Aimbot-PPO/Aimbot-PPO-Python/env.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "5\n",
      "3\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "包含None！\n",
      "[123 None]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def qa(aa = None):\n",
    "    try:\n",
    "        isaanone = aa.any() == None\n",
    "        if aa.all() == None:\n",
    "            print(\"包含None！\")\n",
    "    except:\n",
    "        isaanone =True\n",
    "    if isaanone:\n",
    "        print('none')\n",
    "    else:\n",
    "        print(aa)\n",
    "\n",
    "qa(np.array([123,None]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 1 1 1 1]\n",
      "  [1 2 1 1 1]]\n",
      "\n",
      " [[2 1 3 4 5]\n",
      "  [2 2 3 4 5]]\n",
      "\n",
      " [[3 1 3 4 5]\n",
      "  [3 2 3 4 5]]]\n",
      "-\n",
      "tf.Tensor(\n",
      "[[1 1 1 1 1]\n",
      " [2 1 3 4 5]\n",
      " [3 1 3 4 5]], shape=(3, 5), dtype=int32)\n",
      "-\n",
      "tf.Tensor(\n",
      "[[1 2 1 1 1]\n",
      " [2 2 3 4 5]\n",
      " [3 2 3 4 5]], shape=(3, 5), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "aa = np.array([[[1,1,1,1,1],[1,2,1,1,1],[1,3,1,1,1]],\n",
    "               [[2,1,3,4,5],[2,2,3,4,5],[2,3,3,4,5]],\n",
    "               [[3,1,3,4,5],[3,2,3,4,5],[3,3,3,4,5]]])\n",
    "tt = tf.constant(aa)\n",
    "bb = np.array([6,3,6,3,2,3])\n",
    "\n",
    "print(aa[:,0:2])\n",
    "aa[:,2:]\n",
    "\n",
    "for asd in tf.transpose(aa[:,0:2],perm=[1,0,2]):\n",
    "    print('-')\n",
    "    print(asd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=1>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "aa = tf.constant(0)\n",
    "bb = aa+1\n",
    "bb\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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