#80 -- fix forecasting

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

@@ -80,7 +80,7 @@ public class FuzzyInferenceService {
         output.setName(OUTPUT_VARIABLE_NAME);
         output.setDescription("");
         output.setEnabled(true);
-        output.setRange(-0.1, antecedentValues.size() + 0.1);
+        output.setRange(-0.1, consequentValues.size() + 0.1);
         output.setAggregation(new Maximum());
         output.setDefuzzifier(new Centroid(100));
         output.setDefaultValue(Double.NaN);
@@ -98,7 +98,6 @@ public class FuzzyInferenceService {
         mamdani.setDisjunction(new BoundedSum());
         mamdani.setImplication(new AlgebraicProduct());
         mamdani.setActivation(new Highest());
-        getRulesFromDb(variableValues).forEach(System.out::println);
         getRulesFromDb(variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
         return mamdani;
     }

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

@@ -5,6 +5,7 @@
 
 package ru.ulstu.extractor.ts.service;
 
+import org.json.JSONArray;
 import org.json.JSONObject;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -12,6 +13,7 @@ import org.springframework.stereotype.Service;
 import ru.ulstu.extractor.branch.model.Branch;
 import ru.ulstu.extractor.http.HttpService;
 import ru.ulstu.extractor.http.JsonTimeSeries;
+import ru.ulstu.extractor.http.SmoothingTimeSeries;
 import ru.ulstu.extractor.ts.model.TimeSeries;
 import ru.ulstu.extractor.ts.model.TimeSeriesType;
 import ru.ulstu.extractor.ts.model.TimeSeriesValue;
@@ -114,9 +116,13 @@ public class TimeSeriesService {
     }
 
     public Double getLastTimeSeriesTendency(TimeSeries ts) {
-        //JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(ts)));
-        LOG.debug("Успешно отправлен на сервис сглаживания");
-        //response.get("timeSeries");
+        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");
+            response.get("timeSeries");
+            JSONArray jsonArray = response.getJSONObject("timeSeries").getJSONArray("values");
+            return jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value");
+        }
         return 0.0;
     }
 }