Script 'mail_helper' called by obssrc Hello community, here is the log from the commit of package libsvm for openSUSE:Factory checked in at 2026-01-01 15:01:00 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Comparing /work/SRC/openSUSE:Factory/libsvm (Old) and /work/SRC/openSUSE:Factory/.libsvm.new.1928 (New) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "libsvm" Thu Jan 1 15:01:00 2026 rev:7 rq:1324936 version:3.36 Changes: -------- --- /work/SRC/openSUSE:Factory/libsvm/libsvm.changes 2024-09-27 17:11:58.127845793 +0200 +++ /work/SRC/openSUSE:Factory/.libsvm.new.1928/libsvm.changes 2026-01-01 15:02:07.517626500 +0100 @@ -1,0 +2,8 @@ +Sun Dec 28 23:29:56 UTC 2025 - Dirk Müller <[email protected]> + +- update to 3.36: + * enhanced Python interface. + * Probabilistic outputs for one-class SVM are now supported + - drop libsvm-java8.patch (java 11 is now default) + +------------------------------------------------------------------- Old: ---- libsvm-3.3.tar.gz libsvm-java8.patch New: ---- libsvm-3.36.tar.gz ----------(Old B)---------- Old: * Probabilistic outputs for one-class SVM are now supported - drop libsvm-java8.patch (java 11 is now default) ----------(Old E)---------- ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ libsvm.spec ++++++ --- /var/tmp/diff_new_pack.iPQhAj/_old 2026-01-01 15:02:08.073649304 +0100 +++ /var/tmp/diff_new_pack.iPQhAj/_new 2026-01-01 15:02:08.077649468 +0100 @@ -1,7 +1,7 @@ # # spec file for package libsvm # -# Copyright (c) 2024 SUSE LLC +# Copyright (c) 2025 SUSE LLC and contributors # # All modifications and additions to the file contributed by third parties # remain the property of their copyright owners, unless otherwise agreed @@ -16,18 +16,17 @@ # -%define libname libsvm3 -%define fileversion 3.3 +%define libname libsvm4 +%define fileversion 3.36 Summary: A Library for Support Vector Machines License: BSD-3-Clause Group: Development/Languages/Other Name: libsvm -Version: 3.30 +Version: 3.36 Release: 0 URL: https://www.csie.ntu.edu.tw/~cjlin/libsvm/ Source0: https://www.csie.ntu.edu.tw/~cjlin/libsvm/%{name}-%{fileversion}.tar.gz -Patch0: libsvm-java8.patch Patch1: libsvm-reproducible-jar-mtime.patch BuildRequires: %{python_module devel} BuildRequires: gcc-c++ @@ -97,7 +96,6 @@ %prep %setup -n %{name}-%{fileversion} -%patch -P 0 -p1 # The "--date" option was added into jar in OpenJDK 17 %if %{?pkg_vcmp:%pkg_vcmp java-devel >= 17}%{!?pkg_vcmp:0} %patch -P 1 -p1 @@ -128,8 +126,8 @@ install -m 755 ./tools/subset.py %{buildroot}%{_bindir}/svm-subset install -m 755 ./tools/easy.py %{buildroot}%{_bindir}/svm-easy install -m 644 svm.h %{buildroot}%{_includedir}/libsvm/ -install -m 755 libsvm.so.3 %{buildroot}%{_libdir} -ln -s %{_libdir}/libsvm.so.3 %{buildroot}%{_libdir}/libsvm.so +install -m 755 libsvm.so.4 %{buildroot}%{_libdir} +ln -s %{_libdir}/libsvm.so.4 %{buildroot}%{_libdir}/libsvm.so mv ./python/README README-python mv ./tools/README README-python-tools @@ -156,7 +154,7 @@ %files -n %{libname} %license COPYRIGHT -%{_libdir}/libsvm.so.3 +%{_libdir}/libsvm.so.4 %files devel %{_includedir}/libsvm ++++++ libsvm-3.3.tar.gz -> libsvm-3.36.tar.gz ++++++ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/.github/workflows/wheel.yml new/libsvm-3.36/.github/workflows/wheel.yml --- old/libsvm-3.3/.github/workflows/wheel.yml 2022-08-10 16:06:18.000000000 +0200 +++ new/libsvm-3.36/.github/workflows/wheel.yml 2025-05-12 06:29:48.000000000 +0200 @@ -4,7 +4,7 @@ # on new tag push: tags: - - '*' + - "*" # manually trigger workflow_dispatch: @@ -15,26 +15,31 @@ runs-on: ${{ matrix.os }} strategy: matrix: - os: [windows-2019] + os: [windows-2022, macos-13] steps: - uses: actions/checkout@v2 - # Used to host cibuildwheel - - uses: actions/setup-python@v2 - - - name: Install cibuildwheel - run: python -m pip install cibuildwheel==2.3.1 + - name: Set MacOS compiler + if: runner.os == 'macOS' + run: | + brew install gcc@13; + echo "CXX=gcc-13" >> $GITHUB_ENV - name: Build wheels - working-directory: ./python - run: python -m cibuildwheel --output-dir wheelhouse + uses: pypa/[email protected] env: # don't build for PyPython and windows 32-bit CIBW_SKIP: pp* *win32* + # force compiler on macOS + CXX: ${{ env.CXX }} + CC: ${{ env.CXX }} + with: + package-dir: ./python + output-dir: ./python/wheelhouse - name: Upload a Build Artifact - uses: actions/[email protected] + uses: actions/upload-artifact@v4 with: + name: wheels-${{ matrix.os }} path: ./python/wheelhouse - diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/COPYRIGHT new/libsvm-3.36/COPYRIGHT --- old/libsvm-3.3/COPYRIGHT 2022-08-10 16:06:18.000000000 +0200 +++ new/libsvm-3.36/COPYRIGHT 2025-05-12 06:29:48.000000000 +0200 @@ -1,5 +1,5 @@ -Copyright (c) 2000-2022 Chih-Chung Chang and Chih-Jen Lin +Copyright (c) 2000-2023 Chih-Chung Chang and Chih-Jen Lin All rights reserved. Redistribution and use in source and binary forms, with or without diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/FAQ.html new/libsvm-3.36/FAQ.html --- old/libsvm-3.3/FAQ.html 2022-08-10 16:06:18.000000000 +0200 +++ new/libsvm-3.36/FAQ.html 2025-05-12 06:29:48.000000000 +0200 @@ -9,7 +9,7 @@ <a name="_TOP"><b><h1><a href=http://www.csie.ntu.edu.tw/~cjlin/libsvm>LIBSVM</a> FAQ </h1></b></a> <b>last modified : </b> -Sat, 12 Mar 2022 22:30:54 GMT +Sun, 4 Sep 2022 12:32:38 GMT <class="categories"> <li><a href="#_TOP">All Questions</a>(84)</li> @@ -86,7 +86,7 @@ <li class="headlines_item"><a href="#f427">Why the code gives NaN (not a number) results?</a></li> <li class="headlines_item"><a href="#f430">Why the sign of predicted labels and decision values are sometimes reversed?</a></li> <li class="headlines_item"><a href="#f431">I don't know class labels of test data. What should I put in the first column of the test file?</a></li> -<li class="headlines_item"><a href="#f432">How can I use OpenMP to parallelize LIBSVM on a multicore/shared-memory computer?</a></li> +<li class="headlines_item"><a href="#f432">How can I use OpenMP, tbb, or other tools to parallelize LIBSVM on a multicore/shared-memory computer?</a></li> <li class="headlines_item"><a href="#f433">How could I know which training instances are support vectors?</a></li> <li class="headlines_item"><a href="#f434">Why sv_indices (indices of support vectors) are not stored in the saved model file?</a></li> <li class="headlines_item"><a href="#f501">After doing cross validation, why there is no model file outputted ?</a></li> @@ -1241,14 +1241,15 @@ <a href="#_TOP">[Go Top]</a> <hr/> <a name="/Q04:_Training_and_prediction"></a> -<a name="f432"><b>Q: How can I use OpenMP to parallelize LIBSVM on a multicore/shared-memory computer?</b></a> +<a name="f432"><b>Q: How can I use OpenMP, tbb, or other tools to parallelize LIBSVM on a multicore/shared-memory computer?</b></a> <br/> -<p>It is very easy if you are using GCC 4.2 -or after. - -<p> In Makefile, add -fopenmp to CFLAGS. +<p>It is very easy. Consider OpenMP. For GCC, version 4.2 or after is needed. +<p> In Makefile, add -fopenmp to CFLAGS. In the beginning of svm.cpp, add +<pre> +#include <omp.h> +</pre> <p> In class SVC_Q of svm.cpp, modify the for loop of get_Q to: <pre> @@ -1267,7 +1268,7 @@ a reduction clause for the variable sum: <pre> #pragma omp parallel for private(i) reduction(+:sum) schedule(guided) - for(i=0;i<model->l;i++) + for(i=0;i<model->l;i++) sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param); </pre> @@ -1293,13 +1294,96 @@ </pre> instead. -<p> For Python interface, you need to add the -lgomp link option: +<p> For Python interface, you need to add the -fopenmp link option: <pre> -$(CXX) -lgomp -shared -dynamiclib svm.o -o libsvm.so.$(SHVER) +if [ "$(OS)" = "Darwin" ]; then \ + SHARED_LIB_FLAG="-dynamiclib -Wl,-install_name,libsvm.so.$(SHVER) -fopenmp"; \ +else \ + SHARED_LIB_FLAG="-shared -Wl,-soname,libsvm.so.$(SHVER) -fopenmp"; \ +fi; \ </pre> <p> For MS Windows, you need to add /openmp in CFLAGS of Makefile.win +<!-- <p>To use OpenMP, check that your compiler supports it. For GCC version 4.2 or later is needed. + +<p>In Makefile, there are the lines +<pre> +# Uncomment the following lines to enable parallelization with OpenMP +# CFLAGS += -fopenmp +# SHARED_LIB_FLAG += -fopenmp +</pre> +Uncomment the latter two lines then rebuild the package to use OpenMP. + +<p>Kernel evaluations in training/testing will be parallelized. +An example of running this modification on an 8-core machine using the data set +<a href=../libsvmtools/datasets/binary/real-sim.bz2>real-sim</a>: + +<p> 8 cores: +<pre> +%setenv OMP_NUM_THREADS 8 +%time svm-train -c 8 -g 0.5 -m 1000 real-sim +175.90sec +</pre> +1 core: +<pre> +%setenv OMP_NUM_THREADS 1 +%time svm-train -c 8 -g 0.5 -m 1000 real-sim +588.89sec +</pre> +For this data, kernel evaluations take 91% of training time. In the above example, we assume you use csh. For bash, use +<pre> +export OMP_NUM_THREADS=8 +</pre> +instead. + +<p>For Windows, in Makefile.win there are the lines +<pre> +# Uncomment the following lines to enable parallelization with OpenMP +# CFLAGS = /nologo /O2 /EHsc /I. /D _WIN64 /D _CRT_SECURE_NO_DEPRECATE /openmp +</pre> +Uncomment the latter line and rebuild the package to use OpenMP. --> + +<p> Now consider tbb (thread building blocks). Thank Rob Ward for the following instructions. First the tbb package must be installed. On Ubuntu this can be done by +<pre> +apt-get install -y libtbb-dev +</pre> +In svm.cpp, add +<pre> +#include <atomic> +#include <execution> +#include "tbb/iterators.h" +</pre> +in the beginning. Then the loops mentioned earlier should be as +follows. In class SVC_Q of svm.cpp, +<pre> +std::for_each(std::execution::par, tbb::counting_iterator<int>(start), tbb::counting_iterator<int>(len), + [this, i, data](int j){ + data[j] = (Qfloat)(y[i] * y[j] * (this->*kernel_function)(i, j)); + }); +</pre> +In class SVR_Q of svm.cpp, +<pre> +std::for_each(std::execution::par, tbb::counting_iterator<int>(0), tbb::counting_iterator<int>(l), + [this, i, data, real_i](int j){ + data[j] = (Qfloat)(this->*kernel_function)(real_i, j); + }); +</pre> +In svm_predict_values() of svm.cpp, for SVR and ONE_CLASS, +<pre> +std::atomic<double> sum = 0; +std::for_each(std::execution::par, tbb::counting_iterator<int>(0), tbb::counting_iterator<int>(model->l), + [&sum, sv_coef, x, model](int i){ + sum += sv_coef[i] * Kernel::k_function(x, model->SV[i], model->param); + }); +</pre> +and for SVC +<pre> +std::for_each(std::execution::par, tbb::counting_iterator<int>(0), tbb::counting_iterator<int>(l), + [kvalue, x, model](int i){ + kvalue[i] = Kernel::k_function(x, model->SV[i], model->param); + }); +</pre> <p align="right"> <a href="#_TOP">[Go Top]</a> <hr/> diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/Makefile new/libsvm-3.36/Makefile --- old/libsvm-3.3/Makefile 2022-08-10 16:06:19.000000000 +0200 +++ new/libsvm-3.36/Makefile 2025-05-12 06:29:48.000000000 +0200 @@ -1,18 +1,21 @@ CXX ?= g++ CFLAGS = -Wall -Wconversion -O3 -fPIC -SHVER = 3 +SHVER = 4 OS = $(shell uname) +ifeq ($(OS),Darwin) + SHARED_LIB_FLAG = -dynamiclib -Wl,-install_name,libsvm.so.$(SHVER) +else + SHARED_LIB_FLAG = -shared -Wl,-soname,libsvm.so.$(SHVER) +endif + +# Uncomment the following lines to enable parallelization with OpenMP +# CFLAGS += -fopenmp +# SHARED_LIB_FLAG += -fopenmp all: svm-train svm-predict svm-scale lib: svm.o - if [ "$(OS)" = "Darwin" ]; then \ - SHARED_LIB_FLAG="-dynamiclib -Wl,-install_name,libsvm.so.$(SHVER)"; \ - else \ - SHARED_LIB_FLAG="-shared -Wl,-soname,libsvm.so.$(SHVER)"; \ - fi; \ - $(CXX) $${SHARED_LIB_FLAG} svm.o -o libsvm.so.$(SHVER) - + $(CXX) $(SHARED_LIB_FLAG) svm.o -o libsvm.so.$(SHVER) svm-predict: svm-predict.c svm.o $(CXX) $(CFLAGS) svm-predict.c svm.o -o svm-predict -lm svm-train: svm-train.c svm.o diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/Makefile.win new/libsvm-3.36/Makefile.win --- old/libsvm-3.3/Makefile.win 2022-08-10 16:06:20.000000000 +0200 +++ new/libsvm-3.36/Makefile.win 2025-05-12 06:29:48.000000000 +0200 @@ -8,6 +8,9 @@ CFLAGS = /nologo /O2 /EHsc /I. /D _WIN64 /D _CRT_SECURE_NO_DEPRECATE TARGET = windows +# Uncomment the following lines to enable parallelization with OpenMP +# CFLAGS = /nologo /O2 /EHsc /I. /D _WIN64 /D _CRT_SECURE_NO_DEPRECATE /openmp + all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.exe lib $(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/README new/libsvm-3.36/README --- old/libsvm-3.3/README 2022-08-10 16:06:20.000000000 +0200 +++ new/libsvm-3.36/README 2025-05-12 06:29:51.000000000 +0200 @@ -221,7 +221,7 @@ > svm-train -s 0 -c 5 -t 2 -g 0.5 -e 0.1 data_file -Train a classifier with RBF kernel exp(-0.5|u-v|^2), C=10, and +Train a classifier with RBF kernel exp(-0.5|u-v|^2), C=5, and stopping tolerance 0.1. > svm-train -s 3 -p 0.1 -t 0 data_file @@ -471,7 +471,10 @@ param describes the parameters used to obtain the model. - nr_class is the number of classes. It is 2 for regression and one-class SVM. + nr_class is the number of classes for classification. It is a + non-negative integer with special cases of 0 (no training data at + all) and 1 (all training data in one class). For regression and + one-class SVM, nr_class = 2. l is the number of support vectors. SV and sv_coef are support vectors and the corresponding coefficients, respectively. Assume there are @@ -664,6 +667,27 @@ svm_set_print_string_function(NULL); for default printing to stdout. + Please note that this function is not thread-safe. When multiple threads load or + use the same dynamic library (for example, libsvm.so.4), they actually share the + same memory space of the dynamic library, which results in all threads modifying + the same static function pointer, svm_print_string, in svm.cpp when they call this + function. + + For example, suppose we have threads A and B. They call this function sequentially + and pass their own thread-local print_func into it. After that, they both call (*svm_print_string)(str) + once. When the last thread finishes setting it (say B), svm_print_string is set to + B.print_func. Now, if thread A wants to access svm_print_string, it is actually + accessing B.print_func rather than A.print_func, which is incorrect since we expect + to use the functionality of A.print_func. + + Even if A.print_func and B.print_func have identical functionality, it is still risky. + Suppose svm_print_string is now set to B.print_func, and B deletes B.print_func after + finishing its work. Later, thread A calls svm_print_string, but the address points to, + which is B.print_func, has already been deleted. This invalid memory access will crash + the program. To mitigate this issue, in this example, you should ensure that A.print_func + and B.print_func remain valid after threads finish their work. For example, in Python, + you can assign them as global variables. + Java Version ============ @@ -683,7 +707,7 @@ Library usages are similar to the C version. These functions are available: public class svm { - public static final int LIBSVM_VERSION=330; + public static final int LIBSVM_VERSION=336; public static svm_model svm_train(svm_problem prob, svm_parameter param); public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target); public static int svm_get_svm_type(svm_model model); @@ -716,6 +740,9 @@ }; svm.svm_set_print_string_function(your_print_func); +However, similar to the C version, it is not thread-safe. Please check the +usage of C version svm_set_print_string_function() for details. + Building Windows Binaries ========================= diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/java/Makefile new/libsvm-3.36/java/Makefile --- old/libsvm-3.3/java/Makefile 2022-08-10 16:06:21.000000000 +0200 +++ new/libsvm-3.36/java/Makefile 2025-05-12 06:29:48.000000000 +0200 @@ -5,7 +5,7 @@ svm_train.class svm_predict.class svm_toy.class svm_scale.class #JAVAC = jikes -JAVAC_FLAGS = -target 1.7 -source 1.7 +JAVAC_FLAGS = --release 11 JAVAC = javac # JAVAC_FLAGS = export CLASSPATH := .:$(CLASSPATH) diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/java/libsvm/svm.java new/libsvm-3.36/java/libsvm/svm.java --- old/libsvm-3.3/java/libsvm/svm.java 2022-08-10 16:07:05.000000000 +0200 +++ new/libsvm-3.36/java/libsvm/svm.java 2025-05-12 06:29:48.000000000 +0200 @@ -1295,7 +1295,7 @@ // // construct and solve various formulations // - public static final int LIBSVM_VERSION=330; + public static final int LIBSVM_VERSION=336; public static final Random rand = new Random(); private static svm_print_interface svm_print_stdout = new svm_print_interface() diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/java/libsvm/svm.m4 new/libsvm-3.36/java/libsvm/svm.m4 --- old/libsvm-3.3/java/libsvm/svm.m4 2022-08-10 16:06:22.000000000 +0200 +++ new/libsvm-3.36/java/libsvm/svm.m4 2025-05-12 06:29:48.000000000 +0200 @@ -1295,7 +1295,7 @@ // // construct and solve various formulations // - public static final int LIBSVM_VERSION=330; + public static final int LIBSVM_VERSION=336; public static final Random rand = new Random(); private static svm_print_interface svm_print_stdout = new svm_print_interface() Binary files old/libsvm-3.3/java/libsvm.jar and new/libsvm-3.36/java/libsvm.jar differ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/java/svm_toy.java new/libsvm-3.36/java/svm_toy.java --- old/libsvm-3.3/java/svm_toy.java 2022-08-10 16:06:24.000000000 +0200 +++ new/libsvm-3.36/java/svm_toy.java 2025-05-12 06:29:49.000000000 +0200 @@ -1,11 +1,15 @@ import libsvm.*; -import java.applet.*; import java.awt.*; import java.util.*; import java.awt.event.*; import java.io.*; -public class svm_toy extends Applet { +public class svm_toy { + public static void main(String[] args) { + svm_toy_frame frame = new svm_toy_frame("svm_toy", 500, 500+50); + } +} +class svm_toy_frame extends Frame { static final String DEFAULT_PARAM="-t 2 -c 100"; int XLEN; @@ -43,10 +47,24 @@ Vector<point> point_list = new Vector<point>(); byte current_value = 1; - public void init() + svm_toy_frame(String title, int width, int height) { - setSize(getSize()); + super(title); + this.addWindowListener(new WindowAdapter() { + public void windowClosing(WindowEvent e) { + System.exit(0); + } + }); + this.init(); + this.setSize(width, height); + XLEN = width; + YLEN = height-50; + this.clear_all(); + this.setVisible(true); + } + void init() + { final Button button_change = new Button("Change"); Button button_run = new Button("Run"); Button button_clear = new Button("Clear"); @@ -466,37 +484,4 @@ } g.drawImage(buffer,0,0,this); } - - public Dimension getPreferredSize() { return new Dimension(XLEN,YLEN+50); } - - public void setSize(Dimension d) { setSize(d.width,d.height); } - public void setSize(int w,int h) { - super.setSize(w,h); - XLEN = w; - YLEN = h-50; - clear_all(); - } - - public static void main(String[] argv) - { - new AppletFrame("svm_toy",new svm_toy(),500,500+50); - } -} - -class AppletFrame extends Frame { - AppletFrame(String title, Applet applet, int width, int height) - { - super(title); - this.addWindowListener(new WindowAdapter() { - public void windowClosing(WindowEvent e) { - System.exit(0); - } - }); - applet.init(); - applet.setSize(width,height); - applet.start(); - this.add(applet); - this.pack(); - this.setVisible(true); - } -} +} \ No newline at end of file diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/java/test_applet.html new/libsvm-3.36/java/test_applet.html --- old/libsvm-3.3/java/test_applet.html 2022-08-10 16:06:24.000000000 +0200 +++ new/libsvm-3.36/java/test_applet.html 1970-01-01 01:00:00.000000000 +0100 @@ -1 +0,0 @@ -<APPLET code="svm_toy.class" archive="libsvm.jar" width=300 height=350></APPLET> diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/matlab/libsvmread.c new/libsvm-3.36/matlab/libsvmread.c --- old/libsvm-3.3/matlab/libsvmread.c 2022-08-10 16:06:25.000000000 +0200 +++ new/libsvm-3.36/matlab/libsvmread.c 2025-05-12 06:29:49.000000000 +0200 @@ -188,7 +188,9 @@ void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { - char filename[256]; + #define filename_size 256 + + char filename[filename_size]; if(nrhs != 1 || nlhs != 2) { @@ -197,11 +199,9 @@ return; } - mxGetString(prhs[0], filename, mxGetN(prhs[0]) + 1); - - if(filename == NULL) - { - mexPrintf("Error: filename is NULL\n"); + if(mxGetString(prhs[0], filename, filename_size) == 1){ + mexPrintf("Error: wrong or too long filename\n"); + fake_answer(nlhs, plhs); return; } diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/python/README new/libsvm-3.36/python/README --- old/libsvm-3.3/python/README 2022-08-10 16:06:26.000000000 +0200 +++ new/libsvm-3.36/python/README 2025-05-12 06:29:49.000000000 +0200 @@ -348,7 +348,7 @@ The above command loads svm_train() : train an SVM model svm_predict() : predict testing data - svm_read_problem() : read the data from a LIBSVM-format file. + svm_read_problem() : read the data from a LIBSVM-format file or object. svm_load_model() : load a LIBSVM model. svm_save_model() : save model to a file. evaluations() : evaluate prediction results. @@ -448,6 +448,8 @@ See the usage by examples: >>> y, x = svm_read_problem('data.txt') + >>> with open('data.txt') as f: + >>> y, x = svm_read_problem(f) >>> m = svm_load_model('model_file') >>> svm_save_model('model_file', m) diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/python/libsvm/commonutil.py new/libsvm-3.36/python/libsvm/commonutil.py --- old/libsvm-3.3/python/libsvm/commonutil.py 2022-08-10 16:06:27.000000000 +0200 +++ new/libsvm-3.36/python/libsvm/commonutil.py 2025-05-12 06:29:49.000000000 +0200 @@ -1,5 +1,3 @@ -#!/usr/bin/env python - from __future__ import print_function from array import array import sys @@ -14,12 +12,12 @@ __all__ = ['svm_read_problem', 'evaluations', 'csr_find_scale_param', 'csr_scale'] -def svm_read_problem(data_file_name, return_scipy=False): +def svm_read_problem(data_source, return_scipy=False): """ - svm_read_problem(data_file_name, return_scipy=False) -> [y, x], y: list, x: list of dictionary - svm_read_problem(data_file_name, return_scipy=True) -> [y, x], y: ndarray, x: csr_matrix + svm_read_problem(data_source, return_scipy=False) -> [y, x], y: list, x: list of dictionary + svm_read_problem(data_source, return_scipy=True) -> [y, x], y: ndarray, x: csr_matrix - Read LIBSVM-format data from data_file_name and return labels y + Read LIBSVM-format data from data_source and return labels y and data instances x. """ if scipy != None and return_scipy: @@ -33,30 +31,43 @@ row_ptr = [0] col_idx = [] indx_start = 1 - for i, line in enumerate(open(data_file_name)): - line = line.split(None, 1) - # In case an instance with all zero features - if len(line) == 1: line += [''] - label, features = line - prob_y.append(float(label)) - if scipy != None and return_scipy: - nz = 0 - for e in features.split(): - ind, val = e.split(":") - if ind == '0': - indx_start = 0 - val = float(val) - if val != 0: - col_idx.append(int(ind)-indx_start) - prob_x.append(val) - nz += 1 - row_ptr.append(row_ptr[-1]+nz) - else: - xi = {} - for e in features.split(): - ind, val = e.split(":") - xi[int(ind)] = float(val) - prob_x += [xi] + + if hasattr(data_source, "read"): + file = data_source + else: + file = open(data_source) + try: + for line in file: + line = line.split(None, 1) + # In case an instance with all zero features + if len(line) == 1: line += [''] + label, features = line + prob_y.append(float(label)) + if scipy != None and return_scipy: + nz = 0 + for e in features.split(): + ind, val = e.split(":") + if ind == '0': + indx_start = 0 + val = float(val) + if val != 0: + col_idx.append(int(ind)-indx_start) + prob_x.append(val) + nz += 1 + row_ptr.append(row_ptr[-1]+nz) + else: + xi = {} + for e in features.split(): + ind, val = e.split(":") + xi[int(ind)] = float(val) + prob_x += [xi] + except Exception as err_msg: + raise err_msg + finally: + if not hasattr(data_source, "read"): + # close file only if it was created by us + file.close() + if scipy != None and return_scipy: prob_y = np.frombuffer(prob_y, dtype='d') prob_x = np.frombuffer(prob_x, dtype='d') diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/python/libsvm/svm.py new/libsvm-3.36/python/libsvm/svm.py --- old/libsvm-3.3/python/libsvm/svm.py 2022-08-10 16:06:27.000000000 +0200 +++ new/libsvm-3.36/python/libsvm/svm.py 2025-05-12 06:29:51.000000000 +0200 @@ -1,9 +1,8 @@ -#!/usr/bin/env python - from ctypes import * from ctypes.util import find_library from os import path from glob import glob +from enum import IntEnum import sys try: @@ -19,10 +18,8 @@ from itertools import izip as zip __all__ = ['libsvm', 'svm_problem', 'svm_parameter', - 'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC', - 'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS', - 'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF', - 'SIGMOID', 'c_double', 'svm_model'] + 'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'svm_forms', + 'PRINT_STRING_FUN', 'kernel_names', 'c_double', 'svm_model'] try: dirname = path.dirname(path.abspath(__file__)) @@ -34,7 +31,7 @@ if sys.platform == 'win32': libsvm = CDLL(path.join(dirname, r'..\..\windows\libsvm.dll')) else: - libsvm = CDLL(path.join(dirname, '../../libsvm.so.3')) + libsvm = CDLL(path.join(dirname, '../../libsvm.so.4')) except: # For unix the prefix 'lib' is not considered. if find_library('svm'): @@ -44,22 +41,32 @@ else: raise Exception('LIBSVM library not found.') -C_SVC = 0 -NU_SVC = 1 -ONE_CLASS = 2 -EPSILON_SVR = 3 -NU_SVR = 4 - -LINEAR = 0 -POLY = 1 -RBF = 2 -SIGMOID = 3 -PRECOMPUTED = 4 +class svm_forms(IntEnum): + C_SVC = 0 + NU_SVC = 1 + ONE_CLASS = 2 + EPSILON_SVR = 3 + NU_SVR = 4 + +class kernel_names(IntEnum): + LINEAR = 0 + POLY = 1 + RBF = 2 + SIGMOID = 3 + PRECOMPUTED = 4 PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p) def print_null(s): return +# In multi-threading, all threads share the same memory space of +# the dynamic library (libsvm). Thus, we use a module-level +# variable to keep a reference to ctypes print_null, preventing +# python from garbage collecting it in thread B while thread A +# still needs it. Check the usage of svm_set_print_string_function() +# in LIBSVM README for details. +ctypes_print_null = PRINT_STRING_FUN(print_null) + def genFields(names, types): return list(zip(names, types)) @@ -101,7 +108,7 @@ index_range = index_range[np.where(index_range <= feature_max)] elif isinstance(xi, (dict, list, tuple)): if isinstance(xi, dict): - index_range = xi.keys() + index_range = sorted(xi.keys()) elif isinstance(xi, (list, tuple)): if not isKernel: xi_shift = 1 @@ -110,11 +117,9 @@ index_range = range(0, len(xi)) # index starts from 0 for precomputed kernel if feature_max: - index_range = filter(lambda j: j <= feature_max, index_range) + index_range = list(filter(lambda j: j <= feature_max, index_range)) if not isKernel: - index_range = filter(lambda j:xi[j-xi_shift] != 0, index_range) - - index_range = sorted(index_range) + index_range = list(filter(lambda j:xi[j-xi_shift] != 0, index_range)) else: raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)') @@ -123,9 +128,10 @@ if scipy and isinstance(xi, tuple) and len(xi) == 2\ and isinstance(xi[0], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector - for idx, j in enumerate(index_range): - ret[idx].index = j - ret[idx].value = (xi[1])[idx] + # since xi=(indices, values), we must sort them simultaneously. + for idx, arg in enumerate(np.argsort(index_range)): + ret[idx].index = index_range[arg] + ret[idx].value = (xi[1])[arg] else: for idx, j in enumerate(index_range): ret[idx].index = j @@ -140,10 +146,18 @@ from numba import jit jit_enabled = True except: - jit = lambda x: x + # We need to support two cases: when jit is called with no arguments, and when jit is called with + # a keyword argument. + def jit(func=None, *args, **kwargs): + if func is None: + # This handles the case where jit is used with parentheses: @jit(nopython=True) + return lambda x: x + else: + # This handles the case where jit is used without parentheses: @jit + return func jit_enabled = False -@jit +@jit(nopython=True) def csr_to_problem_jit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start): for i in range(l): b1,e1 = x_rowptr[i], x_rowptr[i+1] @@ -164,7 +178,9 @@ # Extra space for termination node and (possibly) bias term x_space = prob.x_space = np.empty((x.nnz+x.shape[0]), dtype=svm_node) - prob.rowptr = x.indptr.copy() + # rowptr has to be a 64bit integer because it will later be used for pointer arithmetic, + # which overflows when the added pointer points to an address that is numerically high. + prob.rowptr = x.indptr.astype(np.int64, copy=True) prob.rowptr[1:] += np.arange(1,x.shape[0]+1) prob_ind = x_space["index"] prob_val = x_space["value"] @@ -254,8 +270,8 @@ return s def set_to_default_values(self): - self.svm_type = C_SVC; - self.kernel_type = RBF + self.svm_type = svm_forms.C_SVC; + self.kernel_type = kernel_names.RBF self.degree = 3 self.gamma = 0 self.coef0 = 0 @@ -289,10 +305,10 @@ while i < len(argv): if argv[i] == "-s": i = i + 1 - self.svm_type = int(argv[i]) + self.svm_type = svm_forms(int(argv[i])) elif argv[i] == "-t": i = i + 1 - self.kernel_type = int(argv[i]) + self.kernel_type = kernel_names(int(argv[i])) elif argv[i] == "-d": i = i + 1 self.degree = int(argv[i]) @@ -324,7 +340,7 @@ i = i + 1 self.probability = int(argv[i]) elif argv[i] == "-q": - self.print_func = PRINT_STRING_FUN(print_null) + self.print_func = ctypes_print_null elif argv[i] == "-v": i = i + 1 self.cross_validation = 1 diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/python/libsvm/svmutil.py new/libsvm-3.36/python/libsvm/svmutil.py --- old/libsvm-3.3/python/libsvm/svmutil.py 2022-08-10 16:06:27.000000000 +0200 +++ new/libsvm-3.36/python/libsvm/svmutil.py 2025-05-12 06:29:49.000000000 +0200 @@ -1,5 +1,3 @@ -#!/usr/bin/env python - import os, sys from .svm import * from .svm import __all__ as svm_all @@ -95,7 +93,7 @@ assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (np.ndarray, sparse.spmatrix))) y, x, options = arg1, arg2, arg3 param = svm_parameter(options) - prob = svm_problem(y, x, isKernel=(param.kernel_type == PRECOMPUTED)) + prob = svm_problem(y, x, isKernel=(param.kernel_type == kernel_names.PRECOMPUTED)) elif isinstance(arg1, svm_problem): prob = arg1 if isinstance(arg2, svm_parameter): @@ -105,7 +103,7 @@ if prob == None or param == None: raise TypeError("Wrong types for the arguments") - if param.kernel_type == PRECOMPUTED: + if param.kernel_type == kernel_names.PRECOMPUTED: for i in range(prob.l): xi = prob.x[i] idx, val = xi[0].index, xi[0].value @@ -126,7 +124,7 @@ target = (c_double * l)() libsvm.svm_cross_validation(prob, param, nr_fold, target) ACC, MSE, SCC = evaluations(prob.y[:l], target[:l]) - if param.svm_type in [EPSILON_SVR, NU_SVR]: + if param.svm_type in [svm_forms.EPSILON_SVR, svm_forms.NU_SVR]: print("Cross Validation Mean squared error = %g" % MSE) print("Cross Validation Squared correlation coefficient = %g" % SCC) return MSE @@ -214,7 +212,7 @@ if not is_prob_model: raise ValueError("Model does not support probabiliy estimates") - if svm_type in [NU_SVR, EPSILON_SVR]: + if svm_type in [svm_forms.NU_SVR, svm_forms.EPSILON_SVR]: info("Prob. model for test data: target value = predicted value + z,\n" "z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability()); nr_class = 0 @@ -223,9 +221,9 @@ for i in range(nr_instance): if scipy and isinstance(x, sparse.spmatrix): indslice = slice(x.indptr[i], x.indptr[i+1]) - xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED)) + xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED)) else: - xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED)) + xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED)) label = libsvm.svm_predict_probability(m, xi, prob_estimates) values = prob_estimates[:nr_class] pred_labels += [label] @@ -233,7 +231,7 @@ else: if is_prob_model: info("Model supports probability estimates, but disabled in predicton.") - if svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC): + if svm_type in [svm_forms.ONE_CLASS, svm_forms.EPSILON_SVR, svm_forms.NU_SVC]: nr_classifier = 1 else: nr_classifier = nr_class*(nr_class-1)//2 @@ -241,9 +239,9 @@ for i in range(nr_instance): if scipy and isinstance(x, sparse.spmatrix): indslice = slice(x.indptr[i], x.indptr[i+1]) - xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED)) + xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED)) else: - xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED)) + xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED)) label = libsvm.svm_predict_values(m, xi, dec_values) if(nr_class == 1): values = [1] @@ -256,7 +254,7 @@ y = [0] * nr_instance ACC, MSE, SCC = evaluations(y, pred_labels) - if svm_type in [EPSILON_SVR, NU_SVR]: + if svm_type in [svm_forms.EPSILON_SVR, svm_forms.NU_SVR]: info("Mean squared error = %g (regression)" % MSE) info("Squared correlation coefficient = %g (regression)" % SCC) else: diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/python/setup.py new/libsvm-3.36/python/setup.py --- old/libsvm-3.3/python/setup.py 2022-08-10 16:06:27.000000000 +0200 +++ new/libsvm-3.36/python/setup.py 2025-05-12 06:29:49.000000000 +0200 @@ -16,7 +16,7 @@ PACKAGE_DIR = "libsvm" PACKAGE_NAME = "libsvm-official" -VERSION = "3.30.0" +VERSION = "3.36.0" cpp_dir = "cpp-source" # should be consistent with dynamic_lib_name in libsvm/svm.py dynamic_lib_name = "clib" @@ -30,6 +30,11 @@ "svm.def", ] +# license parameters +license_source = path.join("..", "COPYRIGHT") +license_file = "LICENSE" +license_name = "BSD-3-Clause" + kwargs_for_extension = { "sources": [path.join(cpp_dir, f) for f in source_codes], "depends": [path.join(cpp_dir, f) for f in headers], @@ -37,12 +42,20 @@ "language": "c++", } -# see ../Makefile.win +# see ../Makefile.win and enable openmp if sys.platform == "win32": kwargs_for_extension.update( { "define_macros": [("_WIN64", ""), ("_CRT_SECURE_NO_DEPRECATE", "")], - "extra_link_args": ["-DEF:{}\svm.def".format(cpp_dir)], + "extra_link_args": [r"-DEF:{}\svm.def".format(cpp_dir)], + "extra_compile_args": ["/openmp"], + } + ) +else: + kwargs_for_extension.update( + { + "extra_compile_args": ["-fopenmp"], + "extra_link_args": ["-fopenmp"], } ) @@ -59,7 +72,7 @@ class CleanCommand(clean_cmd): def run(self): clean_cmd.run(self) - to_be_removed = ["build/", "dist/", "MANIFEST", cpp_dir, "{}.egg-info".format(PACKAGE_NAME)] + to_be_removed = ["build/", "dist/", "MANIFEST", cpp_dir, "{}.egg-info".format(PACKAGE_NAME), license_file] to_be_removed += glob("./{}/{}.*".format(PACKAGE_DIR, dynamic_lib_name)) for root, dirs, files in os.walk(os.curdir, topdown=False): if "__pycache__" in dirs: @@ -79,6 +92,9 @@ if not path.exists(cpp_dir): create_cpp_source() + if not path.exists(license_file): + copyfile(license_source, license_file) + with open("README") as f: long_description = f.read() @@ -92,6 +108,7 @@ author="ML group @ National Taiwan University", author_email="[email protected]", url="https://www.csie.ntu.edu.tw/~cjlin/libsvm";, + license=license_name, install_requires=["scipy"], ext_modules=[ Extension( diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/svm.cpp new/libsvm-3.36/svm.cpp --- old/libsvm-3.3/svm.cpp 2022-08-10 16:06:29.000000000 +0200 +++ new/libsvm-3.36/svm.cpp 2025-05-12 06:29:50.000000000 +0200 @@ -8,6 +8,10 @@ #include <limits.h> #include <locale.h> #include "svm.h" +#ifdef _OPENMP +#include <omp.h> +#endif + int libsvm_version = LIBSVM_VERSION; typedef float Qfloat; typedef signed char schar; @@ -50,7 +54,7 @@ char buf[BUFSIZ]; va_list ap; va_start(ap,fmt); - vsprintf(buf,fmt,ap); + vsnprintf(buf,BUFSIZ,fmt,ap); va_end(ap); (*svm_print_string)(buf); } @@ -67,7 +71,7 @@ class Cache { public: - Cache(int l,long int size); + Cache(int l,size_t size); ~Cache(); // request data [0,len) @@ -77,7 +81,7 @@ void swap_index(int i, int j); private: int l; - long int size; + size_t size; struct head_t { head_t *prev, *next; // a circular list @@ -91,12 +95,12 @@ void lru_insert(head_t *h); }; -Cache::Cache(int l_,long int size_):l(l_),size(size_) +Cache::Cache(int l_,size_t size_):l(l_),size(size_) { head = (head_t *)calloc(l,sizeof(head_t)); // initialized to 0 size /= sizeof(Qfloat); - size -= l * sizeof(head_t) / sizeof(Qfloat); - size = max(size, 2 * (long int) l); // cache must be large enough for two columns + size_t header_size = l * sizeof(head_t) / sizeof(Qfloat); + size = max(size, 2 * (size_t) l + header_size) - header_size; // cache must be large enough for two columns lru_head.next = lru_head.prev = &lru_head; } @@ -132,7 +136,7 @@ if(more > 0) { // free old space - while(size < more) + while(size < (size_t)more) { head_t *old = lru_head.next; lru_delete(old); @@ -144,7 +148,7 @@ // allocate new space h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len); - size -= more; + size -= more; // previous while loop guarantees size >= more and subtraction of size_t variable will not underflow swap(h->len,len); } @@ -1270,7 +1274,7 @@ :Kernel(prob.l, prob.x, param) { clone(y,y_,prob.l); - cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20))); + cache = new Cache(prob.l,(size_t)(param.cache_size*(1<<20))); QD = new double[prob.l]; for(int i=0;i<prob.l;i++) QD[i] = (this->*kernel_function)(i,i); @@ -1282,6 +1286,9 @@ int start, j; if((start = cache->get_data(i,&data,len)) < len) { +#ifdef _OPENMP +#pragma omp parallel for private(j) schedule(guided) +#endif for(j=start;j<len;j++) data[j] = (Qfloat)(y[i]*y[j]*(this->*kernel_function)(i,j)); } @@ -1319,7 +1326,7 @@ ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { - cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20))); + cache = new Cache(prob.l,(size_t)(param.cache_size*(1<<20))); QD = new double[prob.l]; for(int i=0;i<prob.l;i++) QD[i] = (this->*kernel_function)(i,i); @@ -1366,7 +1373,7 @@ :Kernel(prob.l, prob.x, param) { l = prob.l; - cache = new Cache(l,(long int)(param.cache_size*(1<<20))); + cache = new Cache(l,(size_t)(param.cache_size*(1<<20))); QD = new double[2*l]; sign = new schar[2*l]; index = new int[2*l]; @@ -1397,6 +1404,9 @@ int j, real_i = index[i]; if(cache->get_data(real_i,&data,l) < l) { +#ifdef _OPENMP +#pragma omp parallel for private(j) schedule(guided) +#endif for(j=0;j<l;j++) data[j] = (Qfloat)(this->*kernel_function)(real_i,j); } @@ -2219,11 +2229,9 @@ double *prob_density_marks = Malloc(double,nr_marks); if(svm_one_class_probability(prob,model,prob_density_marks) == 0) - { - model->prob_density_marks = Malloc(double,nr_marks); - for(i=0;i<nr_marks;i++) - model->prob_density_marks[i] = prob_density_marks[i]; - } + model->prob_density_marks = prob_density_marks; + else + free(prob_density_marks); } free(f.alpha); @@ -2436,8 +2444,8 @@ int nr_class; if (nr_fold > l) { + fprintf(stderr,"WARNING: # folds (%d) > # data (%d). Will use # folds = # data instead (i.e., leave-one-out cross validation)\n", nr_fold, l); nr_fold = l; - fprintf(stderr,"WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n"); } fold_start = Malloc(int,nr_fold+1); // stratified cv may not give leave-one-out rate @@ -2598,6 +2606,9 @@ { double *sv_coef = model->sv_coef[0]; double sum = 0; +#ifdef _OPENMP +#pragma omp parallel for private(i) reduction(+:sum) schedule(guided) +#endif for(i=0;i<model->l;i++) sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param); sum -= model->rho[0]; @@ -2614,6 +2625,9 @@ int l = model->l; double *kvalue = Malloc(double,l); +#ifdef _OPENMP +#pragma omp parallel for private(i) schedule(guided) +#endif for(i=0;i<l;i++) kvalue[i] = Kernel::k_function(x,model->SV[i],model->param); diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/svm.h new/libsvm-3.36/svm.h --- old/libsvm-3.3/svm.h 2022-08-10 16:06:29.000000000 +0200 +++ new/libsvm-3.36/svm.h 2025-05-12 06:29:50.000000000 +0200 @@ -1,7 +1,7 @@ #ifndef _LIBSVM_H #define _LIBSVM_H -#define LIBSVM_VERSION 330 +#define LIBSVM_VERSION 336 #ifdef __cplusplus extern "C" { diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/libsvm-3.3/tools/grid.py new/libsvm-3.36/tools/grid.py --- old/libsvm-3.3/tools/grid.py 2022-08-10 16:06:30.000000000 +0200 +++ new/libsvm-3.36/tools/grid.py 2025-05-12 06:29:51.000000000 +0200 @@ -283,7 +283,7 @@ if options.grid_with_g: cmdline += ' -g {0} '.format(g) cmdline += ' -v {0} {1} {2} '.format\ - (options.fold,options.pass_through_string,options.dataset_pathname) + (options.fold,options.pass_through_string,'"' + options.dataset_pathname + '"') return cmdline class LocalWorker(Worker): Binary files old/libsvm-3.3/windows/libsvm.dll and new/libsvm-3.36/windows/libsvm.dll differ Binary files old/libsvm-3.3/windows/libsvmread.mexw64 and new/libsvm-3.36/windows/libsvmread.mexw64 differ Binary files old/libsvm-3.3/windows/libsvmwrite.mexw64 and new/libsvm-3.36/windows/libsvmwrite.mexw64 differ Binary files old/libsvm-3.3/windows/svm-predict.exe and new/libsvm-3.36/windows/svm-predict.exe differ Binary files old/libsvm-3.3/windows/svm-scale.exe and new/libsvm-3.36/windows/svm-scale.exe differ Binary files old/libsvm-3.3/windows/svm-toy.exe and new/libsvm-3.36/windows/svm-toy.exe differ Binary files old/libsvm-3.3/windows/svm-train.exe and new/libsvm-3.36/windows/svm-train.exe differ Binary files old/libsvm-3.3/windows/svmpredict.mexw64 and new/libsvm-3.36/windows/svmpredict.mexw64 differ Binary files old/libsvm-3.3/windows/svmtrain.mexw64 and new/libsvm-3.36/windows/svmtrain.mexw64 differ
