本文整理汇总了C++中std::valarray::sum方法的典型用法代码示例。如果您正苦于以下问题：C++ valarray::sum方法的具体用法？C++ valarray::sum怎么用？C++ valarray::sum使用的例子？那么恭喜您, 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std::valarray的用法示例。
在下文中一共展示了valarray::sum方法的4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: stats
void stats(std:: valarray<float> map, float &mean, float &sigma, float &kurt, float &skew) {
float sum = map.sum();
int n = map.size();
mean = map.sum()/float(n);
std:: valarray <float> maps(n);
valarray<float> maps2(n),maps3(n),maps4(n);
for(int i=0; i<n; i++) {
maps2[i] = gsl_pow_2(map[i] - mean);
maps3[i] = gsl_pow_3(map[i] - mean);
maps4[i] = gsl_pow_4(map[i] - mean);
}
sum = maps2.sum();
sigma = sqrt(sum/(float(n)-1.));
sum = maps3.sum();
double mu3 = sum/(float(n)-1.);
sum = maps4.sum();
double mu4 = sum/(float(n)-1.);
kurt = mu4/gsl_pow_4(sigma) -3;
skew = mu3/gsl_pow_3(sigma);
}示例2: LineFit
bool LineFit(float &kk,float &bb,float &ee,std::valarray <float> &data_x,std::valarray <float> &data_y)
{
if(data_x.size()<=1)
{
ROS_WARN("Line fit point is less than one!");
return false;
}
double A =0.0;
double B =0.0;
double C =0.0;
double D =0.0;
A = (data_x*data_x).sum();
B = data_x.sum();
C = (data_x*data_y).sum();
D = data_y.sum();
float k,b,tmp =0;
if(tmp=(A*data_x.size()-B*B))
{
k = (C*data_x.size()-B*D)/tmp;
b = (A*D-C*B)/tmp;
}
else
{
k=0;
b=B/data_x.size();
ROS_WARN("k is zero!");
}
kk=k;
bb=b;
std::valarray <float> error(data_x.size());
error=data_x*k+b-data_y;
ee=(error*error).sum()/error.size();
return true;
}示例3: _runGrayToneMapping
// run the initilized retina filter in order to perform gray image tone mapping, after this call all retina outputs are updated
void _runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput)
{
// apply tone mapping on the multiplexed image
// -> photoreceptors local adaptation (large area adaptation)
_multiuseFilter->runFilter_LPfilter(grayImageInput, grayImageOutput, 0); // compute low pass filtering modeling the horizontal cells filtering to acess local luminance
_multiuseFilter->setV0CompressionParameterToneMapping(1.f, grayImageOutput.max(), _meanLuminanceModulatorK*grayImageOutput.sum()/(float)_multiuseFilter->getNBpixels());
_multiuseFilter->runFilter_LocalAdapdation(grayImageInput, grayImageOutput, _temp2); // adapt contrast to local luminance
// -> ganglion cells local adaptation (short area adaptation)
_multiuseFilter->runFilter_LPfilter(_temp2, grayImageOutput, 1); // compute low pass filtering (high cut frequency (remove spatio-temporal noise)
_multiuseFilter->setV0CompressionParameterToneMapping(1.f, _temp2.max(), _meanLuminanceModulatorK*grayImageOutput.sum()/(float)_multiuseFilter->getNBpixels());
_multiuseFilter->runFilter_LocalAdapdation(_temp2, grayImageOutput, grayImageOutput); // adapt contrast to local luminance
}示例4: _runGrayToneMapping
// run the initilized retina filter in order to perform gray image tone mapping, after this call all retina outputs are updated
void RetinaFilter::_runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput, const float PhotoreceptorsCompression, const float ganglionCellsCompression)
{
// stability controls value update
++_ellapsedFramesSinceLastReset;
std::valarray<float> temp2(grayImageInput.size());
// apply tone mapping on the multiplexed image
// -> photoreceptors local adaptation (large area adaptation)
_photoreceptorsPrefilter.runFilter_LPfilter(grayImageInput, grayImageOutput, 2); // compute low pass filtering modeling the horizontal cells filtering to acess local luminance
_photoreceptorsPrefilter.setV0CompressionParameterToneMapping(PhotoreceptorsCompression, grayImageOutput.sum()/(float)_photoreceptorsPrefilter.getNBpixels());
_photoreceptorsPrefilter.runFilter_LocalAdapdation(grayImageInput, grayImageOutput, temp2); // adapt contrast to local luminance
// high pass filter
//_spatiotemporalLPfilter(_localBuffer, _filterOutput, 2); // compute low pass filtering (high cut frequency (remove spatio-temporal noise)
//for (unsigned int i=0;i<_NBpixels;++i)
// _localBuffer[i]-= _filterOutput[i]/2.0;
// -> ganglion cells local adaptation (short area adaptation)
_photoreceptorsPrefilter.runFilter_LPfilter(temp2, grayImageOutput, 1); // compute low pass filtering (high cut frequency (remove spatio-temporal noise)
_photoreceptorsPrefilter.setV0CompressionParameterToneMapping(ganglionCellsCompression, temp2.max(), temp2.sum()/(float)_photoreceptorsPrefilter.getNBpixels());
_photoreceptorsPrefilter.runFilter_LocalAdapdation(temp2, grayImageOutput, grayImageOutput); // adapt contrast to local luminance
}