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Merge remote-tracking branch 'origin/74-add-jFuzzyLogic' into 74-add-jFuzzyLogic

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# Conflicts:
#	src/main/java/ru/ulstu/extractor/GitExtractorApplication.java
#	src/main/java/ru/ulstu/extractor/rule/service/FuzzyInferenceService.java
pull/75/head
Anton Romanov 2 years ago
parent f2f53b1980 301a32d610
commit f043e45b49
2 changed files with 119 additions and 131 deletions
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63
src/main/java/ru/ulstu/extractor/GitExtractorApplication.java
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@ -20,69 +20,6 @@ import org.springframework.scheduling.annotation.EnableScheduling;
@EnableScheduling
public class GitExtractorApplication {
public static void main(String[] args) {
Engine engine = new Engine();
engine.setName("Git rules");
engine.setDescription("");
InputVariable obstacle = new InputVariable();
obstacle.setName("obstacle");
obstacle.setDescription("");
obstacle.setEnabled(true);
obstacle.setRange(0.000, 1.000);
obstacle.setLockValueInRange(false);
obstacle.addTerm(new Trapezoid("left", 0.000, 0.700));
obstacle.addTerm(new Trapezoid("right", 0.400, 1.000));
engine.addInputVariable(obstacle);
OutputVariable mSteer = new OutputVariable();
mSteer.setName("mSteer");
mSteer.setDescription("");
mSteer.setEnabled(true);
mSteer.setRange(0.000, 1.000);
mSteer.setLockValueInRange(false);
mSteer.setAggregation(new Maximum());
mSteer.setDefuzzifier(new Centroid(100));
mSteer.setDefaultValue(Double.NaN);
mSteer.setLockPreviousValue(false);
mSteer.addTerm(new Trapezoid("left", 0.000, 0.700));
mSteer.addTerm(new Trapezoid("right", 0.400, 1.000));
engine.addOutputVariable(mSteer);
RuleBlock mamdani = new RuleBlock();
mamdani.setName("mamdani");
mamdani.setDescription("");
mamdani.setEnabled(true);
mamdani.setConjunction(null);
mamdani.setDisjunction(null);
mamdani.setImplication(new AlgebraicProduct());
mamdani.setActivation(new Highest());
mamdani.addRule(Rule.parse("if obstacle is left then mSteer is right", engine));
mamdani.addRule(Rule.parse("if obstacle is right then mSteer is left", engine));
engine.addRuleBlock(mamdani);
StringBuilder status = new StringBuilder();
if (!engine.isReady(status))
throw new RuntimeException("[engine error] engine is not ready:n" + status);
InputVariable obstacle1 = engine.getInputVariable("obstacle");
OutputVariable steer = engine.getOutputVariable("mSteer");
for (int i = 10; i <= 50; ++i) {
double location = obstacle1.getMinimum() + i * (obstacle1.range() / 50);
obstacle1.setValue(location);
engine.process();
if (Double.isNaN(steer.getValue())) {
System.out.println("no decision");
continue;
}
FuzzyLite.logger().info(String.format(
"obstacle.input = %s -> steer.output = %s",
Op.str(location), Op.str(steer.getValue())));
FuzzyLite.logger().info(String.format(
"obstacle.input = %s -> steer.output = %s",
Op.str(location), steer.highestMembership(steer.getValue()).getSecond().getName()));
}
SpringApplication.run(GitExtractorApplication.class, args);
}
}

187
src/main/java/ru/ulstu/extractor/rule/service/FuzzyInferenceService.java
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@ -1,25 +1,27 @@
package ru.ulstu.extractor.rule.service;
import com.fuzzylite.Engine;
import com.fuzzylite.activation.Highest;
import com.fuzzylite.norm.t.AlgebraicProduct;
import com.fuzzylite.rule.Rule;
import com.fuzzylite.rule.RuleBlock;
import com.fuzzylite.term.Triangle;
import com.fuzzylite.variable.InputVariable;
import com.fuzzylite.variable.OutputVariable;
import net.sourceforge.jFuzzyLogic.defuzzifier.DefuzzifierCenterOfGravity;
import net.sourceforge.jFuzzyLogic.membership.MembershipFunctionTriangular;
import net.sourceforge.jFuzzyLogic.rule.FuzzyRule;
import net.sourceforge.jFuzzyLogic.rule.FuzzyRuleExpression;
import net.sourceforge.jFuzzyLogic.rule.FuzzyRuleSet;
import net.sourceforge.jFuzzyLogic.rule.FuzzyRuleTerm;
import net.sourceforge.jFuzzyLogic.rule.LinguisticTerm;
import net.sourceforge.jFuzzyLogic.rule.Variable;
import net.sourceforge.jFuzzyLogic.ruleConnection.RuleConnectionMethodAndMin;
import net.sourceforge.jFuzzyLogic.ruleImplication.RuleImplicationMethodMin;
import org.springframework.stereotype.Service;
import ru.ulstu.extractor.rule.model.AntecedentValue;
import ru.ulstu.extractor.rule.model.DbRule;
import ru.ulstu.extractor.rule.model.Rule;
import java.util.HashMap;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
@Service
public class FuzzyInferenceService {
private final static String RULE_TEMPLATE = "if %s is %s AND %s is %s then state is %s";
private final RuleService ruleService;
private final AntecedentValueService antecedentValueService;
@ -29,69 +31,118 @@ public class FuzzyInferenceService {
this.antecedentValueService = antecedentValueService;
}
public List<String> getRulesFromDb() {
List<DbRule> dbDbRules = ruleService.getList();
return dbDbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
private List<FuzzyRule> getFuzzyRulesFromDb() {
List<FuzzyRule> fuzzyRules = new ArrayList<>();
//List<Variable> variables = getFuzzyVariables();
for (Rule dbRule : ruleService.getList()) {
FuzzyRule fuzzyRule = new FuzzyRule(String.format("Fuzzy rule %s", dbRule.getId()));
// fuzzyRule.setAntecedents(expression);
// fuzzyRule.setConsequents(new LinkedList<>(Collections.singleton(new FuzzyRuleTerm(dbRule.getConsequent(), false))));
fuzzyRules.add(fuzzyRule);
}
return fuzzyRules;
}
private String getFuzzyRule(DbRule dbRule) {
return String.format(RULE_TEMPLATE,
dbRule.getFirstAntecedent().name(),
dbRule.getFirstAntecedentValue().getAntecedentValue(),
dbRule.getSecondAntecedent().name(),
dbRule.getSecondAntecedentValue().getAntecedentValue(),
dbRule.getConsequent());
private List<Variable> getFuzzyVariablesWithoutMembership() {
List<Variable> variables = new ArrayList<>();
List<AntecedentValue> antecedentValues = antecedentValueService.getList();
for (AntecedentValue antecedentValue : antecedentValues) {
variables.add(new Variable(antecedentValue.getAntecedentValue()));
}
return variables;
}
private RuleBlock getRuleBlock(Engine engine, Map<String, Double> variableValues, List<AntecedentValue> antecedentValues) {
variableValues.forEach((key, value) -> {
InputVariable input = new InputVariable();
input.setName(key);
input.setDescription("");
input.setEnabled(true);
input.setRange(0.000, 1.000);
input.setLockValueInRange(false);
for (int i = 0; i < antecedentValues.size(); i++) {
input.addTerm(new Triangle(antecedentValues.get(i).getAntecedentValue(), i, i + 2));
}
engine.addInputVariable(input);
});
RuleBlock mamdani = new RuleBlock();
mamdani.setName("mamdani");
mamdani.setDescription("");
mamdani.setEnabled(true);
mamdani.setConjunction(null);
mamdani.setDisjunction(null);
mamdani.setImplication(new AlgebraicProduct());
mamdani.setActivation(new Highest());
getRulesFromDb().forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
return mamdani;
}
// private FuzzyRuleExpression getFuzzyRulesAntecedents(TimeSeriesType timeSeriesType1, TimeSeriesType timeSeriesType2) {
// return new FuzzyRuleExpression(getFuzzyRuleTerm(), getFuzzyRuleTerm(), new RuleConnectionMethodAndMin());
// }
private Engine getFuzzyEngine() {
Engine engine = new Engine();
engine.setName("Git rules");
engine.setDescription("");
return engine;
private FuzzyRuleExpression getFuzzyRuleExpression(FuzzyRuleTerm term1, FuzzyRuleTerm term2) {
return new FuzzyRuleExpression(term1, term2, new RuleConnectionMethodAndMin());
}
public String run() {
Engine engine = getFuzzyEngine();
List<AntecedentValue> antecedentValues = antecedentValueService.getList();
Map<String, Double> variableValues = new HashMap<>();
engine.addRuleBlock(getRuleBlock(engine, variableValues, antecedentValues));
return getConsequent(engine, variableValues);
private FuzzyRuleTerm getFuzzyRuleTerm(Variable variable, String term) {
return new FuzzyRuleTerm(variable, term, false);
}
private String getConsequent(Engine engine, Map<String, Double> variableValues) {
OutputVariable outputVariable = engine.getOutputVariable("state");
for (Map.Entry<String, Double> variableValue : variableValues.entrySet()) {
InputVariable inputVariable = engine.getInputVariable(variableValue.getKey());
inputVariable.setValue(variableValue.getValue());
}
engine.process();
return outputVariable.highestMembership(outputVariable.getValue()).getSecond().getName();
public void run() {
FuzzyRule fuzzyRule1 = new FuzzyRule("rule 1");
FuzzyRule fuzzyRule2 = new FuzzyRule("rule 2");
FuzzyRule fuzzyRule3 = new FuzzyRule("rule 3");
Variable weather = new Variable("Погода");
weather.getLinguisticTerms().put("солнечно",
new LinguisticTerm("солнечно", new MembershipFunctionTriangular(0, 20, 30)));
weather.getLinguisticTerms().put("мороз",
new LinguisticTerm("мороз", new MembershipFunctionTriangular(-50, -10, 10)));
weather.setDefuzzifier(new DefuzzifierCenterOfGravity(weather));
Variable suit = new Variable("Одежда");
suit.getLinguisticTerms().put("легко одет",
new LinguisticTerm("легко одет", new MembershipFunctionTriangular(0, 5, 10)));
suit.getLinguisticTerms().put("тепло одет",
new LinguisticTerm("тепло одет", new MembershipFunctionTriangular(5, 10, 20)));
suit.setDefuzzifier(new DefuzzifierCenterOfGravity(suit));
Variable feel = new Variable("Ощущение");
feel.getLinguisticTerms().put("Холодно",
new LinguisticTerm("Холодно", new MembershipFunctionTriangular(0, 5, 10)));
feel.getLinguisticTerms().put("Жарко",
new LinguisticTerm("Жарко", new MembershipFunctionTriangular(5, 10, 20)));
feel.setDefuzzifier(new DefuzzifierCenterOfGravity(feel));
FuzzyRuleTerm weatherTerm1 = new FuzzyRuleTerm(weather, "солнечно", false);
FuzzyRuleTerm weatherTerm2 = new FuzzyRuleTerm(weather, "мороз", false);
FuzzyRuleTerm suitTerm1 = new FuzzyRuleTerm(suit, "легко одет", false);
FuzzyRuleTerm suitTerm2 = new FuzzyRuleTerm(suit, "тепло одет", false);
FuzzyRuleTerm feelCold = new FuzzyRuleTerm(feel, "Холодно", false);
FuzzyRuleTerm feelWarm = new FuzzyRuleTerm(feel, "Жарко", false);
FuzzyRuleExpression expression1 = new FuzzyRuleExpression(weatherTerm1, suitTerm2, new RuleConnectionMethodAndMin());
fuzzyRule1.setAntecedents(expression1);
fuzzyRule1.setConsequents(new LinkedList<>(Collections.singleton(feelWarm)));
FuzzyRuleExpression expression2 = new FuzzyRuleExpression(weatherTerm2, suitTerm1, new RuleConnectionMethodAndMin());
fuzzyRule2.setAntecedents(expression2);
fuzzyRule2.setConsequents(new LinkedList<>(Collections.singleton(feelCold)));
FuzzyRuleExpression expression3 = new FuzzyRuleExpression(weatherTerm1, suitTerm1, new RuleConnectionMethodAndMin());
fuzzyRule3.setAntecedents(expression3);
fuzzyRule3.setConsequents(new LinkedList<>(Collections.singleton(feelCold)));
fuzzyRule1.evaluate(new RuleImplicationMethodMin());
fuzzyRule2.evaluate(new RuleImplicationMethodMin());
//fuzzyRule3.evaluate(new RuleImplicationMethodMin());
FuzzyRuleSet set = new FuzzyRuleSet();
set.add(fuzzyRule1);
set.add(fuzzyRule2);
set.add(fuzzyRule3);
set.evaluate();
set.setVariable("Погода", 25);
set.setVariable("Одежда", 7);
// Evaluate fuzzy set
set.evaluate();
// Show output variable's chart
//set.getVariable("Ощущение").chartDefuzzifier(true);
System.out.println(set.getVariable("Ощущение").getLatestDefuzzifiedValue());
System.out.println(set);
System.out.println(set.getVariable("Ощущение"));
System.out.println(
feel.getLinguisticTerms()
.entrySet()
.stream()
.max(Comparator.comparing(e -> e.getValue()
.getMembershipFunction()
.membership(set.getVariable("Ощущение").getLatestDefuzzifiedValue())))
.get()
.getValue().getTermName()
);
set.getVariable("Ощущение").chartDefuzzifier(true);
}
}

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