public override void TrainningModel(TrainningData trainningData)
{
this.possiveValue = trainningData.PositiveValue;
this.negativeValue = trainningData.NegativeValue;
HuanLuyenBayes(trainningData.TrainningAttributes.GetLength(0));
}
//public SupportVectorMachine SVM
//{
// get { return svm; }
// private set { svm = value; }
//}
public override void TrainningModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] outputs = (int[])trainningData.ClassificationAttribute.Clone();
// Create output for SVM (-1 or 1)
for (int index = 0; index < outputs.Length; index++)
{
if (outputs[index] == 0)
{
outputs[index] = -1;
}
}
// Create a Support Vector Machine for the given inputs
//this.svm = new SupportVectorMachine(inputs[0].Length);
//// Create a Kernel Support Vector Machine for the given inputs
this.svm = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, outputs);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run the learning algorithm
double error = smo.Run();
}
public override void TrainningModel(TrainningData trainningData)
{
this.possiveValue = trainningData.PositiveValue;
this.negativeValue = trainningData.NegativeValue;
HuanLuyenBayes(trainningData.TrainningAttributes.GetLength(0));
}
//public SupportVectorMachine SVM
//{
// get { return svm; }
// private set { svm = value; }
//}
public override void TrainningModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] outputs = (int[])trainningData.ClassificationAttribute.Clone();
// Create output for SVM (-1 or 1)
for (int index = 0; index < outputs.Length; index++)
{
if (outputs[index] == 0)
{
outputs[index] = -1;
}
}
// Create a Support Vector Machine for the given inputs
//this.svm = new SupportVectorMachine(inputs[0].Length);
//// Create a Kernel Support Vector Machine for the given inputs
this.svm = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, outputs);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run the learning algorithm
double error = smo.Run();
}
public virtual List<ConfusionMatrix> TestModel(TrainningData trainningData)
{
int[] expected = trainningData.ClassificationAttribute;
int[] predicted = ComputeModel(trainningData.TrainningAttributes);
int positiveValue = trainningData.PositiveValue;
int negativeValue = trainningData.NegativeValue;
ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, expected, positiveValue, negativeValue);
return new List<ConfusionMatrix> { confusionMatrix };
}
public virtual List <ConfusionMatrix> TestModel(TrainningData trainningData)
{
int[] expected = trainningData.ClassificationAttribute;
int[] predicted = ComputeModel(trainningData.TrainningAttributes);
int positiveValue = trainningData.PositiveValue;
int negativeValue = trainningData.NegativeValue;
ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, expected, positiveValue, negativeValue);
return(new List <ConfusionMatrix> {
confusionMatrix
});
}
// buttonXCreateModel click event
private void buttonXCreateModel_Click(object sender, EventArgs e)
{
DialogResult dialogResult;
DataTable orginalTrainningTable = this.dataGridViewXTrainning.DataSource as DataTable;
DataTable tableForTrainning;
int numberOfTrainningRows = 0;
// Get number of row form percent
if (this.comboBoxExTrainningDataPercent.SelectedValue != null)
{
numberOfTrainningRows = Convert.ToInt32(Math.Truncate(
((int)comboBoxExTrainningDataPercent.SelectedValue * 1.0 * orginalTrainningTable.Rows.Count) / 100));
}
// Set trainning data
if (numberOfTrainningRows == 0)
{
tableForTrainning = orginalTrainningTable;
}
else
{
var query = orginalTrainningTable.AsEnumerable().Take(numberOfTrainningRows);
tableForTrainning = query.CopyToDataTable<DataRow>();
}
TrainningData trainningData = new TrainningData(tableForTrainning, this.codification);
Properties.Settings.Default.negativeValue = trainningData.NegativeValue;
// Ask user what to do when selected model already exists
if (this.HaveModel())
{
dialogResult = MessageBox.Show("Mô hình này đã có. Bạn có muốn tạo mô hình mới?", "Lỗi",
MessageBoxButtons.YesNo, MessageBoxIcon.Warning);
if (dialogResult == DialogResult.No || dialogResult == DialogResult.Cancel)
{
return;
}
}
// Check dictionary already have active model. If not add new model
if (!this.modelList.ContainsKey(this.activeLearningAlgorithm))
{
this.modelList.Add(this.activeLearningAlgorithm, ClassificationModel.CreateModel(this.activeLearningAlgorithm));
}
// Trainning model
this.modelList[activeLearningAlgorithm].TrainningModel(trainningData);
}
public override List<ConfusionMatrix> TestModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] expected = trainningData.ClassificationAttribute;
int[] predicted = ComputeModel(inputs);
int positiveValue = trainningData.PositiveValue;
int negativeValue = trainningData.NegativeValue;
ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, expected, positiveValue, negativeValue);
return new List<ConfusionMatrix> { confusionMatrix };
}
public override void TrainningModel(TrainningData trainningData)
{
Codification codification = trainningData.CodificationData;
int[][] inputs = trainningData.TrainningAttributes.ToInt32();
int[] outputs = trainningData.ClassificationAttribute;
int lastIndex = codification.Columns.Count - 1;
List<int> symbolCounts = new List<int>();
for (int indexColumn = 0; indexColumn < lastIndex; indexColumn++)
{
symbolCounts.Add(codification[indexColumn].Symbols);
}
this.bayes = new NaiveBayes(2, symbolCounts.ToArray());
this.bayes.Estimate(inputs, outputs);
}
//public static C45Model CreateC45Model(Codification codification)
//{
// int lastIndex = codification.Columns.Count - 1;
// List<DecisionVariable> attributes = new List<DecisionVariable>();
// for (int indexColumn = 0; indexColumn < lastIndex; indexColumn++)
// {
// attributes.Add(new DecisionVariable(codification.Columns[indexColumn].ColumnName,
// codification[indexColumn].Symbols));
// }
// C45Model model = new C45Model(new DecisionTree(attributes.ToArray(), 2));
// return model;
//}
//public C45Model(DecisionTree tree)
//{
// this.Tree = tree;
//}
// Trainning decision tree with C4.5 algorithm
public override void TrainningModel(TrainningData trainningData)
{
// Get data for trainning tree
Codification codification = trainningData.CodificationData;
double[][] inputs = trainningData.TrainningAttributes;
int[] outputs = trainningData.ClassificationAttribute;
// Create tree
this.Tree = this.CreateDecisionTree(codification);
//var attributes = DecisionVariable.FromCodebook(codification, inputColumns);
//DecisionTree tree = new DecisionTree(attributes, outputClasses: 5);
// Creates a new instance of the C4.5 learning algorithm
C45Learning c45 = new C45Learning(this.Tree);
// Learn the decision tree
double error = c45.Run(inputs, outputs);
}
public override void TrainningModel(TrainningData trainningData)
{
Codification codification = trainningData.CodificationData;
int[][] inputs = trainningData.TrainningAttributes.ToInt32();
int[] outputs = trainningData.ClassificationAttribute;
int lastIndex = codification.Columns.Count - 1;
List <int> symbolCounts = new List <int>();
for (int indexColumn = 0; indexColumn < lastIndex; indexColumn++)
{
symbolCounts.Add(codification[indexColumn].Symbols);
}
this.bayes = new NaiveBayes(2, symbolCounts.ToArray());
this.bayes.Estimate(inputs, outputs);
}
//public static C45Model CreateC45Model(Codification codification)
//{
// int lastIndex = codification.Columns.Count - 1;
// List<DecisionVariable> attributes = new List<DecisionVariable>();
// for (int indexColumn = 0; indexColumn < lastIndex; indexColumn++)
// {
// attributes.Add(new DecisionVariable(codification.Columns[indexColumn].ColumnName,
// codification[indexColumn].Symbols));
// }
// C45Model model = new C45Model(new DecisionTree(attributes.ToArray(), 2));
// return model;
//}
//public C45Model(DecisionTree tree)
//{
// this.Tree = tree;
//}
// Trainning decision tree with C4.5 algorithm
public override void TrainningModel(TrainningData trainningData)
{
// Get data for trainning tree
Codification codification = trainningData.CodificationData;
double[][] inputs = trainningData.TrainningAttributes;
int[] outputs = trainningData.ClassificationAttribute;
// Create tree
this.Tree = this.CreateDecisionTree(codification);
//var attributes = DecisionVariable.FromCodebook(codification, inputColumns);
//DecisionTree tree = new DecisionTree(attributes, outputClasses: 5);
// Creates a new instance of the C4.5 learning algorithm
C45Learning c45 = new C45Learning(this.Tree);
// Learn the decision tree
double error = c45.Run(inputs, outputs);
}
public override List <ConfusionMatrix> TestModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] expected = trainningData.ClassificationAttribute;
int[] predicted = ComputeModel(inputs);
int positiveValue = trainningData.PositiveValue;
int negativeValue = trainningData.NegativeValue;
ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, expected, positiveValue, negativeValue);
return(new List <ConfusionMatrix> {
confusionMatrix
});
}
public abstract void TrainningModel(TrainningData trainningData);
// buttonXTestModel click event
private void buttonXTestModel_Click(object sender, EventArgs e)
{
int numberOfTrainningRows = 0;
DataTable orginalTrainningTable = this.dataGridViewXTrainning.DataSource as DataTable;
DataTable testTable;
// Get number of row form percent
if (this.comboBoxExTrainningDataPercent.SelectedValue != null)
{
numberOfTrainningRows = Convert.ToInt32(Math.Truncate(
((int)comboBoxExTrainningDataPercent.SelectedValue * 1.0 * orginalTrainningTable.Rows.Count) / 100));
}
// Set test data
var query = orginalTrainningTable.AsEnumerable().Skip(numberOfTrainningRows);
testTable = query.CopyToDataTable<DataRow>();
if (!HaveModel())
{
MessageBox.Show("Chưa có mô hình!", "Lỗi", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
TrainningData data = new TrainningData(testTable, this.codification);
// Show test result
dataGridViewXTrainningResult.DataSource = this.modelList[activeLearningAlgorithm].TestModel(data);
List<int> hideColumns = new List<int>(new int[] { 9, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 });
for (int indexColum = 0; indexColum < this.dataGridViewXTrainningResult.Columns.Count; indexColum++)
{
if (hideColumns.Contains(indexColum))
{
this.dataGridViewXTrainningResult.Columns[indexColum].Visible = false;
}
}
}
public abstract void TrainningModel(TrainningData trainningData);
