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#define BASE_IMPLEMENTATION
#include "../base/base_include.h"
#ifdef BTREE_IMPLEMENTATION
internal void
btree_collect(btree_node *node, (void *) *keys, (void *) array *) {
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
// 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;
}
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