examples.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "id": "ac1762d0-d1c1-4ee3-8b57-8456d5cab8cf",
      "metadata": {
        "id": "ac1762d0-d1c1-4ee3-8b57-8456d5cab8cf"
      },
      "source": [
        "# 연습문제 정답"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e4354af-1be8-46ed-970c-c1243a87ac32",
      "metadata": {
        "id": "5e4354af-1be8-46ed-970c-c1243a87ac32"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/rickiepark/math4ds/blob/main/examples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63ecb3e3-21be-46a0-addb-39bffbef0f47",
      "metadata": {
        "id": "63ecb3e3-21be-46a0-addb-39bffbef0f47"
      },
      "source": [
        "## 1장"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07e3d695-4337-44c9-8e11-21d3b8093f89",
      "metadata": {
        "id": "07e3d695-4337-44c9-8e11-21d3b8093f89"
      },
      "source": [
        "1-7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d79c01fa-f6b9-4ede-affb-60841ed41b91",
      "metadata": {
        "id": "d79c01fa-f6b9-4ede-affb-60841ed41b91",
        "outputId": "dd73a6e4-c564-4b71-edc4-063cdae899ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2*exp(2*x - 1)\n"
          ]
        }
      ],
      "source": [
        "from sympy import *\n",
        "\n",
        "x = symbols('x')\n",
        "f = exp(2*x - 1)\n",
        "\n",
        "dx_f = diff(f)\n",
        "print(dx_f)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "558bad30-99dd-4394-b06e-b79e1bdb0866",
      "metadata": {
        "id": "558bad30-99dd-4394-b06e-b79e1bdb0866"
      },
      "source": [
        "1-8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ab7d1b73-dcd2-4a76-a3a5-f49ad10e8024",
      "metadata": {
        "id": "ab7d1b73-dcd2-4a76-a3a5-f49ad10e8024",
        "outputId": "bee087c1-6d4d-49b6-ad7a-dd7df9124fb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2*x/(x**2 - 1)\n"
          ]
        }
      ],
      "source": [
        "from sympy import *\n",
        "\n",
        "x = symbols('x')\n",
        "f = log(x**2 - 1)\n",
        "\n",
        "dx_f = diff(f)\n",
        "print(dx_f)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8efd2580-e313-4f81-97be-07af88217614",
      "metadata": {
        "id": "8efd2580-e313-4f81-97be-07af88217614"
      },
      "source": [
        "1-9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8922448a-4d7d-4faf-a762-49e8c9f38ca0",
      "metadata": {
        "id": "8922448a-4d7d-4faf-a762-49e8c9f38ca0",
        "outputId": "eec5eb8c-d1d0-4875-8c8e-ed372f58b436"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1161.4722313334678\n"
          ]
        }
      ],
      "source": [
        "from math import exp\n",
        "\n",
        "p = 1000\t# 원금\n",
        "r = .05\t# 이자율\n",
        "t = 3\t\t# 년도\n",
        "n = 12\t# 연간 복리 적용 횟수\n",
        "\n",
        "a = p * (1 + (r/n))**(n * t)\n",
        "\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b694a5cf-a73a-4339-a3d4-817eab40503c",
      "metadata": {
        "id": "b694a5cf-a73a-4339-a3d4-817eab40503c"
      },
      "source": [
        "1-10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d95eec34-e5a3-4e29-b765-1d454ce1f6f2",
      "metadata": {
        "id": "d95eec34-e5a3-4e29-b765-1d454ce1f6f2",
        "outputId": "2004d7b2-6704-48e1-e842-c52a8fed7ec0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1161.834242728283\n"
          ]
        }
      ],
      "source": [
        "from math import exp\n",
        "\n",
        "p = 1000 # 원금\n",
        "r = .05 # 이자율\n",
        "t = 3.0 # 년도\n",
        "\n",
        "a = p * exp(r*t)\n",
        "\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83e45f50-da9c-4253-b2a4-20a386b7fbb5",
      "metadata": {
        "id": "83e45f50-da9c-4253-b2a4-20a386b7fbb5"
      },
      "source": [
        "1-11"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c7a9bad-bd10-49a4-b163-9a4636685bcc",
      "metadata": {
        "id": "6c7a9bad-bd10-49a4-b163-9a4636685bcc",
        "outputId": "08b366a1-02bc-4d51-fc14-08dbeebe99b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6*x\n",
            "18\n"
          ]
        }
      ],
      "source": [
        "from sympy import *\n",
        "\n",
        "# 'x'를 SymPy에 선언합니다\n",
        "x = symbols('x')\n",
        "\n",
        "# 파이썬으로 함수를 정의합니다\n",
        "f = 3*x**2 + 1\n",
        "\n",
        "# 함수의 도함수를 계산합니다\n",
        "dx_f = diff(f)\n",
        "print(dx_f)\n",
        "print(dx_f.subs(x,3))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe57c92b-a1cf-4d6a-bd00-4df96fcd884f",
      "metadata": {
        "id": "fe57c92b-a1cf-4d6a-bd00-4df96fcd884f"
      },
      "source": [
        "1-12"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f995bfcd-0c07-4e33-9ba6-3da243e6c4ba",
      "metadata": {
        "id": "f995bfcd-0c07-4e33-9ba6-3da243e6c4ba",
        "outputId": "b1d9c809-4824-4e8e-eba9-e439cfe89507"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3*x**2/y\n"
          ]
        }
      ],
      "source": [
        "from sympy import *\n",
        "\n",
        "# 'x'를 SymPy에 선언합니다\n",
        "x, y = symbols('x y')\n",
        "\n",
        "_y = x**3 - 1\n",
        "z = log(y) + 2\n",
        "\n",
        "dz_dy = diff(z)\n",
        "dy_dx = diff(_y)\n",
        "print(dz_dy * dy_dx)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ade83857-a869-4d2b-868a-67fa3cf10ed9",
      "metadata": {
        "id": "ade83857-a869-4d2b-868a-67fa3cf10ed9"
      },
      "source": [
        "1-13"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a868e26-d258-4b4d-aed0-3b450b893f22",
      "metadata": {
        "id": "7a868e26-d258-4b4d-aed0-3b450b893f22",
        "outputId": "e5bde619-a05f-4f44-e047-b43ac218b810"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10\n"
          ]
        }
      ],
      "source": [
        "from sympy import *\n",
        "\n",
        "# 'x'를 SymPy에 선언합니다\n",
        "x = symbols('x')\n",
        "\n",
        "# 파이썬으로 함수를 정의합니다\n",
        "f = 3*x**2 + 1\n",
        "\n",
        "# x = 0과 2 사이에서 함수를 적분합니다\n",
        "area = integrate(f, (x, 0, 2))\n",
        "\n",
        "print(area)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0342cd0c-b0c1-41e4-8681-3f929c9d4b76",
      "metadata": {
        "id": "0342cd0c-b0c1-41e4-8681-3f929c9d4b76"
      },
      "source": [
        "## 2장"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd4cb7a5-c937-4019-9fde-568673c760ec",
      "metadata": {
        "id": "dd4cb7a5-c937-4019-9fde-568673c760ec"
      },
      "source": [
        "2-5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "10be43b2-0010-481f-a12d-a1a1ca46b066",
      "metadata": {
        "id": "10be43b2-0010-481f-a12d-a1a1ca46b066",
        "outputId": "f48b299d-e926-4575-8683-08ccb4692c0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8220955881474251\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import binom\n",
        "\n",
        "n = 137\n",
        "p = .40\n",
        "\n",
        "p_50_or_more_noshows = 0.0\n",
        "\n",
        "for x in range(50,138):\n",
        "    p_50_or_more_noshows += binom.pmf(x, n, p)\n",
        "\n",
        "print(p_50_or_more_noshows)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "774b2374-d5db-4069-800e-7f6a6f13c7d3",
      "metadata": {
        "id": "774b2374-d5db-4069-800e-7f6a6f13c7d3"
      },
      "source": [
        "2-6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65eb873f-07b2-4f64-b76a-a86f29b8f65d",
      "metadata": {
        "id": "65eb873f-07b2-4f64-b76a-a86f29b8f65d",
        "outputId": "83e2f0a0-a3ec-4f6d-f5b3-0a0969b9fd92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9962310791015625\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import beta\n",
        "\n",
        "heads = 15\n",
        "tails = 4\n",
        "\n",
        "p = 1.0 - beta.cdf(.5, heads, tails)\n",
        "\n",
        "print(p)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ba9cb468-2234-459e-8ac7-a89ae9bcdaa4",
      "metadata": {
        "id": "ba9cb468-2234-459e-8ac7-a89ae9bcdaa4"
      },
      "source": [
        "2-7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "552bb394-253e-490f-b6fd-9af8259d855f",
      "metadata": {
        "id": "552bb394-253e-490f-b6fd-9af8259d855f",
        "outputId": "24ada2df-6f0d-4a9d-8637-4d4b7ddac90b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.33647497661034353\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import beta\n",
        "\n",
        "heads = 15\n",
        "tails = 4\n",
        "\n",
        "p = beta.cdf(0.8, heads, tails) - beta.cdf(0.7, heads, tails)\n",
        "print(p)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "92d489f3-1e04-4c27-913c-b7f96d9da325",
      "metadata": {
        "id": "92d489f3-1e04-4c27-913c-b7f96d9da325"
      },
      "source": [
        "## 3장"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3e5e285c-ccd8-4ac7-a42f-ae82c8bee5a2",
      "metadata": {
        "id": "3e5e285c-ccd8-4ac7-a42f-ae82c8bee5a2"
      },
      "source": [
        "3-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa680332-01a6-42ec-bf7f-81921b8c3285",
      "metadata": {
        "id": "fa680332-01a6-42ec-bf7f-81921b8c3285",
        "outputId": "030170f6-522d-4baf-bfa9-3cc27bc7f037"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "평균:  1.752\n",
            "표준 편차:  0.02135415650406264\n"
          ]
        }
      ],
      "source": [
        "from math import sqrt\n",
        "\n",
        "sample = [1.78, 1.75, 1.72, 1.74, 1.77]\n",
        "\n",
        "def mean_sample(values):\n",
        "    return sum(values) /len(values)\n",
        "\n",
        "def variance_sample(values):\n",
        "    mean = mean_sample(values)\n",
        "    var = sum((v - mean) ** 2 for v in values) / len(values)\n",
        "    return var\n",
        "\n",
        "def std_dev_sample(values):\n",
        "    return sqrt(variance_sample(values))\n",
        "\n",
        "mean = mean_sample(sample)\n",
        "std_dev = std_dev_sample(sample)\n",
        "\n",
        "print(\"평균: \", mean) # 1.752\n",
        "print(\"표준 편차: \", std_dev) # 0.0213542"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "113ce1b5-53f3-4811-a6ff-df25c930985f",
      "metadata": {
        "id": "113ce1b5-53f3-4811-a6ff-df25c930985f"
      },
      "source": [
        "3-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "041861fb-b46d-4cbf-9ca7-4d78dfe68e0d",
      "metadata": {
        "id": "041861fb-b46d-4cbf-9ca7-4d78dfe68e0d",
        "outputId": "5658c0b0-b54c-4a86-9a1e-eec5f0677b03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.031239839196641972\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "mean = 64.43\n",
        "std_dev = 2.99\n",
        "\n",
        "x = 1 - norm.cdf(70, mean, std_dev)\n",
        "\n",
        "print(x) # 0.031239839196641972"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4229cdf2-6863-4ac4-884e-19dd107594ac",
      "metadata": {
        "id": "4229cdf2-6863-4ac4-884e-19dd107594ac"
      },
      "source": [
        "3-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a0feb0c-0260-4fd4-931c-6bc13748cfbf",
      "metadata": {
        "id": "7a0feb0c-0260-4fd4-931c-6bc13748cfbf",
        "outputId": "67892fb8-8f96-47f5-f8c7-a33101626e40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.06382743803380352\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "mean = 42\n",
        "std_dev = 8\n",
        "\n",
        "x = norm.cdf(30, mean, std_dev) - norm.cdf(20, mean, std_dev)\n",
        "\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "79fd16c4-12e0-43eb-a8ca-b9eebae2e40c",
      "metadata": {
        "id": "79fd16c4-12e0-43eb-a8ca-b9eebae2e40c"
      },
      "source": [
        "3-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "b9e023c1-a722-4815-b48c-67e322aecfbe",
      "metadata": {
        "id": "b9e023c1-a722-4815-b48c-67e322aecfbe",
        "outputId": "cca20096-120f-44be-d34d-abe401ed86c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31.747587475643197\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "x = norm.ppf(0.1, loc=42, scale=8)\n",
        "print(x)  # 31.747587475643197"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "db0c3a9a-5820-45ca-9e6a-97b2e314fb0d",
      "metadata": {
        "id": "db0c3a9a-5820-45ca-9e6a-97b2e314fb0d"
      },
      "source": [
        "3-5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "763ffe51-82e3-44bd-b101-33076026c62b",
      "metadata": {
        "id": "763ffe51-82e3-44bd-b101-33076026c62b",
        "outputId": "59dbbda1-441d-4c16-817e-5dcf749d2e3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1.7026658973748656, 1.7285101026251342)\n"
          ]
        }
      ],
      "source": [
        "from math import sqrt\n",
        "from scipy.stats import norm\n",
        "\n",
        "def critical_z_value(p, mean=0.0, std=1.0):\n",
        "    norm_dist = norm(loc=mean, scale=std)\n",
        "    left_area = (1.0 - p) / 2.0\n",
        "    right_area = 1.0 - ((1.0 - p) / 2.0)\n",
        "    return norm_dist.ppf(left_area), norm_dist.ppf(right_area)\n",
        "\n",
        "\n",
        "def ci_large_sample(p, sample_mean, sample_std, n):\n",
        "    # 샘플 크기는 30보다 커야 합니다\n",
        "\n",
        "    lower, upper = critical_z_value(p)\n",
        "    lower_ci = lower * (sample_std / sqrt(n))\n",
        "    upper_ci = upper * (sample_std / sqrt(n))\n",
        "\n",
        "    return sample_mean + lower_ci, sample_mean + upper_ci\n",
        "\n",
        "\n",
        "print(ci_large_sample(p=.99, sample_mean=1.715588,\n",
        "    sample_std=0.029252, n=34))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9cebfd19-c126-49aa-a84d-2ed82f5779d9",
      "metadata": {
        "id": "9cebfd19-c126-49aa-a84d-2ed82f5779d9"
      },
      "source": [
        "3-6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "78f24436-b409-4aa2-970a-75fd1b6a2510",
      "metadata": {
        "id": "78f24436-b409-4aa2-970a-75fd1b6a2510",
        "outputId": "3713c277-96d1-42f7-f7da-f94b8b103600"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "양측 검정의 P 값 0.01888333596496139\n",
            "양측 검정 통과\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "mean = 10345\n",
        "std_dev = 552\n",
        "\n",
        "p1 = 1.0 - norm.cdf(11641, mean, std_dev)\n",
        "\n",
        "# 대칭성을 활용합니다\n",
        "p2 = p1\n",
        "\n",
        "# 양측 검정의 P 값\n",
        "p_value = p1 + p2\n",
        "\n",
        "print(\"양측 검정의 P 값\", p_value)\n",
        "if p_value <= .05:\n",
        "    print(\"양측 검정 통과\")\n",
        "else:\n",
        "    print(\"양측 검정 실패\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e63535e5-7418-4660-ae81-57b04b71aca0",
      "metadata": {
        "id": "e63535e5-7418-4660-ae81-57b04b71aca0"
      },
      "source": [
        "## 4장"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9c18da6-aaeb-4f06-8722-77d548149be2",
      "metadata": {
        "id": "c9c18da6-aaeb-4f06-8722-77d548149be2"
      },
      "source": [
        "4-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31ed695f-f8c4-4e7a-9dde-cc52a2c5b6f5",
      "metadata": {
        "id": "31ed695f-f8c4-4e7a-9dde-cc52a2c5b6f5",
        "outputId": "4cc9ffe4-8327-4b27-eca9-9d1836903a20"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2. 3.]\n"
          ]
        }
      ],
      "source": [
        "from numpy import array\n",
        "\n",
        "v = array([1,2])\n",
        "\n",
        "i_hat = array([2, 0])\n",
        "j_hat = array([0, 1.5])\n",
        "\n",
        "basis = array([i_hat, j_hat])\n",
        "\n",
        "# 벡터 v가 w로 바뀝니다.\n",
        "w = basis.dot(v)\n",
        "\n",
        "print(w)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6ea4d1b-1d41-4bc7-8a89-fa617e5b6d91",
      "metadata": {
        "id": "c6ea4d1b-1d41-4bc7-8a89-fa617e5b6d91"
      },
      "source": [
        "4-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b6cf537-325f-4655-b820-2abd77f70e38",
      "metadata": {
        "id": "3b6cf537-325f-4655-b820-2abd77f70e38",
        "outputId": "f43ed359-2308-4af0-8e9b-e2f1f665f744"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 0 -3]\n"
          ]
        }
      ],
      "source": [
        "from numpy import array\n",
        "\n",
        "v = array([1,2])\n",
        "\n",
        "i_hat = array([-2, 1])\n",
        "j_hat = array([1, -2])\n",
        "\n",
        "basis = array([i_hat, j_hat])\n",
        "\n",
        "# 벡터 v가 w로 바뀝니다.\n",
        "w = basis.dot(v)\n",
        "\n",
        "print(w)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01cb3be2-1eb0-4bf7-bae5-1002ae28fa3f",
      "metadata": {
        "id": "01cb3be2-1eb0-4bf7-bae5-1002ae28fa3f"
      },
      "source": [
        "4-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "020813d5-9de6-40ed-bba5-c678c17cccdd",
      "metadata": {
        "id": "020813d5-9de6-40ed-bba5-c678c17cccdd",
        "outputId": "8d45e6db-068c-4869-c003-6c52cc6aa448"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 9  8]\n",
            " [17 -2]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "a = np.array([[3,-2],[1,4]])\n",
        "b = np.array([[5,2],[3,-1]])\n",
        "c = a.dot(b)\n",
        "print(c)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "efb94a05-8c5c-4ed2-a006-b6d0ad29047d",
      "metadata": {
        "id": "efb94a05-8c5c-4ed2-a006-b6d0ad29047d"
      },
      "source": [
        "4-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d68fa4d5-3944-4a20-a2e2-9d2fade6b3c9",
      "metadata": {
        "id": "d68fa4d5-3944-4a20-a2e2-9d2fade6b3c9",
        "outputId": "aa9cfe1c-e7ed-497d-958c-173a0b832bb5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.0\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from numpy.linalg import det\n",
        "\n",
        "i_hat = np.array([1, 0])\n",
        "j_hat = np.array([2, 2])\n",
        "\n",
        "basis = np.array([i_hat,j_hat]).transpose()\n",
        "\n",
        "determinant = det(basis)\n",
        "\n",
        "print(determinant)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e9a0465-0c43-4724-b2a7-f64a5512bca1",
      "metadata": {
        "id": "8e9a0465-0c43-4724-b2a7-f64a5512bca1"
      },
      "source": [
        "4-6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9ad1d1b7-2645-4c66-ba0c-df64dcf3b135",
      "metadata": {
        "id": "9ad1d1b7-2645-4c66-ba0c-df64dcf3b135",
        "outputId": "3258c078-2383-4314-a044-9d0c10c87432"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0.3875 -0.1     0.0125]\n",
            " [-0.1625  0.3    -0.0375]\n",
            " [-0.125   0.      0.125 ]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "A = np.array([\n",
        "    [3, 1, 0],\n",
        "    [2, 4, 1],\n",
        "    [3, 1, 8]\n",
        "])\n",
        "\n",
        "A_inverse = np.linalg.inv(A)\n",
        "print(A_inverse)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd242c70-9f32-40c5-8828-204ac9c5d08c",
      "metadata": {
        "id": "dd242c70-9f32-40c5-8828-204ac9c5d08c"
      },
      "source": [
        "4-7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4cd319a8-7ff9-43a7-9820-1cf4eec6df1d",
      "metadata": {
        "id": "4cd319a8-7ff9-43a7-9820-1cf4eec6df1d",
        "outputId": "0755a729-227d-4ee4-cd37-5a909c3c5088"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[19.8 -5.4 -6. ]\n"
          ]
        }
      ],
      "source": [
        "from numpy import array\n",
        "from numpy.linalg import inv\n",
        "\n",
        "A = array([\n",
        "    [3, 1, 0],\n",
        "    [2, 4, 1],\n",
        "    [3, 1, 8]\n",
        "])\n",
        "\n",
        "B = array([\n",
        "    54,\n",
        "    12,\n",
        "    6\n",
        "])\n",
        "\n",
        "X = inv(A).dot(B)\n",
        "\n",
        "print(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8e87450-eb34-43db-867b-73a6ecf43270",
      "metadata": {
        "id": "f8e87450-eb34-43db-867b-73a6ecf43270"
      },
      "source": [
        "4-8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b5fc8dad-0c42-44b4-823a-73bd2f391cea",
      "metadata": {
        "id": "b5fc8dad-0c42-44b4-823a-73bd2f391cea",
        "outputId": "d45bb8ff-f205-4cee-8b26-4ff13c4810ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[2 1]\n",
            " [6 3]]\n",
            "0.0\n"
          ]
        }
      ],
      "source": [
        "from numpy.linalg import det\n",
        "from numpy import array\n",
        "\n",
        "i_hat = array([2, 6])\n",
        "j_hat = array([1, 3])\n",
        "\n",
        "basis = array([i_hat, j_hat]).transpose()\n",
        "print(basis)\n",
        "\n",
        "determinant = det(basis)\n",
        "\n",
        "print(determinant)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "462d0533-2c4e-4002-945c-4aee4adb2b83",
      "metadata": {
        "id": "462d0533-2c4e-4002-945c-4aee4adb2b83"
      },
      "source": [
        "4-9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb3e807d-0379-48a5-baae-57a516a87eda",
      "metadata": {
        "id": "eb3e807d-0379-48a5-baae-57a516a87eda",
        "outputId": "858a2623-c978-48d1-b534-76f935beacd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "고윳값\n",
            " [8.38761906 2.12592447 4.48645647]\n",
            "고유벡터\n",
            " [[-0.04479915 -0.72643433 -0.45454378]\n",
            " [-0.24136078  0.63495848 -0.67565954]\n",
            " [-0.96940085  0.26290852  0.5804086 ]]\n",
            "재구성 행렬\n",
            " [[ 3.  1. -0.]\n",
            " [ 2.  4.  1.]\n",
            " [ 3.  1.  8.]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "A = np.array([\n",
        "    [3, 1, 0],\n",
        "    [2, 4, 1],\n",
        "    [3, 1, 8]\n",
        "])\n",
        "\n",
        "eigenvals, eigenvecs = np.linalg.eig(A)\n",
        "print(\"고윳값\\n\", eigenvals)\n",
        "print(\"고유벡터\\n\", eigenvecs)\n",
        "A_reconstruction = eigenvecs @ np.diag(eigenvals) @ np.linalg.inv(eigenvecs)\n",
        "print(\"재구성 행렬\\n\", np.round(A_reconstruction))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "426aa0f2-8bcd-4adc-b54c-7a0d4d0aab56",
      "metadata": {
        "id": "426aa0f2-8bcd-4adc-b54c-7a0d4d0aab56"
      },
      "source": [
        "## 5장"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f70a15e3-8fbb-4d61-adaa-d94b045532f0",
      "metadata": {
        "id": "f70a15e3-8fbb-4d61-adaa-d94b045532f0"
      },
      "source": [
        "5-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9fc99c65-6c2b-4632-9cd8-0af6fd213c0e",
      "metadata": {
        "id": "9fc99c65-6c2b-4632-9cd8-0af6fd213c0e",
        "outputId": "451f466e-2ebd-4d26-f691-b5eee561d9cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "m = [1.75919315]\n",
            "b = [4.69359655]\n"
          ]
        },
        {
          "data": {
            "image/png": 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H8jo2oV+H7KgvM/VXOPp3+FIQnEQ5aeuehW/+DmWHf0nnXHIv1DkloPcV/yhAERGJE3ZbZuqtwZynTiTQ2Z/aCoJ7JXzBXxJn0HrT8TqUFj1h4FRocq7f7yWBU4AiIiIhF2xTsNrqRByYdSL9OmT7PQvkKRwuKDpW6fWbsI/7k/7NIOdq833qnYqj32ToNBQc1p5pikUKUEREJKRCkZYJZ4M5T+HwyJnrcQBJlPE/znmMTnyHuo4SXIaDHW2v5fQhD0GdDL9e21/R7u4a7feviQIUEREJmVClZcLdYM5TODx/zmv8ueRFWieY6ZwNjvYczp1Cr96XBPS6/oj2/jjRfv/a+L2KZ/ny5QwePJimTZvicDh45513Kn3/+uuvx+FwVPrKy8urdM7+/fsZNmwYaWlpZGRkcNNNN3Ho0KGgPoiIiERXQMt3vQh7g7kDO8j7cix/K/sLrRN2cyylId/0epJz7v8kYsGJ331YYuj9feF3gHL48GE6derEc8895/WcvLw8du/eXfE1a9asSt8fNmwYX375JQsXLuTdd99l+fLl3Hzzzf6PXkRELMOv5bu1CFuDufISWD4Vnj0fNs8FhxN6/onUOz6lbb//wekMf/eNUAZydnx/X/md4hkwYAADBgyo8ZyUlBSys7Or/d7mzZtZsGABa9asoVu3bgBMmzaNgQMH8sQTT9C0aVN/hyQiIhYQyrRM1TqRE2+VATeY+2YhzL8b9n9rHp/Wy1ydk3W2768RAtHewDHa7++rsISKS5cupXHjxpx11lmMHDmSffv2VXwvPz+fjIyMiuAEIDc3l4SEBFatWlXt65WUlFBcXFzpS0RErCXUaZmQNZj7+XuYPQxe/Y0ZnNTPgqtehOvfi3hwAtHfwDHa7++rkBfJ5uXlcdVVV9GqVSu2bdvGvffey4ABA8jPz8fpdFJQUEDjxo0rDyIxkczMTAoKCqp9zSlTpjBp0qRQD1VERELI2/JdDwdmcOFPWiaoBnNlx+CTZ+CjJ6H82PF0zkj41ThITfN5DKHiWTHzTeFBn84P1waOdtlAMuQBytChQyv++5xzzuHcc8+ldevWLF26lL59+wb0muPHj2fMmDEVx8XFxbRo0SLosYqISOiEJS1DgA3mvn4f5o+Dn7ebx6dfaKZzGrf373VCpLoVM94EEsj5IxyBZDiEvRrojDPOoFGjRmzduhWA7Oxs9uzZU+mc8vJy9u/f77VuJSUlhbS0tEpfIiJiPVHf9+fn72DWNfDa78zgpH42DHkJhs+NanBS3YqZ6kRiA0e7bCAZ9j4oP/74I/v27aNJE/OXMicnhwMHDrBu3Tq6du0KwOLFi3G73fTo0SPcwxERkTCLyr4/ZUfh47/BiqfMdE5C4i/pnJQG4XvfWtS0YqY62RHqQ+IJJKvO6kTq/X3hd4By6NChitkQgO3bt7NhwwYyMzPJzMxk0qRJDBkyhOzsbLZt28bdd99NmzZt6N+/PwDt27cnLy+PESNG8MILL1BWVsbo0aMZOnSoVvCIiMSIiO77s2UBLBhnzp4AtLoIBkyFxu0i8/41qG3FjMfoS9rQq02jiHZytfoGkn4HKGvXruWSS35pYuOpDRk+fDjTp0/n888/51//+hcHDhygadOmXHrppUyePJmUlJSK57z66quMHj2avn37kpCQwJAhQ3jmmWdC8HFERCRu7N8OC+6BrxeYxw2aQv+H4exfW2bvHF9XwrTNqh+VJb1W3kDS7wDl4osvxjC8T1a9//77tb5GZmYmr732mr9vLSIiYqZzVjxtpnNcJZCQBDmj4KKxkFI/2qOrxC4rZqxIe/GIiEgFK28eh2HAlvnmrMmB783HzrjYTOecemZUh+aNXVbMWJECFBERASy+edy+bWZg8s0H5nFaczOd0+EKy6RzqhOupdfxIPybDoiIiOVZdvO40iOw+CF4vqcZnCQkQe8xMHo1nH2lpYMTj6gvvbYpzaCIiMS52jaPc2BuHtevQ3bk/tI3DPjqPVgwHop+MB9r3QcGPA6N2kZmDCFk9RUzVqQARUQkzllu87h928xN/bZ+aB6nt4D+j0D7wbaYMfHGyitmrEgBiohInLPM5nGlh819cz6ZBq5ScCbDBX+GC++E5LrhfW+xHAUoIiJxztclrj8dLMHlNkKfljAM2PxfWHAvFP9oPta6r7l3TsPWoX0vsQ0VyYqIxDnPUtjawo7J722m92OLQ1sw+9NWmHkVvPEHMzhJbwFXz4Rr/6PgJM4pQBERiXM1bR5XVchW9ZQehg8fNFfnbFtspnMuGgujVtu+1kRCQwGKiIh4XQpblWelz6S5m3C5fd0C78QXMODLt+HZ881OsO4yCrMu5NPLFuC6+D7VmkgFh1FT33qLKi4uJj09naKiItLS0qI9HBGRmOFyG8z4eDuT39tc67mzRvT0b1XK3q9h/lj4dikAu2jMhNLr+NDdBXBYoimcpTvpxgB/7t8qkhURkQrOBAeNGqTUfiJ+rOopOQTLH4f858FdhishmWdLL+P58sspIbniNE/6KFrNyyzdSTcOKcUjIiKVhGyDO8OAjW+Z6ZyP/wbuMoy2/bk68WmeKv9NpeAEQpA+CoJlO+nGMQUoIiJSSW2rehyYMws1bnC3dwu8cjn83w1wcBdknAbXzGZlz+msLc7w+rQTm8JFSm2ddCE6QVO8U4AiIiKV1LSqp9YN7koOwgf3w/QLYPtySEyFi8fDqFVw1oCQNIVzuQ3yt+1jzoad5G/bh8ttVPuYr/zppCuRoxoUERE5iWdVT9WajGxvNRmGARv/YwYnB4+nQ84aaLaoz2xVcVqw6aPq6kQy6iYBcOBIWcVj/tSOWKaTrlSiAEVERKrl8wZ3ezbDvLHw3Ufm8SmtzE39zrz0pNf0pI8Kio5Vm1JxYAZB1aWPPHUiVZ93YmDiUV3BrbcVOiGruZGQUoAiIiJe1bjB3bFiWPYYrHoB3OWQWMfcN+eCWyGp+pu5J300cuZ6HFAp2KgpfVRTnUh1qu7CvHBTgdcVOv06ZAccNEn4qAZFRET8Yxjw+Zvm6pz8Z83gpN1lZp3Jr8Z6DU48vDWFy05P9brEuLY6kWqHiVk78uzirTWu0Fm4qSDwmhsJGzVqE5G4paZcASj80kznfP+xeZx5BgyYCm1z/X4pf67/nA07uW32hoCGnFEniQNHT04DwS+zIyvG9alxlkV9UEJDjdpERGqhplx+OlYES6bA6n+A4TLTORfdZaZzEn1r7FZVjemjKoKp//AWnEDlFTo+19xIRChAEZG4463YMtqdTC3JMODz1+GDB+DwHvOx9pebq3MyWkRsGLUV11bHAaTXMHtyIs8KHX+CJgkv1aCISFxRUy4/FHwBLw+At/9oBicN28C1b8HV/45ocAL+7bh84jk39Drdp9fXCh3rUYAiInFFTbl8cPQAzB8Hf78IfsiHpLrQdyKM/ATa9I3asLwV12bUTaroheLhKbgd3adt8F1xJSqU4hGRuKKmXDVwu+Hz2bBwAhzeaz7W4Uro/zCkN4/q0Dy81YkAXmtHAlnWLNGnAEVE4oqacnmx+3OYdxfsWGUeN2wLA6dC60uiO65qeKsT8VY74ndXXLEEBSgiEleC6WRqJz4v4T16AJY8DGv+Fww3JNWDX90NPf8Eicknn29TWqFjPwpQRCQunHjDHnp+S57+8OuYnfL3aQm12w2fvQYLJ8KRn8zHzv41XPowpDeLwqjDTyt07EUBiojEPF83mIuFKX+fllBnFprN1n5cbX6z0ZlmOueMiyM9XBGvFKCISEzzdsMuOlKGAdyR25bTG9WLiSn/2pZQp3OIw2/djmF8gMOTzrl4HPQYGVPpHIkNClBEJGbVdsN2ALPX7GDFuD62D0xWb9/Px1v3VruE2oGb3zqXMS5xNg3dB80HOw6BSx+CtKYRHm1kaBsD+1OAIiIxy5+eJ3atTagufXWijo5vmZw0g/MStgLwtbsZey+cTK9+QwJ+T6vf/LWNQWxQgCIiMSvWe554S1+Bmc4Zm/g6v3cuJsFhcMhI5enyIcxw9effZ/QO6j2tfPPXNgaxQ51kRSRmxXLPE2/pKwduhjoXsyRlDNcmLiLBYfCO6wL6lDzJS65BnJpeP+Al1J6bf9XZGs/Nf8HG3QF+mtDQNgaxRTMoImIr/qQXYrnnSXXpq3Md2/hL0st0TvgWgC3u5kwsv56V7g5BL6H2pZ5n0txN9OuQHbV0Tzyk9OKJAhQRsQ1/0wueDeZisc35iWmpDA5yd+LrDHUuIcFhcNCow1Plv+EVVz/Kj/8zH+wSajvc/GM9pRdvFKCIiC0EWlsQq23OGzdIJQE3Q51LGJv4Oqc4DgHwlqs3U8quYS+nADD6kjb0atMo6EJWO9z8YzmlF48UoIiI5QWbXgh3m/NorGrpnrydd+tMpIOxDYDN7hZMKLuBNUY74Jf01R39zgzJWHy9qX9TeIj8bfuisrInllN68UgBiohYXijSC+Fqcx7xVS2H98GiB3Gu/zcdMCg26vBU+W95xdUPF04gPOmr2m7+Hs8u2cqzS7ZGZWVPLKf04pFW8YiI5Vk1vRDsqhaX2yB/2z7mbNhJ/rZ9Na8ucbvMDf2mdYH1rwAGdLqGtYMXsqD+lRXBCZizBL4sp/Xn/T03f/jlZl+TaK3s8aT0stMrz/j4ek3EOjSDIiKWF6naAn9SNcGmnfyaedmxBubdCbs/M4+zzoFBT0DLnvQBVnTxP8UUyMyPt3qe6kRzZY92Lo4NDsMwbLcgvLi4mPT0dIqKikhLS4v2cETiXrhrMFxug96PLa61tiCYlvX+3rDzt+3jmhdX1vq6s0b0PCm15K3g1zPyir/0D+2FDx+EDTPNb6SkQ5/7oduN4Az870uf39+LE1vrP7tkW63vV901kPjkz/1bMygiEpRI1GCEu7YgkBVCgaadfGkmdv9bG8jeMpOOW54hsbTYfLDzMMidBPVP9el9vQlFPxNPPY9VU28SG1SDIiIBi2Rn0XDVFgTafTTQtFNtBb9dHF/zr/JxdP7iIRJLi9niaMXKS2bBlc8HHZz48v4nFhzXRst6JZw0gyISAVbfXC0Q0egsGo7agkBXCPmyqiWzXhIFxccqLbv1NpvQiCLuSZrFb5zLASgy6jK1/Gpmufrinm8wveHukMxIhXLWQ8t6JZwUoIiEmdU3VwtUtDqLhnq5cKA37JrSTh77D5dxx+sbgF9+5lVnE5y4uM65kDGJ/0ea4wgAr5dfzGPlQ9mPmaOvGuwFE/CGctZDy3olnBSgiIRRLO+sGiv1B8HcsP1Z1eL5mT/3+/MqZh26Or5ictIM2if8AMAX7tOZUHYDnxptKz33xGCv6GhpUAFvqGc9YrVTr0SfAhSRMLHD5mq1qekv9VipPwj2hn1i2qmg6CiT39vM/sOlJ53n+ZlPfm8zk/s2pmjueIY4PwLggFHveDqnD+4aSgMXbirg5Y+/CyrgDcesh5b1SjgoQBEJEztsrlaT2lJTsVJ/EIobtiftlL9tX7XBiUcCLvIOvc3FC98m0XkINw5ml1/M1PKr+ZnaWya8s2FXSALecMx6hKtTr8QvBSgiYWLnFIivqalYqT8I1Q27pp9ld8dmJiXNoH3CDigDmp6HMeAJWpW2YsLxmZefD5d6DfZOqZdUY/Djb8CrWQ+xOgUoImFi1xSIP6mpWKo/CMUNu7qf5an8zPikWVzlXAHAz0Z99vccT+v+I3EmOMk5fl6dZGeNwd6vOzfjpY+/q3UM/gS8mvUQK1OAIhImdk2B+JuaiqW/xIO9YZ/4M3dSznDnB9ye+B8aOI7iNhzMcvXhlbp/YF7/K6DK9akt2Euvk+xTgGK1gDcWxWLbACtSgCISJnZdghlIakp/iZs8P/MZr77Kg0kzaJewA4AN7tZMKLuBL4wzmH55F68/85qCPZfbsGXAG2titW2AFamTrEgY2XFnVbumpiyheDd5Wx5gdspk2iXsYL9Rn3FlI/h16ST2pnXweZVNTuuGXNG5GTmtG1YEMzXtJmzlgDeWRLJzsmizQJGIsNOUcCQ25os5rjJY9QIsfRRKDwEO3N1uZN0Zo9hVmhrSn7n+go8Oz/8X3tKf+v/CN9osUMRi7JQCsWtqKmq2fwTz7oK9X5nHzc+HgU+Q0LQz54fh7WKp5sdO7N42wI4UoIjISWJpdU7YFO+CD+6Hjf8xj+s2NHcb7jwMEsKbPbdTwBsr7Nw2wK4UoIhItfSXuhflpbBqOix73EznOBKg203Q5z6oc0q0RydhotqsyFOAIiJe6S/1Kr5dCvPGwk9fm8fNu8OgJ6BJp6gOS8LPrm0D7EyreEREalP0I7wxHF65wgxO6jaCK56HG99XcBIntIoq8hSgiIh4U14KH/0Vnj0fNr1jpnO6/xFuXQfnhb/WRKzFjm0D7EwpHhGJCDsttQZg6yKYfzfs22oet+gJA6dCk3OjOy6JKtVmRY4CFBEJO1v17jiwA96/Fzb/1zyu1xj6/QU6DQWHbkKi2qxIUYAiYmG2m3Wohq87I0ddeQl8Mg0+ehLKjoDDCd1vhkvGQ2p6tEcnEncUoIhYlK1mHbzwZ2fkUAdefgV333xopnP2bzOPW+bAwCcgu2NIxyQivlOAImJBtpl1qEW0um/6HNwd+AEWjIev3jWP62dBv8lw7u+UzhGJMpWgi1hMbbMOYM46uNzW30YrGt03fdrQrewYLJsKz3Y3gxOHE3qOgtFrodPVCk5ELMDvAGX58uUMHjyYpk2b4nA4eOeddyp93zAMJkyYQJMmTahTpw65ubl88803lc7Zv38/w4YNIy0tjYyMDG666SYOHToU1AcRiRX+zDpYXaS7b/oS3L3/zkyM6Tmw5CEoPwqn9YJbPoK8RyBVm4+KWIXfAcrhw4fp1KkTzz33XLXff/zxx3nmmWd44YUXWLVqFfXq1aN///4cO/bLP7jDhg3jyy+/ZOHChbz77rssX76cm2++OfBPIRJDYmnPD0/3TW/zEQ7M1Euoum/WFNw1d+zl70lP8lT5Qzj2fwv1s+Gq/4Xr34Oss0Py/iISOn7XoAwYMIABAwZU+z3DMHj66ae5//77ueKKKwB45ZVXyMrK4p133mHo0KFs3ryZBQsWsGbNGrp16wbAtGnTGDhwIE888QRNmzYN4uOI2F8s7fkR6Z2RqwvaUijlZue7jEqcQ6qjjHIjge/aXEeb3z5kixmTWFjJJRKIkNagbN++nYKCAnJzcyseS09Pp0ePHuTn5wOQn59PRkZGRXACkJubS0JCAqtWrar2dUtKSiguLq70JRKrIj3rEG7eum9mpaVwe25bSsrd5G/bF5KamqpB2yUJn/J+8jjuTPo/Uh1l5Ls6MKD0UfZeMNEWwcmCjbvp/dhirnlxJbfN3sA1L66k92OLzToakRgX0lU8BQUFAGRlZVV6PCsrq+J7BQUFNG7cuPIgEhPJzMysOKeqKVOmMGnSpFAOVcSyIj3rEAlVu29+99MRZq3+gac+/KU+LRRLqD3BXVLx9zyQ+G/6OdcDUGCcwsNlw3jXnUN2eh1bBHexspJLJFC2WMUzfvx4ioqKKr527NgR7SGJhFUs7vnh6b6ZkpjA0x9+TUFxDatsAn0P1zFeab2Yhcl308+5njLDyQvll9G35AnedV8AOGwR3MXSSi6RQIV0BiU7OxuAwsJCmjT55R/QwsJCOnfuXHHOnj17Kj2vvLyc/fv3Vzy/qpSUFFJSUkI5VBHLi8U9P8LVuM3lNvj6ozdpuWoSbY/8CA5Y4ziXe0quY5vRDLBXk7to9Y8RsZKQBiitWrUiOzubRYsWVQQkxcXFrFq1ipEjRwKQk5PDgQMHWLduHV27dgVg8eLFuN1uevToEcrhiNherO35EY4b77KVq3F+MJ7e7rUA7DIyeS7pRnoNvpGH6qeGJbgLd+FqLK3kEgmU3wHKoUOH2Lp1a8Xx9u3b2bBhA5mZmbRs2ZLbb7+dhx56iLZt29KqVSseeOABmjZtypVXXglA+/btycvLY8SIEbzwwguUlZUxevRohg4dqhU8IjEupDfesqNsfesv9Nz0IimOMkoNJy+5BjKt/NccLUnltVkbmH5tF67o3CzIUVcWiS0IYmkll0ig/A5Q1q5dyyWXXFJxPGbMGACGDx/OjBkzuPvuuzl8+DA333wzBw4coHfv3ixYsIDU1F/+R3r11VcZPXo0ffv2JSEhgSFDhvDMM8+E4OOIiJWF5MZrGLBlPsaCcbQ58AM4YLnrHB4sH863xi9/5IRjn59IFa56in0Lio5Vmw5zYNYj2aHYVyRQDsMwbFdlVVxcTHp6OkVFRaSlWX+poIiYXG6D3o8trvXGu2Jcn+qDin3bYP442LoQgJ1GQyaXXccC9/ngZWH2rBE9Q5Im84zdW4qq1rH7yRMMQfUruexaLF0b9X2Jbf7cv7VZoIhETMBLqEuPwEdPwifPgKsUEpL4us31XPF5DkepeVYmVHUakS5c9azkqppOyrZRsa+/YmEHbwkdBSgicSpaf6n6deM1DHMzvwXjoeh4e4HWfWDAVPYVncLRz1fW+n6hqtOIRuFqLK7k8kZ9X6QqBSgicSjaf6n6dOP9aSvMvxu2LTKP01tA/0eg/WBwOOieaUS0TiNahauxtpKrOuFafi72ZotGbSISOp6/VKumK0LRKM0fnhvvFZ2bkdO64S83ntLD8OEkmJ5jBifOZLjwLhi1CjpcDg5HxfMnDu4AnFx9Eo6Ou7G2BYGVxNIO3hI6ClBE4oilO5QaBmyaA892hxV/NWtN2vSDP62Evg9Acr2TnhLJjruRDojiifq+SHWU4hGJI5btUPrTNzBvLHy7BIAjdZryQ/cJtL3oapzOmv+OimSdRjwWrkaC+r5IdRSgiMQRy/2lWnoYlk+FT54FdxklJPFC+WVM//lyjr2fQpOVS3y68UeyTiOeClcjRX1fpDoKUETiiGX+UjUM2PQOvH8fFO8EYLGrM5PK/8D3xi97cll1BUc8FK5GUizu4C3BUw2KSByxRKHn3i3wyhXw5vVQvBMjoyVjE+/hxrKxlYITsEBdjERMLO7gLcHRDIpIHInUX6rV9lgpOwTLHoeVz4O7HJwp0PsOVjf9A2++vMHra3nqYp5a+DW92jRSOiWGKX0mJ1Kre5E4cWLQ8N1PR5i1+gcKikPfB+XkHisG19Zfx/2JM0k9tsd86Mw8yJsCmWcwZ8NObpu9wefXV2dREftSq3uRKLPafiLVNWbLTkvhjty2nN6oXsjGWLUbaBvHj0xK/Be9yr+EcjhSrwV1L38CzsqreI6/9S5WrUsRkdBSgCISYtHu0lrdeKprIV5YXMLTH37D9Gu7hGwzPU+PlXoc5bbEt7jBuYAkh4tjRhLPl1/BnLLfsLhtf5wnPK+2FRxVqbOoSHxQkaxEjcttkL9tH3M27CR/276YKIK0SpdWj0g2ZjN7rBzl8oRPWJxyJzcnvkeSw8UHrq7klk7lGddVfF/sPqkbaE0N0LyxY2fRWPx9FwknzaBIVFhtliEUrLifSCQbsx3d+QWzkx+iZ8JmALa7s5hUPpyl7s6Vzquux4q3Bmi1sUtn0Vj8fRcJN82gSMRZbZYhVKy4n0hEGrMdK4b37+OSJVfRM2EzR41kppb9jrzSx04KTsB7zUlexyasGNeHWSN6MvqS1j69tR06i8bq77tIuClAkYiy9F4wQbJcl1bC3JjNMODzN+DZbpD/LA7DxdKEHvQrmcpzrispIbnS6b70WPE0QLuj31nR79cSArH8+y4SbgpQJKKsOMsQKpbp0nqCsDVmK/wSZgyCt0bAoULIbA3X/odjV/2LnZwa9GZ6sbIxXyz/vouEmwIUiSgrzjKEiiW6tFYR8hv9sSLc88dhvHAhfP8xLmcd3H0mwJ/yoU1uSLuBxkJn0Vj+fRcJNxXJSkRZcZYhVKy6n0hIduA1DPhsNiXz7yel5CcA5rm689CxazE+bs7EzP0VrxPKbqB27yway7/vIuGmTrISUS63Qe/HFte6a+mKcX1scxOqyqorNgJuHlfwBcwbCz/kA7DN3YQHy4fzkftc4Jfgyy6zGpEUD7/vIv7w5/6tAEUizrOqAaqfZYiFG53VOskG5OgBWPIIrHkRDDdHSeGZsl/zkmsApSRVOlU3Wu/i4fddxFcKUMTyrDrLEEmWDWLcbvh8NiycAIf3ArCv5QAu+3ogu6m5V8qsET1D0pU21uj3XcSkvXjE8uxeWxAsy96wdn8O8+6CHavM44ZtYeBUVhw8k91fb6j16Sr2rF68/76LBEIBikSNp+dFvPG2N05UN8E7+jMsfhjWvgSGG5Lqwa/uhp5/gsRkGm/b59PLqNjTu3j9fRcJlAIUkQgKRzv8oFJFbjd89hosnAhHzNU5nP1ruPRhSG9WcVptG/p5alCs3jhNROxDAYrIcZGoCQn13jhBpYp2bTDTOT+uMY8bnQUDH4czLj7pVKsuoRaR2KUARYTI1YSEsnFXwKmiI/th8UOw9p+AYaZzLr4HetwCicknn39cSPqpiIj4SAGKxL1I1oSEqnFXQKkitxs+/TcsmgRHjteUdPwNXDoZ0pr6NC4Ve4pIpChAkbgWjpqQmoSqlsPvVNHO9WY6Z+c684RT28PAqdDqQr8/g4o9RSQStBePxLVIb+YWqr1xfE0V/byvAObeDi/2MYOT5AZmAewtHwUUnIiIRIpmUCSuRWMzt1DUctSWAnLg5mrnUvot+g+U/Gw+eM7vzHROg2yvz7Ns8zgRiTsKUCSuRWszt2BrOWpKFZ3r2MbkpJfplPAtlACNzzbTOaf3qvE1Lds8TkTikgIUiWvR7O8RTC1Hdct+T6GYsYmvM9S5lASHQVlifZJy74fzR4Cz5v/VLdk8TkTimmpQJK6FqiYkGjypoqZpSfzeuYjFKXfx+8QlJDgMdra8gqTb1kPPkbUGJ7UVCoNZKOxy227bLhGxMQUoEvc8N/rs9MppnOz0VMvPHORl7GRFw4d5JOklTnEc4vAp7XBdP59mN74CDbJ8eo1IFwqLiPhCKR4RbNjf4/BP8OGD8Om/zZmelDTocz/1ut1U64xJVdEoFBYRqY0CFJHjbNHfw+0yO8AungzHiszHOv0e+k2C+o0DesloFQqLiNREAYqIXexYDe/dCQWfm8fZ58DAJ6Flj6BeVhsBiogVqQZFxOoO7YV3RsFL/czgJDUdBj4BNy8LOjgBexcKi0jsUoAiYlVuF6x+EZ7tChtmmo91vhZGr4PuIyDBGbK3snOhsIjEJqV4RKzoh1Uw704o+MI8btLJTOe0OD9sb2m7QmERiWkKUESs5NBe+HAibHjVPE7NgL4PQNcbQjpj4o0tCoVFJC4oQBGxAlc5rH0JFj8MJcdX55x3HeQ+CPUaRXVoIiLRoABFJNq+z4d5d0HhRvO4SWcY9CQ07xbVYYmIRJMCFJFoOVgICyfA57PN4zqnQN8J0GV4jekc7TgsIvFAAYpIpLnKYfU/YOkUKCkGHNDlD2Y6p27NvUa047CIxAsFKCJB8mtG47uPzXTOnk3mcdMuMOgJaNa11vfRjsMiEk8UoIgEwecZjYMF8MED8MUb5nGdU8wZk/P+AAm1tyOqbcdhB+aOw/06ZCvdIyIxQY3aRALkmdGouhOwZ0Zjwcbd4CqDT56Fad2OBycOc8nwreuh6/U+BSegHYdFJP5oBkUiItYKO32Z0fjvnDfpv+w1HHs3m99o1tVsUd+si9/v5++Ow7F2vUUk/ihAkbCLxcLOmmY0GvMz9yW9yhVln8BeoG5DM53T+VqfZ0xOek0/dhyOxestIvFHKR4JK5/SIDZU3YxGIuWMcL7L4pQ7ucL5CW7DwfZWQ2H0WnOVToDBCfyy47C3ORAHZhDy8+HSmLzeIhJ/FKBI2NSWBgGzsNPlru4Ma6s6o5GT8CXzksdzX9Jr1HccY727DYNLH6Kg9yO1Lh32hS87Dj8wqD2T34vN6y0i8UcBioRNLBd2emY0stnPtKRnmJX8MGcm7OQnI42xZTfzm9IH2Z/Wnu6tgg9OPGrbcfiUeikxe71FJP6oBkXCxt/CTjtxusuYceYnNP98GvUcJbgMBzNduTxZ/lsOUh+AiYM7hLwwtaYdh+ds2OnTa9jxeotI/FGAImHjT2GnrXy7FOaN5ayfvgYHfO44i3tKhrPJOB0If0Gqtx2HY/Z6i0hcUoAiYeNJgxQUHau2LsKBmZ4IZRokrIp+hPfvg03vmMd1G0G/v3D2uUN54LsDUV/SG3PXW0TimgIUCRtPYefImetxQKWbpuf27W8aJND+HkH1BSkvhfxnYflUKDsCjgTofjNcPB7qZOCEamc0Ii0c11tEJFochmHYrqS/uLiY9PR0ioqKSEtLi/ZwpBah6ssR6OsE9f5bF8H8u2HfVvO4ZQ4MnArZ5/g87khTHxQRsSp/7t8KUCTkqputAILqbOptozzPK3jbKC/Q53FgB7x/L2z+r3lcrzFcOhnOvRoc1p+BUCdZEbEif+7fSvFISIXjr/dAN8oL6HnlJfDJNFj+BJQfBYcTevwRLr4HUtMDGn80eCukFRGxC/VBkZAJV9fYQPup+P28bz6E53Ng8WQzOGl5AdzyEeRNsVVwIiISCzSDIiER6CyHLwLtp+Lr8w4WbIM1f4av3jUfqJ8F/SbDub+zRTpHRCQWKUCRkPBntsLf1EOg/T1qe14KpYxwvkffxXPBdex4OueW4+kc1TaJiESTAhQJiVB0jfVW2Blof4+anndxwqc8mPgKpycUggs4rbe5Oierg0+fQ0REwksBioREsF1MayuuDaS/R3V9QZo79jIh8RUuda4D4FjqqaQOnALn/EbpHBERC1GRrISEZ7bC2y3egRlwVNfF1Jfi2to2yvO2QsjzvJZpCfzZ+RYfJt/Fpc51lONk+5k3knrHp3DubxWciIhYTMgDlAcffBCHw1Hpq127dhXfP3bsGKNGjaJhw4bUr1+fIUOGUFhYGOphSIR5ZiuAk4KUmmY5aiuuBbO41uU2yOvYhBXj+jBrRE/+NrQzs0b0ZMW4PrUuX85L/pyl9e5hTNL/keoooyg7B8fIj2n1+6cgpYHfn1VERMIvLCmes88+mw8//PCXN0n85W3uuOMO3nvvPd58803S09MZPXo0V111FR9//HE4hiIR5JmtqJqqya6hD4q/xbV+9ffYvx0WjIev55tBUoOm0P8h0s++SjMmIiIWF5YAJTExkezs7JMeLyoq4qWXXuK1116jT58+ALz88su0b9+elStX0rNnz3AMRyIor2MT+nXI9rmLaSiKa09SdhRWPA0rngJXCSQkQs4ouOhuSKnv++ugjqwiItESlgDlm2++oWnTpqSmppKTk8OUKVNo2bIl69ato6ysjNzc3Ipz27VrR8uWLcnPz/caoJSUlFBSUlJxXFxcHI5hS4j4M8sRbHHtSbbMh/nj4MD35nGrX5mrc049y7fnn0B72oiIRE/Ia1B69OjBjBkzWLBgAdOnT2f79u1ceOGFHDx4kIKCApKTk8nIyKj0nKysLAoKCry+5pQpU0hPT6/4atGiRaiHLVESTHFtJfu/hVd/B7OGmsFJWjP47Qz4w5yAg5NwdMUVERHfhHwGZcCAARX/fe6559KjRw9OO+003njjDerUqRPQa44fP54xY8ZUHBcXFytIiRHVLQX2qKm4tkLpETOV8/HfjqdzkuCC0XDhXX6nczzC2RVXRER8E/ZlxhkZGZx55pls3bqV7OxsSktLOXDgQKVzCgsLq61Z8UhJSSEtLa3Sl8SOgJYQGwZsfhee6wHLHzeDkzMugT/lQ+6DAQcnEPjePyIiEjphb9R26NAhtm3bxnXXXUfXrl1JSkpi0aJFDBkyBIAtW7bwww8/kJOTE+6hiIX5VVy7bxvMvxu2Hl8pltYc8h6B9peHZHVOWAp3RUTELyEPUO666y4GDx7Maaedxq5du5g4cSJOp5NrrrmG9PR0brrpJsaMGUNmZiZpaWnceuut5OTkaAWP1F5cW3oYPnoSPpkGrlJwJsMFt8KFd0JyvZCNI+SFuyIi4reQByg//vgj11xzDfv27ePUU0+ld+/erFy5klNPPRWAp556ioSEBIYMGUJJSQn9+/fn+eefD/UwYlZcLns1DNg8F96/F4p2mI+17gsDHodGbUL+doHu/SMiIqHjMAyjun+DLa24uJj09HSKioriqh4lLpe9/rQV5o+FbYvN4/QWkDcF2l1Wkc4JR9DmWcUD1Rfu1tReX0REqufP/VsBik14bphVf1gxe8MsPQzLnzDTOe6y4+mcPx9P59StOC2cQVtcBoQiImGkACXGuNwGvR9b7HVliSflsGJcH/unewwDNs2B9++D4h/Nx9r0gwGPQcPWlU6NRNAWlyk1EZEw8ef+HfZVPBI8f/ersa29X5urc75dYh5ntIS8R+GsgSetzolUrxK/9v4REZGQUYBiAzG/7LXkkNnLJP/54+mcFOh9O/S+A5Kqb+4XN0GbiEicUoBiAzG77NUw4Mu3zXTOwV3mY237w4BHIfOMGp8a80GbiEicU4BiAzG57HXvFpg3FrYvM48zTjPrTM4aUPPzjovZoE1ERIAItLqX4Hn2qwFO2lTPp/1qrKTkIHxwP0y/wAxOnCnwq3tg1CqfgxMI4SaDIiJiSQpQbCKg/WqsxDDgi/+DZ88/vnS4HM4cYAYml4z3WmviTUwFbSIichItM7YZWy573fMVzLsLvvvIPD7ldLML7Jn9g35p9SoREbEP9UERayg5CEsfhVUvmDMmialmo7UL/gxJoasNsWXQJiISh9QHRaLLk8754H44VGA+dtYgs0X9KaeF/O3Uq0REJPYoQJHQKtxkrs75foV5nHmGmc5p2y+64xIREVtRgCKhcaz4l3SO4YLEOnDRnZBza0jTOSIiEh8UoEhwDAM+fwMWPgCHCs3H2l1mpnMyWkZ3bCIiYlsKUCRwhV8eT+d8bB5ntoaBj0Ob3OiOS0REbE8BivjvWBEsmQKr/2Gmc5LqwkV3Qc5oSEyJ9uhERCQGKECJARFbZmsY8NlsWDgBDu8xH2t/OfR/BDJahP79REQkbilAsbmINSor+ALeuwt2rDSPG7Y10zmt+4TuPURERI5TgGJjCzbuZuTM9SdtIFhQdIyRM9eHpgX+0QOw5GFY879guCGpHvxqLPQcBYnJwb22TakxnIhI+ClAsSmX22DS3E3V7m5sYO5HM2nuJvp1yA7s5ul2w2ez4MOJcHiv+ViHK810TnqzwAceIeEKItRaX0QkMhSg2NTq7fsr3SSrMoDdRcdYvX2//11Wd39mpnN+XG0eNzoTBk6FMy4OeLyRFK4gIiIzViIiAmg3Y9vac9B7cBLIeQAc/RneuxP+cbEZnCTVg35/gVs+tlVwMnLm+pOCN08QsWDj7oBet7YZKzBnrFxu221tJSJiSQpQbKpxA9+6s/p0ntsN6/8N07r+Umty9lUweg30us02tSbhDCL8mbESEZHgKcVjU91bZdIkPZWComPV3pAdQHa6WXtRo12fmumcnWvN41PbmemcVheFeshhF860V1hmrERExCvNoNiUM8HBxMEdADMYOZHneOLgDt4LQ4/sh3fvgH9cYgYnyfXh0ofglhW2DE4gvEFESGesRESkVgpQbCyvYxOmX9uF7PTKN8Xs9FTvBZtuN6z7l5nOWftPwICOv4HRa+GCW8GZFJnBh0E4gwjPjJW3dUAOzELcWmesRETEJ0rx1MAO/S7yOjahX4ds38a5cz3Muwt2rjOPT21/PJ1zYWQHHSYhS3tVwzNjNXLmehxQ6fV9mrESERG/KEDxwk79LpwJjpprKo7sh0WTzJkTDEhuAJeMh+43V5oxsUNAVpNwBxGeGauqvxfZFv29EBGxM4dhGLZbF1lcXEx6ejpFRUWkpaWF/PW99bvw3NZC0e8iIsGA2wXrXzGDk6M/m4+de7W5dLhBdqVT7RSQ1Sbcn8XugZyISLT4c/9WgFKFy23Q+7HFXleDeNIEK8b1CfimFJFg4Md1MO9Oc5UOQOOzYdATcNoF1Y4n3AFZpCmIEBGxHn/u30rxVBHWDq1EoBvp4X2w6EGzrwkGpKTBJffC+SPAefKPO+wt86Ok1rSXiIhYmgKUKsK5VNWXRmL3vv0FR8vcZKf5+Ve/2wXrZsCiv8CxA+Zjna6B3EnQIMvr0/wNyDQzISIikaAApYpwLlWtLRgA2H+4jDte3wD4kfb5cS28N8bcQwcgqyMMfAJOy6l1TP4EZLFUpyIiItamPihVhLPfhb+zLrXuH3P4J5gzCv63rxmcpKTBgMfh5mU+BSfge6D13U9HwrLHjYiISHUUoFThb4dWl9sgf9s+5mzYSf62fTXu8+LvrIvX/WPcLlj9IkzrAp/ONB/r9Hu4dR30+GO1tSbe1BaQAZxSN5F/frxdG+WJiEjEKMVTDV/7Xfib8qitkVh1TirK3bHa3HG44PPjgzoHBj4JLXsE9Flr6h3i8fORcv/GKCIiEiQtM65BTQWhgS7N9TwPqg8GvJn+6xYM2P0CbHjVfCA1Hfo8AN1uhASnfx+sGtUFW/7629DOXNG5WdBjERGR2KQ+KGEWbK8Uf4IBJy6GOT9kQt23SCw7aD543rXm6px6jYL5GCfxBGQFRUeZ/N5m9h8u9ev5s0b01AyKiIh4pT4oYRZsr5QT98/xBAM/Hy49aUalq2MLf0mawdkJ30MZ0KSTmc5pcb7fY/ZlebCnd0j+tn1+BSfB7HEjIiJSHQUoAQhFr5QTG4nVSXZWqgFpRBHjk15jiPMjAEqT0km+dAJ0vSGgdI6/tTL+rDbSRnkiIhIOWsUTgFD3SvEU5TZLS+J65wIWp4xhiPMj3DjY0eq3JN/+KZz/PwEHJ/4uD/ZntVF2eirP/f480usk+7SSyUr8WYElIiKRpRmUANS2GieQlEdeg+30P+VBHKVfAnCo4TnUufJpWrToFvA4A21j78tqo4w6STw3rAtFR8qY/J79mrep6ZyIiLVpBiUA/vZKqdHBQnjrZnh5AI7CL6HOKXDZU9QftQxnEMEJ+Fcrc6LaPp8DeHTIORw8Vsao1+zXvC2QWSUREYksBSgB8qRlstMrp0Oy01N92/DPVQ75z8Oz3eDz1wEHdL0ebl0f9NJhT+pivo832upqTmr7fP06ZNe6r5AVm7f5sh+SFcctIhJvlOIJwomrcfzaPO+7j2HeXbBnk3nctAsMegKadQ16TIH0M/FWc1LT58vfti+suz6HS7h3qxYRkdBQgBKkE1fj1Kp4Nyx8AL540zyukwl9J0CX4ZAQ/GSWt+Zx3vhSK+Pt84Vz1+dwsuu4RUTijQKUSHCVwaq/w9IpUHoIcEC3G8xOsHVD0zukptRFdYJdHhzOXZ/Dya7jFhGJNwpQwm37R2Y6Z+9X5nGzrjDwCWjWJaRvU1vqoqqq+wr5KxwrmSLBruMWEYk3ClDCpXgXfHA/bPyPeVy3IeQ+CJ2vDUk6pypfUxJ/yDmNAR2b+FYrU4OaNhm0cvM2u45bRCTeaBVPqJWXwsfPwLPnHw9OHGaTtdFrocsfwhKcgO8piQEdm5DTumFIbsBBr2SKEruOW0QknmgGJZS+XQrzxsJPX5vHzc830zlNO4f9raOVugh4JVOU2XXcIiLxQgFKKBTthA/ugy/fNo/rNoJ+k6DT78M2Y1JVNFMXfq1kshC7jltEJB4oxROM8lJY8ZSZzvnybXAkQPeb4da1cF54ak1qotSFiIjECs2gBGrbYph3N+z7xjxu0cNM5zQ5N6rDUurC3lxuQz87EREUoPiv6Ed4/17YNMc8rncq9JsMnYaCwxo3EqUu7EkbGIqI/EIpHl+Vl8BHT5rpnE1zjqdz/miuzul8jWWCE7EnbWAoIlKZZlB8sXURzL8b9m01j1vmwMCpkH1OdMcVB+Ih5VHbBoYOzA0M+3XIjrnPLiLijQKUmhzYAe+Ph81zzeN6jeHSyXDu1RGZMYmHm3NN4iXloQ0MRUROpgClOuUl8Mk0WP4ElB8FhxN6/BEuvgdS0yMyhHi5OXvjbeNDT8ojllYlaQNDEZGTqQalqm8+hOdzYPFkMzg5rRfc8hHkTYlocBLP9Qi1pTzATHm43L5ujWht2sBQRORkClBOtPgheHUI7N8G9bPgqhfh+vcg62y/XsblNsjfto85G3aSv22fXzfSeLs5V8eflEcs8HQB9pa8c2DOnmkDQxGJJ0rxnKjdIPj4b2aztV+Ng9Q0v18i2NSM6hHiL+WhDQxFRE6mGZQTNT0Pbt8I/R8OODgJNjUTbzfn6sRjykNdgEVEKtMMSlUNsgJ6WqiWisbjzbmqaG18GG3qAiwi8gvNoIRIqOomVI/wS8oDOOk6xHrKw9MF+IrOzchp3TAmP6OIiC8UoIRIqFIz8XxzPpFSHiIi8U0pnhAJZWrGc3OuWmybHUd9UEApDxGReBbVAOW5555j6tSpFBQU0KlTJ6ZNm0b37t2jOaSAhbpuQjdnkzY+FBGJT1FL8bz++uuMGTOGiRMnsn79ejp16kT//v3Zs2dPtIYUlHCkZlSPICIi8SpqAcpf//pXRowYwQ033ECHDh144YUXqFu3Lv/85z+jNaSgqW5CREQkNKKS4iktLWXdunWMHz++4rGEhARyc3PJz88/6fySkhJKSkoqjouLiyMyzkDYJTUT7xsRioiItUUlQPnpp59wuVxkZVXuOZKVlcVXX3110vlTpkxh0qRJkRpe0Kqrm7BSQBDvGxGKiIj12WIVz/jx4xkzZkzFcXFxMS1atIjiiPxjpYAgnnYJFhER+4pKDUqjRo1wOp0UFhZWerywsJDs7OyTzk9JSSEtLa3Sl11YaWdibUQoIiJ2EZUAJTk5ma5du7Jo0aKKx9xuN4sWLSInJycaQ/KZPzsVWy0giLddgkVExL6iluIZM2YMw4cPp1u3bnTv3p2nn36aw4cPc8MNN0RrSLXyN1VjtZ2JtRGhiIjYRdQClKuvvpq9e/cyYcIECgoK6Ny5MwsWLDipcNYqAqndsFpAoI0IRUTELqK6F8/o0aP5/vvvKSkpYdWqVfTo0SOaw/Eq0FSN1QICbUQoIiJ2oc0CfRBo7YbVAgJtRCgiInahAMUHgaZqrBgQqNutiIjYgS36oERbMKkaK+5MbJdutyIiEr8UoPgg2J2KrRgQaJdgERGxMgUoPvCkakbOXI8DKgUpvqZqFBCIiIj4TjUoPlLthoiISORoBsUPVkzViIiIxCIFKH5SqkZERCT8FKCcwOU2NDsiIiJiAQpQjvN3nx0REREJHxXJ8ss+O1W7xXr22VmwcXeURiYiIhKf4j5ACXSfHREREQmfuA9QAt1nR0RERMIn7gOUQPfZERERkfCJ+wAlmH12REREJDziPkDx7LPjbTGxA3M1j7d9dkRERCT04j5A8eyzA5wUpPi6z46IiIiEVtwHKKB9dkRERKxGjdqO0z47IiIi1qEA5QTaZ0dERMQalOIRERERy1GAIiIiIpajAEVEREQsRwGKiIiIWI4CFBEREbEcBSgiIiJiOQpQRERExHIUoIiIiIjlKEARERERy7FlJ1nDMAAoLi6O8khERETEV577tuc+XhNbBigHDx4EoEWLFlEeiYiIiPjr4MGDpKen13iOw/AljLEYt9vNrl27aNCgAQ5H4Jv5FRcX06JFC3bs2EFaWloIRyjV0fWOLF3vyNL1jixd78gK1fU2DIODBw/StGlTEhJqrjKx5QxKQkICzZs3D9nrpaWl6Rc8gnS9I0vXO7J0vSNL1zuyQnG9a5s58VCRrIiIiFiOAhQRERGxnLgOUFJSUpg4cSIpKSnRHkpc0PWOLF3vyNL1jixd78iKxvW2ZZGsiIiIxLa4nkERERERa1KAIiIiIpajAEVEREQsRwGKiIiIWE5cByjPPfccp59+OqmpqfTo0YPVq1dHe0i2N2XKFM4//3waNGhA48aNufLKK9myZUulc44dO8aoUaNo2LAh9evXZ8iQIRQWFkZpxLHl0UcfxeFwcPvtt1c8pusdWjt37uTaa6+lYcOG1KlTh3POOYe1a9dWfN8wDCZMmECTJk2oU6cOubm5fPPNN1EcsX25XC4eeOABWrVqRZ06dWjdujWTJ0+utI+Lrndwli9fzuDBg2natCkOh4N33nmn0vd9ub779+9n2LBhpKWlkZGRwU033cShQ4eCH5wRp2bPnm0kJycb//znP40vv/zSGDFihJGRkWEUFhZGe2i21r9/f+Pll182Nm7caGzYsMEYOHCg0bJlS+PQoUMV59xyyy1GixYtjEWLFhlr1641evbsaVxwwQVRHHVsWL16tXH66acb5557rnHbbbdVPK7rHTr79+83TjvtNOP66683Vq1aZXz77bfG+++/b2zdurXinEcffdRIT0833nnnHeOzzz4zLr/8cqNVq1bG0aNHozhye3r44YeNhg0bGu+++66xfft248033zTq169v/O1vf6s4R9c7OPPmzTPuu+8+46233jIA4+233670fV+ub15entGpUydj5cqVxkcffWS0adPGuOaaa4IeW9wGKN27dzdGjRpVcexyuYymTZsaU6ZMieKoYs+ePXsMwFi2bJlhGIZx4MABIykpyXjzzTcrztm8ebMBGPn5+dEapu0dPHjQaNu2rbFw4ULjV7/6VUWAousdWuPGjTN69+7t9ftut9vIzs42pk6dWvHYgQMHjJSUFGPWrFmRGGJMGTRokHHjjTdWeuyqq64yhg0bZhiGrneoVQ1QfLm+mzZtMgBjzZo1FefMnz/fcDgcxs6dO4MaT1ymeEpLS1m3bh25ubkVjyUkJJCbm0t+fn4URxZ7ioqKAMjMzARg3bp1lJWVVbr27dq1o2XLlrr2QRg1ahSDBg2qdF1B1zvU/vvf/9KtWzd++9vf0rhxY8477zxefPHFiu9v376dgoKCStc7PT2dHj166HoH4IILLmDRokV8/fXXAHz22WesWLGCAQMGALre4ebL9c3PzycjI4Nu3bpVnJObm0tCQgKrVq0K6v1tuVlgsH766SdcLhdZWVmVHs/KyuKrr76K0qhij9vt5vbbb6dXr1507NgRgIKCApKTk8nIyKh0blZWFgUFBVEYpf3Nnj2b9evXs2bNmpO+p+sdWt9++y3Tp09nzJgx3HvvvaxZs4Y///nPJCcnM3z48IprWt2/Lbre/rvnnnsoLi6mXbt2OJ1OXC4XDz/8MMOGDQPQ9Q4zX65vQUEBjRs3rvT9xMREMjMzg/4ZxGWAIpExatQoNm7cyIoVK6I9lJi1Y8cObrvtNhYuXEhqamq0hxPz3G433bp145FHHgHgvPPOY+PGjbzwwgsMHz48yqOLPW+88Qavvvoqr732GmeffTYbNmzg9ttvp2nTprrecSAuUzyNGjXC6XSetJKhsLCQ7OzsKI0qtowePZp3332XJUuW0Lx584rHs7OzKS0t5cCBA5XO17UPzLp169izZw9dunQhMTGRxMREli1bxjPPPENiYiJZWVm63iHUpEkTOnToUOmx9u3b88MPPwBUXFP92xIaY8eO5Z577mHo0KGcc845XHfdddxxxx1MmTIF0PUON1+ub3Z2Nnv27Kn0/fLycvbv3x/0zyAuA5Tk5GS6du3KokWLKh5zu90sWrSInJycKI7M/gzDYPTo0bz99tssXryYVq1aVfp+165dSUpKqnTtt2zZwg8//KBrH4C+ffvyxRdfsGHDhoqvbt26MWzYsIr/1vUOnV69ep20bP7rr7/mtNNOA6BVq1ZkZ2dXut7FxcWsWrVK1zsAR44cISGh8m3K6XTidrsBXe9w8+X65uTkcODAAdatW1dxzuLFi3G73fTo0SO4AQRVYmtjs2fPNlJSUowZM2YYmzZtMm6++WYjIyPDKCgoiPbQbG3kyJFGenq6sXTpUmP37t0VX0eOHKk455ZbbjFatmxpLF682Fi7dq2Rk5Nj5OTkRHHUseXEVTyGoesdSqtXrzYSExONhx9+2Pjmm2+MV1991ahbt64xc+bMinMeffRRIyMjw5gzZ47x+eefG1dccYWWvQZo+PDhRrNmzSqWGb/11ltGo0aNjLvvvrviHF3v4Bw8eND49NNPjU8//dQAjL/+9a/Gp59+anz//feGYfh2ffPy8ozzzjvPWLVqlbFixQqjbdu2WmYcrGnTphktW7Y0kpOTje7duxsrV66M9pBsD6j26+WXX6445+jRo8af/vQn45RTTjHq1q1r/PrXvzZ2794dvUHHmKoBiq53aM2dO9fo2LGjkZKSYrRr1874xz/+Uen7brfbeOCBB4ysrCwjJSXF6Nu3r7Fly5YojdbeiouLjdtuu81o2bKlkZqaapxxxhnGfffdZ5SUlFSco+sdnCVLllT7b/bw4cMNw/Dt+u7bt8+45pprjPr16xtpaWnGDTfcYBw8eDDosTkM44SWfCIiIiIWEJc1KCIiImJtClBERETEchSgiIiIiOUoQBERERHLUYAiIiIilqMARURERCxHAYqIiIhYjgIUERERsRwFKCIiImI5ClBERETEchSgiIiIiOUoQBERERHL+X9BZbBiiranCAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# 데이터를 로드합니다.\n",
        "df = pd.read_csv('https://bit.ly/3C8JzrM', delimiter=\",\")\n",
        "\n",
        "# (마지막 열을 제외한 모든 열을) 입력 변수로 추출합니다.\n",
        "X = df.values[:, :-1]\n",
        "\n",
        "# 마지막 열을 출력으로 추출합니다.\n",
        "Y = df.values[:, -1]\n",
        "\n",
        "# 모델을 훈련합니다.\n",
        "fit = LinearRegression().fit(X, Y)\n",
        "\n",
        "# m = 1.75919315, b = 4.69359655\n",
        "m = fit.coef_.flatten()\n",
        "b = fit.intercept_.flatten()\n",
        "print(\"m = {0}\".format(m))\n",
        "print(\"b = {0}\".format(b))\n",
        "\n",
        "# 그래프를 그립니다.\n",
        "plt.plot(X, Y, 'o') # 산점도\n",
        "plt.plot(X, m*X+b) # 선 그래프\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23c8e9d1-7698-43f0-a80c-bb04f83a908a",
      "metadata": {
        "id": "23c8e9d1-7698-43f0-a80c-bb04f83a908a"
      },
      "source": [
        "5-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f86e4c5-262c-4667-95cd-6615ab155498",
      "metadata": {
        "id": "6f86e4c5-262c-4667-95cd-6615ab155498",
        "outputId": "e9391d95-e5ec-460c-e896-58ced19d8614"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         x        y\n",
            "x  1.00000  0.92421\n",
            "y  0.92421  1.00000\n",
            "99\n",
            "검정 값: 23.835515323677328\n",
            "임계 범위: -1.9844674544266925, 1.984467454426692\n",
            "상관 관계가 입증되어 귀무 가설을 거부합니다\n",
            "P 값: 0.0\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 판다스 데이 프레임으로 데이터를 읽습니다.\n",
        "df = pd.read_csv('https://bit.ly/3C8JzrM', delimiter=\",\")\n",
        "\n",
        "# 변수 사이의 상관관계를 출력합니다.\n",
        "correlations = df.corr(method='pearson')\n",
        "print(correlations)\n",
        "\n",
        "# 출력:\n",
        "#          x        y\n",
        "# x  1.00000  0.92421\n",
        "# y  0.92421  1.00000\n",
        "\n",
        "# 통계적 유의성을 검정합니다.\n",
        "from scipy.stats import t\n",
        "from math import sqrt\n",
        "\n",
        "# 표본 크기\n",
        "n = df.shape[0]\n",
        "print(n)\n",
        "lower_cv = t(n - 1).ppf(.025)\n",
        "upper_cv = t(n - 1).ppf(.975)\n",
        "\n",
        "# 상관계수를 추출합니다.\n",
        "r = correlations[\"y\"][\"x\"]\n",
        "\n",
        "# 검정을 수행합니다.\n",
        "test_value = r / sqrt((1 - r ** 2) / (n - 2))\n",
        "\n",
        "print(\"검정 값: {}\".format(test_value))\n",
        "print(\"임계 범위: {}, {}\".format(lower_cv, upper_cv))\n",
        "\n",
        "if test_value < lower_cv or test_value > upper_cv:\n",
        "    print(\"상관 관계가 입증되어 귀무 가설을 거부합니다\")\n",
        "else:\n",
        "    print(\"상관 관계가 입증되지 않아 귀무 가설을 거부하지 못합니다\")\n",
        "\n",
        "# p 값을 계산합니다.\n",
        "if test_value > 0:\n",
        "    p_value = 1.0 - t(n - 1).cdf(test_value)\n",
        "else:\n",
        "    p_value = t(n - 1).cdf(test_value)\n",
        "\n",
        "# 양측 검정이므로 2를 곱합니다.\n",
        "p_value = p_value * 2\n",
        "print(\"P 값: {}\".format(p_value))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "194d6702-ce14-4977-8ec0-ce4b43cf59a8",
      "metadata": {
        "id": "194d6702-ce14-4977-8ec0-ce4b43cf59a8"
      },
      "source": [
        "5-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b6ea2e2-bb64-4752-ad13-5c34663fa07a",
      "metadata": {
        "id": "8b6ea2e2-bb64-4752-ad13-5c34663fa07a",
        "outputId": "aae0ef66-4313-4fe9-e30c-4b45a7818349"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "50.792086501055955 134.51442159894404\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from scipy.stats import t\n",
        "from math import sqrt\n",
        "\n",
        "# 데이터를 로드합니다.\n",
        "points = list(pd.read_csv('https://bit.ly/3C8JzrM', delimiter=\",\") \\\n",
        "    .itertuples())\n",
        "\n",
        "n = len(points)\n",
        "\n",
        "# 선형 회귀 파라미터\n",
        "m = 1.75919315\n",
        "b = 4.69359655\n",
        "\n",
        "# x = 50에서 예측 구간은 계산합니다.\n",
        "x_0 = 50\n",
        "x_mean = sum(p.x for p in points) / len(points)\n",
        "\n",
        "t_value = t(n - 2).ppf(.975)\n",
        "\n",
        "standard_error = sqrt(sum((p.y - (m * p.x + b)) ** 2 for p in points) / \\\n",
        "    (n - 2))\n",
        "\n",
        "margin_of_error = t_value * standard_error * \\\n",
        "                  sqrt(1 + (1 / n) + (n * (x_0 - x_mean) ** 2) / \\\n",
        "                       (n * sum(p.x ** 2 for p in points) - \\\n",
        "\tsum(p.x for p in points) ** 2))\n",
        "\n",
        "predicted_y = m*x_0 + b\n",
        "\n",
        "print(predicted_y - margin_of_error, predicted_y + margin_of_error)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4b5fc960-e8c5-46d6-98ec-31958cf9d68a",
      "metadata": {
        "id": "4b5fc960-e8c5-46d6-98ec-31958cf9d68a"
      },
      "source": [
        "5-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa64dcd1-5761-4070-bfb8-be37ae2caf90",
      "metadata": {
        "id": "aa64dcd1-5761-4070-bfb8-be37ae2caf90",
        "outputId": "9265c831-2702-4bb6-dc73-efa0fd31a95f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.86119665 0.78237719 0.85733887]\n",
            "MSE: 평균=0.834 (표준 편차-0.036)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "\n",
        "df = pd.read_csv('https://bit.ly/3C8JzrM', delimiter=\",\")\n",
        "\n",
        "# (마지막 열을 제외한 모든 열을) 입력 변수로 추출합니다.\n",
        "X = df.values[:, :-1]\n",
        "\n",
        "# 마지막 열을 출력으로 추출합니다.\n",
        "Y = df.values[:, -1]\n",
        "\n",
        "# 단순 선형 회귀를 수행합니다.\n",
        "kfold = KFold(n_splits=3, random_state=7, shuffle=True)\n",
        "model = LinearRegression()\n",
        "results = cross_val_score(model, X, Y, cv=kfold)\n",
        "print(results)\n",
        "print(\"MSE: 평균=%.3f (표준 편차-%.3f)\" % (results.mean(), results.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "159ddf78-2f87-4390-997b-d7b4e8a68bf2",
      "metadata": {
        "id": "159ddf78-2f87-4390-997b-d7b4e8a68bf2"
      },
      "source": [
        "## 6장"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b6a45313-cd1b-4c69-975d-2ed5627ebf35",
      "metadata": {
        "id": "b6a45313-cd1b-4c69-975d-2ed5627ebf35"
      },
      "source": [
        "6-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "41bfbdf4-dc3c-4585-a1b6-670bb360b687",
      "metadata": {
        "id": "41bfbdf4-dc3c-4585-a1b6-670bb360b687",
        "outputId": "2207d0eb-6d5a-469e-ddf3-036e1a3e89f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "평균 정확도: 1.000 (stdev=0.000)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "\n",
        "\n",
        "# 데이터를 로드합니다\n",
        "df = pd.read_csv(\"https://bit.ly/3imidqa\", delimiter=\",\")\n",
        "\n",
        "X = df.values[:, :-1]\n",
        "Y = df.values[:, -1]\n",
        "\n",
        "kfold = KFold(n_splits=3, shuffle=True)\n",
        "model = LogisticRegression(penalty=None)\n",
        "results = cross_val_score(model, X, Y, cv=kfold)\n",
        "\n",
        "print(\"평균 정확도: %.3f (stdev=%.3f)\" % (results.mean(),\n",
        "results.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a8eb0560-58ad-4459-b548-a583a1462d90",
      "metadata": {
        "id": "a8eb0560-58ad-4459-b548-a583a1462d90"
      },
      "source": [
        "6-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3e4dfa28-b8a0-4cf8-a476-9fcbce04b698",
      "metadata": {
        "id": "3e4dfa28-b8a0-4cf8-a476-9fcbce04b698",
        "outputId": "8035cc6a-e77d-499f-bb61-80bc216b4d4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[157   6]\n",
            " [  1 280]]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# 데이터를 로드합니다\n",
        "df = pd.read_csv(\"https://bit.ly/3imidqa\", delimiter=\",\")\n",
        "\n",
        "# 입력 변수(마지막 열을 제외한 모든 열)를 추출합니다\n",
        "X = df.values[:, :-1]\n",
        "\n",
        "# 출력 변수(마지막 열)를 추출합니다\n",
        "Y = df.values[:, -1]\n",
        "\n",
        "model = LogisticRegression(solver='liblinear')\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33)\n",
        "model.fit(X_train, Y_train)\n",
        "prediction = model.predict(X_test)\n",
        "\n",
        "\"\"\"\n",
        "오차 행렬은 각 카테고리 내에서 정확도를 평가합니다.\n",
        "[[진짜음성(TP) 거짓양성(FP)]\n",
        " [거짓음성(FN) 진짜양성(TN)]]\n",
        "\n",
        "대각선은 올바른 예측을 나타내므로 이 값이 높아야 합니다\n",
        "\"\"\"\n",
        "matrix = confusion_matrix(y_true=Y_test, y_pred=prediction)\n",
        "print(matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f5bc67e-92bc-4624-9f04-0f9ef7759a83",
      "metadata": {
        "id": "2f5bc67e-92bc-4624-9f04-0f9ef7759a83"
      },
      "source": [
        "6-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa4670b8-bc62-4f97-9873-41a5001515bb",
      "metadata": {
        "id": "fa4670b8-bc62-4f97-9873-41a5001515bb",
        "outputId": "a3c27ed2-b6bf-4b75-e247-3e79a7262787"
      },
      "outputs": [
        {
          "name": "stdin",
          "output_type": "stream",
          "text": [
            "색깔을 입력하세요. {red},{green},{blue}:  255,255,255\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DARK\n"
          ]
        },
        {
          "name": "stdin",
          "output_type": "stream",
          "text": [
            "색깔을 입력하세요. {red},{green},{blue}:  0,0,0\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LIGHT\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# 데이터를 로드합니다.\n",
        "df = pd.read_csv(\"https://bit.ly/3imidqa\", delimiter=\",\")\n",
        "\n",
        "# 입력 변수(마지막 열을 제외한 모든 열)를 추출 합니다\n",
        "X = df.values[:, :-1]\n",
        "\n",
        "# 출력 변수(마지막 열)를 추출합니다\n",
        "Y = df.values[:, -1]\n",
        "\n",
        "model = LogisticRegression(solver='liblinear')\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33)\n",
        "model.fit(X_train, Y_train)\n",
        "prediction = model.predict(X_test)\n",
        "\n",
        "# 예측을 수행합니다\n",
        "for _ in range(2):\n",
        "    n = input(\"색깔을 입력하세요. {red},{green},{blue}: \")\n",
        "    (r, g, b) = n.split(\",\")\n",
        "    x = model.predict(np.array([[int(r), int(g), int(b)]]))\n",
        "    if model.predict(np.array([[int(r), int(g), int(b)]]))[0] == 0.0:\n",
        "        print(\"LIGHT\")\n",
        "    else:\n",
        "        print(\"DARK\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e142f24-5ddd-4e6f-90f5-6da4e79eb9e0",
      "metadata": {
        "id": "5e142f24-5ddd-4e6f-90f5-6da4e79eb9e0"
      },
      "source": [
        "## 7장"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a2bf5a5-1da8-4df9-b1ad-e504aff392a8",
      "metadata": {
        "id": "4a2bf5a5-1da8-4df9-b1ad-e504aff392a8",
        "outputId": "6f5f9a6a-892e-4001-ab2a-10456018dbd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "훈련 세트 점수: 0.555556\n",
            "테스트 세트 점수: 0.500000\n",
            "오차 행렬:\n",
            "[[9 0]\n",
            " [9 0]]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 데이터를 로드합니다\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "df = pd.read_csv('https://tinyurl.com/y6r7qjrp', delimiter=\",\")\n",
        "\n",
        "# 입력 변수(마지막 열을 제외한 모든 열)를 추출합니다\n",
        "X = df.values[:, :-1]\n",
        "\n",
        "# 출력 변수(마지막 열)를 추출합니다\n",
        "Y = df.values[:, -1]\n",
        "\n",
        "# 훈련 데이터와 테스트 데이터를 분할합니다\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=1/3)\n",
        "\n",
        "nn = MLPClassifier(solver='sgd',\n",
        "                   hidden_layer_sizes=(3, ),\n",
        "                   activation='relu',\n",
        "                   max_iter=100_000,\n",
        "                   learning_rate_init=.05)\n",
        "\n",
        "nn.fit(X_train, Y_train)\n",
        "\n",
        "print(\"훈련 세트 점수: %f\" % nn.score(X_train, Y_train))\n",
        "print(\"테스트 세트 점수: %f\" % nn.score(X_test, Y_test))\n",
        "\n",
        "print(\"오차 행렬:\")\n",
        "matrix = confusion_matrix(y_true=Y_test, y_pred=nn.predict(X_test))\n",
        "print(matrix)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    },
    "colab": {
      "provenance": []
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  "nbformat_minor": 5
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