Add standalone cluster-sdm application to installation targets.

Author: mdimura

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 2.8.8)
 project (sparsehc-dm)
 
-if(NOT CMAKE_BUILD_TYPE) 
+if(NOT CMAKE_BUILD_TYPE)
     set(CMAKE_BUILD_TYPE Release)
 endif(NOT CMAKE_BUILD_TYPE)
 
@@ -21,8 +21,8 @@ set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
 
 include_directories(src)
 file(GLOB SOURCES "src/*.cpp")
-add_executable(sparsehc-dm ${SOURCES})
-target_link_libraries(sparsehc-dm ${LIBS})
+add_executable(cluster-sdm ${SOURCES})
+target_link_libraries(cluster-sdm ${LIBS})
 add_definitions(-std=c++11)
 
 file(GLOB SHARED_SOURCES src/averagecluster.cpp src/cluster.cpp src/common.cpp src/completecluster.cpp src/dendrogram.cpp src/inmatrix.cpp src/matrix.cpp src/singlecluster.cpp)
@@ -32,4 +32,5 @@ add_dependencies(sparsehc_dm python_init)
 target_link_libraries(sparsehc_dm ${LIBS})
 set_target_properties(sparsehc_dm PROPERTIES PREFIX "" SUFFIX ".so")
 
-install(CODE "execute_process(COMMAND python ../python/setup.py install --prefix=${CMAKE_INSTALL_PREFIX} WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})")
+install(CODE "execute_process(COMMAND python ../python/setup.py install --prefix=${CMAKE_INSTALL_PREFIX} WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})")
+install(TARGETS cluster-sdm RUNTIME DESTINATION bin)

src/main.cpp

@@ -36,8 +36,9 @@ Dendrogram linkage(InMatrix& mat, const std::string& method) {
 // Program main
 ////////////////////////////////////////////////////////////////////////////////
 bool verbose = false;
-void test(const unsigned nPoints,const long maxRam=2L*1024*1024*1024)
+void test(const unsigned nPoints,const long maxRam=4L*1024*1024*1024)
 {
+	std::cout<<"Clustering "<<nPoints<<" points."<<std::endl;
 	InMatrix inMat(maxRam);
 	using std::chrono::duration_cast;
 	using std::chrono::milliseconds;
@@ -73,7 +74,8 @@ int main(int argc, char **argv) {
 	if (!extractOptions("--linkage=%s", method, argc, argv))
 		strcat(method,"complete");
 
-	InMatrix inMat(64*1024*1024);
+	unsigned maxRam=2L*1024*1024*1024;
+	InMatrix inMat(maxRam);
 
 	using std::chrono::duration_cast;
 	using std::chrono::milliseconds;
@@ -99,7 +101,8 @@ int main(int argc, char **argv) {
 	double clustering=duration_cast<milliseconds>(finishedClust-finishedSort).count()/1000.0;
 	std::cout<<"Clustering took: "<<clustering<<" s"<<std::endl;
 
-	//test(40000);
+
+	//test(350000);
 	return 0;
 }