
self.mean = self.n * self.p
self.mode = tt.cast(tround(self.mean), 'int32')
def __init__(self, n, p, *args, **kwargs):
super(Multinomial, self).__init__(*args, **kwargs)
p = p / tt.sum(p, axis=-1, keepdims=True)
if len(self.shape) == 2:
try:
assert n.shape == (self.shape[0],)
except AttributeError:
# this occurs when n is a scalar Python int or float
n *= tt.ones(self.shape[0])
self.n = tt.shape_padright(n)
self.p = p if p.ndim == 2 else tt.shape_padleft(p)
else:
self.n = tt.as_tensor_variable(n)
self.p = tt.as_tensor_variable(p)
self.mean = self.n * self.p
self.mode = tt.cast(tround(self.mean), 'int32')
def __init__(self, n, p, *args, **kwargs):
super().__init__(*args, **kwargs)
self.n = n = tt.as_tensor_variable(intX(n))
self.p = p = tt.as_tensor_variable(floatX(p))
self.mode = tt.cast(tround(n * p), self.dtype)
def __init__(self, alpha, beta, n, *args, **kwargs):
super().__init__(*args, **kwargs)
self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))
self.beta = beta = tt.as_tensor_variable(floatX(beta))
self.n = n = tt.as_tensor_variable(intX(n))
self.mode = tt.cast(tround(alpha / (alpha + beta)), 'int8')
def __init__(self, n, p, *args, **kwargs):
super(Binomial, self).__init__(*args, **kwargs)
self.n = n = tt.as_tensor_variable(n)
self.p = p = tt.as_tensor_variable(p)
self.mode = tt.cast(tround(n * p), self.dtype)
def __init__(self, p, *args, **kwargs):
super(Bernoulli, self).__init__(*args, **kwargs)
self.p = p = tt.as_tensor_variable(p)
self.mode = tt.cast(tround(p), 'int8')
def __init__(self, alpha, beta, n, *args, **kwargs):
super(BetaBinomial, self).__init__(*args, **kwargs)
self.alpha = alpha = tt.as_tensor_variable(alpha)
self.beta = beta = tt.as_tensor_variable(beta)
self.n = n = tt.as_tensor_variable(n)
self.mode = tt.cast(tround(alpha / (alpha + beta)), 'int8')
def __init__(self, n, p, *args, **kwargs):
super(Binomial, self).__init__(*args, **kwargs)
self.n = n = tt.as_tensor_variable(n)
self.p = p = tt.as_tensor_variable(p)
self.mode = tt.cast(tround(n * p), self.dtype)
def __init__(self, p, *args, **kwargs):
super(Bernoulli, self).__init__(*args, **kwargs)
self.p = p = tt.as_tensor_variable(p)
self.mode = tt.cast(tround(p), 'int8')
def __init__(self, alpha, beta, n, *args, **kwargs):
super(BetaBinomial, self).__init__(*args, **kwargs)
self.alpha = alpha = tt.as_tensor_variable(alpha)
self.beta = beta = tt.as_tensor_variable(beta)
self.n = n = tt.as_tensor_variable(n)
self.mode = tt.cast(tround(alpha / (alpha + beta)), 'int8')
def __init__(self, n, p, *args, **kwargs):
super().__init__(*args, **kwargs)
self.n = n = tt.as_tensor_variable(intX(n))
self.p = p = tt.as_tensor_variable(floatX(p))
self.mode = tt.cast(tround(n * p), self.dtype)
def __init__(self, alpha, beta, n, *args, **kwargs):
super().__init__(*args, **kwargs)
self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))
self.beta = beta = tt.as_tensor_variable(floatX(beta))
self.n = n = tt.as_tensor_variable(intX(n))
self.mode = tt.cast(tround(alpha / (alpha + beta)), 'int8')