testing_stuff.ipynb

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   "cell_type": "code",
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   "source": [
    "from transition_table import TransitionTable\n",
    "from action import Action\n",
    "import numpy as np\n",
    "\n",
    "table = TransitionTable()\n",
    "\n",
    "img = np.ones((84, 84))\n",
    "img2 = 2 * np.ones((84, 84))\n",
    "table.add_transition(img, False, Action(2), 0.5, True)\n",
    "table.add_transition(img2, False, Action(3), 0.7, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "recent = table.get_recent()\n",
    "for i in range(4):\n",
    "    print np.linalg.norm(recent[:, :, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HELLO WE ARE DECREMENTING HERE\n",
      "[ 2.  1.  1.  1.]\n",
      "[ 1.  1.  1.  1.]\n"
     ]
    }
   ],
   "source": [
    "s, t, a, r, sp = table.get_minibatch()\n",
    "print sp[0, 20, 20, :]\n",
    "print s[0, 20, 20, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print table.get_recent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table.transitions[0].was_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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