#91 -- Fix fuzzy inference

src/main/java/ru/ulstu/extractor/assessment/service/AssessmentService.java

@@ -8,7 +8,6 @@ import ru.ulstu.extractor.rule.service.AntecedentValueService;
 import ru.ulstu.extractor.rule.service.DbRuleService;
 import ru.ulstu.extractor.rule.service.FuzzyInferenceService;
 import ru.ulstu.extractor.ts.model.TimeSeries;
-import ru.ulstu.extractor.ts.model.TimeSeriesValue;
 import ru.ulstu.extractor.ts.service.TimeSeriesService;
 
 import java.util.ArrayList;
@@ -57,9 +56,7 @@ public class AssessmentService {
         timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService.getLastTimeSeriesTendency(ts)));
         return fuzzyInferenceService.getFuzzyInference(dbRules,
                 antecedentValueService.getList(),
-                variableValues,
+                variableValues);
-                getTSsMin(timeSeries),
-                getTSsMax(timeSeries));
     }
 
     private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
@@ -75,9 +72,7 @@ public class AssessmentService {
                                     .getLastTimeSeriesTendency(ts)));
                     return fuzzyInferenceService.getFuzzyInference(List.of(dbRule),
                             antecedentValueService.getList(),
-                            variableValues,
+                            variableValues).stream();
-                            getTSsMin(timeSeries),
-                            getTSsMax(timeSeries)).stream();
                 })
                 .sorted(Comparator.comparing(Assessment::getDegree))
                 .collect(Collectors.toList());
@@ -88,44 +83,6 @@ public class AssessmentService {
         timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), ts.getValues().get(ts.getValues().size() - 1).getValue()));
         return fuzzyInferenceService.getFuzzyInference(dbRules,
                 antecedentValueService.getList(),
-                variableValues,
+                variableValues);
-                getTSsMin(timeSeries),
-                getTSsMax(timeSeries));
-    }
-
-    private Double getMin(List<Double> values) {
-        return values.stream().mapToDouble(v -> v).min().getAsDouble();
-    }
-
-    private Map.Entry<String, Double> getTSMin(TimeSeries ts) {
-        return Map.entry(ts.getTimeSeriesType().name(),
-                getMin(ts.getValues().stream().map(TimeSeriesValue::getValue).collect(Collectors.toList())));
-    }
-
-    private Map<String, Double> getTSsMin(List<TimeSeries> tss) {
-        Map<String, Double> res = new HashMap<>();
-        tss.forEach(ts -> {
-            Map.Entry<String, Double> entry = getTSMin(ts);
-            res.put(entry.getKey(), entry.getValue());
-        });
-        return res;
-    }
-
-    private Double getMax(List<Double> values) {
-        return values.stream().mapToDouble(v -> v).max().getAsDouble();
-    }
-
-    private Map.Entry<String, Double> getTSMax(TimeSeries ts) {
-        return Map.entry(ts.getTimeSeriesType().name(),
-                getMax(ts.getValues().stream().map(TimeSeriesValue::getValue).collect(Collectors.toList())));
-    }
-
-    private Map<String, Double> getTSsMax(List<TimeSeries> tss) {
-        Map<String, Double> res = new HashMap<>();
-        tss.forEach(ts -> {
-            Map.Entry<String, Double> entry = getTSMax(ts);
-            res.put(entry.getKey(), entry.getValue());
-        });
-        return res;
     }
 }

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

@@ -34,8 +34,7 @@ public class FuzzyInferenceService {
             + " is %s";
     private final static String NO_RESULT = "Нет результата";
 
-    private List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
+    private List<String> mapRulesToString(List<DbRule> dbRules) {
-        validateVariables(variableValues, dbRules);
         return dbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
     }
 
@@ -51,8 +50,6 @@ public class FuzzyInferenceService {
     private RuleBlock getRuleBlock(Engine engine,
                                    List<DbRule> dbRules,
                                    Map<String, Double> variableValues,
-                                   Map<String, Double> min,
-                                   Map<String, Double> max,
                                    List<AntecedentValue> antecedentValues,
                                    List<Integer> consequentValues) {
         variableValues.forEach((key, value) -> {
@@ -61,16 +58,16 @@ public class FuzzyInferenceService {
             input.setDescription("");
             input.setEnabled(true);
             double delta = antecedentValues.size() > 1
-                    ? (max.get(key) - min.get(key)) / (antecedentValues.size() - 1)
+                    ? 2.0 / (antecedentValues.size() - 1)
-                    : (max.get(key) - min.get(key));
+                    : 2.0;
-            input.setRange(min.get(key), max.get(key));
+            input.setRange(-1, 1);
             input.setLockValueInRange(false);
             for (int i = 0; i < antecedentValues.size(); i++) {
                 input.addTerm(
                         new Triangle(
                                 antecedentValues.get(i).getAntecedentValue(),
-                                min.get(key) + i * delta - 0.5 * delta,
+                                -1 + i * delta - 0.5 * delta,
-                                min.get(key) + i * delta + delta + 0.5 * delta
+                                -1 + i * delta + delta + 0.5 * delta
                         )
                 );
             }
@@ -87,7 +84,7 @@ public class FuzzyInferenceService {
         output.setDefaultValue(Double.NaN);
         output.setLockValueInRange(false);
         for (int i = 0; i < consequentValues.size(); i++) {
-            output.addTerm(new Triangle(consequentValues.get(i).toString(), i, i + 2.1));
+            output.addTerm(new Triangle(consequentValues.get(i).toString(), i, i + 1));
         }
         engine.addOutputVariable(output);
 
@@ -99,7 +96,7 @@ public class FuzzyInferenceService {
         //mamdani.setDisjunction(null);
         mamdani.setImplication(new AlgebraicProduct());
         mamdani.setActivation(new General());
-        getRulesFromDb(dbRules, variableValues).forEach(r -> {
+        mapRulesToString(dbRules).forEach(r -> {
             LOG.info(r);
             mamdani.addRule(Rule.parse(r, engine));
         });
@@ -115,12 +112,11 @@ public class FuzzyInferenceService {
 
     public List<Assessment> getFuzzyInference(List<DbRule> dbRules,
                                               List<AntecedentValue> antecedentValues,
-                                              Map<String, Double> variableValues,
+                                              Map<String, Double> variableValues) {
-                                              Map<String, Double> min,
+        validateVariables(variableValues, dbRules);
-                                              Map<String, Double> max) {
         Engine engine = getFuzzyEngine();
         List<Integer> consequentValues = dbRules.stream().map(DbRule::getId).collect(Collectors.toList());
-        engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, min, max, antecedentValues, consequentValues));
+        engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, antecedentValues, consequentValues));
         Map.Entry<String, Double> consequent = getConsequent(engine, variableValues);
         if (consequent.getKey().equals(NO_RESULT)) {
             return new ArrayList<>();

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

@@ -127,17 +127,29 @@ public class TimeSeriesService {
 
     public double getLastTimeSeriesTendency(TimeSeries ts) {
         if (ts != null && ts.getValues().size() > MIN_TIME_SERIES_LENGTH) {
-            JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(ts)));
+            JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(normalizeTimeSeries(ts))));
             LOG.debug("Успешно отправлен на сервис сглаживания");
             if (response.has("response") && response.getString("response").equals("empty")) {
                 return DEFAULT_TIME_SERIES_TENDENCY;
             }
             JSONArray jsonArray = response.getJSONObject("timeSeries").getJSONArray("values");
-            return jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value");
+            return jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value") -
+                    jsonArray.getJSONObject(jsonArray.length() - 2).getDouble("value");
         }
         return DEFAULT_TIME_SERIES_TENDENCY;
     }
 
+    private TimeSeries normalizeTimeSeries(TimeSeries ts) {
+        double sum = ts.getValues().stream().mapToDouble(TimeSeriesValue::getValue).sum();
+
+        if (sum > 0.0d) {
+            for (int i = 0; i < ts.getValues().size(); i++) {
+                ts.getValues().get(i).setValue(ts.getValues().get(i).getValue() / sum);
+            }
+        }
+        return ts;
+    }
+
     public boolean isBranchContainsAllTimeSeries(Branch b) {
         List<TimeSeries> timeSeries = getByBranch(b.getId());
         return Stream.of(TimeSeriesType.values()).allMatch(type -> timeSeries