How to resolve the algorithm Verify distribution uniformity/Naive step by step in the Erlang programming language

Published on 12 May 2024 09:40 PM
#Erlang

How to resolve the algorithm Verify distribution uniformity/Naive step by step in the Erlang programming language

Table of Contents

Problem Statement

This task is an adjunct to Seven-sided dice from five-sided dice.

Create a function to check that the random integers returned from a small-integer generator function have uniform distribution.

The function should take as arguments:

The function should produce:

Show the distribution checker working when the produced distribution is flat enough and when it is not. (Use a generator from Seven-sided dice from five-sided dice).

See also:

Let's start with the solution:

Step by Step solution about How to resolve the algorithm Verify distribution uniformity/Naive step by step in the Erlang programming language

Source code in the erlang programming language

-module( verify_distribution_uniformity ).

-export( [naive/3] ).

naive( Generator, Times, Delta_percent ) ->
    Dict = lists:foldl( fun update_counter/2, dict:new(), lists:duplicate(Times, Generator) ),
    Values = [dict:fetch(X, Dict) || X <- dict:fetch_keys(Dict)],
    Average = lists:sum( Values ) / dict:size( Dict ),
    Delta = Average * (Delta_percent / 100),
    Fun = fun(_Key, Value) -> erlang:abs(Value - Average) > Delta end,
    Too_large_dict = dict:filter( Fun, Dict ),
    return( dict:size(Too_large_dict), Too_large_dict, Average, Delta_percent ).



return( 0, _Too_large_dict, _Average, _Delta ) -> ok;
return( _N, Too_large_dict, Average, Delta ) ->
	{error, {dict:to_list(Too_large_dict), failed_expected_average, Average, 'with_delta_%', Delta}}.

update_counter( Fun, Dict ) -> dict:update_counter( Fun(), 1, Dict ).

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