/dports/multimedia/vmaf/vmaf-2.3.0/third_party/libsvm/ |
H A D | Makefile | 9 all: svm-train svm-predict svm-scale 11 lib: svm.o 19 svm-predict: svm-predict.c svm.o 20 $(CXX) $(CFLAGS) svm-predict.c svm.o -o svm-predict -lm 21 svm-train: svm-train.c svm.o 22 $(CXX) $(CFLAGS) svm-train.c svm.o -o svm-train -lm 23 svm-scale: svm-scale.c 24 $(CXX) $(CFLAGS) svm-scale.c -o svm-scale 25 svm.o: svm.cpp svm.h 26 $(CXX) $(CFLAGS) -c svm.cpp [all …]
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H A D | Makefile.win | 11 all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.ex… 13 $(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj 14 $(CXX) $(CFLAGS) svm-predict.c svm.obj -Fe$(TARGET)\svm-predict.exe 16 $(TARGET)\svm-train.exe: svm.h svm-train.c svm.obj 17 $(CXX) $(CFLAGS) svm-train.c svm.obj -Fe$(TARGET)\svm-train.exe 19 $(TARGET)\svm-scale.exe: svm.h svm-scale.c 20 $(CXX) $(CFLAGS) svm-scale.c -Fe$(TARGET)\svm-scale.exe 22 $(TARGET)\svm-toy.exe: svm.h svm.obj svm-toy\windows\svm-toy.cpp 23 …$(CXX) $(CFLAGS) svm-toy\windows\svm-toy.cpp svm.obj user32.lib gdi32.lib comdlg32.lib -Fe$(TARGE… 25 svm.obj: svm.cpp svm.h [all …]
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H A D | .gitignore | 2 svm-predict 3 svm-scale 4 svm-train 5 svm.o
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/dports/science/libsvm/libsvm-3.23/ |
H A D | Makefile | 6 all: svm-train svm-predict svm-scale 8 lib: svm.o 16 svm-predict: svm-predict.c svm.o 17 $(CXX) $(CFLAGS) svm-predict.c svm.o -o svm-predict -lm 18 svm-train: svm-train.c svm.o 19 $(CXX) $(CFLAGS) svm-train.c svm.o -o svm-train -lm 20 svm-scale: svm-scale.c 21 $(CXX) $(CFLAGS) svm-scale.c -o svm-scale 22 svm.o: svm.cpp svm.h 23 $(CXX) $(CFLAGS) -c svm.cpp [all …]
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H A D | Makefile.win | 11 all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.ex… 13 $(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj 14 $(CXX) $(CFLAGS) svm-predict.c svm.obj -Fe$(TARGET)\svm-predict.exe 16 $(TARGET)\svm-train.exe: svm.h svm-train.c svm.obj 17 $(CXX) $(CFLAGS) svm-train.c svm.obj -Fe$(TARGET)\svm-train.exe 19 $(TARGET)\svm-scale.exe: svm.h svm-scale.c 20 $(CXX) $(CFLAGS) svm-scale.c -Fe$(TARGET)\svm-scale.exe 22 $(TARGET)\svm-toy.exe: svm.h svm.obj svm-toy\windows\svm-toy.cpp 23 …$(CXX) $(CFLAGS) svm-toy\windows\svm-toy.cpp svm.obj user32.lib gdi32.lib comdlg32.lib -Fe$(TARGE… 25 svm.obj: svm.cpp svm.h [all …]
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/dports/science/libsvm-python/libsvm-3.23/ |
H A D | Makefile | 6 all: svm-train svm-predict svm-scale 8 lib: svm.o 16 svm-predict: svm-predict.c svm.o 17 $(CXX) $(CFLAGS) svm-predict.c svm.o -o svm-predict -lm 18 svm-train: svm-train.c svm.o 19 $(CXX) $(CFLAGS) svm-train.c svm.o -o svm-train -lm 20 svm-scale: svm-scale.c 21 $(CXX) $(CFLAGS) svm-scale.c -o svm-scale 22 svm.o: svm.cpp svm.h 23 $(CXX) $(CFLAGS) -c svm.cpp [all …]
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H A D | Makefile.win | 11 all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.ex… 13 $(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj 14 $(CXX) $(CFLAGS) svm-predict.c svm.obj -Fe$(TARGET)\svm-predict.exe 16 $(TARGET)\svm-train.exe: svm.h svm-train.c svm.obj 17 $(CXX) $(CFLAGS) svm-train.c svm.obj -Fe$(TARGET)\svm-train.exe 19 $(TARGET)\svm-scale.exe: svm.h svm-scale.c 20 $(CXX) $(CFLAGS) svm-scale.c -Fe$(TARGET)\svm-scale.exe 22 $(TARGET)\svm-toy.exe: svm.h svm.obj svm-toy\windows\svm-toy.cpp 23 …$(CXX) $(CFLAGS) svm-toy\windows\svm-toy.cpp svm.obj user32.lib gdi32.lib comdlg32.lib -Fe$(TARGE… 25 svm.obj: svm.cpp svm.h [all …]
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/dports/biology/viennarna/ViennaRNA-2.4.18/src/libsvm-3.24/ |
H A D | Makefile | 6 all: svm-train svm-predict svm-scale 8 lib: svm.o 16 svm-predict: svm-predict.c svm.o 17 $(CXX) $(CFLAGS) svm-predict.c svm.o -o svm-predict -lm 18 svm-train: svm-train.c svm.o 19 $(CXX) $(CFLAGS) svm-train.c svm.o -o svm-train -lm 20 svm-scale: svm-scale.c 21 $(CXX) $(CFLAGS) svm-scale.c -o svm-scale 22 svm.o: svm.cpp svm.h 23 $(CXX) $(CFLAGS) -c svm.cpp [all …]
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H A D | Makefile.win | 11 all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.ex… 13 $(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj 14 $(CXX) $(CFLAGS) svm-predict.c svm.obj -Fe$(TARGET)\svm-predict.exe 16 $(TARGET)\svm-train.exe: svm.h svm-train.c svm.obj 17 $(CXX) $(CFLAGS) svm-train.c svm.obj -Fe$(TARGET)\svm-train.exe 19 $(TARGET)\svm-scale.exe: svm.h svm-scale.c 20 $(CXX) $(CFLAGS) svm-scale.c -Fe$(TARGET)\svm-scale.exe 22 $(TARGET)\svm-toy.exe: svm.h svm.obj svm-toy\windows\svm-toy.cpp 23 …$(CXX) $(CFLAGS) svm-toy\windows\svm-toy.cpp svm.obj user32.lib gdi32.lib comdlg32.lib -Fe$(TARGE… 25 svm.obj: svm.cpp svm.h [all …]
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/dports/devel/p5-Algorithm-SVM/Algorithm-SVM-0.13/ |
H A D | test.pl | 64 ok($svm->predict($ds1) == 10,1); 65 ok($svm->predict($ds2) == 0,1); 103 my $p1 = $svm->predict($ds1); 104 my $p2 = $svm->predict($ds2); 105 my $p3 = $svm->predict($ds3); 109 ok($svm->predict($ds1),$p1); 110 ok($svm->predict($ds2),$p2); 111 ok($svm->predict($ds3),$p3); 122 ok($svm->predict($ds1),$p1); 123 ok($svm->predict($ds2),$p2); [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/svm/tests/ |
H A D | test_svm.py | 103 clf.predict(KT.T) 247 clf.predict(X) 635 ).predict(X) 844 assert_array_equal(clf_unitweight.predict(T), clf.predict(T)) 1199 svm.predict(y) 1220 pred = svm.predict(np.c_[xx.ravel(), yy.ravel()]) 1228 pred = svm.predict(np.c_[xx.ravel(), yy.ravel()]) 1371 assert_array_equal(svc1.predict(data), svc2.predict(X)) 1372 assert_array_equal(svc1.predict(data), svc3.predict(K)) 1374 assert_allclose(svc1.predict(data), svc2.predict(X)) [all …]
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H A D | test_sparse.py | 66 dense_svm.predict(X_test_dense), sparse_svm.predict(X_test) 84 dense_svm.predict(X_test) 172 assert_array_equal(clf_lin.predict(X_sp), clf_mylin.predict(X_sp)) 187 clf.predict(iris.data.toarray()), sp_clf.predict(iris.data) 210 prediction = clf.predict(X) 249 assert_array_almost_equal(clf.predict(X), sp_clf.predict(X_sp)) 269 clf.predict(iris.data.toarray()), sp_clf.predict(iris.data) 298 y_pred = clf.predict(X_[180:]) 530 pred = b.predict(X_sp) 536 pred_dense = dense_svm.fit(X, Y).predict(X) [all …]
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/dports/graphics/opencv/opencv-4.5.3/modules/ml/test/ |
H A D | test_svmtrainauto.cpp | 75 ASSERT_TRUE(svm); in TEST() 80 float result0 = svm->predict( test_point0 ); in TEST() 83 float result1 = svm->predict( test_point1 ); in TEST() 98 ASSERT_TRUE(svm); in TEST() 100 svm->setGamma(10.0); in TEST() 106 EXPECT_FLOAT_EQ(svm->predict( test_point0 ), 0); in TEST() 110 EXPECT_FLOAT_EQ(svm->predict( test_point1 ), 1); in TEST() 122 float result0 = svm->predict( test_point0 ); in TEST() 125 float result1 = svm->predict( test_point1 ); in TEST() 140 ASSERT_TRUE(svm); in TEST() [all …]
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/dports/science/R-cran-e1071/e1071/ |
H A D | NAMESPACE | 11 "predict", "qnorm", "quantile", "rnorm", "runif", "sd", 36 S3method(coef, svm) 49 S3method(plot, svm) 52 S3method(predict, lca) 53 S3method(predict, naiveBayes) 54 S3method(predict, svm) 55 S3method(predict, gknn) 64 S3method(print, svm) 70 S3method(summary, svm) 73 S3method(svm, default) [all …]
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/dports/science/R-cran-e1071/e1071/man/ |
H A D | predict.svm.Rd | 1 \name{predict.svm} 2 \alias{predict.svm} 8 \method{predict}{svm}(object, newdata, decision.values = FALSE, 13 \item{object}{Object of class \code{"svm"}, created by \code{svm}.} 57 \code{\link{svm}} 76 pred <- predict(model, x) 81 pred <- predict(model, x, decision.values = TRUE, probability = TRUE) 91 # estimate model and predict input values 92 m <- svm(x, y) 93 new <- predict(m, x) [all …]
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/dports/science/R-cran-e1071/e1071/inst/doc/ |
H A D | svmdoc.R | 21 svm.model <- svm(Type ~ ., data = trainset, cost = 100, gamma = 1) 22 svm.pred <- predict(svm.model, testset[,-10]) 60 svm.model <- svm(Type ~ ., data = trainset, cost = 100, gamma = 1) 61 svm.pred <- predict(svm.model, testset[,-10]) 97 svm.model <- svm(V4 ~ ., data = trainset, cost = 1000, gamma = 0.0001) 98 svm.pred <- predict(svm.model, testset[,-3]) 103 rpart.pred <- predict(rpart.model, testset[,-3]) 121 svm.model <- svm(V4 ~ ., data = trainset, cost = 1000, gamma = 0.0001) 122 svm.pred <- predict(svm.model, testset[,-3]) 127 rpart.pred <- predict(rpart.model, testset[,-3]) [all …]
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H A D | svmdoc.Rnw | 137 classification. The task is to predict the type of a glass on basis 152 \texttt{rpart()}), we fit the model and try to predict the test set 155 ## svm 157 svm.pred <- predict(svm.model, testset[,-10]) 165 rpart.pred <- predict(rpart.model, testset[,-10], type = "class") 194 svm.pred <- predict(svm.model, testset[,-10]) 201 rpart.pred <- predict(rpart.model, testset[,-10], type = "class") 239 svm.pred <- predict(svm.model, testset[,-3]) 244 rpart.pred <- predict(rpart.model, testset[,-3]) 261 svm.pred <- predict(svm.model, testset[,-3]) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/ |
H A D | test_multiclass.py | 23 from sklearn.svm import LinearSVC, SVC 38 from sklearn import svm 203 clf = svm.SVC() 331 ovr = OneVsRestClassifier(svm.SVC()) 811 clf_precomputed = svm.SVC(kernel="precomputed") 898 clf_precomputed = svm.SVC(kernel="precomputed") 899 clf_notprecomputed = svm.SVC() 913 clf_precomputed = svm.SVC(kernel="precomputed") 914 clf_notprecomputed = svm.SVC() 928 clf_precomputed = svm.SVC(kernel="precomputed") [all …]
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/dports/devel/spark/spark-2.1.1/mllib/src/test/scala/org/apache/spark/mllib/classification/ |
H A D | SVMSuite.scala | 86 val svm = new SVMWithSGD().setIntercept(true) constant 87 svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100) 89 val model = svm.run(testRDD) 96 var predictions = model.predict(validationRDD.map(_.features)).collect() 101 predictions = model.predict(validationRDD.map(_.features)).collect() 106 predictions = model.predict(validationRDD.map(_.features)).collect() 123 val svm = new SVMWithSGD().setIntercept(true) constant 126 val model = svm.run(testRDD) 155 val svm = new SVMWithSGD().setIntercept(true) constant 158 val model = svm.run(testRDD, initialWeights) [all …]
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/dports/math/octave-forge-nan/nan-3.6.1/src/ |
H A D | Makefile.in | 150 svm%_mex.$(MEX_EXT) : svm%_mex.o svm.o svm_model_matlab.o c_mexapi_version.o 158 svm%_mex.mex: svm%_mex.cpp svm_model_octave.o 161 svm.o: svm.cpp 162 $(CC) $(CFLAGS) -c svm.cpp 163 svm%_mex.mex: svm%_mex.cpp svm_model_octave.o svm.o 173 train.$(MEX_EXT) predict.$(MEX_EXT): train.c tron.o linear_model_matlab.c 177 train.mex predict.mex: train.c tron.o linear_model_matlab.c 180 train.$(MEX_EXT) predict.$(MEX_EXT): train.c tron.o linear.o linear_model_matlab.c 184 train.mex predict.mex: train.c tron.o linear.o linear_model_matlab.c 218 train.mexw32 predict.mexw32: train.obj linear.obj linear_model_matlab.obj tron.obj [all …]
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/src/AnomalyDetectors/ |
H A D | OneClassSVM.php | 20 use svm; alias 46 protected $svm; variable in Rubix\\ML\\AnomalyDetectors\\OneClassSVM 97 svm::OPT_TYPE => svm::ONE_CLASS, 98 svm::OPT_NU => $nu, 99 svm::OPT_SHRINKING => $shrinking, 100 svm::OPT_EPS => $tolerance, 106 $svm = new svm(); 108 $svm->setOptions($options); 110 $this->svm = $svm; 190 public function predict(Dataset $dataset) : array function in Rubix\\ML\\AnomalyDetectors\\OneClassSVM [all …]
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/dports/science/R-cran-e1071/e1071/inst/ |
H A D | NEWS.Rd | 56 \item add warning in \code{predict.naiveBayes()} if the variable 70 \item \code{predict.naiveBayes} now fixes the factor levels of 96 \item fix bug in predict.svm (new data with NA in response got 116 \item add warning in \code{predict.svm()} if probabilities should be 120 \code{predict.naiveBayes()} to account for close-zero probabilities 181 to \code{predict.svm()} 246 \code{predict.naiveBayes()} 257 \item \code{predict.naiveBayes()} sped up 293 \item \code{predict.svm()} now adds row numbers to predictions, and 383 \item Small bug fixes in \code{predict.svm()} and \code{plot.svm()} [all …]
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/src/Regressors/ |
H A D | SVR.php | 22 use svm; alias 52 protected $svm; variable in Rubix\\ML\\Regressors\\SVR 110 svm::OPT_TYPE => svm::EPSILON_SVR, 111 svm::OPT_C => $c, 112 svm::OPT_P => $epsilon, 114 svm::OPT_EPS => $tolerance, 120 $svm = new svm(); 122 $svm->setOptions($options); 124 $this->svm = $svm; 215 public function predict(Dataset $dataset) : array function in Rubix\\ML\\Regressors\\SVR [all …]
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/src/Classifiers/ |
H A D | SVC.php | 22 use svm; alias 48 protected $svm; variable in Rubix\\ML\\Classifiers\\SVC 108 svm::OPT_TYPE => svm::C_SVC, 109 svm::OPT_C => $c, 110 svm::OPT_SHRINKING => $shrinking, 111 svm::OPT_EPS => $tolerance, 117 $svm = new svm(); 119 $svm->setOptions($options); 121 $this->svm = $svm; 215 public function predict(Dataset $dataset) : array function in Rubix\\ML\\Classifiers\\SVC [all …]
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/dports/science/R-cran-e1071/e1071/vignettes/ |
H A D | svmdoc.Rnw | 137 classification. The task is to predict the type of a glass on basis 152 \texttt{rpart()}), we fit the model and try to predict the test set 155 ## svm 157 svm.pred <- predict(svm.model, testset[,-10]) 165 rpart.pred <- predict(rpart.model, testset[,-10], type = "class") 194 svm.pred <- predict(svm.model, testset[,-10]) 201 rpart.pred <- predict(rpart.model, testset[,-10], type = "class") 239 svm.pred <- predict(svm.model, testset[,-3]) 244 rpart.pred <- predict(rpart.model, testset[,-3]) 261 svm.pred <- predict(svm.model, testset[,-3]) [all …]
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