search-4e.ipynb

    "    assert f\n",
    "    assert f.pop().state == 2\n",
    "    assert f.pop().state == 3\n",
    "    assert not f\n",
    "    \n",
    "    #### Depth-first search with LIFO Q\n",
    "    f = FrontierQ(Node('a'), LIFO=True)\n",
    "    for s in 'bcdef': f.add(Node(s))\n",
    "    assert len(f) == 6 and 'a' in f and 'c' in f and 'f' in f\n",
    "    for s in 'fedcba': assert f.pop().state == s\n",
    "    assert not f\n",
    "\n",
    "    #### Best-first search with Priority Q\n",
    "    f = FrontierPQ(Node(''), lambda node: len(node.state))\n",
    "    assert '' in f and len(f) == 1 and f\n",
    "    for s in ['book', 'boo', 'bookie', 'bookies', 'cook', 'look', 'b']:\n",
    "        assert s not in f\n",
    "        f.add(Node(s))\n",
    "        assert s in f\n",
    "    assert f.pop().state == ''\n",
    "    assert f.pop().state == 'b'\n",
    "    assert f.pop().state == 'boo'\n",
    "    assert {f.pop().state for _ in '123'} == {'book', 'cook', 'look'}\n",
    "    assert f.pop().state == 'bookie'\n",
    "    \n",
    "    #### Romania: Two paths to Bucharest; cheapest one found first\n",
    "    S    = Node('S')\n",
    "    SF   = Node('F', S, 'S->F', 99)\n",
    "    SFB  = Node('B', SF, 'F->B', 211)\n",
    "    SR   = Node('R', S, 'S->R', 80)\n",
    "    SRP  = Node('P', SR, 'R->P', 97)\n",
    "    SRPB = Node('B', SRP, 'P->B', 101)\n",
    "    f = FrontierPQ(S)\n",
    "    f.add(SF); f.add(SR), f.add(SRP), f.add(SRPB); f.add(SFB)\n",
    "    def cs(n): return (n.path_cost, n.state) # cs: cost and state\n",
    "    assert cs(f.pop()) == (0, 'S')\n",
    "    assert cs(f.pop()) == (80, 'R')\n",
    "    assert cs(f.pop()) == (99, 'F')\n",
    "    assert cs(f.pop()) == (177, 'P')\n",
    "    assert cs(f.pop()) == (278, 'B')\n",
    "    return 'test_frontier ok'\n",
    "\n",
    "test_frontier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106018c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "p = plt.plot([i**2 for i in range(10)])\n",
    "plt.savefig('destination_path.eps', format='eps', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZWTU0NKiyslJVVVX6+OOP13PstM3MzOiLL77Q559/rh9++OGV87/88ou+/vpr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\nobwBADAM5Q0AgGH+P3KmhkzzUJPZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1060e7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "import random\n",
    "# http://stackoverflow.com/questions/10194482/custom-matplotlib-plot-chess-board-like-table-with-colored-cells\n",
    "\n",
    "from matplotlib.table import Table\n",
    "\n",
    "def main():\n",
    "    grid_table(8, 8)\n",
    "    plt.axis('scaled')\n",
    "    plt.show()\n",
    "\n",
    "def grid_table(nrows, ncols):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_axis_off()\n",
    "    colors = ['white', 'lightgrey', 'dimgrey']\n",
    "    tb = Table(ax, bbox=[0,0,2,2])\n",
    "    for i,j in itertools.product(range(ncols), range(nrows)):\n",
    "        tb.add_cell(i, j, 2./ncols, 2./nrows, text='{:0.2f}'.format(0.1234), \n",
    "                    loc='center', facecolor=random.choice(colors), edgecolor='grey') # facecolors=\n",
    "    ax.add_table(tb)\n",
    "    #ax.plot([0, .3], [.2, .2])\n",
    "    #ax.add_line(plt.Line2D([0.3, 0.5], [0.7, 0.7], linewidth=2, color='blue'))\n",
    "    return fig\n",
    "\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class defaultkeydict(collections.defaultdict):\n",
    "    \"\"\"Like defaultdict, but the default_factory is a function of the key.\n",
    "    >>> d = defaultkeydict(abs); d[-42]\n",
    "    42\n",
    "    \"\"\"\n",
    "    def __missing__(self, key):\n",
    "        self[key] = self.default_factory(key)\n",
    "        return self[key]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}