## distributions.Exponential


Exponential distribution implementation.


Usage

``` python
distributions.Exponential(
    mean,
    random_seed=None,
)
```


A probability distribution that models the time between events in a Poisson process, where events occur continuously and independently at a constant average rate.

This class conforms to the Distribution protocol and provides methods to sample from an exponential distribution with a specified mean.


## Methods

| Name | Description |
|----|----|
| [__init__()](#__init__) | Initialize an exponential distribution. |
| [sample()](#sample) | Generate random samples from the exponential distribution. |

------------------------------------------------------------------------


#### \_\_init\_\_()


Initialize an exponential distribution.


Usage

``` python
__init__(mean, random_seed=None)
```


##### Parameters


`mean: float`  
The mean of the exponential distribution. Must be positive.

`random_seed: Optional[Union[int, SeedSequence]] = None`  
A random seed or SeedSequence to reproduce samples. If None, a unique sample sequence is generated.


------------------------------------------------------------------------


#### sample()


Generate random samples from the exponential distribution.


Usage

``` python
sample(size=None)
```


##### Parameters


`size: Optional[Union[int, Tuple[int, …]]] = None`  
The number/shape of samples to generate:

- If None: returns a single sample as a float
- If int: returns a 1-D array with that many samples
- If tuple of ints: returns an array with that shape


##### Returns


`Union[float, NDArray[np.float64]]`  
Random samples from the exponential distribution:

- A single float when size is None
- A numpy array of floats with shape determined by size parameter