def _gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None): # pylint: disable=unused-argument
rng = np.random if seed is None else np.random.RandomState(seed
& 0xffffffff)
dtype = utils.common_dtype([alpha, beta], preferred_dtype=dtype)
scale = 1. if beta is None else (1. / beta)
shape = _shape([alpha, scale], shape)
return rng.gamma(shape=alpha, scale=scale, size=shape).astype(dtype)
def _uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None,
name=None): # pylint: disable=unused-argument
rng = np.random if seed is None else np.random.RandomState(seed & 0xffffffff)
dtype = utils.common_dtype([minval, maxval], dtype_hint=dtype)
maxval = 1 if maxval is None else maxval
shape = _shape([minval, maxval], shape)
return rng.uniform(low=minval, high=maxval, size=shape).astype(dtype)
def _normal(shape,
mean=0.0,
stddev=1.0,
dtype=tf.float32,
seed=None,
name=None): # pylint: disable=unused-argument
rng = np.random if seed is None else np.random.RandomState(seed)
dtype = utils.common_dtype([mean, stddev], preferred_dtype=dtype)
return rng.normal(loc=mean, scale=stddev, size=shape).astype(dtype)
def _poisson(shape, lam, dtype=tf.float32, seed=None, name=None): # pylint: disable=unused-argument
rng = np.random if seed is None else np.random.RandomState(seed
& 0xffffffff)
dtype = utils.common_dtype([lam], preferred_dtype=dtype)
shape = _shape([lam], shape)
return rng.poisson(lam=lam, size=shape).astype(dtype)