public void ConstantDiscreteVariableTest()
{
DecisionTree tree;
int[][] inputs;
int[] outputs;
DataTable data = new DataTable("Degenerated Tennis Example");
data.Columns.Add("Day", "Outlook", "Temperature", "Humidity", "Wind", "PlayTennis");
data.Rows.Add("D1", "Sunny", "Hot", "High", "Weak", "No");
data.Rows.Add("D2", "Sunny", "Hot", "High", "Strong", "No");
data.Rows.Add("D3", "Overcast", "Hot", "High", "Weak", "Yes");
data.Rows.Add("D4", "Rain", "Hot", "High", "Weak", "Yes");
data.Rows.Add("D5", "Rain", "Hot", "Normal", "Weak", "Yes");
data.Rows.Add("D6", "Rain", "Hot", "Normal", "Strong", "No");
data.Rows.Add("D7", "Overcast", "Hot", "Normal", "Strong", "Yes");
data.Rows.Add("D8", "Sunny", "Hot", "High", "Weak", "No");
data.Rows.Add("D9", "Sunny", "Hot", "Normal", "Weak", "Yes");
data.Rows.Add("D10", "Rain", "Hot", "Normal", "Weak", "Yes");
data.Rows.Add("D11", "Sunny", "Hot", "Normal", "Strong", "Yes");
data.Rows.Add("D12", "Overcast", "Hot", "High", "Strong", "Yes");
data.Rows.Add("D13", "Overcast", "Hot", "Normal", "Weak", "Yes");
data.Rows.Add("D14", "Rain", "Hot", "High", "Strong", "No");
// Create a new codification codebook to
// convert strings into integer symbols
Codification codebook = new Codification(data);
DecisionVariable[] attributes =
{
new DecisionVariable("Outlook", codebook["Outlook"].Symbols), // 3 possible values (Sunny, overcast, rain)
new DecisionVariable("Temperature", codebook["Temperature"].Symbols), // 1 constant value (Hot)
new DecisionVariable("Humidity", codebook["Humidity"].Symbols), // 2 possible values (High, normal)
new DecisionVariable("Wind", codebook["Wind"].Symbols) // 2 possible values (Weak, strong)
};
int classCount = codebook["PlayTennis"].Symbols; // 2 possible values (yes, no)
bool thrown = false;
try
{
tree = new DecisionTree(attributes, classCount);
}
catch
{
thrown = true;
}
Assert.IsTrue(thrown);
attributes[1] = new DecisionVariable("Temperature", 2);
tree = new DecisionTree(attributes, classCount);
ID3Learning id3 = new ID3Learning(tree);
// Extract symbols from data and train the classifier
DataTable symbols = codebook.Apply(data);
inputs = symbols.ToArray<int>("Outlook", "Temperature", "Humidity", "Wind");
outputs = symbols.ToArray<int>("PlayTennis");
double error = id3.Run(inputs, outputs);
Assert.AreEqual(0, error);
for (int i = 0; i < inputs.Length; i++)
{
int y = tree.Compute(inputs[i]);
Assert.AreEqual(outputs[i], y);
}
}