{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmzQnrj_qAK2"
      },
      "source": [
        "# 머신 러닝 교과서 3판"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrbmAIqKqAK6"
      },
      "source": [
        "# 2장 - 간단한 분류 알고리즘 훈련"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mi4HcBkqAK7"
      },
      "source": [
        "**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://nbviewer.org/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch02/ch02.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"60\" />주피터 노트북 뷰어로 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch02/ch02.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6ohB3qhqAK7"
      },
      "source": [
        "### 목차\n",
        "\n",
        "- 인공 뉴런: 초기 머신 러닝의 간단한 역사\n",
        "    - 인공 뉴런의 수학적 정의\n",
        "    - 퍼셉트론 학습 규칙\n",
        "- 파이썬으로 퍼셉트론 학습 알고리즘 구현\n",
        "    - 객체 지향 퍼셉트론 API\n",
        "    - 붓꽃 데이터셋에서 퍼셉트론 훈련\n",
        "- 적응형 선형 뉴런과 학습의 수렴\n",
        "    - 경사 하강법으로 비용 함수 최소화\n",
        "    - 파이썬으로 아달린 구현\n",
        "    - 특성 스케일을 조정하여 경사 하강법 결과 향상\n",
        "    - 대규모 머신 러닝과 확률적 경사 하강법"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.421678Z",
          "iopub.status.busy": "2021-10-23T05:49:14.420609Z",
          "iopub.status.idle": "2021-10-23T05:49:14.423029Z",
          "shell.execute_reply": "2021-10-23T05:49:14.422347Z"
        },
        "id": "H7hW_HzPqAK8"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjRNovxBqAK8"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fr9HSqsoqAK9"
      },
      "source": [
        "# 2.1 인공 뉴런: 초기 머신 러닝의 간단한 역사"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.434031Z",
          "iopub.status.busy": "2021-10-23T05:49:14.431909Z",
          "iopub.status.idle": "2021-10-23T05:49:14.437885Z",
          "shell.execute_reply": "2021-10-23T05:49:14.437327Z"
        },
        "id": "-q5FhqX_qAK9",
        "outputId": "70df2112-d749-4dcb-c8de-aa9c7cda34be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbf\" width=\"500\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "Image(url='https://git.io/JtIbf', width=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpLFrQkI-Iwh"
      },
      "source": [
        "- 1943년 워렌 맥컬록(Warren McCulloch)과 월터 피츠(Walter Pitts)가 맥컬록-피츠(MCP) 뉴런 발표\n",
        "- 프랑크 로젠블라트(Frank Rosenblatt)는 MCP 뉴런 모델을 기반으로 퍼셉트론 학습 개념을 발표"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H98ahObaqALK"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcS1fpThqAK-"
      },
      "source": [
        "## 2.1.1 인공 뉴런의 수학적 정의"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GT0gjzyV-Iwh"
      },
      "source": [
        "$\\boldsymbol{w}=\\begin{bmatrix} w_1 \\\\ \\vdots \\\\ w_m\\end{bmatrix}$ ,\n",
        "$\\boldsymbol{x}=\\begin{bmatrix} x_1 \\\\ \\vdots \\\\ x_m\\end{bmatrix}$\n",
        "\n",
        "최종 입력(net input) : $z = w_1x_1+w_2x_2+\\cdots+w_mx_m$\n",
        "\n",
        "\n",
        "벡터 점곱(dot product), 행렬 곱셈(matrix multiplication) :\n",
        "$\\begin{bmatrix}1&2&3\\end{bmatrix}\\times\\begin{bmatrix}4\\\\5\\\\6\\end{bmatrix}=1\\times4+2\\times5+3\\times6=32$\n",
        "\n",
        "점곱을 사용한 표현 : $z = w_1x_1+\\cdots+w_mx_m=\\sum_{j=1}^mx_jw_j=\\boldsymbol{w}^T\\boldsymbol{x}$\n",
        "\n",
        "퍼셉트론의 결정 함수 : $\\phi(z)=\\begin{cases}1&z\\ge\\theta\\mbox{ 일 때} \\\\ -1&\\mbox{그 외}\\end{cases}$\n",
        "$\\;\\;\\;\\;\\;$\n",
        "$\\phi(z)=\\begin{cases}1&z-\\theta\\ge0\\mbox{ 일 때} \\\\ -1&\\mbox{그 외}\\end{cases}$\n",
        "\n",
        "$w_0=-\\theta , x_0=1$ 일 때 : $z_{new} = w_0x_0+w_1x_1+\\cdots+w_mx_m=\\sum_{j=0}^mx_jw_j=\\boldsymbol{w}^T\\boldsymbol{x}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.444031Z",
          "iopub.status.busy": "2021-10-23T05:49:14.443018Z",
          "iopub.status.idle": "2021-10-23T05:49:14.446779Z",
          "shell.execute_reply": "2021-10-23T05:49:14.447226Z"
        },
        "id": "m2egC3TBqAK-",
        "outputId": "d35a383e-2892-4929-b643-f22cee664f2f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbL\" width=\"500\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# 퍼셉트론 결정 함수(단위 계단 함수)와 결정 경계\n",
        "Image(url='https://git.io/JtIbL', width=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAuPvuta-Iwi"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4CbGXjyqAK-"
      },
      "source": [
        "## 2.1.2 퍼셉트론 학습 규칙"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1smPkjyqAK_"
      },
      "source": [
        "$\\Delta w_j = \\eta(y^{(i)}-\\hat{y}^{(i)})x_j^{(i)}$\n",
        "\n",
        "$w_j := w_j + \\Delta w_j$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XApIteAcqAK_"
      },
      "source": [
        "클래스 레이블을 정확히 예측한 경우 :\n",
        "\n",
        "$y^{(i)}=-1,\\; \\hat{y}^{(i)}=-1, \\qquad \\Delta w_j=\\eta(-1-(-1))x_j^{(i)}=0$\n",
        "\n",
        "$y^{(i)}=1,\\; \\hat{y}^{(i)}=1, \\qquad \\Delta w_j=\\eta(1-1)x_j^{(i)}=0$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u55ceXR9qAK_"
      },
      "source": [
        "클래스 레이블을 잘못 예측한 경우 :\n",
        "\n",
        "$y^{(i)}=1,\\;\\hat{y}^{(i)}=-1, \\qquad \\Delta w_j=\\eta(1-(-1))x_j^{(i)}=\\eta(2)x_j^{(i)}$\n",
        "\n",
        "$y^{(i)}=-1,\\;\\hat{y}^{(i)}=1, \\qquad \\Delta w_j=\\eta(-1-1)x_j^{(i)}=\\eta(-2)x_j^{(i)}$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6jDxcEoqAK_"
      },
      "source": [
        "예를 들어 $\\hat{y}^{(i)}=-1,\\;y^{(i)}=+1,\\;\\eta=1$ 이고, $x_j^{(i)}=0.5$ 일 때 :\n",
        "\n",
        "$\\Delta w_j=(1-(-1))0.5=(2)0.5=1$\n",
        "\n",
        "$x_j^{(i)}=2$ 일 때 :\n",
        "\n",
        "$\\Delta w_j=(1-(-1))2=(2)2=4$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.452950Z",
          "iopub.status.busy": "2021-10-23T05:49:14.452160Z",
          "iopub.status.idle": "2021-10-23T05:49:14.455702Z",
          "shell.execute_reply": "2021-10-23T05:49:14.456150Z"
        },
        "id": "QeJHXlU4qAK_",
        "outputId": "b26b1ad0-c9de-427e-c145-9f766751a0a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbq\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "Image(url='https://git.io/JtIbq', width=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.461662Z",
          "iopub.status.busy": "2021-10-23T05:49:14.460639Z",
          "iopub.status.idle": "2021-10-23T05:49:14.464422Z",
          "shell.execute_reply": "2021-10-23T05:49:14.464881Z"
        },
        "id": "kRRcFM_BqALA",
        "outputId": "2a6ae18e-359a-4fe7-aa8b-ad81e8f1f13d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbO\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# 퍼셉트론 알고리즘\n",
        "Image(url='https://git.io/JtIbO', width=600)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7g3fMsmaqALA"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E813AMpDqALA"
      },
      "source": [
        "# 2.2 파이썬으로 퍼셉트론 학습 알고리즘 구현"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X53-_PH3qALA"
      },
      "source": [
        "## 2.2.1 객체 지향 퍼셉트론 API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.476082Z",
          "iopub.status.busy": "2021-10-23T05:49:14.473555Z",
          "iopub.status.idle": "2021-10-23T05:49:14.611511Z",
          "shell.execute_reply": "2021-10-23T05:49:14.612056Z"
        },
        "id": "UYXKL2ixqALA"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "class Perceptron(object):\n",
        "    \"\"\"퍼셉트론 분류기\n",
        "\n",
        "    매개변수\n",
        "    ------------\n",
        "    eta : float\n",
        "      학습률 (0.0과 1.0 사이)\n",
        "    n_iter : int\n",
        "      훈련 데이터셋 반복 횟수\n",
        "    random_state : int\n",
        "      가중치 무작위 초기화를 위한 난수 생성기 시드\n",
        "\n",
        "    속성\n",
        "    -----------\n",
        "    w_ : 1d-array\n",
        "      학습된 가중치\n",
        "    errors_ : list\n",
        "      에포크마다 누적된 분류 오류\n",
        "\n",
        "    \"\"\"\n",
        "    def __init__(self, eta=0.01, n_iter=50, random_state=1):\n",
        "        self.eta = eta\n",
        "        self.n_iter = n_iter\n",
        "        self.random_state = random_state\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        \"\"\"훈련 데이터 학습\n",
        "\n",
        "        매개변수\n",
        "        ----------\n",
        "        X : array-like, shape = [n_samples, n_features]\n",
        "          n_samples개의 샘플과 n_features개의 특성으로 이루어진 훈련 데이터\n",
        "        y : array-like, shape = [n_samples]\n",
        "          타깃값\n",
        "\n",
        "        반환값\n",
        "        -------\n",
        "        self : object\n",
        "\n",
        "        \"\"\"\n",
        "        rgen = np.random.RandomState(self.random_state)\n",
        "        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])\n",
        "        self.errors_ = []\n",
        "\n",
        "        for _ in range(self.n_iter):\n",
        "            errors = 0\n",
        "            for xi, target in zip(X, y):\n",
        "                update = self.eta * (target - self.predict(xi))\n",
        "                self.w_[1:] += update * xi\n",
        "                self.w_[0] += update\n",
        "                errors += int(update != 0.0)\n",
        "            self.errors_.append(errors)\n",
        "        return self\n",
        "\n",
        "    def net_input(self, X):\n",
        "        \"\"\"입력 계산\"\"\"\n",
        "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
        "\n",
        "    def predict(self, X):\n",
        "        \"\"\"단위 계단 함수를 사용하여 클래스 레이블을 반환합니다\"\"\"\n",
        "        return np.where(self.net_input(X) >= 0.0, 1, -1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-VI4sBt-Iwj"
      },
      "source": [
        "$\\boldsymbol{w}^{(1)} = \\boldsymbol{w}^{(0)}+\\mathit\\Delta\\boldsymbol{w}=\\boldsymbol{w}^{(0)}+\\eta(\\boldsymbol{y}-\\boldsymbol{\\hat{y}})\\boldsymbol{x}=\\eta(\\boldsymbol{y}-\\phi(\\boldsymbol{w}^{(0)}\\boldsymbol{x}))\\boldsymbol{x}=\\eta(\\boldsymbol{y}+1)\\boldsymbol{x}$\n",
        "\n",
        "$a\\cdot b =\\vert a\\vert\\vert b\\vert\\text{cos}\\theta$\n",
        "\n",
        "$\\theta=\\text{cos}^{-1}\\dfrac{a\\cdot b}{\\vert a\\vert\\vert b\\vert}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.616043Z",
          "iopub.status.busy": "2021-10-23T05:49:14.615368Z",
          "iopub.status.idle": "2021-10-23T05:49:14.620231Z",
          "shell.execute_reply": "2021-10-23T05:49:14.620702Z"
        },
        "id": "Wi9_qzykqALB",
        "outputId": "e1596c1b-9022-417d-b6da-60ff297676c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "v1 = np.array([1, 2, 3])\n",
        "v2 = 0.5 * v1\n",
        "np.arccos(v1.dot(v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PETPUylh-Iwk"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXzkl6OdqALB"
      },
      "source": [
        "## 2.2.2 붓꽃 데이터셋에서 퍼셉트론 훈련"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "raRwqKBRqALC"
      },
      "source": [
        "### 붓꽃 데이터셋 읽기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:14.624758Z",
          "iopub.status.busy": "2021-10-23T05:49:14.624131Z",
          "iopub.status.idle": "2021-10-23T05:49:15.647834Z",
          "shell.execute_reply": "2021-10-23T05:49:15.647022Z"
        },
        "id": "7fZajroDqALC",
        "outputId": "c82e883c-0d5a-4395-8d54-b916aec2f358"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "URL: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0    1    2    3               4\n",
              "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
              "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
              "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
              "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30331501776206193,\n        \"min\": 5.9,\n        \"max\": 6.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.3,\n          5.9,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31937438845342625,\n        \"min\": 2.5,\n        \"max\": 3.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          2.5,\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14832396974191348,\n        \"min\": 5.0,\n        \"max\": 5.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          5.1,\n          5.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23021728866442667,\n        \"min\": 1.8,\n        \"max\": 2.3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.9,\n          1.8,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "s = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
        "print('URL:', s)\n",
        "\n",
        "df = pd.read_csv(s, header=None, encoding='utf-8')\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFUKi7gUqALC"
      },
      "source": [
        "<hr>\n",
        "\n",
        "### 노트:\n",
        "\n",
        "이 깃허브에는 붓꽃 데이터셋이 포함되어 있습니다(이 책에서 사용하는 다른 데이터셋도 모두 포함되어 있습니다). 인터넷에 연결되어 있지 않거나 이따금 UCI 서버(https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)에 접근할 수 없을 때 사용할 수 있습니다. 이런 경우 로컬 디렉터리에서 붓꽃 데이터를 로드하려면 다음 코드를\n",
        "```python\n",
        "    df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
        "        'machine-learning-databases/iris/iris.data', header=None)\n",
        "```\n",
        "다음과 같이 바꿉니다.\n",
        "```python\n",
        "    df = pd.read_csv('your/local/path/to/iris.data', header=None)\n",
        "```\n",
        "그다음 다음 코드를 실행합니다.\n",
        "```python\n",
        "    df = pd.read_csv('iris.data', header=None, encoding='utf-8')\n",
        "    df.tail()\n",
        "```\n",
        "\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lc6Bt3ogqALD"
      },
      "source": [
        "### 붓꽃 데이터 그래프 그리기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:15.658083Z",
          "iopub.status.busy": "2021-10-23T05:49:15.657338Z",
          "iopub.status.idle": "2021-10-23T05:49:16.078313Z",
          "shell.execute_reply": "2021-10-23T05:49:16.077583Z"
        },
        "id": "XcshxMesqALD",
        "outputId": "9fe46147-c856-4331-81e3-e6e62fc7e35b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# setosa와 versicolor를 선택합니다\n",
        "y = df.iloc[0:100, 4].values\n",
        "y = np.where(y == 'Iris-setosa', -1, 1)\n",
        "\n",
        "# 꽃받침 길이와 꽃잎 길이를 추출합니다\n",
        "X = df.iloc[0:100, [0, 2]].values\n",
        "\n",
        "# 산점도를 그립니다, setosa-음성, versicolor-양성\n",
        "plt.scatter(X[:50, 0], X[:50, 1],\n",
        "            color='red', marker='o', label='setosa')\n",
        "plt.scatter(X[50:100, 0], X[50:100, 1],\n",
        "            color='blue', marker='x', label='versicolor')\n",
        "\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('petal length [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "# plt.savefig('images/02_06.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1Iv80F_qALD"
      },
      "source": [
        "### 퍼셉트론 모델 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.137478Z",
          "iopub.status.busy": "2021-10-23T05:49:16.083504Z",
          "iopub.status.idle": "2021-10-23T05:49:16.234584Z",
          "shell.execute_reply": "2021-10-23T05:49:16.235431Z"
        },
        "id": "i7rZoVY9qALE",
        "outputId": "16abee57-b552-4fbe-8e96-3f09b04f4a1f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ppn = Perceptron(eta=0.1, n_iter=10)\n",
        "\n",
        "ppn.fit(X, y)\n",
        "\n",
        "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Number of updates')\n",
        "\n",
        "# plt.savefig('images/02_07.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPBaThajqALE"
      },
      "source": [
        "### 결정 경계 그래프 함수"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.248170Z",
          "iopub.status.busy": "2021-10-23T05:49:16.246966Z",
          "iopub.status.idle": "2021-10-23T05:49:16.249608Z",
          "shell.execute_reply": "2021-10-23T05:49:16.248983Z"
        },
        "id": "RKhIj0ynqALE"
      },
      "outputs": [],
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "\n",
        "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
        "\n",
        "    # 마커와 컬러맵을 설정합니다\n",
        "    markers = ('s', 'x', 'o', '^', 'v')\n",
        "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
        "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
        "\n",
        "    # 결정 경계를 그립니다\n",
        "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1  # 꽃받침 길이 최소/최대\n",
        "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1  # 꽃잎 길이 최소/최대\n",
        "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
        "                           np.arange(x2_min, x2_max, resolution))\n",
        "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
        "    Z = Z.reshape(xx1.shape)\n",
        "    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)\n",
        "    plt.xlim(xx1.min(), xx1.max())\n",
        "    plt.ylim(xx2.min(), xx2.max())\n",
        "\n",
        "    # 샘플의 산점도를 그립니다\n",
        "    for idx, cl in enumerate(np.unique(y)):\n",
        "        plt.scatter(x=X[y == cl, 0],\n",
        "                    y=X[y == cl, 1],\n",
        "                    alpha=0.8,\n",
        "                    c=colors[idx],\n",
        "                    marker=markers[idx],\n",
        "                    label=cl,\n",
        "                    edgecolor=None if idx==1 else 'black')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.255014Z",
          "iopub.status.busy": "2021-10-23T05:49:16.254186Z",
          "iopub.status.idle": "2021-10-23T05:49:16.462238Z",
          "shell.execute_reply": "2021-10-23T05:49:16.462696Z"
        },
        "id": "ux8HGXQSqALE",
        "outputId": "c83ce198-7e9f-498a-8e9a-e403cb78722e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_decision_regions(X, y, classifier=ppn)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('petal length [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "# plt.savefig('images/02_08.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEdYJwaXqALF"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwzhuiRWqALF"
      },
      "source": [
        "# 2.3 적응형 선형 뉴런과 학습의 수렴"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.468172Z",
          "iopub.status.busy": "2021-10-23T05:49:16.467421Z",
          "iopub.status.idle": "2021-10-23T05:49:16.471404Z",
          "shell.execute_reply": "2021-10-23T05:49:16.470761Z"
        },
        "id": "H3SPVRtIqALF",
        "outputId": "49b310f0-233e-43f2-f6f9-5f838ac71272"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbn\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "Image(url='https://git.io/JtIbn', width=600)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_QZBd5q-Iwq"
      },
      "source": [
        "아달린의 활성화 함수 : $\\phi(z) = z$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqhqlofW-Iwq"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhERmxUDqALF"
      },
      "source": [
        "## 2.3.1 경사 하강법으로 비용 함수 최소화"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqUN1U7m-Iwq"
      },
      "source": [
        "목적 함수 == 비용 함수 == 손실 함수\n",
        "\n",
        "$J(\\boldsymbol{w})=\\dfrac{1}{2}\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)^2$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.476935Z",
          "iopub.status.busy": "2021-10-23T05:49:16.476018Z",
          "iopub.status.idle": "2021-10-23T05:49:16.479602Z",
          "shell.execute_reply": "2021-10-23T05:49:16.478970Z"
        },
        "id": "tJS1Uk5ZqALF",
        "outputId": "69185026-f586-4a4d-e6dd-c4d58d0c0ede",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbc\" width=\"500\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "Image(url='https://git.io/JtIbc', width=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gqRpFrG-Iwr"
      },
      "source": [
        "$\\\n",
        "\\begin{align*}\n",
        "\\dfrac{\\partial J}{\\partial w_j}\\\n",
        "&=\\dfrac{\\partial}{\\partial w_j}\\dfrac{1}{2}\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)^2 \\\\\n",
        "&=\\dfrac{1}{2}\\dfrac{\\partial}{\\partial w_j}\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)^2 \\\\\n",
        "&=\\dfrac{1}{2}\\sum_i2\\left(y^{(i)}-\\phi(z^{(i)})\\right)\\dfrac{\\partial}{\\partial w_j}\\left(y^{(i)}-\\phi(z^{(i)})\\right) \\\\\n",
        "&=\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)\\dfrac{\\partial}{\\partial w_j}\\left(y^{(i)}-\\sum(w_kx_k^{(i)})\\right) \\\\\n",
        "&=\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)\\left(-x_j^{(i)}\\right) \\\\\n",
        "&=-\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)x_j^{(i)} \\\\\n",
        "\\end{align*}\n",
        "$\n",
        "\n",
        "$\\mathit\\Delta w_j=-\\eta\\dfrac{\\partial J}{\\partial w_j}=\\eta\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)x_j^{(i)}$\n",
        "\n",
        "$\\boldsymbol{w} := \\boldsymbol{w} + \\mathit\\Delta \\boldsymbol{w}$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csOcD18f-Iwr"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJ0OEDXpqALG"
      },
      "source": [
        "## 2.3.2 파이썬으로 아달린 구현하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.490203Z",
          "iopub.status.busy": "2021-10-23T05:49:16.488420Z",
          "iopub.status.idle": "2021-10-23T05:49:16.492385Z",
          "shell.execute_reply": "2021-10-23T05:49:16.492812Z"
        },
        "id": "r_X9q8k_qALG"
      },
      "outputs": [],
      "source": [
        "class AdalineGD(object):\n",
        "    \"\"\"적응형 선형 뉴런 분류기\n",
        "\n",
        "    매개변수\n",
        "    ------------\n",
        "    eta : float\n",
        "      학습률 (0.0과 1.0 사이)\n",
        "    n_iter : int\n",
        "      훈련 데이터셋 반복 횟수\n",
        "    random_state : int\n",
        "      가중치 무작위 초기화를 위한 난수 생성기 시드\n",
        "\n",
        "    속성\n",
        "    -----------\n",
        "    w_ : 1d-array\n",
        "      학습된 가중치\n",
        "    cost_ : list\n",
        "      에포크마다 누적된 비용 함수의 제곱합\n",
        "\n",
        "    \"\"\"\n",
        "    def __init__(self, eta=0.01, n_iter=50, random_state=1):\n",
        "        self.eta = eta\n",
        "        self.n_iter = n_iter\n",
        "        self.random_state = random_state\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        \"\"\"훈련 데이터 학습\n",
        "\n",
        "        매개변수\n",
        "        ----------\n",
        "        X : array-like, shape = [n_samples, n_features]\n",
        "          n_samples 개의 샘플과 n_features 개의 특성으로 이루어진 훈련 데이터\n",
        "        y : array-like, shape = [n_samples]\n",
        "          타깃값\n",
        "\n",
        "        반환값\n",
        "        -------\n",
        "        self : object\n",
        "\n",
        "        \"\"\"\n",
        "        rgen = np.random.RandomState(self.random_state)\n",
        "        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])\n",
        "        self.cost_ = []\n",
        "\n",
        "        for i in range(self.n_iter):\n",
        "            net_input = self.net_input(X)\n",
        "            # 이 코드의 활성화 함수는 항등 함수(identity function)이기 때문에\n",
        "            # 아무런 효과가 없습니다.\n",
        "            # 이 대신 `output = self.net_input(X)`로 바로 쓸 수 있습니다.\n",
        "            # 이 활성화 함수는 개념적인 목적을 위해 만들었습니다.\n",
        "            # (잠시 후에 보게 될) 로지스틱 회귀의 경우 이 함수를 시그모이드 함수로\n",
        "            # 바꾸어 로지스틱 회귀 분류기를 구현합니다.\n",
        "            output = self.activation(net_input)\n",
        "            errors = (y - output)\n",
        "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
        "            self.w_[0] += self.eta * errors.sum()\n",
        "            cost = (errors**2).sum() / 2.0\n",
        "            self.cost_.append(cost)\n",
        "        return self\n",
        "\n",
        "    def net_input(self, X):\n",
        "        \"\"\"최종 입력 계산\"\"\"\n",
        "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
        "\n",
        "    def activation(self, X):\n",
        "        \"\"\"선형 활성화 계산\"\"\"\n",
        "        return X\n",
        "\n",
        "    def predict(self, X):\n",
        "        \"\"\"단위 계단 함수를 사용하여 클래스 레이블을 반환합니다\"\"\"\n",
        "        return np.where(self.activation(self.net_input(X)) >= 0.0, 1, -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.536447Z",
          "iopub.status.busy": "2021-10-23T05:49:16.535626Z",
          "iopub.status.idle": "2021-10-23T05:49:16.722476Z",
          "shell.execute_reply": "2021-10-23T05:49:16.722981Z"
        },
        "id": "duwyjN71qALG",
        "outputId": "d4ff9a48-2a5e-4468-b253-f974075e3734"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
        "\n",
        "ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)\n",
        "ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')\n",
        "ax[0].set_xlabel('Epochs')\n",
        "ax[0].set_ylabel('log(Sum-squared-error)')\n",
        "ax[0].set_title('Adaline - Learning rate 0.01')\n",
        "\n",
        "ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)\n",
        "ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')\n",
        "ax[1].set_xlabel('Epochs')\n",
        "ax[1].set_ylabel('Sum-squared-error')\n",
        "ax[1].set_title('Adaline - Learning rate 0.0001')\n",
        "\n",
        "# plt.savefig('images/02_11.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.728868Z",
          "iopub.status.busy": "2021-10-23T05:49:16.728186Z",
          "iopub.status.idle": "2021-10-23T05:49:16.732377Z",
          "shell.execute_reply": "2021-10-23T05:49:16.731441Z"
        },
        "id": "4w5q2QKqqALH",
        "outputId": "8a72cce6-9822-498c-972a-29991ca6430c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIb4\" width=\"700\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "Image(url='https://git.io/JtIb4', width=700)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kq7qlF0A-Iws"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u0jOIB3qALH"
      },
      "source": [
        "## 2.3.3 특성 스케일을 조정하여 경사 하강법 결과 향상시키기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rw3sByh0-Iws"
      },
      "source": [
        "표준화(standardization) : $\\boldsymbol{x}'_j=\\dfrac{\\boldsymbol{x}_j-\\mu_j}{\\sigma_j}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.739316Z",
          "iopub.status.busy": "2021-10-23T05:49:16.738133Z",
          "iopub.status.idle": "2021-10-23T05:49:16.742617Z",
          "shell.execute_reply": "2021-10-23T05:49:16.743133Z"
        },
        "id": "PZ5nowNeqALI",
        "outputId": "d5ce1a0b-140b-426a-e741-19fef54ae586",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JtIbB\" width=\"700\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "Image(url='https://git.io/JtIbB', width=700)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.750242Z",
          "iopub.status.busy": "2021-10-23T05:49:16.749194Z",
          "iopub.status.idle": "2021-10-23T05:49:16.752265Z",
          "shell.execute_reply": "2021-10-23T05:49:16.751380Z"
        },
        "id": "nEBXmUTkqALI"
      },
      "outputs": [],
      "source": [
        "# 특성을 표준화합니다.\n",
        "X_std = np.copy(X)\n",
        "X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()\n",
        "X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:16.764833Z",
          "iopub.status.busy": "2021-10-23T05:49:16.759885Z",
          "iopub.status.idle": "2021-10-23T05:49:17.130905Z",
          "shell.execute_reply": "2021-10-23T05:49:17.131810Z"
        },
        "id": "OGiKd5kvqALI",
        "outputId": "61994823-1037-45d8-8b30-7d6e00f45b88"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ada_gd = AdalineGD(n_iter=15, eta=0.01)\n",
        "ada_gd.fit(X_std, y)\n",
        "\n",
        "plot_decision_regions(X_std, y, classifier=ada_gd)\n",
        "plt.title('Adaline - Gradient Descent')\n",
        "plt.xlabel('sepal length [standardized]')\n",
        "plt.ylabel('petal length [standardized]')\n",
        "plt.legend(loc='upper left')\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/02_14_1.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "plt.plot(range(1, len(ada_gd.cost_) + 1), ada_gd.cost_, marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Sum-squared-error')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/02_14_2.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9aKnN4fqALJ"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hj-m4ewrqALJ"
      },
      "source": [
        "## 2.3.4 대규모 머신 러닝과 확률적 경사 하강법"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exqIOEeO-Iws"
      },
      "source": [
        "배치 경사 하강법 : $\\mathit\\Delta \\boldsymbol{w}=\\eta\\sum_i\\left(y^{(i)}-\\phi(z^{(i)})\\right)\\boldsymbol{x}^{(i)}$\n",
        "\n",
        "확률적 경사 하강법 : $\\mathit\\Delta \\boldsymbol{w}=\\eta\\left(y^{(i)}-\\phi(z^{(i)})\\right)\\boldsymbol{x}^{(i)}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:17.150367Z",
          "iopub.status.busy": "2021-10-23T05:49:17.148625Z",
          "iopub.status.idle": "2021-10-23T05:49:17.151833Z",
          "shell.execute_reply": "2021-10-23T05:49:17.152692Z"
        },
        "id": "Fmyd49AbqALJ"
      },
      "outputs": [],
      "source": [
        "class AdalineSGD(object):\n",
        "    \"\"\"ADAptive LInear NEuron 분류기\n",
        "\n",
        "    Parameters\n",
        "    ------------\n",
        "    eta : float\n",
        "      학습률 (0.0과 1.0 사이)\n",
        "    n_iter : int\n",
        "      훈련 데이터셋 반복 횟수\n",
        "    shuffle : bool (default: True)\n",
        "      True로 설정하면 같은 반복이 되지 않도록 에포크마다 훈련 데이터를 섞습니다\n",
        "    random_state : int\n",
        "      가중치 무작위 초기화를 위한 난수 생성기 시드\n",
        "\n",
        "    Attributes\n",
        "    -----------\n",
        "    w_ : 1d-array\n",
        "      학습된 가중치\n",
        "    cost_ : list\n",
        "      모든 훈련 샘플에 대해 에포크마다 누적된 평균 비용 함수의 제곱합\n",
        "\n",
        "    \"\"\"\n",
        "    def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):\n",
        "        self.eta = eta\n",
        "        self.n_iter = n_iter\n",
        "        self.w_initialized = False\n",
        "        self.shuffle = shuffle\n",
        "        self.random_state = random_state\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        \"\"\"훈련 데이터 학습\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : {array-like}, shape = [n_samples, n_features]\n",
        "          n_samples 개의 샘플과 n_features 개의 특성으로 이루어진 훈련 데이터\n",
        "        y : array-like, shape = [n_samples]\n",
        "          타깃 벡터\n",
        "\n",
        "        반환값\n",
        "        -------\n",
        "        self : object\n",
        "\n",
        "        \"\"\"\n",
        "        self._initialize_weights(X.shape[1])\n",
        "        self.cost_ = []\n",
        "        for i in range(self.n_iter):\n",
        "            if self.shuffle:\n",
        "                X, y = self._shuffle(X, y)\n",
        "            cost = []\n",
        "            for xi, target in zip(X, y):\n",
        "                cost.append(self._update_weights(xi, target))\n",
        "            avg_cost = sum(cost) / len(y)\n",
        "            self.cost_.append(avg_cost)\n",
        "        return self\n",
        "\n",
        "    def partial_fit(self, X, y):\n",
        "        \"\"\"가중치를 다시 초기화하지 않고 훈련 데이터를 학습합니다\"\"\"\n",
        "        if not self.w_initialized:\n",
        "            self._initialize_weights(X.shape[1])\n",
        "        if y.ravel().shape[0] > 1:\n",
        "            for xi, target in zip(X, y):\n",
        "                self._update_weights(xi, target)\n",
        "        else:\n",
        "            self._update_weights(X, y)\n",
        "        return self\n",
        "\n",
        "    def _shuffle(self, X, y):\n",
        "        \"\"\"훈련 데이터를 섞습니다\"\"\"\n",
        "        r = self.rgen.permutation(len(y))\n",
        "        return X[r], y[r]\n",
        "\n",
        "    def _initialize_weights(self, m):\n",
        "        \"\"\"랜덤한 작은 수로 가중치를 초기화합니다\"\"\"\n",
        "        self.rgen = np.random.RandomState(self.random_state)\n",
        "        self.w_ = self.rgen.normal(loc=0.0, scale=0.01, size=1 + m)\n",
        "        self.w_initialized = True\n",
        "\n",
        "    def _update_weights(self, xi, target):\n",
        "        \"\"\"아달린 학습 규칙을 적용하여 가중치를 업데이트합니다\"\"\"\n",
        "        output = self.activation(self.net_input(xi))\n",
        "        error = (target - output)\n",
        "        self.w_[1:] += self.eta * xi.dot(error)\n",
        "        self.w_[0] += self.eta * error\n",
        "        cost = 0.5 * error**2\n",
        "        return cost\n",
        "\n",
        "    def net_input(self, X):\n",
        "        \"\"\"입력 계산\"\"\"\n",
        "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
        "\n",
        "    def activation(self, X):\n",
        "        \"\"\"선형 활성화 계산\"\"\"\n",
        "        return X\n",
        "\n",
        "    def predict(self, X):\n",
        "        \"\"\"단위 계단 함수를 사용하여 클래스 레이블을 반환합니다\"\"\"\n",
        "        return np.where(self.activation(self.net_input(X)) >= 0.0, 1, -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:17.166778Z",
          "iopub.status.busy": "2021-10-23T05:49:17.163351Z",
          "iopub.status.idle": "2021-10-23T05:49:17.626666Z",
          "shell.execute_reply": "2021-10-23T05:49:17.627707Z"
        },
        "id": "CL_zIPSEqALJ",
        "outputId": "06f6858f-9b95-4567-bbcb-7c9047a0c408"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ada_sgd = AdalineSGD(n_iter=15, eta=0.01, random_state=1)\n",
        "ada_sgd.fit(X_std, y)\n",
        "\n",
        "plot_decision_regions(X_std, y, classifier=ada_sgd)\n",
        "plt.title('Adaline - Stochastic Gradient Descent')\n",
        "plt.xlabel('sepal length [standardized]')\n",
        "plt.ylabel('petal length [standardized]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/02_15_1.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "plt.plot(range(1, len(ada_sgd.cost_) + 1), ada_sgd.cost_, marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Average Cost')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('images/02_15_2.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-10-23T05:49:17.633926Z",
          "iopub.status.busy": "2021-10-23T05:49:17.632735Z",
          "iopub.status.idle": "2021-10-23T05:49:17.638571Z",
          "shell.execute_reply": "2021-10-23T05:49:17.639385Z"
        },
        "id": "8MIHsvCsqALK",
        "outputId": "73dfc618-efa4-45a4-d5ad-7a287d47be90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.AdalineSGD at 0x7e6475919130>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "ada_sgd.partial_fit(X_std[0, :], y[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdf0YMrA-Iwt"
      },
      "source": [
        "<br>"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "name": "ch02.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}