TD(lambda), and a big rework

NOTE: 6x6 weights are as-yet untrained

On the performance side:
* switch from linked-list chaining hash table to direct mapping
* cache check_win by hash
* persist transposition tables across plies (this was a big deal and difficult to puzzle out)
* add align directives to the network floats
* prepare_nn_input gained a small LUT to avoid branching (i suppose you might call this 1D embedding)
* apply mtune=native for the native builds

On the correctness side:
* Zobrist has now includes the ply parity
* the network now predicts something in [-1, 1] using tanh (approximated by polynomials)
* the training object is now MSE because we're learning a value estimate
* use .clangd instead of compile_commands.json

On the training side:
* completely forgo human-derived data set
* implement td(lambda), self-play, tournaments

1 files changed, 6 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index 52a279d..b013cd7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,9 +1,15 @@
 *.o
 data/
+include/cand_weights_*.c
 ct1986*
 ctlm*
 pptdb*
 cttei*
 geminict*
+selfplay*
+match*
+__pycache__/
+production-*.log
 buildroot*
+test/
 .cache