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, 12 insertions, 2 deletions

diff --git a/include/xorshift64.h b/include/xorshift64.h
index c3542c6..2b13f60 100644
--- a/include/xorshift64.h
+++ b/include/xorshift64.h
@@ -1,5 +1,5 @@
 /*
-  This file is part of ct.
+  This file is part of ctak.
 
   This program is free software: you can redistribute it and/or modify
   it under the terms of the GNU General Public License as published by
@@ -12,7 +12,7 @@
   General Public License for more details.
 
   You should have received a copy of the GNU General Public License
-  along with ct. If not, see <https://www.gnu.org/licenses/>.
+  along with ctak. If not, see <https://www.gnu.org/licenses/>.
 */
 
 #ifndef XORSHIFT_H
@@ -32,4 +32,14 @@ extern uint64_t xors;
 #define RANDOM64 (xors * 0x2545F4914F6CDD1D)
 #define RANDOM32 ((uint32_t)RANDOM64)
 
+static inline uint64_t rand64(void) {
+  XORSHIFT64;
+  return RANDOM64;
+}
+
+static inline uint32_t rand32(void) {
+  XORSHIFT64;
+  return RANDOM32;
+}
+
 #endif