MergeSorts.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/jsilryan/Algorithms/blob/master/MergeSorts.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import time\n",
        "from tqdm.auto import tqdm\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "AXTHtTBzOp1h"
      },
      "execution_count": 214,
      "outputs": []
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      "execution_count": 215,
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        "def merge(left, right):\n",
        "    result = []\n",
        "    i, j = 0, 0\n",
        "    while i < len(left) and j < len(right):\n",
        "        if left[i] < right[j]:\n",
        "            result.append(left[i])\n",
        "            i += 1\n",
        "        else:\n",
        "            result.append(right[j])\n",
        "            j += 1\n",
        "    # Append remaining elements from left and right, if any\n",
        "    for k in range(i, len(left)):\n",
        "        result.append(left[k])\n",
        "\n",
        "    for k in range(j, len(right)):\n",
        "        result.append(right[k])\n",
        "\n",
        "    return result\n",
        "\n",
        "\n",
        "def basic_merge_sort(array):\n",
        "    if len(array) <= 1:\n",
        "        return array\n",
        "\n",
        "    mid = len(array) // 2\n",
        "\n",
        "    left_array = []\n",
        "    for i in range(mid):\n",
        "        left_array.append(array[i])\n",
        "\n",
        "    right_array = []\n",
        "    for i in range(mid, len(array)):\n",
        "        right_array.append(array[i])\n",
        "\n",
        "    left = basic_merge_sort(left_array)\n",
        "    right = basic_merge_sort(right_array)\n",
        "\n",
        "    return merge(left, right)\n"
      ]
    },
    {
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      "source": [
        "def top_down_merge_sort(data):\n",
        "    if len(data) <= 1:\n",
        "        return data\n",
        "\n",
        "    mid = len(data) // 2\n",
        "\n",
        "    left = []\n",
        "    for i in range(mid):\n",
        "        left.append(data[i])\n",
        "\n",
        "    right = []\n",
        "    for i in range(mid, len(data)):\n",
        "        right.append(data[i])\n",
        "\n",
        "    left = top_down_merge_sort(left)\n",
        "    right = top_down_merge_sort(right)\n",
        "\n",
        "    return merge(left, right)\n",
        "\n",
        "\n",
        "def merge(left, right):\n",
        "    result = []\n",
        "    i = j = 0\n",
        "\n",
        "    while i < len(left) and j < len(right):\n",
        "        if left[i] < right[j]:\n",
        "            result.append(left[i])\n",
        "            i += 1\n",
        "        else:\n",
        "            result.append(right[j])\n",
        "            j += 1\n",
        "\n",
        "    for k in range(i, len(left)):\n",
        "        result.append(left[k])\n",
        "\n",
        "    for k in range(j, len(right)):\n",
        "        result.append(right[k])\n",
        "\n",
        "    return result\n"
      ],
      "metadata": {
        "id": "70U-CwIHD0qL"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_merge(\n",
        "    test_num: int,\n",
        "    input_size: int\n",
        "):\n",
        "  results = {\n",
        "      \"Avg_basic_time\" : None,\n",
        "      \"Avg_top_down_time\" : None,\n",
        "      \"Input_size\" : input_size\n",
        "  }\n",
        "\n",
        "  total_basic_time = 0\n",
        "  total_top_down_time = 0\n",
        "\n",
        "  for test in range(test_num):\n",
        "    # 1. Create an array for both merge sorts\n",
        "    array = list(range(1, input_size+1))\n",
        "    basic_array = array.copy()\n",
        "    random.shuffle(basic_array)\n",
        "    top_down_array = array.copy()\n",
        "    random.shuffle(top_down_array)\n",
        "    # print(f\"Basic Array : {basic_array} \\nTop-Down Array : {top_down_array}\")\n",
        "\n",
        "    # 2. Basic Merge Sort\n",
        "    basic_start = time.time()\n",
        "    basic_sorted = basic_merge_sort(basic_array)\n",
        "    basic_end = time.time()\n",
        "    basic_duration = basic_end - basic_start\n",
        "\n",
        "    # 3. Top-Down Merge Sort\n",
        "    top_down_start = time.time()\n",
        "    top_down_sorted = top_down_merge_sort(top_down_array)\n",
        "    top_down_end = time.time()\n",
        "    top_down_duration = top_down_end - top_down_start\n",
        "\n",
        "    # 4. Print what's happening\n",
        "    print(f\"Basic Merge Sort Duration : {basic_duration:.4f} || Top-Down Merge Sort Duration : {top_down_duration:.4f}\")\n",
        "\n",
        "    # 5. Update Dictionary\n",
        "    total_basic_time += basic_duration\n",
        "    total_top_down_time += top_down_duration\n",
        "\n",
        "  results[\"Avg_basic_time\"] = total_basic_time / test_num\n",
        "  results[\"Avg_top_down_time\"] = total_top_down_time / test_num\n",
        "\n",
        "  # 6. Return results\n",
        "  # print(f\"First 10 values: {basic_sorted[:10]} || Last 10 values: {basic_sorted[-10:]}\")\n",
        "  return results\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "I3f0RuSrEevM"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def input_size_init(\n",
        "    input_size: int,\n",
        "    tests_per_input_size: int\n",
        "):\n",
        "  # 1. Input the test sizes\n",
        "  sizes = [input_size, input_size * 10, input_size * 50, input_size * 100, input_size * 200]\n",
        "\n",
        "  # 2. Initialize a dictionary to store the averages and their corresponding input_size\n",
        "  new_results = {\n",
        "      \"basic_time_avg\" : [],\n",
        "      \"top_down_time_avg\" : [],\n",
        "      \"input_size\" : []\n",
        "  }\n",
        "\n",
        "  # 3. Get the averages per input size\n",
        "  for num in tqdm(range(len(sizes))):\n",
        "    print(f\"Test: {num + 1} | Input Size: {sizes[num]}\\n--------\")\n",
        "    time_results = test_merge(\n",
        "        test_num = tests_per_input_size,\n",
        "        input_size = sizes[num]\n",
        "    )\n",
        "    new_results[\"basic_time_avg\"].append(time_results[\"Avg_basic_time\"])\n",
        "    new_results[\"top_down_time_avg\"].append(time_results[\"Avg_top_down_time\"])\n",
        "    new_results[\"input_size\"].append(time_results[\"Input_size\"])\n",
        "    print(\"\\n\")\n",
        "\n",
        "  return new_results\n"
      ],
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      "execution_count": 218,
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        "# Get time averages per input size\n",
        "time_averages_per_size = input_size_init(\n",
        "    input_size = 20,\n",
        "    tests_per_input_size = 10\n",
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            "Test: 1 | Input Size: 20\n",
            "--------\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0003 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "Basic Merge Sort Duration : 0.0001 || Top-Down Merge Sort Duration : 0.0001\n",
            "\n",
            "\n",
            "Test: 2 | Input Size: 200\n",
            "--------\n",
            "Basic Merge Sort Duration : 0.0012 || Top-Down Merge Sort Duration : 0.0009\n",
            "Basic Merge Sort Duration : 0.0009 || Top-Down Merge Sort Duration : 0.0008\n",
            "Basic Merge Sort Duration : 0.0013 || Top-Down Merge Sort Duration : 0.0027\n",
            "Basic Merge Sort Duration : 0.0025 || Top-Down Merge Sort Duration : 0.0024\n",
            "Basic Merge Sort Duration : 0.0011 || Top-Down Merge Sort Duration : 0.0007\n",
            "Basic Merge Sort Duration : 0.0008 || Top-Down Merge Sort Duration : 0.0007\n",
            "Basic Merge Sort Duration : 0.0008 || Top-Down Merge Sort Duration : 0.0008\n",
            "Basic Merge Sort Duration : 0.0008 || Top-Down Merge Sort Duration : 0.0007\n",
            "Basic Merge Sort Duration : 0.0008 || Top-Down Merge Sort Duration : 0.0008\n",
            "Basic Merge Sort Duration : 0.0008 || Top-Down Merge Sort Duration : 0.0007\n",
            "\n",
            "\n",
            "Test: 3 | Input Size: 1000\n",
            "--------\n",
            "Basic Merge Sort Duration : 0.0048 || Top-Down Merge Sort Duration : 0.0045\n",
            "Basic Merge Sort Duration : 0.0053 || Top-Down Merge Sort Duration : 0.0059\n",
            "Basic Merge Sort Duration : 0.0088 || Top-Down Merge Sort Duration : 0.0097\n",
            "Basic Merge Sort Duration : 0.0061 || Top-Down Merge Sort Duration : 0.0046\n",
            "Basic Merge Sort Duration : 0.0046 || Top-Down Merge Sort Duration : 0.0047\n",
            "Basic Merge Sort Duration : 0.0048 || Top-Down Merge Sort Duration : 0.0045\n",
            "Basic Merge Sort Duration : 0.0047 || Top-Down Merge Sort Duration : 0.0045\n",
            "Basic Merge Sort Duration : 0.0045 || Top-Down Merge Sort Duration : 0.0049\n",
            "Basic Merge Sort Duration : 0.0051 || Top-Down Merge Sort Duration : 0.0045\n",
            "Basic Merge Sort Duration : 0.0053 || Top-Down Merge Sort Duration : 0.0045\n",
            "\n",
            "\n",
            "Test: 4 | Input Size: 2000\n",
            "--------\n",
            "Basic Merge Sort Duration : 0.0179 || Top-Down Merge Sort Duration : 0.0100\n",
            "Basic Merge Sort Duration : 0.0136 || Top-Down Merge Sort Duration : 0.0099\n",
            "Basic Merge Sort Duration : 0.0098 || Top-Down Merge Sort Duration : 0.0099\n",
            "Basic Merge Sort Duration : 0.0108 || Top-Down Merge Sort Duration : 0.0100\n",
            "Basic Merge Sort Duration : 0.0099 || Top-Down Merge Sort Duration : 0.0098\n",
            "Basic Merge Sort Duration : 0.0099 || Top-Down Merge Sort Duration : 0.0098\n",
            "Basic Merge Sort Duration : 0.0104 || Top-Down Merge Sort Duration : 0.0103\n",
            "Basic Merge Sort Duration : 0.0108 || Top-Down Merge Sort Duration : 0.0111\n",
            "Basic Merge Sort Duration : 0.0113 || Top-Down Merge Sort Duration : 0.0100\n",
            "Basic Merge Sort Duration : 0.0107 || Top-Down Merge Sort Duration : 0.0099\n",
            "\n",
            "\n",
            "Test: 5 | Input Size: 4000\n",
            "--------\n",
            "Basic Merge Sort Duration : 0.0265 || Top-Down Merge Sort Duration : 0.0216\n",
            "Basic Merge Sort Duration : 0.0223 || Top-Down Merge Sort Duration : 0.0212\n",
            "Basic Merge Sort Duration : 0.0227 || Top-Down Merge Sort Duration : 0.0223\n",
            "Basic Merge Sort Duration : 0.0223 || Top-Down Merge Sort Duration : 0.0240\n",
            "Basic Merge Sort Duration : 0.0228 || Top-Down Merge Sort Duration : 0.0216\n",
            "Basic Merge Sort Duration : 0.0266 || Top-Down Merge Sort Duration : 0.0223\n",
            "Basic Merge Sort Duration : 0.0238 || Top-Down Merge Sort Duration : 0.0218\n",
            "Basic Merge Sort Duration : 0.0229 || Top-Down Merge Sort Duration : 0.0210\n",
            "Basic Merge Sort Duration : 0.0221 || Top-Down Merge Sort Duration : 0.0211\n",
            "Basic Merge Sort Duration : 0.0330 || Top-Down Merge Sort Duration : 0.0550\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_averages_per_size.keys()"
      ],
      "metadata": {
        "id": "OSQECmDVH6d-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d0a1bd5-60f9-4401-ac65-285d9ce912c1"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['basic_time_avg', 'top_down_time_avg', 'input_size'])"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "averages_df = pd.DataFrame(time_averages_per_size)\n",
        "averages_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dkVqusOJlPsy",
        "outputId": "bb8db442-1717-4af4-cd97-35296104fa55"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   basic_time_avg  top_down_time_avg  input_size\n",
              "0        0.000139           0.000105          20\n",
              "1        0.001084           0.001128         200\n",
              "2        0.005406           0.005251        1000\n",
              "3        0.011523           0.010071        2000\n",
              "4        0.024490           0.025182        4000"
            ],
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "basic_time_avg = time_averages_per_size[\"basic_time_avg\"]\n",
        "length = len(basic_time_avg)\n",
        "length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qatKzdQxjt3g",
        "outputId": "2b35e22b-ed8c-477b-d812-b04f83547039"
      },
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_durations(\n",
        "    results: dict[str, list[float]]\n",
        "):\n",
        "  \"\"\"Plot the durations of Basic and Top-Down Merge Sorts\"\"\"\n",
        "  # Get the values from the dictionary\n",
        "  basic_time_avg = results[\"basic_time_avg\"]\n",
        "  top_down_time_avg = results[\"top_down_time_avg\"]\n",
        "  input_size = results[\"input_size\"]\n",
        "  # length = range(len(basic_time_avg))\n",
        "\n",
        "  # Set the figure size\n",
        "  plt.figure(figsize=(8, 6))\n",
        "\n",
        "  # Plot the durations\n",
        "  plt.plot(input_size, basic_time_avg, label = \"Basic Merge Sort Time Average\", marker='o')\n",
        "  plt.plot(input_size, top_down_time_avg, label = \"Top-Down Merge Sort Time Average\", marker='o')\n",
        "  plt.title(\"Basic and Top-Down Merge Sorts Average Durations\")\n",
        "  plt.xlabel(\"Input Sizes\")\n",
        "  plt.legend()"
      ],
      "metadata": {
        "id": "4p8UNLMvNmyg"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_durations(time_averages_per_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zV5-9fsPPnoK",
        "outputId": "7d4dd34d-8647-45e2-e88c-0e8c1036ab89"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}