#84 -- multiple assessments
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@ -0,0 +1,7 @@
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package ru.ulstu.extractor.rule.model;
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public class AssessmentException extends RuntimeException {
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public AssessmentException(String message) {
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super(message);
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}
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}
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@ -15,6 +15,7 @@ import org.springframework.stereotype.Service;
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import ru.ulstu.extractor.assessment.model.Assessment;
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import ru.ulstu.extractor.gitrepository.service.GitRepositoryService;
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import ru.ulstu.extractor.rule.model.AntecedentValue;
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import ru.ulstu.extractor.rule.model.AssessmentException;
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import ru.ulstu.extractor.rule.model.DbRule;
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import ru.ulstu.extractor.ts.model.TimeSeries;
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import ru.ulstu.extractor.ts.service.TimeSeriesService;
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@ -48,10 +49,9 @@ public class FuzzyInferenceService {
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this.timeSeriesService = timeSeriesService;
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}
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public List<String> getRulesFromDb(Map<String, Double> variableValues) {
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List<DbRule> dbDbRules = ruleService.getList();
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validateVariables(variableValues, dbDbRules);
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return dbDbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
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public List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
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validateVariables(variableValues, dbRules);
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return dbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
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}
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private String getFuzzyRule(DbRule dbRule) {
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@ -64,6 +64,7 @@ public class FuzzyInferenceService {
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}
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private RuleBlock getRuleBlock(Engine engine,
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List<DbRule> dbRules,
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Map<String, Double> variableValues,
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List<AntecedentValue> antecedentValues,
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List<Integer> consequentValues) {
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@ -102,7 +103,7 @@ public class FuzzyInferenceService {
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mamdani.setDisjunction(new BoundedSum());
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mamdani.setImplication(new AlgebraicProduct());
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mamdani.setActivation(new Highest());
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getRulesFromDb(variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
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getRulesFromDb(dbRules, variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
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return mamdani;
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}
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@ -116,7 +117,11 @@ public class FuzzyInferenceService {
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public List<Assessment> getAssessmentsByForecastTendencies(Integer branchId) {
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List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
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List<DbRule> dbRules = ruleService.getList();
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return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
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try {
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return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
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} catch (AssessmentException ex) {
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return new ArrayList<>();
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}
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}
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public List<Assessment> getAssessmentsByLastValues(Integer branchId) {
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@ -130,7 +135,7 @@ public class FuzzyInferenceService {
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List<AntecedentValue> antecedentValues = Stream.concat(dbRules.stream().map(DbRule::getFirstAntecedentValue),
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dbRules.stream().map(DbRule::getSecondAntecedentValue)).distinct().collect(Collectors.toList());
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List<Integer> consequentValues = dbRules.stream().map(DbRule::getId).collect(Collectors.toList());
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engine.addRuleBlock(getRuleBlock(engine, variableValues, antecedentValues, consequentValues));
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engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, antecedentValues, consequentValues));
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String consequent = getConsequent(engine, variableValues);
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if (consequent.equals(NO_RESULT)) {
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return new ArrayList<>();
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@ -142,12 +147,30 @@ public class FuzzyInferenceService {
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.collect(Collectors.toList());
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}
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private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
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private List<Assessment> getSingleAssessmentByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) throws AssessmentException {
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Map<String, Double> variableValues = new HashMap<>();
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService.getLastTimeSeriesTendency(ts)));
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(),
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timeSeriesService.getLastTimeSeriesTendency(ts)
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.orElseThrow(() -> new AssessmentException(""))));
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return getFuzzyInference(dbRules, variableValues);
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}
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private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
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return dbRules
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.stream()
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.flatMap(dbRule -> {
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Map<String, Double> variableValues = new HashMap<>();
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timeSeries
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.stream()
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.filter(ts -> ts.getTimeSeriesType() == dbRule.getFirstAntecedent()
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|| ts.getTimeSeriesType() == dbRule.getSecondAntecedent())
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.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService
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.getLastTimeSeriesTendency(ts)
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.orElseThrow(() -> new AssessmentException(""))));
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return getFuzzyInference(List.of(dbRule), variableValues).stream();
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}).collect(Collectors.toList());
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}
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private List<Assessment> getAssessmentsByLastValues(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
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Map<String, Double> variableValues = new HashMap<>();
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), ts.getValues().get(ts.getValues().size() - 1).getValue()));
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@ -115,16 +115,16 @@ public class TimeSeriesService {
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return timeSeriesRepository.getTimeSeriesByBranchId(branchId);
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}
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public Double getLastTimeSeriesTendency(TimeSeries ts) {
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public Optional<Double> getLastTimeSeriesTendency(TimeSeries ts) {
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if (ts != null && ts.getValues().size() > 5) {
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JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(ts)));
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LOG.debug("Успешно отправлен на сервис сглаживания");
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if (response.has("response") && response.getString("response").equals("empty")) {
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return 0.0;
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return Optional.empty();
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}
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JSONArray jsonArray = response.getJSONObject("timeSeries").getJSONArray("values");
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return jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value");
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return Optional.of(jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value"));
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}
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return 0.0;
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return Optional.empty();
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}
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}
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