examples.ipynb

{
  "cells": [
    {
      "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": "code",
      "source": [
        "import sklearn\n",
        "import pandas\n",
        "import sympy\n",
        "\n",
        "print('sklearn', sklearn.__version__)\n",
        "print('pandas', pandas.__version__)\n",
        "print('sympy', sympy.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRmxFlc7vJZ4",
        "outputId": "2ddb915d-7b95-4b1d-dc66-e0d59173c818"
      },
      "id": "KRmxFlc7vJZ4",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sklearn 1.6.0\n",
            "pandas 2.2.2\n",
            "sympy 1.13.1\n"
          ]
        }
      ]
    },
    {
      "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": 2,
      "id": "d79c01fa-f6b9-4ede-affb-60841ed41b91",
      "metadata": {
        "id": "d79c01fa-f6b9-4ede-affb-60841ed41b91",
        "outputId": "c9a6db7a-11b6-4d77-950f-e1693396e674",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 3,
      "id": "ab7d1b73-dcd2-4a76-a3a5-f49ad10e8024",
      "metadata": {
        "id": "ab7d1b73-dcd2-4a76-a3a5-f49ad10e8024",
        "outputId": "47bc4f88-aef0-4be9-8e20-f4a6d3f94447",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 4,
      "id": "8922448a-4d7d-4faf-a762-49e8c9f38ca0",
      "metadata": {
        "id": "8922448a-4d7d-4faf-a762-49e8c9f38ca0",
        "outputId": "5c378e84-e17b-4b86-c8e2-a6a96e8e068c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 5,
      "id": "d95eec34-e5a3-4e29-b765-1d454ce1f6f2",
      "metadata": {
        "id": "d95eec34-e5a3-4e29-b765-1d454ce1f6f2",
        "outputId": "434bc1e6-0da2-4c81-e809-e93e29d940ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 6,
      "id": "6c7a9bad-bd10-49a4-b163-9a4636685bcc",
      "metadata": {
        "id": "6c7a9bad-bd10-49a4-b163-9a4636685bcc",
        "outputId": "a9ca730d-4f84-4348-df87-be63aa03f9f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 7,
      "id": "f995bfcd-0c07-4e33-9ba6-3da243e6c4ba",
      "metadata": {
        "id": "f995bfcd-0c07-4e33-9ba6-3da243e6c4ba",
        "outputId": "ad2158a9-70d1-4489-db9f-1a7ccb484961",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 8,
      "id": "7a868e26-d258-4b4d-aed0-3b450b893f22",
      "metadata": {
        "id": "7a868e26-d258-4b4d-aed0-3b450b893f22",
        "outputId": "172f5c12-8df4-4198-9819-fca4b15f6420",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 9,
      "id": "10be43b2-0010-481f-a12d-a1a1ca46b066",
      "metadata": {
        "id": "10be43b2-0010-481f-a12d-a1a1ca46b066",
        "outputId": "ac67202f-67ec-4aad-e0f9-9ef2e38547ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 10,
      "id": "65eb873f-07b2-4f64-b76a-a86f29b8f65d",
      "metadata": {
        "id": "65eb873f-07b2-4f64-b76a-a86f29b8f65d",
        "outputId": "d152332b-5b64-4612-d518-284c0a5a2031",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 11,
      "id": "552bb394-253e-490f-b6fd-9af8259d855f",
      "metadata": {
        "id": "552bb394-253e-490f-b6fd-9af8259d855f",
        "outputId": "2d83b675-c630-4c86-e957-a051cbd05c20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 12,
      "id": "fa680332-01a6-42ec-bf7f-81921b8c3285",
      "metadata": {
        "id": "fa680332-01a6-42ec-bf7f-81921b8c3285",
        "outputId": "208ee169-a7a3-4fc6-b3eb-5df4a936610a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 13,
      "id": "041861fb-b46d-4cbf-9ca7-4d78dfe68e0d",
      "metadata": {
        "id": "041861fb-b46d-4cbf-9ca7-4d78dfe68e0d",
        "outputId": "c0886052-b1f1-410f-9b7f-7f83bbaa8f27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 14,
      "id": "7a0feb0c-0260-4fd4-931c-6bc13748cfbf",
      "metadata": {
        "id": "7a0feb0c-0260-4fd4-931c-6bc13748cfbf",
        "outputId": "41937a89-c0c5-4c56-f605-9fd5b249dbc4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 15,
      "id": "b9e023c1-a722-4815-b48c-67e322aecfbe",
      "metadata": {
        "id": "b9e023c1-a722-4815-b48c-67e322aecfbe",
        "outputId": "4767683a-01d9-4063-e3f0-bc3815eaae41",
        "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": 16,
      "id": "763ffe51-82e3-44bd-b101-33076026c62b",
      "metadata": {
        "id": "763ffe51-82e3-44bd-b101-33076026c62b",
        "outputId": "38ab6c3c-73b5-4c08-87af-e9140b92121b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 17,
      "id": "78f24436-b409-4aa2-970a-75fd1b6a2510",
      "metadata": {
        "id": "78f24436-b409-4aa2-970a-75fd1b6a2510",
        "outputId": "a8a04155-0511-4717-82bb-a5fc5a67d128",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 18,
      "id": "31ed695f-f8c4-4e7a-9dde-cc52a2c5b6f5",
      "metadata": {
        "id": "31ed695f-f8c4-4e7a-9dde-cc52a2c5b6f5",
        "outputId": "462daf10-99dd-4ff4-da27-2b6607a449ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 19,
      "id": "3b6cf537-325f-4655-b820-2abd77f70e38",
      "metadata": {
        "id": "3b6cf537-325f-4655-b820-2abd77f70e38",
        "outputId": "d8ae6bc3-725f-425c-c748-8ecc1ae383d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 20,
      "id": "020813d5-9de6-40ed-bba5-c678c17cccdd",
      "metadata": {
        "id": "020813d5-9de6-40ed-bba5-c678c17cccdd",
        "outputId": "f0d58166-cd7d-4441-fc70-5c6103a71480",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 21,
      "id": "d68fa4d5-3944-4a20-a2e2-9d2fade6b3c9",
      "metadata": {
        "id": "d68fa4d5-3944-4a20-a2e2-9d2fade6b3c9",
        "outputId": "b5881583-3515-424a-88e8-b69082345e7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 22,
      "id": "9ad1d1b7-2645-4c66-ba0c-df64dcf3b135",
      "metadata": {
        "id": "9ad1d1b7-2645-4c66-ba0c-df64dcf3b135",
        "outputId": "1813aed5-6aef-4cac-f8fb-6976593c1940",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 23,
      "id": "4cd319a8-7ff9-43a7-9820-1cf4eec6df1d",
      "metadata": {
        "id": "4cd319a8-7ff9-43a7-9820-1cf4eec6df1d",
        "outputId": "86e2642d-5e2f-47f0-b465-88982c2a5d68",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 24,
      "id": "b5fc8dad-0c42-44b4-823a-73bd2f391cea",
      "metadata": {
        "id": "b5fc8dad-0c42-44b4-823a-73bd2f391cea",
        "outputId": "8910b433-f756-4c3b-fcfd-95cd2e8e3eb5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 25,
      "id": "eb3e807d-0379-48a5-baae-57a516a87eda",
      "metadata": {
        "id": "eb3e807d-0379-48a5-baae-57a516a87eda",
        "outputId": "904ba2c4-aea8-4a2b-ef45-3ef7f56a949d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 26,
      "id": "9fc99c65-6c2b-4632-9cd8-0af6fd213c0e",
      "metadata": {
        "id": "9fc99c65-6c2b-4632-9cd8-0af6fd213c0e",
        "outputId": "f2265902-7640-4212-87cf-f82e25965127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "m = [1.75919315]\n",
            "b = [4.69359655]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": 27,
      "id": "6f86e4c5-262c-4667-95cd-6615ab155498",
      "metadata": {
        "id": "6f86e4c5-262c-4667-95cd-6615ab155498",
        "outputId": "24838ccf-b47c-4b51-ee3d-f7f097fa1f72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 28,
      "id": "8b6ea2e2-bb64-4752-ad13-5c34663fa07a",
      "metadata": {
        "id": "8b6ea2e2-bb64-4752-ad13-5c34663fa07a",
        "outputId": "bb40f4f7-af01-44d6-dcc1-81d31490d437",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50.7920864989629 134.5144216010371\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": 29,
      "id": "aa64dcd1-5761-4070-bfb8-be37ae2caf90",
      "metadata": {
        "id": "aa64dcd1-5761-4070-bfb8-be37ae2caf90",
        "outputId": "4709fc4c-61ba-44a5-fae6-c5555b7875e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": 30,
      "id": "41bfbdf4-dc3c-4585-a1b6-670bb360b687",
      "metadata": {
        "id": "41bfbdf4-dc3c-4585-a1b6-670bb360b687",
        "outputId": "e7f03746-c952-45d4-9764-3d8f330c3b5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "평균 정확도: 0.999 (stdev=0.002)\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/3BVJ2ip\", 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": 31,
      "id": "3e4dfa28-b8a0-4cf8-a476-9fcbce04b698",
      "metadata": {
        "id": "3e4dfa28-b8a0-4cf8-a476-9fcbce04b698",
        "outputId": "ed291a6f-3c23-498b-fc0e-efcb458bbcc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[282   0]\n",
            " [  9 153]]\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/3BVJ2ip\", 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": 32,
      "id": "fa4670b8-bc62-4f97-9873-41a5001515bb",
      "metadata": {
        "id": "fa4670b8-bc62-4f97-9873-41a5001515bb",
        "outputId": "96d6c7f3-a306-4161-b63f-137016b65252",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "색깔을 입력하세요. {red},{green},{blue}: 0,0,0\n",
            "LIGHT\n",
            "색깔을 입력하세요. {red},{green},{blue}: 255,255,255\n",
            "DARK\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/3BVJ2ip\", 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()\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 x[0] == 0.0:\n",
        "        print(\"DARK\")\n",
        "    else:\n",
        "        print(\"LIGHT\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e142f24-5ddd-4e6f-90f5-6da4e79eb9e0",
      "metadata": {
        "id": "5e142f24-5ddd-4e6f-90f5-6da4e79eb9e0"
      },
      "source": [
        "## 7장"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "4a2bf5a5-1da8-4df9-b1ad-e504aff392a8",
      "metadata": {
        "id": "4a2bf5a5-1da8-4df9-b1ad-e504aff392a8",
        "outputId": "d69a005f-6696-437c-8441-ccfdbbf918c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "훈련 세트 점수: 0.527778\n",
            "테스트 세트 점수: 0.333333\n",
            "오차 행렬:\n",
            "[[ 0 12]\n",
            " [ 0  6]]\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"
    },
    "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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