iris_teste.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/frkr/mlearn/blob/master/iris_teste.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "raw",
      "source": [
        "---\n",
        "title: kNN\n",
        "description:\n",
        "show-code: false\n",
        "---"
      ],
      "metadata": {
        "collapsed": false,
        "id": "bCRLM0baTO5Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# kNN"
      ],
      "metadata": {
        "collapsed": false,
        "id": "duyU4RE4TO5c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5],\n",
              "       [4.9, 3. ],\n",
              "       [4.7, 3.2],\n",
              "       [4.6, 3.1],\n",
              "       [5. , 3.6]])"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import sklearn\n",
        "from sklearn import datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "\n",
        "X = iris.data[:, :2]\n",
        "Y = iris.target\n",
        "\n",
        "X[:5]"
      ],
      "metadata": {
        "id": "mCQpZAm8TO5h",
        "outputId": "c5b075b3-e4fb-4cee-869f-a598f5c3d943",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "egu9pWZbTO5j",
        "outputId": "d37e9448-0560-4d53-8df2-ca84b8b258e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate min, max and limits\n",
        "H = 0.5  # step size in the mesh\n",
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, H), np.arange(y_min, y_max, H))\n",
        "\n",
        "# Put the result into a color plot\n",
        "plt.figure()\n",
        "plt.scatter(X[:, 0], X[:, 1])\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.title(\"Data points\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jn7N85krTO5l",
        "outputId": "15625f9e-9935-4bd0-8a34-e1ddac5dfe0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import ListedColormap\n",
        "from sklearn import neighbors\n",
        "\n",
        "# rank\n",
        "n_neighbors = 3\n",
        "\n",
        "# Create color maps\n",
        "cmap_light = ListedColormap(['#FF4B46', '#92FF7B', '#878DFF'])\n",
        "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#00AAFF'])\n",
        "\n",
        "# we create an instance of Neighbours Classifier and fit the data.\n",
        "clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')\n",
        "clf.fit(X, Y)\n",
        "\n",
        "# predict class using data and kNN classifier\n",
        "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "# Put the result into a color plot\n",
        "Z = Z.reshape(xx.shape)\n",
        "plt.figure()\n",
        "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
        "\n",
        "# Plot also the training points\n",
        "plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=cmap_bold)\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.title(\"3-Class classification (k = %i)\" % (n_neighbors))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0rUI8OgcTO5n",
        "outputId": "db32a917-dac6-49de-c5eb-86b1243e1079",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: \n",
            "Versicolour\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# make prediction\n",
        "#sepal length\n",
        "sl = 6.5\n",
        "#sepal width\n",
        "sw = 2.9\n",
        "dataClass = clf.predict([[sl, sw]])\n",
        "print('Prediction: '),\n",
        "\n",
        "if dataClass == 0:\n",
        "    print('Setosa')\n",
        "elif dataClass == 1:\n",
        "    print('Versicolour')\n",
        "else:\n",
        "    print('Virginica')\n"
      ]
    }
  ],
  "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": "3.10.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}