mfinzi/OMGchess

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chess\n",
    "import chess.uci\n",
    "import chess.pgn\n",
    "import sys,os\n",
    "import numpy as np\n",
    "import torch\n",
    "import io\n",
    "import pandas as pd\n",
    "import concurrent\n",
    "import dill\n",
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch,dill\n",
    "import matplotlib.pyplot as plt\n",
    "from oil.utils.utils import Eval, cosLr\n",
    "from chess_dataset import ChessDataset, legal_opponent_moves,legal_moves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "startfen = chess.Board().fen()\n",
    "r = torch.arange(64)[:,None,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.,  ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sum(r.float()*legal_moves(startfen).float(),dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 9.,  9., 11., 11., 12., 19., 14., 21.],\n",
       "        [ 8.,  9., 10., 11., 12., 13., 14., 15.],\n",
       "        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sum(r.float()*legal_moves(startfen).reshape(64,8,8).float(),dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [ 48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.],\n",
       "        [105.,  49., 107.,  51.,  52., 115.,  54., 117.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sum(r.float()*legal_opponent_moves(startfen).reshape(64,8,8).float(),dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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