/dports/math/gravity/Gravity-da941e9/examples/MachineLearning/Supervised/Classification/SVM/ |
H A D | SVM_main.cpp | 30 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() [all …]
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/dports/math/octave-forge-communications/communications-1.2.3/inst/ |
H A D | dpcmopt.m | 17 ## @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);
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/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/ml/include/pcl/ml/ |
H A D | svm_wrapper.h | 160 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()
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/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/ml/src/ |
H A D | svm_wrapper.cpp | 124 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() [all …]
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/dports/misc/vxl/vxl-3.3.2/contrib/mul/clsfy/tests/ |
H A D | test_binary_hyperplane.cxx | 149 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() [all …]
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/dports/math/eprover/eprover-E-2.0/PROVER/ |
H A D | tsm_classify.c | 134 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()
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/dports/biology/py-biopython/biopython-1.79/Bio/ |
H A D | NaiveBayes.py | 135 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]
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H A D | MaxEntropy.py | 262 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)
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/dports/textproc/p5-AI-Categorizer/AI-Categorizer-0.09/lib/AI/ |
H A D | Categorizer.pm | 23 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} );
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/dports/math/gravity/Gravity-da941e9/examples/MachineLearning/Supervised/Classification/NeuralNets/ |
H A D | NeuralNet_main.cpp | 61 DataSet<> training_set; in main() local 62 training_set.parse(file); in main() 63 training_set.print_stats(); in main()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/cpp-package/example/ |
H A D | mlp_csv.cpp | 92 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()
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/cpp-package/example/ |
H A D | mlp_csv.cpp | 92 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()
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/dports/textproc/py-nltk/nltk-3.4.1/nltk/sentiment/ |
H A D | util.py | 578 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)
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H A D | sentiment_analyzer.py | 163 def train(self, trainer, training_set, save_classifier=None, **kwargs): argument 183 self.classifier = trainer(training_set, **kwargs)
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/dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/utils/ |
H A D | download_datasets_and_models.py | 56 training_set, _, _ = pickle_load(file) 57 data, labels = training_set
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/dports/science/afni/afni-AFNI_21.3.16/src/pkundu/meica.libs/mdp/nodes/ |
H A D | shogun_svm_classifier.py | 356 def training_set(self, ordered=False): member in ShogunSVMClassifier
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/dports/textproc/p5-AI-Categorizer/AI-Categorizer-0.09/ |
H A D | README | 237 training_set 247 A shortcut for setting the "training_set", "test_set", and 248 "category_file" parameters separately. Sets "training_set" to
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/dports/textproc/py-nltk/nltk-3.4.1/nltk/test/ |
H A D | sentiment.doctest | 48 >>> training_set = sentim_analyzer.apply_features(training_docs) 55 >>> classifier = sentim_analyzer.train(trainer, training_set)
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/dports/science/py-mdp/MDP-3.5/mdp/nodes/ |
H A D | shogun_svm_classifier.py | 359 def training_set(self, ordered=False): member in ShogunSVMClassifier
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/dports/math/cgal/CGAL-5.3/include/CGAL/Classification/ |
H A D | Sum_of_weighted_features_classifier.h | 82 Compute_iou (std::vector<std::size_t>& training_set, in Compute_iou() argument 91 : m_training_set (training_set) in Compute_iou()
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/dports/math/octave-forge-communications/communications-1.2.3/doc/ |
H A D | comms.texi | 4017 @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)
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H A D | comms.info | 3293 '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)'
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