mackey.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "learning a [Mackey-Glass](http://www.scholarpedia.org/article/Mackey-Glass_equation) system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test error: \n",
      "0.139603909616\n"
     ]
    },
    {
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     "metadata": {},
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   ],
   "source": [
    "import numpy as np\n",
    "from pyESN import ESN\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "data = np.load('mackey_glass_t17.npy') #  http://minds.jacobs-university.de/mantas/code\n",
    "esn = ESN(n_inputs = 1,\n",
    "          n_outputs = 1,\n",
    "          n_reservoir = 500,\n",
    "          spectral_radius = 1.5,\n",
    "          random_state=42)\n",
    "\n",
    "trainlen = 2000\n",
    "future = 2000\n",
    "pred_training = esn.fit(np.ones(trainlen),data[:trainlen])\n",
    "\n",
    "prediction = esn.predict(np.ones(future))\n",
    "print(\"test error: \\n\"+str(np.sqrt(np.mean((prediction.flatten() - data[trainlen:trainlen+future])**2))))\n",
    "\n",
    "plt.figure(figsize=(11,1.5))\n",
    "plt.plot(range(0,trainlen+future),data[0:trainlen+future],'k',label=\"target system\")\n",
    "plt.plot(range(trainlen,trainlen+future),prediction,'r', label=\"free running ESN\")\n",
    "lo,hi = plt.ylim()\n",
    "plt.plot([trainlen,trainlen],[lo+np.spacing(1),hi-np.spacing(1)],'k:')\n",
    "plt.legend(loc=(0.61,1.1),fontsize='x-small')"
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