private static void repeat(double[][] inputs, int[] outputs,
DecisionTree tree, int training, double threshold,
out int nodeCount2)
{
int nodeCount = 0;
foreach (var node in tree)
{
nodeCount++;
}
var pruningInputs = inputs.Submatrix(training, inputs.Length - 1);
var pruningOutputs = outputs.Submatrix(training, inputs.Length - 1);
ErrorBasedPruning prune = new ErrorBasedPruning(tree, pruningInputs, pruningOutputs);
prune.Threshold = threshold;
double lastError;
double error = Double.PositiveInfinity;
do
{
lastError = error;
error = prune.Run();
} while (error < lastError);
nodeCount2 = 0;
foreach (var node in tree)
{
nodeCount2++;
}
}
public void RunTest()
{
Accord.Math.Random.Generator.Seed = 0;
double[][] inputs;
int[] outputs;
int trainingSamplesCount = 6000;
DecisionTree tree = ReducedErrorPruningTest.createNurseryExample(out inputs, out outputs, trainingSamplesCount);
int nodeCount = 0;
foreach (var node in tree)
{
nodeCount++;
}
var pruningInputs = inputs.Submatrix(trainingSamplesCount, inputs.Length - 1);
var pruningOutputs = outputs.Submatrix(trainingSamplesCount, inputs.Length - 1);
ErrorBasedPruning prune = new ErrorBasedPruning(tree, pruningInputs, pruningOutputs);
prune.Threshold = 0.1;
double lastError, error = Double.PositiveInfinity;
do
{
lastError = error;
error = prune.Run();
} while (error < lastError);
int nodeCount2 = 0;
foreach (var node in tree)
{
nodeCount2++;
}
Assert.AreEqual(0.28922413793103446, error, 5e-4);
Assert.AreEqual(447, nodeCount);
Assert.AreEqual(424, nodeCount2);
}
public void RunTest()
{
double[][] inputs;
int[] outputs;
int training = 6000;
DecisionTree tree = ReducedErrorPruningTest.createNurseryExample(out inputs, out outputs, training);
int nodeCount = 0;
foreach (var node in tree)
{
nodeCount++;
}
var pruningInputs = inputs.Submatrix(training, inputs.Length - 1);
var pruningOutputs = outputs.Submatrix(training, inputs.Length - 1);
ErrorBasedPruning prune = new ErrorBasedPruning(tree, pruningInputs, pruningOutputs);
prune.Threshold = 0.1;
double lastError, error = Double.PositiveInfinity;
do
{
lastError = error;
error = prune.Run();
} while (error < lastError);
int nodeCount2 = 0;
foreach (var node in tree)
{
nodeCount2++;
}
Assert.AreEqual(0.25459770114942532, error);
Assert.AreEqual(447, nodeCount);
Assert.AreEqual(193, nodeCount2);
}
/// <summary>
/// 构建决策树
/// </summary>
public void BuildTree()
{
// 采样
List <Cell> samplePoints = getSample();
updateConsoleEvent("-----------");
updateConsoleEvent("起始城市栅格数目：" + this.BeginCityCnt);
updateConsoleEvent("目标城市栅格数目：" + this.EndCityCnt);
updateConsoleEvent("-----------");
updateConsoleEvent("-----------开始训练----------");
// 样本数目
int COUNT = samplePoints.Count;
// 构造输入和输出数据集
double[][] inputs = new double[COUNT][];
int[] outputs = new int[COUNT];
for (int i = 0; i < COUNT; i++)
{
Cell cell = samplePoints[i];
int pos = cell.row * width + cell.col;
List <double> input = (from buffer in driveBufferList
select buffer[pos]).ToList <double>();
input.Add(GetNeighbourAffect(beginBuffer, width, height, cell.row, cell.col, 3));
inputs[i] = input.ToArray <double>();
if (this.landInfo.UrbanInfos[0].LandUseTypeValue == (int)beginBuffer[pos])
{
outputs[i] = 1;
}
else
{
outputs[i] = 0;
}
}
// 训练数据集
var trainingInputs = inputs.Submatrix(0, COUNT / 2 - 1);
var trainingOutput = outputs.Submatrix(0, COUNT / 2 - 1);
// 检验数据集
var pruningInputs = inputs.Submatrix(COUNT / 2, COUNT - 1);
var pruningOutput = outputs.Submatrix(COUNT / 2, COUNT - 1);
// 设置驱动因子的名字
List <DecisionVariable> featuresList = (from column in this.driveLayerNames
select new DecisionVariable(column, DecisionVariableKind.Continuous)).ToList <DecisionVariable>();
featuresList.Add(new DecisionVariable("affectofneighbour", DecisionVariableKind.Continuous));
// 训练树
var tree = new DecisionTree(inputs: featuresList, classes: 2);
var teacher = new C45Learning(tree);
teacher.Learn(trainingInputs, trainingOutput);
// 剪枝
ErrorBasedPruning prune = new ErrorBasedPruning(tree, pruningInputs, pruningOutput);
prune.Threshold = 0.1;// Gain threshold ？
double lastError;
double error = Double.PositiveInfinity;
do
{
lastError = error;
error = prune.Run();
} while (error < lastError);
updateConsoleEvent("错误率：" + error);
this.func = tree.ToExpression().Compile();
//UpdateUi("错误率" + error);
DecisionSet rules = tree.ToRules();
string ruleText = rules.ToString();
//consolePad.addLineToInfo(ruleText);
updateConsoleEvent("规则:");
updateConsoleEvent(ruleText);
updateConsoleEvent("-----------训练结束----------");
}
