algorithm.ipynb

   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "DELTA=0.01 #don't threshold\n",
    "COMBINEFLAG=True #don't combine features\n",
    "alpha_e_uncombined=myEM.findfeatures(alpha_e=alpha_e,delta=DELTA,combineFlag=COMBINEFLAG)\n",
    "print(\"there are \",myEM.feature_count,\"features\", flush=True)\n",
    "makeplot(myEM,alpha_e_uncombined)\n",
    "print(makefname(IMEI,KAP,DELTA,COMBINEFLAG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(myEM.intervals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "harvester 3 (CURRENT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "IMEI=gd.IMEISet(3)\n",
    "raw_data=gd.get(IMEI,DATE)\n",
    "data=raw_data#[0:500]\n",
    "TVALS=numpy.array(data[\"locationTimestamp\"])\n",
    "SHIFTS=TVALS\n",
    "N_ITER=30\n",
    "\n",
    "print(\"making feature list\",flush=True)\n",
    "FEATURES=[]\n",
    "for s,w in itertools.product(sorted(SHIFTS),sorted(WIDTHS,reverse=True)):\n",
    "    FEATURES.append(Box(height=HEIGHT,width=w,shift=s))    \n",
    "print(\"there are \",len(FEATURES), \"features\", flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "KAP=1.5E-7 #for box\n",
    "myEM=L0_EM(data,FEATURES,KAP)\n",
    "\n",
    "print(\"L0_EM created\", flush=True)\n",
    "alpha_e=myEM.initialize()\n",
    "print(\"about to iterate\", flush=True)\n",
    "N_iter=20\n",
    "for n in range(N_iter):\n",
    "    print(\"n=\",n,flush=True)\n",
    "    alpha_e=myEM.iterate(alpha_e)\n",
    "    myEM.findfeatures()\n",
    "    if (myEM.stopFlag):\n",
    "        break\n",
    "        \n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "DELTA=0.01 #don't threshold\n",
    "COMBINEFLAG=False #don't combine features\n",
    "alpha_e_uncombined=myEM.findfeatures(alpha_e=alpha_e,delta=DELTA,combineFlag=COMBINEFLAG)\n",
    "print(\"there are \",myEM.feature_count,\"features\", flush=True)\n",
    "makeplot(myEM,alpha_e_uncombined)\n",
    "print(makefname(IMEI,KAP,DELTA,COMBINEFLAG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "DELTA=0.01 #don't threshold\n",
    "COMBINEFLAG=True #combine features\n",
    "alpha_e_uncombined=myEM.findfeatures(alpha_e=alpha_e,delta=DELTA,combineFlag=COMBINEFLAG)\n",
    "print(\"there are \",myEM.feature_count,\"features\", flush=True)\n",
    "makeplot(myEM,alpha_e_uncombined)\n",
    "print(makefname(IMEI,KAP,DELTA,COMBINEFLAG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(myEM.intervals)"
   ]
  }
 ],
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