public void CanHandleZeroLengthEndSegments()
{
const string featureClassName = "TLM_STEHENDES_GEWAESSER";
var locator = TestDataUtils.GetTestDataLocator();
string path = locator.GetPath("QaBorderSense.gdb");
IFeatureWorkspace workspace = WorkspaceUtils.OpenFileGdbFeatureWorkspace(path);
IFeatureClass featureClass = workspace.OpenFeatureClass(featureClassName);
// expect counter-clockwise: 0 errors
var runnerCounterClockwise = new QaContainerTestRunner(1000,
new QaBorderSense(
featureClass, false));
Assert.AreEqual(0, runnerCounterClockwise.Execute());
// expect clockwise: 1 error
var runnerClockwise = new QaContainerTestRunner(1000,
new QaBorderSense(featureClass,
true));
Assert.AreEqual(1, runnerClockwise.Execute());
}
public static void CreateAnAdditionalAggregateCalculation_Number()
{
CreateCalculationPage createcalculationpage = new CreateCalculationPage();
ManagePoliciesPage managepoliciespage = new ManagePoliciesPage();
string newname = "TestAggCalculationName";
string descriptiontext = "This is a Description for: ";
var randomSpecCalcName = newname + TestDataUtils.RandomString(6);
ScenarioContext.Current["AddAggCalcName"] = randomSpecCalcName;
managepoliciespage.CreateCalculation.Click();
Thread.Sleep(2000);
createcalculationpage.CalculationName.SendKeys(randomSpecCalcName);
createcalculationpage.CalculationDescription.SendKeys(descriptiontext + randomSpecCalcName);
Actions.CreateCalculationSpecificationpageSelectPolicyOrSubpolicyDropDown();
var calctype = createcalculationpage.CalculationTypeDropDown;
var selectElement = new SelectElement(calctype);
selectElement.SelectByValue("Number");
createcalculationpage.SaveCalculation.Click();
Thread.Sleep(2000);
var aggCalcNumName = ScenarioContext.Current["AddAggCalcName"];
string aggCalcCreated = aggCalcNumName.ToString();
Console.WriteLine(aggCalcCreated + " has been created successfully");
Thread.Sleep(5000);
}
private void InitializeWeightsAndDistributionsMixture()
{
_distributionWeights = new double[_NumberOfStates][];
for (var i = 0; i < _NumberOfStates; i++)
{
_distributionWeights[i] = new double[_NumberOfDistributionsInState];
_distributionWeights[i][0] = 0.5;
_distributionWeights[i][1] = 0.5;
}
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var distributions = CreateEmissions(series, _NumberOfStates * _NumberOfComponents * _NumberOfDistributionsInState);
_distributions = new Mixture <IMultivariateDistribution> [_NumberOfStates][];
var index = 0;
for (int i = 0; i < _NumberOfStates; i++)
{
_distributions[i] = new Mixture <IMultivariateDistribution> [_NumberOfDistributionsInState];
for (int k = 0; k < _NumberOfDistributionsInState; k++)
{
_distributions[i][k] = new Mixture <IMultivariateDistribution>(_NumberOfComponents, series[0].Length);
for (int j = 0; j < _NumberOfComponents; j++)
{
((Mixture <IMultivariateDistribution>)_distributions[i][k]).Components[j] = distributions[index];
index++;
}
}
}
}
private void InitializeWeightsAndDistributionsMultivariate()
{
_distributionWeights = new double[_NumberOfStates][];
for (var i = 0; i < _NumberOfStates; i++)
{
_distributionWeights[i] = new double[_NumberOfDistributionsInState];
_distributionWeights[i][0] = 0.5;
_distributionWeights[i][1] = 0.5;
}
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var distributions = CreateEmissions(series, _NumberOfStates * _NumberOfDistributionsInState);
_distributions = new IMultivariateDistribution[_NumberOfStates][];
var index = 0;
for (int i = 0; i < _NumberOfStates; i++)
{
_distributions[i] = new IMultivariateDistribution[_NumberOfDistributionsInState];
for (int k = 0; k < _NumberOfDistributionsInState; k++)
{
_distributions[i][k] = distributions[index];
index++;
}
}
}
public void Gen2847_NonZawarePolylineWithInteriorLoops()
{
var locator = TestDataUtils.GetTestDataLocator();
string path = locator.GetPath("DKM25_HOEHENKURVE_OID_2178.xml");
var nonZAwarePolyline = (IPolyline)GeometryUtils.FromXmlFile(path);
var fc = new FeatureClassMock(1, "Contours", esriGeometryType.esriGeometryPolyline,
esriFeatureType.esriFTSimple,
nonZAwarePolyline.SpatialReference,
hasZ: false, hasM: false);
IFeature f = fc.CreateFeature(nonZAwarePolyline);
const double toleranceFactor = 1.0;
var test = new QaSimpleGeometry(fc, false, toleranceFactor);
var runner = new QaTestRunner(test)
{
KeepGeometry = true
};
runner.Execute(f);
Assert.AreEqual(1, runner.Errors.Count);
Assert.AreEqual(3, ((IPointCollection)runner.ErrorGeometries[0]).PointCount);
}
public void HiddenMarkovModelMultivariateGaussianDistribution_ModelState_ModelCreated()
{
var pi = new double[NumberOfStates] {
0.5, 0.5
};
var tpm = new double[NumberOfStates][] { new [] { 0.5, 0.5 }, new [] { 0.5, 0.5 } };
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var emissions = CreateEmissions(series, NumberOfStates);
var model = HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <IMultivariateDistribution>()
{
Pi = pi, TransitionProbabilityMatrix = tpm, Emissions = emissions
}); //new HiddenMarkovModelMultivariateGaussianDistribution(pi, tpm, emissions);
Assert.AreEqual(ModelType.Ergodic, model.Type);
Assert.AreEqual(NumberOfStates, model.N);
Assert.IsFalse(model.Normalized);
for (int n = 0; n < NumberOfStates; n++)
{
Assert.AreEqual(0.5, model.Pi[n]);
Assert.IsNotNull(model.Emission[n]);
Assert.IsInstanceOfType(model.Emission[n], typeof(NormalDistribution));
for (int i = 0; i < NumberOfStates; i++)
{
Assert.AreEqual(0.5, model.TransitionProbabilityMatrix[n][i]);
}
}
}
public void Predict_HMMMultivariateAndFTSESeriesLength20_Predicted20Days()
{
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var test = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 12, 18), new DateTime(2012, 01, 18));
var model = (HiddenMarkovModelMultivariateGaussianDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <IMultivariateDistribution>()
{
NumberOfStates = _NumberOfStates
});
model.Normalized = true;
model.Train(series, _NumberOfIterations, _LikelihoodTolerance);
var pred = new ViterbiBasedPredictor();
var request = new PredictionRequest {
TrainingSet = series, NumberOfDays = 20, TestSet = test
};
pred.NumberOfIterations = _NumberOfIterations;
pred.LikelihoodTolerance = _LikelihoodTolerance;
var predictions = pred.Predict(model, request);
Assert.AreEqual(20, predictions.Predicted.Length);
}
public static void CreateANewProviderDataset()
{
ManageSpecificationPage managespecficationpage = new ManageSpecificationPage();
CreateSpecificationPage createspecificationpage = new CreateSpecificationPage();
ManagePoliciesPage managepoliciespage = new ManagePoliciesPage();
ChooseDatasetRelationshipPage choosedatasetrelationshippage = new ChooseDatasetRelationshipPage();
string newname = "TestDatasetName";
string descriptiontext = "This is a Datset Description for: ";
managepoliciespage.datasetsTab.Click();
managepoliciespage.Createdatatyperelationship.Should().NotBeNull();
managepoliciespage.Createdatatyperelationship.Click();
Thread.Sleep(2000);
Actions.SelectDatasetDataSchemaDropDown();
var randomDatasetName = newname + TestDataUtils.RandomString(6);
ScenarioContext.Current["DatasetSchemaName"] = randomDatasetName;
choosedatasetrelationshippage.datasetSchemaRelationshipName.SendKeys(randomDatasetName);
choosedatasetrelationshippage.datasetSchemaRelationshipDescription.SendKeys(descriptiontext + randomDatasetName);
choosedatasetrelationshippage.createDatasetSetAsDataProvider.Click();
choosedatasetrelationshippage.datasetSchemaRelationshipSaveButton.Click();
Thread.Sleep(2000);
managepoliciespage.datasetsTab.Should().NotBeNull();
Console.WriteLine(randomDatasetName + " has been created successfully");
}
public void Alpha_ErgodicAndNotNormalized_AlphaCalculated()
{
const int numberOfStates = 2;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = numberOfStates, Emissions = CreateEmissions(observations, numberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(numberOfStates, CreateEmissions(observations, numberOfStates)) { LogNormalized = true };
model.Normalized = false;
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
});
Assert.IsNotNull(alpha);
for (int i = 0; i < observations.Length; i++)
{
for (int j = 0; j < numberOfStates; j++)
{
Assert.IsTrue(alpha[i][j] > 0 && alpha[i][j] < 1, string.Format("Failed Alpha [{0}][{1}] : {2}", i, j, alpha[i][j]));
}
}
}
public void TestTopgisTMultiPatches()
{
IWorkspace workspace = TestDataUtils.OpenTopgisTlm();
IFeatureClass gebaeude =
((IFeatureWorkspace)workspace).OpenFeatureClass(
"TOPGIS_TLM.TLM_GEBAEUDE");
var test = new QaMpVertexNotNearFace(
gebaeude, new[] { gebaeude }, 1, 0.2);
test.VerifyWithinFeature = true;
test.PointCoincidence = 0.1;
test.EdgeCoincidence = 0.1;
test.IgnoreNonCoplanarFaces = true;
test.MinimumSlopeDegrees = 15;
var runner = new QaContainerTestRunner(10000, test);
runner.LogErrors = false;
IFeatureWorkspace ws =
TestWorkspaceUtils.CreateTestFgdbWorkspace("QaMpVertexNotNearFaceTest");
var logger = new SimpleErrorWorkspace(ws);
runner.TestContainer.QaError += logger.TestContainer_QaError;
runner.Execute();
Console.WriteLine(runner.Errors.Count);
}
public void Gamma_RightLeftNotNormalized_EachEntryMatrixIsSummedToOne()
{
var delta = 3;
var numberOfStatesRightLeft = 4;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = numberOfStatesRightLeft, Delta = delta, Emissions = CreateEmissions(observations, numberOfStatesRightLeft)
}); //new HiddenMarkovModelState<NormalDistribution>(numberOfStatesRightLeft, delta, CreateEmissions(observations, numberOfStatesRightLeft)) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new GammaEstimator <NormalDistribution>();
for (int i = 0; i < observations.Length; i++)
{
Assert.AreEqual(1.0d, Math.Round(estimator.Estimate(@params)[i].Sum(), 5), string.Format("Failed Gamma Component [{1}] : {0}", estimator.Estimate(@params)[i], i));
}
}
public void Initialize()
{
var util = new TestDataUtils();
// Create 2 Distributions
// Stock + Ema7, Stock + Ema28 => Data : Stock + Index
_observations = util.GetSvcData(util.MSFTFilePath, new DateTime(2011, 10, 7), new DateTime(2012, 3, 1));//SecurityDailyDataManager.GetRange("MSFT", new DateTime(2011, 10, 7), new DateTime(2012, 3, 1));
_observed = util.GetSvcData(util.MSFTFilePath, new DateTime(2012, 3, 2), new DateTime(2012, 3, 29));
var trainData1 = util.GetSvcData(util.MSFTFilePath, new DateTime(2010, 1, 1), new DateTime(2011, 1, 1));
var trainData2 = util.GetSvcData(util.MSFTFilePath, new DateTime(2009, 1, 1), new DateTime(2010, 1, 1));
var d0 = new NormalDistribution(4);
var likelihood0 = 0.0d;
_distributions[0] = (NormalDistribution)d0.Evaluate(trainData1, out likelihood0);
var d1 = new NormalDistribution(4);
var likelihood1 = 0.0d;
_distributions[1] = (NormalDistribution)d1.Evaluate(trainData2, out likelihood1);
for (var k = 0; k < K; k++)
{
_coefficients[k] = 1d / K;
}
}
public ApplySchemaChangeTestData()
{
tables = new TableConf[2];
//first table has no column list
tables[0] = new TableConf();
tables[0].SchemaName = "dbo";
tables[0].Name = "test1";
//second one has column list
tables[1] = new TableConf();
tables[1].SchemaName = "dbo";
tables[1].Name = "test2";
tables[1].ColumnList = new string[] { "column1", "column2" };
sourceDataUtils = new TestDataUtils(TServer.RELAY);
destDataUtils = new TestDataUtils(TServer.SLAVE);
testData = new DataSet();
sourceDataUtils.testData = new DataSet();
destDataUtils.testData = new DataSet();
//this method, conveniently, sets up the datatable schema we need
sourceDataUtils.CreateSchemaChangeTable("CT_testdb", 1);
Config.Tables = tables.ToList();
Config.RelayDB = "CT_testdb";
var logger = new Logger(null, null, null, "");
slave = new Slave(sourceDataUtils, destDataUtils, logger);
}
public void Ksi_ErgodicNotNormalized_EachEntryMatrixIsSummedToOne()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(NumberOfStates, CreateEmissions(observations, NumberOfStates)) { LogNormalized = true };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new KsiEstimator <NormalDistribution>();
for (int t = 0; t < observations.Length - 1; t++)
{
Assert.AreEqual(1.0d, Math.Round(estimator.Estimate(@params)[t].Sum(), 5), string.Format("Failed Ksi [{1}] :{0}", new Matrix(estimator.Estimate(@params)[t]), t));
}
}
/// <summary>
/// xunit will automatically create an instance of DDLEventTestData and pass it to this method before running tests
/// </summary>
public void SetFixture(ApplySchemaChangeTestData data)
{
this.tables = data.tables;
this.sourceDataUtils = data.sourceDataUtils;
this.destDataUtils = data.destDataUtils;
this.slave = data.slave;
}
public void Search_OneNodeAndKis3AndNis10_3Nodes()
{
var util = new TestDataUtils();
var crf = new ChangeRatioFinder();
var search = new BeamLikeSearch();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var ratios = crf.GetMaximumChangeRatios(series);
var population = new double[1][]
{
series[series.Length]
};
var k = 3;
var n = 10;
var numberOfIterations = 10;
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfComponents = _NumberOfComponents, NumberOfStates = _NumberOfStates
});
model.Normalized = true;
model.Train(series, _NumberOfIterations, _LikelihoodTolerance);
var result = search.Search(population, k, n, numberOfIterations, ratios, model);
Assert.IsNull(result);
}
public static void CreateANewSpecificationSubPolicy()
{
ManagePoliciesPage managepoliciespage = new ManagePoliciesPage();
CreateSubPolicyPage createsubpolicypage = new CreateSubPolicyPage();
string newname = "Test Sub Policy Name ";
string descriptiontext = "This is a Description for: ";
var randomSpecSubPolicyName = newname + TestDataUtils.RandomString(6);
ScenarioContext.Current["SpecSubPolicyName"] = randomSpecSubPolicyName;
managepoliciespage.CreateSubPolicy.Click();
Thread.Sleep(2000);
createsubpolicypage.SubPolicyName.SendKeys(randomSpecSubPolicyName);
Actions.SelectPolicyForSubPolicyCreationDropdownOption();
createsubpolicypage.SubPolicyDescription.Click();
Thread.Sleep(2000);
createsubpolicypage.SubPolicyDescription.SendKeys(descriptiontext + randomSpecSubPolicyName);
createsubpolicypage.SaveSubPolicy.Click();
Thread.Sleep(2000);
var specPolicyName = ScenarioContext.Current["SpecSubPolicyName"];
string specSubPolicyCreated = specPolicyName.ToString();
Console.WriteLine(specSubPolicyCreated + " has been created successfully");
Thread.Sleep(1000);
}
private static IList <QaError> GetErrors([NotNull] string tableName,
int maximumLength)
{
var locator = TestDataUtils.GetTestDataLocator();
string path = locator.GetPath("QaSchemaTests.mdb");
IFeatureWorkspace workspace = WorkspaceUtils.OpenPgdbFeatureWorkspace(path);
const int minimumValueCount = 1;
const int minimumNonEqualNameValueCount = 1;
const bool allowEmptyName = false;
ITable table = workspace.OpenTable(tableName);
var test = new QaSchemaFieldDomainCodedValues(table, maximumLength,
UniqueStringsConstraint.UniqueAnyCase,
minimumValueCount,
minimumNonEqualNameValueCount,
allowEmptyName);
var runner = new QaTestRunner(test);
runner.Execute();
return(runner.Errors);
}
public void GammaComponents_ErgodicNotNormalized_EachEntryMatrixIsSummedToOne()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStates, CreateEmissions(observations, NumberOfStates, NumberOfComponents)) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var estimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureAdvancedEstimationParameters <Mixture <IMultivariateDistribution> >
{
Alpha = alpha,
Beta = beta,
L = model.Emission[0].Components.Length,
Model = model,
Normalized = model.Normalized,
Observations = Helper.Convert(observations)
};
var gammaComponents = estimator.Estimate(@params);
for (int t = 0; t < observations.Length; t++)
{
Assert.AreEqual(1.0d, Math.Round(gammaComponents[t].Sum(), 5), string.Format("Failed Gamma Components {0} at time {1}", new Matrix(gammaComponents[t]), t));
}
}
public void Beta_ErgodicLogNormalized_BetaCalculated()
{
const int numberOfStates = 2;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = numberOfStates, Emissions = CreateEmissions(observations, numberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(numberOfStates, CreateEmissions(observations, numberOfStates)) { LogNormalized = true };
model.Normalized = true;
var estimator = new BetaEstimator <NormalDistribution>();
var beta = estimator.Estimate(new BasicEstimationParameters <NormalDistribution>()
{
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
});
Assert.IsNotNull(beta);
for (int i = 0; i < observations.Length - 1; i++)
{
for (int j = 0; j < numberOfStates; j++)
{
Assert.IsTrue(beta[i][j] < 0, string.Format("Failed Beta [{0}][{1}] : {2}", i, j, beta[i][j]));
}
}
// Last observation has probability == 1
Assert.IsTrue(beta[observations.Length - 1][0] == 0);
Assert.IsTrue(beta[observations.Length - 1][1] == 0);
}
public void MixtureGammaEstimator_Parameters_MixtureGammaComponentsAndGammaInitialized()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStates, CreateEmissions(observations, NumberOfStates, NumberOfComponents)) { LogNormalized = true };
model.Normalized = true;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var gamma = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureAdvancedEstimationParameters <Mixture <IMultivariateDistribution> >
{
Alpha = alpha,
Beta = beta,
L = model.Emission[0].Components.Length,
Model = model,
Normalized = model.Normalized,
Observations = Helper.Convert(observations)
};
Assert.IsNotNull(gamma.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >));
Assert.IsNotNull(gamma.Estimate(@params));
}
public void WhenIHaveEnteredATestNameForMyQATest()
{
var randomQATestName = qatestname + TestDataUtils.RandomString(6);
ScenarioContext.Current["QATestName"] = randomQATestName;
createqatestpage.createQATestName.Click();
createqatestpage.createQATestName.SendKeys(randomQATestName);
}
public void GivenIHaveCompletedTheRequiredQATestScenarioFields()
{
Actions.SelectSpecificationCreateQATestPage();
createqatestpage.createQATestName.Click();
createqatestpage.createQATestName.SendKeys(qatestname + TestDataUtils.RandomString(6));
createqatestpage.createQATestDescription.Click();
createqatestpage.createQATestDescription.SendKeys(qatestdescription);
Thread.Sleep(2000);
}
private static string GetTestJson(string fileName)
{
var path = TestDataUtils.GetLocalFilePath(fileName);
using (var reader = File.OpenText(path))
{
return(reader.ReadToEnd());
}
}
public void TestInitialized()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(NumberOfStates, CreateEmissions(observations, NumberOfStates)) { LogNormalized = true };
model.Normalized = true;
}
public void Sigma_ErgodicAndObservationAndLogNormalized_SigmaCalculated()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var sequence = Helper.Convert(observations);
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(NumberOfStates, CreateEmissions(observations, NumberOfStates)) { LogNormalized = true };
model.Normalized = true;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = sequence, Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = sequence,
Model = model
};
var gammaEstimator = new GammaEstimator <NormalDistribution>();
var muEstimator = new MuMultivariateEstimator <NormalDistribution>();
var estimator = new SigmaMultivariateEstimator <NormalDistribution>();
var muParams = new MuEstimationParameters <NormalDistribution>
{
Gamma = gammaEstimator.Estimate(@params),
Model = model,
Normalized = model.Normalized,
Observations = Helper.Convert(observations)
};
Assert.IsNotNull(estimator);
var sigma = estimator.Estimate(new SigmaEstimationParameters <NormalDistribution, double[][]>(muParams)
{
Mean = muEstimator.Estimate(muParams)
});
for (int n = 0; n < NumberOfStates; n++)
{
for (int i = 0; i < sequence[0].Dimention; i++)
{
for (int j = 0; j < sequence[0].Dimention; j++)
{
Assert.IsTrue(sigma[n][i, j] > 0, string.Format("Failed Sigma {0}", sigma[n][i, j]));
}
}
}
}
public void Sigma_RightLeftAndParametersAnnNotNormalized_SigmaCalculated()
{
var delta = 3;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStatesRightLeft, Delta = delta, Emissions = CreateEmissions(observations, NumberOfStatesRightLeft, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStatesRightLeft, delta, CreateEmissions(observations, NumberOfStatesRightLeft, NumberOfComponents)) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var parameters = new ParameterEstimations <Mixture <IMultivariateDistribution> >(model, Helper.Convert(observations), alpha, beta);
var sigma = new MixtureSigmaEstimator <Mixture <IMultivariateDistribution> >();
var mixtureGammaEstimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var mixtureMuEstimator = new MixtureMuEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureSigmaEstimationParameters <Mixture <IMultivariateDistribution> >
{
Model = model,
Normalized = model.Normalized,
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
L = model.Emission[0].Components.Length
};
var gamma = mixtureGammaEstimator.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >);
var gammaComponens = mixtureGammaEstimator.Estimate(@params);
@params.Gamma = gamma;
@params.GammaComponents = gammaComponens;
@params.Mu = mixtureMuEstimator.Estimate(@params);
for (int i = 0; i < NumberOfStatesRightLeft; i++)
{
for (int l = 0; l < NumberOfComponents; l++)
{
for (int rows = 0; rows < parameters.Observation[0].Dimention; rows++)
{
for (int cols = 0; cols < parameters.Observation[0].Dimention; cols++)
{
Assert.IsTrue(sigma.Estimate(@params)[i, l][rows, cols] > 0, string.Format("Failed Sigma {0}", sigma.Estimate(@params)[i, l][rows, cols]));
}
}
}
}
}
public void VerifyTOP4472_20140117_2()
{
IWorkspace ws = TestDataUtils.OpenPgdb(@"QA_Fehler\QA_Fehler2.mdb");
IFeatureClass fc =
((IFeatureWorkspace)ws).OpenFeatureClass("TLM_GEBAEUDE");
var test = new QaMpFootprintHoles(fc, InnerRingHandling.None);
var runner = new QaTestRunner(test);
runner.Execute();
Assert.AreEqual(0, runner.Errors.Count);
}
public void Estimate_KsiGammaParameters_TransitionProbabilityMatrixCalculatedAndReturned()
{
const int numberOfStates = 2;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = numberOfStates, Emissions = CreateEmissions(observations, numberOfStates)
});
model.Normalized = true;
var observationsList = new List <IObservation>();
for (var i = 0; i < observations.Length; i++)
{
observationsList.Add(new Observation(observations[i], i.ToString()));
}
var baseEstimator = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseEstimator);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseEstimator);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = observationsList,
Model = model,
Normalized = model.Normalized
};
var gammaEstimator = new GammaEstimator <NormalDistribution>();
var ksiEstimator = new KsiEstimator <NormalDistribution>();
var gamma = gammaEstimator.Estimate(@params);
var ksi = ksiEstimator.Estimate(@params);
var estimator = new TransitionProbabilityEstimator <NormalDistribution>();
var parameters = new KsiGammaTransitionProbabilityMatrixParameters <NormalDistribution>
{
Model = model,
Ksi = ksi,
Gamma = gamma,
T = observations.Length,
Normalized = model.Normalized
};
var estimatedTransitionProbabilityMatrix = estimator.Estimate(parameters);
Assert.AreEqual(1d, Math.Round(estimatedTransitionProbabilityMatrix[0][0] + estimatedTransitionProbabilityMatrix[0][1], 5));
Assert.AreEqual(1d, Math.Round(estimatedTransitionProbabilityMatrix[1][0] + estimatedTransitionProbabilityMatrix[1][1], 5));
}
public void Denormalized_NormalizedEstimator_SigmaDenormalized()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStates, CreateEmissions(observations, NumberOfStates, NumberOfComponents)) { LogNormalized = true };
model.Normalized = true;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var parameters = new ParameterEstimations <Mixture <IMultivariateDistribution> >(model, Helper.Convert(observations), alpha, beta);
var coefficients = new MixtureCoefficientsEstimator <Mixture <IMultivariateDistribution> >();
var mixtureGammaEstimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureCoefficientEstimationParameters <Mixture <IMultivariateDistribution> >
{
Model = model,
Normalized = model.Normalized,
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
L = model.Emission[0].Components.Length
};
var gamma = mixtureGammaEstimator.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >);
var gammaComponens = mixtureGammaEstimator.Estimate(@params);
@params.Gamma = gamma;
@params.GammaComponents = gammaComponens;
for (int i = 0; i < NumberOfStates; i++)
{
for (int l = 0; l < NumberOfComponents; l++)
{
Assert.IsTrue(coefficients.Estimate(@params)[i][l] < 0, string.Format("Failed Coefficients {0}", coefficients.Estimate(@params)[i][l]));
}
}
coefficients.Denormalize();
for (int i = 0; i < NumberOfStates; i++)
{
for (int l = 0; l < NumberOfComponents; l++)
{
Assert.IsTrue(coefficients.Estimate(@params)[i][l] > 0 && coefficients.Estimate(@params)[i][l] < 1, string.Format("Failed Coefficients {0}", coefficients.Estimate(@params)[i][l]));
}
}
}
