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/dports/math/gravity/Gravity-da941e9/examples/MachineLearning/Supervised/Classification/SVM/
H A DSVM_main.cpp30 auto nf = training_set._nb_features; in build_svm()
31 auto m1 = training_set._class_sizes[0]; in build_svm()
32 auto m2 = training_set._class_sizes[1]; in build_svm()
65 auto m = training_set._nb_points; in build_svm_dual()
67 auto y = training_set.get_classes(); in build_svm_dual()
92 auto nf = training_set._nb_features; in build_lazy_svm()
93 auto m = training_set._nb_points; in build_lazy_svm()
157 DataSet<> training_set; in main() local
158 training_set.parse(fname); in main()
159 training_set.print_stats(); in main()
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/dports/math/octave-forge-communications/communications-1.2.3/inst/
H A Ddpcmopt.m17 ## @deftypefn {Function File} {@var{predictor} =} dpcmopt (@var{training_set}, @var{ord})
18 … {[@var{predictor}, @var{partition}, @var{codebook}] =} dpcmopt (@var{training_set}, @var{ord}, @v…
27 ## @item predictor = dpcmopt (training_set, ord)
35 ## training_set is the training data used to find the best predictor.
39 ## @item [predictor, partition, codebook] = dpcmopt (training_set,ord,cb)
50 function [predictor, partition, codebook] = dpcmopt (training_set, ord, cb)
56 training_set = training_set(:); variable
57 L = length (training_set);
58 corr_tr = xcorr (training_set'); # autocorrelation
68 e(i-ord) = training_set(i) - fliplr (predictor) * training_set(i-ord:i);
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/ml/include/pcl/ml/
H A Dsvm_wrapper.h160 adaptInputToLibSVM(std::vector<SVMData> training_set, svm_problem& prob);
164 adaptLibSVMToInput(std::vector<SVMData>& training_set, svm_problem prob) const;
262 scaleFactors(std::vector<SVMData> training_set, svm_scaling& scaling);
303 setInputTrainingSet(std::vector<SVMData> training_set) in setInputTrainingSet() argument
305 training_set_.insert(training_set_.end(), training_set.begin(), training_set.end()); in setInputTrainingSet()
412 setInputTrainingSet(std::vector<SVMData> training_set) in setInputTrainingSet() argument
414 assert(training_set.size() > 0); in setInputTrainingSet()
423 training_set_.insert(training_set_.end(), training_set.begin(), training_set.end()); in setInputTrainingSet()
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/ml/src/
H A Dsvm_wrapper.cpp124 for (const auto& svm_data : training_set) in scaleFactors()
141 for (const auto& svm_data : training_set) in scaleFactors()
153 training_set.clear(); // Reset input in adaptLibSVMToInput()
175 training_set.push_back(parent); in adaptLibSVMToInput()
182 assert(training_set.size() > 0); in adaptInputToLibSVM()
198 prob.y[i] = training_set[i].label; in adaptInputToLibSVM()
209 if (training_set[i].SV[j].idx != -1 && in adaptInputToLibSVM()
210 std::isfinite(training_set[i].SV[j].value)) { in adaptInputToLibSVM()
211 prob.x[i][k].index = training_set[i].SV[j].idx; in adaptInputToLibSVM()
212 if (training_set[i].SV[j].idx < scaling_.max && in adaptInputToLibSVM()
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/dports/misc/vxl/vxl-3.3.2/contrib/mul/clsfy/tests/
H A Dtest_binary_hyperplane.cxx149 mbl_data_array_wrapper<vnl_vector<double> > training_set(trainingVectors); in test_binary_hyperplane() local
151 double train_error = builder.build(*pClassifier, training_set, labels); in test_binary_hyperplane()
332 mbl_data_array_wrapper<vnl_vector<double> > training_set(trainingVectors); in test_clsfy_geman_mcclure_build() local
334 double train_errorLS = builder.build(*pClassifier, training_set, labels); in test_clsfy_geman_mcclure_build()
359 training_set.reset(); in test_clsfy_geman_mcclure_build()
360 unsigned num_vars_ = training_set.current().size(); in test_clsfy_geman_mcclure_build()
361 unsigned num_examples_ = training_set.size(); in test_clsfy_geman_mcclure_build()
367 std::copy(training_set.current().begin(),training_set.current().end(),row); in test_clsfy_geman_mcclure_build()
368 } while (training_set.next()); in test_clsfy_geman_mcclure_build()
408 double train_error = pBase->build(*pClassifier, training_set, 1,labels); in test_clsfy_geman_mcclure_build()
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/dports/math/eprover/eprover-E-2.0/PROVER/
H A Dtsm_classify.c134 AnnoSet_p training_set, test_set; in main() local
164 training_set = AnnoSetParse(in, bank, 2); /* (Sources, Class) ->2 */ in main()
166 AnnoSetFlatten(training_set, ANNOTATIONS_MERGE_ALL); in main()
182 FlatAnnoSetTranslate(ftrain_set, training_set, weights->array); in main()
190 AnnoSetComputePatternSubst(subst, training_set); in main()
210 AnnoSetFree(training_set); in main()
/dports/biology/py-biopython/biopython-1.79/Bio/
H A DNaiveBayes.py135 def train(training_set, results, priors=None, typecode=None): argument
147 if not len(training_set):
149 if len(training_set) != len(results):
159 dimensions = [len(x) for x in training_set]
192 klass, obs = results[i], training_set[i]
H A DMaxEntropy.py262 training_set, argument
287 if not training_set:
289 if len(training_set) != len(results):
293 xs, ys = training_set, results
299 features = [_eval_feature_fn(fn, training_set, classes) for fn in feature_fns]
301 f_sharp = _calc_f_sharp(len(training_set), len(classes), features)
/dports/textproc/p5-AI-Categorizer/AI-Categorizer-0.09/lib/AI/
H A DCategorizer.pm23 training_set => { type => SCALAR, optional => 1 },
43 $defaults{training_set} = File::Spec->catfile($args{data_root}, 'training');
74 $self->knowledge_set->scan_features( path => $self->{training_set} );
82 $self->knowledge_set->read( path => $self->{training_set} );
/dports/math/gravity/Gravity-da941e9/examples/MachineLearning/Supervised/Classification/NeuralNets/
H A DNeuralNet_main.cpp61 DataSet<> training_set; in main() local
62 training_set.parse(file); in main()
63 training_set.print_stats(); in main()
/dports/misc/mxnet/incubator-mxnet-1.9.0/cpp-package/example/
H A Dmlp_csv.cpp92 std::string training_set; in main() local
99 training_set = argv[index]; in main()
122 if (training_set.empty() || test_set.empty() || hidden_units_string.empty()) { in main()
145 .SetParam("data_csv", training_set) in main()
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/cpp-package/example/
H A Dmlp_csv.cpp92 std::string training_set; in main() local
99 training_set = argv[index]; in main()
122 if (training_set.empty() || test_set.empty() || hidden_units_string.empty()) { in main()
145 .SetParam("data_csv", training_set) in main()
/dports/textproc/py-nltk/nltk-3.4.1/nltk/sentiment/
H A Dutil.py578 training_set = sentim_analyzer.apply_features(training_tweets)
581 classifier = sentim_analyzer.train(trainer, training_set)
647 training_set = sentim_analyzer.apply_features(training_docs)
650 classifier = sentim_analyzer.train(trainer, training_set)
717 training_set = sentim_analyzer.apply_features(training_docs)
720 classifier = sentim_analyzer.train(trainer, training_set)
H A Dsentiment_analyzer.py163 def train(self, trainer, training_set, save_classifier=None, **kwargs): argument
183 self.classifier = trainer(training_set, **kwargs)
/dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/utils/
H A Ddownload_datasets_and_models.py56 training_set, _, _ = pickle_load(file)
57 data, labels = training_set
/dports/science/afni/afni-AFNI_21.3.16/src/pkundu/meica.libs/mdp/nodes/
H A Dshogun_svm_classifier.py356 def training_set(self, ordered=False): member in ShogunSVMClassifier
/dports/textproc/p5-AI-Categorizer/AI-Categorizer-0.09/
H A DREADME237 training_set
247 A shortcut for setting the "training_set", "test_set", and
248 "category_file" parameters separately. Sets "training_set" to
/dports/textproc/py-nltk/nltk-3.4.1/nltk/test/
H A Dsentiment.doctest48 >>> training_set = sentim_analyzer.apply_features(training_docs)
55 >>> classifier = sentim_analyzer.train(trainer, training_set)
/dports/science/py-mdp/MDP-3.5/mdp/nodes/
H A Dshogun_svm_classifier.py359 def training_set(self, ordered=False): member in ShogunSVMClassifier
/dports/math/cgal/CGAL-5.3/include/CGAL/Classification/
H A DSum_of_weighted_features_classifier.h82 Compute_iou (std::vector<std::size_t>& training_set, in Compute_iou() argument
91 : m_training_set (training_set) in Compute_iou()
/dports/math/octave-forge-communications/communications-1.2.3/doc/
H A Dcomms.texi4017 @deftypefn {Function File} {@var{predictor} =} dpcmopt (@var{training_set}, @var{ord})
4018 … {[@var{predictor}, @var{partition}, @var{codebook}] =} dpcmopt (@var{training_set}, @var{ord}, @v…
4027 @item predictor = dpcmopt (training_set, ord)
4035 training_set is the training data used to find the best predictor
4039 @item [predictor, partition, codebook] = dpcmopt (training_set,ord,cb)
H A Dcomms.info3293 'predictor = dpcmopt (training_set, ord)'
3301 training_set is the training data used to find the best
3306 '[predictor, partition, codebook] = dpcmopt (training_set,ord,cb)'