## distributions.Triangular


Triangular distribution implementation.


Usage

``` python
distributions.Triangular(
    low,
    mode,
    high,
    random_seed=None,
)
```


A continuous probability distribution with lower limit, upper limit, and mode, forming a triangular-shaped probability density function.

This class conforms to the Distribution protocol and provides methods to sample from a triangular distribution with specified parameters.


## Methods

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

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


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


Initialize a triangular distribution.


Usage

``` python
__init__(low, mode, high, random_seed=None)
```


##### Parameters


`low: float`  
Lower limit of the distribution.

`mode: float`  
Mode (peak) of the distribution. Must be between low and high.

`high: float`  
Upper limit of the distribution.

`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 triangular 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 triangular distribution:

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