## pricing.fit_references()


A model that reproduces every reference: expected layer losses (which


Usage


``` python
pricing.fit_references(
    layers=Ellipsis, frequencies=Ellipsis, default_alpha=2.0, rule="minimize"
)
```


may overlap or leave gaps) and excess frequencies.


## Parameters


`layers: list of tuple of float = Ellipsis`  
`(limit, attachment, expected_loss)` per layer.

`frequencies: list of tuple of float = Ellipsis`  
`(threshold, frequency)` per excess frequency.

`default_alpha: float = ``2.0`  
Alpha above the highest point, unless an unlimited layer sets it.

`rule: (minimize, midpoint) = ``"minimize"`  


## Returns


`TowerModel`  


## Examples

``` python
>>> from prospicio.pricing import fit_references
>>> m = fit_references([(1000.0, 1000.0, 150.0), (3000.0, 1500.0, 160.0)], [(1000.0, 0.3)])
>>> round(m.layer_loss(3000.0, 1500.0), 6)
```

160.0