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所以我使用emguCV来使用OpenCV中的机器学习算法。我的代码如下，当它进入dtree.Train方法它给了我异常（exc1），如果我等待ir给我和错误消息（err1）。如果我尝试调试并进入此方法，如果给我另一个异常（exc2）并且调试器不前进。 EXC1： 类型“Emgu.CV.Util.CvException”的第一次机会异常发生在Emgu.CV.dllemgucv抛出异常的代码示例？
EXC2： 步骤为：步进比非用户代码“Emgu.CV.ML.RTrees .Train' 类型的第一次机会异常‘Emgu.CV.Util.CvException’发生在Emgu.CV.dll 步骤为：跨过非用户代码‘MS.Internal.Threading.ExceptionFilterHelper.TryCatchWhen’
err1： CLR无法从COM上下文0x795fa8转换到COM上下文0x796118。拥有目的地上下文/公寓的线程很可能要么进行非抽水等待，要么处理非常长的运行操作而不抽取Windows消息。这种情况通常会对性能产生负面影响，甚至可能导致应用程序无法响应或内存使用量不断累积。为了避免这个问题，所有的单线程单元（STA）线程都应该使用抽取等待原语（比如CoWaitForMultipleHandles），并在长时间运行的操作中定期抽取消息。
我的代码，使用这个例子 - 大量的试验和试错我发现这之后http://www.emgu.com/wiki/index.php/Mushroom_Poisonous_Prediction_(Decision_Tree)_in_CSharp
public void train()
{
Matrix<float> data, response;
// data = new Matrix<float>(15, 200);
// response = new Matrix<float>(15, 200);
Console.WriteLine("reading shroom data");
ReadMushroomData(out data, out response);
///data = new Matrix<float>(1, 5);
//Use the first 80% of data as training sample
int trainingSampleCount = (int)(data.Rows * 0.8);
Matrix<Byte> varType = new Matrix<byte>(data.Cols + 1, 1);
varType.SetValue((byte)Emgu.CV.ML.MlEnum.VAR_TYPE.CATEGORICAL); //the data is categorical
Matrix<byte> sampleIdx = new Matrix<byte>(data.Rows, 1);
using (Matrix<byte> sampleRows = sampleIdx.GetRows(0, trainingSampleCount, 1))
sampleRows.SetValue(255);
float[] priors = new float[] { 1, 0.5f };
GCHandle priorsHandle = GCHandle.Alloc(priors, GCHandleType.Pinned);
MCvRTParams param = new MCvRTParams();
param.maxDepth = 8;// max depth
param.minSampleCount = 10;// min sample count
param.regressionAccuracy = 0;// regression accuracy: N/A here
param.useSurrogates = true; //compute surrogate split, no missing data
param.maxCategories = 15;// max number of categories (use sub-optimal algorithm for larger numbers)
param.cvFolds = 10;
//param.use1seRule = true;
param.truncatePrunedTree = true;
param.priors = priorsHandle.AddrOfPinnedObject(); // the array of priors
Console.WriteLine("starting train");
using (RTrees dtree = new RTrees())
{
bool success = dtree.Train(data,
Emgu.CV.ML.MlEnum.DATA_LAYOUT_TYPE.ROW_SAMPLE,
response,
null,
sampleIdx,
varType,
null,
param);
Console.WriteLine("starting tests");
if (!success) return;
double trainDataCorrectRatio = 0;
double testDataCorrectRatio = 0;
for (int i = 0; i < data.Rows; i++)
{
using (Matrix<float> sample = data.GetRow(i))
{
double r = dtree.Predict(sample, null);
r = Math.Abs(r - response[i, 0]);
if (r < 1.0e-5)
{
if (i < trainingSampleCount)
trainDataCorrectRatio++;
else
testDataCorrectRatio++;
}
}
}
trainDataCorrectRatio /= trainingSampleCount;
testDataCorrectRatio /= (data.Rows - trainingSampleCount);
Console.WriteLine(String.Format("Prediction accuracy for training data :{0}%", trainDataCorrectRatio * 100));
Console.WriteLine(String.Format("Prediction accuracy for test data :{0}%", testDataCorrectRatio * 100));
}
}