From 96631c14c9bee9b6893b120bf98241e6b0274c1e Mon Sep 17 00:00:00 2001
From: anon <anon@anon.anon>
Date: Tue, 10 Dec 2024 20:39:01 +0100
Subject: [PATCH] Added 'C_C++/xor_nn.cpp'
---
C_C++/xor_nn.cpp | 98 ++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 98 insertions(+)
create mode 100644 C_C++/xor_nn.cpp
diff --git a/C_C++/xor_nn.cpp b/C_C++/xor_nn.cpp
new file mode 100644
index 0000000..a98775a
--- /dev/null
+++ b/C_C++/xor_nn.cpp
@@ -0,0 +1,98 @@
+// @BAKE g++ $@ -fpermissive -Wextra -Wpedantic -o $*.out
+#include <stdlib.h>
+#include <stdio.h>
+#include <math.h>
+#include <time.h>
+#include <vector>
+#include <algorithm>
+#include <numeric>
+
+const int TRAINING_SESSIONS = 1000;
+
+using namespace std;
+
+typedef struct nn {
+ static const int INPUT_COUNT = 2;
+ static const int B = 1;
+ static const int NUMBER_OF_LAYERS = 2;
+ static const int INPUT_SIZE = INPUT_COUNT + B;
+
+ double weights[INPUT_SIZE][NUMBER_OF_LAYERS];
+
+ nn() {
+ this->random_fill();
+ }
+
+ ~nn() {
+ this->print();
+ }
+
+ void random_fill() {
+ srand((unsigned int)time(NULL));
+ for (int i = 0; i < INPUT_SIZE; i++) {
+ for (int h = 0; h < NUMBER_OF_LAYERS; h++) {
+ weights[i][h] = ((double)rand() / RAND_MAX) * 2.0 - 1.0;
+ }
+ }
+ }
+
+ void print() {
+ for (int j = 0; j < NUMBER_OF_LAYERS; j++) {
+ for (int i = 0; i < INPUT_SIZE; i++) {
+ printf("%.4f ", weights[i][j]);
+ }
+ printf("\n");
+ }
+ }
+} neural_network_t;
+
+typedef struct {
+ int inputs[neural_network_t::INPUT_SIZE];
+ int output;
+} data_t;
+
+
+data_t training_data[] = {
+ (data_t){{0, 0, neural_network_t::B}, 0},
+ (data_t){{1, 0, neural_network_t::B}, 1},
+ (data_t){{0, 1, neural_network_t::B}, 1},
+ (data_t){{1, 1, neural_network_t::B}, 0},
+};
+
+double sigmoid_derivative(int i) {
+ return (double)i * ((double)1 - (double)i);
+}
+
+double sigmoid_activation(int i) {
+ return (double)i / ((double)1 + exp(i));
+}
+
+signed main() {
+ neural_network_t my_neural_network;
+ int training_sessions_counter;
+ double sumerr = 0;
+ for (training_sessions_counter = 0;
+ training_sessions_counter < TRAINING_SESSIONS;
+ training_sessions_counter++) {
+ for (const auto &data : training_data) {
+ int y[neural_network_t::INPUT_COUNT];
+ for (int h = 0; h < neural_network_t::INPUT_COUNT; h++) {
+ //double r = std::accumulate(myList.begin(),
+ // myList.end(),
+ // 0,
+ // [](int sum, int element) {
+ // return sum + (element * 2);
+ // }
+ //);
+ }
+ }
+ }
+
+
+ printf("Number of steps: %d\n"
+ "??: %lf\n",
+ training_sessions_counter, sumerr
+ );
+
+ return 0;
+}