refactor: improve code readability and maintainability in neural network implementation

Author: dlidstrom

C/Makefile

@@ -16,7 +16,13 @@
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
 CC = clang
-CC_FLAGS = -Wfatal-errors -Wall -Wextra -Wpedantic -Wconversion -Wshadow -lm
+CC_FLAGS = -Wfatal-errors -Wall -Wextra -Wpedantic -Wconversion -Wshadow
+
+# Detect OS and add -lm on Linux
+UNAME_S := $(shell uname -s)
+ifeq ($(UNAME_S),Linux)
+    LDFLAGS = -lm
+endif
 
 # Final binary
 BIN = cmain
@@ -25,7 +31,7 @@ BIN = cmain
 BUILD_DIR = ./build
 
 # List of all .c source files.
-CCS = main.c $(wildcard *.c)
+CCS = $(wildcard *.c)
 
 # All .o files go to build dir.
 OBJ = $(CCS:%.c=$(BUILD_DIR)/%.o)
@@ -39,7 +45,7 @@ $(BIN) : $(BUILD_DIR)/$(BIN)
 # Actual target of the binary - depends on all .o files.
 $(BUILD_DIR)/$(BIN) : $(OBJ)
 	mkdir -p $(@D)
-	$(CC) $(CC_FLAGS) $^ -o $@
+	$(CC) $(CC_FLAGS) $^ -o $@ $(LDFLAGS)
 
 # Include all .d files
 -include $(DEP)

C/main.c

@@ -18,110 +18,98 @@ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */
 
 #include "neural.h"
-#include <stdlib.h>
 #include <stdio.h>
+#include <stdlib.h>
 
 uint32_t P = 2147483647;
 uint32_t A = 16807;
 uint32_t current = 1;
 
-double Rand() {
-    current = current * A % P;
-    double result = (double)current / P;
-    return result;
+double Rand(void) {
+  current = current * A % P;
+  double result = (double)current / P;
+  return result;
 }
 
-void print_network(const Network* network);
+void print_network(const Network *network);
 
-static uint32_t xor(uint32_t i, uint32_t j) { return i ^ j; }
-static uint32_t xnor(uint32_t i, uint32_t j) { return 1 - xor(i, j); }
-static uint32_t or(uint32_t i, uint32_t j) { return i | j; }
-static uint32_t and(uint32_t i, uint32_t j) { return i & j; }
-static uint32_t nor(uint32_t i, uint32_t j) { return 1 - or(i, j); }
+static uint32_t xor (uint32_t i, uint32_t j) { return i ^ j; } static uint32_t
+    xnor(uint32_t i, uint32_t j) {
+  return 1 - xor(i, j);
+}
+static uint32_t or (uint32_t i, uint32_t j) { return i | j; }
+static uint32_t and (uint32_t i, uint32_t j) { return i & j; }
+static uint32_t nor(uint32_t i, uint32_t j) { return 1 - or (i, j); }
 static uint32_t nand(uint32_t i, uint32_t j) { return 1 - and(i, j); }
 
 const int ITERS = 4000;
 
-int main() {
-    Network network = {0};
-    network_init(&network, 2, 2, 6, Rand);
-    Trainer trainer = {0};
-    trainer_init(&trainer, &network);
-    double inputs[4][2] = {
-        {0, 0},
-        {0, 1},
-        {1, 0},
-        {1, 1}
-    };
-    double outputs[4][6] = {
-        { xor(0, 0), xnor(0, 0), or(0, 0), and(0, 0), nor(0, 0), nand(0, 0) },
-        { xor(0, 1), xnor(0, 1), or(0, 1), and(0, 1), nor(0, 1), nand(0, 1) },
-        { xor(1, 0), xnor(1, 0), or(1, 0), and(1, 0), nor(1, 0), nand(1, 0) },
-        { xor(1, 1), xnor(1, 1), or(1, 1), and(1, 1), nor(1, 1), nand(1, 1) }
-    };
-
-    for (size_t i = 0; i < ITERS; i++) {
-        double* input = inputs[i % 4];
-        double* output = outputs[i % 4];
-
-        trainer_train(&trainer, &network, input, output, 1.0);
-    }
-
-    printf(
-        "Result after %d iterations\n        XOR  XNOR    OR   AND   NOR  NAND\n",
-        ITERS);
-    for (size_t i = 0; i < 4; i++)
-    {
-        double* input = inputs[i % 4];
-        network_predict(&network, input);
-        printf(
-            "%.0f,%.0f = %.3f %.3f %.3f %.3f %.3f %.3f\n",
-            input[0],
-            input[1],
-            network.output[0],
-            network.output[1],
-            network.output[2],
-            network.output[3],
-            network.output[4],
-            network.output[5]);
-    }
-
-    print_network(&network);
-    trainer_free(&trainer);
-    network_free(&network);
-    return 0;
+int main(void) {
+  Network network = {0};
+  network_init(&network, 2, 2, 6, Rand);
+  Trainer trainer = {0};
+  trainer_init(&trainer, &network);
+  double inputs[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
+  double outputs[4][6] = {
+      {xor(0, 0), xnor(0, 0), or (0, 0), and(0, 0), nor(0, 0), nand(0, 0)},
+      {xor(0, 1), xnor(0, 1), or (0, 1), and(0, 1), nor(0, 1), nand(0, 1)},
+      {xor(1, 0), xnor(1, 0), or (1, 0), and(1, 0), nor(1, 0), nand(1, 0)},
+      {xor(1, 1), xnor(1, 1), or (1, 1), and(1, 1), nor(1, 1), nand(1, 1)}};
+
+  for (size_t i = 0; i < ITERS; i++) {
+    double *input = inputs[i % 4];
+    double *output = outputs[i % 4];
+
+    trainer_train(&trainer, &network, input, output, 1.0);
+  }
+
+  printf(
+      "Result after %d iterations\n        XOR  XNOR    OR   AND   NOR  NAND\n",
+      ITERS);
+  for (size_t i = 0; i < 4; i++) {
+    double *input = inputs[i % 4];
+    network_predict(&network, input);
+    printf("%.0f,%.0f = %.3f %.3f %.3f %.3f %.3f %.3f\n", input[0], input[1],
+           network.output[0], network.output[1], network.output[2],
+           network.output[3], network.output[4], network.output[5]);
+  }
+
+  print_network(&network);
+  trainer_free(&trainer);
+  network_free(&network);
+  return 0;
 }
 
-void print_network(const Network* network) {
-    printf("weights hidden:\n");
-    for (size_t i = 0; i < network->n_inputs; i++) {
-        for (size_t j = 0; j < network->n_hidden; j++) {
-            printf(" %9.6f", network->weights_hidden[network->n_inputs * i + j]);
-        }
-
-        printf("\n");
-    }
-
-    printf("biases hidden:\n");
-    for (size_t i = 0; i < network->n_hidden; i++) {
-        printf(" %9.6f", network->biases_hidden[i]);
+void print_network(const Network *network) {
+  printf("weights hidden:\n");
+  for (size_t i = 0; i < network->n_inputs; i++) {
+    for (size_t j = 0; j < network->n_hidden; j++) {
+      printf(" %9.6f", network->weights_hidden[network->n_inputs * i + j]);
     }
 
     printf("\n");
+  }
 
-    printf("weights output:\n");
-    for (size_t i = 0; i < network->n_hidden; i++) {
-        for (size_t j = 0; j < network->n_outputs; j++) {
-            printf(" %9.6f", network->weights_output[i * network->n_outputs + j]);
-        }
+  printf("biases hidden:\n");
+  for (size_t i = 0; i < network->n_hidden; i++) {
+    printf(" %9.6f", network->biases_hidden[i]);
+  }
 
-        printf("\n");
-    }
+  printf("\n");
 
-    printf("biases output:\n");
-    for (size_t i = 0; i < network->n_outputs; i++) {
-        printf(" %9.6f", network->biases_output[i]);
+  printf("weights output:\n");
+  for (size_t i = 0; i < network->n_hidden; i++) {
+    for (size_t j = 0; j < network->n_outputs; j++) {
+      printf(" %9.6f", network->weights_output[i * network->n_outputs + j]);
     }
 
     printf("\n");
+  }
+
+  printf("biases output:\n");
+  for (size_t i = 0; i < network->n_outputs; i++) {
+    printf(" %9.6f", network->biases_output[i]);
+  }
+
+  printf("\n");
 }