http://robowiki.net/w/index.php?title=Dynamic_Clustering&feed=atom&action=historyDynamic Clustering - Revision history2024-03-29T14:37:21ZRevision history for this page on the wikiMediaWiki 1.34.1http://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=51773&oldid=prevXor: /* See Also */ Add link to TronsGun2017-09-14T23:09:29Z<p><span dir="auto"><span class="autocomment">See Also: </span> Add link to TronsGun</span></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 23:09, 14 September 2017</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Visit Count Stats]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Visit Count Stats]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[kd-tree]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[kd-tree]]</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* [[TronsGun]] — the initial form of dynamic clustering</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Log-Based Algorithms]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Log-Based Algorithms]]</div></td></tr>
</table>Xorhttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=51754&oldid=prevXor: Add link2017-09-14T08:57:17Z<p>Add link</p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 08:57, 14 September 2017</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l8" >Line 8:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[Tron]] & [[Shadow]]: First two bots using this technique. This technique used to be called "Trons Gun", but later it was renamed.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Tron]] & [[Shadow]]: First two bots using this technique. This technique used to be called "<ins class="diffchange diffchange-inline">[[TronsGun|</ins>Trons Gun<ins class="diffchange diffchange-inline">]]</ins>", but later it was renamed.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Chalk]]: First dynamic clustering bot that was released with [[Open Source|source code]].</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Chalk]]: First dynamic clustering bot that was released with [[Open Source|source code]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[DCBot]]: A simplified version of gun used by earlier version of [[Tron]] and [[Shadow]].</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[DCBot]]: A simplified version of gun used by earlier version of [[Tron]] and [[Shadow]].</div></td></tr>
</table>Xorhttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=11363&oldid=prevNat: update math2009-08-30T00:09:53Z<p>update math</p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 00:09, 30 August 2009</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The idea is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<<del class="diffchange diffchange-inline">code</del>>sqrt<del class="diffchange diffchange-inline">(</del>(dist1 - dist2)<del class="diffchange diffchange-inline">{{sups|</del>2<del class="diffchange diffchange-inline">}} </del>+ (lat1 - lat2)<del class="diffchange diffchange-inline">{{sups|</del>2}<del class="diffchange diffchange-inline">} + &middot;&middot;&middot;)</del></<del class="diffchange diffchange-inline">code</del>>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]] (<<del class="diffchange diffchange-inline">code</del>>|dist1 - dist2| + |lat1 - lat2| + <del class="diffchange diffchange-inline">&middot;&middot;&middot;</del></<del class="diffchange diffchange-inline">code</del>>). Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The idea is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<<ins class="diffchange diffchange-inline">math</ins>><ins class="diffchange diffchange-inline">\</ins>sqrt<ins class="diffchange diffchange-inline">{</ins>(dist1 - dist2)<ins class="diffchange diffchange-inline">^</ins>2 + (lat1 - lat2)<ins class="diffchange diffchange-inline">^</ins>2 <ins class="diffchange diffchange-inline">+ \cdots</ins>}</<ins class="diffchange diffchange-inline">math</ins>>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]] (<<ins class="diffchange diffchange-inline">math</ins>>|dist1 - dist2| + |lat1 - lat2| + <ins class="diffchange diffchange-inline">\cdots</ins></<ins class="diffchange diffchange-inline">math</ins>>). Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[kd-tree]]s have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[RoboWiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]].</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[kd-tree]]s have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[RoboWiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]].</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l18" >Line 18:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Dynamic Clustering Tutorial]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Dynamic Clustering Tutorial]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Visit Count Stats]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Visit Count Stats]]</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[<del class="diffchange diffchange-inline">Kd</del>-<del class="diffchange diffchange-inline">Tree</del>]]</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[<ins class="diffchange diffchange-inline">kd</ins>-<ins class="diffchange diffchange-inline">tree</ins>]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Log-Based Algorithms]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Log-Based Algorithms]]</div></td></tr>
</table>Nathttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=11322&oldid=prevNat: remove sentence about Simonton's tree2009-08-29T07:22:16Z<p>remove sentence about Simonton's tree</p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 07:22, 29 August 2009</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l5" >Line 5:</td>
<td colspan="2" class="diff-lineno">Line 5:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The idea is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt((dist1 - dist2){{sups|2}} + (lat1 - lat2){{sups|2}} + &middot;&middot;&middot;)</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]] (<code>|dist1 - dist2| + |lat1 - lat2| + &middot;&middot;&middot;</code>). Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The idea is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt((dist1 - dist2){{sups|2}} + (lat1 - lat2){{sups|2}} + &middot;&middot;&middot;)</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]] (<code>|dist1 - dist2| + |lat1 - lat2| + &middot;&middot;&middot;</code>). Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[<del class="diffchange diffchange-inline">Kd</del>-<del class="diffchange diffchange-inline">Tree</del>]]s have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[RoboWiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]<del class="diffchange diffchange-inline">. As of now, Simonton's Kd-tree implementation is one of the faster ones</del>.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[<ins class="diffchange diffchange-inline">kd</ins>-<ins class="diffchange diffchange-inline">tree</ins>]]s have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[RoboWiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td></tr>
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</table>Nathttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=10733&oldid=prevCrazyBassoonist: Add a link to the DC tutorial2009-08-17T23:54:20Z<p>Add a link to the DC tutorial</p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== See Also ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== See Also ==</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* [[Dynamic Clustering Tutorial]]</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Visit Count Stats]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Visit Count Stats]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Kd-Tree]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Kd-Tree]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Log-Based Algorithms]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Log-Based Algorithms]]</div></td></tr>
</table>CrazyBassoonisthttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=10673&oldid=prevPositive: Youtube: dynamic clustering2009-08-16T22:55:17Z<p>Youtube: dynamic clustering</p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 22:55, 16 August 2009</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{Wikipedia|K-nearest neighbor algorithm}}</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{Wikipedia|K-nearest neighbor algorithm}}</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{Youtube|eqlPbtO3rQY}}</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td></tr>
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</table>Positivehttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=10244&oldid=prevRednaxela: Add {{Wikipedia}} template2009-08-11T06:27:37Z<p>Add {{Wikipedia}} template</p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 06:27, 11 August 2009</td>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{Wikipedia|K-nearest neighbor algorithm}}</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
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</table>Rednaxelahttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=9627&oldid=prevNat: change a little2009-07-29T11:02:08Z<p>change a little</p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 11:02, 29 July 2009</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
<td colspan="2" class="diff-lineno">Line 1:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentially, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the Robocode community.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The idea is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt(<del class="diffchange diffchange-inline">sqr</del>(dist1 - dist2) + <del class="diffchange diffchange-inline">sqr</del>(lat1 - lat2))</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]]. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The idea is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt((dist1 - dist2)<ins class="diffchange diffchange-inline">{{sups|2}} </ins>+ (lat1 - lat2)<ins class="diffchange diffchange-inline">{{sups|2}} + &middot;&middot;&middot;</ins>)</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]] <ins class="diffchange diffchange-inline">(<code>|dist1 - dist2| + |lat1 - lat2| + &middot;&middot;&middot;</code>)</ins>. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[Kd-Tree<del class="diffchange diffchange-inline">|Kd-Trees</del>]] have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[<del class="diffchange diffchange-inline">Robowiki</del>]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]. As of now, Simonton's Kd-tree implementation is one of the faster ones.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[Kd-Tree]]<ins class="diffchange diffchange-inline">s </ins>have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[<ins class="diffchange diffchange-inline">RoboWiki</ins>]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]. As of now, Simonton's Kd-tree implementation is one of the faster ones.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td></tr>
</table>Nathttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=6331&oldid=prevVoidious: minor cleanup2009-05-07T19:10:15Z<p>minor cleanup</p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 19:10, 7 May 2009</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. <del class="diffchange diffchange-inline">Essentialy</del>, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the <del class="diffchange diffchange-inline">robocode </del>community.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. <ins class="diffchange diffchange-inline">Essentially</ins>, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this misnomer, the term "Dynamic Clustering" has stuck with the <ins class="diffchange diffchange-inline">Robocode </ins>community.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The <del class="diffchange diffchange-inline">ideas </del>is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt(sqr(dist1 - dist2) + sqr(lat1 - lat2))</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]]. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The <ins class="diffchange diffchange-inline">idea </ins>is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you deem valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the data, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt(sqr(dist1 - dist2) + sqr(lat1 - lat2))</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]]. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[Kd-Tree|Kd-Trees]] have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[Robowiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]. As of now, Simonton's Kd-tree implementation is one of the faster ones.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[Kd-Tree|Kd-Trees]] have been used. [[User:Corbos|Corbos]] was the first one to mention them on the [[Robowiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]. As of now, Simonton's Kd-tree implementation is one of the faster ones.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[Tron]] & [[Shadow]]: First two bots using this technique. This technique used to called "Trons Gun", but later it was renamed.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Tron]] & [[Shadow]]: First two bots using this technique. This technique used to <ins class="diffchange diffchange-inline">be </ins>called "Trons Gun", but later it was renamed.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Chalk]]: First dynamic clustering bot that was released with [[Open Source|source code]].</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Chalk]]: First dynamic clustering bot that was released with [[Open Source|source code]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[DCBot]]: A simplified version of gun used by earlier version of [[Tron]] and [[Shadow]].</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[DCBot]]: A simplified version of gun used by earlier version of [[Tron]] and [[Shadow]].</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[Lukious]] & [[Firebird]] & [[Hydra]]: Dynamic Clustering version of [[Dookious]], [[Phoenix]], [[WaveSerpent]] respectively.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Lukious]] & [[Firebird]] & [[Hydra]]: Dynamic Clustering version of [[Dookious]], [[Phoenix]], [[WaveSerpent]]<ins class="diffchange diffchange-inline">, </ins>respectively.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Horizon]] & [[RougeDC]] & [[YersiniaPestis]]: Newer dynamic clustering bots.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Horizon]] & [[RougeDC]] & [[YersiniaPestis]]: Newer dynamic clustering bots.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[X2]] & [[Ali]] & [[DrussGT]]: These bots use Dynamic Clustering only for their gun.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[X2]] & [[Ali]] & [[DrussGT]]: These bots use Dynamic Clustering only for their gun.</div></td></tr>
</table>Voidioushttp://robowiki.net/w/index.php?title=Dynamic_Clustering&diff=5640&oldid=prevVoidious: a few typos / small fixes2009-04-27T03:34:07Z<p>a few typos / small fixes</p>
<table class="diff diff-contentalign-left" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 03:34, 27 April 2009</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentialy, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this <del class="diffchange diffchange-inline">midnomer</del>, the term "Dynamic Clustering" has stuck with the robocode community.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''Dynamic clustering''' is a technique to find entries in your [[:Category:Log-Based Algorithms|log]] similar to the current situation. Essentialy, it is a [[wikipedia:K-nearest neighbor algorithm|K-nearest neighbor algorithm]], and not actually [[wikipedia:Cluster analysis|clustering]] at all. Despite this <ins class="diffchange diffchange-inline">misnomer</ins>, the term "Dynamic Clustering" has stuck with the robocode community.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The ideas is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you <del class="diffchange diffchange-inline">seem </del>valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the <del class="diffchange diffchange-inline">day</del>, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt(sqr(dist1 - dist2) + sqr(lat1 - lat2))</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]]. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The ideas is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you <ins class="diffchange diffchange-inline">deem </ins>valuable, such as [[lateral velocity]], [[advancing velocity]], or [[enemy distance]]. Save this along with your data. Then to use the <ins class="diffchange diffchange-inline">data</ins>, you find a "distance" between current state and past states. Distance can be [[wikipedia:Euclidean distance|Euclidian]] (<code>sqrt(sqr(dist1 - dist2) + sqr(lat1 - lat2))</code>) or another way, such as [[wikipedia:Manhattan distance|Manhattan distance]]. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this<del class="diffchange diffchange-inline">, </del>was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[Kd-Tree|Kd-Trees]] have been used. [[User:Corbos|Corbos]] was the first one to mention them <del class="diffchange diffchange-inline">in </del>[[Robowiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]. As of now, Simonton's Kd-tree implementation is one of faster ones.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The earliest method doing this was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently [[Kd-Tree|Kd-Trees]] have been used. [[User:Corbos|Corbos]] was the first one to mention them <ins class="diffchange diffchange-inline">on the </ins>[[Robowiki]], which caught the interest of [[User:Chase-san|Chase-san]] and [[User:Simonton|Simonton]]. As of now, Simonton's Kd-tree implementation is one of <ins class="diffchange diffchange-inline">the </ins>faster ones.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Bot using this technique ===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[Tron]] & [[Shadow]]: First <del class="diffchange diffchange-inline">tow </del>bots using this technique. This technique used to called "Trons Gun", but later renamed.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Tron]] & [[Shadow]]: First <ins class="diffchange diffchange-inline">two </ins>bots using this technique. This technique used to called "Trons Gun", but later <ins class="diffchange diffchange-inline">it was </ins>renamed.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[Chalk]]: First dynamic clustering bot that released with [[Open Source|source code]].</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Chalk]]: First dynamic clustering bot that <ins class="diffchange diffchange-inline">was </ins>released with [[Open Source|source code]].</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[DCBot]]: A simplified version of gun <del class="diffchange diffchange-inline">using </del>by earlier version of [[Tron]] and [[Shadow]].</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[DCBot]]: A simplified version of gun <ins class="diffchange diffchange-inline">used </ins>by earlier version of [[Tron]] and [[Shadow]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Lukious]] & [[Firebird]] & [[Hydra]]: Dynamic Clustering version of [[Dookious]], [[Phoenix]], [[WaveSerpent]] respectively.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Lukious]] & [[Firebird]] & [[Hydra]]: Dynamic Clustering version of [[Dookious]], [[Phoenix]], [[WaveSerpent]] respectively.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Horizon]] & [[RougeDC]] & [[YersiniaPestis]]: Newer dynamic clustering bots.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Horizon]] & [[RougeDC]] & [[YersiniaPestis]]: Newer dynamic clustering bots.</div></td></tr>
</table>Voidious