Implement a new column distribution¶

You are not limited to use only the distributions that are currently implemented in edo.

Say, for example, you wanted to implement a triangular distribution class. The first step would be to import the edo.distributions.Distribution base class:

>>> from edo.distributions import Distribution

Now, you define your class as normal, inheriting from the base class. The requirements on your class are as follows:

There must be a class attribute name giving the name of the distribution.
There must be a class attribute dtype detailing the preferred data type of the distribution.
There must be a class attribute hard_limits that gives extreme limits on the parameters of the distribution.
There must be a class attribute param_limits that gives the original limits on the parameters of the distribution.
It must have a sample method that takes as argument: itself, an integer number of rows nrows and an instance of numpy.random.RandomState.
The __init__ takes only an instance of numpy.random.RandomState.
The only attributes defined in the __init__ are the parameters of that particular instance of the distribution and match the keys of param_limits.

So, bearing that in mind, a triangular distribution class would look something like this:

>>> class Triangular(Distribution):
...     """ A continuous column distribution given by the triangular
...     distribution. """
...
...     name = "Triangular"
...     dtype = float
...     hard_limits = {"bounds": [-10, 10]}
...     param_limits = {"bounds": [-10, 10]}
...
...     def __init__(self, random_state):
...
...         left, mode, right = sorted(
...             random_state.uniform(*self.param_limits["bounds"], size=3)
...         )
...         self.bounds = [left, mode, right]
...
...     def sample(self, nrows, random_state):
...         """ Take a sample of size ``nrows`` from the triangular
...         distribution with the given bounds. """
...
...         return random_state.triangular(*self.bounds, size=nrows)