Merge remote-tracking branch 'origin/74-add-jFuzzyLogic' into 74-add-jFuzzyLogic
# Conflicts: # src/main/java/ru/ulstu/extractor/GitExtractorApplication.java # src/main/java/ru/ulstu/extractor/rule/service/FuzzyInferenceService.java
This commit is contained in:
commit
f043e45b49
@ -20,69 +20,6 @@ import org.springframework.scheduling.annotation.EnableScheduling;
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@EnableScheduling
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@EnableScheduling
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public class GitExtractorApplication {
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public class GitExtractorApplication {
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public static void main(String[] args) {
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public static void main(String[] args) {
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Engine engine = new Engine();
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engine.setName("Git rules");
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engine.setDescription("");
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InputVariable obstacle = new InputVariable();
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obstacle.setName("obstacle");
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obstacle.setDescription("");
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obstacle.setEnabled(true);
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obstacle.setRange(0.000, 1.000);
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obstacle.setLockValueInRange(false);
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obstacle.addTerm(new Trapezoid("left", 0.000, 0.700));
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obstacle.addTerm(new Trapezoid("right", 0.400, 1.000));
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engine.addInputVariable(obstacle);
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OutputVariable mSteer = new OutputVariable();
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mSteer.setName("mSteer");
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mSteer.setDescription("");
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mSteer.setEnabled(true);
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mSteer.setRange(0.000, 1.000);
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mSteer.setLockValueInRange(false);
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mSteer.setAggregation(new Maximum());
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mSteer.setDefuzzifier(new Centroid(100));
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mSteer.setDefaultValue(Double.NaN);
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mSteer.setLockPreviousValue(false);
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mSteer.addTerm(new Trapezoid("left", 0.000, 0.700));
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mSteer.addTerm(new Trapezoid("right", 0.400, 1.000));
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engine.addOutputVariable(mSteer);
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RuleBlock mamdani = new RuleBlock();
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mamdani.setName("mamdani");
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mamdani.setDescription("");
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mamdani.setEnabled(true);
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mamdani.setConjunction(null);
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mamdani.setDisjunction(null);
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mamdani.setImplication(new AlgebraicProduct());
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mamdani.setActivation(new Highest());
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mamdani.addRule(Rule.parse("if obstacle is left then mSteer is right", engine));
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mamdani.addRule(Rule.parse("if obstacle is right then mSteer is left", engine));
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engine.addRuleBlock(mamdani);
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StringBuilder status = new StringBuilder();
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if (!engine.isReady(status))
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throw new RuntimeException("[engine error] engine is not ready:n" + status);
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InputVariable obstacle1 = engine.getInputVariable("obstacle");
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OutputVariable steer = engine.getOutputVariable("mSteer");
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for (int i = 10; i <= 50; ++i) {
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double location = obstacle1.getMinimum() + i * (obstacle1.range() / 50);
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obstacle1.setValue(location);
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engine.process();
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if (Double.isNaN(steer.getValue())) {
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System.out.println("no decision");
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continue;
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}
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FuzzyLite.logger().info(String.format(
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"obstacle.input = %s -> steer.output = %s",
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Op.str(location), Op.str(steer.getValue())));
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FuzzyLite.logger().info(String.format(
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"obstacle.input = %s -> steer.output = %s",
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Op.str(location), steer.highestMembership(steer.getValue()).getSecond().getName()));
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}
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SpringApplication.run(GitExtractorApplication.class, args);
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SpringApplication.run(GitExtractorApplication.class, args);
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}
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}
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}
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}
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@ -1,25 +1,27 @@
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package ru.ulstu.extractor.rule.service;
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package ru.ulstu.extractor.rule.service;
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import com.fuzzylite.Engine;
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import net.sourceforge.jFuzzyLogic.defuzzifier.DefuzzifierCenterOfGravity;
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import com.fuzzylite.activation.Highest;
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import net.sourceforge.jFuzzyLogic.membership.MembershipFunctionTriangular;
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import com.fuzzylite.norm.t.AlgebraicProduct;
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import net.sourceforge.jFuzzyLogic.rule.FuzzyRule;
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import com.fuzzylite.rule.Rule;
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import net.sourceforge.jFuzzyLogic.rule.FuzzyRuleExpression;
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import com.fuzzylite.rule.RuleBlock;
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import net.sourceforge.jFuzzyLogic.rule.FuzzyRuleSet;
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import com.fuzzylite.term.Triangle;
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import net.sourceforge.jFuzzyLogic.rule.FuzzyRuleTerm;
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import com.fuzzylite.variable.InputVariable;
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import net.sourceforge.jFuzzyLogic.rule.LinguisticTerm;
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import com.fuzzylite.variable.OutputVariable;
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import net.sourceforge.jFuzzyLogic.rule.Variable;
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import net.sourceforge.jFuzzyLogic.ruleConnection.RuleConnectionMethodAndMin;
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import net.sourceforge.jFuzzyLogic.ruleImplication.RuleImplicationMethodMin;
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import org.springframework.stereotype.Service;
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import org.springframework.stereotype.Service;
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import ru.ulstu.extractor.rule.model.AntecedentValue;
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import ru.ulstu.extractor.rule.model.AntecedentValue;
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import ru.ulstu.extractor.rule.model.DbRule;
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import ru.ulstu.extractor.rule.model.Rule;
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import java.util.HashMap;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.Comparator;
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import java.util.LinkedList;
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import java.util.List;
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import java.util.List;
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import java.util.Map;
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import java.util.stream.Collectors;
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@Service
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@Service
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public class FuzzyInferenceService {
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public class FuzzyInferenceService {
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private final static String RULE_TEMPLATE = "if %s is %s AND %s is %s then state is %s";
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private final RuleService ruleService;
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private final RuleService ruleService;
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private final AntecedentValueService antecedentValueService;
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private final AntecedentValueService antecedentValueService;
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@ -29,69 +31,118 @@ public class FuzzyInferenceService {
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this.antecedentValueService = antecedentValueService;
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this.antecedentValueService = antecedentValueService;
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}
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}
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public List<String> getRulesFromDb() {
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private List<FuzzyRule> getFuzzyRulesFromDb() {
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List<DbRule> dbDbRules = ruleService.getList();
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List<FuzzyRule> fuzzyRules = new ArrayList<>();
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return dbDbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
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//List<Variable> variables = getFuzzyVariables();
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for (Rule dbRule : ruleService.getList()) {
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FuzzyRule fuzzyRule = new FuzzyRule(String.format("Fuzzy rule %s", dbRule.getId()));
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// fuzzyRule.setAntecedents(expression);
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// fuzzyRule.setConsequents(new LinkedList<>(Collections.singleton(new FuzzyRuleTerm(dbRule.getConsequent(), false))));
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fuzzyRules.add(fuzzyRule);
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}
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return fuzzyRules;
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}
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}
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private String getFuzzyRule(DbRule dbRule) {
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private List<Variable> getFuzzyVariablesWithoutMembership() {
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return String.format(RULE_TEMPLATE,
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List<Variable> variables = new ArrayList<>();
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dbRule.getFirstAntecedent().name(),
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dbRule.getFirstAntecedentValue().getAntecedentValue(),
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dbRule.getSecondAntecedent().name(),
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dbRule.getSecondAntecedentValue().getAntecedentValue(),
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dbRule.getConsequent());
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}
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private RuleBlock getRuleBlock(Engine engine, Map<String, Double> variableValues, List<AntecedentValue> antecedentValues) {
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variableValues.forEach((key, value) -> {
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InputVariable input = new InputVariable();
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input.setName(key);
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input.setDescription("");
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input.setEnabled(true);
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input.setRange(0.000, 1.000);
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input.setLockValueInRange(false);
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for (int i = 0; i < antecedentValues.size(); i++) {
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input.addTerm(new Triangle(antecedentValues.get(i).getAntecedentValue(), i, i + 2));
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}
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engine.addInputVariable(input);
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});
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RuleBlock mamdani = new RuleBlock();
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mamdani.setName("mamdani");
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mamdani.setDescription("");
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mamdani.setEnabled(true);
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mamdani.setConjunction(null);
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mamdani.setDisjunction(null);
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mamdani.setImplication(new AlgebraicProduct());
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mamdani.setActivation(new Highest());
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getRulesFromDb().forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
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return mamdani;
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}
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private Engine getFuzzyEngine() {
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Engine engine = new Engine();
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engine.setName("Git rules");
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engine.setDescription("");
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return engine;
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}
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public String run() {
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Engine engine = getFuzzyEngine();
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List<AntecedentValue> antecedentValues = antecedentValueService.getList();
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List<AntecedentValue> antecedentValues = antecedentValueService.getList();
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Map<String, Double> variableValues = new HashMap<>();
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for (AntecedentValue antecedentValue : antecedentValues) {
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engine.addRuleBlock(getRuleBlock(engine, variableValues, antecedentValues));
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variables.add(new Variable(antecedentValue.getAntecedentValue()));
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return getConsequent(engine, variableValues);
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}
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return variables;
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}
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}
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private String getConsequent(Engine engine, Map<String, Double> variableValues) {
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// private FuzzyRuleExpression getFuzzyRulesAntecedents(TimeSeriesType timeSeriesType1, TimeSeriesType timeSeriesType2) {
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OutputVariable outputVariable = engine.getOutputVariable("state");
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// return new FuzzyRuleExpression(getFuzzyRuleTerm(), getFuzzyRuleTerm(), new RuleConnectionMethodAndMin());
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for (Map.Entry<String, Double> variableValue : variableValues.entrySet()) {
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// }
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InputVariable inputVariable = engine.getInputVariable(variableValue.getKey());
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inputVariable.setValue(variableValue.getValue());
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private FuzzyRuleExpression getFuzzyRuleExpression(FuzzyRuleTerm term1, FuzzyRuleTerm term2) {
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return new FuzzyRuleExpression(term1, term2, new RuleConnectionMethodAndMin());
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}
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}
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engine.process();
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return outputVariable.highestMembership(outputVariable.getValue()).getSecond().getName();
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private FuzzyRuleTerm getFuzzyRuleTerm(Variable variable, String term) {
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return new FuzzyRuleTerm(variable, term, false);
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}
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public void run() {
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FuzzyRule fuzzyRule1 = new FuzzyRule("rule 1");
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FuzzyRule fuzzyRule2 = new FuzzyRule("rule 2");
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FuzzyRule fuzzyRule3 = new FuzzyRule("rule 3");
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Variable weather = new Variable("Погода");
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weather.getLinguisticTerms().put("солнечно",
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new LinguisticTerm("солнечно", new MembershipFunctionTriangular(0, 20, 30)));
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weather.getLinguisticTerms().put("мороз",
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new LinguisticTerm("мороз", new MembershipFunctionTriangular(-50, -10, 10)));
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weather.setDefuzzifier(new DefuzzifierCenterOfGravity(weather));
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Variable suit = new Variable("Одежда");
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suit.getLinguisticTerms().put("легко одет",
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new LinguisticTerm("легко одет", new MembershipFunctionTriangular(0, 5, 10)));
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suit.getLinguisticTerms().put("тепло одет",
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new LinguisticTerm("тепло одет", new MembershipFunctionTriangular(5, 10, 20)));
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suit.setDefuzzifier(new DefuzzifierCenterOfGravity(suit));
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Variable feel = new Variable("Ощущение");
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feel.getLinguisticTerms().put("Холодно",
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new LinguisticTerm("Холодно", new MembershipFunctionTriangular(0, 5, 10)));
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feel.getLinguisticTerms().put("Жарко",
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new LinguisticTerm("Жарко", new MembershipFunctionTriangular(5, 10, 20)));
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feel.setDefuzzifier(new DefuzzifierCenterOfGravity(feel));
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FuzzyRuleTerm weatherTerm1 = new FuzzyRuleTerm(weather, "солнечно", false);
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FuzzyRuleTerm weatherTerm2 = new FuzzyRuleTerm(weather, "мороз", false);
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FuzzyRuleTerm suitTerm1 = new FuzzyRuleTerm(suit, "легко одет", false);
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FuzzyRuleTerm suitTerm2 = new FuzzyRuleTerm(suit, "тепло одет", false);
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FuzzyRuleTerm feelCold = new FuzzyRuleTerm(feel, "Холодно", false);
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FuzzyRuleTerm feelWarm = new FuzzyRuleTerm(feel, "Жарко", false);
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FuzzyRuleExpression expression1 = new FuzzyRuleExpression(weatherTerm1, suitTerm2, new RuleConnectionMethodAndMin());
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fuzzyRule1.setAntecedents(expression1);
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fuzzyRule1.setConsequents(new LinkedList<>(Collections.singleton(feelWarm)));
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FuzzyRuleExpression expression2 = new FuzzyRuleExpression(weatherTerm2, suitTerm1, new RuleConnectionMethodAndMin());
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fuzzyRule2.setAntecedents(expression2);
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fuzzyRule2.setConsequents(new LinkedList<>(Collections.singleton(feelCold)));
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FuzzyRuleExpression expression3 = new FuzzyRuleExpression(weatherTerm1, suitTerm1, new RuleConnectionMethodAndMin());
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fuzzyRule3.setAntecedents(expression3);
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fuzzyRule3.setConsequents(new LinkedList<>(Collections.singleton(feelCold)));
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fuzzyRule1.evaluate(new RuleImplicationMethodMin());
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fuzzyRule2.evaluate(new RuleImplicationMethodMin());
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//fuzzyRule3.evaluate(new RuleImplicationMethodMin());
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FuzzyRuleSet set = new FuzzyRuleSet();
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set.add(fuzzyRule1);
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set.add(fuzzyRule2);
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set.add(fuzzyRule3);
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set.evaluate();
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set.setVariable("Погода", 25);
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set.setVariable("Одежда", 7);
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// Evaluate fuzzy set
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set.evaluate();
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// Show output variable's chart
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//set.getVariable("Ощущение").chartDefuzzifier(true);
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System.out.println(set.getVariable("Ощущение").getLatestDefuzzifiedValue());
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System.out.println(set);
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System.out.println(set.getVariable("Ощущение"));
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System.out.println(
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feel.getLinguisticTerms()
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.entrySet()
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.stream()
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.max(Comparator.comparing(e -> e.getValue()
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.getMembershipFunction()
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.membership(set.getVariable("Ощущение").getLatestDefuzzifiedValue())))
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.get()
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.getValue().getTermName()
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);
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set.getVariable("Ощущение").chartDefuzzifier(true);
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}
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}
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}
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}
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Loading…
Reference in New Issue
Block a user