Added CMake and C89 support

.gitignore

@@ -0,0 +1,8 @@
+*.o
+*.log
+*.tmp
+
+.idea
+cmake-build-*
+build*
+*_export.h

CMakeLists.txt

@@ -0,0 +1,63 @@
+cmake_minimum_required(VERSION 3.19)
+
+# set the project name and version
+project(genann VERSION 0.0.1 LANGUAGES "C")
+string(TOLOWER "${PROJECT_NAME}" PROJECT_LOWER_NAME)
+
+set(CMAKE_C_STANDARD 90)
+set(CMAKE_VERBOSE_MAKEFILE ON)
+
+add_library("${PROJECT_LOWER_NAME}_compiler_flags" INTERFACE)
+target_compile_features("${PROJECT_LOWER_NAME}_compiler_flags" INTERFACE "c_std_90")
+
+# add compiler warning flags just when building this project via
+# the BUILD_INTERFACE genex
+set(gcc_like "$<COMPILE_LANG_AND_ID:C,CXX,ARMClang,AppleClang,Clang,GNU>")
+set(msvc "$<COMPILE_LANG_AND_ID:C,CXX,MSVC>")
+target_compile_options(
+        "${PROJECT_LOWER_NAME}_compiler_flags"
+        INTERFACE
+        "$<${gcc_like}:$<BUILD_INTERFACE:-Wshadow;-Wformat=2;-Wall;-Wunused-macros;-pedantic;-march=native>>"
+        # "-Wgnu-zero-variadic-macro-arguments"
+        "$<${msvc}:$<BUILD_INTERFACE:-W3;-WX;-Zi;-permissive->>"
+)
+
+# control where the static and shared libraries are built so that on windows
+# we don't need to tinker with the path to run the executable
+set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}")
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}")
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}")
+
+option(BUILD_SHARED_LIBS "Build using shared libraries" OFF)
+
+if(APPLE)
+    set(CMAKE_INSTALL_RPATH "@executable_path/../lib")
+elseif(UNIX)
+    set(CMAKE_INSTALL_RPATH "$ORIGIN/../lib")
+endif()
+
+configure_file(
+        "${PROJECT_NAME}Config.h.in"
+        "config/${PROJECT_NAME}Config.h"
+)
+
+set(original_deps "genann" "example" "test")
+foreach (_lib ${original_deps})
+    add_subdirectory("${_lib}")
+    message(STATUS "Built ${_lib}")
+endforeach ()
+
+#set_target_properties(
+#        "${PROJECT_NAME}"
+#        PROPERTIES
+#        LINKER_LANGUAGE
+#        C
+#)
+
+## add the binary tree to the search path for include files
+## so that we will find "${PROJECT_NAME}Config.h"
+target_include_directories(
+        "${PROJECT_NAME}"
+        PUBLIC
+        "${PROJECT_BINARY_DIR}/config"
+)

README.md

@@ -11,11 +11,11 @@ functions and little extra.
 
 ## Features
 
-- **C99 with no dependencies**.
+- **C89 with no dependencies**.
 - Contained in a single source code and header file.
 - Simple.
 - Fast and thread-safe.
-- Easily extendible.
+- Easily extendable.
 - Implements backpropagation training.
 - *Compatible with alternative training methods* (classic optimization, genetic algorithms, etc)
 - Includes examples and test suite.

example/CMakeLists.txt

@@ -0,0 +1,48 @@
+get_filename_component(LIBRARY_NAME "${CMAKE_CURRENT_SOURCE_DIR}" NAME)
+string(REPLACE " " "_" LIBRARY_NAME "${LIBRARY_NAME}")
+
+file(
+        COPY "iris.data" "iris.names" "xor.ann"
+        DESTINATION "${CMAKE_ARCHIVE_OUTPUT_DIRECTORY}/example"
+)
+
+foreach(example RANGE 1 4)
+  set(EXEC_NAME "example${example}")
+
+  set(Source_Files "../${EXEC_NAME}.c")
+  source_group("Source Files" FILES "${Source_Files}")
+
+  set(TARGET_NAME "${PROJECT_NAME}_${EXEC_NAME}")
+  add_executable("${TARGET_NAME}" "${Source_Files}")
+
+  target_include_directories(
+          "${TARGET_NAME}"
+          INTERFACE
+          "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>"
+          "$<INSTALL_INTERFACE:include>"
+  )
+
+  target_link_libraries(
+          "${TARGET_NAME}"
+          INTERFACE
+          "${PROJECT_LOWER_NAME}_compiler_flags"
+  )
+  target_link_libraries("${TARGET_NAME}" PRIVATE "genann")
+
+  set_target_properties(
+          "${TARGET_NAME}"
+          PROPERTIES
+          LINKER_LANGUAGE
+          C
+  )
+
+  # install rules
+  set(installable_libs "${TARGET_NAME}" "${PROJECT_LOWER_NAME}_compiler_flags")
+  if (TARGET "${DEPENDANT_LIBRARY}")
+    list(APPEND installable_libs "${DEPENDANT_LIBRARY}")
+  endif ()
+  install(TARGETS ${installable_libs}
+          DESTINATION "bin/"
+          EXPORT "${TARGET_NAME}Targets")
+
+endforeach ()

example1.c

@@ -5,36 +5,42 @@
 
 int main(int argc, char *argv[])
 {
+    genann *ann;
     printf("GENANN example 1.\n");
     printf("Train a small ANN to the XOR function using backpropagation.\n");
 
     /* This will make the neural network initialize differently each run. */
     /* If you don't get a good result, try again for a different result. */
     srand(time(0));
 
-    /* Input and expected out data for the XOR function. */
-    const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
-    const double output[4] = {0, 1, 1, 0};
-    int i;
+    {
+        /* Input and expected out data for the XOR function. */
+        const double input[4][2] = {{0, 0},
+                                    {0, 1},
+                                    {1, 0},
+                                    {1, 1}};
+        const double output[4] = {0, 1, 1, 0};
+        int i;
 
-    /* New network with 2 inputs,
-     * 1 hidden layer of 2 neurons,
-     * and 1 output. */
-    genann *ann = genann_init(2, 1, 2, 1);
+        /* New network with 2 inputs,
+         * 1 hidden layer of 2 neurons,
+         * and 1 output. */
+        ann = genann_init(2, 1, 2, 1);
 
-    /* Train on the four labeled data points many times. */
-    for (i = 0; i < 500; ++i) {
-        genann_train(ann, input[0], output + 0, 3);
-        genann_train(ann, input[1], output + 1, 3);
-        genann_train(ann, input[2], output + 2, 3);
-        genann_train(ann, input[3], output + 3, 3);
-    }
+        /* Train on the four labeled data points many times. */
+        for (i = 0; i < 500; ++i) {
+            genann_train(ann, input[0], output + 0, 3);
+            genann_train(ann, input[1], output + 1, 3);
+            genann_train(ann, input[2], output + 2, 3);
+            genann_train(ann, input[3], output + 3, 3);
+        }
 
-    /* Run the network and see what it predicts. */
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
+        /* Run the network and see what it predicts. */
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
+    }
 
     genann_free(ann);
     return 0;

example2.c

@@ -6,65 +6,72 @@
 
 int main(int argc, char *argv[])
 {
+    genann *ann;
     printf("GENANN example 2.\n");
     printf("Train a small ANN to the XOR function using random search.\n");
 
     srand(time(0));
 
     /* Input and expected out data for the XOR function. */
-    const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
-    const double output[4] = {0, 1, 1, 0};
-    int i;
+    {
+        const double input[4][2] = {{0, 0},
+                                    {0, 1},
+                                    {1, 0},
+                                    {1, 1}};
+        const double output[4] = {0, 1, 1, 0};
+        int i;
 
-    /* New network with 2 inputs,
-     * 1 hidden layer of 2 neurons,
-     * and 1 output. */
-    genann *ann = genann_init(2, 1, 2, 1);
+        double err;
+        double last_err = 1000;
+        int count = 0;
 
-    double err;
-    double last_err = 1000;
-    int count = 0;
+        /* New network with 2 inputs,
+         * 1 hidden layer of 2 neurons,
+         * and 1 output. */
+        ann = genann_init(2, 1, 2, 1);
 
-    do {
-        ++count;
-        if (count % 1000 == 0) {
-            /* We're stuck, start over. */
-            genann_randomize(ann);
-            last_err = 1000;
-        }
+        do {
+            genann *save;
+            ++count;
+            if (count % 1000 == 0) {
+                /* We're stuck, start over. */
+                genann_randomize(ann);
+                last_err = 1000;
+            }
 
-        genann *save = genann_copy(ann);
+            save = genann_copy(ann);
 
-        /* Take a random guess at the ANN weights. */
-        for (i = 0; i < ann->total_weights; ++i) {
-            ann->weight[i] += ((double)rand())/RAND_MAX-0.5;
-        }
+            /* Take a random guess at the ANN weights. */
+            for (i = 0; i < ann->total_weights; ++i) {
+                ann->weight[i] += ((double) rand()) / RAND_MAX - 0.5;
+            }
 
-        /* See how we did. */
-        err = 0;
-        err += pow(*genann_run(ann, input[0]) - output[0], 2.0);
-        err += pow(*genann_run(ann, input[1]) - output[1], 2.0);
-        err += pow(*genann_run(ann, input[2]) - output[2], 2.0);
-        err += pow(*genann_run(ann, input[3]) - output[3], 2.0);
+            /* See how we did. */
+            err = 0;
+            err += pow(*genann_run(ann, input[0]) - output[0], 2.0);
+            err += pow(*genann_run(ann, input[1]) - output[1], 2.0);
+            err += pow(*genann_run(ann, input[2]) - output[2], 2.0);
+            err += pow(*genann_run(ann, input[3]) - output[3], 2.0);
 
-        /* Keep these weights if they're an improvement. */
-        if (err < last_err) {
-            genann_free(save);
-            last_err = err;
-        } else {
-            genann_free(ann);
-            ann = save;
-        }
+            /* Keep these weights if they're an improvement. */
+            if (err < last_err) {
+                genann_free(save);
+                last_err = err;
+            } else {
+                genann_free(ann);
+                ann = save;
+            }
 
-    } while (err > 0.01);
+        } while (err > 0.01);
 
-    printf("Finished in %d loops.\n", count);
+        printf("Finished in %d loops.\n", count);
 
-    /* Run the network and see what it predicts. */
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
+        /* Run the network and see what it predicts. */
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
+    }
 
     genann_free(ann);
     return 0;

example3.c

@@ -6,33 +6,41 @@ const char *save_name = "example/xor.ann";
 
 int main(int argc, char *argv[])
 {
+    genann *ann;
+
     printf("GENANN example 3.\n");
     printf("Load a saved ANN to solve the XOR function.\n");
 
+    {
+        FILE *saved = fopen(save_name, "r");
+        if (!saved) {
+            printf("Couldn't open file: %s\n", save_name);
+            exit(1);
+        }
 
-    FILE *saved = fopen(save_name, "r");
-    if (!saved) {
-        printf("Couldn't open file: %s\n", save_name);
-        exit(1);
+        ann = genann_read(saved);
+        fclose(saved);
     }
 
-    genann *ann = genann_read(saved);
-    fclose(saved);
-
     if (!ann) {
         printf("Error loading ANN from file: %s.", save_name);
         exit(1);
     }
 
 
     /* Input data for the XOR function. */
-    const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
-
-    /* Run the network and see what it predicts. */
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
-    printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
+    {
+        const double input[4][2] = {{0, 0},
+                                    {0, 1},
+                                    {1, 0},
+                                    {1, 1}};
+
+        /* Run the network and see what it predicts. */
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
+        printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
+    }
 
     genann_free(ann);
     return 0;