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);
}