private void Window_Loaded(object sender, RoutedEventArgs e) { DataSetFactory.SearchFolder("."); // read input data var dataset = sds.DataSet.Open("Tutorial2.csv?inferDims=true&appendMetadata=true"); if (!dataset.Any(var => var.Name == "Model")) { var x = dataset.GetData <double[]>("X"); var y = dataset.GetData <double[]>("Observation"); // compute var xm = x.Sum() / x.Length; var ym = y.Sum() / y.Length; double a = 0, d = 0; for (int i = 0; i < x.Length; i++) { a += (x[i] - xm) * (y[i] - ym); d += (x[i] - xm) * (x[i] - xm); } a /= d; var b = ym - a * xm; var model = x.Select(xx => a * xx + b).ToArray(); // var varid = dataset.Add <double[]>("Model", dataset.Dimensions[0].Name).ID; dataset.PutData <double[]>(varid, model); } Viewer.DataSet = dataset; }
public void GetAnswer_Success() { DataSetFactory dataSetFactory = new DataSetFactory(); AnswerResponse answerResponse = dataSetFactory.GetAndPostAnswer().Result; Assert.IsTrue(answerResponse.Success); }
internal static void backPropogationDemo() { try { DataSet irisDataSet = DataSetFactory.getIrisDataSet(); INumerizer numerizer = new IrisDataSetNumerizer(); NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet(); innds.CreateExamplesFromDataSet(irisDataSet, numerizer); NeuralNetworkConfig config = new NeuralNetworkConfig(); config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4); config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3); config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6); config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0); config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0); FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config); ffnn.SetTrainingScheme(new BackPropagationLearning(0.1, 0.9)); ffnn.TrainOn(innds, 1000); innds.RefreshDataset(); int[] result = ffnn.TestOnDataSet(innds); System.Console.WriteLine(result[0] + " right, " + result[1] + " wrong"); } catch (Exception e) { throw e; } }
public void testLoadsIrisDataSetWithNumericAndStringAttributes() { DataSet ds = DataSetFactory.getIrisDataSet(); Example first = ds.getExample(0); Assert.AreEqual("5,1", first.getAttributeValueAsString("sepal_length")); }
static void decisionListDemo() { try { DataSet ds = DataSetFactory.getRestaurantDataSet(); DecisionListLearner learner = new DecisionListLearner("Yes", "No", new DecisionListTestFactory()); learner.Train(ds); System.Console.WriteLine("The Induced DecisionList is"); System.Console.WriteLine(learner.getDecisionList()); int[] result = learner.Test(ds); System.Console.WriteLine("\nThis Decision List classifies the data set with " + result[0] + " successes" + " and " + result[1] + " failures"); System.Console.WriteLine("\n"); } catch (Exception e) { System.Console.WriteLine("Decision ListDemo Failed"); throw e; } }
public void testDataSetPopulation() { DataSet irisDataSet = DataSetFactory.getIrisDataSet(); INumerizer numerizer = new IrisDataSetNumerizer(); NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet(); innds.CreateExamplesFromDataSet(irisDataSet, numerizer); NeuralNetworkConfig config = new NeuralNetworkConfig(); config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4); config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3); config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6); config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0); config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0); FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config); ffnn.SetTrainingScheme(new BackPropagationLearning(0.1, 0.9)); ffnn.TrainOn(innds, 10); innds.RefreshDataset(); ffnn.TestOnDataSet(innds); }
public void testDecisonListWithNoTestsReturnsDefaultValue() { DecisionList dlist = new DecisionList("Yes", "No"); DataSet ds = DataSetFactory.getRestaurantDataSet(); Assert.AreEqual("No", dlist.predict(ds.getExample(0))); }
internal ConstDataSet LoadDataSetFromXml() { var ds = new ConstDataSet(); ds.ReadXml(new StringReader(DataSetXml), XmlReadMode.Auto); if (DataSetFactory != null) { var dsWithSchema = new ConstDataSet(); foreach (DataTable t in ds.Tables) { var schemaTblName = t.TableName; if (SchemaMapping.ContainsKey(schemaTblName)) { schemaTblName = SchemaMapping[schemaTblName]; } var tblDs = DataSetFactory.GetDataSet(schemaTblName); if (tblDs != null) { var schemaTbl = tblDs.Tables[schemaTblName].Clone(); schemaTbl.TableName = t.TableName; dsWithSchema.Tables.Add(schemaTbl); } } dsWithSchema.ReadXml(new StringReader(DataSetXml), XmlReadMode.Auto); } return(ds); }
public void addNewDataSet() { DataSet newDataSet = DataSetFactory.create(Globals.ExcelAddIn.getActiveWorksheet(), null, "Data Set " + (model.numberOfDataSets() + 1), COLUMNS, true); model.addDataSet(newDataSet); view.selectDataSet(newDataSet); }
private void addDataSetOfCurrentSelection() { _Worksheet workSheet = Globals.ExcelAddIn.getActiveWorksheet(); Range range = Globals.ExcelAddIn.getCurrentSelectionRange(); if (model.hasIntersectionWith(range)) { if (DataSetManagerForm.ignoreIntersection(model.getFirstIntersectingDataSetWith(range))) { return; } } if (range.Rows.Count == 1 && range.Columns.Count == 1) { range = Globals.ExcelAddIn.getExpandedCurrentRange(); range.Select(); } bool addNewDataSet = (range.Rows.Count != 1 || range.Columns.Count != 1) && DataSetManagerForm.addNewDataSet(range); if (!addNewDataSet) { return; } DataSet newDataSet = DataSetFactory.create(workSheet, range, "Data Set " + (model.numberOfDataSets() + 1), COLUMNS, true); model.addDataSet(newDataSet); view.selectDataSet(newDataSet); }
static void ensembleLearningDemo() { try { DataSet ds = DataSetFactory.getRestaurantDataSet(); ICollection <DecisionTree> stumps = DecisionTree.getStumpsFor(ds, "Yes", "No"); ICollection <ILearner> learners = CollectionFactory.CreateQueue <ILearner>(); System.Console.WriteLine("\nStump Learners vote to decide in this algorithm"); foreach (object stump in stumps) { DecisionTree sl = (DecisionTree)stump; StumpLearner stumpLearner = new StumpLearner(sl, "No"); learners.Add(stumpLearner); } AdaBoostLearner learner = new AdaBoostLearner(learners, ds); learner.Train(ds); var answer = learner.Predict(ds.getExample(0)); int[] result = learner.Test(ds); System.Console.WriteLine("\nThis Ensemble Learner classifies the data set with " + result[0] + " successes" + " and " + result[1] + " failures"); System.Console.WriteLine("\n"); } catch (Exception e) { throw e; } }
public void testDecisionList() { DataSet ds = DataSetFactory.getRestaurantDataSet(); List <DLTest> dlTests = new DLTestFactory() .createDLTestsWithAttributeCount(ds, 1); Assert.AreEqual(26, dlTests.Count); }
public MainWindow() { DataSetFactory.SearchFolder(Environment.CurrentDirectory); InitializeComponent(); InitModel(); dsvc.DataSet = dataset; }
public void testNonDestructiveRemoveExample() { DataSet ds1 = DataSetFactory.getRestaurantDataSet(); DataSet ds2 = ds1.removeExample(ds1.getExample(0)); Assert.AreEqual(12, ds1.size()); Assert.AreEqual(11, ds2.size()); }
public void testStumpCreationForDataSet() { DataSet ds = DataSetFactory.getRestaurantDataSet(); List <DecisionTree> dt = DecisionTree.getStumpsFor(ds, YES, "Unable to classify"); Assert.AreEqual(26, dt.Count); }
public void testDecisionList() { DataSet ds = DataSetFactory.getRestaurantDataSet(); ICollection <aima.net.learning.inductive.DecisionListTest> dlTests = new DecisionListTestFactory() .createDLTestsWithAttributeCount(ds, 1); Assert.AreEqual(26, dlTests.Size()); }
public void testDLTestMatchFailsOnMismatchedExample() { DataSet ds = DataSetFactory.getRestaurantDataSet(); Example e = ds.getExample(0); aima.net.learning.inductive.DecisionListTest test = new aima.net.learning.inductive.DecisionListTest(); test.add("type", "Thai"); Assert.IsFalse(test.matches(e)); }
public void testDLTestMatchSucceedsWithMatchedExample() { DataSet ds = DataSetFactory.getRestaurantDataSet(); Example e = ds.getExample(0); DLTest test = new DLTest(); test.add("type", "French"); Assert.IsTrue(test.matches(e)); }
public void HasRoleは引数のクラスがDataSetでありかつInitializeで指定されたテストケースにパラメタセクションがあればtrueを返す() { // setup Sheet sheet = TempActors.Book.GetSheet("HasRole"); DataSetFactory factory = GetDataSetFactory(sheet.GetCase("ロールあり")); // expect Assert.IsTrue(factory.HasRole <DataSet>(null)); }
public void HasRoleは引数のクラスがDictionaryでなければfalseを返す() { // setup Sheet sheet = TempActors.Book.GetSheet("HasRole"); DataSetFactory factory = GetDataSetFactory(sheet.GetCase("ロールあり")); // expect Assert.IsFalse(factory.HasRole <object>(null)); }
public void HasRoleはInitializeで指定されたテストケースにパラメタセクションがなければfalseを返す() { // setup Sheet sheet = TempActors.Book.GetSheet("HasRole"); DataSetFactory factory = GetDataSetFactory(sheet.GetCase("ロールなし")); // expect Assert.IsFalse(factory.HasRole <object>(null)); }
public void testDLTestMatchFailsOnMismatchedExample() { DataSet ds = DataSetFactory.getRestaurantDataSet(); Example e = ds.getExample(0); DLTest test = new DLTest(); test.add("type", "Thai"); Assert.IsFalse(test.matches(e)); }
public void testDLTestMatchesEvenOnMismatchedTargetAttributeValue() { DataSet ds = DataSetFactory.getRestaurantDataSet(); Example e = ds.getExample(0); DLTest test = new DLTest(); test.add("type", "French"); Assert.IsTrue(test.matches(e)); }
public void testInducedDecisionTreeClassifiesRestaurantDataSetCorrectly() { DecisionTreeLearner learner = new DecisionTreeLearner( createInducedRestaurantDecisionTree(), "Unable to clasify"); int[] results = learner.test(DataSetFactory.getRestaurantDataSet()); Assert.AreEqual(12, results[0]); Assert.AreEqual(0, results[1]); }
public void testGainCalculation() { DataSet ds = DataSetFactory.getRestaurantDataSet(); double gain = ds.calculateGainFor("patrons"); Assert.AreEqual(0.541, gain, 0.001); gain = ds.calculateGainFor("type"); Assert.AreEqual(0.0, gain, 0.001); }
public void testDLTestMatchSucceedsWithMatchedExample() { DataSet ds = DataSetFactory.getRestaurantDataSet(); Example e = ds.getExample(0); aima.net.learning.inductive.DecisionListTest test = new aima.net.learning.inductive.DecisionListTest(); test.add("type", "French"); Assert.IsTrue(test.matches(e)); }
public void testMajorityLearner() { MajorityLearner learner = new MajorityLearner(); DataSet ds = DataSetFactory.getRestaurantDataSet(); learner.train(ds); int[] result = learner.test(ds); Assert.AreEqual(6, result[0]); Assert.AreEqual(6, result[1]); }
public void testInducedTreeClassifiesDataSetCorrectly() { DataSet ds = DataSetFactory.getRestaurantDataSet(); DecisionTreeLearner learner = new DecisionTreeLearner(); learner.train(ds); int[] result = learner.test(ds); Assert.AreEqual(12, result[0]); Assert.AreEqual(0, result[1]); }
public void testBasicDataSetInformationCalculation() { DataSet ds = DataSetFactory.getRestaurantDataSet(); double infoForTargetAttribute = ds.getInformationFor();// this should // be the // generic // distribution Assert.AreEqual(1.0, infoForTargetAttribute, 0.001); }
public void setVariableNamesInFirstRowOrColumn(DataSet dataSet, bool variableNamesInFirstRowOrColumn) { if (dataSet == null) { return; } DataSet newDataSet = DataSetFactory.create(dataSet.getWorksheet(), dataSet.getRange(), dataSet.getName(), dataSet.getRangeLayout(), variableNamesInFirstRowOrColumn); model.swapDataSets(dataSet, newDataSet); }