feature(main): working on the design. i dont know how machine learning works pfff...main

1 files changed, 50 insertions, 8 deletions

diff --git a/source/tb_ml/tb_ml.c b/source/tb_ml/tb_ml.c
index cf9625a..8211a60 100644
--- a/source/tb_ml/tb_ml.c
+++ b/source/tb_ml/tb_ml.c
@@ -1,20 +1,62 @@
-#define BASE_UNITY
+#define BASE_IMPLEMENTATION
 #include "../base/base_include.h"
 
-#if 0
+#ifdef BTREE_IMPLEMENTATION
+internal void
+btree_collect(btree_node *node, (void *) *keys, (void *) array *) {
 
-#include "../third_party/btree_impl.h"
+    if (!n) return;
+
+    for (s32 index = 0; index < node->count; ++index)
+    {
+        TODO(nasr): traverse the tree to use the tree as training data
+    }
+}
 #endif
 
 
+/**
+* calculating a derviate requires a building an algebraic system
+* so we do a dump approximation of what the derivative could be
+* we want this to be able to normalize the data which is necessary for proper training
+**/
+#if 1
+internal f32
+internal training_sample_data
+normalize_training_data_btree(btree *btree) {
+
+
+
+}
+
+#endif
 
-//- dataset is an implementation of the btree
 
-int main(int c, char **v)
-{
-    unused(c);
-    unused(v);
+// using gradient descent
+internal void
+train(f32 *weights, b_tree, s32 learning_rate) {
+
+    btree_collect(btree_node *node, (void *) *keys, (void *) array *);
+
+    for (s32 epoch = 0; epoch < learning_rate; ++epoch)
+    {
+
+    }
+
+}
+
+internal f32
+predict(f32 b1, f32 b2) {
+    return b1 + (b2 * log(b1)) + M_E;
+}
+
+//- dataset is an implementation of the btree
+int main(void) {
 
+    for (s32 index = 0; index < 10; ++index)
+    {
+        printf("value [%4.f]\n",predict(10, 10));
+    }
 
     return 0;
 }