chore: import upstream snapshot with attribution

modules/ml/perf/perf_svm.cpp

@@ -0,0 +1,48 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+#include "perf_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+// SVM::predict is dominated by the per-feature kernel reduction over the support
+// vectors: calc_non_rbf_base (LINEAR/POLY/SIGMOID dot product), calc_rbf
+// (squared distance), calc_intersec (min-sum) and calc_chi2. Train data is kept
+// non-negative so the INTER and CHI2 kernels stay in their valid domain.
+typedef TestBaseWithParam< tuple<int, int, int> > SVMPredict;  // (samples, dims, kernelType)
+
+PERF_TEST_P(SVMPredict, kernels, testing::Combine(
+            testing::Values(1500),
+            testing::Values(512),
+            testing::Values((int)SVM::RBF, (int)SVM::POLY, (int)SVM::INTER, (int)SVM::CHI2)))
+{
+    const int nsamples = get<0>(GetParam());
+    const int dims     = get<1>(GetParam());
+    const int kernel   = get<2>(GetParam());
+    const int nquery   = 1000;
+
+    Mat train(nsamples, dims, CV_32F), query(nquery, dims, CV_32F);
+    Mat responses(nsamples, 1, CV_32S);
+    RNG& rng = theRNG();
+    rng.fill(train, RNG::UNIFORM, 0.f, 1.f);
+    rng.fill(query, RNG::UNIFORM, 0.f, 1.f);
+    for (int i = 0; i < nsamples; i++)
+        responses.at<int>(i) = i & 1;
+
+    Ptr<SVM> svm = SVM::create();
+    svm->setType(SVM::C_SVC);
+    svm->setKernel(kernel);
+    svm->setC(1);
+    svm->setGamma(0.1);
+    svm->setDegree(3);   // POLY
+    svm->setCoef0(1);    // POLY
+    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 1000, 1e-3));
+    svm->train(train, ROW_SAMPLE, responses);
+
+    Mat results;
+    TEST_CYCLE() svm->predict(query, results);
+
+    SANITY_CHECK_NOTHING();
+}
+
+}} // namespace