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      "source": "#Manager function\n\nimport joblib\nfrom joblib import Parallel,delayed\n\nimport importlib\nimport MCTS2\nimportlib.reload(MCTS2)\n\n\nimport numpy as np\n#protein = \"hhppppphhppphppphp\" #Best score -4 length 18\n#protein,maxValue = \"hphphhhppphhhhpphh\",8 #Best score -8 length 18\nprotein, maxValue = \"BWBWWBBWBWWBWBBWWBWB\",9 #Best score -9 length = 20 #B = H\n#protein,maxValue = \"WWWBBWWBBWWWWWBBBBBBBWWBBWWWWBBWWBWW\",14 #Best score -14 length 36\n#protein,maxValue = \"WWBWWBBWWWWBBWWWWBBWWWWBB\",8 #Best score -8 length 25\n#protein,maxValue = \"pphpphhpphhppppphhhhhhhhhhpppppphhpphhpphpphhhhh\",23 #Best score -23 length 48\nprotein,maxValue = \"PPPHHPPHHHHPPHHHPHHPHHPHHHHPPPPPPPPHHHHHHPPHHHHHHPPPPPPPPPHPHHPHHHHHHHHHHHPPHHHPHHPHPPHPHHHPPPPPPHHH\" ,50 #length 100, score-50\n#protein,maxValue = \"HHPHPHPHPHHHHPHPPPHPPPHPPPPHPPPHPPPHPHHHHPHPHPHPHH\",21 #Length 50, score -21\nl = []\nfor char in protein:\n    if char == \"H\":\n        l.append(2)\n    else:\n        l.append(1)\n\nproteinArray = np.array(l)\nprint(\"Length of protein\", proteinArray.shape[0])\n\n\nparams = MCTS2.params(alpha=0.04,\n                      nRuns = 100_000,\n                      maxValue = maxValue,\n                      selectionAlgorithm=MCTS2.MCTS,\n                      updateAlgorithm= MCTS2.UpdateNodesMCTS)\n\n#---------------------------------------------------------\n# Serial runs\n#---------------------------------------------------------\n#maxScore,foundRun,maxRun,board,nIllegalRuns ,alpha= MCTS2.MainLoop(proteinArray,maxValue,MCTS2.GreedyEpsilon,MCTS2.UpdateNodesEpsilon,.95)\nmaxScore,foundRun,maxRun,board,nIllegalRuns ,alpha, nRepeated,analysisNodes = MCTS2.MainLoop(proteinArray,params)\nprint(f\"Score {maxScore} run {foundRun} repeated routes {nRepeated} incomplete {nIllegalRuns}\")\n#MCTS2.ShowMolecule1(maxRun,board)\n\n#---------------------------------------------------------\n# Parallel runs\n#---------------------------------------------------------\n#results = Parallel(n_jobs=8)(delayed(MCTS2.MainLoop)(proteinArray,params) for i in range(8))\n# #results = Parallel(n_jobs=8)(delayed(MainLoop)(proteinArray,maxValue,GreedyEpsilon,UpdateNodesEpsilon,.9) for i in range(10))\n#formattedResults = [f\"Alpha {results[i][5]:.2f} Max found {results[i][0]} on iteration {results[i][1]}\" for i in range(len(results))]\n#print(*formattedResults,sep=\"\\n\")\n#MCTS2.ShowMolecule1(results[0][2],board)\n\n ",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Length of protein 100\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "Max score 39: 100%|████████████████████████████████████████| 100000/100000 [02:25<00:00, 687.10it/s]\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "Score 39 run 36549 repeated routes 42967 incomplete 0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-06-12T08:54:15.749544Z",
          "start_time": "2019-06-12T08:54:15.706530Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport qgrid\nqgrid.set_grid_option('forceFitColumns',False)\nqgrid.set_grid_option('filterable',False)\nqgrid.enable()\ndf = pd.DataFrame(analysisNodes[1:15,6:9],columns=['Left','Forwards','Right'])\ndf",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-06-12T09:12:43.726256Z",
          "start_time": "2019-06-12T09:12:43.460747Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "MCTS2.ShowMolecule1(maxRun,board)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-06-12T09:21:16.477131Z",
          "start_time": "2019-06-12T09:21:13.019113Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "MCTS2.drawTree()",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-06-12T09:26:15.543294Z",
          "start_time": "2019-06-12T09:26:15.499794Z"
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        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.DataFrame(analysisNodes)\ndf[1:100]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-06-12T13:16:49.416532Z",
          "start_time": "2019-06-12T13:16:49.238036Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(analysisNodes[-100:])\nprint(np.sum(analysisNodes[:,9]))",
      "execution_count": null,
      "outputs": []
    },
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        "trusted": true
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