WIP: страницы для правил #62
@ -5,8 +5,8 @@ import org.springframework.ui.Model;
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import org.springframework.web.bind.annotation.GetMapping;
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import org.springframework.web.bind.annotation.RequestParam;
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import ru.ulstu.extractor.assessment.model.FilterBranchForm;
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import ru.ulstu.extractor.assessment.service.AssessmentService;
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import ru.ulstu.extractor.branch.service.BranchService;
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import ru.ulstu.extractor.rule.service.FuzzyInferenceService;
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import springfox.documentation.annotations.ApiIgnore;
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import java.util.Optional;
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@ -16,12 +16,12 @@ import static ru.ulstu.extractor.core.Route.ASSESSMENTS;
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@Controller
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@ApiIgnore
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public class AssessmentController {
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private final FuzzyInferenceService fuzzyInferenceService;
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private final AssessmentService assessmentService;
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private final BranchService branchService;
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public AssessmentController(FuzzyInferenceService fuzzyInferenceService,
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public AssessmentController(AssessmentService assessmentService,
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BranchService branchService) {
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this.fuzzyInferenceService = fuzzyInferenceService;
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this.assessmentService = assessmentService;
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this.branchService = branchService;
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}
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@ -29,7 +29,7 @@ public class AssessmentController {
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public String getAssessments(Model model, @RequestParam Optional<Integer> branchId) {
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model.addAttribute("branches", branchService.findAll());
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if (branchId.isPresent()) {
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model.addAttribute("assessments", fuzzyInferenceService.getAssessmentsByForecastTendencies(branchId.get()));
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model.addAttribute("assessments", assessmentService.getAssessmentsByForecastTendencies(branchId.get()));
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model.addAttribute("filterBranchForm", new FilterBranchForm(branchId.get()));
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} else {
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model.addAttribute("filterBranchForm", new FilterBranchForm());
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@ -9,13 +9,15 @@ public class Assessment {
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private final String firstAntecedentTendency;
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private final TimeSeriesType secondAntecedent;
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private final String secondAntecedentTendency;
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private final Double degree;
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public Assessment(DbRule dbRule) {
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public Assessment(DbRule dbRule, Double degree) {
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this.consequent = dbRule.getConsequent();
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this.firstAntecedent = dbRule.getFirstAntecedent();
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this.firstAntecedentTendency = dbRule.getFirstAntecedentValue().getAntecedentValue();
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this.secondAntecedent = dbRule.getSecondAntecedent();
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this.secondAntecedentTendency = dbRule.getSecondAntecedentValue().getAntecedentValue();
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this.degree = degree;
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}
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public String getConsequent() {
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@ -37,4 +39,8 @@ public class Assessment {
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public String getSecondAntecedentTendency() {
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return secondAntecedentTendency;
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}
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public Double getDegree() {
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return degree;
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}
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}
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@ -0,0 +1,134 @@
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package ru.ulstu.extractor.assessment.service;
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import org.springframework.stereotype.Service;
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import ru.ulstu.extractor.assessment.model.Assessment;
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import ru.ulstu.extractor.rule.model.AssessmentException;
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import ru.ulstu.extractor.rule.model.DbRule;
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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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import java.util.Comparator;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.stream.Collectors;
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@Service
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public class AssessmentService {
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private final DbRuleService ruleService;
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private final AntecedentValueService antecedentValueService;
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private final TimeSeriesService timeSeriesService;
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private final FuzzyInferenceService fuzzyInferenceService;
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public AssessmentService(DbRuleService ruleService,
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AntecedentValueService antecedentValueService,
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TimeSeriesService timeSeriesService,
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FuzzyInferenceService fuzzyInferenceService) {
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this.ruleService = ruleService;
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this.antecedentValueService = antecedentValueService;
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this.timeSeriesService = timeSeriesService;
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this.fuzzyInferenceService = fuzzyInferenceService;
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}
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public List<Assessment> getAssessmentsByForecastTendencies(Integer branchId) {
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List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
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List<DbRule> dbRules = ruleService.getList();
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try {
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return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
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} catch (AssessmentException ex) {
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ex.printStackTrace();
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return new ArrayList<>();
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}
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}
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public List<Assessment> getAssessmentsByLastValues(Integer branchId) {
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List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
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List<DbRule> dbRules = ruleService.getList();
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return getAssessmentsByLastValues(dbRules, timeSeries);
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}
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private List<Assessment> getSingleAssessmentByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) throws AssessmentException {
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Map<String, Double> variableValues = new HashMap<>();
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(),
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timeSeriesService.getLastTimeSeriesTendency(ts)
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.orElseThrow(() -> new AssessmentException(""))));
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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 List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
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return dbRules
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.stream()
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.flatMap(dbRule -> {
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Map<String, Double> variableValues = new HashMap<>();
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timeSeries
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.stream()
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.filter(ts -> ts.getTimeSeriesType() == dbRule.getFirstAntecedent()
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|| ts.getTimeSeriesType() == dbRule.getSecondAntecedent())
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.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService
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.getLastTimeSeriesTendency(ts)
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.orElse(ts.getValues().get(ts.getValues().size() - 1).getValue())));
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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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})
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.sorted(Comparator.comparing(Assessment::getDegree))
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.collect(Collectors.toList());
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}
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private List<Assessment> getAssessmentsByLastValues(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
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Map<String, Double> variableValues = new HashMap<>();
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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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}
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}
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@ -283,7 +283,6 @@ public class GitRepositoryService {
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List<FileChange> changes = new ArrayList<>();
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String[] strings = output.split("\n");
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Map<String, List<String>> filesContent = getFilesContent(strings);
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System.out.println(filesContent);
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for(Map.Entry<String, List<String>> fileSterings: filesContent.entrySet()) {
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FileChange fileChange = new FileChange();
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fileChange.setFile(fileSterings.getKey());
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@ -11,7 +11,8 @@ public class AntecedentValue extends BaseEntity {
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public AntecedentValue() {
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}
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public AntecedentValue(String antecedentValue) {
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public AntecedentValue(Integer id, String antecedentValue) {
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this.setId(id);
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this.antecedentValue = antecedentValue;
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}
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@ -1,61 +1,46 @@
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package ru.ulstu.extractor.rule.service;
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import com.fuzzylite.Engine;
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import com.fuzzylite.activation.Highest;
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import com.fuzzylite.activation.General;
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import com.fuzzylite.defuzzifier.Centroid;
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import com.fuzzylite.norm.s.BoundedSum;
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import com.fuzzylite.norm.s.Maximum;
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import com.fuzzylite.norm.t.AlgebraicProduct;
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import com.fuzzylite.norm.t.Minimum;
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import com.fuzzylite.rule.Rule;
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import com.fuzzylite.rule.RuleBlock;
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import com.fuzzylite.term.Triangle;
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import com.fuzzylite.variable.InputVariable;
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import com.fuzzylite.variable.OutputVariable;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.stereotype.Service;
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import ru.ulstu.extractor.assessment.model.Assessment;
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import ru.ulstu.extractor.gitrepository.service.GitRepositoryService;
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import ru.ulstu.extractor.rule.model.AntecedentValue;
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import ru.ulstu.extractor.rule.model.AssessmentException;
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import ru.ulstu.extractor.rule.model.DbRule;
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import ru.ulstu.extractor.ts.model.TimeSeries;
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import ru.ulstu.extractor.ts.service.TimeSeriesService;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.stream.Collectors;
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import java.util.stream.Stream;
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import static java.lang.String.format;
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@Service
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public class FuzzyInferenceService {
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private final static Logger LOG = LoggerFactory.getLogger(FuzzyInferenceService.class);
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private final static String OUTPUT_VARIABLE_NAME = "state";
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private final static String RULE_TEMPLATE = "if %s is %s and %s is %s then "
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+ OUTPUT_VARIABLE_NAME
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+ " is %s";
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private final static String NO_RESULT = "Нет результата";
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private final DbRuleService ruleService;
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private final AntecedentValueService antecedentValueService;
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private final GitRepositoryService gitRepositoryService;
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private final TimeSeriesService timeSeriesService;
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public FuzzyInferenceService(DbRuleService ruleService,
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AntecedentValueService antecedentValueService,
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GitRepositoryService gitRepositoryService,
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TimeSeriesService timeSeriesService) {
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this.ruleService = ruleService;
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this.antecedentValueService = antecedentValueService;
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this.gitRepositoryService = gitRepositoryService;
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this.timeSeriesService = timeSeriesService;
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}
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public List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
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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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return dbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
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}
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private String getFuzzyRule(DbRule dbRule) {
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return String.format(RULE_TEMPLATE,
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return format(RULE_TEMPLATE,
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dbRule.getFirstAntecedent().name(),
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dbRule.getFirstAntecedentValue().getAntecedentValue(),
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dbRule.getSecondAntecedent().name(),
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@ -66,6 +51,8 @@ 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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@ -73,10 +60,19 @@ public class FuzzyInferenceService {
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input.setName(key);
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input.setDescription("");
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input.setEnabled(true);
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input.setRange(-0.1, antecedentValues.size() + 1.1);
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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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input.setLockValueInRange(false);
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for (int i = 0; i < antecedentValues.size(); i++) {
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input.addTerm(new Triangle(antecedentValues.get(i).getAntecedentValue(), i - 0.1, i + 2.1));
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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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)
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);
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}
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engine.addInputVariable(input);
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});
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@ -85,13 +81,13 @@ 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, consequentValues.size() + 0.1);
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output.setRange(0, 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.setDefuzzifier(new Centroid(10));
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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 - 0.1, i + 2.1));
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output.addTerm(new Triangle(consequentValues.get(i).toString(), i, i + 2.1));
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}
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engine.addOutputVariable(output);
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@ -99,11 +95,14 @@ public class FuzzyInferenceService {
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mamdani.setName("mamdani");
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mamdani.setDescription("");
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mamdani.setEnabled(true);
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mamdani.setConjunction(new AlgebraicProduct());
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mamdani.setDisjunction(new BoundedSum());
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mamdani.setConjunction(new Minimum());
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//mamdani.setDisjunction(null);
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mamdani.setImplication(new AlgebraicProduct());
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mamdani.setActivation(new Highest());
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getRulesFromDb(dbRules, variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
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mamdani.setActivation(new General());
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getRulesFromDb(dbRules, variableValues).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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return mamdani;
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}
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@ -114,83 +113,40 @@ public class FuzzyInferenceService {
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return engine;
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}
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public List<Assessment> getAssessmentsByForecastTendencies(Integer branchId) {
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List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
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List<DbRule> dbRules = ruleService.getList();
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try {
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return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
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} catch (AssessmentException ex) {
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return new ArrayList<>();
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}
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}
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public List<Assessment> getAssessmentsByLastValues(Integer branchId) {
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List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
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List<DbRule> dbRules = ruleService.getList();
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return getAssessmentsByLastValues(dbRules, timeSeries);
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}
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private List<Assessment> getFuzzyInference(List<DbRule> dbRules, Map<String, Double> variableValues) {
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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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Engine engine = getFuzzyEngine();
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List<AntecedentValue> antecedentValues = Stream.concat(dbRules.stream().map(DbRule::getFirstAntecedentValue),
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dbRules.stream().map(DbRule::getSecondAntecedentValue)).distinct().collect(Collectors.toList());
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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, antecedentValues, consequentValues));
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String consequent = getConsequent(engine, variableValues);
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if (consequent.equals(NO_RESULT)) {
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engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, min, max, 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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}
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return dbRules
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.stream()
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.filter(r -> r.getId().equals(Integer.valueOf(consequent)))
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.map(Assessment::new)
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.filter(r -> r.getId().equals(Integer.valueOf(consequent.getKey())))
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.map(r -> new Assessment(r, consequent.getValue()))
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.collect(Collectors.toList());
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}
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private List<Assessment> getSingleAssessmentByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) throws AssessmentException {
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Map<String, Double> variableValues = new HashMap<>();
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timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(),
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timeSeriesService.getLastTimeSeriesTendency(ts)
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.orElseThrow(() -> new AssessmentException(""))));
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return getFuzzyInference(dbRules, variableValues);
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}
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private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
|
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return dbRules
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.stream()
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.flatMap(dbRule -> {
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Map<String, Double> variableValues = new HashMap<>();
|
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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()));
|
||||
return getFuzzyInference(dbRules, variableValues);
|
||||
}
|
||||
|
||||
private void validateVariables(Map<String, Double> variableValues, List<DbRule> dbDbRules) {
|
||||
for (DbRule dbRule : dbDbRules) {
|
||||
if (!variableValues.containsKey(dbRule.getFirstAntecedent().name())) {
|
||||
throw new RuntimeException(String.format("Переменной в правиле не задано значение (нет временного ряда): %s ",
|
||||
throw new RuntimeException(format("Переменной в правиле не задано значение (нет временного ряда): %s ",
|
||||
dbRule.getFirstAntecedent().name()));
|
||||
}
|
||||
if (!variableValues.containsKey(dbRule.getSecondAntecedent().name())) {
|
||||
throw new RuntimeException(String.format("Переменной в правиле не задано значение (нет временного ряда): %s ",
|
||||
throw new RuntimeException(format("Переменной в правиле не задано значение (нет временного ряда): %s ",
|
||||
dbRule.getSecondAntecedent().name()));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private String getConsequent(Engine engine, Map<String, Double> variableValues) {
|
||||
private Map.Entry<String, Double> getConsequent(Engine engine, Map<String, Double> variableValues) {
|
||||
OutputVariable outputVariable = engine.getOutputVariable(OUTPUT_VARIABLE_NAME);
|
||||
for (Map.Entry<String, Double> variableValue : variableValues.entrySet()) {
|
||||
InputVariable inputVariable = engine.getInputVariable(variableValue.getKey());
|
||||
@ -198,10 +154,10 @@ public class FuzzyInferenceService {
|
||||
}
|
||||
engine.process();
|
||||
if (outputVariable != null) {
|
||||
outputVariable.defuzzify();
|
||||
LOG.info("Output: {}", outputVariable.getValue());
|
||||
}
|
||||
return (outputVariable == null || Double.isNaN(outputVariable.getValue()))
|
||||
? NO_RESULT
|
||||
: outputVariable.highestMembership(outputVariable.getValue()).getSecond().getName();
|
||||
? Map.entry(NO_RESULT, 0.0)
|
||||
: Map.entry(outputVariable.highestMembershipTerm(outputVariable.getValue()).getName(), outputVariable.getValue());
|
||||
}
|
||||
}
|
||||
|
@ -42,6 +42,7 @@
|
||||
th:text="${assessment.firstAntecedent.description}"></span>'
|
||||
и тенденции '<span th:text="${assessment.secondAntecedentTendency}"></span>' показателя '<span
|
||||
th:text="${assessment.secondAntecedent.description}"></span>';
|
||||
<span class="badge badge-warning" th:text="${assessment.degree}"></span>
|
||||
</div>
|
||||
</div>
|
||||
<div th:if="${assessments != null && #lists.size(assessments) == 0}">
|
||||
|
@ -2,7 +2,9 @@ package ru.ulstu;
|
||||
|
||||
import org.junit.Assert;
|
||||
import org.junit.Test;
|
||||
import ru.ulstu.extractor.branch.model.Branch;
|
||||
import ru.ulstu.extractor.ts.model.TimeSeries;
|
||||
import ru.ulstu.extractor.ts.model.TimeSeriesType;
|
||||
import ru.ulstu.extractor.ts.model.TimeSeriesValue;
|
||||
import ru.ulstu.extractor.ts.util.TimeSeriesDateMapper;
|
||||
|
||||
@ -17,13 +19,13 @@ public class TimeSeriesMapperTest {
|
||||
c1.set(2020, 5, 1, 1, 1, 1);
|
||||
Calendar c2 = GregorianCalendar.getInstance();
|
||||
c2.set(2020, 5, 2, 2, 1, 1);
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый",
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый", new Branch(), TimeSeriesType.COMMITS,
|
||||
Arrays.asList(new TimeSeriesValue(c1.getTime(), 10.0),
|
||||
new TimeSeriesValue(c2.getTime(), 10.0)));
|
||||
TimeSeriesDateMapper mapper = new TimeSeriesDateMapper();
|
||||
timeSeries = mapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.MONTH, timeSeries);
|
||||
timeSeries = TimeSeriesDateMapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.MONTH, timeSeries);
|
||||
Assert.assertEquals(1, timeSeries.getValues().size());
|
||||
Assert.assertEquals(Integer.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
Assert.assertEquals(Double.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
}
|
||||
|
||||
@Test
|
||||
@ -32,13 +34,13 @@ public class TimeSeriesMapperTest {
|
||||
c1.set(2020, 5, 1, 1, 1, 1);
|
||||
Calendar c2 = GregorianCalendar.getInstance();
|
||||
c2.set(2020, 5, 2, 1, 1, 1);
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый",
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый", new Branch(), TimeSeriesType.COMMITS,
|
||||
Arrays.asList(new TimeSeriesValue(c1.getTime(), 10.0),
|
||||
new TimeSeriesValue(c2.getTime(), 10.0)));
|
||||
TimeSeriesDateMapper mapper = new TimeSeriesDateMapper();
|
||||
timeSeries = mapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.MONTH, timeSeries);
|
||||
Assert.assertEquals(1, timeSeries.getValues().size());
|
||||
Assert.assertEquals(Integer.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
Assert.assertEquals(Double.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
}
|
||||
|
||||
@Test
|
||||
@ -47,13 +49,13 @@ public class TimeSeriesMapperTest {
|
||||
c1.set(2020, 5, 1, 1, 1, 1);
|
||||
Calendar c2 = GregorianCalendar.getInstance();
|
||||
c2.set(2020, 5, 2, 1, 1, 1);
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый",
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый", new Branch(), TimeSeriesType.COMMITS,
|
||||
Arrays.asList(new TimeSeriesValue(c1.getTime(), 10.0),
|
||||
new TimeSeriesValue(c2.getTime(), 10.0)));
|
||||
TimeSeriesDateMapper mapper = new TimeSeriesDateMapper();
|
||||
timeSeries = mapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.MONTH, timeSeries);
|
||||
Assert.assertEquals(1, timeSeries.getValues().size());
|
||||
Assert.assertEquals(Integer.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
Assert.assertEquals(Double.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
}
|
||||
|
||||
@Test
|
||||
@ -62,13 +64,13 @@ public class TimeSeriesMapperTest {
|
||||
c1.set(2020, 5, 1, 1, 1, 1);
|
||||
Calendar c2 = GregorianCalendar.getInstance();
|
||||
c2.set(2020, 6, 2, 1, 1, 1);
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый",
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый", new Branch(), TimeSeriesType.COMMITS,
|
||||
Arrays.asList(new TimeSeriesValue(c1.getTime(), 10.0),
|
||||
new TimeSeriesValue(c2.getTime(), 10.0)));
|
||||
TimeSeriesDateMapper mapper = new TimeSeriesDateMapper();
|
||||
timeSeries = mapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.MONTH, timeSeries);
|
||||
Assert.assertEquals(2, timeSeries.getValues().size());
|
||||
Assert.assertEquals(Integer.valueOf(10), timeSeries.getValues().get(0).getValue());
|
||||
Assert.assertEquals(Double.valueOf(10), timeSeries.getValues().get(0).getValue());
|
||||
}
|
||||
|
||||
@Test
|
||||
@ -77,13 +79,13 @@ public class TimeSeriesMapperTest {
|
||||
c1.set(2020, 5, 1, 1, 1, 1);
|
||||
Calendar c2 = GregorianCalendar.getInstance();
|
||||
c2.set(2020, 5, 2, 1, 1, 1);
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый",
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый", new Branch(), TimeSeriesType.COMMITS,
|
||||
Arrays.asList(new TimeSeriesValue(c1.getTime(), 10.0),
|
||||
new TimeSeriesValue(c2.getTime(), 10.0)));
|
||||
TimeSeriesDateMapper mapper = new TimeSeriesDateMapper();
|
||||
timeSeries = mapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.YEAR, timeSeries);
|
||||
Assert.assertEquals(1, timeSeries.getValues().size());
|
||||
Assert.assertEquals(Integer.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
Assert.assertEquals(Double.valueOf(20), timeSeries.getValues().get(0).getValue());
|
||||
}
|
||||
|
||||
@Test
|
||||
@ -92,12 +94,12 @@ public class TimeSeriesMapperTest {
|
||||
c1.set(2020, 5, 1, 1, 1, 1);
|
||||
Calendar c2 = GregorianCalendar.getInstance();
|
||||
c2.set(2021, 5, 2, 1, 1, 1);
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый",
|
||||
TimeSeries timeSeries = new TimeSeries("Тестовый", new Branch(), TimeSeriesType.COMMITS,
|
||||
Arrays.asList(new TimeSeriesValue(c1.getTime(), 10.0),
|
||||
new TimeSeriesValue(c2.getTime(), 10.0)));
|
||||
TimeSeriesDateMapper mapper = new TimeSeriesDateMapper();
|
||||
timeSeries = mapper.mapTimeSeriesToInterval(TimeSeriesDateMapper.TimeSeriesInterval.YEAR, timeSeries);
|
||||
Assert.assertEquals(2, timeSeries.getValues().size());
|
||||
Assert.assertEquals(Integer.valueOf(10), timeSeries.getValues().get(0).getValue());
|
||||
Assert.assertEquals(Double.valueOf(10), timeSeries.getValues().get(0).getValue());
|
||||
}
|
||||
}
|
||||
|
Loading…
Reference in New Issue
Block a user