class tf.contrib.distributions.ExponentialWithSoftplusLam Exponential with softplus transform on lam.

tf.contrib.distributions.Exponential.__init__(lam, validate_args=False, allow_nan_stats=True, name='Exponential') Construct Exponential distribution with parameter lam. Args: lam: Floating point tensor, the rate of the distribution(s). lam must contain only positive values. validate_args: Boolean, default False. Whether to assert that lam > 0, and that x > 0 in the methods prob(x) and log_prob(x). If validate_args is False and the inputs are invalid, correct behavior is not guaranteed.

tf.contrib.distributions.Exponential.variance(name='variance') Variance.

tf.contrib.distributions.Exponential.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.Exponential.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.Exponential.std(name='std') Standard deviation.

tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from Gamma: See the documentation for tf.random_gamma for more details. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.Exponential.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Exponential.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.