#84 -- multiple assessments

src/main/java/ru/ulstu/extractor/rule/model/AssessmentException.java

@@ -0,0 +1,7 @@
+package ru.ulstu.extractor.rule.model;
+
+public class AssessmentException extends RuntimeException {
+    public AssessmentException(String message) {
+        super(message);
+    }
+}

src/main/java/ru/ulstu/extractor/rule/service/FuzzyInferenceService.java

@@ -15,6 +15,7 @@ import org.springframework.stereotype.Service;
 import ru.ulstu.extractor.assessment.model.Assessment;
 import ru.ulstu.extractor.gitrepository.service.GitRepositoryService;
 import ru.ulstu.extractor.rule.model.AntecedentValue;
+import ru.ulstu.extractor.rule.model.AssessmentException;
 import ru.ulstu.extractor.rule.model.DbRule;
 import ru.ulstu.extractor.ts.model.TimeSeries;
 import ru.ulstu.extractor.ts.service.TimeSeriesService;
@@ -48,10 +49,9 @@ public class FuzzyInferenceService {
         this.timeSeriesService = timeSeriesService;
     }
 
-    public List<String> getRulesFromDb(Map<String, Double> variableValues) {
+    public List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
-        List<DbRule> dbDbRules = ruleService.getList();
+        validateVariables(variableValues, dbRules);
-        validateVariables(variableValues, dbDbRules);
+        return dbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
-        return dbDbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
     }
 
     private String getFuzzyRule(DbRule dbRule) {
@@ -64,6 +64,7 @@ public class FuzzyInferenceService {
     }
 
     private RuleBlock getRuleBlock(Engine engine,
+                                   List<DbRule> dbRules,
                                    Map<String, Double> variableValues,
                                    List<AntecedentValue> antecedentValues,
                                    List<Integer> consequentValues) {
@@ -102,7 +103,7 @@ public class FuzzyInferenceService {
         mamdani.setDisjunction(new BoundedSum());
         mamdani.setImplication(new AlgebraicProduct());
         mamdani.setActivation(new Highest());
-        getRulesFromDb(variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
+        getRulesFromDb(dbRules, variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
         return mamdani;
     }
 
@@ -116,7 +117,11 @@ public class FuzzyInferenceService {
     public List<Assessment> getAssessmentsByForecastTendencies(Integer branchId) {
         List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
         List<DbRule> dbRules = ruleService.getList();
+        try {
             return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
+        } catch (AssessmentException ex) {
+            return new ArrayList<>();
+        }
     }
 
     public List<Assessment> getAssessmentsByLastValues(Integer branchId) {
@@ -130,7 +135,7 @@ public class FuzzyInferenceService {
         List<AntecedentValue> antecedentValues = Stream.concat(dbRules.stream().map(DbRule::getFirstAntecedentValue),
                 dbRules.stream().map(DbRule::getSecondAntecedentValue)).distinct().collect(Collectors.toList());
         List<Integer> consequentValues = dbRules.stream().map(DbRule::getId).collect(Collectors.toList());
-        engine.addRuleBlock(getRuleBlock(engine, variableValues, antecedentValues, consequentValues));
+        engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, antecedentValues, consequentValues));
         String consequent = getConsequent(engine, variableValues);
         if (consequent.equals(NO_RESULT)) {
             return new ArrayList<>();
@@ -142,12 +147,30 @@ public class FuzzyInferenceService {
                 .collect(Collectors.toList());
     }
 
-    private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
+    private List<Assessment> getSingleAssessmentByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) throws AssessmentException {
         Map<String, Double> variableValues = new HashMap<>();
-        timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService.getLastTimeSeriesTendency(ts)));
+        timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(),
+                timeSeriesService.getLastTimeSeriesTendency(ts)
+                        .orElseThrow(() -> new AssessmentException(""))));
         return getFuzzyInference(dbRules, variableValues);
     }
 
+    private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
+        return dbRules
+                .stream()
+                .flatMap(dbRule -> {
+                    Map<String, Double> variableValues = new HashMap<>();
+                    timeSeries
+                            .stream()
+                            .filter(ts -> ts.getTimeSeriesType() == dbRule.getFirstAntecedent()
+                                    || ts.getTimeSeriesType() == dbRule.getSecondAntecedent())
+                            .forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService
+                                    .getLastTimeSeriesTendency(ts)
+                                    .orElseThrow(() -> new AssessmentException(""))));
+                    return getFuzzyInference(List.of(dbRule), variableValues).stream();
+                }).collect(Collectors.toList());
+    }
+
     private List<Assessment> getAssessmentsByLastValues(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
         Map<String, Double> variableValues = new HashMap<>();
         timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), ts.getValues().get(ts.getValues().size() - 1).getValue()));

src/main/java/ru/ulstu/extractor/ts/service/TimeSeriesService.java

@@ -115,16 +115,16 @@ public class TimeSeriesService {
         return timeSeriesRepository.getTimeSeriesByBranchId(branchId);
     }
 
-    public Double getLastTimeSeriesTendency(TimeSeries ts) {
+    public Optional<Double> getLastTimeSeriesTendency(TimeSeries ts) {
         if (ts != null && ts.getValues().size() > 5) {
             JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(ts)));
             LOG.debug("Успешно отправлен на сервис сглаживания");
             if (response.has("response") && response.getString("response").equals("empty")) {
-                return 0.0;
+                return Optional.empty();
             }
             JSONArray jsonArray = response.getJSONObject("timeSeries").getJSONArray("values");
-            return jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value");
+            return Optional.of(jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value"));
         }
-        return 0.0;
+        return Optional.empty();
     }
 }