本文整理汇总了C++中eigen::MatrixXf::topRows方法的典型用法代码示例。如果您正苦于以下问题：C++ MatrixXf::topRows方法的具体用法？C++ MatrixXf::topRows怎么用？C++ MatrixXf::topRows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eigen::MatrixXf的用法示例。
在下文中一共展示了MatrixXf::topRows方法的2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: main
// run from build folder of the project (see path below)
int main (int argc, char** argv) {
Eigen::MatrixXf X;
Eigen::MatrixXf y;
eigen_extensions::loadASCII("../test/svmtestx.eig.txt", &X);
eigen_extensions::loadASCII("../test/svmtesty.eig.txt", &y);
// Classify
cout << X.topRows(5) << endl;
cout << y.topRows(5) << endl;
vector<int> yhat;
SVMClassifier svm;
svm.train(X, y);
svm.test(X, yhat);
Eigen::MatrixXf w;
float b;
svm.getw(w, b);
Eigen::MatrixXf margin= ((X * w).array() + b).matrix(); // ahh eigen...
// Evaluate accuracy and print results
int match=0;
for(int i=0;i<yhat.size();i++) {
if(yhat[i]==(int)y(i)) match++; else printf("WRONG! ");
printf("y= %d, yhat= %d margin= %f\n", (int)y(i), yhat[i], margin(i));
}
printf("MATLAB cross-check: last margin should be around -3.107\n");
printf("Accuracy= %f. From MATLAB cross-check, expect this to be around 0.945\n", 1.0*match/yhat.size());
// Save the model
svm.saveModel("temp.svmmodel");
// Test loading model in new instance of SVMClassifier
SVMClassifier svm2;
svm2.loadModel("temp.svmmodel");
yhat.clear();
svm2.test(X, yhat);
match=0;
for(int i=0;i<yhat.size();i++) if(yhat[i]==(int)y(i)) match++;
printf("Accuracy= %f from loaded model. Should be 0.945 again.\n", 1.0*match/yhat.size());
printf("you may want to rm the temporary file temp.svmmodel\n");
}示例2: split
int Shell::split(QVariantList points)
{
QPointF point;
//omit last value, because it is always zero
int dataSize = points.size()-1;
Eigen::MatrixXf x; x.resize(dataSize,2);
Eigen::VectorXf y(dataSize);
//create the linear eq system in the form of y = beta1*x + beta2*1
for( int i = 0; i < dataSize; i++)
{
point = points[i].toPointF();
x(i, 0) = 1.0f; //beta for y-intercept
x(i, 1) = point.x(); //beta for slope (dependent on x)
y(i) = point.y();
}
//Error function (least squares of ax+b)
auto error = [](Regression reg, int b, int a)->float
{
float result = 0;
for(int i=0 ; i< reg.y.size(); i++)
{
float functionValue = a*reg.x(i, 1)+ b;
float squarederror = std::pow(reg.y(i) - functionValue, 2);
result+=squarederror;
}
return result;
};
//Perform all pairs of regressions
float lowestError = std::numeric_limits<float>::max();
float r1a, r1b;
float r2a, r2b;
int splitIndex = 0;
for( int i = 2; i < dataSize; i++)
{
Regression reg1; reg1.x = x.topRows(i); reg1.y = y.head(i);
Regression reg2; reg2.x = x.bottomRows(dataSize-i); reg2.y = y.tail(dataSize-i);
Eigen::MatrixXf reg1Result = ((reg1.x.transpose() * reg1.x).inverse() * reg1.x.transpose()) * reg1.y;
Eigen::MatrixXf reg2Result = ((reg2.x.transpose() * reg2.x).inverse() * reg2.x.transpose()) * reg2.y;
float currentError = error(reg1,reg1Result(0),reg1Result(1)) + error(reg2,reg2Result(0),reg2Result(1));
if (currentError < lowestError)
{
r1a = reg1Result(1); r1b = reg1Result(0);
r2a = reg2Result(1); r2b = reg2Result(0);
lowestError = currentError;
splitIndex = i;
}
}
std::cout << "r1:" << r1a << "x + " << r1b << std::endl;
std::cout << "r2:" << r2a << "x + " << r2b << std::endl;
std::cout << "(smallest error:" << lowestError << ")" << std::endl;
return splitIndex;
}