public void Build4dDataSet()
{
var data = new[]
{
new double [] { 1, 2, 2, 1 },
new double [] { 1, 2, 3, 2 },
new double [] { 1, 2, 2, 3 },
new double [] { 2, 2, 2, 1 },
new double [] { 2, 3, 2, 2 },
new double [] { 1, 3, 2, 1 },
new double [] { 1, 2, 3, 1 },
new double [] { 2, 3, 1, 2 },
new double [] { 1, 2, 2, 2 },
new double [] { 1, 1, 3, 2 },
new double [] { 2, 1, 2, 2 },
new double [] { 1, 1, 2, 3 }
};
var classes = new[]
{
1, 1, 1, 1, 2,
1, 2, 1, 1, 1,
2, 1
};
var tree = DecisionTree.Build(data, classes);
Assert.NotNull(tree);
Assert.NotNull(tree.Root);
}
public void Build2dDataSet()
{
// Built by estimating from the sample set in https://victorzhou.com/blog/gini-impurity/#picking-the-best-split
var data = new[]
{
// Class 1
new [] { 0.2, 1.5 },
new [] { 0.5, 0.2 },
new [] { 0.6, 1.2 },
new [] { 1.0, 2.3 },
new [] { 1.8, 0.3 },
// Class 2
new [] { 2.3, 1.6 },
new [] { 2.4, 1.4 },
new [] { 2.5, 3.1 },
new [] { 2.5, 0.3 },
new [] { 2.9, 2.1 }
};
var classes = new[]
{
1, 1, 1, 1, 1,
2, 2, 2, 2, 2
};
var tree = DecisionTree.Build(data, classes);
Assert.NotNull(tree);
Assert.NotNull(tree.Root);
Assert.False(tree.IsEmpty);
Assert.True(tree.Root.IsLeaf);
Assert.Null(tree.Root.Left);
Assert.Null(tree.Root.Right);
var predictionA = tree.Predict(new[] { 0.7, 5 });
var predictionB = tree.Predict(new[] { 6, 0.3 });
Assert.Equal(1, predictionA);
Assert.Equal(2, predictionB);
}