BernoulliZip__Lossless_Erdos_Renyi_Graph_Compressor.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPZZFbP2Wun1J8EtitEXT4f"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# BernoulliZip: Efficient Compression and Decompression of Graph Sequences\n",
        "##Overview\n",
        "This notebook demonstrates the implementation of the BernoulliZip algorithm, which is used for compressing and decompressing graph sequences generated from Erdős-Rényi (ER) random graphs. The algorithm utilizes the Bernoulli distribution to efficiently compress sequences that represent the presence or absence of edges in a graph. The process is lossless, meaning that the original sequence can be perfectly reconstructed from the compressed data.\n",
        "\n",
        "##Code Breakdown\n",
        "1. `Bernoulli_Ordering` Function\n",
        "\n",
        "The `Bernoulli_Ordering` function generates an \"order\" array that helps determine the rank of the compressed sequence. This array is crucial in ensuring that the sequence is encoded and decoded correctly. The function performs the following steps:\n",
        "\n",
        "Input Parameters:\n",
        "\n",
        "$n$: The length of the sequence.\n",
        "\n",
        "$p$: The probability parameter from the Bernoulli distribution.\n",
        "Computation:\n",
        "\n",
        "The function computes an initial value $m$ which is the ceiling of $n * p$.\n",
        "It then iteratively adjusts this value to generate an ordered sequence that will be used during compression.\n",
        "\n",
        "2. `Bernoulli_Compress` Function\n",
        "\n",
        "The `Bernoulli_Compress` function compresses the sequence generated from the graph into a binary code. The steps involved are:\n",
        "\n",
        "Input Parameters:\n",
        "\n",
        "`p`: The probability parameter from the Bernoulli distribution.\n",
        "\n",
        "`seq`: The sequence representing the presence/absence of edges.\n",
        "\n",
        "`order`: The ordering array generated by Bernoulli_Ordering.\n",
        "Process:\n",
        "\n",
        "The function calculates the rank of the sequence and generates a prefix code based on this rank.\n",
        "\n",
        "The sequence is then encoded into a compressed binary format using combinatorial encoding.\n",
        "\n",
        "3. `Bernoulli_Decompress`Function\n",
        "The `Bernoulli_Decompress` function reverses the compression process, reconstructing the original sequence from the compressed data.\n",
        "\n",
        "Input Parameters:\n",
        "\n",
        "`p`: The probability parameter from the Bernoulli distribution.\n",
        "\n",
        "`compressed`: The compressed binary sequence.\n",
        "\n",
        "`order`: The ordering array generated by `Bernoulli_Ordering`.\n",
        "`Process`: The function decodes the binary sequence back into the original sequence using the rank information.\n",
        "\n",
        "4. Graph Generation and Sequence Extraction\n",
        "The functions `generate_erdos_renyi_graph` and `generate_erdos_renyi_sequence` generate an Erdős-Rényi random graph and extract the upper triangular portion of its adjacency matrix as a sequence. This sequence represents the edges of the graph.\n",
        "\n",
        "Input Parameters:\n",
        "\n",
        "`n`: Number of nodes in the graph.\n",
        "\n",
        "`p`: Probability of edge creation.\n",
        "\n",
        "5. Graph Compression and Decompression Workflow\n",
        "Generate ER Graph: The graph is generated based on the given parameters `n` and `p`.\n",
        "\n",
        "Compression: The sequence representing the graph is compressed using `Bernoulli_Compress`.\n",
        "\n",
        "Decompression: The compressed sequence is decompressed using `Bernoulli_Decompress`, and the original sequence is reconstructed.\n",
        "\n",
        "Hamming Distance Calculation: The Hamming distance between the original and decompressed sequences is computed to verify lossless compression.\n",
        "\n",
        "6. Graph Reconstruction and Statistics\n",
        "The decompressed sequence is used to reconstruct the graph, and various graph statistics (such as clustering coefficient, triangle count, degree centrality, etc.) are calculated for both the original and reconstructed graphs.\n",
        "\n",
        "Comparison: The statistics of the original and reconstructed graphs are compared to ensure that the compression process has preserved the structural properties of the graph.\n",
        "\n",
        "7. Visualization\n",
        "Finally, the original and reconstructed graphs are visualized side-by-side to provide a visual confirmation of the integrity of the compression and decompression process.\n",
        "\n",
        "Summary\n",
        "This notebook provides a complete implementation of the BernoulliZip algorithm, showcasing its application in compressing and decompressing graph sequences from Erdős-Rényi graphs. The process is validated through statistical comparisons and visual inspection of the reconstructed graph.\n",
        "\n"
      ],
      "metadata": {
        "id": "itig_ZhAGfL5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        },
        "id": "tbzama5gFZUs",
        "outputId": "b869e383-bd05-48ea-8e84-73b75324a877"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original sequence: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
            "Compressed sequence: [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0]\n",
            "Decompressed sequence: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n",
            "Compression ratio: 2.76\n",
            "Hamming distance: 0\n",
            "Original Graph Statistics:\n",
            "Clustering Coefficient: 0.046663558663558646\n",
            "Triangle Count: 41\n",
            "\n",
            "\n",
            "Reconstructed Graph Statistics:\n",
            "Clustering Coefficient: 0.046663558663558646\n",
            "Triangle Count: 41\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "Created on Wed Jun 19 16:07:30 2024\n",
        "\n",
        "@author: Martin Wafula\n",
        "\"\"\"\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "import math\n",
        "from scipy.special import comb\n",
        "\n",
        "def Bernoulli_Ordering(n, p):\n",
        "    m = math.ceil(n * p)\n",
        "    q = 1 - p\n",
        "\n",
        "    flag0 = False\n",
        "    flagn = False\n",
        "\n",
        "    order = np.zeros(n + 1, dtype=int)\n",
        "    order[0] = m\n",
        "\n",
        "    if m == 0:\n",
        "        order[1:] = np.arange(1, n + 1)\n",
        "    elif m == n:\n",
        "        order[1:] = np.arange(n - 1, -1, -1)\n",
        "    else:\n",
        "        dif = -2 * math.log(p) + 2 * math.log(q) + math.log(m * (m + 1)) - math.log((n - m) * (n - m + 1))\n",
        "        u = m + 1\n",
        "        d = m - 1\n",
        "        for i in range(1, n + 1):\n",
        "            if dif >= 0:\n",
        "                if d != 0:\n",
        "                    dif = dif + math.log(d) - math.log(n - (d - 1)) - 2 * math.log(p) + 2 * math.log(q)\n",
        "                    order[i] = d\n",
        "                    d -= 1\n",
        "                else:\n",
        "                    if not flag0:\n",
        "                        dif = dif - math.log(n) - 2 * math.log(p) + 2 * math.log(q)\n",
        "                        order[i] = d\n",
        "                        flag0 = True\n",
        "                    else:\n",
        "                        if (u + 1) > 0 and (n - u) > 0:\n",
        "                            dif = dif + math.log(u + 1) - math.log(n - u) - 2 * math.log(p) + 2 * math.log(q)\n",
        "                            order[i] = u\n",
        "                            u += 1\n",
        "                        else:\n",
        "                            break\n",
        "            else:\n",
        "                if u != n:\n",
        "                    if (u + 1) > 0 and (n - u) > 0:\n",
        "                        dif = dif + math.log(u + 1) - math.log(n - u) - 2 * math.log(p) + 2 * math.log(q)\n",
        "                        order[i] = u\n",
        "                        u += 1\n",
        "                    else:\n",
        "                        break\n",
        "                else:\n",
        "                    if not flagn:\n",
        "                        dif = dif + math.log(n) - 2 * math.log(p) + 2 * math.log(q)\n",
        "                        order[i] = u\n",
        "                        flagn = True\n",
        "                    else:\n",
        "                        if d > 0 and (n - (d - 1)) > 0:\n",
        "                            dif = dif + math.log(d) - math.log(n - (d - 1)) - 2 * math.log(p) + 2 * math.log(q)\n",
        "                            order[i] = d\n",
        "                            d -= 1\n",
        "\n",
        "    return order\n",
        "\n",
        "def Bernoulli_Compress(p, seq, order):\n",
        "    n = len(seq)\n",
        "    m = math.ceil((n + 1) * p - 1)\n",
        "    q = 1 - p\n",
        "\n",
        "    k = np.sum(seq)\n",
        "    index = np.where(order == k)[0][0] + 1\n",
        "    log_rank = math.floor(math.log2(index))\n",
        "    prefix = [0] * log_rank + [1]\n",
        "    dif = index - 2**log_rank\n",
        "    bin_dif = list(map(int, bin(dif)[2:]))\n",
        "    ldif = log_rank - len(bin_dif)\n",
        "    bin_dif = [0] * ldif + bin_dif\n",
        "    prefix.extend(bin_dif)\n",
        "\n",
        "    code = 0\n",
        "    for i in range(n):\n",
        "        if seq[i] == 1:\n",
        "            if k > n - i - 1:\n",
        "                break\n",
        "            code += comb(n - i - 1, k, exact=True)\n",
        "            k -= 1\n",
        "\n",
        "    code_bin = list(map(int, bin(code)[2:]))\n",
        "    compressed = prefix + code_bin\n",
        "\n",
        "    return compressed\n",
        "\n",
        "def Bernoulli_Decompress(p, compressed, order):\n",
        "    log_rank = 0\n",
        "    while compressed[log_rank] == 0:\n",
        "        log_rank += 1\n",
        "    index = 2**log_rank\n",
        "    prefix_len = log_rank + 1\n",
        "    bin_dif = compressed[prefix_len:prefix_len + log_rank]\n",
        "\n",
        "    if bin_dif:\n",
        "        dif = int(''.join(map(str, bin_dif)), 2)\n",
        "    else:\n",
        "        dif = 0\n",
        "\n",
        "    index += dif\n",
        "\n",
        "    k = order[index - 1]\n",
        "    seq_len = len(order) - 1\n",
        "    code = compressed[prefix_len + log_rank:]\n",
        "    code = int(''.join(map(str, code)), 2) if code else 0\n",
        "\n",
        "    seq = [0] * seq_len\n",
        "    for i in range(seq_len):\n",
        "        if k == 0:\n",
        "            break\n",
        "        if code >= comb(seq_len - i - 1, k, exact=True):\n",
        "            seq[i] = 1\n",
        "            code -= comb(seq_len - i - 1, k, exact=True)\n",
        "            k -= 1\n",
        "\n",
        "    return seq\n",
        "\n",
        "def generate_erdos_renyi_graph(n, p):\n",
        "    return nx.erdos_renyi_graph(n, p, seed= 10000000)\n",
        "\n",
        "def generate_erdos_renyi_sequence(n, p):\n",
        "    graph = generate_erdos_renyi_graph(n, p)\n",
        "    upper_triangle = nx.to_numpy_array(graph)[np.triu_indices(n, 1)]\n",
        "    return upper_triangle.tolist()\n",
        "\n",
        "def calculate_entropy(p, n):\n",
        "    return n * (p * math.log2(1 / p) + (1 - p) * math.log2(1 / (1 - p)))\n",
        "\n",
        "def hamming_distance(seq1, seq2):\n",
        "    return sum(el1 != el2 for el1, el2 in zip(seq1, seq2))\n",
        "\n",
        "def graph_statistics(graph):\n",
        "    clustering_coeff = nx.average_clustering(graph)\n",
        "    triangle_count = sum(nx.triangles(graph).values()) // 3\n",
        "    degree_centrality = nx.degree_centrality(graph)\n",
        "    betweenness_centrality = nx.betweenness_centrality(graph)\n",
        "    spectral_centrality = nx.eigenvector_centrality(graph, max_iter=1000)\n",
        "\n",
        "    stats = {\n",
        "        'clustering_coeff': clustering_coeff,\n",
        "        'triangle_count': triangle_count,\n",
        "        'degree_centrality': degree_centrality,\n",
        "        'betweenness_centrality': betweenness_centrality,\n",
        "        'spectral_centrality': spectral_centrality\n",
        "    }\n",
        "    return stats\n",
        "\n",
        "def print_statistics(name, stats):\n",
        "    print(f\"{name} Statistics:\")\n",
        "    print(f\"Clustering Coefficient: {stats['clustering_coeff']}\")\n",
        "    print(f\"Triangle Count: {stats['triangle_count']}\")\n",
        "    #print(\"Degree Centrality:\", stats['degree_centrality'])\n",
        "    #print(\"Betweenness Centrality:\", stats['betweenness_centrality'])\n",
        "    #print(\"Spectral Centrality:\", stats['spectral_centrality'])\n",
        "    print(\"\\n\")\n",
        "\n",
        "# Parameters\n",
        "n = 100\n",
        "p = np.log2(n) / n\n",
        "\n",
        "# Generate ER graph and sequence\n",
        "original_graph = generate_erdos_renyi_graph(n, p)\n",
        "original_sequence = generate_erdos_renyi_sequence(n, p)\n",
        "\n",
        "# Compress the sequence\n",
        "edge_count = n * (n - 1) // 2\n",
        "order = Bernoulli_Ordering(edge_count, p)\n",
        "compressed_sequence = Bernoulli_Compress(p, original_sequence, order)\n",
        "\n",
        "# Decompress the sequence\n",
        "decompressed_sequence = Bernoulli_Decompress(p, compressed_sequence, order)\n",
        "\n",
        "# Calculate compression ratio\n",
        "original_size = len(original_sequence)\n",
        "compressed_size = len(compressed_sequence)\n",
        "compression_ratio = original_size / compressed_size\n",
        "\n",
        "# Verify lossless compression\n",
        "assert original_sequence == decompressed_sequence, \"The decompressed sequence does not match the original sequence.\"\n",
        "\n",
        "# Calculate Hamming distance\n",
        "hamming_dist = hamming_distance(original_sequence, decompressed_sequence)\n",
        "\n",
        "# Reconstruct the graph from the decompressed sequence\n",
        "reconstructed_graph = nx.Graph()\n",
        "reconstructed_graph.add_nodes_from(range(n))\n",
        "edge_index = 0\n",
        "for i in range(n):\n",
        "    for j in range(i + 1, n):\n",
        "        if decompressed_sequence[edge_index] == 1:\n",
        "            reconstructed_graph.add_edge(i, j)\n",
        "        edge_index += 1\n",
        "\n",
        "# Print results\n",
        "print(f\"Original sequence: {original_sequence}\")\n",
        "print(f\"Compressed sequence: {compressed_sequence}\")\n",
        "print(f\"Decompressed sequence: {decompressed_sequence}\")\n",
        "print(f\"Compression ratio: {compression_ratio:.2f}\")\n",
        "print(f\"Hamming distance: {hamming_dist}\")\n",
        "\n",
        "# Calculate and print statistics\n",
        "original_stats = graph_statistics(original_graph)\n",
        "reconstructed_stats = graph_statistics(reconstructed_graph)\n",
        "\n",
        "print_statistics(\"Original Graph\", original_stats)\n",
        "print_statistics(\"Reconstructed Graph\", reconstructed_stats)\n",
        "\n",
        "# Plot the original and reconstructed graphs\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))\n",
        "\n",
        "nx.draw(original_graph, ax=ax1, with_labels=False, node_color='blue', edge_color='gray', node_size=15)\n",
        "ax1.set_title('Original Erdos-Renyi Graph')\n",
        "\n",
        "nx.draw(reconstructed_graph, ax=ax2, with_labels=False, node_color='green', edge_color='gray', node_size=15)\n",
        "ax2.set_title('Reconstructed Graph from Decompressed Sequence')\n",
        "\n",
        "plt.show()"
      ]
    }
  ]
}