WIP: страницы для правил #62
@ -8,7 +8,6 @@ import ru.ulstu.extractor.rule.service.AntecedentValueService;
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import ru.ulstu.extractor.rule.service.DbRuleService;
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import ru.ulstu.extractor.rule.service.FuzzyInferenceService;
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import ru.ulstu.extractor.ts.model.TimeSeries;
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import ru.ulstu.extractor.ts.model.TimeSeriesValue;
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import ru.ulstu.extractor.ts.service.TimeSeriesService;
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import java.util.ArrayList;
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@ -57,9 +56,7 @@ public class AssessmentService {
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService.getLastTimeSeriesTendency(ts)));
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return fuzzyInferenceService.getFuzzyInference(dbRules,
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antecedentValueService.getList(),
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variableValues,
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getTSsMin(timeSeries),
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getTSsMax(timeSeries));
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variableValues);
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}
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private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
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@ -75,9 +72,7 @@ public class AssessmentService {
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.getLastTimeSeriesTendency(ts)));
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return fuzzyInferenceService.getFuzzyInference(List.of(dbRule),
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antecedentValueService.getList(),
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variableValues,
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getTSsMin(timeSeries),
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getTSsMax(timeSeries)).stream();
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variableValues).stream();
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})
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.sorted(Comparator.comparing(Assessment::getDegree))
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.collect(Collectors.toList());
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@ -88,44 +83,6 @@ public class AssessmentService {
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), ts.getValues().get(ts.getValues().size() - 1).getValue()));
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return fuzzyInferenceService.getFuzzyInference(dbRules,
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antecedentValueService.getList(),
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variableValues,
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getTSsMin(timeSeries),
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getTSsMax(timeSeries));
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}
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private Double getMin(List<Double> values) {
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return values.stream().mapToDouble(v -> v).min().getAsDouble();
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}
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private Map.Entry<String, Double> getTSMin(TimeSeries ts) {
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return Map.entry(ts.getTimeSeriesType().name(),
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getMin(ts.getValues().stream().map(TimeSeriesValue::getValue).collect(Collectors.toList())));
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}
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private Map<String, Double> getTSsMin(List<TimeSeries> tss) {
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Map<String, Double> res = new HashMap<>();
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tss.forEach(ts -> {
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Map.Entry<String, Double> entry = getTSMin(ts);
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res.put(entry.getKey(), entry.getValue());
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});
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return res;
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}
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private Double getMax(List<Double> values) {
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return values.stream().mapToDouble(v -> v).max().getAsDouble();
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}
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private Map.Entry<String, Double> getTSMax(TimeSeries ts) {
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return Map.entry(ts.getTimeSeriesType().name(),
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getMax(ts.getValues().stream().map(TimeSeriesValue::getValue).collect(Collectors.toList())));
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}
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private Map<String, Double> getTSsMax(List<TimeSeries> tss) {
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Map<String, Double> res = new HashMap<>();
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tss.forEach(ts -> {
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Map.Entry<String, Double> entry = getTSMax(ts);
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res.put(entry.getKey(), entry.getValue());
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});
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return res;
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variableValues);
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}
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}
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@ -34,8 +34,7 @@ public class FuzzyInferenceService {
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+ " is %s";
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private final static String NO_RESULT = "Нет результата";
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private List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
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validateVariables(variableValues, dbRules);
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private List<String> mapRulesToString(List<DbRule> dbRules) {
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return dbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
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}
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@ -51,8 +50,6 @@ public class FuzzyInferenceService {
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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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Map<String, Double> min,
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Map<String, Double> max,
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List<AntecedentValue> antecedentValues,
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List<Integer> consequentValues) {
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variableValues.forEach((key, value) -> {
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@ -61,16 +58,16 @@ public class FuzzyInferenceService {
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input.setDescription("");
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input.setEnabled(true);
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double delta = antecedentValues.size() > 1
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? (max.get(key) - min.get(key)) / (antecedentValues.size() - 1)
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: (max.get(key) - min.get(key));
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input.setRange(min.get(key), max.get(key));
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? 2.0 / (antecedentValues.size() - 1)
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: 2.0;
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input.setRange(-1, 1);
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input.setLockValueInRange(false);
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for (int i = 0; i < antecedentValues.size(); i++) {
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input.addTerm(
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new Triangle(
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antecedentValues.get(i).getAntecedentValue(),
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min.get(key) + i * delta - 0.5 * delta,
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min.get(key) + i * delta + delta + 0.5 * delta
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-1 + i * delta - 0.5 * delta,
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-1 + i * delta + delta + 0.5 * delta
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)
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);
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}
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@ -87,7 +84,7 @@ public class FuzzyInferenceService {
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output.setDefaultValue(Double.NaN);
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output.setLockValueInRange(false);
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for (int i = 0; i < consequentValues.size(); i++) {
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output.addTerm(new Triangle(consequentValues.get(i).toString(), i, i + 2.1));
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output.addTerm(new Triangle(consequentValues.get(i).toString(), i, i + 1));
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}
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engine.addOutputVariable(output);
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@ -99,7 +96,7 @@ public class FuzzyInferenceService {
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//mamdani.setDisjunction(null);
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mamdani.setImplication(new AlgebraicProduct());
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mamdani.setActivation(new General());
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getRulesFromDb(dbRules, variableValues).forEach(r -> {
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mapRulesToString(dbRules).forEach(r -> {
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LOG.info(r);
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mamdani.addRule(Rule.parse(r, engine));
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});
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@ -115,12 +112,11 @@ public class FuzzyInferenceService {
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public List<Assessment> getFuzzyInference(List<DbRule> dbRules,
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List<AntecedentValue> antecedentValues,
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Map<String, Double> variableValues,
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Map<String, Double> min,
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Map<String, Double> max) {
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Map<String, Double> variableValues) {
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validateVariables(variableValues, dbRules);
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Engine engine = getFuzzyEngine();
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List<Integer> consequentValues = dbRules.stream().map(DbRule::getId).collect(Collectors.toList());
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engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, min, max, antecedentValues, consequentValues));
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engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, antecedentValues, consequentValues));
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Map.Entry<String, Double> consequent = getConsequent(engine, variableValues);
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if (consequent.getKey().equals(NO_RESULT)) {
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return new ArrayList<>();
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@ -127,17 +127,29 @@ public class TimeSeriesService {
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public double getLastTimeSeriesTendency(TimeSeries ts) {
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if (ts != null && ts.getValues().size() > MIN_TIME_SERIES_LENGTH) {
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JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(ts)));
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JSONObject response = httpService.post(TIME_SERIES_TENDENCY_URL, new JSONObject(new SmoothingTimeSeries(normalizeTimeSeries(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 DEFAULT_TIME_SERIES_TENDENCY;
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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 jsonArray.getJSONObject(jsonArray.length() - 1).getDouble("value") -
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jsonArray.getJSONObject(jsonArray.length() - 2).getDouble("value");
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}
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return DEFAULT_TIME_SERIES_TENDENCY;
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}
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private TimeSeries normalizeTimeSeries(TimeSeries ts) {
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double sum = ts.getValues().stream().mapToDouble(TimeSeriesValue::getValue).sum();
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if (sum > 0.0d) {
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for (int i = 0; i < ts.getValues().size(); i++) {
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ts.getValues().get(i).setValue(ts.getValues().get(i).getValue() / sum);
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}
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}
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return ts;
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}
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public boolean isBranchContainsAllTimeSeries(Branch b) {
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List<TimeSeries> timeSeries = getByBranch(b.getId());
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return Stream.of(TimeSeriesType.values()).allMatch(type -> timeSeries
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