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/external/opencv3/modules/flann/include/opencv2/flann/
Dall_indices.h52 …static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const Inde… in create()
59 nnIndex = new LinearIndex<Distance>(dataset, params, distance); in create()
62 nnIndex = new KDTreeSingleIndex<Distance>(dataset, params, distance); in create()
65 nnIndex = new KDTreeIndex<Distance>(dataset, params, distance); in create()
68 nnIndex = new KMeansIndex<Distance>(dataset, params, distance); in create()
71 nnIndex = new CompositeIndex<Distance>(dataset, params, distance); in create()
74 nnIndex = new AutotunedIndex<Distance>(dataset, params, distance); in create()
77 nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance); in create()
80 nnIndex = new LshIndex<Distance>(dataset, params, distance); in create()
93 …static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const Inde…
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Dhierarchical_clustering_index.h127 … DistanceType sq = distance(dataset[centers[index]], dataset[centers[j]], dataset.cols); in chooseCentersRandom()
164 … DistanceType dist = distance(dataset[centers[0]],dataset[dsindices[j]],dataset.cols); in chooseCentersGonzales()
166 … DistanceType tmp_dist = distance(dataset[centers[i]],dataset[dsindices[j]],dataset.cols); in chooseCentersGonzales()
215 … closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); in chooseCentersKMeanspp()
243 … DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); in chooseCentersKMeanspp()
258 …DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols… in chooseCentersKMeanspp()
300 … closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); in GroupWiseCenterChooser()
320 … newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols) in GroupWiseCenterChooser()
336 …closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dat… in GroupWiseCenterChooser()
359 : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d) in dataset() function
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Dhdf5.h76 void save_to_file(const cvflann::Matrix<T>& dataset, const String& filename, const String& name) in save_to_file() argument
94 dimsf[0] = dataset.rows; in save_to_file()
95 dimsf[1] = dataset.cols; in save_to_file()
116 …status = H5Dwrite(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, H5P_DEFAULT, dataset.data… in save_to_file()
128 void load_from_file(cvflann::Matrix<T>& dataset, const String& filename, const String& name) in load_from_file() argument
147 dataset = cvflann::Matrix<T>(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]); in load_from_file()
149 status = H5Dread(dataset_id, get_hdf5_type<T>(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]); in load_from_file()
169 void load_from_file(cvflann::Matrix<T>& dataset, const String& filename, const String& name) in load_from_file() argument
212 dataset.rows = count[0]; in load_from_file()
213 dataset.cols = count[1]; in load_from_file()
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Dground_truth.h42 void find_nearest(const Matrix<typename Distance::ElementType>& dataset, typename Distance::Element…
51 dists[0] = distance(dataset[0], query, dataset.cols);
55 for (size_t i=1; i<dataset.rows; ++i) {
56 DistanceType tmp = distance(dataset[i], query, dataset.cols);
83 void compute_ground_truth(const Matrix<typename Distance::ElementType>& dataset, const Matrix<typen…
87 find_nearest<Distance>(dataset, testset[i], matches[i], (int)matches.cols, skip, d);
Dflann_base.hpp73 NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const cv… in load_saved_index() argument
85 if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) { in load_saved_index()
91 NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance); in load_saved_index()
Dlsh_table.h195 void add(Matrix<ElementType> dataset) in add() argument
198 buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2); in add()
201 for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]); in add()
Dkmeans_index.h281 , dataset(_dataset) in KMeansDistanceComputer()
301 DistanceType sq_dist = distance(dataset[indices[i]], dcenters[0], veclen); in operator()
304 DistanceType new_sq_dist = distance(dataset[indices[i]], dcenters[j], veclen); in operator()
326 const Matrix<ElementType>& dataset; variable
/external/opencv3/modules/flann/include/opencv2/
Dflann.hpp251 GenericIndex<Distance>::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Dist… in GenericIndex() argument
253 CV_Assert(dataset.type() == CvType<ElementType>::type()); in GenericIndex()
254 CV_Assert(dataset.isContinuous()); in GenericIndex()
255 …flann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, data… in GenericIndex()
405 Index_<T>::Index_(const Mat& dataset, const ::cvflann::IndexParams& params) in Index_() argument
409 CV_Assert(dataset.type() == CvType<ElementType>::type()); in Index_()
410 CV_Assert(dataset.isContinuous()); in Index_()
411 …flann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, data… in Index_()
/external/autotest/tko/
Dplotgraph.py96 for dataset in self.datasets:
99 if label in dataset:
100 data = dataset[label]
/external/skia/bench/
Dgen_bench_expectations.py93 for idx, dataset in extra_data:
94 for data in dataset:
/external/opencv3/modules/flann/src/
Dminiflann.cpp319 ::cvflann::Matrix<ElementType> dataset((ElementType*)data.data, data.rows, data.cols); in buildIndex_()
320 IndexType* _index = new IndexType(dataset, get_params(params), dist); in buildIndex_()
707 ::cvflann::Matrix<ElementType> dataset((ElementType*)data.data, data.rows, data.cols); in loadIndex_()
711 IndexType* _index = new IndexType(dataset, params, dist); in loadIndex_()
/external/opencv3/doc/tutorials/objdetect/
Dtraincascade.markdown20 training: the quality of training dataset first of all and training parameters too. It's possible to
34 - opencv_createsamples is used to prepare a training dataset of positive and test samples.
35 opencv_createsamples produces dataset of positive samples in a format that is supported by
85 Please note that you need a large dataset of positive samples before you give it to the mentioned
/external/mockftpserver/tags/1.0/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/1.2.4/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/branches/1.x_Branch/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/1.2.2/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/2.x_Before_IDEA/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/1.2/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/1.1/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/1.2.1/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/1.2.3/src/main/resources/
DReplyText.properties60 # Exceeded storage allocation (for current directory or dataset).
/external/opencv3/doc/tutorials/ml/introduction_to_pca/
Dintroduction_to_pca.markdown14 …t Analysis (PCA) is a statistical procedure that extracts the most important features of a dataset.
18 …ion is the process of reducing the number of the dimensions of the given dataset. For example, in …
/external/opencv3/doc/tutorials/features2d/akaze_matching/
Dakaze_matching.markdown19 We are going to use images 1 and 3 from *Graffity* sequence of Oxford dataset.
/external/mockftpserver/tags/2.0-rc1/src/main/resources/
DReplyText.properties62 # Exceeded storage allocation (for current directory or dataset).
/external/mockftpserver/tags/2.3/src/main/resources/
DReplyText.properties63 # Exceeded storage allocation (for current directory or dataset).

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