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git-extractor
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Декомпозиция #87

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romanov73 merged 2 commits from 86-bl-decomposition into master 1 year ago
Conversation 0 Commits 2 Files Changed 8
8 changed files with 212 additions and 113 deletions
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10
src/main/java/ru/ulstu/extractor/assessment/controller/AssessmentController.java
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@ -5,8 +5,8 @@ import org.springframework.ui.Model;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import ru.ulstu.extractor.assessment.model.FilterBranchForm;
import ru.ulstu.extractor.assessment.service.AssessmentService;
import ru.ulstu.extractor.branch.service.BranchService;
import ru.ulstu.extractor.rule.service.FuzzyInferenceService;
import springfox.documentation.annotations.ApiIgnore;
import java.util.Optional;
@ -16,12 +16,12 @@ import static ru.ulstu.extractor.core.Route.ASSESSMENTS;
@Controller
@ApiIgnore
public class AssessmentController {
private final FuzzyInferenceService fuzzyInferenceService;
private final AssessmentService assessmentService;
private final BranchService branchService;
public AssessmentController(FuzzyInferenceService fuzzyInferenceService,
public AssessmentController(AssessmentService assessmentService,
BranchService branchService) {
this.fuzzyInferenceService = fuzzyInferenceService;
this.assessmentService = assessmentService;
this.branchService = branchService;
}
@ -29,7 +29,7 @@ public class AssessmentController {
public String getAssessments(Model model, @RequestParam Optional<Integer> branchId) {
model.addAttribute("branches", branchService.findAll());
if (branchId.isPresent()) {
model.addAttribute("assessments", fuzzyInferenceService.getAssessmentsByForecastTendencies(branchId.get()));
model.addAttribute("assessments", assessmentService.getAssessmentsByForecastTendencies(branchId.get()));
model.addAttribute("filterBranchForm", new FilterBranchForm(branchId.get()));
} else {
model.addAttribute("filterBranchForm", new FilterBranchForm());

8
src/main/java/ru/ulstu/extractor/assessment/model/Assessment.java
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@ -9,13 +9,15 @@ public class Assessment {
private final String firstAntecedentTendency;
private final TimeSeriesType secondAntecedent;
private final String secondAntecedentTendency;
private final Double degree;
public Assessment(DbRule dbRule) {
public Assessment(DbRule dbRule, Double degree) {
this.consequent = dbRule.getConsequent();
this.firstAntecedent = dbRule.getFirstAntecedent();
this.firstAntecedentTendency = dbRule.getFirstAntecedentValue().getAntecedentValue();
this.secondAntecedent = dbRule.getSecondAntecedent();
this.secondAntecedentTendency = dbRule.getSecondAntecedentValue().getAntecedentValue();
this.degree = degree;
}
public String getConsequent() {
@ -37,4 +39,8 @@ public class Assessment {
public String getSecondAntecedentTendency() {
return secondAntecedentTendency;
}
public Double getDegree() {
return degree;
}
}

134
src/main/java/ru/ulstu/extractor/assessment/service/AssessmentService.java
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@ -0,0 +1,134 @@
package ru.ulstu.extractor.assessment.service;
import org.springframework.stereotype.Service;
import ru.ulstu.extractor.assessment.model.Assessment;
import ru.ulstu.extractor.rule.model.AssessmentException;
import ru.ulstu.extractor.rule.model.DbRule;
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;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
@Service
public class AssessmentService {
private final DbRuleService ruleService;
private final AntecedentValueService antecedentValueService;
private final TimeSeriesService timeSeriesService;
private final FuzzyInferenceService fuzzyInferenceService;
public AssessmentService(DbRuleService ruleService,
AntecedentValueService antecedentValueService,
TimeSeriesService timeSeriesService,
FuzzyInferenceService fuzzyInferenceService) {
this.ruleService = ruleService;
this.antecedentValueService = antecedentValueService;
this.timeSeriesService = timeSeriesService;
this.fuzzyInferenceService = fuzzyInferenceService;
}
public List<Assessment> getAssessmentsByForecastTendencies(Integer branchId) {
List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
List<DbRule> dbRules = ruleService.getList();
try {
return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
} catch (AssessmentException ex) {
ex.printStackTrace();
return new ArrayList<>();
}
}
public List<Assessment> getAssessmentsByLastValues(Integer branchId) {
List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
List<DbRule> dbRules = ruleService.getList();
return getAssessmentsByLastValues(dbRules, timeSeries);
}
private List<Assessment> getSingleAssessmentByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) throws AssessmentException {
Map<String, Double> variableValues = new HashMap<>();
timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(),
timeSeriesService.getLastTimeSeriesTendency(ts)
.orElseThrow(() -> new AssessmentException(""))));
return fuzzyInferenceService.getFuzzyInference(dbRules,
antecedentValueService.getList(),
variableValues,
getTSsMin(timeSeries),
getTSsMax(timeSeries));
}
private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
return dbRules
.stream()
.flatMap(dbRule -> {
Map<String, Double> variableValues = new HashMap<>();
timeSeries
.stream()
.filter(ts -> ts.getTimeSeriesType() == dbRule.getFirstAntecedent()
|| ts.getTimeSeriesType() == dbRule.getSecondAntecedent())
.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(), timeSeriesService
.getLastTimeSeriesTendency(ts)
.orElse(ts.getValues().get(ts.getValues().size() - 1).getValue())));
return fuzzyInferenceService.getFuzzyInference(List.of(dbRule),
antecedentValueService.getList(),
variableValues,
getTSsMin(timeSeries),
getTSsMax(timeSeries)).stream();
})
.sorted(Comparator.comparing(Assessment::getDegree))
.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 fuzzyInferenceService.getFuzzyInference(dbRules,
antecedentValueService.getList(),
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;
}
}

1
src/main/java/ru/ulstu/extractor/gitrepository/service/GitRepositoryService.java
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@ -283,7 +283,6 @@ public class GitRepositoryService {
List<FileChange> changes = new ArrayList<>();
String[] strings = output.split("\n");
Map<String, List<String>> filesContent = getFilesContent(strings);
System.out.println(filesContent);
for(Map.Entry<String, List<String>> fileSterings: filesContent.entrySet()) {
FileChange fileChange = new FileChange();
fileChange.setFile(fileSterings.getKey());

3
src/main/java/ru/ulstu/extractor/rule/model/AntecedentValue.java
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@ -11,7 +11,8 @@ public class AntecedentValue extends BaseEntity {
public AntecedentValue() {
}
public AntecedentValue(String antecedentValue) {
public AntecedentValue(Integer id, String antecedentValue) {
this.setId(id);
this.antecedentValue = antecedentValue;
}

140
src/main/java/ru/ulstu/extractor/rule/service/FuzzyInferenceService.java
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@ -1,61 +1,46 @@
package ru.ulstu.extractor.rule.service;
import com.fuzzylite.Engine;
import com.fuzzylite.activation.Highest;
import com.fuzzylite.activation.General;
import com.fuzzylite.defuzzifier.Centroid;
import com.fuzzylite.norm.s.BoundedSum;
import com.fuzzylite.norm.s.Maximum;
import com.fuzzylite.norm.t.AlgebraicProduct;
import com.fuzzylite.norm.t.Minimum;
import com.fuzzylite.rule.Rule;
import com.fuzzylite.rule.RuleBlock;
import com.fuzzylite.term.Triangle;
import com.fuzzylite.variable.InputVariable;
import com.fuzzylite.variable.OutputVariable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.stereotype.Service;
import ru.ulstu.extractor.assessment.model.Assessment;
import ru.ulstu.extractor.gitrepository.service.GitRepositoryService;
import ru.ulstu.extractor.rule.model.AntecedentValue;
import ru.ulstu.extractor.rule.model.AssessmentException;
import ru.ulstu.extractor.rule.model.DbRule;
import ru.ulstu.extractor.ts.model.TimeSeries;
import ru.ulstu.extractor.ts.service.TimeSeriesService;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
import java.util.stream.Stream;
import static java.lang.String.format;
@Service
public class FuzzyInferenceService {
private final static Logger LOG = LoggerFactory.getLogger(FuzzyInferenceService.class);
private final static String OUTPUT_VARIABLE_NAME = "state";
private final static String RULE_TEMPLATE = "if %s is %s and %s is %s then "
+ OUTPUT_VARIABLE_NAME
+ " is %s";
private final static String NO_RESULT = "Нет результата";
private final DbRuleService ruleService;
private final AntecedentValueService antecedentValueService;
private final GitRepositoryService gitRepositoryService;
private final TimeSeriesService timeSeriesService;
public FuzzyInferenceService(DbRuleService ruleService,
AntecedentValueService antecedentValueService,
GitRepositoryService gitRepositoryService,
TimeSeriesService timeSeriesService) {
this.ruleService = ruleService;
this.antecedentValueService = antecedentValueService;
this.gitRepositoryService = gitRepositoryService;
this.timeSeriesService = timeSeriesService;
}
public List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
private List<String> getRulesFromDb(List<DbRule> dbRules, Map<String, Double> variableValues) {
validateVariables(variableValues, dbRules);
return dbRules.stream().map(this::getFuzzyRule).collect(Collectors.toList());
}
private String getFuzzyRule(DbRule dbRule) {
return String.format(RULE_TEMPLATE,
return format(RULE_TEMPLATE,
dbRule.getFirstAntecedent().name(),
dbRule.getFirstAntecedentValue().getAntecedentValue(),
dbRule.getSecondAntecedent().name(),
@ -66,6 +51,8 @@ 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) -> {
@ -73,10 +60,19 @@ public class FuzzyInferenceService {
input.setName(key);
input.setDescription("");
input.setEnabled(true);
input.setRange(-0.1, antecedentValues.size() + 1.1);
double delta = antecedentValues.size() > 1
? (max.get(key) - min.get(key)) / (antecedentValues.size() - 1)
: (max.get(key) - min.get(key));
input.setRange(min.get(key), max.get(key));
input.setLockValueInRange(false);
for (int i = 0; i < antecedentValues.size(); i++) {
input.addTerm(new Triangle(antecedentValues.get(i).getAntecedentValue(), i - 0.1, i + 2.1));
input.addTerm(
new Triangle(
antecedentValues.get(i).getAntecedentValue(),
min.get(key) + i * delta - 0.5 * delta,
min.get(key) + i * delta + delta + 0.5 * delta
)
);
}
engine.addInputVariable(input);
});
@ -85,13 +81,13 @@ public class FuzzyInferenceService {
output.setName(OUTPUT_VARIABLE_NAME);
output.setDescription("");
output.setEnabled(true);
output.setRange(-0.1, consequentValues.size() + 0.1);
output.setRange(0, consequentValues.size() + 0.1);
output.setAggregation(new Maximum());
output.setDefuzzifier(new Centroid(100));
output.setDefuzzifier(new Centroid(10));
output.setDefaultValue(Double.NaN);
output.setLockValueInRange(false);
for (int i = 0; i < consequentValues.size(); i++) {
output.addTerm(new Triangle(consequentValues.get(i).toString(), i - 0.1, i + 2.1));
output.addTerm(new Triangle(consequentValues.get(i).toString(), i, i + 2.1));
}
engine.addOutputVariable(output);
@ -99,11 +95,14 @@ public class FuzzyInferenceService {
mamdani.setName("mamdani");
mamdani.setDescription("");
mamdani.setEnabled(true);
mamdani.setConjunction(new AlgebraicProduct());
mamdani.setDisjunction(new BoundedSum());
mamdani.setConjunction(new Minimum());
//mamdani.setDisjunction(null);
mamdani.setImplication(new AlgebraicProduct());
mamdani.setActivation(new Highest());
getRulesFromDb(dbRules, variableValues).forEach(r -> mamdani.addRule(Rule.parse(r, engine)));
mamdani.setActivation(new General());
getRulesFromDb(dbRules, variableValues).forEach(r -> {
LOG.info(r);
mamdani.addRule(Rule.parse(r, engine));
});
return mamdani;
}
@ -114,83 +113,40 @@ public class FuzzyInferenceService {
return engine;
}
public List<Assessment> getAssessmentsByForecastTendencies(Integer branchId) {
List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
List<DbRule> dbRules = ruleService.getList();
try {
return getAssessmentsByTimeSeriesTendencies(dbRules, timeSeries);
} catch (AssessmentException ex) {
return new ArrayList<>();
}
}
public List<Assessment> getAssessmentsByLastValues(Integer branchId) {
List<TimeSeries> timeSeries = timeSeriesService.getByBranch(branchId);
List<DbRule> dbRules = ruleService.getList();
return getAssessmentsByLastValues(dbRules, timeSeries);
}
private List<Assessment> getFuzzyInference(List<DbRule> dbRules, Map<String, Double> variableValues) {
public List<Assessment> getFuzzyInference(List<DbRule> dbRules,
List<AntecedentValue> antecedentValues,
Map<String, Double> variableValues,
Map<String, Double> min,
Map<String, Double> max) {
Engine engine = getFuzzyEngine();
List<AntecedentValue> antecedentValues = Stream.concat(dbRules.stream().map(DbRule::getFirstAntecedentValue),
dbRules.stream().map(DbRule::getSecondAntecedentValue)).distinct().collect(Collectors.toList());
List<Integer> consequentValues = dbRules.stream().map(DbRule::getId).collect(Collectors.toList());
engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, antecedentValues, consequentValues));
String consequent = getConsequent(engine, variableValues);
if (consequent.equals(NO_RESULT)) {
engine.addRuleBlock(getRuleBlock(engine, dbRules, variableValues, min, max, antecedentValues, consequentValues));
Map.Entry<String, Double> consequent = getConsequent(engine, variableValues);
if (consequent.getKey().equals(NO_RESULT)) {
return new ArrayList<>();
}
return dbRules
.stream()
.filter(r -> r.getId().equals(Integer.valueOf(consequent)))
.map(Assessment::new)
.filter(r -> r.getId().equals(Integer.valueOf(consequent.getKey())))
.map(r -> new Assessment(r, consequent.getValue()))
.collect(Collectors.toList());
}
private List<Assessment> getSingleAssessmentByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) throws AssessmentException {
Map<String, Double> variableValues = new HashMap<>();
timeSeries.forEach(ts -> variableValues.put(ts.getTimeSeriesType().name(),
timeSeriesService.getLastTimeSeriesTendency(ts)
.orElseThrow(() -> new AssessmentException(""))));
return getFuzzyInference(dbRules, variableValues);
}
private List<Assessment> getAssessmentsByTimeSeriesTendencies(List<DbRule> dbRules, List<TimeSeries> timeSeries) {
return dbRules
.stream()
.flatMap(dbRule -> {
Map<String, Double> variableValues = new HashMap<>();
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());
}
}

1
src/main/resources/templates/assessments.html
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@ -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}">

28
src/test/java/ru/ulstu/TimeSeriesMapperTest.java
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@ -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());
}
}

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