Free tennis software for linux

Pzn Tennis project isnt a tennis game... is like a pong game but with different rules and modified game styles.

Contrary to the classic pong game, you will be able to move your racket in all your area. PznTennis is a entertained game for 2 Players in the same PC.

Tennix! SDL Port is a simple two-player tennis game.

It features simple image loading (with all game graphics being customizable by simply editing them with a graphics editor like The GIMP), sound effects, stadium audience sounds, and ball shadows.

The source code for this SDL port is released under the terms of the GNU General Public License, Version 2 (or later).

Controls

Player 1 uses the keys W, S and D and Player 2 uses O, L and K.

AI::DecisionTree is Perl module for automatically Learns Decision Trees.

SYNOPSIS

use AI::DecisionTree;
my $dtree = new AI::DecisionTree;

# A set of training data for deciding whether to play tennis
$dtree->add_instance
(attributes => {outlook => sunny,
temperature => hot,
humidity => high},
result => no);

$dtree->add_instance
(attributes => {outlook => overcast,
temperature => hot,
humidity => normal},
result => yes);

... repeat for several more instances, then:
$dtree->train;

# Find results for unseen instances
my $result = $dtree->get_result
(attributes => {outlook => sunny,
temperature => hot,
humidity => normal});

The AI::DecisionTree module automatically creates so-called "decision trees" to explain a set of training data. A decision tree is a kind of categorizer that use a flowchart-like process for categorizing new instances. For instance, a learned decision tree might look like the following, which classifies for the concept "play tennis":

OUTLOOK
/ |
/ |
/ |
sunny/ overcast rainy
/ |
HUMIDITY | WIND
/ *no* /
/ /
high/ normal /
/ strong/ weak
*no* *yes* /
*no* *yes*

(This example, and the inspiration for the AI::DecisionTree module, come directly from Tom Mitchells excellent book "Machine Learning", available from McGraw Hill.)

A decision tree like this one can be learned from training data, and then applied to previously unseen data to obtain results that are consistent with the training data.

The usual goal of a decision tree is to somehow encapsulate the training data in the smallest possible tree. This is motivated by an "Occams Razor" philosophy, in which the simplest possible explanation for a set of phenomena should be preferred over other explanations. Also, small trees will make decisions faster than large trees, and they are much easier for a human to look at and understand. One of the biggest reasons for using a decision tree instead of many other machine learning techniques is that a decision tree is a much more scrutable decision maker than, say, a neural network.

The current implementation of this module uses an extremely simple method for creating the decision tree based on the training instances. It uses an Information Gain metric (based on expected reduction in entropy) to select the "most informative" attribute at each node in the tree. This is essentially the ID3 algorithm, developed by J. R. Quinlan in 1986. The idea is that the attribute with the highest Information Gain will (probably) be the best attribute to split the tree on at each point if were interested in making small trees.