Created
June 16, 2009 08:13
-
-
Save russelldb/130592 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
%%% File : recomendations.erl | |
%%% Author : Russell Brown <[email protected]> | |
%%% Description : The first chapter of Programming Collective Inteligence, but in elrang, like. | |
%%% Created : 15 Jun 2009 by Russell Brown <[email protected]> | |
-module(recomendations). | |
-compile(export_all). | |
%% | |
%% @spec data() -> List | |
%% A list of prefernce data for use in the recomendations functions | |
%% | |
data() -> | |
[{"Lisa Rose", [{"Lady in the Water", 2.5}, { "Snakes on a Plane", 3.5}, {"Just My Luck", 3.0}, {"Superman Returns", 3.5}, {"You, Me and Dupree", 2.5}, {"The Night Listener", 3.0}]}, | |
{"Gene Seymour", [{"Lady in the Water", 3.0}, {"Snakes on a Plane", 3.5}, {"Just My Luck", 1.5}, {"Superman Returns", 5.0}, {"The Night Listener", 3.0}, {"You, Me and Dupree", 3.5}]}, | |
{ "Michael Phillips", [{"Lady in the Water", 2.5}, {"Snakes on a Plane", 3.0}, {"Superman Returns", 3.5}, {"The Night Listener", 4.0}]}, | |
{ "Claudia Puig", [{"Snakes on a Plane", 3.5}, {"Just My Luck", 3.0}, {"The Night Listener", 4.5}, {"Superman Returns", 4.0}, {"You, Me and Dupree", 2.5}]}, | |
{ "Mick LaSalle", [{"Lady in the Water", 3.0}, {"Snakes on a Plane", 4.0}, {"Just My Luck", 2.0}, {"Superman Returns", 3.0}, {"The Night Listener", 3.0}, {"You, Me and Dupree", 2.0}]}, | |
{ "Jack Matthews", [{"Lady in the Water", 3.0}, {"Snakes on a Plane", 4.0},{"The Night Listener", 3.0}, {"Superman Returns", 5.0}, {"You, Me and Dupree", 3.5}]}, | |
{ "Toby", [{"Snakes on a Plane",4.5},{"You, Me and Dupree",1.0},{"Superman Returns",4.0}]} | |
]. | |
%% | |
%% @spec euclidean_similarity(Data::prefs(), Person1::string(), Person2::string()) -> float() | |
%% @type prefs() | |
%% Figures out how close Person1 and Person2 are using the Euclidean Distance Score. | |
%% | |
%% | |
euclidean_similarity(Data, Person1, Person2) -> | |
Person1s = proplists:get_value(Person1, Data), | |
Person2s = proplists:get_value(Person2, Data), | |
SumOfSquares = lists:sum( [ math:pow(X - Y, 2) || {T, X} <- Person1s, {T1, Y} <- Person2s, T =:= T1] ), | |
1 / ( 1 + math:sqrt( SumOfSquares ) ). | |
%% | |
%% @spec pearson_similarity(Data::prefs(), Person1::string(), Person2::string()) -> float() | |
%% @type prefs() | |
%% Figures out how close Person1 and Person2 are using the Pearson Correlation Score. | |
%% | |
%% | |
pearson_similarity(Data, Person1, Person2) -> | |
Person1s = proplists:get_value(Person1, Data), | |
Person2s = proplists:get_value(Person2, Data), | |
{Xs, Ys} = lists:unzip([ {X, Y} || {T, X} <- Person1s, {T1, Y} <- Person2s, | |
T =:= T1]), | |
N = length(Xs), | |
Sum1 = lists:sum( Xs), | |
Sum2 = lists:sum(Ys ), | |
Sum1Sq = lists:foldl( fun(X, Acc) -> Acc + math:pow(X, 2) end, 0, Xs), | |
Sum2Sq = lists:foldl( fun(Y, Acc) -> Acc + math:pow(Y, 2) end, 0, Ys), | |
SumProd = lists:sum( lists:zipwith(fun(X,Y) -> X * Y end, Xs, Ys) ), | |
Num = SumProd - (Sum1 * Sum2 /N), | |
Den = math:sqrt( (Sum1Sq - math:pow(Sum1, 2) / N) * ( Sum2Sq - math:pow(Sum2, 2) / N )), | |
case Den of | |
0 -> | |
0; | |
_ -> | |
Num/Den | |
end. | |
%% | |
%% @spec top_matches(Data::prefs(), Person::string, SimalarityFun:function()) -> List | |
%% returns a list in order of closeness for Person against all others in Data, measured by SimalarityFun | |
%% | |
top_matches(Data, Person, SimalarityFun) -> | |
Peeps = [ Peep || {Peep, _} <- Data, Peep =/= Person ], | |
Similarities = lists:foldl(fun(X, Acc) -> [{X, SimalarityFun(Data, X, Person)}|Acc] end, [], Peeps), | |
lists:sort(fun( {_,A}, {_,B} ) -> A>B end, Similarities). |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment