notebookcdc59cceba.ipynb

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      "started ARC workflow\n"
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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "\n",
    "# import numpy as np # linear algebra\n",
    "# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "\n",
    "# import os\n",
    "\n",
    "\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "# print('testing arc')\n",
    "        \n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n",
    "\n",
    "\n",
    "\n",
    "# Original work\n",
    "# %%\n",
    "\n",
    "# import relevant libraries\n",
    "import json\n",
    "import numpy as np\n",
    "from typing import List, Dict, Any, Tuple\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "# %% [markdown]\n",
    "### Entity-Based AGI Implementation\n",
    "\n",
    "# %%\n",
    "\n",
    "# An entity is an independent individual or character containing two parameters of interest:\n",
    "# first parameter is it's Ego\n",
    "# second parameter is it's interest.\n",
    "\n",
    "class Entity:\n",
    "    def __init__(self, interest_threshold=0.5, ego_weight=0.7):\n",
    "        self.interest_threshold = interest_threshold\n",
    "        self.ego_weight = ego_weight\n",
    "        self.memory = []\n",
    "        \n",
    "    def assess_interest(self, pattern: np.ndarray) -> float:\n",
    "        \"\"\"Evaluate pattern interestingness based on complexity and novelty\"\"\"\n",
    "        complexity = self._calculate_complexity(pattern)\n",
    "        novelty = self._calculate_novelty(pattern)\n",
    "        return (complexity + novelty) / 2\n",
    "    \n",
    "    def _calculate_complexity(self, pattern: np.ndarray) -> float:\n",
    "        # Implement pattern complexity measurement\n",
    "        unique_vals = len(np.unique(pattern))\n",
    "        size = pattern.size\n",
    "        return unique_vals / size\n",
    "    \n",
    "    def _calculate_novelty(self, pattern: np.ndarray) -> float:\n",
    "        if not self.memory:\n",
    "            return 1.0\n",
    "        # Compare with memorized patterns\n",
    "        similarities = [np.mean(m == pattern) for m in self.memory]\n",
    "        return 1 - max(similarities)\n",
    "\n",
    "# %%\n",
    "# solve the grid based on above assumptions.\n",
    "class GridSolver:\n",
    "    def __init__(self):\n",
    "        self.entity = Entity()\n",
    "        \n",
    "    def solve_task(self, task: Dict[str, Any]) -> Dict[str, List[np.ndarray]]:\n",
    "        train_pairs = task['train']\n",
    "        test_inputs = task['test']\n",
    "        \n",
    "        predictions = {\n",
    "            'attempt_1': [],\n",
    "            'attempt_2': []\n",
    "        }\n",
    "        \n",
    "        for test_input in test_inputs:\n",
    "            # Generate two attempts based on different interest/ego parameters\n",
    "            pred1 = self._generate_prediction(train_pairs, test_input, ego_boost=0)\n",
    "            pred2 = self._generate_prediction(train_pairs, test_input, ego_boost=0.2)\n",
    "            \n",
    "            predictions['attempt_1'].append(pred1.tolist())\n",
    "            predictions['attempt_2'].append(pred2.tolist())\n",
    "            \n",
    "        return predictions\n",
    "    \n",
    "    def _generate_prediction(self, train_pairs, test_input, ego_boost=0):\n",
    "        # Implementation here\n",
    "        return np.zeros_like(test_input)\n",
    "\n",
    "# %% [markdown]\n",
    "### Data Loading and Testing\n",
    "\n",
    "# %%\n",
    "def load_tasks(path: str) -> Dict[str, Any]:\n",
    "    with open(path, 'r') as f:\n",
    "        return json.load(f)\n",
    "\n",
    "def save_submission(predictions: Dict[str, Any], path: str):\n",
    "    with open(path, 'w') as f:\n",
    "        json.dump(predictions, f)\n",
    "\n",
    "# %%\n",
    "# Test implementation\n",
    "if __name__ == \"__main__\":\n",
    "    print(\"started ARC workflow\")\n",
    "    solver = GridSolver()\n",
    "    # Add test cases here\n"
   ]
  }
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