ml_pln_01.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOk9WamY+y1dim1LkgTK+bv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LeoSousaJesus/semana_machine_learning/blob/main/ml_pln_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Algebra Linear - Revisão 1\n",
        "\n",
        "1.   A equação linear tem **x¹** (x elevado a 1)\n",
        "2.   \n",
        "\n"
      ],
      "metadata": {
        "id": "imTVF_hpqH4M"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "OiCN1dUbqGli"
      },
      "outputs": [],
      "source": [
        "def verifica_equacao(base, expoente):\n",
        "  msg = \"\"\n",
        "  if expoente == 1:\n",
        "    msg = \"equação linear\"\n",
        "  else:\n",
        "    msg = \"equação não linear\"\n",
        "    return f\" resposta da potência: {pow(base,expoente)} - {msg}\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3 é coeficiente da variável x\n",
        "# 5 (sem letra e variável) é o termo independente\n",
        "# Se o termo independente for igual a 0 = Equação Linear Homogênea\n",
        "# Quando não tem coeficiente é igual a 1"
      ],
      "metadata": {
        "id": "b4rlNCO5uFvU"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# chamar a função (paradigma estruturado)\n",
        "# overflow - estouro de pilha de memória\n",
        "verifica_equacao(5,1)"
      ],
      "metadata": {
        "id": "4tzgFU4jwhaY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POO - Paradigma Orientado a Objetos\n",
        "\n",
        "- Classe\n",
        "- Objeto\n",
        "- Método\n",
        "- Construtor\n",
        "\n",
        "**Biblioteca abc** (Abstract Basics Classes)\n",
        "\n",
        "- Classes Abstratas\n",
        "- Métodos Abstratos\n",
        "- Polimorfismo"
      ],
      "metadata": {
        "id": "vPZtMyTSwt3o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class EquacaoLinear:\n",
        "  # atributo - base e expoente\n",
        "  # atributos do objeto\n",
        "  def __init__(self, base, expoente):\n",
        "    self.base = base\n",
        "    self.expoente = expoente\n",
        "\n",
        "  def verifica_equacao(self):\n",
        "  # variáveis - msg\n",
        "    msg =\"\"\n",
        "    if self.expoente == 1:\n",
        "      msg = \"equação linear\"\n",
        "    else:\n",
        "      msg = \"equação não linear\"\n",
        "    return f\" resposta da potência: {pow(self.base,self.expoente)} - {msg}\""
      ],
      "metadata": {
        "id": "DC_YN2hCx3Nl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "i0UYeiiuuEfE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = EquacaoLinear(6,1)"
      ],
      "metadata": {
        "id": "cRBSChRO0GpJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a.verifica_equacao()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "WK44qP_JARb6",
        "outputId": "5d25875b-e6c7-4a59-a79b-bd472eba8c49"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' resposta da potência: 6 - equação linear'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao = lambda x : pow(x,1)"
      ],
      "metadata": {
        "id": "j9Eq_XkuBBHp"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(verifica_equacao(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kv3BICjAiih",
        "outputId": "925ef21d-9157-4d1b-95c9-fbb303f18ddb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: crie uma lambda desse função:\n",
        "# def __init__(self, base, expoente):\n",
        "#     self.base = base\n",
        "#     self.expoente = expoente\n",
        "#   def verifica_equacao(self):\n",
        "#   # variáveis - msg\n",
        "#     msg =\"\"\n",
        "#     if self.expoente == 1:\n",
        "#       msg = \"equação linear\"\n",
        "#     else:\n",
        "#       msg = \"equação não linear\"\n",
        "#     return f\" resposta da potência: {pow(self.base,self.expoente)} - {msg}\"\n",
        "\n",
        "verifica_equacao_lambda = lambda base, expoente: (\n",
        "    \"equação linear\" if expoente == 1 else \"equação não linear\"\n",
        ")\n",
        "\n",
        "# Exemplo de uso:\n",
        "base = 5\n",
        "expoente = 2\n",
        "resultado = verifica_equacao_lambda(base, expoente)\n",
        "print(resultado)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VtmUZWtBJbI",
        "outputId": "7d3375db-01cf-486a-e33b-387a52f4cb03"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "equação não linear\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao = lambda x, y : pow(x,y)\n",
        "verifica_equacao(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4VM6Z9MEIYh",
        "outputId": "d98520a0-491b-4c52-ac51-634ed00d319e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Gere uma função que calcule a raiz quadrada de x\n",
        "# imortando a bibliotecha\n",
        "import math\n",
        "# criando a função com um parâmetro\n",
        "def raiz_quadrada(x):\n",
        "    return math.sqrt(x)"
      ],
      "metadata": {
        "id": "aUIjobtGFo5z"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"a raiz quadrada de x é: {raiz_quadrada(256)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8VXzs2RGnF7",
        "outputId": "591846f3-4d5f-4309-dd07-f3c60a1a4f49"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a raiz quadrada de x é: 16.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exercício 01: Considere a função criada anteriormente da raiz quadrada e crie duas versões multiparadigmas: uma solução com orientação a objetos e uma classe que terá o nome de Matemática, e outra solução usando lambda e ai você define o nome."
      ],
      "metadata": {
        "id": "JseRp0Y3G1xi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Considere a função criada anteriormente da raiz quadrada e crie duas versões multiparadigma: uma solução com orientação a objetos e uma classe que terá o nome de Matemática, e outra solução usando lambda e ai você define o nome.\n",
        "\n",
        "import math\n",
        "\n",
        "class Matematica:\n",
        "  def raiz_quadrada(self, x):\n",
        "    return math.sqrt(x)\n",
        "\n",
        "# Solução com Orientação a Objetos\n",
        "matematica = Matematica()\n",
        "resultado_oo = matematica.raiz_quadrada(25)\n",
        "print(f\"Resultado: {resultado_oo}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKvaNF9EHjXP",
        "outputId": "1a6808c0-bf32-4c13-9490-bf15022881d7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado: 5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Solução com Lambda\n",
        "raiz_quadrada_lambda = lambda x: math.sqrt(x)\n",
        "resultado_lambda = raiz_quadrada_lambda(25)\n",
        "print(f\"Resultado: {resultado_lambda}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqmksBESHw5W",
        "outputId": "c4f60c34-56dd-4a80-88f9-7d8947f9ef3e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado: 5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Considere a função criada anteriormente da raiz quadrada e crie duas versões multiparadigma: uma solução com orientação a objetos e uma classe que terá o nome de Matemática, e outra solução usando lambda e ai você define o nome.\n",
        "\n",
        "import math\n",
        "\n",
        "class Matematica:\n",
        "  def __init__(self, x):\n",
        "    self.x = x\n",
        "\n",
        "  def raiz_quadrada(self):\n",
        "    return math.sqrt(self.x) # math - biblioteca sqtr\n",
        "  def calcular_fatorial(self):\n",
        "    return math.factorial(self.x)\n",
        "  def angulo(self):\n",
        "    return math.radians(self.x)\n",
        "\n",
        "# Solução com Orientação a Objetos\n",
        "matematica = Matematica(5)\n",
        "resultado_oo = matematica.raiz_quadrada()\n",
        "print(f\"Resultado: {resultado_oo}\")\n",
        "\n",
        "print(f\"O fatorial de x é: {matematica.calcular_fatorial()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gOvwtrENtsE",
        "outputId": "24334e77-e1e7-4d5b-eced-b610ee5fd5ce"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado: 2.23606797749979\n",
            "O fatorial de x é: 120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"O fatorial de x é: {matematica.calcular_fatorial()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlqVBLjTNxpb",
        "outputId": "124f7bf2-fe45-4f8a-ef18-de03a0eabdfe"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O fatorial de x é: 120\n"
          ]
        }
      ]
    }
  ]
}