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/dports/multimedia/vmaf/vmaf-2.3.0/third_party/libsvm/
H A DMakefile9 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 …]
H A DMakefile.win11 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 …]
H A D.gitignore2 svm-predict
3 svm-scale
4 svm-train
5 svm.o
/dports/science/libsvm/libsvm-3.23/
H A DMakefile6 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 …]
H A DMakefile.win11 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 …]
/dports/science/libsvm-python/libsvm-3.23/
H A DMakefile6 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 …]
H A DMakefile.win11 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
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/dports/biology/viennarna/ViennaRNA-2.4.18/src/libsvm-3.24/
H A DMakefile6 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 …]
H A DMakefile.win11 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
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/dports/devel/p5-Algorithm-SVM/Algorithm-SVM-0.13/
H A Dtest.pl64 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);
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/svm/tests/
H A Dtest_svm.py103 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))
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H A Dtest_sparse.py66 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)
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/dports/graphics/opencv/opencv-4.5.3/modules/ml/test/
H A Dtest_svmtrainauto.cpp75 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()
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/dports/science/R-cran-e1071/e1071/
H A DNAMESPACE11 "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 …]
/dports/science/R-cran-e1071/e1071/man/
H A Dpredict.svm.Rd1 \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)
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/dports/science/R-cran-e1071/e1071/inst/doc/
H A Dsvmdoc.R21 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 …]
H A Dsvmdoc.Rnw137 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 …]
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/
H A Dtest_multiclass.py23 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 …]
/dports/devel/spark/spark-2.1.1/mllib/src/test/scala/org/apache/spark/mllib/classification/
H A DSVMSuite.scala86 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 …]
/dports/math/octave-forge-nan/nan-3.6.1/src/
H A DMakefile.in150 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
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/src/AnomalyDetectors/
H A DOneClassSVM.php20 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
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/dports/science/R-cran-e1071/e1071/inst/
H A DNEWS.Rd56 \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 …]
/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/src/Regressors/
H A DSVR.php22 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
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/src/Classifiers/
H A DSVC.php22 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
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/dports/science/R-cran-e1071/e1071/vignettes/
H A Dsvmdoc.Rnw137 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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