WIP: страницы для правил #62
@ -80,7 +80,7 @@ public class FuzzyInferenceService {
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output.setName(OUTPUT_VARIABLE_NAME);
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output.setDescription("");
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output.setEnabled(true);
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output.setRange(-0.1, antecedentValues.size() + 0.1);
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output.setRange(-0.1, consequentValues.size() + 0.1);
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output.setAggregation(new Maximum());
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output.setDefuzzifier(new Centroid(100));
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output.setDefaultValue(Double.NaN);
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@ -98,7 +98,6 @@ 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(System.out::println);
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getRulesFromDb(variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
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return mamdani;
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}
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@ -5,6 +5,7 @@
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package ru.ulstu.extractor.ts.service;
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import org.json.JSONArray;
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import org.json.JSONObject;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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@ -12,6 +13,7 @@ import org.springframework.stereotype.Service;
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import ru.ulstu.extractor.branch.model.Branch;
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import ru.ulstu.extractor.http.HttpService;
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import ru.ulstu.extractor.http.JsonTimeSeries;
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import ru.ulstu.extractor.http.SmoothingTimeSeries;
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import ru.ulstu.extractor.ts.model.TimeSeries;
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import ru.ulstu.extractor.ts.model.TimeSeriesType;
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import ru.ulstu.extractor.ts.model.TimeSeriesValue;
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@ -114,9 +116,13 @@ public class TimeSeriesService {
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}
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public Double getLastTimeSeriesTendency(TimeSeries ts) {
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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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//response.get("timeSeries");
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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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response.get("timeSeries");
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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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}
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return 0.0;
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}
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}
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