## distributions.RawDiscreteEmpirical


Raw Empirical distribution implementation.


Usage

``` python
distributions.RawDiscreteEmpirical(
    values,
    random_seed=None,
)
```


Samples with replacement from a list of empirical values. Useful when no theoretical distribution fits the observed data well.

This class conforms to the Distribution protocol.


## Attributes

| Name | Description |
|----|----|
| [mean](#mean) | Calculate the theoretical mean of the distribution. |
| [variance](#variance) | Calculate the theoretical variance of the distribution. |

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


#### mean


Calculate the theoretical mean of the distribution.


`mean: float`


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


#### variance


Calculate the theoretical variance of the distribution.


`variance: float`


## Methods

| Name | Description |
|----|----|
| [__init__()](#__init__) | Initialize a raw empirical distribution. |
| [sample()](#sample) | Generate random samples from the raw empirical data with replacement. |

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


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


Initialize a raw empirical distribution.


Usage

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


##### Parameters


`values: ArrayLike`  
List of empirical sample values to sample from with replacement.

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


##### Notes

If the sample size is small, consider whether the upper and lower limits in the raw data are representative of the real-world system.

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


#### sample()


Generate random samples from the raw empirical data with replacement.


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
- If int: returns a 1-D array with that many samples
- If tuple of ints: returns an array with that shape


##### Returns


`Union[Any, NDArray]`  
Random samples from the empirical data:

- A single value when size is None
- A numpy array of values with shape determined by size parameter