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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "from __future__ import division\n",
    "import os\n",
    "\n",
    "numOfItems = 10\n",
    "minweight = 0      # minimum weight of the items\n",
    "maxweight = 100    # maximum weight of the items\n",
    "knapsackweight = numOfItems/2*(maxweight-minweight) + minweight\n",
    "numOfInstances = 3 # number of created random instances\n",
    "filenamebase = 'instances/KP-random-%ditems-' % numOfItems\n",
    "if not os.path.isdir('instances'):\n",
    "    os.mkdir('instances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for k in range(1, numOfInstances+1):\n",
    "    filename = filenamebase + str(k) + '.txt'\n",
    "    mystring = 'n = %d # number of items\\n W = %d  # maximum weight of knapsack (capacity)\\n' % (numOfItems, knapsackweight)\n",
    "    A = np.round( (maxweight-minweight) * np.random.rand(numOfItems,2) + minweight )\n",
    "    for i in range(A.shape[0]):\n",
    "        mystring = mystring + '%d %d\\n' % (A[i][0], A[i][1])\n",
    "    with open(filename, \"w\") as text_file:\n",
    "        text_file.write(mystring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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