本文整理汇总了C#中CvMat.Sum方法的典型用法代码示例。如果您正苦于以下问题：C# CvMat.Sum方法的具体用法？C# CvMat.Sum怎么用？C# CvMat.Sum使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类CvMat的用法示例。
在下文中一共展示了CvMat.Sum方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: BuildBoostClassifier
//.........这里部分代码省略.........
using (CvMat newResponses = new CvMat(ntrainSamples * ClassCount, 1, MatrixType.S32C1))
{
// 1. unroll the database type mask
Console.WriteLine("Unrolling the database...");
for (int i = 0; i < ntrainSamples; i++)
{
unsafe
{
float* dataRow = (float*)(data.DataByte + data.Step * i);
for (int j = 0; j < ClassCount; j++)
{
float* newDataRow = (float*)(newData.DataByte + newData.Step * (i * ClassCount + j));
for (int k = 0; k < varCount; k++)
{
newDataRow[k] = dataRow[k];
}
newDataRow[varCount] = (float)j;
newResponses.DataInt32[i * ClassCount + j] = (responses.DataSingle[i] == j + 'A') ? 1 : 0;
}
}
}
// 2. create type mask
varType = new CvMat(varCount + 2, 1, MatrixType.U8C1);
varType.Set(CvScalar.ScalarAll(CvStatModel.CV_VAR_ORDERED));
// the last indicator variable, as well
// as the new (binary) response are categorical
varType.SetReal1D(varCount, CvStatModel.CV_VAR_CATEGORICAL);
varType.SetReal1D(varCount + 1, CvStatModel.CV_VAR_CATEGORICAL);
// 3. train classifier
Console.Write("Training the classifier (may take a few minutes)...");
boost.Train(
newData, DTreeDataLayout.RowSample, newResponses, null, null, varType, null,
new CvBoostParams(CvBoost.REAL, 100, 0.95, 5, false, null)
);
}
Console.WriteLine();
}
tempSample = new CvMat(1, varCount + 1, MatrixType.F32C1);
weakResponses = new CvMat(1, boost.GetWeakPredictors().Total, MatrixType.F32C1);
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAall; i++)
{
int bestClass = 0;
double maxSum = double.MinValue;
double r;
CvMat sample;
Cv.GetRow(data, out sample, i);
for (int k = 0; k < varCount; k++)
{
tempSample.DataArraySingle[k] = sample.DataArraySingle[k];
}
for (int j = 0; j < ClassCount; j++)
{
tempSample.DataArraySingle[varCount] = (float)j;
boost.Predict(tempSample, null, weakResponses);
double sum = weakResponses.Sum().Val0;
if (maxSum < sum)
{
maxSum = sum;
bestClass = j + 'A';
}
}
r = (Math.Abs(bestClass - responses.DataArraySingle[i]) < float.Epsilon) ? 1 : 0;
if (i < ntrainSamples)
trainHr += r;
else
testHr += r;
}
testHr /= (double)(nsamplesAall - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
Console.WriteLine("Number of trees: {0}", boost.GetWeakPredictors().Total);
// Save classifier to file if needed
if (filenameToSave != null)
{
boost.Save(filenameToSave);
}
Console.Read();
tempSample.Dispose();
weakResponses.Dispose();
if (varType != null) varType.Dispose();
data.Dispose();
responses.Dispose();
boost.Dispose();
}