public ClassifierWReview()
{
//runData2 and runData2_1
//string filedata = System.IO.File.ReadAllText("../runData2.txt");
string filedata = System.IO.File.ReadAllText("../runData2_1.txt");
string[] inputColumns =
{
"Day", "Outlook", "Temperature", "Humidity", "Wind", "SprintReview"
};
string outputColumn = "GoRun";
DataTable data = new DataTable("Internet Services Run Calculator");
data.Columns.Add(inputColumns);
data.Columns.Add(outputColumn);
string[] lines = filedata.Split(
new[] { "\r\n" }, StringSplitOptions.RemoveEmptyEntries);
foreach (var line in lines)
{
data.Rows.Add(line.Split(','));
}
//create codebook to turn the strings into number representations
codebook = new Accord.Statistics.Filters.Codification(data);
// Translate our training data into integer symbols using our codebook:
DataTable symbols = codebook.Apply(data);
int[][] inputs = symbols.ToJagged <int>("Outlook", "Temperature", "Humidity", "Wind", "SprintReview");
int[] outputs = symbols.ToArray <int>("GoRun");
string[] decisionVariables = { "Outlook", "Temperature", "Humidity", "Wind", "SprintReview" };
DecisionVariable[] attributes = DecisionVariable.FromCodebook(codebook, decisionVariables);
// Create a teacher ID3 algorithm
var id3learning = new ID3Learning(attributes);
tree = id3learning.Learn(inputs, outputs);
// Compute the training error when predicting training instances
double error = new ZeroOneLoss(outputs).Loss(tree.Decide(inputs));
}
//4, 10, 2, 1, 0, 0, 0, 0
public string BankPrediction(int a, int b, int c, int d, int e, int f, int g, int h, string name)
{
string salida = "Vacio";
try
{
var codebook = new Accord.Statistics.Filters.Codification(table);
System.Data.DataTable symbols = codebook.Apply(table);
int[][] inputs = DataTableToMatrix(symbols, new string[] { "AGE", "JOB", "MARITAL", "EDUCATION", "DEBT", "BALANCE", "HOUSING", "LOAN" });
int[][] outputs = DataTableToMatrix(symbols, new string[] { "DEPOSIT" });
int[] output = new int[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
output[i] = outputs[i][0];
}
ID3Learning teacher = new ID3Learning()
{
new DecisionVariable("AGE", 5),
new DecisionVariable("JOB", 12),
new DecisionVariable("MARITAL", 3),
new DecisionVariable("EDUCATION", 4),
new DecisionVariable("DEBT", 2),
new DecisionVariable("BALANCE", 6),
new DecisionVariable("HOUSING", 2),
new DecisionVariable("LOAN", 2)
};
DecisionTree tree = teacher.Learn(inputs, output);
//mandar la variable error a un label que me muestre el error que tiene el arbol
double error = new Accord.Math.Optimization.Losses.ZeroOneLoss(output).Loss(tree.Decide(inputs));
double ep = Math.Floor(error * 100);
errorLabel.Text = ep + "%" + " " + "-" + " " + "(" + error + ")";
int[] input = new int[] { a, b, c, d, e, f, g, h };
int prediccion = tree.Decide(input);
string predijo = prediccion == 1 ? "yes" : "no";
if (predijo.Equals("yes"))
{
outputLabel.ForeColor = Color.FromArgb(0, 255, 0);
}
else if (predijo.Equals("no"))
{
outputLabel.ForeColor = Color.FromArgb(255, 0, 0);
}
outputLabel.Text = predijo;
salida = predijo;
subjectLabel.Text = "for" + " " + name + " " + "the prediction is";
}
catch (Exception) {
string message = "Yo cannot make predictions without\nloading the data";
string title = "Warning";
MessageBox.Show(message, title);
}
return(salida);
}
