diff --git a/2017/03-classificacao-scikit-learn/hands-on-knn.ipynb b/2017/03-classificacao-scikit-learn/hands-on-knn.ipynb index ee18647..7dd1d2a 100644 --- a/2017/03-classificacao-scikit-learn/hands-on-knn.ipynb +++ b/2017/03-classificacao-scikit-learn/hands-on-knn.ipynb @@ -514,7 +514,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/2017/04-knn-exercicio/knn_joel_ribeiro.ipynb b/2017/04-knn-exercicio/knn_joel_ribeiro.ipynb new file mode 100644 index 0000000..ae0e8e2 --- /dev/null +++ b/2017/04-knn-exercicio/knn_joel_ribeiro.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Garimpagem de Dados\n", + "\n", + "## Aula 4 - Exercício de Classificação com kNN\n", + "\n", + "Aluno : Joel Oliveira Ribeiro ~ 371822" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Importando todas as bibliotecas para uso\n", + "\n", + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pandas import DataFrame\n", + "import math \n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Atualizando a função de classificação para medir na forma distância euclidiana para o pacote do scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def someKNeighborsClassifier(k):\n", + " try:\n", + " return KNeighborsClassifier(n_neighbors=k, metric='euclidean')\n", + " except (k<=1) and (k%2==0) : # Para o algorítmo o k deve ser maior que um e ímpar\n", + " return ('Valor de k inválido, espera-se um k > 1 e ímpar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Carregando o arquivo contendo o Banco de Dados requisitado" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n" + ] + } + ], + "source": [ + "df = pd.read_csv(\"train.csv\")\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Selecionando e tratando a lista de features que deverão ser essências para a classificação " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Retirando features que de cara não parecem ser essências para a classificação\n", + "df.drop(labels=['Name','Ticket','Cabin'], inplace=True, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/joelribeiro/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " \n", + "/Users/joelribeiro/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " if sys.path[0] == '':\n" + ] + } + ], + "source": [ + "# Transformando as features categoricas ['Sex' e 'Embarked') em numéricas\n", + "df['Sex'] = pd.factorize(df['Sex'])[0]\n", + "df['Embarked'] = pd.factorize(df['Embarked'])[0]\n", + "\n", + "# Tratando a feature 'Age' por conter valores do tipo NaN, substituindo pelo valor médio de todas as idades\n", + "for i in range(len(df['Age'])):\n", + " if(math.isnan(df['Age'][i])):\n", + " df['Age'][i] = 0\n", + "\n", + "for i in range(len(df['Age'])):\n", + " if(df['Age'][i]==0):\n", + " df['Age'][i] = df['Age'].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Pclass Sex Age SibSp Parch Fare Embarked\n", + "Pclass 1.000000 -0.131900 -0.356187 0.083081 0.018443 -0.549500 0.050992\n", + "Sex -0.131900 1.000000 -0.073377 0.114631 0.245489 0.182333 0.111249\n", + "Age -0.356187 -0.073377 1.000000 -0.232411 -0.155118 0.107554 -0.053111\n", + "SibSp 0.083081 0.114631 -0.232411 1.000000 0.414838 0.159651 -0.058008\n", + "Parch 0.018443 0.245489 -0.155118 0.414838 1.000000 0.216225 -0.076625\n", + "Fare -0.549500 0.182333 0.107554 0.159651 0.216225 1.000000 0.058462\n", + "Embarked 0.050992 0.111249 -0.053111 -0.058008 -0.076625 0.058462 1.000000\n" + ] + } + ], + "source": [ + "#Matriz de Correlação\n", + "corr = df.loc[:,'Pclass':].corr()\n", + "print(corr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Separando o dataset em treino (75%) / teste (25%) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(normalize(df.loc[:,'Pclass':],norm='max'), df['Survived'], test_size=0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Executando o classificador para 30 k's pulando de 4 em 4 e apresente todas as acurácias utilizando o dataset de validação (Qual o melhor k?) [plotar um gráfico com os resultados]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "results = []\n", + "\n", + "for k in range(5,120,4):\n", + " neigh = someKNeighborsClassifier(k)\n", + " neigh.fit(X_train, y_train)\n", + " \n", + " results.append([neigh.score(X_test,y_test),k])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.70403587443946192, 5], [0.71300448430493268, 9], [0.70403587443946192, 13], [0.70403587443946192, 17], [0.71748878923766812, 21], [0.68161434977578472, 25], [0.66367713004484308, 29], [0.6547085201793722, 33], [0.64573991031390132, 37], [0.65022421524663676, 41], [0.64573991031390132, 45], [0.64125560538116588, 49], [0.65022421524663676, 53], [0.65022421524663676, 57], [0.6547085201793722, 61], [0.6547085201793722, 65], [0.6547085201793722, 69], [0.6547085201793722, 73], [0.65022421524663676, 77], [0.65022421524663676, 81], [0.6547085201793722, 85], [0.65022421524663676, 89], [0.6547085201793722, 93], [0.65022421524663676, 97], [0.65022421524663676, 101], [0.65022421524663676, 105], [0.65022421524663676, 109], [0.65022421524663676, 113], [0.65022421524663676, 117]]\n" + ] + } + ], + "source": [ + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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858LpXDUpL+RzO+ubDzwr2NTus4KuXDhqMA/fOZPj1fXcvmozp8/qTfNwCCn4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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = np.asarray(results)\n", + "plt.plot(results[:,1],results[:,0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.717488789238 21.0\n" + ] + } + ], + "source": [ + "for i in range(len(results)):\n", + " if(results[i,0]==max(results[:,0])):\n", + " max_Accuracie,max_k = [results[i,0],results[i,1]]\n", + "\n", + "print(max_Accuracie,max_k)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Executando o classificador para o melhor k encontrado utilizando o dataset de teste e apresentar um relatório da precisão " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### ainda implementando ..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/04-knn-exercicio/train.csv b/2017/04-knn-exercicio/train.csv new file mode 100644 index 0000000..63b68ab --- /dev/null +++ b/2017/04-knn-exercicio/train.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. 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Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C +829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q +830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28, +831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C +832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S +833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C +834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S +835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S +836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C +837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S +838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S +839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S +840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C +841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S +842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S +843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C +844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C +845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S +846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S +847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S +848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C +849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S +850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C +851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S +852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S +853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C +854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S +855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S +856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S +857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S +858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S +859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C +860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C +861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S +862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S +863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S +864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S +865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S +866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S +867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C +868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S +869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S +870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S +871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S +872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S +873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S +874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S +875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C +876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C +877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S +878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S +879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S +880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C +881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S +882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S +883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S +884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S +885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S +886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q +887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S +888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S +889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/2017/05-naive-bayes/Naive_Bayes_Francisco_Carlos.ipynb b/2017/05-naive-bayes/Naive_Bayes_Francisco_Carlos.ipynb index e962530..d141aba 100644 --- a/2017/05-naive-bayes/Naive_Bayes_Francisco_Carlos.ipynb +++ b/2017/05-naive-bayes/Naive_Bayes_Francisco_Carlos.ipynb @@ -167,7 +167,9 @@ { "cell_type": "code", "execution_count": 535, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#Separa em conjunto de treino (70%) e teste (30%)\n", @@ -181,7 +183,9 @@ { "cell_type": "code", "execution_count": 536, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#separa conjunto de teste em features e classe e calcula o tamanho total de treino\n", @@ -535,7 +539,9 @@ { "cell_type": "code", "execution_count": 546, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#feature_index = [(frequency_label.loc[i] == data.loc[i]).argmax() for i in data.index]\n", @@ -545,7 +551,9 @@ { "cell_type": "code", "execution_count": 547, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#Faz a predição do conjunto de teste\n", @@ -569,7 +577,9 @@ { "cell_type": "code", "execution_count": 548, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "result_y = np.squeeze(np.asarray(predicts))\n", @@ -644,7 +654,9 @@ { "cell_type": "code", "execution_count": 552, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#Transforma os valores string em númericos\n", @@ -666,7 +678,9 @@ { "cell_type": "code", "execution_count": 553, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#Faz o fit e o predict com a implementação do scikit\n", @@ -787,7 +801,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/2017/05-naive-bayes/Naive_Bayes_Joel_Ribeiro.ipynb b/2017/05-naive-bayes/Naive_Bayes_Joel_Ribeiro.ipynb new file mode 100644 index 0000000..66daf8e --- /dev/null +++ b/2017/05-naive-bayes/Naive_Bayes_Joel_Ribeiro.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Naive Bayes - Trabalho\n", + "### Aluno : Joel Oliveira Ribeiro - 371822\n", + "------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Import de bibliotecas \n", + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pandas import DataFrame\n", + "import math \n", + "from sklearn.model_selection import train_test_split\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Questão 1\n", + "\n", + "Implementar um classifacor Naive Bayes para o problema de predizer a qualidade de um carro. Para este fim, utilizar um conjunto de dados referente a qualidade de carros, disponível no [UCI](https://archive.ics.uci.edu/ml/datasets/car+evaluation). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Carregando o dataFrame e tratando os dados" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " vhigh vhigh.1 2 2.1 small low unacc\n", + "0 vhigh vhigh 2 2 small med unacc\n", + "1 vhigh vhigh 2 2 small high unacc\n", + "2 vhigh vhigh 2 2 med low unacc\n", + "3 vhigh vhigh 2 2 med med unacc\n", + "4 vhigh vhigh 2 2 med high unacc\n", + "5 vhigh vhigh 2 2 big low unacc\n", + "6 vhigh vhigh 2 2 big med unacc\n", + "7 vhigh vhigh 2 2 big high unacc\n", + "8 vhigh vhigh 2 4 small low unacc\n", + "9 vhigh vhigh 2 4 small med unacc\n", + "10 vhigh vhigh 2 4 small high unacc\n", + "11 vhigh vhigh 2 4 med low unacc\n", + "12 vhigh vhigh 2 4 med med unacc\n", + "13 vhigh vhigh 2 4 med high unacc\n", + "14 vhigh vhigh 2 4 big low unacc\n", + "15 vhigh vhigh 2 4 big med unacc\n", + "16 vhigh vhigh 2 4 big high unacc\n", + "17 vhigh vhigh 2 more small low unacc\n", + "18 vhigh vhigh 2 more small med unacc\n", + "19 vhigh vhigh 2 more small high unacc\n", + "20 vhigh vhigh 2 more med low unacc\n", + "21 vhigh vhigh 2 more med med unacc\n", + "22 vhigh vhigh 2 more med high unacc\n", + "23 vhigh vhigh 2 more big low unacc\n", + "24 vhigh vhigh 2 more big med unacc\n", + "25 vhigh vhigh 2 more big high unacc\n", + "26 vhigh vhigh 3 2 small low unacc\n", + "27 vhigh vhigh 3 2 small med unacc\n", + "28 vhigh vhigh 3 2 small high unacc\n", + "29 vhigh vhigh 3 2 med low unacc\n", + "... ... ... ... ... ... ... ...\n", + "1697 low low 4 more big low unacc\n", + "1698 low low 4 more big med good\n", + "1699 low low 4 more big high vgood\n", + "1700 low low 5more 2 small low unacc\n", + "1701 low low 5more 2 small med unacc\n", + "1702 low low 5more 2 small high unacc\n", + "1703 low low 5more 2 med low unacc\n", + "1704 low low 5more 2 med med unacc\n", + "1705 low low 5more 2 med high unacc\n", + "1706 low low 5more 2 big low unacc\n", + "1707 low low 5more 2 big med unacc\n", + "1708 low low 5more 2 big high unacc\n", + "1709 low low 5more 4 small low unacc\n", + "1710 low low 5more 4 small med acc\n", + "1711 low low 5more 4 small high good\n", + "1712 low low 5more 4 med low unacc\n", + "1713 low low 5more 4 med med good\n", + "1714 low low 5more 4 med high vgood\n", + "1715 low low 5more 4 big low unacc\n", + "1716 low low 5more 4 big med good\n", + "1717 low low 5more 4 big high vgood\n", + "1718 low low 5more more small low unacc\n", + "1719 low low 5more more small med acc\n", + "1720 low low 5more more small high good\n", + "1721 low low 5more more med low unacc\n", + "1722 low low 5more more med med good\n", + "1723 low low 5more more med high vgood\n", + "1724 low low 5more more big low unacc\n", + "1725 low low 5more more big med good\n", + "1726 low low 5more more big high vgood\n", + "\n", + "[1727 rows x 7 columns]\n" + ] + } + ], + "source": [ + "# Carregando o dataFrame\n", + "df = pd.read_csv(\"carData.csv\")\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " vhigh vhigh.1 2 2.1 small low unacc\n", + "0 0 0 2 2 0 0 0\n", + "1 0 0 2 2 0 1 0\n", + "2 0 0 2 2 1 2 0\n", + "3 0 0 2 2 1 0 0\n", + "4 0 0 2 2 1 1 0\n", + "5 0 0 2 2 2 2 0\n", + "6 0 0 2 2 2 0 0\n", + "7 0 0 2 2 2 1 0\n", + "8 0 0 2 4 0 2 0\n", + "9 0 0 2 4 0 0 0\n", + "10 0 0 2 4 0 1 0\n", + "11 0 0 2 4 1 2 0\n", + "12 0 0 2 4 1 0 0\n", + "13 0 0 2 4 1 1 0\n", + "14 0 0 2 4 2 2 0\n", + "15 0 0 2 4 2 0 0\n", + "16 0 0 2 4 2 1 0\n", + "17 0 0 2 more 0 2 0\n", + "18 0 0 2 more 0 0 0\n", + "19 0 0 2 more 0 1 0\n", + "20 0 0 2 more 1 2 0\n", + "21 0 0 2 more 1 0 0\n", + "22 0 0 2 more 1 1 0\n", + "23 0 0 2 more 2 2 0\n", + "24 0 0 2 more 2 0 0\n", + "25 0 0 2 more 2 1 0\n", + "26 0 0 3 2 0 2 0\n", + "27 0 0 3 2 0 0 0\n", + "28 0 0 3 2 0 1 0\n", + "29 0 0 3 2 1 2 0\n", + "... ... ... ... ... ... ... ...\n", + "1697 3 3 4 more 2 2 0\n", + "1698 3 3 4 more 2 0 3\n", + "1699 3 3 4 more 2 1 2\n", + "1700 3 3 5more 2 0 2 0\n", + "1701 3 3 5more 2 0 0 0\n", + "1702 3 3 5more 2 0 1 0\n", + "1703 3 3 5more 2 1 2 0\n", + "1704 3 3 5more 2 1 0 0\n", + "1705 3 3 5more 2 1 1 0\n", + "1706 3 3 5more 2 2 2 0\n", + "1707 3 3 5more 2 2 0 0\n", + "1708 3 3 5more 2 2 1 0\n", + "1709 3 3 5more 4 0 2 0\n", + "1710 3 3 5more 4 0 0 1\n", + "1711 3 3 5more 4 0 1 3\n", + "1712 3 3 5more 4 1 2 0\n", + "1713 3 3 5more 4 1 0 3\n", + "1714 3 3 5more 4 1 1 2\n", + "1715 3 3 5more 4 2 2 0\n", + "1716 3 3 5more 4 2 0 3\n", + "1717 3 3 5more 4 2 1 2\n", + "1718 3 3 5more more 0 2 0\n", + "1719 3 3 5more more 0 0 1\n", + "1720 3 3 5more more 0 1 3\n", + "1721 3 3 5more more 1 2 0\n", + "1722 3 3 5more more 1 0 3\n", + "1723 3 3 5more more 1 1 2\n", + "1724 3 3 5more more 2 2 0\n", + "1725 3 3 5more more 2 0 3\n", + "1726 3 3 5more more 2 1 2\n", + "\n", + "[1727 rows x 7 columns]\n", + "--------------------\n", + " vhigh vhigh.1 2 2.1 small low unacc\n", + "0 0 0 0 0 0 0 0\n", + "1 0 0 0 0 0 1 0\n", + "2 0 0 0 0 1 2 0\n", + "3 0 0 0 0 1 0 0\n", + "4 0 0 0 0 1 1 0\n", + "5 0 0 0 0 2 2 0\n", + "6 0 0 0 0 2 0 0\n", + "7 0 0 0 0 2 1 0\n", + "8 0 0 0 1 0 2 0\n", + "9 0 0 0 1 0 0 0\n", + "10 0 0 0 1 0 1 0\n", + "11 0 0 0 1 1 2 0\n", + "12 0 0 0 1 1 0 0\n", + "13 0 0 0 1 1 1 0\n", + "14 0 0 0 1 2 2 0\n", + "15 0 0 0 1 2 0 0\n", + "16 0 0 0 1 2 1 0\n", + "17 0 0 0 2 0 2 0\n", + "18 0 0 0 2 0 0 0\n", + "19 0 0 0 2 0 1 0\n", + "20 0 0 0 2 1 2 0\n", + "21 0 0 0 2 1 0 0\n", + "22 0 0 0 2 1 1 0\n", + "23 0 0 0 2 2 2 0\n", + "24 0 0 0 2 2 0 0\n", + "25 0 0 0 2 2 1 0\n", + "26 0 0 1 0 0 2 0\n", + "27 0 0 1 0 0 0 0\n", + "28 0 0 1 0 0 1 0\n", + "29 0 0 1 0 1 2 0\n", + "... ... ... .. ... ... ... ...\n", + "1697 3 3 2 2 2 2 0\n", + "1698 3 3 2 2 2 0 3\n", + "1699 3 3 2 2 2 1 2\n", + "1700 3 3 3 0 0 2 0\n", + "1701 3 3 3 0 0 0 0\n", + "1702 3 3 3 0 0 1 0\n", + "1703 3 3 3 0 1 2 0\n", + "1704 3 3 3 0 1 0 0\n", + "1705 3 3 3 0 1 1 0\n", + "1706 3 3 3 0 2 2 0\n", + "1707 3 3 3 0 2 0 0\n", + "1708 3 3 3 0 2 1 0\n", + "1709 3 3 3 1 0 2 0\n", + "1710 3 3 3 1 0 0 1\n", + "1711 3 3 3 1 0 1 3\n", + "1712 3 3 3 1 1 2 0\n", + "1713 3 3 3 1 1 0 3\n", + "1714 3 3 3 1 1 1 2\n", + "1715 3 3 3 1 2 2 0\n", + "1716 3 3 3 1 2 0 3\n", + "1717 3 3 3 1 2 1 2\n", + "1718 3 3 3 2 0 2 0\n", + "1719 3 3 3 2 0 0 1\n", + "1720 3 3 3 2 0 1 3\n", + "1721 3 3 3 2 1 2 0\n", + "1722 3 3 3 2 1 0 3\n", + "1723 3 3 3 2 1 1 2\n", + "1724 3 3 3 2 2 2 0\n", + "1725 3 3 3 2 2 0 3\n", + "1726 3 3 3 2 2 1 2\n", + "\n", + "[1727 rows x 7 columns]\n", + "--------------------\n", + "[[0 0 0 ..., 0 0 0]\n", + " [0 0 0 ..., 0 1 0]\n", + " [0 0 0 ..., 1 2 0]\n", + " ..., \n", + " [3 3 3 ..., 2 2 0]\n", + " [3 3 3 ..., 2 0 3]\n", + " [3 3 3 ..., 2 1 2]]\n" + ] + } + ], + "source": [ + "# Tratamento dos dados, necessário passar algumas features de caracteres para números\n", + "df['vhigh'] = pd.factorize(df['vhigh'])[0]\n", + "df['vhigh.1'] = pd.factorize(df['vhigh.1'])[0]\n", + "df['small'] = pd.factorize(df['small'])[0]\n", + "df['low'] = pd.factorize(df['low'])[0]\n", + "df['unacc'] = pd.factorize(df['unacc'])[0]\n", + "\n", + "print(df)\n", + "\n", + "df['2'] = pd.factorize(df['2'])[0]\n", + "df['2.1'] = pd.factorize(df['2.1'])[0]\n", + "\n", + "print('--------------------')\n", + "print(df)\n", + "\n", + "# Mudando para tipo numpy array\n", + "df = df.values\n", + "print('--------------------')\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Criando as funções para o classificador do tipo Naive Bayes\n", + "Adaptando as funções do próprio notebook da aula " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def splitDataset(dataset, splitRatio):\n", + " trainSize = int(len(dataset) * splitRatio)\n", + " trainSet = []\n", + " copy = list(dataset)\n", + " while len(trainSet) < trainSize:\n", + " index = random.randrange(len(copy))\n", + " trainSet.append(copy.pop(index))\n", + " \n", + " return [trainSet, copy]\n", + " \n", + "def separateByClass(dataset):\n", + " separated = {}\n", + " for i in range(len(dataset)):\n", + " vector = dataset[i]\n", + " if (vector[-1] not in separated):\n", + " separated[vector[-1]] = []\n", + " separated[vector[-1]].append(vector)\n", + " return separated\n", + "\n", + "def mean(numbers):\n", + " return sum(numbers)/float(len(numbers))\n", + " \n", + "def stdev(numbers):\n", + " avg = mean(numbers)\n", + " variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)\n", + " return math.sqrt(variance)\n", + " \n", + "def summarize(dataset):\n", + " summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]\n", + " del summaries[-1]\n", + " return summaries\n", + " \n", + "def summarizeByClass(dataset):\n", + " separated = separateByClass(dataset)\n", + " summaries = {}\n", + " for classValue, instances in separated.items():\n", + " summaries[classValue] = summarize(instances)\n", + " return summaries\n", + " \n", + "def calculateProbability(x, mean, stdev):\n", + " exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))\n", + " return (1 / (math.sqrt(2*math.pi) * math.pow(stdev, 2))) * exponent\n", + " \n", + "def calculateClassProbabilities(summaries, inputVector):\n", + " probabilities = {}\n", + " for classValue, classSummaries in summaries.items():\n", + " probabilities[classValue] = 1\n", + " for i in range(len(classSummaries)):\n", + " mean, stdev = classSummaries[i]\n", + " x = inputVector[i]\n", + " probabilities[classValue] *= calculateProbability(x, mean, stdev)\n", + " return probabilities\n", + " \n", + "def predict(summaries, inputVector):\n", + " probabilities = calculateClassProbabilities(summaries, inputVector)\n", + " bestLabel, bestProb = None, -1\n", + " for classValue, probability in probabilities.items():\n", + " if bestLabel is None or probability > bestProb:\n", + " bestProb = probability\n", + " bestLabel = classValue\n", + " return bestLabel\n", + " \n", + "def getPredictions(summaries, testSet):\n", + " predictions = []\n", + " for i in range(len(testSet)):\n", + " result = predict(summaries, testSet[i])\n", + " predictions.append(result)\n", + " return predictions\n", + " \n", + "def getAccuracy(testSet, predictions):\n", + " correct = 0\n", + " for i in range(len(testSet)):\n", + " if testSet[i][-1] == predictions[i]:\n", + " correct += 1\n", + " return (correct/float(len(testSet))) * 100.0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Separando treino (67%) de teste (33%)\n", + "X_train,X_test = splitDataset(df,0.67)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1.4935177182368193, 1.1095688643707255), (1.497839239412273, 1.1056807537218771), (1.5073465859982713, 1.1095634736654678), (1.0051858254105446, 0.81506622156905), (1.0008643042350907, 0.8230035798648657), (1.0121002592912705, 0.8096683269265)]\n", + "--------------------------------------------------------\n", + "{0: [(1.3506815365551426, 1.1215004157709034), (1.3605947955390334, 1.1105555550338533), (1.4745972738537794, 1.1167733364496824), (0.78934324659231725, 0.8306984952291961), (0.92812887236679054, 0.8274384780660208), (1.1970260223048328, 0.8495166210242404)], 1: [(1.5521235521235521, 1.0039579271859078), (1.5945945945945945, 1.0462788110792012), (1.5598455598455598, 1.0922957720211002), (1.501930501930502, 0.5009643210501636), (1.1158301158301158, 0.8033966085081455), (0.53281853281853286, 0.4998877489153622)], 3: [(2.6222222222222222, 0.49031014715590004), (2.6666666666666665, 0.4767312946227963), (1.4888888888888889, 1.1000459127241053), (1.4666666666666666, 0.5045249791095131), (1.0, 0.7977240352174656), (0.46666666666666667, 0.5045249791095131)], 2: [(2.5652173913043477, 0.5012062743707417), (2.2173913043478262, 0.727645929717068), (1.8043478260869565, 1.0670741975116678), (1.5434782608695652, 0.503610155185335), (1.6304347826086956, 0.48802074874715046), (1.0, 0.0)]}\n" + ] + } + ], + "source": [ + "summ = summarize(X_train)\n", + "print(summ)\n", + "print('--------------------------------------------------------')\n", + "summBy = summarizeByClass(X_train)\n", + "print(summBy)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "float division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetPredictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummBy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mgetPredictions\u001b[0;34m(summaries, testSet)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestSet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummaries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestSet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(summaries, inputVector)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummaries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mprobabilities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalculateClassProbabilities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummaries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0mbestLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbestProb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mclassValue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprobabilities\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mcalculateClassProbabilities\u001b[0;34m(summaries, inputVector)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassSummaries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputVector\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mprobabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclassValue\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcalculateProbability\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mprobabilities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mcalculateProbability\u001b[0;34m(x, mean, stdev)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalculateProbability\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mexponent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstdev\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstdev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mexponent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero" + ] + } + ], + "source": [ + "print(getPredictions(summBy,X_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Questão 2\n", + "Criar uma versão de sua implementação usando as funções disponíveis na biblioteca SciKitLearn para o Naive Bayes ([veja aqui](http://scikit-learn.org/stable/modules/naive_bayes.html)) " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of mislabeled points out of a total 7 points : 0\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "gnb = GaussianNB()\n", + "y_pred = gnb.fit(df, df[:,6]).predict(df)\n", + "print(\"Number of mislabeled points out of a total %d points : %d\"% (df.shape[0],(df[:,6] != y_pred).sum()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/05-naive-bayes/Naive_Bayes_Trabalho.ipynb b/2017/05-naive-bayes/Naive_Bayes_Trabalho.ipynb index 61a431f..1acb584 100644 --- a/2017/05-naive-bayes/Naive_Bayes_Trabalho.ipynb +++ b/2017/05-naive-bayes/Naive_Bayes_Trabalho.ipynb @@ -55,7 +55,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/2017/05-naive-bayes/Naive_Bayes_Tutorial_01.ipynb b/2017/05-naive-bayes/Naive_Bayes_Tutorial_01.ipynb index 104d3b4..0753502 100644 --- a/2017/05-naive-bayes/Naive_Bayes_Tutorial_01.ipynb +++ b/2017/05-naive-bayes/Naive_Bayes_Tutorial_01.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "metadata": { "collapsed": true }, @@ -71,13 +71,790 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - 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0.0, 42.9, 1.394, 22.0, 1.0],\n", + " [8.0, 167.0, 106.0, 46.0, 231.0, 37.6, 0.165, 43.0, 1.0],\n", + " [9.0, 145.0, 80.0, 46.0, 130.0, 37.9, 0.637, 40.0, 1.0],\n", + " [6.0, 115.0, 60.0, 39.0, 0.0, 33.7, 0.245, 40.0, 1.0],\n", + " [1.0, 112.0, 80.0, 45.0, 132.0, 34.8, 0.217, 24.0, 0.0],\n", + " [4.0, 145.0, 82.0, 18.0, 0.0, 32.5, 0.235, 70.0, 1.0],\n", + " [10.0, 111.0, 70.0, 27.0, 0.0, 27.5, 0.141, 40.0, 1.0],\n", + " [6.0, 98.0, 58.0, 33.0, 190.0, 34.0, 0.43, 43.0, 0.0],\n", + " [9.0, 154.0, 78.0, 30.0, 100.0, 30.9, 0.164, 45.0, 0.0],\n", + " [6.0, 165.0, 68.0, 26.0, 168.0, 33.6, 0.631, 49.0, 0.0],\n", + " [1.0, 99.0, 58.0, 10.0, 0.0, 25.4, 0.551, 21.0, 0.0],\n", + " [10.0, 68.0, 106.0, 23.0, 49.0, 35.5, 0.285, 47.0, 0.0],\n", + " [3.0, 123.0, 100.0, 35.0, 240.0, 57.3, 0.88, 22.0, 0.0],\n", + " [8.0, 91.0, 82.0, 0.0, 0.0, 35.6, 0.587, 68.0, 0.0],\n", + " [6.0, 195.0, 70.0, 0.0, 0.0, 30.9, 0.328, 31.0, 1.0],\n", + " [9.0, 156.0, 86.0, 0.0, 0.0, 24.8, 0.23, 53.0, 1.0],\n", + " [0.0, 93.0, 60.0, 0.0, 0.0, 35.3, 0.263, 25.0, 0.0],\n", + " [3.0, 121.0, 52.0, 0.0, 0.0, 36.0, 0.127, 25.0, 1.0],\n", + " [2.0, 101.0, 58.0, 17.0, 265.0, 24.2, 0.614, 23.0, 0.0],\n", + " [2.0, 56.0, 56.0, 28.0, 45.0, 24.2, 0.332, 22.0, 0.0],\n", + " [0.0, 162.0, 76.0, 36.0, 0.0, 49.6, 0.364, 26.0, 1.0],\n", + " [0.0, 95.0, 64.0, 39.0, 105.0, 44.6, 0.366, 22.0, 0.0],\n", + " [4.0, 125.0, 80.0, 0.0, 0.0, 32.3, 0.536, 27.0, 1.0],\n", + " [5.0, 136.0, 82.0, 0.0, 0.0, 0.0, 0.64, 69.0, 0.0],\n", + " [2.0, 129.0, 74.0, 26.0, 205.0, 33.2, 0.591, 25.0, 0.0],\n", + " [3.0, 130.0, 64.0, 0.0, 0.0, 23.1, 0.314, 22.0, 0.0],\n", + " [1.0, 107.0, 50.0, 19.0, 0.0, 28.3, 0.181, 29.0, 0.0],\n", + " [1.0, 140.0, 74.0, 26.0, 180.0, 24.1, 0.828, 23.0, 0.0],\n", + " [1.0, 144.0, 82.0, 46.0, 180.0, 46.1, 0.335, 46.0, 1.0],\n", + " [8.0, 107.0, 80.0, 0.0, 0.0, 24.6, 0.856, 34.0, 0.0],\n", + " [13.0, 158.0, 114.0, 0.0, 0.0, 42.3, 0.257, 44.0, 1.0],\n", + " [2.0, 121.0, 70.0, 32.0, 95.0, 39.1, 0.886, 23.0, 0.0],\n", + " [7.0, 129.0, 68.0, 49.0, 125.0, 38.5, 0.439, 43.0, 1.0],\n", + " [2.0, 90.0, 60.0, 0.0, 0.0, 23.5, 0.191, 25.0, 0.0],\n", + " [7.0, 142.0, 90.0, 24.0, 480.0, 30.4, 0.128, 43.0, 1.0],\n", + " [3.0, 169.0, 74.0, 19.0, 125.0, 29.9, 0.268, 31.0, 1.0],\n", + " [0.0, 99.0, 0.0, 0.0, 0.0, 25.0, 0.253, 22.0, 0.0],\n", + " [4.0, 127.0, 88.0, 11.0, 155.0, 34.5, 0.598, 28.0, 0.0],\n", + " [4.0, 118.0, 70.0, 0.0, 0.0, 44.5, 0.904, 26.0, 0.0],\n", + " [2.0, 122.0, 76.0, 27.0, 200.0, 35.9, 0.483, 26.0, 0.0],\n", + " [6.0, 125.0, 78.0, 31.0, 0.0, 27.6, 0.565, 49.0, 1.0],\n", + " [1.0, 168.0, 88.0, 29.0, 0.0, 35.0, 0.905, 52.0, 1.0],\n", + " [2.0, 129.0, 0.0, 0.0, 0.0, 38.5, 0.304, 41.0, 0.0],\n", + " [4.0, 110.0, 76.0, 20.0, 100.0, 28.4, 0.118, 27.0, 0.0],\n", + " [6.0, 80.0, 80.0, 36.0, 0.0, 39.8, 0.177, 28.0, 0.0],\n", + " [10.0, 115.0, 0.0, 0.0, 0.0, 0.0, 0.261, 30.0, 1.0],\n", + " [2.0, 127.0, 46.0, 21.0, 335.0, 34.4, 0.176, 22.0, 0.0],\n", + " [9.0, 164.0, 78.0, 0.0, 0.0, 32.8, 0.148, 45.0, 1.0],\n", + " [2.0, 93.0, 64.0, 32.0, 160.0, 38.0, 0.674, 23.0, 1.0],\n", + " [3.0, 158.0, 64.0, 13.0, 387.0, 31.2, 0.295, 24.0, 0.0],\n", + " [5.0, 126.0, 78.0, 27.0, 22.0, 29.6, 0.439, 40.0, 0.0],\n", + " [10.0, 129.0, 62.0, 36.0, 0.0, 41.2, 0.441, 38.0, 1.0],\n", + " [0.0, 134.0, 58.0, 20.0, 291.0, 26.4, 0.352, 21.0, 0.0],\n", + " [3.0, 102.0, 74.0, 0.0, 0.0, 29.5, 0.121, 32.0, 0.0],\n", + " [7.0, 187.0, 50.0, 33.0, 392.0, 33.9, 0.826, 34.0, 1.0],\n", + " [3.0, 173.0, 78.0, 39.0, 185.0, 33.8, 0.97, 31.0, 1.0],\n", + " [10.0, 94.0, 72.0, 18.0, 0.0, 23.1, 0.595, 56.0, 0.0],\n", + " [1.0, 108.0, 60.0, 46.0, 178.0, 35.5, 0.415, 24.0, 0.0],\n", + " [5.0, 97.0, 76.0, 27.0, 0.0, 35.6, 0.378, 52.0, 1.0],\n", + " [4.0, 83.0, 86.0, 19.0, 0.0, 29.3, 0.317, 34.0, 0.0],\n", + " [1.0, 114.0, 66.0, 36.0, 200.0, 38.1, 0.289, 21.0, 0.0],\n", + " [1.0, 149.0, 68.0, 29.0, 127.0, 29.3, 0.349, 42.0, 1.0],\n", + " [5.0, 117.0, 86.0, 30.0, 105.0, 39.1, 0.251, 42.0, 0.0],\n", + " [1.0, 111.0, 94.0, 0.0, 0.0, 32.8, 0.265, 45.0, 0.0],\n", + " [4.0, 112.0, 78.0, 40.0, 0.0, 39.4, 0.236, 38.0, 0.0],\n", + " [1.0, 116.0, 78.0, 29.0, 180.0, 36.1, 0.496, 25.0, 0.0],\n", + " [0.0, 141.0, 84.0, 26.0, 0.0, 32.4, 0.433, 22.0, 0.0],\n", + " [2.0, 175.0, 88.0, 0.0, 0.0, 22.9, 0.326, 22.0, 0.0],\n", + " [2.0, 92.0, 52.0, 0.0, 0.0, 30.1, 0.141, 22.0, 0.0],\n", + " [3.0, 130.0, 78.0, 23.0, 79.0, 28.4, 0.323, 34.0, 1.0],\n", + " [8.0, 120.0, 86.0, 0.0, 0.0, 28.4, 0.259, 22.0, 1.0],\n", + " [2.0, 174.0, 88.0, 37.0, 120.0, 44.5, 0.646, 24.0, 1.0],\n", + " [2.0, 106.0, 56.0, 27.0, 165.0, 29.0, 0.426, 22.0, 0.0],\n", + " [2.0, 105.0, 75.0, 0.0, 0.0, 23.3, 0.56, 53.0, 0.0],\n", + " [4.0, 95.0, 60.0, 32.0, 0.0, 35.4, 0.284, 28.0, 0.0],\n", + " [0.0, 126.0, 86.0, 27.0, 120.0, 27.4, 0.515, 21.0, 0.0],\n", + " [8.0, 65.0, 72.0, 23.0, 0.0, 32.0, 0.6, 42.0, 0.0],\n", + " [2.0, 99.0, 60.0, 17.0, 160.0, 36.6, 0.453, 21.0, 0.0],\n", + " [1.0, 102.0, 74.0, 0.0, 0.0, 39.5, 0.293, 42.0, 1.0],\n", + " [11.0, 120.0, 80.0, 37.0, 150.0, 42.3, 0.785, 48.0, 1.0],\n", + " [3.0, 102.0, 44.0, 20.0, 94.0, 30.8, 0.4, 26.0, 0.0],\n", + " [1.0, 109.0, 58.0, 18.0, 116.0, 28.5, 0.219, 22.0, 0.0],\n", + " [9.0, 140.0, 94.0, 0.0, 0.0, 32.7, 0.734, 45.0, 1.0],\n", + " [13.0, 153.0, 88.0, 37.0, 140.0, 40.6, 1.174, 39.0, 0.0],\n", + " [12.0, 100.0, 84.0, 33.0, 105.0, 30.0, 0.488, 46.0, 0.0],\n", + " [1.0, 147.0, 94.0, 41.0, 0.0, 49.3, 0.358, 27.0, 1.0],\n", + " [1.0, 81.0, 74.0, 41.0, 57.0, 46.3, 1.096, 32.0, 0.0],\n", + " [3.0, 187.0, 70.0, 22.0, 200.0, 36.4, 0.408, 36.0, 1.0],\n", + " [6.0, 162.0, 62.0, 0.0, 0.0, 24.3, 0.178, 50.0, 1.0],\n", + " [4.0, 136.0, 70.0, 0.0, 0.0, 31.2, 1.182, 22.0, 1.0],\n", + " [1.0, 121.0, 78.0, 39.0, 74.0, 39.0, 0.261, 28.0, 0.0],\n", + " [3.0, 108.0, 62.0, 24.0, 0.0, 26.0, 0.223, 25.0, 0.0],\n", + " [0.0, 181.0, 88.0, 44.0, 510.0, 43.3, 0.222, 26.0, 1.0],\n", + " [8.0, 154.0, 78.0, 32.0, 0.0, 32.4, 0.443, 45.0, 1.0],\n", + " [1.0, 128.0, 88.0, 39.0, 110.0, 36.5, 1.057, 37.0, 1.0],\n", + " [7.0, 137.0, 90.0, 41.0, 0.0, 32.0, 0.391, 39.0, 0.0],\n", + " [0.0, 123.0, 72.0, 0.0, 0.0, 36.3, 0.258, 52.0, 1.0],\n", + " [1.0, 106.0, 76.0, 0.0, 0.0, 37.5, 0.197, 26.0, 0.0],\n", + " [6.0, 190.0, 92.0, 0.0, 0.0, 35.5, 0.278, 66.0, 1.0],\n", + " [2.0, 88.0, 58.0, 26.0, 16.0, 28.4, 0.766, 22.0, 0.0],\n", + " [9.0, 170.0, 74.0, 31.0, 0.0, 44.0, 0.403, 43.0, 1.0],\n", + " [9.0, 89.0, 62.0, 0.0, 0.0, 22.5, 0.142, 33.0, 0.0],\n", + " [10.0, 101.0, 76.0, 48.0, 180.0, 32.9, 0.171, 63.0, 0.0],\n", + " [2.0, 122.0, 70.0, 27.0, 0.0, 36.8, 0.34, 27.0, 0.0],\n", + " [5.0, 121.0, 72.0, 23.0, 112.0, 26.2, 0.245, 30.0, 0.0],\n", + " [1.0, 126.0, 60.0, 0.0, 0.0, 30.1, 0.349, 47.0, 1.0],\n", + " [1.0, 93.0, 70.0, 31.0, 0.0, 30.4, 0.315, 23.0, 0.0]]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "loadCsv('pima-indians-diabetes.data')" ] }, { @@ -93,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -122,13 +899,21 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[4], [5], [3]], [[2], [1]]]\n" + ] + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "a = [[2],[5],[4],[1],[3]]\n", + "print(splitDataset(a,0.67))" ] }, { @@ -159,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -188,13 +973,20 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: [[1, 20, 1], [3, 22, 1]], 0: [[2, 21, 0]]}\n" + ] + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "print(separateByClass([[1,20,1],[3,22,1],[2,21,0]]))" ] }, { @@ -212,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -241,13 +1033,21 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "média=3.0, stdev=1.5811388300841898\n" + ] + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "a=[1,2,3,4,5]\n", + "print('média={}, stdev={}'.format(mean(a),stdev(a)))" ] }, { @@ -263,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -288,13 +1088,20 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(11.0, 9.539392014169456)]\n" + ] + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "print(summarize([[2,1],[21,1],[10,0]]))" ] }, { @@ -309,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 38, "metadata": { "collapsed": true }, @@ -336,13 +1143,20 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: [(4.666666666666667, 4.725815626252609), (15.333333333333334, 9.865765724632494)]}\n" + ] + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "print(summarizeByClass([[1,20,0],[3,22,0],[10,4,0]]))" ] }, { @@ -374,7 +1188,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 62, "metadata": { "collapsed": true }, @@ -398,7 +1212,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 63, "metadata": { "collapsed": true }, @@ -420,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 64, "metadata": { "collapsed": true }, @@ -450,7 +1264,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 65, "metadata": { "collapsed": true }, @@ -472,7 +1286,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 66, "metadata": { "collapsed": true }, @@ -501,13 +1315,28 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "float division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m### COLOQUE SUA RESPOSTA AQUI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummarizeByClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36msummarizeByClass\u001b[0;34m(dataset)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msummaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mclassValue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstances\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseparated\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0msummaries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclassValue\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummarize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msummaries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36msummarize\u001b[0;34m(dataset)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msummarize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msummaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattribute\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdev\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattribute\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mdel\u001b[0m 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\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0msummaries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msummaries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mstdev\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstdev\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mvariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero" + ] + } + ], "source": [ - "### COLOQUE SUA RESPOSTA AQUI" + "### COLOQUE SUA RESPOSTA AQUI\n", + "predict(summarizeByClass([[1,1],[20,2]]),[1.1])" ] }, { @@ -653,7 +1482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/2017/05-naive-bayes/naiveBayes_bruno_mourao.ipynb b/2017/05-naive-bayes/naiveBayes_bruno_mourao.ipynb index 0971251..1eac789 100644 --- a/2017/05-naive-bayes/naiveBayes_bruno_mourao.ipynb +++ b/2017/05-naive-bayes/naiveBayes_bruno_mourao.ipynb @@ -423,7 +423,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/2017/05-naive-bayes/resp_Naive_Bayes_Trabalho_Vilma.ipynb b/2017/05-naive-bayes/resp_Naive_Bayes_Trabalho_Vilma.ipynb index 793ae4d..c94fad0 100755 --- a/2017/05-naive-bayes/resp_Naive_Bayes_Trabalho_Vilma.ipynb +++ b/2017/05-naive-bayes/resp_Naive_Bayes_Trabalho_Vilma.ipynb @@ -32,7 +32,9 @@ { "cell_type": "code", "execution_count": 83, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import csv\n", @@ -268,7 +270,9 @@ { "cell_type": "code", "execution_count": 86, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "y_pred = getPredictions(summaries, X_test)" @@ -385,7 +389,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/2017/05-naive-bayes/resp_naive_bayes_moesio.ipynb b/2017/05-naive-bayes/resp_naive_bayes_moesio.ipynb index 1c765c8..ddc9262 100644 --- a/2017/05-naive-bayes/resp_naive_bayes_moesio.ipynb +++ b/2017/05-naive-bayes/resp_naive_bayes_moesio.ipynb @@ -137,7 +137,9 @@ { "cell_type": "code", "execution_count": 36, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "ds = loadCsv('carData.csv')" @@ -146,7 +148,9 @@ { "cell_type": "code", "execution_count": 37, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "j = 0;\n", @@ -478,7 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.1" } }, "nbformat": 4,