Bayes_2.ipynb

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    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Neste código, implementamos um classificador Bayesiano multivariado em um problema de classificação. O exercício exige a aplicação deste classificador em dados da base de dados \"heart\" e a variação do tamanho da amostra de treinamento. Vamos seguir as etapas específicas:"
      ],
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        "id": "qMd-hjIgwOzd"
      }
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    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "id": "H6BeD70A-JAF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random as rd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tabulate import tabulate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizamos a função read.csv para carregar os dados da base de dados \"heart\"."
      ],
      "metadata": {
        "id": "ALqxpxtIwRr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "heart = pd.read_csv('/content/drive/MyDrive/UFMG/Reconhecimento de padrões/heart.dat', sep=' ')\n",
        "print(heart.shape)\n",
        "heart"
      ],
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              "    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-31515a11-86ad-4740-b53b-1eb80884799c 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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##90% treino 10% Teste"
      ],
      "metadata": {
        "id": "U4K5vpczr5Va"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizamos a expressão da densidade multivariada vista em sala de aula para estimar as densidades das duas classes. Isso envolve cálculos com base na matriz de covariâncias, vetor de médias e número de características."
      ],
      "metadata": {
        "id": "SKT5IEfYwV5X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pdf_multivariate(x, mean, cov, n):\n",
        "    norm = (1 / np.sqrt(((2 * np.pi) ** n) * np.linalg.det(cov)))\n",
        "    exp_part = -0.5 * np.dot(np.transpose(x - mean), np.linalg.solve(cov, x - mean))\n",
        "    return norm * np.exp(exp_part)"
      ],
      "metadata": {
        "id": "Wu5_QzH1XPdo"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dividimos os dados em conjuntos de treinamento (90%) e teste (10%) usando a função train_test_split."
      ],
      "metadata": {
        "id": "BPDZo698wTVA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xc0 = heart[heart.iloc[:, -1] == 1]\n",
        "xc1 = heart[heart.iloc[:, -1] == 2]\n",
        "\n",
        "xc0_train, xc0_test = train_test_split(xc0, test_size=0.1, random_state=42)\n",
        "xc1_train, xc1_test = train_test_split(xc1, test_size=0.1, random_state=42)"
      ],
      "metadata": {
        "id": "qt_aWk5jjPB-"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Após isso é calculado a matriz de covariância e média"
      ],
      "metadata": {
        "id": "BK7bs4Mywggb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k0 = xc0_train.iloc[:, 0:13].cov()\n",
        "k1 = xc1_train.iloc[:, 0:13].cov()\n",
        "m0 = xc0_train.iloc[:, 0:13].mean()\n",
        "m1 = xc1_train.iloc[:, 0:13].mean()\n",
        "n = 13\n",
        "\n",
        "print(\"Matriz de Covariância - Classe 0:\")\n",
        "print(k0.to_string())\n",
        "\n",
        "print(\"\\nMatriz de Covariância - Classe 1:\")\n",
        "print(k1.to_string())\n",
        "\n",
        "print(\"\\nMédia - Classe 0:\")\n",
        "print(m0.to_string())\n",
        "\n",
        "print(\"\\nMédia - Classe 1:\")\n",
        "print(m1.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ia09-VfajS69",
        "outputId": "0b1c8c29-aea8-4141-c9b8-0418353410e4"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Covariância - Classe 0:\n",
            "             70.0       1.0       4.0       130.0        322.0       0.0        2.0       109.0     0.0.1       2.4     2.0.1       3.0     3.0.1\n",
            "70.0    83.903593 -0.810835  0.692040   40.046324   116.490105  0.425207   0.786954  -90.425760  0.140575  0.843925  0.516639  1.223604  1.661194\n",
            "1.0     -0.810835  0.247872 -0.051244    0.360752    -6.279049  0.031177  -0.026976    1.734439  0.009729  0.006064 -0.006247  0.009674  0.292537\n",
            "4.0      0.692040 -0.051244  0.802985   -2.329353     3.112438 -0.046269  -0.021891   -3.272139  0.048756 -0.060647 -0.006965  0.019900 -0.064179\n",
            "130.0   40.046324  0.360752 -2.329353  260.577888   107.956495  1.085738   2.149696   14.426423 -0.144887  2.330669 -0.142178 -0.210337 -0.523881\n",
            "322.0  116.490105 -6.279049  3.112438  107.956495  3075.800774 -0.984411  12.583527   26.397070  0.497402  1.686987 -1.715810  2.411553 -1.674627\n",
            "0.0      0.425207  0.031177 -0.046269    1.085738    -0.984411  0.132338   0.003151    0.062521 -0.007131 -0.012454 -0.001824  0.048922  0.053731\n",
            "2.0      0.786954 -0.026976 -0.021891    2.149696    12.583527  0.003151   0.973245   -0.097125  0.015644  0.040802  0.094306 -0.039525 -0.211940\n",
            "109.0  -90.425760  1.734439 -3.272139   14.426423    26.397070  0.062521  -0.097125  373.247872 -1.126092 -2.245727 -2.777888 -1.102266 -3.486567\n",
            "0.0.1    0.140575  0.009729  0.048756   -0.144887     0.497402 -0.007131   0.015644   -1.126092  0.121835 -0.007999  0.004035 -0.006910  0.058209\n",
            "2.4      0.843925  0.006064 -0.060647    2.330669     1.686987 -0.012454   0.040802   -2.245727 -0.007999  0.617759  0.235661 -0.011553  0.115373\n",
            "2.0.1    0.516639 -0.006247 -0.006965   -0.142178    -1.715810 -0.001824   0.094306   -2.777888  0.004035  0.235661  0.329795 -0.027916  0.131343\n",
            "3.0      1.223604  0.009674  0.019900   -0.210337     2.411553  0.048922  -0.039525   -1.102266 -0.006910 -0.011553 -0.027916  0.402432  0.089552\n",
            "3.0.1    1.661194  0.292537 -0.064179   -0.523881    -1.674627  0.053731  -0.211940   -3.486567  0.058209  0.115373  0.131343  0.089552  2.489552\n",
            "\n",
            "Matriz de Covariância - Classe 1:\n",
            "            70.0       1.0       4.0       130.0        322.0       0.0       2.0       109.0     0.0.1       2.4     2.0.1       3.0     3.0.1\n",
            "70.0   67.383178 -0.481485 -0.485100   32.220772    51.309998  0.262388  0.786634  -19.360166 -0.174925  0.464239  0.578205  2.890231 -2.921795\n",
            "1.0    -0.481485  0.141245 -0.038353   -2.428760    -4.720332 -0.020190 -0.009081   -0.730030  0.010316 -0.058896 -0.033327 -0.032798  0.136484\n",
            "4.0    -0.485100 -0.038353  0.636925   -2.075913    -2.636748 -0.012520  0.051137   -4.109064  0.137277  0.061947  0.035443  0.102716  0.205255\n",
            "130.0  32.220772 -2.428760 -2.075913  387.800212   205.870922  1.129783  1.684888   13.187445  0.410333  3.455775  2.589402  0.267678  3.381855\n",
            "322.0  51.309998 -4.720332 -2.636748  205.870922  2134.567625  2.464380  4.425851   20.177482  2.828778 -7.514257 -1.035355  4.009081 -7.469318\n",
            "0.0     0.262388 -0.020190 -0.012520    1.129783     2.464380  0.134897  0.030065    0.614001  0.013842 -0.002028  0.022042  0.052989  0.022042\n",
            "2.0     0.786634 -0.009081  0.051137    1.684888     4.425851  0.030065  0.949215    0.992241  0.014548  0.050855  0.035003  0.081467 -0.087639\n",
            "109.0 -19.360166 -0.730030 -4.109064   13.187445    20.177482  0.614001  0.992241  536.287075 -3.481661 -5.975040 -5.068683 -0.627491 -0.285664\n",
            "0.0.1  -0.174925  0.010316  0.137277    0.410333     2.828778  0.013842  0.014548   -3.481661  0.248633  0.093176  0.063922 -0.003879  0.101658\n",
            "2.4     0.464239 -0.058896  0.061947    3.455775    -7.514257 -0.002028  0.050855   -5.975040  0.093176  1.538309  0.430973  0.155369  0.289464\n",
            "2.0.1   0.578205 -0.033327  0.035443    2.589402    -1.035355  0.022042  0.035003   -5.068683  0.063922  0.430973  0.310175  0.027773  0.074326\n",
            "3.0     2.890231 -0.032798  0.102716    0.267678     4.009081  0.052989  0.081467   -0.627491 -0.003879  0.155369  0.027773  1.027332  0.140980\n",
            "3.0.1  -2.921795  0.136484  0.205255    3.381855    -7.469318  0.022042 -0.087639   -0.285664  0.101658  0.289464  0.074326  0.140980  3.159231\n",
            "\n",
            "Média - Classe 0:\n",
            "70.0      52.429630\n",
            "1.0        0.562963\n",
            "4.0        2.866667\n",
            "130.0    128.548148\n",
            "322.0    245.281481\n",
            "0.0        0.155556\n",
            "2.0        0.837037\n",
            "109.0    158.362963\n",
            "0.0.1      0.140741\n",
            "2.4        0.598519\n",
            "2.0.1      1.392593\n",
            "3.0        0.259259\n",
            "3.0.1      3.800000\n",
            "\n",
            "Média - Classe 1:\n",
            "70.0      56.775701\n",
            "1.0        0.831776\n",
            "4.0        3.607477\n",
            "130.0    135.308411\n",
            "322.0    254.869159\n",
            "0.0        0.158879\n",
            "2.0        1.224299\n",
            "109.0    138.700935\n",
            "0.0.1      0.560748\n",
            "2.4        1.542056\n",
            "2.0.1      1.803738\n",
            "3.0        1.140187\n",
            "3.0.1      5.803738\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aplicamos o classificador ao conjunto de teste e calculamos a acurácia. A acurácia é a proporção de classificações corretas no conjunto de testes em relação ao número total de amostras."
      ],
      "metadata": {
        "id": "fhJVWbyowmL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Amostras de teste\n",
        "test_samples = pd.concat([xc0_test, xc1_test])\n",
        "test_samples = test_samples.sample(frac=1)  # Aleatorizar as linhas\n",
        "\n",
        "# Calcular as probabilidades\n",
        "M0 = np.zeros(len(test_samples))\n",
        "M1 = np.zeros(len(test_samples))\n",
        "\n",
        "for i in range(len(test_samples)):\n",
        "    M0[i] = pdf_multivariate(test_samples.iloc[i, 0:13].values, m0, k0, n)\n",
        "    M1[i] = pdf_multivariate(test_samples.iloc[i, 0:13].values, m1, k1, n)\n",
        "    print(f'\\nProbabilidade da Amostra {i+1} ser das classes.')\n",
        "    print(\"\\033[31m\", f'C0: {M0[i]}', \"\\033[0m\", end=\" \")\n",
        "    print(\"\\033[34m\", f'C1: {M1[i]}', \"\\033[0m\", end=\" \")\n",
        "    print('\\n\\n---------------------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "i-JYA0l6kJwL",
        "outputId": "4e04eb36-850c-4d8c-e846-5f32b22627e9"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Probabilidade da Amostra 1 ser das classes.\n",
            "\u001b[31m C0: 1.4486490704759817e-10 \u001b[0m \u001b[34m C1: 3.177532480775281e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 2 ser das classes.\n",
            "\u001b[31m C0: 1.3402534178898638e-11 \u001b[0m \u001b[34m C1: 3.966490301352143e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 3 ser das classes.\n",
            "\u001b[31m C0: 6.164226733059961e-16 \u001b[0m \u001b[34m C1: 1.9342293287667277e-10 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 4 ser das classes.\n",
            "\u001b[31m C0: 7.063324474198588e-15 \u001b[0m \u001b[34m C1: 2.9302944441960104e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 5 ser das classes.\n",
            "\u001b[31m C0: 6.7236646372814725e-15 \u001b[0m \u001b[34m C1: 4.953461465571553e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 6 ser das classes.\n",
            "\u001b[31m C0: 1.9990002009874823e-13 \u001b[0m \u001b[34m C1: 1.7124039274594446e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 7 ser das classes.\n",
            "\u001b[31m C0: 3.9486341649711805e-14 \u001b[0m \u001b[34m C1: 4.196678880000708e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 8 ser das classes.\n",
            "\u001b[31m C0: 4.481202547722381e-14 \u001b[0m \u001b[34m C1: 1.557706151547134e-10 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 9 ser das classes.\n",
            "\u001b[31m C0: 3.267868605673199e-17 \u001b[0m \u001b[34m C1: 8.927285791813109e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 10 ser das classes.\n",
            "\u001b[31m C0: 3.545399640685847e-11 \u001b[0m \u001b[34m C1: 3.220346192046962e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 11 ser das classes.\n",
            "\u001b[31m C0: 1.1972392312482543e-11 \u001b[0m \u001b[34m C1: 5.422847476317955e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 12 ser das classes.\n",
            "\u001b[31m C0: 1.8626814423083064e-11 \u001b[0m \u001b[34m C1: 4.6721039084075936e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 13 ser das classes.\n",
            "\u001b[31m C0: 2.794602194922417e-21 \u001b[0m \u001b[34m C1: 5.358711704780792e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 14 ser das classes.\n",
            "\u001b[31m C0: 1.5228986665065744e-11 \u001b[0m \u001b[34m C1: 1.1293831577892917e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 15 ser das classes.\n",
            "\u001b[31m C0: 6.150600957253154e-13 \u001b[0m \u001b[34m C1: 2.005103238954473e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 16 ser das classes.\n",
            "\u001b[31m C0: 4.005664489053964e-14 \u001b[0m \u001b[34m C1: 1.242790617092458e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 17 ser das classes.\n",
            "\u001b[31m C0: 2.6194585082353573e-13 \u001b[0m \u001b[34m C1: 5.298937976868894e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 18 ser das classes.\n",
            "\u001b[31m C0: 1.7358552844296927e-12 \u001b[0m \u001b[34m C1: 6.9578620248290365e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 19 ser das classes.\n",
            "\u001b[31m C0: 2.8922734029679055e-13 \u001b[0m \u001b[34m C1: 3.1592462593233116e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 20 ser das classes.\n",
            "\u001b[31m C0: 4.550598058631345e-13 \u001b[0m \u001b[34m C1: 1.553187817443792e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 21 ser das classes.\n",
            "\u001b[31m C0: 8.968428615102315e-12 \u001b[0m \u001b[34m C1: 1.459276136797541e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 22 ser das classes.\n",
            "\u001b[31m C0: 2.013019531766034e-21 \u001b[0m \u001b[34m C1: 2.3053105962083277e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 23 ser das classes.\n",
            "\u001b[31m C0: 8.132589809643572e-13 \u001b[0m \u001b[34m C1: 7.369591233584588e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 24 ser das classes.\n",
            "\u001b[31m C0: 4.302540799624771e-15 \u001b[0m \u001b[34m C1: 3.046687472881391e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 25 ser das classes.\n",
            "\u001b[31m C0: 8.149290930712678e-12 \u001b[0m \u001b[34m C1: 1.3193234570448083e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 26 ser das classes.\n",
            "\u001b[31m C0: 3.1800287346376725e-11 \u001b[0m \u001b[34m C1: 5.3568188073414283e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 27 ser das classes.\n",
            "\u001b[31m C0: 4.9404791557093526e-14 \u001b[0m \u001b[34m C1: 1.0068734094381624e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificar de acordo com a regra bayesiana\n",
        "prob0 = len(xc0) / (len(xc0) + len(xc1))\n",
        "prob1 = len(xc1) / (len(xc0) + len(xc1))\n",
        "M0 = M0 * prob0\n",
        "M1 = M1 * prob1\n",
        "M = M0 / M1\n",
        "\n",
        "for elemento in np.nditer(M):\n",
        "    if elemento > 1:\n",
        "        print(\"\\n\\033[34m\", elemento, \"\\033[0m\", end=\" \")  # Azul\n",
        "    else:\n",
        "        print(\"\\n\\033[31m\", elemento, \"\\033[0m\", end=\" \")  # Vermelho"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "f7aGSbgVmrLW",
        "outputId": "19ea99ad-0cc8-4533-cb42-cf7c48ed7350"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[34m 574.6686308145136 \u001b[0m \n",
            "\u001b[34m 4.259168524857239 \u001b[0m \n",
            "\u001b[31m 4.017121228375029e-06 \u001b[0m \n",
            "\u001b[34m 3.038380731737036 \u001b[0m \n",
            "\u001b[31m 0.00017109666815601525 \u001b[0m \n",
            "\u001b[34m 1.471468332967197 \u001b[0m \n",
            "\u001b[34m 118.60020978886968 \u001b[0m \n",
            "\u001b[31m 0.0003626213220237949 \u001b[0m \n",
            "\u001b[31m 0.004614125955023152 \u001b[0m \n",
            "\u001b[34m 138773.6248592892 \u001b[0m \n",
            "\u001b[34m 2.7829015807577893 \u001b[0m \n",
            "\u001b[31m 0.5025397188185574 \u001b[0m \n",
            "\u001b[31m 6.573609484501713e-09 \u001b[0m \n",
            "\u001b[34m 16997.06742238822 \u001b[0m \n",
            "\u001b[34m 386.65631768274255 \u001b[0m \n",
            "\u001b[31m 0.4062757514848498 \u001b[0m \n",
            "\u001b[34m 62.311324838527135 \u001b[0m \n",
            "\u001b[31m 0.31447201334622465 \u001b[0m \n",
            "\u001b[31m 0.11539849943926782 \u001b[0m \n",
            "\u001b[31m 0.3693080713512808 \u001b[0m \n",
            "\u001b[34m 774.6814784910069 \u001b[0m \n",
            "\u001b[31m 1.1006844726388629e-06 \u001b[0m \n",
            "\u001b[34m 1391.0084427593074 \u001b[0m \n",
            "\u001b[34m 17.80087653920215 \u001b[0m \n",
            "\u001b[34m 77.85971972248996 \u001b[0m \n",
            "\u001b[34m 748.2873185821705 \u001b[0m \n",
            "\u001b[34m 618.4982814833074 \u001b[0m "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  # Classificar as amostras\n",
        "  M[M >= 1] = 1\n",
        "  M[M < 1] = 2\n",
        "\n",
        "  for elemento in np.nditer(M):\n",
        "      if elemento > 1:\n",
        "          print(\"\\n\\033[31m\", elemento, \"\\033[0m\", end=\" \")  # Vermelho\n",
        "      else:\n",
        "          print(\"\\n\\033[34m\", elemento, \"\\033[0m\", end=\" \")  # Azul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "D6zkHRF9pZT6",
        "outputId": "672d42cf-0462-49c4-fee7-b2c744bda860"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar quantidade de erros e acertos\n",
        "truthMatrix = np.zeros((len(test_samples), 4))\n",
        "\n",
        "for i in range(len(M)):\n",
        "    if M[i] == 1 and test_samples.iloc[i, 13] == 1:\n",
        "        truthMatrix[i, 0] = 1\n",
        "    elif M[i] == 2 and test_samples.iloc[i, 13] == 2:\n",
        "        truthMatrix[i, 1] = 1\n",
        "    elif M[i] == 2 and test_samples.iloc[i, 13] == 1:\n",
        "        truthMatrix[i, 2] = 1\n",
        "    else:\n",
        "        truthMatrix[i, 3] = 1\n",
        "\n",
        "right0 = round(np.sum(truthMatrix[:, 0]) / len(test_samples) * 100, 2)\n",
        "right1 = round(np.sum(truthMatrix[:, 1]) / len(test_samples) * 100, 2)\n",
        "wrong0 = round(np.sum(truthMatrix[:, 2]) / len(test_samples) * 100, 2)\n",
        "wrong1 = round(np.sum(truthMatrix[:, 3]) / len(test_samples) * 100, 2)\n",
        "\n",
        "train_teste = {\n",
        "    '90 treino 10 teste': right0+right1,\n",
        "    '70 treino 30 teste': [],\n",
        "    '20 treino 80 teste': []\n",
        "}\n",
        "\n",
        "data = {\n",
        "    'classe 0': [f'{round(right0,2)}%', f'{round(wrong0,2)}%'],\n",
        "    'classe 1': [f'{round(right1,2)}%', f'{round(wrong1,2)}%'],\n",
        "    'Total': [f'{round(right0+right1,2)}%', f'{round(wrong0+wrong1,2)}%']\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data, index=['acertos (%)', 'erro (%)'])\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lHQYFg6cg_Ot",
        "outputId": "d413dbd2-f5cf-4492-8f10-5466235da90a"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            classe 0 classe 1   Total\n",
            "acertos (%)   48.15%   33.33%  81.48%\n",
            "erro (%)       7.41%   11.11%  18.52%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##70% treino 30% Teste\n"
      ],
      "metadata": {
        "id": "GdPz2OSgr_xD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Repetimos os Passos anteriores, variando o tamanho da amostra de treinamento para 70% e 20%. Isso envolve executar novamente as etapas de divisão de dados, estimativa de densidade e aplicação do classificador."
      ],
      "metadata": {
        "id": "TW85kkL0wtGl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xc0 = heart[heart.iloc[:, -1] == 1]\n",
        "xc1 = heart[heart.iloc[:, -1] == 2]\n",
        "\n",
        "xc0_train, xc0_test = train_test_split(xc0, test_size=0.3, random_state=42)\n",
        "xc1_train, xc1_test = train_test_split(xc1, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "wRO4SxvOr_xE"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular as matrizes de covariância e médias\n",
        "k0 = xc0_train.iloc[:, 0:13].cov()\n",
        "k1 = xc1_train.iloc[:, 0:13].cov()\n",
        "m0 = xc0_train.iloc[:, 0:13].mean()\n",
        "m1 = xc1_train.iloc[:, 0:13].mean()\n",
        "n = 13\n",
        "\n",
        "print(\"Matriz de Covariância - Classe 0:\")\n",
        "print(k0.to_string())\n",
        "\n",
        "print(\"\\nMatriz de Covariância - Classe 1:\")\n",
        "print(k1.to_string())\n",
        "\n",
        "print(\"\\nMédia - Classe 0:\")\n",
        "print(m0.to_string())\n",
        "\n",
        "print(\"\\nMédia - Classe 1:\")\n",
        "print(m1.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d3fafda5-20cf-44cc-fcc7-ebf9504edb2c",
        "id": "BdxDrTWBr_xF"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Covariância - Classe 0:\n",
            "             70.0       1.0       4.0       130.0        322.0       0.0        2.0       109.0     0.0.1       2.4     2.0.1       3.0     3.0.1\n",
            "70.0    92.640659 -0.790110  0.841484   46.201374   150.417033  0.310440   1.352473  -90.002198  0.297802  0.659863  0.407967  1.127473  1.791209\n",
            "1.0     -0.790110  0.245788 -0.061081   -0.252473    -8.466300  0.031593  -0.043681    0.967674  0.006777 -0.011227 -0.002289  0.028755  0.304029\n",
            "4.0      0.841484 -0.061081  0.705678   -1.502198     3.152106 -0.046703  -0.013187   -2.944689  0.059799  0.021822  0.038919  0.003480  0.014194\n",
            "130.0   46.201374 -0.252473 -1.502198  242.316484   129.971703  0.773352   2.648901   21.198626 -0.351374  0.548929 -0.940934  0.412363 -3.784341\n",
            "322.0  150.417033 -8.466300  3.152106  129.971703  3598.172711 -0.243132  14.637912   71.854762  0.657326  0.273471 -1.866300  3.587271 -2.518773\n",
            "0.0      0.310440  0.031593 -0.046703    0.773352    -0.243132  0.123626   0.015110    0.526099 -0.012363 -0.005220  0.012363  0.072802  0.075549\n",
            "2.0      1.352473 -0.043681 -0.013187    2.648901    14.637912  0.015110   0.970330   -1.414011  0.026374  0.067610  0.075549 -0.016209 -0.215659\n",
            "109.0  -90.002198  0.967674 -2.944689   21.198626    71.854762  0.526099  -1.414011  344.461172 -1.351007 -1.259734 -2.738095 -1.459524 -4.794414\n",
            "0.0.1    0.297802  0.006777  0.059799   -0.351374     0.657326 -0.012363   0.026374   -1.351007  0.130403 -0.015760 -0.000916 -0.006960  0.058150\n",
            "2.4      0.659863 -0.011227  0.021822    0.548929     0.273471 -0.005220   0.067610   -1.259734 -0.015760  0.558832  0.215888  0.003059  0.021383\n",
            "2.0.1    0.407967 -0.002289  0.038919   -0.940934    -1.866300  0.012363   0.075549   -2.738095 -0.000916  0.215888  0.315018 -0.017399  0.121337\n",
            "3.0      1.127473  0.028755  0.003480    0.412363     3.587271  0.072802  -0.016209   -1.459524 -0.006960  0.003059 -0.017399  0.440842  0.143315\n",
            "3.0.1    1.791209  0.304029  0.014194   -3.784341    -2.518773  0.075549  -0.215659   -4.794414  0.058150  0.021383  0.121337  0.143315  2.521062\n",
            "\n",
            "Matriz de Covariância - Classe 1:\n",
            "            70.0       1.0       4.0       130.0        322.0       0.0       2.0       109.0     0.0.1       2.4     2.0.1       3.0      3.0.1\n",
            "70.0   74.139877 -0.453864 -0.734646   37.267852    68.659271  0.164855  0.866148  -24.272554  0.050691  0.333808  0.887599  3.812371  -3.740523\n",
            "1.0    -0.453864  0.141934 -0.033353   -2.342198    -4.604467 -0.017925 -0.019835   -0.096532  0.023509 -0.102189 -0.040993 -0.048487   0.131649\n",
            "4.0    -0.734646 -0.033353  0.635028   -1.137673    -4.908757 -0.022627  0.075522   -4.460917  0.125037 -0.005143 -0.008963  0.084337   0.222157\n",
            "130.0  37.267852 -2.342198 -1.137673  350.501910   214.013077  1.127094  1.490597  -27.497796 -0.134293  5.802277  2.816339  1.516309   2.952836\n",
            "322.0  68.659271 -4.604467 -4.908757  214.013077  1989.096092  3.560535  0.023803  -41.369527  2.008375 -8.835909 -0.470320  3.936233 -12.397003\n",
            "0.0     0.164855 -0.017925 -0.022627    1.127094     3.560535  0.149868  0.041287    0.590361  0.006171 -0.013944  0.030855  0.034528   0.005289\n",
            "2.0     0.866148 -0.019835  0.075522    1.490597     0.023803  0.041287  0.941228   -1.038055  0.032618  0.085880  0.065530  0.050103  -0.163679\n",
            "109.0 -24.272554 -0.096532 -4.460917  -27.497796   -41.369527  0.590361 -1.038055  505.228622 -3.643697 -5.267823 -4.877020 -2.805760  -2.797385\n",
            "0.0.1   0.050691  0.023509  0.125037   -0.134293     2.008375  0.006171  0.032618   -3.643697  0.248604  0.047855  0.035704 -0.021892   0.070232\n",
            "2.4     0.333808 -0.102189 -0.005143    5.802277    -8.835909 -0.013944  0.085880   -5.267823  0.047855  1.574470  0.430738  0.166162   0.112621\n",
            "2.0.1   0.887599 -0.040993 -0.008963    2.816339    -0.470320  0.030855  0.065530   -4.877020  0.035704  0.430738  0.312665  0.049074   0.034088\n",
            "3.0     3.812371 -0.048487  0.084337    1.516309     3.936233  0.034528  0.050103   -2.805760 -0.021892  0.166162  0.049074  1.027623  -0.051866\n",
            "3.0.1  -3.740523  0.131649  0.222157    2.952836   -12.397003  0.005289 -0.163679   -2.797385  0.070232  0.112621  0.034088 -0.051866   3.227446\n",
            "\n",
            "Média - Classe 0:\n",
            "70.0      52.314286\n",
            "1.0        0.580952\n",
            "4.0        2.923810\n",
            "130.0    126.971429\n",
            "322.0    248.352381\n",
            "0.0        0.142857\n",
            "2.0        0.828571\n",
            "109.0    158.219048\n",
            "0.0.1      0.152381\n",
            "2.4        0.596190\n",
            "2.0.1      1.380952\n",
            "3.0        0.295238\n",
            "3.0.1      3.809524\n",
            "\n",
            "Média - Classe 1:\n",
            "70.0      56.698795\n",
            "1.0        0.831325\n",
            "4.0        3.590361\n",
            "130.0    134.638554\n",
            "322.0    249.602410\n",
            "0.0        0.180723\n",
            "2.0        1.240964\n",
            "109.0    138.506024\n",
            "0.0.1      0.566265\n",
            "2.4        1.622892\n",
            "2.0.1      1.831325\n",
            "3.0        1.144578\n",
            "3.0.1      5.771084\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Amostras de teste\n",
        "test_samples = pd.concat([xc0_test, xc1_test])\n",
        "test_samples = test_samples.sample(frac=1)  # Aleatorizar as linhas\n",
        "\n",
        "# Calcular as probabilidades\n",
        "M0 = np.zeros(len(test_samples))\n",
        "M1 = np.zeros(len(test_samples))\n",
        "\n",
        "for i in range(len(test_samples)):\n",
        "    M0[i] = pdf_multivariate(test_samples.iloc[i, 0:13].values, m0, k0, n)\n",
        "    M1[i] = pdf_multivariate(test_samples.iloc[i, 0:13].values, m1, k1, n)\n",
        "    print(f'\\nProbabilidade da Amostra {i+1} ser das classes.')\n",
        "    print(\"\\033[31m\", f'C0: {M0[i]}', \"\\033[0m\", end=\" \")\n",
        "    print(\"\\033[34m\", f'C1: {M1[i]}', \"\\033[0m\", end=\" \")\n",
        "    print('\\n\\n---------------------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8956f517-0524-47c4-839c-53717dd96cae",
        "id": "5xdeO21tr_xF"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Probabilidade da Amostra 1 ser das classes.\n",
            "\u001b[31m C0: 8.283910005198094e-11 \u001b[0m \u001b[34m C1: 1.4802308168439429e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 2 ser das classes.\n",
            "\u001b[31m C0: 1.045074187434933e-11 \u001b[0m \u001b[34m C1: 4.9458390570468215e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 3 ser das classes.\n",
            "\u001b[31m C0: 1.464949764893025e-14 \u001b[0m \u001b[34m C1: 2.8715816312110426e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 4 ser das classes.\n",
            "\u001b[31m C0: 1.1359718659593743e-17 \u001b[0m \u001b[34m C1: 9.629601674842097e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 5 ser das classes.\n",
            "\u001b[31m C0: 1.95410597024715e-19 \u001b[0m \u001b[34m C1: 1.4806897692469157e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 6 ser das classes.\n",
            "\u001b[31m C0: 1.195379601396653e-13 \u001b[0m \u001b[34m C1: 1.781071933504194e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 7 ser das classes.\n",
            "\u001b[31m C0: 9.402221769518844e-12 \u001b[0m \u001b[34m C1: 3.9060246021439804e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 8 ser das classes.\n",
            "\u001b[31m C0: 9.813709088137622e-11 \u001b[0m \u001b[34m C1: 2.9757709990517147e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 9 ser das classes.\n",
            "\u001b[31m C0: 1.2409665551709422e-15 \u001b[0m \u001b[34m C1: 7.552969366523738e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 10 ser das classes.\n",
            "\u001b[31m C0: 4.66754475228611e-14 \u001b[0m \u001b[34m C1: 3.43693063490046e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 11 ser das classes.\n",
            "\u001b[31m C0: 1.0292939880920299e-11 \u001b[0m \u001b[34m C1: 3.297187529664002e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 12 ser das classes.\n",
            "\u001b[31m C0: 4.725521135167439e-13 \u001b[0m \u001b[34m C1: 3.202971351917572e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 13 ser das classes.\n",
            "\u001b[31m C0: 3.1745900495637267e-17 \u001b[0m \u001b[34m C1: 2.5514394282816815e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 14 ser das classes.\n",
            "\u001b[31m C0: 1.518557625554028e-15 \u001b[0m \u001b[34m C1: 2.5623381502955514e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 15 ser das classes.\n",
            "\u001b[31m C0: 1.2832714548082759e-23 \u001b[0m \u001b[34m C1: 2.7889116224870057e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 16 ser das classes.\n",
            "\u001b[31m C0: 1.7665823856749508e-14 \u001b[0m \u001b[34m C1: 5.5491409309890796e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 17 ser das classes.\n",
            "\u001b[31m C0: 1.867734332156858e-11 \u001b[0m \u001b[34m C1: 2.8770502269259625e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 18 ser das classes.\n",
            "\u001b[31m C0: 1.3584406192680647e-13 \u001b[0m \u001b[34m C1: 6.967476643952052e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 19 ser das classes.\n",
            "\u001b[31m C0: 3.23314027499803e-17 \u001b[0m \u001b[34m C1: 6.566064307176664e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 20 ser das classes.\n",
            "\u001b[31m C0: 1.972677137634047e-13 \u001b[0m \u001b[34m C1: 6.3955143750853e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 21 ser das classes.\n",
            "\u001b[31m C0: 4.9081889102809786e-11 \u001b[0m \u001b[34m C1: 2.5990198171882578e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 22 ser das classes.\n",
            "\u001b[31m C0: 1.364704987610156e-13 \u001b[0m \u001b[34m C1: 4.1816253494869526e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 23 ser das classes.\n",
            "\u001b[31m C0: 2.4620585507227244e-19 \u001b[0m \u001b[34m C1: 5.025464519741039e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 24 ser das classes.\n",
            "\u001b[31m C0: 3.391105573571957e-13 \u001b[0m \u001b[34m C1: 1.2323996918893078e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 25 ser das classes.\n",
            "\u001b[31m C0: 3.1418166692794215e-14 \u001b[0m \u001b[34m C1: 1.5000464443496686e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 26 ser das classes.\n",
            "\u001b[31m C0: 2.0988191962720466e-11 \u001b[0m \u001b[34m C1: 3.96179800571518e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 27 ser das classes.\n",
            "\u001b[31m C0: 8.519571349461966e-12 \u001b[0m \u001b[34m C1: 1.400906686480889e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 28 ser das classes.\n",
            "\u001b[31m C0: 3.483850617767561e-21 \u001b[0m \u001b[34m C1: 1.3958252470461698e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 29 ser das classes.\n",
            "\u001b[31m C0: 4.0747900035000174e-11 \u001b[0m \u001b[34m C1: 5.367233799690577e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 30 ser das classes.\n",
            "\u001b[31m C0: 4.289859297653201e-17 \u001b[0m \u001b[34m C1: 1.687144793460256e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 31 ser das classes.\n",
            "\u001b[31m C0: 1.6998713713127593e-15 \u001b[0m \u001b[34m C1: 8.308350548152466e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 32 ser das classes.\n",
            "\u001b[31m C0: 1.3581294951688406e-14 \u001b[0m \u001b[34m C1: 2.3085565320726922e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 33 ser das classes.\n",
            "\u001b[31m C0: 8.518261800905179e-16 \u001b[0m \u001b[34m C1: 5.212032453451581e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 34 ser das classes.\n",
            "\u001b[31m C0: 5.626251487647918e-15 \u001b[0m \u001b[34m C1: 4.163580042189466e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 35 ser das classes.\n",
            "\u001b[31m C0: 8.758586786115165e-14 \u001b[0m \u001b[34m C1: 1.083988810554727e-10 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 36 ser das classes.\n",
            "\u001b[31m C0: 6.654387285477693e-17 \u001b[0m \u001b[34m C1: 2.5134352422470334e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 37 ser das classes.\n",
            "\u001b[31m C0: 1.5142645063737986e-11 \u001b[0m \u001b[34m C1: 5.94806353914389e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 38 ser das classes.\n",
            "\u001b[31m C0: 2.0488892193451812e-10 \u001b[0m \u001b[34m C1: 1.085887980940862e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 39 ser das classes.\n",
            "\u001b[31m C0: 2.58699599263052e-11 \u001b[0m \u001b[34m C1: 1.312289067689504e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 40 ser das classes.\n",
            "\u001b[31m C0: 8.29700330914372e-12 \u001b[0m \u001b[34m C1: 3.824462349994822e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 41 ser das classes.\n",
            "\u001b[31m C0: 9.95245883879091e-11 \u001b[0m \u001b[34m C1: 4.0752589095662635e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 42 ser das classes.\n",
            "\u001b[31m C0: 1.879320449032907e-15 \u001b[0m \u001b[34m C1: 2.7191280011939245e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 43 ser das classes.\n",
            "\u001b[31m C0: 2.0567856295416885e-10 \u001b[0m \u001b[34m C1: 6.462071657027346e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 44 ser das classes.\n",
            "\u001b[31m C0: 1.4300393938496656e-15 \u001b[0m \u001b[34m C1: 1.4191362314894561e-10 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 45 ser das classes.\n",
            "\u001b[31m C0: 8.223012878779696e-11 \u001b[0m \u001b[34m C1: 1.6889803794468177e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 46 ser das classes.\n",
            "\u001b[31m C0: 3.1085151374015323e-16 \u001b[0m \u001b[34m C1: 4.405028664161826e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 47 ser das classes.\n",
            "\u001b[31m C0: 2.222879439443194e-14 \u001b[0m \u001b[34m C1: 2.270838542505289e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 48 ser das classes.\n",
            "\u001b[31m C0: 2.0500308308732869e-13 \u001b[0m \u001b[34m C1: 1.2158080095461431e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 49 ser das classes.\n",
            "\u001b[31m C0: 1.9320592194826594e-12 \u001b[0m \u001b[34m C1: 3.59846817330484e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 50 ser das classes.\n",
            "\u001b[31m C0: 4.758601938251345e-14 \u001b[0m \u001b[34m C1: 6.87097745407744e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 51 ser das classes.\n",
            "\u001b[31m C0: 4.334131620521631e-10 \u001b[0m \u001b[34m C1: 8.331320023204594e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 52 ser das classes.\n",
            "\u001b[31m C0: 1.2107646496671848e-11 \u001b[0m \u001b[34m C1: 2.8239318729148226e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 53 ser das classes.\n",
            "\u001b[31m C0: 1.0685181828178775e-10 \u001b[0m \u001b[34m C1: 1.0186899182780451e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 54 ser das classes.\n",
            "\u001b[31m C0: 5.709226962129765e-13 \u001b[0m \u001b[34m C1: 3.9635213963850107e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 55 ser das classes.\n",
            "\u001b[31m C0: 2.9309496895678754e-11 \u001b[0m \u001b[34m C1: 2.3704382720389463e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 56 ser das classes.\n",
            "\u001b[31m C0: 2.7046649421121567e-12 \u001b[0m \u001b[34m C1: 3.8631129573583794e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 57 ser das classes.\n",
            "\u001b[31m C0: 8.018236860618929e-11 \u001b[0m \u001b[34m C1: 3.017209499742955e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 58 ser das classes.\n",
            "\u001b[31m C0: 1.3438010364624227e-10 \u001b[0m \u001b[34m C1: 1.3494931325280798e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 59 ser das classes.\n",
            "\u001b[31m C0: 1.57266777443131e-11 \u001b[0m \u001b[34m C1: 1.0911967565013148e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 60 ser das classes.\n",
            "\u001b[31m C0: 1.3430881890388658e-11 \u001b[0m \u001b[34m C1: 6.479488523015297e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 61 ser das classes.\n",
            "\u001b[31m C0: 2.1374628385740367e-13 \u001b[0m \u001b[34m C1: 1.1007609720550292e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 62 ser das classes.\n",
            "\u001b[31m C0: 5.724129540972795e-11 \u001b[0m \u001b[34m C1: 8.250604226783555e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 63 ser das classes.\n",
            "\u001b[31m C0: 7.478656568737128e-11 \u001b[0m \u001b[34m C1: 2.588896478313348e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 64 ser das classes.\n",
            "\u001b[31m C0: 1.957578907908796e-13 \u001b[0m \u001b[34m C1: 5.6997387332386284e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 65 ser das classes.\n",
            "\u001b[31m C0: 1.0379833053245469e-14 \u001b[0m \u001b[34m C1: 4.401898147639965e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 66 ser das classes.\n",
            "\u001b[31m C0: 5.798708806775602e-13 \u001b[0m \u001b[34m C1: 9.816027353707444e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 67 ser das classes.\n",
            "\u001b[31m C0: 2.2327040044997933e-12 \u001b[0m \u001b[34m C1: 9.912316511993543e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 68 ser das classes.\n",
            "\u001b[31m C0: 6.8684144500355756e-12 \u001b[0m \u001b[34m C1: 2.211534297930584e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 69 ser das classes.\n",
            "\u001b[31m C0: 1.5504170366325967e-14 \u001b[0m \u001b[34m C1: 1.076452014277943e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 70 ser das classes.\n",
            "\u001b[31m C0: 2.3422319108042186e-13 \u001b[0m \u001b[34m C1: 1.0747787581058562e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 71 ser das classes.\n",
            "\u001b[31m C0: 1.7013249412919628e-13 \u001b[0m \u001b[34m C1: 3.3064695342232826e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 72 ser das classes.\n",
            "\u001b[31m C0: 3.490397144382218e-15 \u001b[0m \u001b[34m C1: 5.877196577540623e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 73 ser das classes.\n",
            "\u001b[31m C0: 7.540949228021182e-15 \u001b[0m \u001b[34m C1: 1.8345152818885126e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 74 ser das classes.\n",
            "\u001b[31m C0: 1.436437393437268e-18 \u001b[0m \u001b[34m C1: 1.0443735801348684e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 75 ser das classes.\n",
            "\u001b[31m C0: 2.1547295004260503e-15 \u001b[0m \u001b[34m C1: 9.211146327920382e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 76 ser das classes.\n",
            "\u001b[31m C0: 3.1837145001679494e-14 \u001b[0m \u001b[34m C1: 5.465262480373103e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 77 ser das classes.\n",
            "\u001b[31m C0: 8.601589282548436e-15 \u001b[0m \u001b[34m C1: 1.3965931626046893e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 78 ser das classes.\n",
            "\u001b[31m C0: 3.34937306253665e-11 \u001b[0m \u001b[34m C1: 4.315588084934416e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 79 ser das classes.\n",
            "\u001b[31m C0: 1.0382056603370716e-11 \u001b[0m \u001b[34m C1: 8.745988514398729e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 80 ser das classes.\n",
            "\u001b[31m C0: 2.831576282768974e-16 \u001b[0m \u001b[34m C1: 3.327511416020944e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 81 ser das classes.\n",
            "\u001b[31m C0: 1.4777291492203555e-11 \u001b[0m \u001b[34m C1: 1.532554378575036e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificar de acordo com a regra bayesiana\n",
        "prob0 = len(xc0) / (len(xc0) + len(xc1))\n",
        "prob1 = len(xc1) / (len(xc0) + len(xc1))\n",
        "M0 = M0 * prob0\n",
        "M1 = M1 * prob1\n",
        "M = M0 / M1\n",
        "\n",
        "for elemento in np.nditer(M):\n",
        "    if elemento > 1:\n",
        "        print(\"\\n\\033[34m\", elemento, \"\\033[0m\", end=\" \")  # Azul\n",
        "    else:\n",
        "        print(\"\\n\\033[31m\", elemento, \"\\033[0m\", end=\" \")  # Vermelho"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d53c282e-20d2-4e41-be62-0337903f92d0",
        "id": "9xLnH753r_xF"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[34m 70542.39953036745 \u001b[0m \n",
            "\u001b[31m 0.2663492259119981 \u001b[0m \n",
            "\u001b[34m 64.30516596945942 \u001b[0m \n",
            "\u001b[31m 0.00014869746001786775 \u001b[0m \n",
            "\u001b[31m 1.6635211758629165e-05 \u001b[0m \n",
            "\u001b[31m 0.008459967179423863 \u001b[0m \n",
            "\u001b[34m 3.03416932886362 \u001b[0m \n",
            "\u001b[34m 415.6980340090442 \u001b[0m \n",
            "\u001b[31m 0.00020710312474868508 \u001b[0m \n",
            "\u001b[34m 1.7118354708836336 \u001b[0m \n",
            "\u001b[34m 39349.57854480523 \u001b[0m \n",
            "\u001b[34m 185.96917023448506 \u001b[0m \n",
            "\u001b[31m 0.01568363352754049 \u001b[0m \n",
            "\u001b[34m 7.470318729338246 \u001b[0m \n",
            "\u001b[31m 5.800001146111032e-11 \u001b[0m \n",
            "\u001b[31m 0.40128454970802824 \u001b[0m \n",
            "\u001b[34m 818.2988782307236 \u001b[0m \n",
            "\u001b[31m 0.024575900226481963 \u001b[0m \n",
            "\u001b[31m 0.0006206742289751337 \u001b[0m \n",
            "\u001b[34m 388.79872277887796 \u001b[0m \n",
            "\u001b[34m 23804.330783228957 \u001b[0m \n",
            "\u001b[34m 41.13750580616283 \u001b[0m \n",
            "\u001b[31m 6.175419477699825e-05 \u001b[0m \n",
            "\u001b[31m 0.34684387313317566 \u001b[0m \n",
            "\u001b[34m 26.40100329862804 \u001b[0m \n",
            "\u001b[34m 6.677701416509726 \u001b[0m \n",
            "\u001b[34m 766.5717914082911 \u001b[0m \n",
            "\u001b[31m 3.146101813974827e-05 \u001b[0m \n",
            "\u001b[34m 9.569715261284655 \u001b[0m \n",
            "\u001b[31m 3.205051333039686e-06 \u001b[0m \n",
            "\u001b[31m 2.5789655761851453e-05 \u001b[0m \n",
            "\u001b[31m 0.7415577272217602 \u001b[0m \n",
            "\u001b[31m 0.20600993733157494 \u001b[0m \n",
            "\u001b[34m 17.033210765807617 \u001b[0m \n",
            "\u001b[31m 0.0010184824176398266 \u001b[0m \n",
            "\u001b[31m 0.033372187164274154 \u001b[0m \n",
            "\u001b[31m 0.32090053513698824 \u001b[0m \n",
            "\u001b[34m 23.783608577425493 \u001b[0m \n",
            "\u001b[34m 248490.93075073807 \u001b[0m \n",
            "\u001b[34m 27.34608573816464 \u001b[0m \n",
            "\u001b[34m 307.83605316220473 \u001b[0m \n",
            "\u001b[31m 0.0008711952218763684 \u001b[0m \n",
            "\u001b[34m 4012.005848270577 \u001b[0m \n",
            "\u001b[31m 1.2701886010087242e-05 \u001b[0m \n",
            "\u001b[34m 6.1369228502055915 \u001b[0m \n",
            "\u001b[31m 0.0889505310955375 \u001b[0m \n",
            "\u001b[34m 12.33882911885268 \u001b[0m \n",
            "\u001b[31m 0.021253951739101662 \u001b[0m \n",
            "\u001b[34m 67.67792979580878 \u001b[0m \n",
            "\u001b[31m 0.0008729817231072533 \u001b[0m \n",
            "\u001b[34m 65.57413594830548 \u001b[0m \n",
            "\u001b[34m 5.404428990620863 \u001b[0m \n",
            "\u001b[34m 132.21605857166352 \u001b[0m \n",
            "\u001b[34m 1815.6845528011606 \u001b[0m \n",
            "\u001b[34m 1558.5617403304689 \u001b[0m \n",
            "\u001b[34m 88.25114774800804 \u001b[0m \n",
            "\u001b[34m 334.9791008460649 \u001b[0m \n",
            "\u001b[34m 125.51874565752786 \u001b[0m \n",
            "\u001b[34m 181.6679096329471 \u001b[0m \n",
            "\u001b[34m 2.6128116432303448 \u001b[0m \n",
            "\u001b[34m 2.4476529940272784 \u001b[0m \n",
            "\u001b[34m 8745.16476494898 \u001b[0m \n",
            "\u001b[34m 36.412726838585186 \u001b[0m \n",
            "\u001b[31m 0.004329209730528173 \u001b[0m \n",
            "\u001b[31m 0.00029723139285661574 \u001b[0m \n",
            "\u001b[34m 7.446288148843324 \u001b[0m \n",
            "\u001b[31m 0.28392281212731774 \u001b[0m \n",
            "\u001b[34m 39.14777754636403 \u001b[0m \n",
            "\u001b[34m 1815.507949366034 \u001b[0m \n",
            "\u001b[31m 0.027469775920045514 \u001b[0m \n",
            "\u001b[31m 0.06485852099122424 \u001b[0m \n",
            "\u001b[31m 0.7485984530177299 \u001b[0m \n",
            "\u001b[34m 51.81422188424695 \u001b[0m \n",
            "\u001b[31m 0.00017337046860617516 \u001b[0m \n",
            "\u001b[31m 0.00029486510061615094 \u001b[0m \n",
            "\u001b[31m 0.07342896189936364 \u001b[0m \n",
            "\u001b[31m 0.7763420101214333 \u001b[0m \n",
            "\u001b[34m 97.82904983591985 \u001b[0m \n",
            "\u001b[34m 149.63003837805798 \u001b[0m \n",
            "\u001b[34m 10726.37582736746 \u001b[0m \n",
            "\u001b[34m 1215.4112294991978 \u001b[0m "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificar as amostras\n",
        "M[M >= 1] = 1\n",
        "M[M < 1] = 2\n",
        "\n",
        "for elemento in np.nditer(M):\n",
        "    if elemento > 1:\n",
        "        print(\"\\n\\033[31m\", elemento, \"\\033[0m\", end=\" \")  # Vermelho\n",
        "    else:\n",
        "        print(\"\\n\\033[34m\", elemento, \"\\033[0m\", end=\" \")  # Azul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "82dd30c3-fe11-4e84-fe9d-feec4b114db4",
        "id": "xA23ByINr_xF"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar quantidade de erros e acertos\n",
        "truthMatrix = np.zeros((len(test_samples), 4))\n",
        "\n",
        "for i in range(len(M)):\n",
        "    if M[i] == 1 and test_samples.iloc[i, 13] == 1:\n",
        "        truthMatrix[i, 0] = 1\n",
        "    elif M[i] == 2 and test_samples.iloc[i, 13] == 2:\n",
        "        truthMatrix[i, 1] = 1\n",
        "    elif M[i] == 2 and test_samples.iloc[i, 13] == 1:\n",
        "        truthMatrix[i, 2] = 1\n",
        "    else:\n",
        "        truthMatrix[i, 3] = 1\n",
        "\n",
        "right0 = round(np.sum(truthMatrix[:, 0]) / len(test_samples) * 100, 2)\n",
        "right1 = round(np.sum(truthMatrix[:, 1]) / len(test_samples) * 100, 2)\n",
        "wrong0 = round(np.sum(truthMatrix[:, 2]) / len(test_samples) * 100, 2)\n",
        "wrong1 = round(np.sum(truthMatrix[:, 3]) / len(test_samples) * 100, 2)\n",
        "\n",
        "train_teste['70 treino 30 teste'] = right0+right1\n",
        "\n",
        "data = {\n",
        "    'classe 0': [f'{round(right0,2)}%', f'{round(wrong0,2)}%'],\n",
        "    'classe 1': [f'{round(right1,2)}%', f'{round(wrong1,2)}%'],\n",
        "    'Total': [f'{round(right0+right1,2)}%', f'{round(wrong0+wrong1,2)}%']\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data, index=['acertos (%)', 'erro (%)'])\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e51544f1-a2f5-4f10-dd40-99e6210a924d",
        "id": "35dXIBeLr_xG"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            classe 0 classe 1   Total\n",
            "acertos (%)   48.15%    35.8%  83.95%\n",
            "erro (%)       7.41%    8.64%  16.05%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##20% treino 80% Teste\n"
      ],
      "metadata": {
        "id": "Uz9SpCmZtQ7z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Repetimos os Passos anteriores, variando o tamanho da amostra de treinamento para 70% e 20%. Isso envolve executar novamente as etapas de divisão de dados, estimativa de densidade e aplicação do classificador."
      ],
      "metadata": {
        "id": "pq3oTaW-wwe2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xc0 = heart[heart.iloc[:, -1] == 1]\n",
        "xc1 = heart[heart.iloc[:, -1] == 2]\n",
        "\n",
        "xc0_train, xc0_test = train_test_split(xc0, test_size=0.8, random_state=42)\n",
        "xc1_train, xc1_test = train_test_split(xc1, test_size=0.8, random_state=42)"
      ],
      "metadata": {
        "id": "Y7_jjD_-tQ7z"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular as matrizes de covariância e médias\n",
        "k0 = xc0_train.iloc[:, 0:13].cov()\n",
        "k1 = xc1_train.iloc[:, 0:13].cov()\n",
        "m0 = xc0_train.iloc[:, 0:13].mean()\n",
        "m1 = xc1_train.iloc[:, 0:13].mean()\n",
        "n = 13\n",
        "\n",
        "print(\"Matriz de Covariância - Classe 0:\")\n",
        "print(k0.to_string())\n",
        "\n",
        "print(\"\\nMatriz de Covariância - Classe 1:\")\n",
        "print(k1.to_string())\n",
        "\n",
        "print(\"\\nMédia - Classe 0:\")\n",
        "print(m0.to_string())\n",
        "\n",
        "print(\"\\nMédia - Classe 1:\")\n",
        "print(m1.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2fb31dad-ebd8-40e2-b6a4-5c5df7f09fa2",
        "id": "eY8JkuQztQ70"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Covariância - Classe 0:\n",
            "            70.0        1.0       4.0       130.0        322.0       0.0       2.0       109.0     0.0.1        2.4     2.0.1       3.0      3.0.1\n",
            "70.0   76.654023  -0.180460 -0.075862   74.518391    78.221839 -0.110345 -0.231034  -70.608046  0.717241   2.180920  1.318391  1.158621   3.257471\n",
            "1.0    -0.180460   0.254023  0.020690   -0.785057   -11.929885  0.055172 -0.065517   -0.083908  0.020690   0.094023  0.021839  0.110345   0.388506\n",
            "4.0    -0.075862   0.020690  0.648276    0.282759     6.400000 -0.041379 -0.075862   -2.544828  0.062069  -0.162759 -0.020690  0.020690   0.027586\n",
            "130.0  74.518391  -0.785057  0.282759  300.378161   195.812644 -0.096552 -2.182759   41.780460  0.420690   0.981149 -1.180460 -2.606897  -9.464368\n",
            "322.0  78.221839 -11.929885  6.400000  195.812644  2845.495402 -1.220690  9.727586   79.111494  3.434483 -11.941609 -7.208046  0.903448 -20.105747\n",
            "0.0    -0.110345   0.055172 -0.041379   -0.096552    -1.220690  0.165517  0.062069    0.489655 -0.006897   0.061379  0.013793  0.124138  -0.041379\n",
            "2.0    -0.231034  -0.065517 -0.075862   -2.182759     9.727586  0.062069  0.906897    1.300000 -0.006897   0.213103  0.203448 -0.220690  -0.351724\n",
            "109.0 -70.608046  -0.083908 -2.544828   41.780460    79.111494  0.489655  1.300000  372.171264 -1.993103  -2.693563 -5.260920 -2.227586 -13.901149\n",
            "0.0.1   0.717241   0.020690  0.062069    0.420690     3.434483 -0.006897 -0.006897   -1.993103  0.165517  -0.062759 -0.020690  0.020690   0.200000\n",
            "2.4     2.180920   0.094023 -0.162759    0.981149   -11.941609  0.061379  0.213103   -2.693563 -0.062759   0.573609  0.271494 -0.039310   0.222299\n",
            "2.0.1   1.318391   0.021839 -0.020690   -1.180460    -7.208046  0.013793  0.203448   -5.260920 -0.020690   0.271494  0.322989 -0.041379   0.232184\n",
            "3.0     1.158621   0.110345  0.020690   -2.606897     0.903448  0.124138 -0.220690   -2.227586  0.020690  -0.039310 -0.041379  0.731034   0.744828\n",
            "3.0.1   3.257471   0.388506  0.027586   -9.464368   -20.105747 -0.041379 -0.351724  -13.901149  0.200000   0.222299  0.232184  0.744828   2.602299\n",
            "\n",
            "Matriz de Covariância - Classe 1:\n",
            "             70.0       1.0       4.0       130.0        322.0       0.0       2.0       109.0     0.0.1        2.4     2.0.1        3.0     3.0.1\n",
            "70.0   110.885375  0.001976  0.695652   90.796443    36.563241 -0.075099  1.695652  -79.832016  0.166008   0.564032  1.918972   7.707510 -7.533597\n",
            "1.0      0.001976  0.177866 -0.118577   -4.320158    -0.944664 -0.031621 -0.118577    0.664032  0.077075  -0.409881 -0.156126  -0.187747 -0.114625\n",
            "4.0      0.695652 -0.118577  0.897233    0.531621    -3.476285 -0.084980  0.351779   -8.942688  0.039526   0.067194 -0.001976   0.185771  0.152174\n",
            "130.0   90.796443 -4.320158  0.531621  505.885375    99.982213  3.893281  4.849802 -113.622530  0.833992  14.563241  7.171937  12.247036  5.260870\n",
            "322.0   36.563241 -0.944664 -3.476285   99.982213  2040.225296  9.351779 -1.703557  -24.523715  7.466403 -16.620751  1.776680  -6.416996 -4.122530\n",
            "0.0     -0.075099 -0.031621 -0.084980    3.893281     9.351779  0.201581  0.142292   -0.233202  0.025692   0.075494  0.114625  -0.138340  0.128458\n",
            "2.0      1.695652 -0.118577  0.351779    4.849802    -1.703557  0.142292  0.806324   -4.260870 -0.051383   0.321739  0.225296   0.185771 -0.256917\n",
            "109.0  -79.832016  0.664032 -8.942688 -113.622530   -24.523715 -0.233202 -4.260870  495.079051 -2.721344  -1.349012 -6.952569  -6.322134  2.075099\n",
            "0.0.1    0.166008  0.077075  0.039526    0.833992     7.466403  0.025692 -0.051383   -2.721344  0.201581  -0.075494 -0.023715  -0.088933  0.189723\n",
            "2.4      0.564032 -0.409881  0.067194   14.563241   -16.620751  0.075494  0.321739   -1.349012 -0.075494   2.664387  0.809684   0.280632  0.131621\n",
            "2.0.1    1.918972 -0.156126 -0.001976    7.171937     1.776680  0.114625  0.225296   -6.952569 -0.023715   0.809684  0.537549   0.197628 -0.118577\n",
            "3.0      7.707510 -0.187747  0.185771   12.247036    -6.416996 -0.138340  0.185771   -6.322134 -0.088933   0.280632  0.197628   1.332016 -0.581028\n",
            "3.0.1   -7.533597 -0.114625  0.152174    5.260870    -4.122530  0.128458 -0.256917    2.075099  0.189723   0.131621 -0.118577  -0.581028  3.403162\n",
            "\n",
            "Média - Classe 0:\n",
            "70.0      53.366667\n",
            "1.0        0.566667\n",
            "4.0        3.200000\n",
            "130.0    128.633333\n",
            "322.0    244.233333\n",
            "0.0        0.200000\n",
            "2.0        0.700000\n",
            "109.0    156.966667\n",
            "0.0.1      0.200000\n",
            "2.4        0.586667\n",
            "2.0.1      1.433333\n",
            "3.0        0.400000\n",
            "3.0.1      3.866667\n",
            "\n",
            "Média - Classe 1:\n",
            "70.0      57.608696\n",
            "1.0        0.782609\n",
            "4.0        3.478261\n",
            "130.0    137.391304\n",
            "322.0    253.043478\n",
            "0.0        0.260870\n",
            "2.0        1.478261\n",
            "109.0    140.521739\n",
            "0.0.1      0.739130\n",
            "2.4        1.756522\n",
            "2.0.1      1.913043\n",
            "3.0        1.173913\n",
            "3.0.1      5.695652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Amostras de teste\n",
        "test_samples = pd.concat([xc0_test, xc1_test])\n",
        "test_samples = test_samples.sample(frac=1)  # Aleatorizar as linhas\n",
        "\n",
        "# Calcular as probabilidades\n",
        "M0 = np.zeros(len(test_samples))\n",
        "M1 = np.zeros(len(test_samples))\n",
        "\n",
        "for i in range(len(test_samples)):\n",
        "    M0[i] = pdf_multivariate(test_samples.iloc[i, 0:13].values, m0, k0, n)\n",
        "    M1[i] = pdf_multivariate(test_samples.iloc[i, 0:13].values, m1, k1, n)\n",
        "    print(f'\\nProbabilidade da Amostra {i+1} ser das classes.')\n",
        "    print(\"\\033[31m\", f'C0: {M0[i]}', \"\\033[0m\", end=\" \")\n",
        "    print(\"\\033[34m\", f'C1: {M1[i]}', \"\\033[0m\", end=\" \")\n",
        "    print('\\n\\n---------------------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "53c3dd82-0e1c-4745-8150-e14fa068aabe",
        "id": "EGhBiWAetQ70"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Probabilidade da Amostra 1 ser das classes.\n",
            "\u001b[31m C0: 2.4387307500348115e-12 \u001b[0m \u001b[34m C1: 3.3646922369356556e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 2 ser das classes.\n",
            "\u001b[31m C0: 1.6602026771545037e-14 \u001b[0m \u001b[34m C1: 1.9133183201565572e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 3 ser das classes.\n",
            "\u001b[31m C0: 6.2767571198314116e-12 \u001b[0m \u001b[34m C1: 7.271531739948712e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 4 ser das classes.\n",
            "\u001b[31m C0: 1.6876808766900138e-12 \u001b[0m \u001b[34m C1: 4.282170242702715e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 5 ser das classes.\n",
            "\u001b[31m C0: 5.452604562247882e-15 \u001b[0m \u001b[34m C1: 1.2142584556754273e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 6 ser das classes.\n",
            "\u001b[31m C0: 2.529295653521354e-12 \u001b[0m \u001b[34m C1: 1.896796559530551e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 7 ser das classes.\n",
            "\u001b[31m C0: 3.7297368819510205e-12 \u001b[0m \u001b[34m C1: 5.240367572348514e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 8 ser das classes.\n",
            "\u001b[31m C0: 1.5494949370629516e-13 \u001b[0m \u001b[34m C1: 1.1566399841437255e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 9 ser das classes.\n",
            "\u001b[31m C0: 1.2468203999054782e-13 \u001b[0m \u001b[34m C1: 6.34842290692464e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 10 ser das classes.\n",
            "\u001b[31m C0: 6.07332972305621e-17 \u001b[0m \u001b[34m C1: 9.374472669347195e-47 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 11 ser das classes.\n",
            "\u001b[31m C0: 6.6026767631611286e-18 \u001b[0m \u001b[34m C1: 1.0261060512525751e-22 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 12 ser das classes.\n",
            "\u001b[31m C0: 2.0156676835467528e-17 \u001b[0m \u001b[34m C1: 8.387606614230446e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 13 ser das classes.\n",
            "\u001b[31m C0: 9.180906995216708e-11 \u001b[0m \u001b[34m C1: 1.393738635052979e-26 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 14 ser das classes.\n",
            "\u001b[31m C0: 3.7729078196825304e-15 \u001b[0m \u001b[34m C1: 8.978040150349896e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 15 ser das classes.\n",
            "\u001b[31m C0: 1.081850472329297e-11 \u001b[0m \u001b[34m C1: 2.392948655877842e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 16 ser das classes.\n",
            "\u001b[31m C0: 3.0817751809681495e-19 \u001b[0m \u001b[34m C1: 3.2824563861973266e-24 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 17 ser das classes.\n",
            "\u001b[31m C0: 3.793459432015801e-12 \u001b[0m \u001b[34m C1: 3.376217595073228e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 18 ser das classes.\n",
            "\u001b[31m C0: 5.680100317956941e-15 \u001b[0m \u001b[34m C1: 2.015201509223878e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 19 ser das classes.\n",
            "\u001b[31m C0: 1.5531737993512894e-15 \u001b[0m \u001b[34m C1: 4.2027900494672e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 20 ser das classes.\n",
            "\u001b[31m C0: 1.122242909470917e-10 \u001b[0m \u001b[34m C1: 3.645683555958162e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 21 ser das classes.\n",
            "\u001b[31m C0: 2.43209179016798e-18 \u001b[0m \u001b[34m C1: 2.429227476153192e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 22 ser das classes.\n",
            "\u001b[31m C0: 1.467759293231557e-16 \u001b[0m \u001b[34m C1: 1.1489744658860388e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 23 ser das classes.\n",
            "\u001b[31m C0: 1.1748961546556314e-10 \u001b[0m \u001b[34m C1: 2.0889768813683902e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 24 ser das classes.\n",
            "\u001b[31m C0: 1.524993888207559e-10 \u001b[0m \u001b[34m C1: 4.036542720148132e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 25 ser das classes.\n",
            "\u001b[31m C0: 1.8415642226986757e-17 \u001b[0m \u001b[34m C1: 4.09220650612763e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 26 ser das classes.\n",
            "\u001b[31m C0: 4.245720841141088e-19 \u001b[0m \u001b[34m C1: 2.677575624533763e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 27 ser das classes.\n",
            "\u001b[31m C0: 2.5920700484604246e-13 \u001b[0m \u001b[34m C1: 5.598177249214423e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 28 ser das classes.\n",
            "\u001b[31m C0: 3.5784705567973455e-10 \u001b[0m \u001b[34m C1: 4.4306289419814137e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 29 ser das classes.\n",
            "\u001b[31m C0: 1.7305297419038328e-15 \u001b[0m \u001b[34m C1: 1.7524676376262445e-10 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 30 ser das classes.\n",
            "\u001b[31m C0: 2.014279741192147e-18 \u001b[0m \u001b[34m C1: 2.4173793115834347e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 31 ser das classes.\n",
            "\u001b[31m C0: 2.624698670417751e-23 \u001b[0m \u001b[34m C1: 2.6060794500831855e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 32 ser das classes.\n",
            "\u001b[31m C0: 2.9116685365603082e-28 \u001b[0m \u001b[34m C1: 2.381124521400857e-28 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 33 ser das classes.\n",
            "\u001b[31m C0: 3.472381997888197e-14 \u001b[0m \u001b[34m C1: 1.554175413021093e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 34 ser das classes.\n",
            "\u001b[31m C0: 4.2283330265486046e-11 \u001b[0m \u001b[34m C1: 1.0000621760111458e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 35 ser das classes.\n",
            "\u001b[31m C0: 8.15517315409226e-16 \u001b[0m \u001b[34m C1: 1.6313501082743385e-10 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 36 ser das classes.\n",
            "\u001b[31m C0: 1.0656255231513127e-13 \u001b[0m \u001b[34m C1: 2.4246150022862375e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 37 ser das classes.\n",
            "\u001b[31m C0: 6.355554994594071e-11 \u001b[0m \u001b[34m C1: 3.6671859431277666e-27 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 38 ser das classes.\n",
            "\u001b[31m C0: 7.516260911913023e-16 \u001b[0m \u001b[34m C1: 4.362322944571326e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 39 ser das classes.\n",
            "\u001b[31m C0: 5.3260532527472025e-14 \u001b[0m \u001b[34m C1: 1.1870604809228408e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 40 ser das classes.\n",
            "\u001b[31m C0: 2.1049276749834133e-11 \u001b[0m \u001b[34m C1: 4.010818144003762e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 41 ser das classes.\n",
            "\u001b[31m C0: 7.017497200742829e-15 \u001b[0m \u001b[34m C1: 2.974944216878753e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 42 ser das classes.\n",
            "\u001b[31m C0: 9.805005626742845e-13 \u001b[0m \u001b[34m C1: 1.3372966073560243e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 43 ser das classes.\n",
            "\u001b[31m C0: 1.9753808029365704e-12 \u001b[0m \u001b[34m C1: 2.135968723500045e-35 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 44 ser das classes.\n",
            "\u001b[31m C0: 7.348775857195401e-21 \u001b[0m \u001b[34m C1: 2.0987475316637993e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 45 ser das classes.\n",
            "\u001b[31m C0: 2.3504809958851404e-11 \u001b[0m \u001b[34m C1: 3.15073890427164e-33 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 46 ser das classes.\n",
            "\u001b[31m C0: 2.724714250218902e-13 \u001b[0m \u001b[34m C1: 3.2480519449004484e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 47 ser das classes.\n",
            "\u001b[31m C0: 5.462422216302112e-12 \u001b[0m \u001b[34m C1: 1.3569197429447612e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 48 ser das classes.\n",
            "\u001b[31m C0: 7.033150603812516e-15 \u001b[0m \u001b[34m C1: 2.377728135455703e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 49 ser das classes.\n",
            "\u001b[31m C0: 5.252191505373631e-11 \u001b[0m \u001b[34m C1: 3.3218499018247263e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 50 ser das classes.\n",
            "\u001b[31m C0: 5.066896251004078e-13 \u001b[0m \u001b[34m C1: 1.7018302705607873e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 51 ser das classes.\n",
            "\u001b[31m C0: 1.4427347193554643e-12 \u001b[0m \u001b[34m C1: 1.9909928529045848e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 52 ser das classes.\n",
            "\u001b[31m C0: 1.1347839147825645e-18 \u001b[0m \u001b[34m C1: 8.233695608279922e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 53 ser das classes.\n",
            "\u001b[31m C0: 4.8875797855676197e-11 \u001b[0m \u001b[34m C1: 2.2506045849739278e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 54 ser das classes.\n",
            "\u001b[31m C0: 2.185024404615994e-11 \u001b[0m \u001b[34m C1: 1.1540335540013454e-28 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 55 ser das classes.\n",
            "\u001b[31m C0: 5.544070365956106e-12 \u001b[0m \u001b[34m C1: 1.7954431878065686e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 56 ser das classes.\n",
            "\u001b[31m C0: 2.7800419926861126e-14 \u001b[0m \u001b[34m C1: 1.2874861991302116e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 57 ser das classes.\n",
            "\u001b[31m C0: 3.554723772602468e-10 \u001b[0m \u001b[34m C1: 9.683382476994227e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 58 ser das classes.\n",
            "\u001b[31m C0: 5.357977495649852e-12 \u001b[0m \u001b[34m C1: 5.049901354996406e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 59 ser das classes.\n",
            "\u001b[31m C0: 2.3963559024266392e-17 \u001b[0m \u001b[34m C1: 7.70790861554016e-39 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 60 ser das classes.\n",
            "\u001b[31m C0: 4.411763066266941e-12 \u001b[0m \u001b[34m C1: 6.418792554037927e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 61 ser das classes.\n",
            "\u001b[31m C0: 3.980181346912511e-15 \u001b[0m \u001b[34m C1: 4.730773380923425e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 62 ser das classes.\n",
            "\u001b[31m C0: 3.4487644127090453e-14 \u001b[0m \u001b[34m C1: 8.340214891852732e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 63 ser das classes.\n",
            "\u001b[31m C0: 1.154915090003919e-18 \u001b[0m \u001b[34m C1: 1.3966000460391653e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 64 ser das classes.\n",
            "\u001b[31m C0: 9.086613216547702e-12 \u001b[0m \u001b[34m C1: 2.390443540531968e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 65 ser das classes.\n",
            "\u001b[31m C0: 1.6091454059196514e-13 \u001b[0m \u001b[34m C1: 4.331792446828579e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 66 ser das classes.\n",
            "\u001b[31m C0: 2.2840758142001957e-11 \u001b[0m \u001b[34m C1: 7.235547413899774e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 67 ser das classes.\n",
            "\u001b[31m C0: 2.8290274800185206e-29 \u001b[0m \u001b[34m C1: 7.467884228629753e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 68 ser das classes.\n",
            "\u001b[31m C0: 2.51026761089074e-15 \u001b[0m \u001b[34m C1: 8.933609339324378e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 69 ser das classes.\n",
            "\u001b[31m C0: 7.413633503573026e-14 \u001b[0m \u001b[34m C1: 7.863793357613538e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 70 ser das classes.\n",
            "\u001b[31m C0: 3.886864607529722e-15 \u001b[0m \u001b[34m C1: 8.423200872520695e-24 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 71 ser das classes.\n",
            "\u001b[31m C0: 2.8304198921053387e-10 \u001b[0m \u001b[34m C1: 2.3092174219803954e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 72 ser das classes.\n",
            "\u001b[31m C0: 5.356814795459307e-16 \u001b[0m \u001b[34m C1: 2.022186297498364e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 73 ser das classes.\n",
            "\u001b[31m C0: 4.6571731337869624e-15 \u001b[0m \u001b[34m C1: 1.1687221081235195e-33 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 74 ser das classes.\n",
            "\u001b[31m C0: 2.5091605141675944e-16 \u001b[0m \u001b[34m C1: 1.631761497741129e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 75 ser das classes.\n",
            "\u001b[31m C0: 5.819320682439528e-18 \u001b[0m \u001b[34m C1: 7.974156981001303e-22 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 76 ser das classes.\n",
            "\u001b[31m C0: 3.202981719852374e-33 \u001b[0m \u001b[34m C1: 1.2078277049658403e-28 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 77 ser das classes.\n",
            "\u001b[31m C0: 1.900218231864342e-14 \u001b[0m \u001b[34m C1: 9.119207220568244e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 78 ser das classes.\n",
            "\u001b[31m C0: 1.4936385264944929e-21 \u001b[0m \u001b[34m C1: 1.4908244793878795e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 79 ser das classes.\n",
            "\u001b[31m C0: 5.001193105166296e-13 \u001b[0m \u001b[34m C1: 1.3935706152358422e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 80 ser das classes.\n",
            "\u001b[31m C0: 6.887768158735565e-20 \u001b[0m \u001b[34m C1: 9.745121526530758e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 81 ser das classes.\n",
            "\u001b[31m C0: 1.3684984450657623e-18 \u001b[0m \u001b[34m C1: 2.893635267080595e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 82 ser das classes.\n",
            "\u001b[31m C0: 6.011457481948047e-16 \u001b[0m \u001b[34m C1: 1.925525644092478e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 83 ser das classes.\n",
            "\u001b[31m C0: 1.2982726762478155e-10 \u001b[0m \u001b[34m C1: 4.93089581995793e-30 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 84 ser das classes.\n",
            "\u001b[31m C0: 1.219895570991353e-13 \u001b[0m \u001b[34m C1: 5.915088875169505e-22 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 85 ser das classes.\n",
            "\u001b[31m C0: 4.155519171835954e-12 \u001b[0m \u001b[34m C1: 2.9696859788186247e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 86 ser das classes.\n",
            "\u001b[31m C0: 2.1306850639943274e-26 \u001b[0m \u001b[34m C1: 1.07947745962655e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 87 ser das classes.\n",
            "\u001b[31m C0: 8.266420183943615e-11 \u001b[0m \u001b[34m C1: 6.681099339165917e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 88 ser das classes.\n",
            "\u001b[31m C0: 2.498136217359972e-12 \u001b[0m \u001b[34m C1: 3.4705973520169854e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 89 ser das classes.\n",
            "\u001b[31m C0: 1.3213066162480499e-12 \u001b[0m \u001b[34m C1: 8.21160772271413e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 90 ser das classes.\n",
            "\u001b[31m C0: 9.809705649878483e-12 \u001b[0m \u001b[34m C1: 1.04265806914177e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 91 ser das classes.\n",
            "\u001b[31m C0: 7.4344627618845e-16 \u001b[0m \u001b[34m C1: 1.0577653180131723e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 92 ser das classes.\n",
            "\u001b[31m C0: 2.5824058735910472e-17 \u001b[0m \u001b[34m C1: 1.579597800024547e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 93 ser das classes.\n",
            "\u001b[31m C0: 9.799121195079496e-12 \u001b[0m \u001b[34m C1: 1.147102998591585e-22 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 94 ser das classes.\n",
            "\u001b[31m C0: 2.852536017621704e-14 \u001b[0m \u001b[34m C1: 3.020134294278163e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 95 ser das classes.\n",
            "\u001b[31m C0: 2.4935567142081856e-12 \u001b[0m \u001b[34m C1: 9.471266887258359e-28 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 96 ser das classes.\n",
            "\u001b[31m C0: 2.2180773592164986e-14 \u001b[0m \u001b[34m C1: 3.416407005918628e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 97 ser das classes.\n",
            "\u001b[31m C0: 2.1613825910237785e-15 \u001b[0m \u001b[34m C1: 7.593973592145665e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 98 ser das classes.\n",
            "\u001b[31m C0: 4.893215299752682e-16 \u001b[0m \u001b[34m C1: 3.2888503832510157e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 99 ser das classes.\n",
            "\u001b[31m C0: 2.563170722923158e-16 \u001b[0m \u001b[34m C1: 1.3832404434176056e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 100 ser das classes.\n",
            "\u001b[31m C0: 2.3522328963402388e-12 \u001b[0m \u001b[34m C1: 9.667398002599565e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 101 ser das classes.\n",
            "\u001b[31m C0: 2.5977313647511815e-20 \u001b[0m \u001b[34m C1: 1.026282093123421e-28 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 102 ser das classes.\n",
            "\u001b[31m C0: 1.210598449748666e-24 \u001b[0m \u001b[34m C1: 2.2234755500409485e-24 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 103 ser das classes.\n",
            "\u001b[31m C0: 1.3908098361829374e-11 \u001b[0m \u001b[34m C1: 5.259919915172587e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 104 ser das classes.\n",
            "\u001b[31m C0: 5.218399633468179e-10 \u001b[0m \u001b[34m C1: 7.759070051783038e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 105 ser das classes.\n",
            "\u001b[31m C0: 3.204631314060684e-14 \u001b[0m \u001b[34m C1: 3.823109102841408e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 106 ser das classes.\n",
            "\u001b[31m C0: 9.83929700351425e-10 \u001b[0m \u001b[34m C1: 1.610292899317517e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 107 ser das classes.\n",
            "\u001b[31m C0: 1.1988272676234156e-12 \u001b[0m \u001b[34m C1: 1.2297236182736064e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 108 ser das classes.\n",
            "\u001b[31m C0: 7.331582232202993e-13 \u001b[0m \u001b[34m C1: 3.2319957413297667e-34 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 109 ser das classes.\n",
            "\u001b[31m C0: 7.791952059983928e-18 \u001b[0m \u001b[34m C1: 7.798900990573018e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 110 ser das classes.\n",
            "\u001b[31m C0: 5.044529278726375e-13 \u001b[0m \u001b[34m C1: 6.282653185565248e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 111 ser das classes.\n",
            "\u001b[31m C0: 3.415832566561715e-17 \u001b[0m \u001b[34m C1: 4.065397615430662e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 112 ser das classes.\n",
            "\u001b[31m C0: 3.407084954412383e-15 \u001b[0m \u001b[34m C1: 2.0027167934594093e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 113 ser das classes.\n",
            "\u001b[31m C0: 7.346480365042292e-22 \u001b[0m \u001b[34m C1: 7.587863207172981e-26 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 114 ser das classes.\n",
            "\u001b[31m C0: 3.583462882385497e-25 \u001b[0m \u001b[34m C1: 1.6619678892418071e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 115 ser das classes.\n",
            "\u001b[31m C0: 6.8971510939389666e-15 \u001b[0m \u001b[34m C1: 5.884343049474342e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 116 ser das classes.\n",
            "\u001b[31m C0: 3.643515730681747e-12 \u001b[0m \u001b[34m C1: 3.849281345198985e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 117 ser das classes.\n",
            "\u001b[31m C0: 2.1619852844511607e-19 \u001b[0m \u001b[34m C1: 2.493768594069843e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 118 ser das classes.\n",
            "\u001b[31m C0: 2.6213078648074383e-12 \u001b[0m \u001b[34m C1: 1.9465214129364396e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 119 ser das classes.\n",
            "\u001b[31m C0: 7.70263644567534e-13 \u001b[0m \u001b[34m C1: 3.706863993162093e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 120 ser das classes.\n",
            "\u001b[31m C0: 6.775568863177152e-12 \u001b[0m \u001b[34m C1: 6.638874386536752e-36 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 121 ser das classes.\n",
            "\u001b[31m C0: 1.3539062622579511e-11 \u001b[0m \u001b[34m C1: 3.405901069674682e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 122 ser das classes.\n",
            "\u001b[31m C0: 2.8052763704104395e-12 \u001b[0m \u001b[34m C1: 2.3789654533722757e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 123 ser das classes.\n",
            "\u001b[31m C0: 4.86109104658848e-19 \u001b[0m \u001b[34m C1: 2.2362591974870377e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 124 ser das classes.\n",
            "\u001b[31m C0: 6.085592975764066e-14 \u001b[0m \u001b[34m C1: 3.036953014276776e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 125 ser das classes.\n",
            "\u001b[31m C0: 1.5673807753034818e-11 \u001b[0m \u001b[34m C1: 6.161687442256988e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 126 ser das classes.\n",
            "\u001b[31m C0: 7.3149722454859e-18 \u001b[0m \u001b[34m C1: 2.3313540293626916e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 127 ser das classes.\n",
            "\u001b[31m C0: 2.6486993756566173e-12 \u001b[0m \u001b[34m C1: 5.592931124333831e-17 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 128 ser das classes.\n",
            "\u001b[31m C0: 1.7777500330173627e-11 \u001b[0m \u001b[34m C1: 2.1547804041117597e-28 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 129 ser das classes.\n",
            "\u001b[31m C0: 9.427343194929725e-12 \u001b[0m \u001b[34m C1: 7.14185615110557e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 130 ser das classes.\n",
            "\u001b[31m C0: 2.723946842233111e-12 \u001b[0m \u001b[34m C1: 7.0572656141449296e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 131 ser das classes.\n",
            "\u001b[31m C0: 6.184428673741618e-19 \u001b[0m \u001b[34m C1: 5.5601859831800485e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 132 ser das classes.\n",
            "\u001b[31m C0: 2.3545544372233126e-22 \u001b[0m \u001b[34m C1: 3.1291983176100416e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 133 ser das classes.\n",
            "\u001b[31m C0: 3.739981527238022e-26 \u001b[0m \u001b[34m C1: 1.2904153578516937e-43 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 134 ser das classes.\n",
            "\u001b[31m C0: 2.202309149490157e-17 \u001b[0m \u001b[34m C1: 1.3707632574856511e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 135 ser das classes.\n",
            "\u001b[31m C0: 2.801625133435815e-15 \u001b[0m \u001b[34m C1: 2.2500070026451724e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 136 ser das classes.\n",
            "\u001b[31m C0: 4.4010810505046943e-11 \u001b[0m \u001b[34m C1: 3.113046876118031e-32 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 137 ser das classes.\n",
            "\u001b[31m C0: 2.3813563164075177e-10 \u001b[0m \u001b[34m C1: 1.9368962072626742e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 138 ser das classes.\n",
            "\u001b[31m C0: 4.627680974044843e-23 \u001b[0m \u001b[34m C1: 1.0552684466647425e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 139 ser das classes.\n",
            "\u001b[31m C0: 2.922829621309553e-17 \u001b[0m \u001b[34m C1: 1.6149760296577644e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 140 ser das classes.\n",
            "\u001b[31m C0: 9.470092204415815e-12 \u001b[0m \u001b[34m C1: 3.616797682696519e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 141 ser das classes.\n",
            "\u001b[31m C0: 3.1171724706139285e-14 \u001b[0m \u001b[34m C1: 6.245293804656847e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 142 ser das classes.\n",
            "\u001b[31m C0: 1.3869497619651696e-14 \u001b[0m \u001b[34m C1: 3.652837033199796e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 143 ser das classes.\n",
            "\u001b[31m C0: 3.55058922349515e-14 \u001b[0m \u001b[34m C1: 4.8609217233989925e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 144 ser das classes.\n",
            "\u001b[31m C0: 4.0292252227837607e-13 \u001b[0m \u001b[34m C1: 1.6892333356241446e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 145 ser das classes.\n",
            "\u001b[31m C0: 1.6699146449328847e-13 \u001b[0m \u001b[34m C1: 1.561978208279089e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 146 ser das classes.\n",
            "\u001b[31m C0: 5.089744811768088e-13 \u001b[0m \u001b[34m C1: 1.579815005016284e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 147 ser das classes.\n",
            "\u001b[31m C0: 1.2336103605454979e-11 \u001b[0m \u001b[34m C1: 2.7195964545704356e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 148 ser das classes.\n",
            "\u001b[31m C0: 2.931066017640337e-16 \u001b[0m \u001b[34m C1: 7.064251461674306e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 149 ser das classes.\n",
            "\u001b[31m C0: 1.2381862044921663e-11 \u001b[0m \u001b[34m C1: 2.2448169443540822e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 150 ser das classes.\n",
            "\u001b[31m C0: 1.9259080894748183e-16 \u001b[0m \u001b[34m C1: 1.1357844891809128e-26 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 151 ser das classes.\n",
            "\u001b[31m C0: 2.2386564414782478e-17 \u001b[0m \u001b[34m C1: 2.2197541361111655e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 152 ser das classes.\n",
            "\u001b[31m C0: 4.0927367529673027e-10 \u001b[0m \u001b[34m C1: 6.246140224482511e-25 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 153 ser das classes.\n",
            "\u001b[31m C0: 2.0339268613496932e-13 \u001b[0m \u001b[34m C1: 2.007764726509925e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 154 ser das classes.\n",
            "\u001b[31m C0: 2.7165628782886882e-11 \u001b[0m \u001b[34m C1: 1.3025590645033863e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 155 ser das classes.\n",
            "\u001b[31m C0: 1.0371908033451478e-11 \u001b[0m \u001b[34m C1: 1.2022771218198348e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 156 ser das classes.\n",
            "\u001b[31m C0: 8.439163932471964e-17 \u001b[0m \u001b[34m C1: 2.621252802155207e-44 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 157 ser das classes.\n",
            "\u001b[31m C0: 2.1450967251666004e-18 \u001b[0m \u001b[34m C1: 4.073154803017353e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 158 ser das classes.\n",
            "\u001b[31m C0: 4.5361139627604294e-12 \u001b[0m \u001b[34m C1: 1.0199433143406276e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 159 ser das classes.\n",
            "\u001b[31m C0: 9.371363573798019e-11 \u001b[0m \u001b[34m C1: 1.9203098596583894e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 160 ser das classes.\n",
            "\u001b[31m C0: 4.993295344438432e-16 \u001b[0m \u001b[34m C1: 4.1060836373229335e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 161 ser das classes.\n",
            "\u001b[31m C0: 1.5872974999938174e-19 \u001b[0m \u001b[34m C1: 7.130732418297203e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 162 ser das classes.\n",
            "\u001b[31m C0: 3.038136127251876e-19 \u001b[0m \u001b[34m C1: 6.633897702859532e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 163 ser das classes.\n",
            "\u001b[31m C0: 8.342776140661291e-16 \u001b[0m \u001b[34m C1: 5.6658658290813106e-30 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 164 ser das classes.\n",
            "\u001b[31m C0: 1.8829381701123576e-14 \u001b[0m \u001b[34m C1: 6.257657013322991e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 165 ser das classes.\n",
            "\u001b[31m C0: 2.099287804273203e-17 \u001b[0m \u001b[34m C1: 1.586190397306261e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 166 ser das classes.\n",
            "\u001b[31m C0: 9.801148985179092e-10 \u001b[0m \u001b[34m C1: 1.9996981479371523e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 167 ser das classes.\n",
            "\u001b[31m C0: 5.045602899812774e-19 \u001b[0m \u001b[34m C1: 6.331499237824821e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 168 ser das classes.\n",
            "\u001b[31m C0: 1.5225666456738585e-11 \u001b[0m \u001b[34m C1: 5.139894267220784e-19 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 169 ser das classes.\n",
            "\u001b[31m C0: 2.769926892630664e-16 \u001b[0m \u001b[34m C1: 1.6679809636025976e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 170 ser das classes.\n",
            "\u001b[31m C0: 1.8921899789915375e-21 \u001b[0m \u001b[34m C1: 4.769033385829955e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 171 ser das classes.\n",
            "\u001b[31m C0: 1.555526503061571e-11 \u001b[0m \u001b[34m C1: 3.27660034165133e-29 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 172 ser das classes.\n",
            "\u001b[31m C0: 4.7534382221149276e-11 \u001b[0m \u001b[34m C1: 4.639185610609111e-27 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 173 ser das classes.\n",
            "\u001b[31m C0: 1.3807340771601293e-12 \u001b[0m \u001b[34m C1: 7.027839459097843e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 174 ser das classes.\n",
            "\u001b[31m C0: 2.7904503416096906e-11 \u001b[0m \u001b[34m C1: 2.8890360835906587e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 175 ser das classes.\n",
            "\u001b[31m C0: 3.3588015306058896e-11 \u001b[0m \u001b[34m C1: 1.2418269873327735e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 176 ser das classes.\n",
            "\u001b[31m C0: 1.5054020573471832e-10 \u001b[0m \u001b[34m C1: 2.0693195924948745e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 177 ser das classes.\n",
            "\u001b[31m C0: 4.8751173303862555e-32 \u001b[0m \u001b[34m C1: 5.210052972095198e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 178 ser das classes.\n",
            "\u001b[31m C0: 2.1382938392252388e-13 \u001b[0m \u001b[34m C1: 2.6849508436755184e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 179 ser das classes.\n",
            "\u001b[31m C0: 5.199561112316807e-16 \u001b[0m \u001b[34m C1: 9.253957754745888e-24 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 180 ser das classes.\n",
            "\u001b[31m C0: 3.713712218029212e-12 \u001b[0m \u001b[34m C1: 8.012006400657958e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 181 ser das classes.\n",
            "\u001b[31m C0: 5.398815109857793e-11 \u001b[0m \u001b[34m C1: 6.486467958282352e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 182 ser das classes.\n",
            "\u001b[31m C0: 1.70232103167412e-13 \u001b[0m \u001b[34m C1: 5.650758173333249e-27 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 183 ser das classes.\n",
            "\u001b[31m C0: 2.722041937590812e-13 \u001b[0m \u001b[34m C1: 6.405140294849464e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 184 ser das classes.\n",
            "\u001b[31m C0: 2.876342299463415e-14 \u001b[0m \u001b[34m C1: 1.6345652350820886e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 185 ser das classes.\n",
            "\u001b[31m C0: 9.675925341724768e-12 \u001b[0m \u001b[34m C1: 1.9049449586621474e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 186 ser das classes.\n",
            "\u001b[31m C0: 2.1173966988059533e-10 \u001b[0m \u001b[34m C1: 1.7203995963772338e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 187 ser das classes.\n",
            "\u001b[31m C0: 8.020081931926663e-15 \u001b[0m \u001b[34m C1: 2.7973151049522617e-18 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 188 ser das classes.\n",
            "\u001b[31m C0: 2.263799937775826e-13 \u001b[0m \u001b[34m C1: 2.499998566895773e-41 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 189 ser das classes.\n",
            "\u001b[31m C0: 1.2600469249259584e-12 \u001b[0m \u001b[34m C1: 6.311404890944478e-35 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 190 ser das classes.\n",
            "\u001b[31m C0: 5.13743636184175e-13 \u001b[0m \u001b[34m C1: 3.0497790582401275e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 191 ser das classes.\n",
            "\u001b[31m C0: 2.0709297425462122e-17 \u001b[0m \u001b[34m C1: 1.0448432144041018e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 192 ser das classes.\n",
            "\u001b[31m C0: 4.322141699867737e-18 \u001b[0m \u001b[34m C1: 6.39106307623643e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 193 ser das classes.\n",
            "\u001b[31m C0: 4.786559235427998e-15 \u001b[0m \u001b[34m C1: 6.680129982213531e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 194 ser das classes.\n",
            "\u001b[31m C0: 2.012963830817514e-13 \u001b[0m \u001b[34m C1: 5.297172179109317e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 195 ser das classes.\n",
            "\u001b[31m C0: 4.29773216016778e-13 \u001b[0m \u001b[34m C1: 3.1813466659566964e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 196 ser das classes.\n",
            "\u001b[31m C0: 5.906614940911379e-18 \u001b[0m \u001b[34m C1: 1.1183636217115103e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 197 ser das classes.\n",
            "\u001b[31m C0: 1.0496024147583275e-23 \u001b[0m \u001b[34m C1: 1.8232109957087522e-16 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 198 ser das classes.\n",
            "\u001b[31m C0: 1.7496660041962948e-12 \u001b[0m \u001b[34m C1: 2.877661530920374e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 199 ser das classes.\n",
            "\u001b[31m C0: 1.463361389031255e-11 \u001b[0m \u001b[34m C1: 7.332482513013976e-32 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 200 ser das classes.\n",
            "\u001b[31m C0: 5.148819889534953e-22 \u001b[0m \u001b[34m C1: 8.336034193253286e-23 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 201 ser das classes.\n",
            "\u001b[31m C0: 4.0215312864089515e-17 \u001b[0m \u001b[34m C1: 1.0989819527016268e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 202 ser das classes.\n",
            "\u001b[31m C0: 6.101268675261768e-15 \u001b[0m \u001b[34m C1: 2.5649712143878224e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 203 ser das classes.\n",
            "\u001b[31m C0: 1.1700339062824296e-11 \u001b[0m \u001b[34m C1: 6.777130856638068e-26 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 204 ser das classes.\n",
            "\u001b[31m C0: 8.751808090023357e-14 \u001b[0m \u001b[34m C1: 4.4502290316266385e-26 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 205 ser das classes.\n",
            "\u001b[31m C0: 2.2816640339799505e-16 \u001b[0m \u001b[34m C1: 1.5764384159637755e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 206 ser das classes.\n",
            "\u001b[31m C0: 7.577851859448716e-13 \u001b[0m \u001b[34m C1: 7.316270454343282e-15 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 207 ser das classes.\n",
            "\u001b[31m C0: 1.4812693594099557e-20 \u001b[0m \u001b[34m C1: 3.8324130526216064e-13 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 208 ser das classes.\n",
            "\u001b[31m C0: 1.4752815743346514e-16 \u001b[0m \u001b[34m C1: 1.1310376595141649e-11 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 209 ser das classes.\n",
            "\u001b[31m C0: 2.385748648138899e-12 \u001b[0m \u001b[34m C1: 2.3073405788001067e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 210 ser das classes.\n",
            "\u001b[31m C0: 1.2421797004493562e-23 \u001b[0m \u001b[34m C1: 7.15970582778368e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 211 ser das classes.\n",
            "\u001b[31m C0: 1.2658675242113296e-17 \u001b[0m \u001b[34m C1: 1.9872205615855563e-14 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 212 ser das classes.\n",
            "\u001b[31m C0: 1.9331945924444856e-15 \u001b[0m \u001b[34m C1: 1.2298245481827455e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 213 ser das classes.\n",
            "\u001b[31m C0: 8.205117515321521e-26 \u001b[0m \u001b[34m C1: 4.434234657604463e-21 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 214 ser das classes.\n",
            "\u001b[31m C0: 5.1558685948957774e-15 \u001b[0m \u001b[34m C1: 2.9812075348301362e-12 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 215 ser das classes.\n",
            "\u001b[31m C0: 7.289699500388213e-11 \u001b[0m \u001b[34m C1: 1.5555360612143532e-22 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n",
            "\n",
            "Probabilidade da Amostra 216 ser das classes.\n",
            "\u001b[31m C0: 6.842639450198742e-19 \u001b[0m \u001b[34m C1: 3.652286574578519e-20 \u001b[0m \n",
            "\n",
            "---------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificar de acordo com a regra bayesiana\n",
        "prob0 = len(xc0) / (len(xc0) + len(xc1))\n",
        "prob1 = len(xc1) / (len(xc0) + len(xc1))\n",
        "M0 = M0 * prob0\n",
        "M1 = M1 * prob1\n",
        "M = M0 / M1\n",
        "\n",
        "for elemento in np.nditer(M):\n",
        "    if elemento > 1:\n",
        "        print(\"\\n\\033[34m\", elemento, \"\\033[0m\", end=\" \")  # Azul\n",
        "    else:\n",
        "        print(\"\\n\\033[31m\", elemento, \"\\033[0m\", end=\" \")  # Vermelho"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e37ace87-eea3-429b-991f-8b8fbb6a62c4",
        "id": "W3xtBHDQtQ70"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[34m 91361.41259642714 \u001b[0m \n",
            "\u001b[34m 1093.7502809378414 \u001b[0m \n",
            "\u001b[34m 108.80621862667375 \u001b[0m \n",
            "\u001b[31m 0.49678754360340616 \u001b[0m \n",
            "\u001b[34m 5.660270207460535 \u001b[0m \n",
            "\u001b[34m 1680.827489135362 \u001b[0m \n",
            "\u001b[34m 897.1410775972882 \u001b[0m \n",
            "\u001b[34m 16.886368320534206 \u001b[0m \n",
            "\u001b[34m 2.475610676641848 \u001b[0m \n",
            "\u001b[34m 8.166280818265552e+29 \u001b[0m \n",
            "\u001b[34m 81109.56749689141 \u001b[0m \n",
            "\u001b[31m 3.029180672340988e-07 \u001b[0m \n",
            "\u001b[34m 8303258266403901.0 \u001b[0m \n",
            "\u001b[31m 0.005297109480044318 \u001b[0m \n",
            "\u001b[34m 5.698730988697458 \u001b[0m \n",
            "\u001b[34m 118344.01153295455 \u001b[0m \n",
            "\u001b[34m 1.416280621230926 \u001b[0m \n",
            "\u001b[34m 355289050.9450801 \u001b[0m \n",
            "\u001b[31m 0.00046582914610993417 \u001b[0m \n",
            "\u001b[34m 38.8018290940956 \u001b[0m \n",
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            "\u001b[34m 108742264.3110917 \u001b[0m \n",
            "\u001b[34m 4.058212770171045e+27 \u001b[0m \n",
            "\u001b[31m 6.63835175888951e-06 \u001b[0m \n",
            "\u001b[34m 56.059887142437134 \u001b[0m \n",
            "\u001b[34m 615.1425563346843 \u001b[0m \n",
            "\u001b[31m 0.015328644805688282 \u001b[0m \n",
            "\u001b[31m 0.028058761017668608 \u001b[0m \n",
            "\u001b[31m 0.5772750086315168 \u001b[0m \n",
            "\u001b[34m 185604542998681.47 \u001b[0m \n",
            "\u001b[31m 0.0003792875624020777 \u001b[0m \n",
            "\u001b[31m 0.0016682493490801568 \u001b[0m \n",
            "\u001b[34m 617.8127178774977 \u001b[0m \n",
            "\u001b[31m 1.0045020012370969e-08 \u001b[0m \n",
            "\u001b[34m 37339321.67535592 \u001b[0m \n",
            "\u001b[34m 209.32519990924214 \u001b[0m \n",
            "\u001b[31m 5.001251251424853e-08 \u001b[0m \n",
            "\u001b[34m 5.984091706303683e+17 \u001b[0m \n",
            "\u001b[34m 1.2915475590593314e+16 \u001b[0m \n",
            "\u001b[34m 247646.67943730246 \u001b[0m \n",
            "\u001b[34m 121.74906364647025 \u001b[0m \n",
            "\u001b[34m 340.93182747087997 \u001b[0m \n",
            "\u001b[34m 91.69997835941105 \u001b[0m \n",
            "\u001b[31m 1.17947090203325e-17 \u001b[0m \n",
            "\u001b[34m 1003.865070796449 \u001b[0m \n",
            "\u001b[34m 70824492.6405625 \u001b[0m \n",
            "\u001b[34m 58.426686407467756 \u001b[0m \n",
            "\u001b[34m 104.914248768985 \u001b[0m \n",
            "\u001b[34m 37973361223647.24 \u001b[0m \n",
            "\u001b[34m 5.356862053815259 \u001b[0m \n",
            "\u001b[34m 22.181075897918507 \u001b[0m \n",
            "\u001b[34m 640.257057976009 \u001b[0m \n",
            "\u001b[34m 15513.764599166167 \u001b[0m \n",
            "\u001b[34m 3613.946442829981 \u001b[0m \n",
            "\u001b[34m 1.1414123876375165e+28 \u001b[0m \n",
            "\u001b[34m 2.5165465861061228e+22 \u001b[0m \n",
            "\u001b[31m 0.2123353854920043 \u001b[0m \n",
            "\u001b[31m 0.24983802410524855 \u001b[0m \n",
            "\u001b[31m 8.524525118833267e-06 \u001b[0m \n",
            "\u001b[34m 90319769.88344711 \u001b[0m \n",
            "\u001b[34m 47.900073487951765 \u001b[0m \n",
            "\u001b[34m 1.7028353129697493 \u001b[0m \n",
            "\u001b[31m 0.006657327550885949 \u001b[0m \n",
            "\u001b[31m 7.256583341209719e-08 \u001b[0m \n",
            "\u001b[34m 76.64074895986398 \u001b[0m \n",
            "\u001b[34m 2.5156189273923826e+20 \u001b[0m \n",
            "\u001b[34m 7.785607585089502 \u001b[0m \n",
            "\u001b[31m 0.004612593565569713 \u001b[0m \n",
            "\u001b[34m 2.9983474113122357 \u001b[0m \n",
            "\u001b[34m 217619031737196.34 \u001b[0m \n",
            "\u001b[34m 2478904072437.157 \u001b[0m \n",
            "\u001b[31m 0.18243954679936497 \u001b[0m \n",
            "\u001b[34m 130.55714886644654 \u001b[0m \n",
            "\u001b[31m 4.871985941285332e-08 \u001b[0m \n",
            "\u001b[31m 1.644152701254609e-05 \u001b[0m \n",
            "\u001b[34m 130333866.73661038 \u001b[0m \n",
            "\u001b[31m 0.002186923274951849 \u001b[0m \n",
            "\u001b[31m 0.0008029462677088922 \u001b[0m \n",
            "\u001b[31m 0.001981420772616204 \u001b[0m \n",
            "\u001b[31m 2.3324397335647324e-05 \u001b[0m \n",
            "\u001b[31m 0.002179987119732626 \u001b[0m \n",
            "\u001b[34m 590709343122.2382 \u001b[0m \n",
            "\u001b[34m 23.615824228022966 \u001b[0m "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificar as amostras\n",
        "M[M >= 1] = 1\n",
        "M[M < 1] = 2\n",
        "\n",
        "for elemento in np.nditer(M):\n",
        "    if elemento > 1:\n",
        "        print(\"\\n\\033[31m\", elemento, \"\\033[0m\", end=\" \")  # Vermelho\n",
        "    else:\n",
        "        print(\"\\n\\033[34m\", elemento, \"\\033[0m\", end=\" \")  # Azul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a5a32413-4979-478a-ba4a-d4b7c95d252f",
        "id": "vRA-WY9ttQ70"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
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            "\u001b[31m 2.0 \u001b[0m \n",
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            "\u001b[34m 1.0 \u001b[0m \n",
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            "\u001b[34m 1.0 \u001b[0m \n",
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            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
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            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[31m 2.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m \n",
            "\u001b[34m 1.0 \u001b[0m "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar quantidade de erros e acertos\n",
        "truthMatrix = np.zeros((len(test_samples), 4))\n",
        "\n",
        "for i in range(len(M)):\n",
        "    if M[i] == 1 and test_samples.iloc[i, 13] == 1:\n",
        "        truthMatrix[i, 0] = 1\n",
        "    elif M[i] == 2 and test_samples.iloc[i, 13] == 2:\n",
        "        truthMatrix[i, 1] = 1\n",
        "    elif M[i] == 2 and test_samples.iloc[i, 13] == 1:\n",
        "        truthMatrix[i, 2] = 1\n",
        "    else:\n",
        "        truthMatrix[i, 3] = 1\n",
        "\n",
        "right0 = round(np.sum(truthMatrix[:, 0]) / len(test_samples) * 100, 2)\n",
        "right1 = round(np.sum(truthMatrix[:, 1]) / len(test_samples) * 100, 2)\n",
        "wrong0 = round(np.sum(truthMatrix[:, 2]) / len(test_samples) * 100, 2)\n",
        "wrong1 = round(np.sum(truthMatrix[:, 3]) / len(test_samples) * 100, 2)\n",
        "\n",
        "train_teste['20 treino 80 teste'] = right0+right1\n",
        "\n",
        "data = {\n",
        "    'classe 0': [f'{round(right0,2)}%', f'{round(wrong0,2)}%'],\n",
        "    'classe 1': [f'{round(right1,2)}%', f'{round(wrong1,2)}%'],\n",
        "    'Total': [f'{round(right0+right1,2)}%', f'{round(wrong0+wrong1,2)}%']\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data, index=['acertos (%)', 'erro (%)'])\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ca1c8113-6c2b-4ea6-8dc0-e34330eb40df",
        "id": "tOKfv44atQ70"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            classe 0 classe 1   Total\n",
            "acertos (%)   45.83%   23.15%  68.98%\n",
            "erro (%)       9.72%    21.3%  31.02%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Considerações finais"
      ],
      "metadata": {
        "id": "alEx62DgteFF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_teste"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Nva3dOSpt6_y",
        "outputId": "b9162b85-0168-4e9a-94fc-edf9b98d6935"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'90 treino 10 teste': 81.47999999999999,\n",
              " '70 treino 30 teste': 83.94999999999999,\n",
              " '20 treino 80 teste': 68.97999999999999}"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_axis = list(train_teste.keys())\n",
        "Y_axis = list(train_teste.values())\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(X_axis, Y_axis)\n",
        "plt.xlabel('Divisão de dados (Treino/Teste)')\n",
        "plt.ylabel('Acurácia')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "bJntNkXctdkr",
        "outputId": "78d0f03e-aeb4-420f-b569-57f9404d0f81"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O método funcionou bem na maioria dos casos, resultando em classificações precisas e um desvio padrão consistente. No entanto, notamos que a eficácia da classificação foi reduzida quando usamos um número menor de amostras de treinamento. Em resumo, o modelo foi eficaz na maioria dos cenários, mas teve dificuldade em generalizar quando o tamanho do conjunto de treinamento foi reduzido."
      ],
      "metadata": {
        "id": "XsWuEL3_w067"
      }
    }
  ]
}