PAVIC_ML_00_Perceptron_aula02.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wallisonferreira/machine-learning-pavic/blob/main/PAVIC_ML_00_Perceptron_aula02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ppwVt8hLUoZq"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from random import random\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.datasets import make_blobs\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1X_BQV1RTReI"
      },
      "outputs": [],
      "source": [
        "#Algoritimo perceptron\n",
        "\n",
        "# Inicializar o 'w' e o 'b'\n",
        "# Para cada amostra\n",
        "  # calcular o y_pred = f(E xi*wi + b)onde f é  função de ativação step, para regressão trocamos para linear\n",
        "  # calcular o erro erro = y - y_pred\n",
        "  # atualizar os pesos wi = wi + lambda * erro * xi\n",
        "  # atualizar o bias bi = bi + lambda*erro\n",
        "# repetir até encontrar o menor erro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0wtpAKYUa6-"
      },
      "source": [
        "# Classificação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUBaa87EUfGd"
      },
      "source": [
        "## Porta OR ou AND"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55RyBl-KUmtg",
        "outputId": "7c2baa15-a5c3-4f79-882d-8ab7a321237b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4, 2) (4,)\n"
          ]
        }
      ],
      "source": [
        "x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "#y = np.array([0, 1, 1, 1]) # porta OR\n",
        "y = np.array([0, 0, 0, 1]).T # porta AND\n",
        "\n",
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4QN4wPX0VAAb",
        "outputId": "da0009fd-4d80-49dd-e93e-2157a69a0bac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.7062687 , -0.92792573]), 0.8985873994523752)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "#Initilize w and b\n",
        "D = x.shape[1] # qtd entradas\n",
        "w = 2*np.random.random(D)-1\n",
        "b = 2*np.random.random()-1\n",
        "w,b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "tlHkYZ2LV_du",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbb7ffa3-c416-4ed0-c38b-24541b7e31ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 0: 0\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "Step 10: 0\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "Step 20: 0\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "Step 30: 0\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "Step 40: 0\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "Step 50: 0\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "Step 60: 0\n",
            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "Step 70: 0\n",
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            "w [1.2937313  1.07207427]\n",
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            "w [1.2937313  1.07207427]\n",
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            "Step 80: 0\n",
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            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "Step 90: 0\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n",
            "w [1.2937313  1.07207427]\n",
            "b -2.101412600547625\n",
            "y_pred: [-2.1014126  -1.02933833 -0.8076813   0.26439297]\n"
          ]
        }
      ],
      "source": [
        "#LOOP TRAIN - PUT YOU CODE HERE\n",
        "for step in range(100):\n",
        "    learning_rate = 1.0\n",
        "    cost = 0\n",
        "    for x_n, y_n in zip(x, y):\n",
        "        y_pred = np.dot(x_n, w) + b\n",
        "        y_pred = np.where(y_pred > 0, 1, 0) # activation function # where is > 0 turns 1 else 0\n",
        "        error = y_n - y_pred # calculates error\n",
        "        w = w + learning_rate * np.dot(error, x_n) # updates weights of objective function / lambda is the learning rate\n",
        "        b = b + learning_rate * error\n",
        "        cost += error ** 2\n",
        "    if step % 10 == 0:\n",
        "        print('Step {0}: {1}'.format(step, cost))\n",
        "\n",
        "    print('w', w)\n",
        "    print('b', b)\n",
        "    print( 'y_pred: {0}'.format( np.dot( x, np.array(w) ) + b ) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eSKk_QEGY07-"
      },
      "outputs": [],
      "source": [
        "#podemos aplicar esses pesos no notebook de intuição"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCoqjMbJbNa_"
      },
      "source": [
        "## Exercício de Classificação\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "AUOmxveJY6mE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "35a3ee7a-6f14-410a-b4a5-6847820bee2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 2) (100,)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f173b3ff2e0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x, y = make_blobs(n_samples=100, n_features=2, centers=2, random_state=1234)\n",
        "\n",
        "print(x.shape, y.shape)\n",
        "plt.scatter(x[:,0], x[:,1], c=y.ravel(), cmap='bwr')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "YQ_j__8-bP0g"
      },
      "outputs": [],
      "source": [
        "def plot_linear_classifier(x, y, w, b):\n",
        "    x1_min, x1_max = x[:,0].min(), x[:,0].max()\n",
        "    x2_min, x2_max = x[:,1].min(), x[:,1].max()\n",
        "\n",
        "    x1, x2 = np.meshgrid(np.linspace(x1_min-1, x1_max+1,100), np.linspace(x2_min-1, x2_max+1, 100))\n",
        "    x_mesh = np.array([x1.ravel(), x2.ravel()]).T\n",
        "\n",
        "    plt.scatter(x[:,0], x[:,1], c=y.ravel(), cmap='bwr')\n",
        "\n",
        "    y_mesh = np.dot(x_mesh, np.array(w).reshape(1, -1).T) + b\n",
        "    y_mesh = np.where(y_mesh <= 0, 0, 1)\n",
        "\n",
        "    plt.contourf(x1, x2, y_mesh.reshape(x1.shape), cmap='bwr', alpha=0.5)\n",
        "    plt.xlim(x1_min-1, x1_max+1)\n",
        "    plt.ylim(x2_min-1, x2_max+1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "z7UBA97nbuWd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da7ca429-9c0f-4199-abf3-8fcc2fab7aa7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.42102156,  0.10261262]), -0.5110406590397871)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#DEFINE W AND B - PUT YOU CODE HERE\n",
        "D = x.shape[1]\n",
        "w = 2*np.random.random(D)-1\n",
        "b = 2*np.random.random()-1\n",
        "\n",
        "w, b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "fQf-8mZzbw-X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db371b67-c48c-4f0a-bc58-9b167e5318dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 0: 0\n",
            "Step 10: 0\n",
            "Step 20: 0\n",
            "Step 30: 0\n",
            "Step 40: 0\n",
            "Step 50: 0\n",
            "Step 60: 0\n",
            "Step 70: 0\n",
            "Step 80: 0\n",
            "Step 90: 0\n",
            "w [13.16416117 13.19948499]\n",
            "b 0.4889593409602129\n",
            "y_pred: [ 59.65413975 -20.26090404 -62.6231465   89.04991973  63.38007655\n",
            " -51.51975357  53.94928021 -20.89699682  48.369784    44.38245982\n",
            " -29.08744438  27.06770492 -62.24685657 -35.70751049 -52.0044845\n",
            " -59.8194627   64.90970625  77.52029435 -36.40211995  52.16376871\n",
            " -69.3222466   56.19422532  58.63596933  49.34191148 -38.64199753\n",
            "  51.20194203 -47.19126095  70.16438028  51.09152459  56.67357561\n",
            "  30.7203459   43.11687597 -63.58405706 -49.27656594 -57.6193913\n",
            "  57.45293485  65.25684342  35.14907985 -20.64396587 -38.09821711\n",
            " -69.74210597 -56.5061094  -51.11232007  64.57511196 -57.07336736\n",
            "  57.4499541   35.87509096  52.43700295 -38.73476858  85.00693024\n",
            "  58.32052203 -52.86322327 -75.40619373 -36.84428752 -46.26918861\n",
            "  81.64308261  68.26707531  95.16275483  55.14738826  69.04063337\n",
            "  66.67631588  -5.85394729  28.19870034  49.1686851   13.36399953\n",
            " -66.56896856 -50.05271763  54.33621788 -52.72160571  64.01674675\n",
            " -60.72007144 -38.82522113  83.79794514 -54.96702721 -39.34001751\n",
            "  42.9652566  -45.57935833  42.20348712 -36.92595874  76.47139204\n",
            " -68.2812068  -37.33556486 -44.30170924 -25.39113592  88.99601326\n",
            " -32.31272688  67.99622685 -39.65230991  48.00163537  54.35616143\n",
            "  52.55747388 -89.41658558 -52.73398145 -46.05876024 -34.18420447\n",
            " -77.88899313  47.03509738  57.04192579 -37.18190899 -26.55450867]\n"
          ]
        }
      ],
      "source": [
        "#LOOP TRAIN - PUT YOU CODE HERE\n",
        "for step in range(100):\n",
        "    learning_rate = 1.0\n",
        "    cost = 0\n",
        "    for x_n, y_n in zip(x, y):\n",
        "        y_pred = np.dot(x_n, w) + b\n",
        "        y_pred = np.where(y_pred > 1, 1, 0)\n",
        "        error = y_n - y_pred\n",
        "        w = w + learning_rate * np.dot(error, x_n)\n",
        "        b = b + learning_rate * error\n",
        "        cost += error ** 2\n",
        "    if step % 10 == 0:\n",
        "        print('Step {0}: {1}'.format(step, cost))\n",
        "\n",
        "print('w', w)\n",
        "print('b', b)\n",
        "print( 'y_pred: {0}'.format( np.dot( x, np.array(w) ) + b ) )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "2zojwKMJcDxM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "29847767-0cf6-45dc-fbe7-4811e089d354"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_linear_classifier(x, y, w, b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8uH0NOYYcUgc"
      },
      "source": [
        "# Perceptron Regressão linear"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsH2FJKEcdnK"
      },
      "source": [
        "Para transformar o Perceptron em um __regressor linear__, só o que temos de fazer é __remover a função de ativação _step___, transformando-a em uma função de ativação linear.\n",
        "\n",
        "Apesar dessa modificação, __a fórmula de atualização dos pesos não sofre nenhuma alteração__.\n",
        "\n",
        "Vamos, então, implementar nosso perceptron para classificação em Python, Numpy, Keras e TensorFlow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "nghw_DgzcXMn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "30fe6503-252e-4048-89fe-27ef5db82b77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Altura    Peso\n",
              "0     187  109.72\n",
              "1     177   91.09\n",
              "2     180   88.93\n",
              "3     177   89.39\n",
              "4     177   92.02\n",
              "5     183   94.70\n",
              "6     178   83.57\n",
              "7     177   85.19\n",
              "8     183   92.96\n",
              "9     188  108.21"
            ],
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ff5daf65-b22c-4581-9c89-7202564b1ad8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ff5daf65-b22c-4581-9c89-7202564b1ad8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-78536860-0c6a-4b31-8c72-2c5d9dd0fd46\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78536860-0c6a-4b31-8c72-2c5d9dd0fd46')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-78536860-0c6a-4b31-8c72-2c5d9dd0fd46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df = pd.read_csv('/content/medidas.csv')\n",
        "print(df.shape)\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "DcQpk36CcsXK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "d780b564-ca66-4312-aa0e-87423333ad26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Peso')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = df.Altura.values\n",
        "y = df.Peso.values\n",
        "\n",
        "plt.figure()\n",
        "plt.scatter(x, y)\n",
        "plt.xlabel('Altura')\n",
        "plt.ylabel('Peso')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "lkZbYowldjEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dad15a78-e323-41b0-ad24-0c907e308938"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100,) (100,)\n"
          ]
        }
      ],
      "source": [
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Ll-E8KHbdkvv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "704f4b5a-7e3b-4e17-f1ed-30cf705f3620"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 1) (100,)\n"
          ]
        }
      ],
      "source": [
        "x = x.reshape(-1, 1)\n",
        "\n",
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "hCLDytuCdqyS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7de494b7-adfe-4071-8f5e-deb04874194b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "w [0.34208926]\n",
            "b 71.5179135365518\n",
            "error:  -4.8717237117101035\n",
            "###################################\n",
            "###################################\n",
            "w [0.34215133]\n",
            "b 71.51791901014711\n",
            "error:  -4.8717228423300725\n",
            "###################################\n",
            "###################################\n",
            "w [0.34221341]\n",
            "b 71.51792448373227\n",
            "error:  -4.8717219729517325\n",
            "###################################\n",
            "###################################\n",
            "w [0.34227548]\n",
            "b 71.51792995730715\n",
            "error:  -4.8717211035749415\n",
            "###################################\n",
            "###################################\n",
            "w [0.34233756]\n",
            "b 71.51793543087187\n",
            "error:  -4.871720234199842\n",
            "###################################\n",
            "###################################\n",
            "w [0.34239964]\n",
            "b 71.51794090442635\n",
            "error:  -4.871719364826319\n",
            "###################################\n",
            "###################################\n",
            "w [0.34246171]\n",
            "b 71.5179463779706\n",
            "error:  -4.871718495454402\n",
            "###################################\n",
            "###################################\n",
            "w [0.34252379]\n",
            "b 71.51795185150473\n",
            "error:  -4.871717626084219\n",
            "###################################\n",
            "###################################\n",
            "w [0.34258586]\n",
            "b 71.51795732502855\n",
            "error:  -4.871716756715557\n",
            "###################################\n",
            "###################################\n",
            "w [0.34264794]\n",
            "b 71.51796279854213\n",
            "error:  -4.871715887348472\n",
            "###################################\n",
            "###################################\n",
            "w [0.34271001]\n",
            "b 71.51796827204561\n",
            "error:  -4.871715017983135\n",
            "###################################\n",
            "###################################\n",
            "w [0.34277209]\n",
            "b 71.51797374553887\n",
            "error:  -4.871714148619418\n",
            "###################################\n",
            "###################################\n",
            "w [0.34283416]\n",
            "b 71.5179792190218\n",
            "error:  -4.871713279257207\n",
            "###################################\n",
            "###################################\n",
            "w [0.34289624]\n",
            "b 71.5179846924946\n",
            "error:  -4.871712409896688\n",
            "###################################\n",
            "###################################\n",
            "w [0.34295831]\n",
            "b 71.51799016595716\n",
            "error:  -4.871711540537774\n",
            "###################################\n",
            "###################################\n",
            "w [0.34302038]\n",
            "b 71.5179956394095\n",
            "error:  -4.87171067118048\n",
            "###################################\n",
            "###################################\n",
            "w [0.34308246]\n",
            "b 71.51800111285164\n",
            "error:  -4.871709801824807\n",
            "###################################\n",
            "###################################\n",
            "w [0.34314453]\n",
            "b 71.51800658628359\n",
            "error:  -4.871708932470796\n",
            "###################################\n",
            "###################################\n",
            "w [0.34320661]\n",
            "b 71.5180120597053\n",
            "error:  -4.871708063118362\n",
            "###################################\n",
            "###################################\n",
            "w [0.34326868]\n",
            "b 71.51801753311683\n",
            "error:  -4.871707193767591\n",
            "###################################\n",
            "###################################\n",
            "w [0.34333075]\n",
            "b 71.51802300651812\n",
            "error:  -4.871706324418426\n",
            "###################################\n",
            "###################################\n",
            "w [0.34339283]\n",
            "b 71.51802847990915\n",
            "error:  -4.871705455070824\n",
            "###################################\n",
            "###################################\n",
            "w [0.3434549]\n",
            "b 71.51803395329001\n",
            "error:  -4.871704585724885\n",
            "###################################\n",
            "###################################\n",
            "w [0.34351697]\n",
            "b 71.51803942666066\n",
            "error:  -4.871703716380594\n",
            "###################################\n",
            "###################################\n",
            "w [0.34357905]\n",
            "b 71.5180449000211\n",
            "error:  -4.871702847037895\n",
            "###################################\n",
            "###################################\n",
            "w [0.34364112]\n",
            "b 71.5180503733713\n",
            "error:  -4.871701977696816\n",
            "###################################\n",
            "###################################\n",
            "w [0.34370319]\n",
            "b 71.51805584671135\n",
            "error:  -4.871701108357399\n",
            "###################################\n",
            "###################################\n",
            "w [0.34376527]\n",
            "b 71.51806132004121\n",
            "error:  -4.8717002390196456\n",
            "###################################\n",
            "###################################\n",
            "w [0.34382734]\n",
            "b 71.51806679336073\n",
            "error:  -4.871699369683384\n",
            "###################################\n",
            "###################################\n",
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            "error:  -4.870862083639267\n",
            "###################################\n",
            "###################################\n",
            "w [0.40367367]\n",
            "b 71.52334378943193\n",
            "error:  -4.870861215867009\n",
            "###################################\n",
            "###################################\n",
            "w [0.40373563]\n",
            "b 71.52334925289473\n",
            "error:  -4.870860348096372\n",
            "###################################\n",
            "###################################\n",
            "w [0.40379759]\n",
            "b 71.52335471634737\n",
            "error:  -4.870859480327397\n",
            "###################################\n",
            "###################################\n",
            "w [0.40385955]\n",
            "b 71.52336017978976\n",
            "error:  -4.870858612559985\n",
            "###################################\n",
            "###################################\n",
            "w [0.40392151]\n",
            "b 71.52336564322198\n",
            "error:  -4.870857744794222\n",
            "###################################\n",
            "###################################\n",
            "w [0.40398347]\n",
            "b 71.52337110664405\n",
            "error:  -4.870856877030107\n",
            "###################################\n",
            "Step 10000: 23221.556531166265\n",
            "###################################\n",
            "w [0.40404543]\n",
            "b 71.52337657005587\n",
            "error:  -4.870856009267584\n",
            "###################################\n"
          ]
        }
      ],
      "source": [
        "#LOOP TRAIN - PUT YOU CODE HERE\n",
        "D = x.shape[1]\n",
        "w = 2 * np.random.random(D)-1\n",
        "b = 2 * np.random.random()-1\n",
        "lr = 1e-7\n",
        "for step in range(10001):\n",
        "    cost = 0\n",
        "    for x_n, y_n in zip(x, y):\n",
        "        y_pred = np.dot(x_n, w) + b\n",
        "        #y_pred = np.where(y_pred > 1, 1, 0) # na regressão, tiramos a função de ativação\n",
        "        error = y_n - y_pred\n",
        "        w = w + lr * np.dot(error, x_n)\n",
        "        b = b + 1e-2 * error\n",
        "        cost += error ** 2\n",
        "    if step % 1000 == 0:\n",
        "        print('Step {0}: {1}'.format(step, cost))\n",
        "    print('###################################')\n",
        "    print('w', w)\n",
        "    print('b', b)\n",
        "    print('error: ', error)\n",
        "    print('###################################')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "Mi5hAgBIdt3A"
      },
      "outputs": [],
      "source": [
        "# os dados nao estao normalizados peso em kg e altura em m\n",
        "# vamos normalizar\n",
        "# PUT YOU CODE HERE\n",
        "minmax = MinMaxScaler(feature_range=(-1, 1))\n",
        "x = minmax.fit_transform(x.astype(np.float64))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "6_gFCFNTfVMO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "b0d525cd-5102-4ef1-dbaf-e45616bf227c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Peso')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.scatter(x, y)\n",
        "plt.xlabel('Altura')\n",
        "plt.ylabel('Peso')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "BlKqU6JefEGM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16710413-bc6d-483c-a52e-712944af6a70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w:  [33.60164767]\n",
            "b:  74.99636286981102\n"
          ]
        }
      ],
      "source": [
        "reg = LinearRegression()\n",
        "reg.fit(x,y)\n",
        "\n",
        "print('w: ', reg.coef_)\n",
        "print('b: ', reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "UHZ5f1jvfGpz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1717dfd2-4bc8-404b-dd89-29625718d2d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "###################################\n",
            "w [2.75406425]\n",
            "b 24.958183673992384\n",
            "error:  42.02530617775427\n",
            "###################################\n",
            "###################################\n",
            "w [3.254859]\n",
            "b 29.64079150236254\n",
            "error:  37.38911539045671\n",
            "###################################\n",
            "###################################\n",
            "w [3.75311809]\n",
            "b 33.878768952199586\n",
            "error:  33.19774569348875\n",
            "###################################\n",
            "###################################\n",
            "w [4.24824607]\n",
            "b 37.71444691148295\n",
            "error:  29.408762561987118\n",
            "###################################\n",
            "###################################\n",
            "w [4.73971972]\n",
            "b 41.18612409459595\n",
            "error:  25.983775101370135\n",
            "###################################\n",
            "###################################\n",
            "w [5.22708088]\n",
            "b 44.32845116024985\n",
            "error:  22.888050808642305\n",
            "###################################\n",
            "###################################\n",
            "w [5.70993]\n",
            "b 47.17277823637667\n",
            "error:  20.09016703404488\n",
            "###################################\n",
            "###################################\n",
            "w [6.18792022]\n",
            "b 49.74746933804249\n",
            "error:  17.561695646767\n",
            "###################################\n",
            "###################################\n",
            "w [6.66075216]\n",
            "b 52.07818683233256\n",
            "error:  15.27691774150778\n",
            "###################################\n",
            "###################################\n",
            "w [7.12816905]\n",
            "b 54.1881488036967\n",
            "error:  13.212565524024768\n",
            "###################################\n",
            "###################################\n",
            "w [7.58995245]\n",
            "b 56.09836190140453\n",
            "error:  11.347588786441058\n",
            "###################################\n",
            "###################################\n",
            "w [8.04591836]\n",
            "b 57.82783200481777\n",
            "error:  9.6629436297479\n",
            "###################################\n",
            "###################################\n",
            "w [8.49591362]\n",
            "b 59.393754819673944\n",
            "error:  8.141401314105956\n",
            "###################################\n",
            "###################################\n",
            "w [8.93981281]\n",
            "b 60.81168831726286\n",
            "error:  6.767375319452022\n",
            "###################################\n",
            "###################################\n",
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            "Step 500: 2611.2445682077805\n",
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            "###################################\n",
            "###################################\n",
            "w [33.60180831]\n",
            "b 74.93896426420241\n",
            "error:  -4.85511398134031\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180832]\n",
            "b 74.93896426444033\n",
            "error:  -4.855113980827184\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180832]\n",
            "b 74.93896426467379\n",
            "error:  -4.855113980323523\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180833]\n",
            "b 74.93896426490298\n",
            "error:  -4.855113979829241\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180834]\n",
            "b 74.93896426512777\n",
            "error:  -4.855113979343969\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180834]\n",
            "b 74.93896426534855\n",
            "error:  -4.855113978867848\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180835]\n",
            "b 74.93896426556515\n",
            "error:  -4.855113978400453\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180836]\n",
            "b 74.93896426577774\n",
            "error:  -4.855113977941727\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180836]\n",
            "b 74.9389642659864\n",
            "error:  -4.855113977491513\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180837]\n",
            "b 74.93896426619114\n",
            "error:  -4.8551139770495695\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180838]\n",
            "b 74.93896426639216\n",
            "error:  -4.855113976615911\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180838]\n",
            "b 74.93896426658948\n",
            "error:  -4.855113976190296\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180839]\n",
            "b 74.93896426678309\n",
            "error:  -4.855113975772525\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180839]\n",
            "b 74.93896426697307\n",
            "error:  -4.855113975362457\n",
            "###################################\n",
            "###################################\n",
            "w [33.6018084]\n",
            "b 74.93896426715963\n",
            "error:  -4.855113974960062\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180841]\n",
            "b 74.93896426734275\n",
            "error:  -4.855113974565171\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180841]\n",
            "b 74.93896426752251\n",
            "error:  -4.855113974177598\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180842]\n",
            "b 74.93896426769881\n",
            "error:  -4.855113973797103\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180842]\n",
            "b 74.93896426787191\n",
            "error:  -4.8551139734237125\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180843]\n",
            "b 74.93896426804174\n",
            "error:  -4.855113973057186\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180843]\n",
            "b 74.93896426820847\n",
            "error:  -4.855113972697481\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180844]\n",
            "b 74.9389642683721\n",
            "error:  -4.855113972344455\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180844]\n",
            "b 74.9389642685326\n",
            "error:  -4.855113971997852\n",
            "###################################\n",
            "###################################\n",
            "w [33.60180845]\n",
            "b 74.93896426869027\n",
            "error:  -4.855113971657829\n",
            "###################################\n",
            "Step 1000: 2611.2443542779138\n",
            "###################################\n",
            "w [33.60180845]\n",
            "b 74.93896426884496\n",
            "error:  -4.8551139713240445\n",
            "###################################\n"
          ]
        }
      ],
      "source": [
        "##LOOP TRAIN - PUT YOU CODE HERE\n",
        "D = x.shape[1]\n",
        "w = 2 * np.random.random(D)-1\n",
        "b = 2 * np.random.random()-1\n",
        "lr = 1e-3\n",
        "for step in range(1001):\n",
        "    cost = 0\n",
        "    for x_n, y_n in zip(x, y):\n",
        "        y_pred = np.dot(x_n, w) + b\n",
        "        #y_pred = np.where(y_pred > 1, 1, 0) # na regressão, tiramos a função de ativação\n",
        "        error = y_n - y_pred\n",
        "        w = w + lr * np.dot(error, x_n)\n",
        "        b = b + lr * error\n",
        "        cost += error ** 2\n",
        "    if step % 100 == 0:\n",
        "        print('Step {0}: {1}'.format(step, cost))\n",
        "    print('###################################')\n",
        "    print('w', w)\n",
        "    print('b', b)\n",
        "    print('error: ', error)\n",
        "    print('###################################')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "GVdwrzozfk0F"
      },
      "outputs": [],
      "source": [
        "def plot_line(w, b):\n",
        "    plt.figure(0, figsize=(8,8))\n",
        "    plt.subplot(1,1,1)\n",
        "    plt.scatter(x, y)\n",
        "    y_pred = x*w + b\n",
        "    plt.plot(x, y_pred, c='red')\n",
        "    plt.xlim(140, 210)\n",
        "    plt.ylim(40, 120)\n",
        "\n",
        "    plt.subplot(1,1,1)\n",
        "    x_ = np.array([0, x.max()])\n",
        "    y_ = x_*w + b\n",
        "    plt.scatter(x, y)\n",
        "    plt.plot(x_, y_, c='red')\n",
        "    plt.xlim(0, 1)\n",
        "    plt.ylim(-160, 120)\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "eDPKk0ZvfxNl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "39882bf6-7f77-4a01-994a-5f7119f4557f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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viLZYVtzzm4i5lZ3LNkJuRGncGBRCSk8MoNQJugxqU+pnxCNHXxfPl43tsf65srElM+1koS56r61fl9PttmfZ4gXxwtcOislLTo2pD30hJi85NV742kGxbPGCAe+7EDcGg+4m6nOf22aQjbKyiH33zc1xGht7DbLdy5w5vX6s5G4MiiylJwZQ6gZ3VRhE6bcNK9RFr+3l53O63bbku1lBoW4MCtbBpq2tINPEZrtGZBdX9torfqA3hcNHjIgnaz4Q45+9KbKsZ9OYLOv8MVhT84GS+Z3Mt1SaXUEK1OhSErrahk390Fkx+ZiTBn1zhc0VqjZsxG575nS73hSiWUEha8Om1M+I3eesjIa3Xxz373lKNLz94thjzsq+hcI77th+bWyuQu4vf7nd2tjlv5iftycf7W1tsX/joojYuv131+v9GhflrPlKqRt0TwxgCPNbBnlWqHEod91z75xu15tCdLJ5+5EnRPsdZTEssl7bG2dZRHsMi7cfOfAJA7pGdqjrqtF9IaLpmp/2HNnh4IMj/vznAR9rh1pbI8rL+/3xfD752NF5L8vBeU+NIblgcBB0oQAKcdErREAsRLOCvzx8Z0wu20b1d3SGqhHRESsevnNAoWrZ4gVx+B2zYti3Xu2xvjpaojpmRsTMfu97K//3/0YsGHj75R3JV694nav6p9SbXUEK/LYxIGbg2nn5vugVIiAWollBTkPVnDkRX/96r29N6UuhdmT16s6a30TpXNV/huSC4pJI6LeUZuBKQSFq3QrRyWanQ1XVhK0bjOZLtu0biKFA5yqgVAm69EtKM3AVSr5vDApR61aI9sbdoWpNYwz7t9e3ud3kK/+h38foob4i4t0Vvb71+ynXxLs+fG5ujlPCCtXOHCDX/FVKWL6aFaQ4vWVEfpthFOLGoFC1bjlrb1xbG/H001utHh4R1QMq4RZaWiJGj+6xquGmr0bdX765w4+2vfJCLktS0nSuAkpR6aQQ+iSftYcpTm+Zz+9XoW4MClnrtsP2xi0tEVVVAz7ODh12WMQjj/T5YyNGj8vpdkOFzlVAqfHXKUGpDOhfKPn+fhXyxuCtWrcvR3W82L3+ubI94tm6L+eu1u3ii2P4P/9zTM7N3rap/be/jdUj38h5LfuuY2tzut1QUqjOVTq6Arngr0ZiClF7mFIP7EJ8vwbDjUGfu2wVqpNXe3vEsG3PWzM8Ii9huquZx/hs/TaHYmvSuapodHQFcsXMaIlZ/cDiqI7e22hGvFV7uPqBxf0+xqRp9bEhdttmR/Qsi3gpRpdESCjE96uQNwZdtdPjsxd7rB+fvRiH33d+LFu8IOL3v9/+TF65Crnnnbfdmbwiy7YbcvOpq5lHFr3PXJWFmauKpetneFy2vsf6cdn6t36GAXaSv+KJKVTt4Y4HWyqN4ZgK8f0q1Exf7W1tsW/DP0XZd1qirKXne2VvLlNyOAlC+xNPxOpnV5Xso2WdqwafVDu6AsXjL0ViClF72Nnm9JXtTge6e7xSEp3RCvH9yulEDm+8ETFqVK9vDY+I3ftdyl5sZ+zYZYsXxMR/m17yj5Z1rhpcUuzoChSXpgtbaG9rixX3LoyHfvPDWHHvwmhvayt2kfpk0rT6aIqxWz2O7dKRRTTGwNoeDoY2p7ky6L5f//Iv229SsI2Q22c/+tGOmxVsQ2qPlrs6V0390Fkx+ZiT1BQWUUp/W4DBwV/0zaTQAaIQQ0yl1Bmt4N+vK1u2ud3UOLvfx9jKl3aLGN77feyDo/8+jvrsZ/u1W4+WyaeU/rYAg4Ma3TelVEs1pX5GPHL0dfF82dge658rGxuPHH1dbiYmyHMtaCHl7Pu1alWvtbCTj/1QZ8DdTsjtk8MO67UGtuEH50fMrexcthFyIyLaRw+s2Uq+O+8xdKX2twUoPlUukWYtVT7bHqY4HehOfb8OOSRi9er8F+aRRzrDbB/tdsgJEc/M37nt+qnQj5aNpTq0pPi3BSgufy2iOB0gCnEBz+fA7sn1WG9vj+G77JL3CRAiorPGdTNdY7aO+9KfB/QzcGjdibHhzt2iKntlm6M7bCgbHYfWndjvYxR6qLRSb0pE3yX3twUoKkE3Cl9LlcoFvJA91gd8Y/CDH0Scc07Oy7WV88/v7FC2mRX3LozJS07d5kfKcnQjNXzEiFhTd3W8877zI8t6Dofb1bfsr3VXxZQBnJ+uiRbGZb03X+jIOgPJQB8t53u2OgY3o2EAueKvRhRnQP+ISOICXojpQHfqxqBQM3k1N0dUVu54u80U8kZqe7VhubiRKsSj5RSbEtF3hZpqGEibq0QUrpbKBbzvli1eEIcvnhXDvv1qj/XV0RLVMTMihxMgRMR2h9Xqr0L3JN9WbVh1jn6m8v1o2ViqAOSKNBWF6wDhAr4NJ5wQcdddvb41JZfHue22iI98JJd73CmFupHaXL5rw/L5aNlYqgDkiqD7pkJ0gBiyF/BCNSvIQ21sLqTakzxfYdpYqgDkSmldWfMs3x0gkryAL1oU8cEP5v84+wyLmLlbr289NPUbMfVDZ+W/DAOgJ/nOK0YNOABpEnS3kM9HviV5AS9Ubey6dRF77dVj1Y5GK+hSKjcGepLvnFRrwAEoPFeKAir0BXyHQ3K9/HKfRxDot340KyjJG4Md0JN856gBByAXBN0t5Hsihyn1M6JhzQNx1LM3R8Rb4S+LYfHghE9FXY4u4MsWL4iDrvh8TH7w5Zzsb7t+8IOIs3LfdEDN3tCmBhyAgSrLskHagyeHWlpaoqqqKpqbm6NyOzWYXeO1bl6D1BS5nchh83F0ewtujxx93c4fq1DNCjZtiihiuGi48bw46tmbY3jZWz+q7dmbNwbn3FC0cgEAubOzea0vhuVkLwnoCqDjsvU91o/L1sfh950fyxYvGPAxdjSObsSb4+i2tUU8/HBnkN3ekgt7793ZrGB7SxFD7rLFC2LaszdFWfS8HyuLjpj27E05OS8pam9rixX3LoyHfvPDWHHvws6fqRI8BgAMhBrd6Lxgv/C1g3bYFnTcl/48oGYMPTpXfb0l4o1+72rnnbNrRPXWZV7x9zcP+raihTovqSnUk4l8HwOAoSUfNbrSQeRhIodNmyLKy7daPXmA5ezVFvcpD/3mhzH1oS/s8GOlMFavCTb6rhBTTKc2jTUA6dJ0IfoxkcO3vrX9JgW9hNx+mTNnx80KtpDSWL1DdoKNfupT05hBfAwAyBU1utFL6LuypdftpsbZEXF27g586W4R5W/da+TiUXxKQ3KlFNoLoRA14GrZASglQy/orl0bsc8+PVblpUlBRK81rjsadWGgw2WlNCRXSqG9EApRA66WHYBSMrSaLlRVbRVy++23v+1zs4KIzraLjxx9XTxfNrbH+ufKxvZtaLHtKMQxCqErtEe8FdK7bB7adUTrVIgacLXsAJSSoTXqQkTsdB++uVtv2fC2T0fdmd/JSZnyPTFFoY5RCL318G8MM2RtqRCjVBgJA4B8MepCrsyYETF/fvfLhh/Njrr//clOfDB3EzQUYirYVKabNUPWzilEs5WUmsYAkL6hdTVqbo7o5Q5ht0nHR+xE0N1t0vH5KBU7IZXQnm9T6mfEsoitasCfK8tdDXghjgEAuTC0mi5sZ8KIl7+2T1Rlr/Q64ViWRWwoGx2VX/qrx7GUBE1jACg1mi7kyfARI2JN3dXxzvvOjyzrObtu123AX+uuiik5vIgLCeSTpjEAIOh2297j2FxPa9rVuWry5tOnLjF9KgBALmm6sIV817TuaBzdUhr+CwAgVzRdKIB8Po7d0fSpHdmb06eecJpmDAAAAzS0JowostUPLI7q6H380Yi3pk9d/cDiwhYMACBBgm4BmT4VAKBwBN0CMn0qAEDhCLoFNGlafTTF2O6OZ1vqyDqntp00rb6wBQMASJCgW0Bd06dGxFZhd/PpU3VEAwAYOEG3wKbUz4hHjr4uni8b22P9c2VjDS0GAJBDxtEtEjOjAQC8xTi6CTF9KgBAfmm6AABAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkFT3ofvnLX46ysrIey6RJk7rff+ONN2LWrFkxduzY2G233eKUU06JpqamIpYYAIBSUPSgGxExefLkePbZZ7uXe+65p/u92bNnx69//ev4+c9/Hv/93/8dzzzzTHz0ox8tYmkBACgFI4pdgIiIESNGRE1NzVbrm5ub48c//nHcfPPN8b73vS8iIn7605/GIYccEvfff3+8+93v7nV/ra2t0dra2v26paUlPwUHAGDQGhQ1un/5y19i4sSJsf/++8dpp50Wa9eujYiIhx9+ODZt2hTTp0/v3nbSpEmx9957R0NDwzb3N2/evKiqqupeamtr8/41AAAwuBQ96E6bNi3mz58fixYtiu9///uxZs2aOO644+Lll1+OxsbGKC8vjzFjxvT4THV1dTQ2Nm5zn5deemk0Nzd3L+vWrcvzVwEAwGBT9KYLH/zgB7v/f9hhh8W0adNin332iVtvvTVGjRrVr31WVFRERUVFrooIAEAJKnqN7pbGjBkTBx10UDz++ONRU1MTGzdujA0bNvTYpqmpqdc2vQAA0GXQBd1XXnklnnjiiZgwYUIceeSRscsuu8Sdd97Z/f5jjz0Wa9eujbq6uiKWEgCAwa7oTRcuvvjiOPnkk2OfffaJZ555JubOnRvDhw+PT33qU1FVVRVnnHFGXHjhhbHHHntEZWVlfP7zn4+6urptjrgAAAARgyDoPv300/GpT30q1q9fH+PGjYtjjz027r///hg3blxERHz729+OYcOGxSmnnBKtra1RX18fN9xwQ5FLDQDAYFeWZVlW7ELkW0tLS1RVVUVzc3NUVlYWuzgAAGwhH3lt0LXRBQCAXBB0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSSiboXn/99bHvvvvGyJEjY9q0afHggw8Wu0gAAAxiJRF0f/azn8WFF14Yc+fOjT/84Q9x+OGHR319fTz33HPFLhoAAINUWZZlWbELsSPTpk2Ld73rXfG9730vIiI6OjqitrY2Pv/5z8ecOXO22r61tTVaW1u7X7e0tERtbW00NzdHZWVlwcoNAMDOaWlpiaqqqpzmtUFfo7tx48Z4+OGHY/r06d3rhg0bFtOnT4+GhoZePzNv3ryoqqrqXmprawtVXAAABolBH3RfeOGFaG9vj+rq6h7rq6uro7GxsdfPXHrppdHc3Ny9rFu3rhBFBQBgEBlR7ALkQ0VFRVRUVBS7GAAAFNGgr9Hdc889Y/jw4dHU1NRjfVNTU9TU1BSpVAAADHaDPuiWl5fHkUceGXfeeWf3uo6Ojrjzzjujrq6uiCUDAGAwK4mmCxdeeGHMmDEjpk6dGkcddVR85zvfiVdffTU+/elPF7toAAAMUiURdD/xiU/E888/H1dccUU0NjbGO9/5zli0aNFWHdQAAKBLSYyjO1D5GJcNAIDcGZLj6AIAQH8IugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSUUNuvvuu2+UlZX1WK655poe2/zxj3+M4447LkaOHBm1tbVx7bXXFqm0AACUkhHFLsBXvvKVOPPMM7tfjx49uvv/LS0t8f73vz+mT58eN954Yzz66KPxmc98JsaMGRNnnXVWMYoLAECJKHrQHT16dNTU1PT63k033RQbN26Mn/zkJ1FeXh6TJ0+O5cuXx7e+9a3tBt3W1tZobW3tft3S0pLzcgMAMLgVvY3uNddcE2PHjo0pU6bEN77xjWhra+t+r6GhIf7u7/4uysvLu9fV19fHY489Fi+99NI29zlv3ryoqqrqXmpra/P6NQAAMPgUNeief/75ccstt8Tdd98dZ599dlx99dVxySWXdL/f2NgY1dXVPT7T9bqxsXGb+7300kujubm5e1m3bl1+vgAAAAatnDddmDNnTnz961/f7jarVq2KSZMmxYUXXti97rDDDovy8vI4++yzY968eVFRUdHvMlRUVAzo8wAAlL6cB92LLrooZs6cud1t9t9//17XT5s2Ldra2uKvf/1rHHzwwVFTUxNNTU09tul6va12vQAAEJGHoDtu3LgYN25cvz67fPnyGDZsWIwfPz4iIurq6uKyyy6LTZs2xS677BIREUuWLImDDz44dt9995yVGQCA9BStjW5DQ0N85zvfiUceeSSefPLJuOmmm2L27Nnxj//4j90h9tRTT43y8vI444wzYsWKFfGzn/0s/uVf/qVHkwcAAOhN0YYXq6ioiFtuuSW+/OUvR2tra+y3334xe/bsHiG2qqoq7rjjjpg1a1YceeSRseeee8YVV1xhDF0AAHaoLMuyrNiFyLeWlpaoqqqK5ubmqKysLHZxAADYQj7yWtHH0QUAgHwQdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkvIWdK+66qo4+uijY9ddd40xY8b0us3atWvjpJNOil133TXGjx8fX/jCF6Ktra3HNkuXLo0jjjgiKioq4sADD4z58+fnq8gAACQkb0F348aN8bGPfSzOPffcXt9vb2+Pk046KTZu3Bj33XdfLFiwIObPnx9XXHFF9zZr1qyJk046KY4//vhYvnx5XHDBBfHZz342Fi9enK9iAwCQiLIsy7J8HmD+/PlxwQUXxIYNG3qs/6//+q/40Ic+FM8880xUV1dHRMSNN94YX/ziF+P555+P8vLy+OIXvxgLFy6MP/3pT92f++QnPxkbNmyIRYsWbfOYra2t0dra2v26paUlamtro7m5OSorK3P7BQIAMGAtLS1RVVWV07xWtDa6DQ0N8Y53vKM75EZE1NfXR0tLS6xYsaJ7m+nTp/f4XH19fTQ0NGx33/PmzYuqqqrupba2NvdfAAAAg1rRgm5jY2OPkBsR3a8bGxu3u01LS0u8/vrr29z3pZdeGs3Nzd3LunXrclx6AAAGuz4F3Tlz5kRZWdl2l9WrV+errDutoqIiKisreywAAAwtI/qy8UUXXRQzZ87c7jb777//Tu2rpqYmHnzwwR7rmpqaut/r+rdr3ebbVFZWxqhRo3ay1AAADEV9Crrjxo2LcePG5eTAdXV1cdVVV8Vzzz0X48ePj4iIJUuWRGVlZRx66KHd29x+++09PrdkyZKoq6vLSRkAAEhX3trorl27NpYvXx5r166N9vb2WL58eSxfvjxeeeWViIh4//vfH4ceemicfvrp8cgjj8TixYvjS1/6UsyaNSsqKioiIuKcc86JJ598Mi655JJYvXp13HDDDXHrrbfG7Nmz81VsAAASkbfhxWbOnBkLFizYav3dd98d733veyMi4qmnnopzzz03li5dGn/zN38TM2bMiGuuuSZGjHironnp0qUxe/bsWLlyZey1115x+eWX77D5xJbyMVwFAAC5k4+8lvdxdAcDQRcAYHBLahxdAADIJ0EXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSBF0AAJIk6AIAkCRBFwCAJAm6AAAkSdAFACBJgi4AAEkSdAEASJKgCwBAkgRdAACSJOgCAJAkQRcAgCQJugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACQpb0H3qquuiqOPPjp23XXXGDNmTK/blJWVbbXccsstPbZZunRpHHHEEVFRUREHHnhgzJ8/P19FBgAgIXkLuhs3boyPfexjce655253u5/+9Kfx7LPPdi8f+chHut9bs2ZNnHTSSXH88cfH8uXL44ILLojPfvazsXjx4nwVGwCARIzI146vvPLKiIgd1sCOGTMmampqen3vxhtvjP322y/++Z//OSIiDjnkkLjnnnvi29/+dtTX129zn62trdHa2tr9uqWlpY+lBwCg1BW9je6sWbNizz33jKOOOip+8pOfRJZl3e81NDTE9OnTe2xfX18fDQ0N293nvHnzoqqqqnupra3NS9kBABi8ihp0v/KVr8Stt94aS5YsiVNOOSXOO++8+O53v9v9fmNjY1RXV/f4THV1dbS0tMTrr7++zf1eeuml0dzc3L2sW7cub18DAACDU5+aLsyZMye+/vWvb3ebVatWxaRJk3Zqf5dffnn3/6dMmRKvvvpqfOMb34jzzz+/L8XaSkVFRVRUVAxoHwAAlLY+Bd2LLrooZs6cud1t9t9//34XZtq0afHVr341Wltbo6KiImpqaqKpqanHNk1NTVFZWRmjRo3q93EAAEhfn4LuuHHjYty4cfkqSyxfvjx233337trYurq6uP3223tss2TJkqirq8tbGQAASEPeRl1Yu3ZtvPjii7F27dpob2+P5cuXR0TEgQceGLvttlv8+te/jqampnj3u98dI0eOjCVLlsTVV18dF198cfc+zjnnnPje974Xl1xySXzmM5+Ju+66K2699dZYuHBhvooNAEAiyrLNhznIoZkzZ8aCBQu2Wn/33XfHe9/73li0aFFceuml8fjjj0eWZXHggQfGueeeG2eeeWYMG/ZWH7mlS5fG7NmzY+XKlbHXXnvF5ZdfvsPmE1tqaWmJqqqqaG5ujsrKyoF+aQAA5Fg+8lregu5gIugCAAxu+chrRR9HFwAA8kHQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEmCLgAASRJ0AQBIkqALAECSRhS7AIWQZVlERLS0tBS5JAAA9KYrp3XltlwYEkF3/fr1ERFRW1tb5JIAALA969evj6qqqpzsa0gE3T322CMiItauXZuzbxyDV0tLS9TW1sa6deuisrKy2MUhz5zvocX5Hlqc76Glubk59t577+7clgtDIugOG9bZFLmqqsovyhBSWVnpfA8hzvfQ4nwPLc730NKV23Kyr5ztCQAABhFBFwCAJA2JoFtRURFz586NioqKYheFAnC+hxbne2hxvocW53toycf5LstyOYYDAAAMEkOiRhcAgKFH0AUAIEmCLgAASRJ0AQBIkqALAECSkgm6119/fey7774xcuTImDZtWjz44IPb3f7nP/95TJo0KUaOHBnveMc74vbbby9QScmFvpzvH/3oR3HcccfF7rvvHrvvvntMnz59hz8fDC59/f3ucsstt0RZWVl85CMfyW8Byam+nu8NGzbErFmzYsKECVFRUREHHXSQv+klpK/n+zvf+U4cfPDBMWrUqKitrY3Zs2fHG2+8UaDS0l+/+93v4uSTT46JEydGWVlZ/PKXv9zhZ5YuXRpHHHFEVFRUxIEHHhjz58/v+4GzBNxyyy1ZeXl59pOf/CRbsWJFduaZZ2ZjxozJmpqaet3+3nvvzYYPH55de+212cqVK7MvfelL2S677JI9+uijBS45/dHX833qqadm119/fbZs2bJs1apV2cyZM7Oqqqrs6aefLnDJ6Y++nu8ua9asyd72trdlxx13XPbhD3+4MIVlwPp6vltbW7OpU6dmJ554YnbPPfdka9asyZYuXZotX768wCWnP/p6vm+66aasoqIiu+mmm7I1a9ZkixcvziZMmJDNnj27wCWnr26//fbssssuy37xi19kEZHddttt293+ySefzHbdddfswgsvzFauXJl997vfzYYPH54tWrSoT8dNIugeddRR2axZs7pft7e3ZxMnTszmzZvX6/Yf//jHs5NOOqnHumnTpmVnn312XstJbvT1fG+pra0tGz16dLZgwYJ8FZEc6s/5bmtry44++ujsX//1X7MZM2YIuiWkr+f7+9//frb//vtnGzduLFQRyaG+nu9Zs2Zl73vf+3qsu/DCC7Njjjkmr+Ukt3Ym6F5yySXZ5MmTe6z7xCc+kdXX1/fpWCXfdGHjxo3x8MMPx/Tp07vXDRs2LKZPnx4NDQ29fqahoaHH9hER9fX129yewaM/53tLr732WmzatCn22GOPfBWTHOnv+f7KV74S48ePjzPOOKMQxSRH+nO+f/WrX0VdXV3MmjUrqqur42//9m/j6quvjvb29kIVm37qz/k++uij4+GHH+5u3vDkk0/G7bffHieeeGJBykzh5CqrjchloYrhhRdeiPb29qiuru6xvrq6OlavXt3rZxobG3vdvrGxMW/lJDf6c7639MUvfjEmTpy41S8Qg09/zvc999wTP/7xj2P58uUFKCG51J/z/eSTT8Zdd90Vp512Wtx+++3x+OOPx3nnnRebNm2KuXPnFqLY9FN/zvepp54aL7zwQhx77LGRZVm0tbXFOeecE//0T/9UiCJTQNvKai0tLfH666/HqFGjdmo/JV+jC31xzTXXxC233BK33XZbjBw5stjFIcdefvnlOP300+NHP/pR7LnnnsUuDgXQ0dER48ePjx/+8Idx5JFHxic+8Ym47LLL4sYbbyx20ciDpUuXxtVXXx033HBD/OEPf4hf/OIXsXDhwvjqV79a7KIxSJV8je6ee+4Zw4cPj6amph7rm5qaoqamptfP1NTU9Gl7Bo/+nO8u3/zmN+Oaa66J3/72t3HYYYfls5jkSF/P9xNPPBF//etf4+STT+5e19HRERERI0aMiMceeywOOOCA/BaafuvP7/eECRNil112ieHDh3evO+SQQ6KxsTE2btwY5eXleS0z/def83355ZfH6aefHp/97GcjIuId73hHvPrqq3HWWWfFZZddFsOGqb9LxbayWmVl5U7X5kYkUKNbXl4eRx55ZNx5553d6zo6OuLOO++Murq6Xj9TV1fXY/uIiCVLlmxzewaP/pzviIhrr702vvrVr8aiRYti6tSphSgqOdDX8z1p0qR49NFHY/ny5d3LP/zDP8Txxx8fy5cvj9ra2kIWnz7qz+/3McccE48//nj3DU1ExJ///OeYMGGCkDvI9ed8v/baa1uF2a6bnM4+TqQiZ1mtb/3kBqdbbrklq6ioyObPn5+tXLkyO+uss7IxY8ZkjY2NWZZl2emnn57NmTOne/t77703GzFiRPbNb34zW7VqVTZ37lzDi5WQvp7va665JisvL8/+8z//M3v22We7l5dffrlYXwJ90NfzvSWjLpSWvp7vtWvXZqNHj84+97nPZY899lj2m9/8Jhs/fnz2ta99rVhfAn3Q1/M9d+7cbPTo0dl//Md/ZE8++WR2xx13ZAcccED28Y9/vFhfAjvp5ZdfzpYtW5YtW7Ysi4jsW9/6VrZs2bLsqaeeyrIsy+bMmZOdfvrp3dt3DS/2hS98IVu1alV2/fXXD93hxbIsy7773e9me++9d1ZeXp4dddRR2f3339/93nve855sxowZPba/9dZbs4MOOigrLy/PJk+enC1cuLDAJWYg+nK+99lnnywitlrmzp1b+ILTL339/d6coFt6+nq+77vvvmzatGlZRUVFtv/++2dXXXVV1tbWVuBS0199Od+bNm3KvvzlL2cHHHBANnLkyKy2tjY777zzspdeeqnwBadP7r777l6vxV3nd8aMGdl73vOerT7zzne+MysvL8/233//7Kc//Wmfj1uWZer6AQBIT8m30QUAgN4IugAAJEnQBQAgSYIuAABJEnQBAEiSoAsAQJIEXQAAkiToAgCQJEEXAIAkCboAACRJ0AUAIEn/H00WYpWBr/1mAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_line(w, b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "No-m4_3TglJP"
      },
      "source": [
        "## Exercício"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "dLaa2tkggo2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "7373f4b4-385f-4fee-d20c-cebd89fbbd8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   prova1  prova2  prova3  final\n",
              "0      73      80      75    152\n",
              "1      93      88      93    185\n",
              "2      89      91      90    180\n",
              "3      96      98     100    196\n",
              "4      73      66      70    142\n",
              "5      53      46      55    101\n",
              "6      69      74      77    149\n",
              "7      47      56      60    115\n",
              "8      87      79      90    175\n",
              "9      79      70      88    164"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5855cc9c-1a59-4925-8fd4-7f672091dc6e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prova1</th>\n",
              "      <th>prova2</th>\n",
              "      <th>prova3</th>\n",
              "      <th>final</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>73</td>\n",
              "      <td>80</td>\n",
              "      <td>75</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>93</td>\n",
              "      <td>88</td>\n",
              "      <td>93</td>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>89</td>\n",
              "      <td>91</td>\n",
              "      <td>90</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>96</td>\n",
              "      <td>98</td>\n",
              "      <td>100</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>73</td>\n",
              "      <td>66</td>\n",
              "      <td>70</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>53</td>\n",
              "      <td>46</td>\n",
              "      <td>55</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>69</td>\n",
              "      <td>74</td>\n",
              "      <td>77</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>47</td>\n",
              "      <td>56</td>\n",
              "      <td>60</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>87</td>\n",
              "      <td>79</td>\n",
              "      <td>90</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>79</td>\n",
              "      <td>70</td>\n",
              "      <td>88</td>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5855cc9c-1a59-4925-8fd4-7f672091dc6e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5855cc9c-1a59-4925-8fd4-7f672091dc6e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5855cc9c-1a59-4925-8fd4-7f672091dc6e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c4927665-d6ad-4eaa-b4ae-328742a9f38c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c4927665-d6ad-4eaa-b4ae-328742a9f38c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c4927665-d6ad-4eaa-b4ae-328742a9f38c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "df = pd.read_csv('/content/notas.csv')\n",
        "\n",
        "print(df.shape)\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "p3RWQdGUgxdw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "59e209d7-d108-4eea-c78b-7745695204f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Final')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 4))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.scatter(df.prova1.values, df.final.values)\n",
        "plt.xlabel('Prova 1')\n",
        "plt.ylabel('Final')\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.scatter(df.prova2.values, df.final.values)\n",
        "plt.xlabel('Prova 2')\n",
        "plt.ylabel('Final')\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.scatter(df.prova3.values, df.final.values)\n",
        "plt.xlabel('Prova 3')\n",
        "plt.ylabel('Final')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "aTXgJjN8g1Ij",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f12f001-01ab-4faa-f7ee-caebb1ad3998"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25, 3) (25,)\n"
          ]
        }
      ],
      "source": [
        "x = df[['prova1', 'prova2', 'prova3']].values\n",
        "y = df['final'].values\n",
        "\n",
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "2EVTWBCUg3jA"
      },
      "outputs": [],
      "source": [
        "minmax = MinMaxScaler(feature_range=(-1,1))\n",
        "x = minmax.fit_transform(x.astype(np.float64))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "bYbI1Yzqg5Lw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "933606ca-5ab4-483f-bf87-c7ffbec71171"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w:  [ 8.72048636 14.1054877  26.26749487]\n",
            "b:  150.65175754349872\n"
          ]
        }
      ],
      "source": [
        "reg = LinearRegression()\n",
        "reg.fit(x, y)\n",
        "\n",
        "print('w: ', reg.coef_)\n",
        "print('b: ', reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "4WKgn2zNg7eJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78d15810-4790-4355-9d06-7322cd4787cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "###################################\n",
            "Learning Rate =  1e-07\n",
            "w [ 0.04093674  0.63370278 -0.01050816]\n",
            "b 162.2702068109798\n",
            "error:  29.47999925531039\n",
            "###################################\n",
            "###################################\n",
            "Learning Rate =  1e-06\n",
            "w [1.08976742 1.62164302 1.11807956]\n",
            "b 161.43955674218665\n",
            "error:  27.567002060006928\n",
            "###################################\n",
            "###################################\n",
            "Learning Rate =  1e-05\n",
            "w [8.4238404  8.53915036 9.15211742]\n",
            "b 155.60071460456442\n",
            "error:  14.1014582820292\n",
            "###################################\n",
            "###################################\n",
            "Learning Rate =  0.0001\n",
            "w [14.69886306 14.81721194 19.76212562]\n",
            "b 149.80776213137534\n",
            "error:  0.15668678944084036\n",
            "###################################\n",
            "###################################\n",
            "Learning Rate =  0.001\n",
            "w [ 9.39517396 13.76785509 25.9221793 ]\n",
            "b 150.66690728438655\n",
            "error:  0.664174081642841\n",
            "###################################\n"
          ]
        }
      ],
      "source": [
        "#LOOP TRAIN - PUT YOU CODE HERE\n",
        "D = x.shape[1]\n",
        "w = 2 * np.random.random(D)-1\n",
        "b = 2 * np.random.random()-1\n",
        "#lr = 1e-7\n",
        "\n",
        "lrs = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]\n",
        "\n",
        "for lr in lrs:\n",
        "    print('###################################')\n",
        "    print('Learning Rate = ', lr)\n",
        "    for step in range(4001):\n",
        "        cost = 0\n",
        "        for x_n, y_n in zip(x, y):\n",
        "            y_pred = np.dot(x_n, w) + b\n",
        "            #y_pred = np.where(y_pred > 1, 1, 0) # na regressão, tiramos a função de ativação\n",
        "            error = y_n - y_pred\n",
        "            w = w + lr * np.dot(error, x_n)\n",
        "            b = b + 1e-2 * error\n",
        "            cost += error ** 2\n",
        "        #if step % 1000 == 0:\n",
        "            #print('Step {0}: {1}'.format(step, cost))\n",
        "\n",
        "    print('w', w)\n",
        "    print('b', b)\n",
        "    print('error: ', error)\n",
        "    print('###################################')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i7PclM3rcTr3"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}