| /hardware/bsp/intel/peripheral/libupm/src/gas/ |
| gas.cxx | 44 int sampleIdx = 0;
56 while (sampleIdx < numberOfSamples) {
57 buffer[sampleIdx++] = getSample();
61 return sampleIdx;
|
| /hardware/bsp/intel/peripheral/libupm/src/mic/ |
| mic.cxx | 59 int sampleIdx = 0;
71 while (sampleIdx < numberOfSamples) {
72 buffer[sampleIdx++] = mraa_aio_read (m_micCtx);
76 return sampleIdx;
|
| /external/opencv3/apps/traincascade/ |
| haarfeatures.h | 37 virtual float operator()(int featureIdx, int sampleIdx) const;
73 inline float CvHaarEvaluator::operator()(int featureIdx, int sampleIdx) const
75 float nf = normfactor.at<float>(0, sampleIdx);
76 return !nf ? 0.0f : (features[featureIdx].calc( sum, tilted, sampleIdx)/nf);
|
| HOGfeatures.h | 25 virtual float operator()(int varIdx, int sampleIdx) const;
52 inline float CvHOGEvaluator::operator()(int varIdx, int sampleIdx) const
56 //return features[featureIdx].calc( hist, sampleIdx, componentIdx);
57 return features[featureIdx].calc( hist, normSum, sampleIdx, componentIdx);
|
| lbpfeatures.h | 20 virtual float operator()(int featureIdx, int sampleIdx) const
21 { return (float)features[featureIdx].calc( sum, sampleIdx); }
|
| old_ml.hpp | 198 const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
201 const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
207 const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
209 const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
243 const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 );
246 const CvMat* sampleIdx=0, bool is_regression=false,
253 const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
256 const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false,
473 const CvMat* varIdx=0, const CvMat* sampleIdx=0,
477 const CvMat* varIdx=0, const CvMat* sampleIdx=0
[all...] |
| boost.h | 56 virtual CvDTreeNode* predict( int sampleIdx ) const;
70 virtual float predict( int sampleIdx, bool returnSum = false ) const;
|
| cascadeclassifier.h | 100 int predict( int sampleIdx );
|
| traincascade_features.h | 83 virtual float operator()(int featureIdx, int sampleIdx) const = 0;
|
| boost.cpp | 954 CvDTreeNode* CvCascadeBoostTree::predict( int sampleIdx ) const
[all...] |
| cascadeclassifier.cpp | 288 int CvCascadeClassifier::predict( int sampleIdx )
290 CV_DbgAssert( sampleIdx < numPos + numNeg );
294 if ( (*it)->predict( sampleIdx ) == 0.f )
|
| old_ml_precomp.hpp | 114 uchar* sidx;int sistep; - sampleIdx
215 ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \
|
| /external/opencv/ml/src/ |
| mlestimate.cpp | 404 const CvMat* sampleIdx)
454 if (sampleIdx)
459 if (!CV_IS_MAT (sampleIdx))
460 CV_ERROR (CV_StsBadArg, "Invalid sampleIdx array");
462 if (sampleIdx->rows != 1 && sampleIdx->cols != 1)
463 CV_ERROR (CV_StsBadSize, "sampleIdx array must be 1-dimensional");
465 s_len = sampleIdx->rows + sampleIdx->cols - 1;
466 s_step = sampleIdx->rows == 1 ?
[all...] |
| _ml.h | 118 uchar* sidx;int sistep; - sampleIdx
219 ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \
|
| /external/opencv3/modules/ml/src/ |
| data.cpp | 131 return !sampleIdx.empty() ? (int)sampleIdx.total() :
160 Mat getTrainSampleIdx() const { return !trainSampleIdx.empty() ? trainSampleIdx : sampleIdx; }
217 sampleIdx.release();
242 sampleIdx = _sampleIdx.getMat();
253 if( !sampleIdx.empty() )
255 CV_Assert( (sampleIdx.checkVector(1, CV_32S, true) > 0 &&
256 checkRange(sampleIdx, true, 0, 0, nsamples-1)) ||
257 sampleIdx.checkVector(1, CV_8U, true) == nsamples );
258 if( sampleIdx.type() == CV_8U
[all...] |
| gbt.cpp | [all...] |
| /external/webrtc/webrtc/modules/audio_device/mac/ |
| audio_device_mac.cc | [all...] |
| /external/opencv3/modules/ml/include/opencv2/ |
| ml.hpp | 159 sampleIdx)
275 @param sampleIdx vector specifying which samples to use for training. It can be an integer
284 InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
[all...] |
| /external/opencv/ml/include/ |
| ml.h | [all...] |
