<?xml version="1.0" encoding="UTF-8"?>
<program xmlns="http://www.nomoseed.org/program" xmlns:xi="http://www.w3.org/2001/XInclude" name="tutorial_2_ans">
  <header xmlns="http://www.nomoseed.org/project">
    <author>Cédric Coussinet (cedric.coussinet@nomoseed.net)</author>
    <copyright>
      <mention xml:lang="en">
        Copyright (2013) Cédric Coussinet
      </mention>
    </copyright>
    <license name="GPLv3">
      <mention xml:lang="en">
        This program is free software: you can redistribute it and/or modify
        it under the terms of the GNU General Public License as published
        by the Free Software Foundation, either version 3 of the License, or
        (at your option) any later version.
        
        This program is distributed in the hope that it will be useful,
        but WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
        GNU General Public License for more details.
        
        You should have received a copy of the GNU General Public License
        along with this program. If not, see http://www.gnu.org/licenses
      </mention>
    </license>
    <datetime>2013-08-29T16:54:06Z</datetime>
    <version/>
    <annotation>
      <documentation xml:lang="fr">
        Programme illustrant une classifications non surpervisé dans le cadre de la deuxième parties du tutoriel portant sur les propriétés dynamiques..
      </documentation>
    </annotation>
  </header>
  <body>
    <models>
      <new instance="tutorial_2">
        <xi:include href="tutorial_2_ans.mod" parse="xml"/>
      </new>
    </models>    
    <scheme>
<scheme name="points">
<rule name="point_in_1" fitting_nbr="0" relevance="0.9">
<premise model="tutorial_2" category="input" type="point">
<information value="5.531711" tolerance="0.15"/>
<information value="3.168476" tolerance="0.1"/>
<information value="2.55142" tolerance="0.25"/>
<information value="1.86295" tolerance="0.1"/>
</premise>
<conclusion model="tutorial_2" category="perception" type="point">
<information value="cluster_1"/>
</conclusion>
</rule>
<rule name="point_out_1">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_1" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="point">
<information value="cluster_1"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
</conclusion>
</rule>
<rule name="point_in_2" fitting_nbr="0" relevance="0.9">
<premise model="tutorial_2" category="input" type="point">
<information value="6.585435" tolerance="0.15"/>
<information value="3.495687" tolerance="0.1"/>
<information value="5.355395" tolerance="0.25"/>
<information value="0.7164725" tolerance="0.1"/>
</premise>
<conclusion model="tutorial_2" category="perception" type="point">
<information value="cluster_2"/>
</conclusion>
</rule>
<rule name="point_out_2">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_2" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="point">
<information value="cluster_2"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
</conclusion>
</rule>
<rule name="point_in_3" fitting_nbr="0" relevance="0.9">
<premise model="tutorial_2" category="input" type="point">
<information value="6.112236" tolerance="0.15"/>
<information value="4.179901" tolerance="0.1"/>
<information value="5.87746" tolerance="0.25"/>
<information value="1.873950" tolerance="0.1"/>
</premise>
<conclusion model="tutorial_2" category="perception" type="point">
<information value="cluster_3"/>
</conclusion>
</rule>
<rule name="point_out_3">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_3" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="point">
<information value="cluster_3"/>
<output value="0.0"/>
<output value="0.0"/>
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<output value="0.0"/>
</conclusion>
</rule>
<rule name="point_in_4" fitting_nbr="0" relevance="0.9">
<premise model="tutorial_2" category="input" type="point">
<information value="6.262408" tolerance="0.15"/>
<information value="3.034113" tolerance="0.1"/>
<information value="4.650424" tolerance="0.25"/>
<information value="2.146584" tolerance="0.1"/>
</premise>
<conclusion model="tutorial_2" category="perception" type="point">
<information value="cluster_4"/>
</conclusion>
</rule>
<rule name="point_out_4">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_4" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="point">
<information value="cluster_4"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
</conclusion>
</rule>
<rule name="point_in_5" fitting_nbr="0" relevance="0.9">
<premise model="tutorial_2" category="input" type="point">
<information value="5.883819" tolerance="0.15"/>
<information value="4.319814" tolerance="0.1"/>
<information value="2.643757" tolerance="0.25"/>
<information value="1.452570" tolerance="0.1"/>
</premise>
<conclusion model="tutorial_2" category="perception" type="point">
<information value="cluster_5"/>
</conclusion>
</rule>
<rule name="point_out_5">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_5" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="point">
<information value="cluster_5"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
</conclusion>
</rule>
<rule name="point_in_6" fitting_nbr="0" relevance="0.9">
<premise model="tutorial_2" category="input" type="point">
<information value="5.67912" tolerance="0.15"/>
<information value="3.111359" tolerance="0.1"/>
<information value="1.066484" tolerance="0.25"/>
<information value="0.4176339" tolerance="0.1"/>
</premise>
<conclusion model="tutorial_2" category="perception" type="point">
<information value="cluster_6"/>
</conclusion>
</rule>
<rule name="point_out_6">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_6" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="point">
<information value="cluster_6"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
<output value="0.0"/>
</conclusion>
</rule>
</scheme>
<scheme name="labels">
<rule name="label_out_1">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_1" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="label">
<information value="cluster_1"/>
<output value="1"/>
</conclusion>
</rule>
<rule name="label_out_2">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_2" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="label">
<information value="cluster_2"/>
<output value="2"/>
</conclusion>
</rule>
<rule name="label_out_3">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_3" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="label">
<information value="cluster_3"/>
<output value="3"/>
</conclusion>
</rule>
<rule name="label_out_4">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_4" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="label">
<information value="cluster_4"/>
<output value="4"/>
</conclusion>
</rule>
<rule name="label_out_5">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_5" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="label">
<information value="cluster_5"/>
<output value="5"/>
</conclusion>
</rule>
<rule name="label_out_6">
<premise model="tutorial_2" category="perception" type="point">
<information value="cluster_6" tolerance="0"/>
<credibility value="1" tolerance="INF"/>
<timespan value="0" tolerance="0"/>
</premise>
<conclusion model="tutorial_2" category="command" type="label">
<information value="cluster_6"/>
<output value="6"/>
</conclusion>
</rule>
</scheme>
</scheme>
  </body>
  <sdk>
    <macro xmlns="http://www.nomoseed.org/sdk" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" active="true" name="points" xsi:type="template">
      <csv xsi:type="extern">
        <xi:include href="tutorial_2_ans.csv" parse="text"/>
      </csv>
      <template xmlns="http://www.nomoseed.org/template">
        <rule name="point_in" fitting_nbr="0" relevance="0.9">
          <premise model="tutorial_2" category="input" type="point">
            <information value="#Comp_1" tolerance="0.15"/>
            <information value="#Comp_2" tolerance="0.1"/>
            <information value="#Comp_3" tolerance="0.25"/>
            <information value="#Comp_4" tolerance="0.1"/>
          </premise>
          <conclusion model="tutorial_2" category="perception" type="point">
            <information value="#cluster"/>
          </conclusion>
        </rule>
        <rule name="point_out">
          <premise model="tutorial_2" category="perception" type="point">
            <information value="#cluster" tolerance="0"/>
            <credibility value="1" tolerance="INF"/>
            <timespan value="0" tolerance="0"/>
          </premise>
          <conclusion model="tutorial_2" category="command" type="point">
            <information value="#cluster"/>
            <output value="0.0"/>
            <output value="0.0"/>
            <output value="0.0"/>
            <output value="0.0"/>
          </conclusion>
        </rule>
      </template>
    </macro>
    <macro xmlns="http://www.nomoseed.org/sdk" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" active="true" name="labels" xsi:type="template">
      <csv xsi:type="extern">
        <xi:include href="tutorial_2_ans.csv" parse="text"/>
      </csv>
      <template xmlns="http://www.nomoseed.org/template">
        <rule name="label_out">
          <premise model="tutorial_2" category="perception" type="point">
            <information value="#cluster" tolerance="0"/>
            <credibility value="1" tolerance="INF"/>
            <timespan value="0" tolerance="0"/>
          </premise>
          <conclusion model="tutorial_2" category="command" type="label">
            <information value="#cluster"/>
            <output value="#label"/>
          </conclusion>
        </rule>
      </template>
    </macro>
  </sdk>
</program>
