public void TestTwoAttributeAxes()
{
List <DataPoint> dataPoints = new List <DataPoint> {
new DataPoint {
Class = 1,
Attributes = new float[] { 123, 987 },
Categories = new uint[] { },
Weight = 1.0f,
},
new DataPoint {
Class = 0,
Attributes = new float[] { 123, 876 },
Categories = new uint[] { },
Weight = 1.0f,
},
new DataPoint {
Class = 1,
Attributes = new float[] { 456, 765 },
Categories = new uint[] { },
Weight = 1.0f,
},
new DataPoint {
Class = 1,
Attributes = new float[] { 456, 543 },
Categories = new uint[] { },
Weight = 1.0f,
},
};
using (CudaManager cudaManager = Provider.CudaManagerPool.GetCudaManagerForThread(Provider.Logger))
using (DecisionLearner decisionLearner = new DecisionLearner(cudaManager, dataPoints)) {
IDataNode root = decisionLearner.FitDecisionTree().Node;
Assert.AreEqual(1.0, Accuracy(root, dataPoints));
}
}
public void TestSimpleAttributeSplit()
{
List <DataPoint> dataPoints = new List <DataPoint> {
new DataPoint {
Class = 0,
Attributes = new float[] { 123 },
Categories = new uint[] { },
Weight = 1.0f,
},
new DataPoint {
Class = 1,
Attributes = new float[] { 456 },
Categories = new uint[] { },
Weight = 1.0f,
},
};
using (CudaManager cudaManager = Provider.CudaManagerPool.GetCudaManagerForThread(Provider.Logger))
using (DecisionLearner decisionLearner = new DecisionLearner(cudaManager, dataPoints)) {
IDataNode root = decisionLearner.FitDecisionTree().Node;
AttributeSplit attributeSplit = (AttributeSplit)root;
Assert.AreEqual(0, attributeSplit.Axis);
Assert.AreEqual(456, attributeSplit.SplitValue);
DataLeaf left = (DataLeaf)attributeSplit.Left;
Assert.AreEqual(1, left.ClassDistribution[0]);
Assert.AreEqual(0, left.ClassDistribution[1]);
DataLeaf right = (DataLeaf)attributeSplit.Right;
Assert.AreEqual(0, right.ClassDistribution[0]);
Assert.AreEqual(1, right.ClassDistribution[1]);
Assert.AreEqual(1.0, Accuracy(root, dataPoints));
}
}
public void TestCategoryPlusAttributeSplit()
{
List <DataPoint> dataPoints = new List <DataPoint> {
new DataPoint {
Class = 1,
Attributes = new float[] { 99, 987 },
Categories = new uint[] { 0x3, 0x1 },
Weight = 1.0f,
},
new DataPoint {
Class = 0,
Attributes = new float[] { 99, 987 },
Categories = new uint[] { 0x3, 0x1 | 0x2 },
Weight = 1.0f,
},
new DataPoint {
Class = 1,
Attributes = new float[] { 99, 765 },
Categories = new uint[] { 0x3, 0x1 | 0x2 },
Weight = 1.0f,
},
new DataPoint {
Class = 1,
Attributes = new float[] { 99, 765 },
Categories = new uint[] { 0x3, 0x1 },
Weight = 1.0f,
},
};
using (CudaManager cudaManager = Provider.CudaManagerPool.GetCudaManagerForThread(Provider.Logger))
using (DecisionLearner decisionLearner = new DecisionLearner(cudaManager, dataPoints)) {
IDataNode root = decisionLearner.FitDecisionTree().Node;
Assert.AreEqual(1.0, Accuracy(root, dataPoints));
}
}
