├── .gitattributes ├── ArvoreDecisão ├── .DS_Store ├── .ipynb_checkpoints │ └── Aula_3_Decision_tree_Einstein_Covid-checkpoint.ipynb ├── crashcourse_decisiontree.py └── dataset_einstein.csv ├── GradienteDescendente ├── gradiente_descendente_regressao.py └── readme.txt ├── KNN ├── Aula_5_KNN_eistein.ipynb ├── readme.txt └── teleCust1000t.csv ├── Kmeans ├── Cust_Segmentation.csv ├── dataset_einstein.csv ├── dataset_titanic.csv ├── readme.txt └── teleCust1000t.csv ├── RegressaoLinear ├── CrashCourse_Regressão_Linear.ipynb ├── FuelConsumptionCo2.csv └── crashcourse_regressão_linear.py ├── RegressaoLogistica ├── Logistic_Regression_Pytorch.py └── readme.txt └── RegressaoSoftmax ├── pytorch_regressão_softmax.py └── readme.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /ArvoreDecisão/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/diogocortiz/Curso-IA-para-todos/48ce9636fbd3f17d32349599dc3654cc0cd15e47/ArvoreDecisão/.DS_Store -------------------------------------------------------------------------------- /ArvoreDecisão/crashcourse_decisiontree.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Einstein_Covid_Decision_tree.ipynb 3 | # Crash Course IA - Aula 2 - Regressão Linear 4 | 5 | Este é o primeiro exercício prático do Crash Course de IA, 6 | uma iniciativa em adaptar a disciplina de IA do TIDD/PUC-SP para uma versão online 7 | e acessível para todos. 8 | 9 | Neste tutorial, vamos treinar um modelo de regressão linear para predição de valores contínuos. 10 | 11 | As aulas estão disponíveis no YouTube: https://youtu.be/Ze-Q6ZNWpco 12 | 13 | Qualquer dúvida, entre em contato: 14 | https://instagram.com/diogocortiz 15 | https://twitter.com/diogocortiz 16 | 17 | """ 18 | 19 | Automatically generated by Colaboratory. 20 | 21 | Original file is located at 22 | https://colab.research.google.com/drive/1I3so8oVR9QO47_Cn1nF96aOfB_coxloC 23 | """ 24 | 25 | import numpy as np 26 | import itertools 27 | import pandas as pd 28 | import matplotlib.pyplot as plt 29 | from sklearn.tree import DecisionTreeClassifier, export_graphviz 30 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix 31 | from sklearn.model_selection import train_test_split 32 | from sklearn.externals.six import StringIO 33 | import pydotplus 34 | from IPython.display import Image 35 | 36 | # IMPORTANDO O DATASET PARA O DATAFRAME 37 | df = pd.read_csv('dataset_einstein.csv', delimiter=';') 38 | 39 | # MOSTRANDO AS PRIMEIRAS CINCO LINHAS 40 | print(df.head(5)) 41 | 42 | count_row = df.shape[0] # PEGANDO OS NÚMEROS DE REGISTROS 43 | count_col = df.shape[1] # PEGANDO OS NUMEROS DE COLUNAS 44 | 45 | print(count_row) 46 | print(count_col) 47 | # REPAREM QUE HÁ MUITOS REGISTROS EM QUE HÁ DADOS FALTANDO NOS CAMPOS 48 | 49 | """Precisamos deixar o dataset somente com os registros que tenham todos os campos (para evitar ruídos e distorções)""" 50 | 51 | # REMOVENDO OS REGISTROS NOS QUAIS PELO MENOS UM CAMPO ESTÁ EM BRANCO (NAN) 52 | df = df.dropna() 53 | 54 | print(df.head(5)) 55 | 56 | print('Quantidade de campos(colunas): ', df.shape[1]) 57 | print('Total de registros:', df.shape[0]) 58 | 59 | #VAMOS VERIFICAR SE O BANCO DE DADOS ESTÁ BALANCEADO OU DESBALANCEADO 60 | print ('Total de registros negativos: ', df[df['SARS-Cov-2 exam result'] =='negative'].shape[0]) 61 | print ('Total de registros positivos: ', df[df['SARS-Cov-2 exam result'] =='positive'].shape[0]) 62 | 63 | """Precisamos converter o Dataframe para um Array Numpy, que é o tipo de dados que iremos usar no treinamento. Também iremos já separar o Dataset em dois. Um com as features de entrada, e outro com os labels (etiquetas, rótulos do registro). 64 | 65 | Neste caso, estamos tentando fazer um classificador para o teste do Covid, neste caso, queremos treinar o nosso modelo com a etiqueta presente no campo 'SARS-Cov-2 exam result' 66 | """ 67 | 68 | # VAMOS JOGAR AS ETIQUETAS PARA Y 69 | Y = df['SARS-Cov-2 exam result'].values 70 | print(Y) 71 | 72 | # X SERÁ A NOSSA MATRIZ COM AS FEATURES 73 | # VAMOS PEGAR OS CAMPOS DE TREINAMENTO (Hemoglobin, Leukocytes, Basophils, Proteina C reativa mg/dL) 74 | 75 | X = df[['Hemoglobin', 'Leukocytes', 'Basophils','Proteina C reativa mg/dL']].values 76 | 77 | # VAMOS MOSTRAR X 78 | print(X) 79 | 80 | """Agora precisamos dividir o nosso Dataset em dois: um para o treino (80% dos dados) e outro para o teste (20% dos dados)""" 81 | 82 | X_treino, X_teste, Y_treino, Y_teste = train_test_split(X, Y, test_size=0.2, random_state=3) 83 | 84 | """Agora vamos criar o e treinar o nosso modelo. Lembram da diferença entre algortimo e modelo? Existe o algortimo de treinamento (que neste caso é o de árvore de decisão) que irá exportar um modelo treinado (que também é um algoritmo).""" 85 | 86 | # CRIAR UM ALGORTIMO QUE SERÁ DO TIPO DE ÁRVORE DE DECISÃO 87 | 88 | algortimo_arvore = DecisionTreeClassifier(criterion='entropy', max_depth=5) 89 | # AGORA EM MINHA_ARVORE EU TENHO ASSOCIADA A ELA O ALGORITMO DE TREINAMENTO, 90 | # BASICAMENTE A RECEITA QUE VIMOS NA PARTE TÉORICA. 91 | 92 | #AGORA PRECISAMOS TREINÁ-LA 93 | modelo = algortimo_arvore.fit(X_treino, Y_treino) 94 | 95 | """A árvore de decisão pode ser considerada um modelo White Box, ou seja, um modelo que podemos entender melhor o que ele aprendeu e como ele decide. Podemos mostrar a árvore para isso.""" 96 | 97 | #PODEMOS MOSTRAR A FEATURE MAIS IMPORTANTE (WHITE BOX?) 98 | print (modelo.feature_importances_) 99 | 100 | 101 | nome_features = ['Hemoglobin', 'Leukocytes', 'Basophils','Proteina C reativa mg/dL'] 102 | nome_classes = modelo.classes_ 103 | 104 | # MONTAR A IMAGEM DA ÁRVORE 105 | dot_data = StringIO() 106 | #dot_data = tree.export_graphviz(my_tree_one, out_file=None, feature_names=featureNames) 107 | export_graphviz(modelo, out_file=dot_data, filled=True, feature_names=nome_features, class_names=nome_classes, rounded=True, special_characters=True) 108 | graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 109 | Image(graph.create_png()) 110 | graph.write_png("arvore.png") 111 | Image('arvore.png') 112 | 113 | """E podemos entender também quais as features de maior importância para o modelo treinado""" 114 | 115 | importances = modelo.feature_importances_ 116 | indices = np.argsort(importances)[::-1] 117 | print("Feature ranking:") 118 | 119 | for f in range(X.shape[1]): 120 | print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]])) 121 | f, ax = plt.subplots(figsize=(11, 9)) 122 | plt.title("Feature ranking", fontsize = 20) 123 | plt.bar(range(X.shape[1]), importances[indices], 124 | color="b", 125 | align="center") 126 | plt.xticks(range(X.shape[1]), indices) 127 | plt.xlim([-1, X.shape[1]]) 128 | plt.ylabel("importance", fontsize = 18) 129 | plt.xlabel("index of the feature", fontsize = 18) 130 | plt.show() 131 | 132 | #Indice das features 133 | # 0 - 'Hemoglobin', 134 | # 1 - 'Leukocytes' 135 | # 2 - 'Basophils', 136 | # 3 - 'Proteina C reativa mg/dL'] 137 | 138 | """Vamos testar o modelo, fazendo as predições no dataset de teste.""" 139 | 140 | # APLICANDO O MODELO NA BASE DE TESTES E ARMAZENDO O RESULTADO EM Y_PREDICOES 141 | Y_predicoes = modelo.predict(X_teste) 142 | 143 | #AVALIAÇÃO DO MODELO 144 | #VAMOS AVALIAR O VALOR REAL DO DATASET Y_TESTE COM AS PREDIÇÕES 145 | print("ACURÁCIA DA ÁRVORE: ", accuracy_score(Y_teste, Y_predicoes)) 146 | print (classification_report(Y_teste, Y_predicoes)) 147 | 148 | # PRECISÃO: DAS CLASSIFICAÇÕES QUE O MODELO FEZ PARA UMA DETERMINADA CLASSE, QUANTAS EFETIVAMENTE ERAM CORRETAS? 149 | # RECALL: DOS POSSÍVEIS DATAPOINTS PERTECENTES A UMA DETERMINADA CLASSE, QUANTOS O MODELO CONSEGIU CLASSIFICAR CORRETAMENTE? 150 | 151 | """Vamos entender a Matriz de Confusão""" 152 | 153 | def plot_confusion_matrix(cm, classes, 154 | normalize=False, 155 | title='Confusion matrix', 156 | cmap=plt.cm.Blues): 157 | """ 158 | This function prints and plots the confusion matrix. 159 | Normalization can be applied by setting `normalize=True`. 160 | """ 161 | plt.imshow(cm, interpolation='nearest', cmap=cmap) 162 | plt.title(title) 163 | plt.colorbar() 164 | tick_marks = np.arange(len(classes)) 165 | plt.xticks(tick_marks, classes, rotation=45) 166 | plt.yticks(tick_marks, classes) 167 | 168 | if normalize: 169 | cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] 170 | print("Matriz de Confusão Normalizada") 171 | else: 172 | print('Matriz de Confusão sem normalizacão ') 173 | 174 | print(cm) 175 | 176 | thresh = cm.max() / 2. 177 | for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): 178 | plt.text(j, i, cm[i, j], 179 | horizontalalignment="center", 180 | color="white" if cm[i, j] > thresh else "black") 181 | 182 | plt.tight_layout() 183 | plt.ylabel('Rótulo real') 184 | plt.xlabel('Rótulo prevista') 185 | 186 | matrix_confusao = confusion_matrix(Y_teste, Y_predicoes) 187 | plt.figure() 188 | plot_confusion_matrix(matrix_confusao, classes=nome_classes, 189 | title='Matrix de Confusao') 190 | -------------------------------------------------------------------------------- /GradienteDescendente/gradiente_descendente_regressao.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """PyTorch Introdução - Regressão Linear.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1bKC_MDzluD0HkcZyp611e6iSx0xBj5jK 8 | 9 | #REGRESSÃO LINEAR 10 | """ 11 | 12 | # RECEITA DE TREINAMENTO 13 | # 1 - DESIGN DO MODELO (INPUT, OUTPUT, FORWARD PASS) 14 | # 2 - DEFINIÇAO DA FUNÇÃO DE CUSTO E OTIMIZADOR 15 | # 3 - LOOP DE TREINAMENTO: 16 | # - FORWARD PASS: CALCULAR A PREDIÇÃO E O CUSTO 17 | # - BACKWARPASS: CALCULAR OS GRADIENTES 18 | # - ATUALIZAR OS PESOS 19 | 20 | import torch 21 | import time 22 | import torch.nn as nn 23 | import numpy as np 24 | from sklearn import datasets 25 | import matplotlib.pyplot as plt 26 | 27 | # PREPARAÇÃP DA DATA 28 | x_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=1) 29 | 30 | x = torch.from_numpy(x_numpy.astype(np.float32)) 31 | y = torch.from_numpy(y_numpy.astype(np.float32)) 32 | y = y.view(y.shape[0], 1) 33 | 34 | print(x.shape) 35 | print(y.shape) 36 | 37 | plt.plot(x_numpy, y_numpy, 'ro') 38 | 39 | # DEFINICIÇÃO DE MODELO 40 | input_size = 1 41 | output_size = 1 42 | model = nn.Linear(input_size, output_size) 43 | 44 | # DEFINIÇÃO DA FUNÇAO DE CUSTO E OTIMIZADOR 45 | learning_rate = 0.01 46 | criterion = nn.MSELoss() 47 | optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) 48 | print (model.parameters()) 49 | 50 | # LOOP DE TREINAMENTO 51 | num_epochs = 1000 52 | contador_custo = [] 53 | for epoch in range(num_epochs): 54 | #forward pass and loos 55 | y_hat = model(x) 56 | loss = criterion(y_hat, y) 57 | contador_custo.append(loss) 58 | 59 | 60 | #backward pass (calcular gradientes) 61 | loss.backward() 62 | 63 | #update (atualizar os pesos) 64 | optimizer.step() 65 | 66 | if (epoch+1)%10 == 0: 67 | print('Epoch: ', epoch) 68 | print('Custo: {:.20f}'.format(loss.item())) 69 | print('Coeficientes: ') 70 | print('m: {:.20f}'.format(model.weight.data.detach().item())) 71 | print('m (gradiente): {:.20f}'.format(model.weight.grad.detach().item())) 72 | print('b: {:.20f}'.format(model.bias.data.detach().item())) 73 | print('b (gradiente): {:.20f}'.format(model.bias.grad.detach().item())) 74 | #for p in model.parameters(): 75 | # print('{:.2f}'.format(p.data.detach().item())) 76 | # print('{:.2f}'.format(p.grad.detach().item())) 77 | previsao_final = y_hat.detach().numpy() 78 | plt.plot(x_numpy, y_numpy, 'ro') 79 | plt.plot(x_numpy, previsao_final, 'b') 80 | plt.show() 81 | 82 | #limpar o otimizador 83 | optimizer.zero_grad() 84 | 85 | # PLOTANDO O GRÁFICO DA FUNÇÃO DE CUSTO 86 | print("GRÁFICO DA FUNÇÃO DE CUSTO") 87 | plt.plot(contador_custo, 'b') 88 | plt.show() 89 | 90 | -------------------------------------------------------------------------------- /GradienteDescendente/readme.txt: -------------------------------------------------------------------------------- 1 | Implementação de Regressão Linear usando o algortimo de otimização de Gradiente Descendente, no Pytorch. 2 | -------------------------------------------------------------------------------- /KNN/Aula_5_KNN_eistein.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Aula 5 - KNN_eistein", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | } 14 | }, 15 | "cells": [ 16 | { 17 | "cell_type": "code", 18 | "metadata": { 19 | "id": "wttQUqca-d4X", 20 | "colab": { 21 | "base_uri": "https://localhost:8080/" 22 | }, 23 | "outputId": "f3ce7686-3a93-4319-8b25-372980d21df3" 24 | }, 25 | "source": [ 26 | "import numpy as np \n", 27 | "import pandas as pd\n", 28 | "import matplotlib.pyplot as plt\n", 29 | "from sklearn import preprocessing, metrics\n", 30 | "from sklearn.model_selection import train_test_split\n", 31 | "from sklearn.externals.six import StringIO\n", 32 | "import pydotplus\n", 33 | "import matplotlib.image as mpimg\n", 34 | "from sklearn.metrics import classification_report, confusion_matrix\n", 35 | "from IPython.display import Image \n", 36 | "from sklearn.neighbors import KNeighborsClassifier\n", 37 | "\n", 38 | "\n", 39 | "# FAZENDO O DOWNLOAD DO DATASET\n", 40 | "!pip install wget\n", 41 | "!wget https://raw.githubusercontent.com/diogocortiz/Crash-Course-IA/master/ArvoreDecis%C3%A3o/dataset_einstein.csv\n", 42 | "\n", 43 | "\n", 44 | "# IMPORTANDO O DATASET PARA O DATAFRAME\n", 45 | "df = pd.read_csv('dataset_einstein.csv', delimiter=';')\n", 46 | "\n", 47 | "# MOSTRANDO AS PRIMEIRAS CINCO LINHAS\n", 48 | "print(df.head(5))\n", 49 | "\n", 50 | "count_row = df.shape[0] # PEGANDO OS NÚMEROS DE REGISTROS\n", 51 | "count_col = df.shape[1] # PEGANDO OS NUMEROS DE COLUNAS\n", 52 | "\n", 53 | "print(count_row)\n", 54 | "print(count_col)\n", 55 | "# REPAREM QUE HÁ MUITOS REGISTROS EM QUE HÁ DADOS FALTANDO NOS CAMPOS \n", 56 | "\n", 57 | "\n", 58 | "\n" 59 | ], 60 | "execution_count": 1, 61 | "outputs": [ 62 | { 63 | "output_type": "stream", 64 | "text": [ 65 | "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n", 66 | " \"(https://pypi.org/project/six/).\", FutureWarning)\n" 67 | ], 68 | "name": "stderr" 69 | }, 70 | { 71 | "output_type": "stream", 72 | "text": [ 73 | "Collecting wget\n", 74 | " Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n", 75 | "Building wheels for collected packages: wget\n", 76 | " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", 77 | " Created wheel for wget: filename=wget-3.2-cp37-none-any.whl size=9681 sha256=afee4c102a7ef223e9e70f33e54b0369150f18728a7eef01e8db37f83f0d2486\n", 78 | " Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n", 79 | "Successfully built wget\n", 80 | "Installing collected packages: wget\n", 81 | "Successfully installed wget-3.2\n", 82 | "--2021-04-15 14:56:27-- https://raw.githubusercontent.com/diogocortiz/Crash-Course-IA/master/ArvoreDecis%C3%A3o/dataset_einstein.csv\n", 83 | "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n", 84 | "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", 85 | "HTTP request sent, awaiting response... 200 OK\n", 86 | "Length: 248868 (243K) [text/plain]\n", 87 | "Saving to: ‘dataset_einstein.csv’\n", 88 | "\n", 89 | "dataset_einstein.cs 100%[===================>] 243.04K --.-KB/s in 0.06s \n", 90 | "\n", 91 | "2021-04-15 14:56:27 (4.19 MB/s) - ‘dataset_einstein.csv’ saved [248868/248868]\n", 92 | "\n", 93 | " Patient ID Patient age quantile ... Proteina C reativa mg/dL Creatinine\n", 94 | "0 44477f75e8169d2 13 ... NaN NaN\n", 95 | "1 126e9dd13932f68 17 ... -0.147895 2.089928\n", 96 | "2 a46b4402a0e5696 8 ... NaN NaN\n", 97 | "3 f7d619a94f97c45 5 ... NaN NaN\n", 98 | "4 d9e41465789c2b5 15 ... NaN NaN\n", 99 | "\n", 100 | "[5 rows x 11 columns]\n", 101 | "5644\n", 102 | "11\n" 103 | ], 104 | "name": "stdout" 105 | } 106 | ] 107 | }, 108 | { 109 | "cell_type": "markdown", 110 | "metadata": { 111 | "id": "xhuKozveEjH0" 112 | }, 113 | "source": [ 114 | "Precisamos deixar o dataset somente com os registros que tenham todos os campos (para evitar ruídos e distorções)" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "metadata": { 120 | "id": "TN1X7zRcEqra", 121 | "colab": { 122 | "base_uri": "https://localhost:8080/" 123 | }, 124 | "outputId": "4160f3c1-c2fc-4e29-b3e0-ddb7bd6f2812" 125 | }, 126 | "source": [ 127 | "# REMOVENDO OS REGISTROS NOS QUAIS PELO MENOS UM CAMPO ESTÁ EM BRANCO (NAN) \n", 128 | "df = df.dropna()\n", 129 | "\n", 130 | "print(df.head(5))\n", 131 | "\n", 132 | "print('Quantidade de campos(colunas): ', df.shape[1])\n", 133 | "print('Total de registros:', df.shape[0])\n", 134 | "\n", 135 | "#VAMOS VERIFICAR SE O BANCO DE DADOS ESTÁ BALANCEADO OU DESBALANCEADO\n", 136 | "print ('Total de registros negativos: ', df[df['SARS-Cov-2 exam result'] =='negative'].shape[0])\n", 137 | "print ('Total de registros positivos: ', df[df['SARS-Cov-2 exam result'] =='positive'].shape[0])\n" 138 | ], 139 | "execution_count": 2, 140 | "outputs": [ 141 | { 142 | "output_type": "stream", 143 | "text": [ 144 | " Patient ID Patient age quantile ... Proteina C reativa mg/dL Creatinine\n", 145 | "1 126e9dd13932f68 17 ... -0.147895 2.089928\n", 146 | "8 8bb9d64f0215244 1 ... -0.286986 -1.838623\n", 147 | "28 fc41531ca4faf1e 13 ... -0.434025 -0.701411\n", 148 | "29 891d0f6449ff3d7 14 ... -0.529401 0.332418\n", 149 | "30 ebdd7c67fcb21b4 9 ... 0.545572 1.021638\n", 150 | "\n", 151 | "[5 rows x 11 columns]\n", 152 | "Quantidade de campos(colunas): 11\n", 153 | "Total de registros: 357\n", 154 | "Total de registros negativos: 302\n", 155 | "Total de registros positivos: 55\n" 156 | ], 157 | "name": "stdout" 158 | } 159 | ] 160 | }, 161 | { 162 | "cell_type": "markdown", 163 | "metadata": { 164 | "id": "y2hftRNFLsaZ" 165 | }, 166 | "source": [ 167 | "Precisamos converter o Dataframe para um Array Numpy, que é o tipo de dados que iremos usar no treinamento. Também iremos já separar o Dataset em dois. Um com as features de entrada, e outro com os labels (etiquetas, rótulos do registro). \n", 168 | "\n", 169 | "Neste caso, estamos tentando fazer um classificador para o teste do Covid, neste caso, queremos treinar o nosso modelo com a etiqueta presente no campo 'SARS-Cov-2 exam result'" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "metadata": { 175 | "id": "YGrd4eZpErPi", 176 | "colab": { 177 | "base_uri": "https://localhost:8080/" 178 | }, 179 | "outputId": "a8e30420-05c6-4791-8cec-9e046add4ff7" 180 | }, 181 | "source": [ 182 | "# VAMOS JOGAR AS ETIQUETAS PARA Y\n", 183 | "Y = df['SARS-Cov-2 exam result'].values \n", 184 | "print(Y)\n", 185 | "\n", 186 | "# X SERÁ A NOSSA MATRIZ COM AS FEATURES\n", 187 | "# VAMOS PEGAR OS CAMPOS DE TREINAMENTO (Hemoglobin, Leukocytes, Basophils, Proteina C reativa mg/dL)\n", 188 | "\n", 189 | "X = df[['Hemoglobin', 'Leukocytes', 'Basophils','Proteina C reativa mg/dL']].values\n", 190 | "\n", 191 | "# VAMOS MOSTRAR X \n", 192 | "print(X)\n", 193 | "\n", 194 | "\n" 195 | ], 196 | "execution_count": 3, 197 | "outputs": [ 198 | { 199 | "output_type": "stream", 200 | "text": [ 201 | "['negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 202 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 203 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 204 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 205 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 206 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 207 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 208 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 209 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 210 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 211 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 212 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 213 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 214 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 215 | " 'negative' 'negative' 'negative' 'positive' 'negative' 'negative'\n", 216 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 217 | " 'negative' 'negative' 'negative' 'negative' 'positive' 'negative'\n", 218 | " 'negative' 'positive' 'negative' 'negative' 'negative' 'negative'\n", 219 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 220 | " 'negative' 'negative' 'negative' 'positive' 'negative' 'negative'\n", 221 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 222 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 223 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 224 | " 'negative' 'positive' 'negative' 'negative' 'negative' 'negative'\n", 225 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 226 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 227 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 228 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 229 | " 'positive' 'negative' 'positive' 'negative' 'negative' 'negative'\n", 230 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 231 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 232 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 233 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 234 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'positive'\n", 235 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 236 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 237 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 238 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 239 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 240 | " 'positive' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 241 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 242 | " 'positive' 'negative' 'negative' 'positive' 'negative' 'positive'\n", 243 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'negative'\n", 244 | " 'negative' 'negative' 'positive' 'negative' 'positive' 'negative'\n", 245 | " 'negative' 'negative' 'positive' 'negative' 'negative' 'negative'\n", 246 | " 'negative' 'negative' 'positive' 'negative' 'positive' 'negative'\n", 247 | " 'negative' 'negative' 'negative' 'negative' 'negative' 'positive'\n", 248 | " 'negative' 'positive' 'negative' 'positive' 'negative' 'negative'\n", 249 | " 'negative' 'negative' 'negative' 'positive' 'positive' 'negative'\n", 250 | " 'positive' 'negative' 'positive' 'positive' 'negative' 'negative'\n", 251 | " 'positive' 'negative' 'negative' 'negative' 'positive' 'negative'\n", 252 | " 'positive' 'positive' 'negative' 'positive' 'positive' 'positive'\n", 253 | " 'negative' 'negative' 'positive' 'positive' 'positive' 'positive'\n", 254 | " 'positive' 'negative' 'negative' 'positive' 'negative' 'positive'\n", 255 | " 'positive' 'negative' 'positive' 'negative' 'negative' 'positive'\n", 256 | " 'positive' 'negative' 'negative' 'positive' 'positive' 'positive'\n", 257 | " 'negative' 'positive' 'positive' 'negative' 'negative' 'negative'\n", 258 | " 'negative' 'positive' 'positive' 'positive' 'negative' 'positive'\n", 259 | " 'positive' 'negative' 'negative' 'positive' 'negative' 'negative'\n", 260 | " 'negative' 'negative' 'positive']\n", 261 | "[[-0.02234027 -0.09461035 -0.22376651 -0.14789495]\n", 262 | " [-0.774212 0.36455047 -0.22376651 -0.28698576]\n", 263 | " [ 0.85484386 -0.07513076 2.52536511 -0.4340246 ]\n", 264 | " ...\n", 265 | " [ 1.10546756 -0.5509882 -0.22376651 0.5614683 ]\n", 266 | " [-2.77920342 -1.73367476 -1.14014375 0.60915661]\n", 267 | " [ 0.54156393 -1.28842807 -1.14014375 -0.50357002]]\n" 268 | ], 269 | "name": "stdout" 270 | } 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "metadata": { 276 | "id": "w3KiQHyHNq0w" 277 | }, 278 | "source": [ 279 | "Agora precisamos dividir o nosso Dataset em dois: um para o treino (80% dos dados) e outro para o teste (20% dos dados)" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "metadata": { 285 | "id": "leQhYx_ENzmz" 286 | }, 287 | "source": [ 288 | "X_treino, X_teste, Y_treino, Y_teste = train_test_split(X, Y, test_size=0.2, random_state=3)\n" 289 | ], 290 | "execution_count": 4, 291 | "outputs": [] 292 | }, 293 | { 294 | "cell_type": "markdown", 295 | "metadata": { 296 | "id": "wCyb_DK0OM3z" 297 | }, 298 | "source": [ 299 | "Agora vamos criar o e treinar o nosso modelo. Lembram da diferença entre algortimo e modelo? Existe o algortimo de treinamento (que neste caso é o de árvore de decisão) que irá exportar um modelo treinado (que também é um algoritmo)." 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "metadata": { 305 | "id": "yPawptV4O0Mw" 306 | }, 307 | "source": [ 308 | "\n", 309 | "\n", 310 | "#DEFINE O NÚMERO DE VIZINHOS EM 4\n", 311 | "K = 4\n", 312 | "#CRIA O MODELO DE KNN\n", 313 | "knn_algoritmo = KNeighborsClassifier(n_neighbors = K)\n", 314 | "\n", 315 | "#AGORA PRECISAMOS TREINÁ-LO\n", 316 | "knn_modelo = knn_algoritmo.fit(X_treino, Y_treino)\n", 317 | "\n", 318 | "\n" 319 | ], 320 | "execution_count": 7, 321 | "outputs": [] 322 | }, 323 | { 324 | "cell_type": "markdown", 325 | "metadata": { 326 | "id": "Z2rFjNDidvZ3" 327 | }, 328 | "source": [ 329 | "Vamos testar o modelo, fazendo as predições no dataset de teste." 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "metadata": { 335 | "id": "tkKuwKH3d0-3", 336 | "colab": { 337 | "base_uri": "https://localhost:8080/" 338 | }, 339 | "outputId": "dcb5069e-b64c-4e70-9777-198e8431474a" 340 | }, 341 | "source": [ 342 | "# APLICANDO O MODELO NA BASE DE TESTES E ARMAZENDO O RESULTADO EM Y_PREDICOES\n", 343 | "Y_predicoes = knn_modelo.predict(X_teste)\n", 344 | "\n", 345 | "\n", 346 | "#AVALIAÇÃO DO MODELO \n", 347 | "#VAMOS AVALIAR O VALOR REAL DO DATASET Y_TESTE COM AS PREDIÇÕES\n", 348 | "print(\"ACURÁCIA DA ÁRVORE: \", metrics.accuracy_score(Y_teste, Y_predicoes))\n", 349 | "print (classification_report(Y_teste, Y_predicoes))\n" 350 | ], 351 | "execution_count": 8, 352 | "outputs": [ 353 | { 354 | "output_type": "stream", 355 | "text": [ 356 | "ACURÁCIA DA ÁRVORE: 0.8611111111111112\n", 357 | " precision recall f1-score support\n", 358 | "\n", 359 | " negative 0.87 0.98 0.92 60\n", 360 | " positive 0.75 0.25 0.38 12\n", 361 | "\n", 362 | " accuracy 0.86 72\n", 363 | " macro avg 0.81 0.62 0.65 72\n", 364 | "weighted avg 0.85 0.86 0.83 72\n", 365 | "\n" 366 | ], 367 | "name": "stdout" 368 | } 369 | ] 370 | }, 371 | { 372 | "cell_type": "markdown", 373 | "metadata": { 374 | "id": "-r8QCHEACTKk" 375 | }, 376 | "source": [ 377 | "#TESTAR PARA DIFERENTES VALORES DE K" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "metadata": { 383 | "colab": { 384 | "base_uri": "https://localhost:8080/", 385 | "height": 314 386 | }, 387 | "id": "XArIYCt5CWrz", 388 | "outputId": "ebd27efb-affe-45fe-d57a-7f913a54e530" 389 | }, 390 | "source": [ 391 | "#TESTAR DIFERENTES VALORES PARA K\n", 392 | "Ks = 100\n", 393 | "mean_acc = np.zeros((Ks-1))\n", 394 | "std_acc = np.zeros((Ks-1))\n", 395 | "ConfustionMx = [];\n", 396 | "for n in range(1,Ks):\n", 397 | "\n", 398 | " #CRIA O MODELO DE KNN\n", 399 | " knn_algoritmo = KNeighborsClassifier(n_neighbors = n)\n", 400 | "\n", 401 | " #AGORA PRECISAMOS TREINÁ-LO\n", 402 | " knn_modelo = knn_algoritmo.fit(X_treino, Y_treino)\n", 403 | "\n", 404 | " #FAZ AS PREDIÇÕES\n", 405 | " yhat=knn_modelo.predict(X_teste)\n", 406 | " mean_acc[n-1] = metrics.accuracy_score(Y_teste, yhat)\n", 407 | "\n", 408 | " \n", 409 | " std_acc[n-1]=np.std(yhat==Y_teste)/np.sqrt(yhat.shape[0])\n", 410 | "\n", 411 | "mean_acc\n", 412 | "\n", 413 | "#GRAFICO PARA OS DIFERENTES VALORES DE K\n", 414 | "plt.plot(range(1,Ks),mean_acc,'g')\n", 415 | "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n", 416 | "plt.legend(('Accuracy ', '+/- 3xstd'))\n", 417 | "plt.ylabel('Accuracy ')\n", 418 | "plt.xlabel('Number of Nabors (K)')\n", 419 | "plt.tight_layout()\n", 420 | "plt.show()\n", 421 | "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) \n" 422 | ], 423 | "execution_count": 13, 424 | "outputs": [ 425 | { 426 | "output_type": "display_data", 427 | "data": { 428 | "image/png": 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\n", 429 | "text/plain": [ 430 | "
" 431 | ] 432 | }, 433 | "metadata": { 434 | "tags": [], 435 | "needs_background": "light" 436 | } 437 | }, 438 | { 439 | "output_type": "stream", 440 | "text": [ 441 | "The best accuracy was with 0.8888888888888888 with k= 1\n" 442 | ], 443 | "name": "stdout" 444 | } 445 | ] 446 | } 447 | ] 448 | } -------------------------------------------------------------------------------- /KNN/readme.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /KNN/teleCust1000t.csv: -------------------------------------------------------------------------------- 1 | region,tenure,age,marital,address,income,ed,employ,retire,gender,reside,custcat 2 | 2,13,44,1,9,64.000,4,5,0.000,0,2,1 3 | 3,11,33,1,7,136.000,5,5,0.000,0,6,4 4 | 3,68,52,1,24,116.000,1,29,0.000,1,2,3 5 | 2,33,33,0,12,33.000,2,0,0.000,1,1,1 6 | 2,23,30,1,9,30.000,1,2,0.000,0,4,3 7 | 2,41,39,0,17,78.000,2,16,0.000,1,1,3 8 | 3,45,22,1,2,19.000,2,4,0.000,1,5,2 9 | 2,38,35,0,5,76.000,2,10,0.000,0,3,4 10 | 3,45,59,1,7,166.000,4,31,0.000,0,5,3 11 | 1,68,41,1,21,72.000,1,22,0.000,0,3,2 12 | 2,5,33,0,10,125.000,4,5,0.000,1,1,1 13 | 3,7,35,0,14,80.000,2,15,0.000,1,1,3 14 | 1,41,38,1,8,37.000,2,9,0.000,1,3,1 15 | 2,57,54,1,30,115.000,4,23,0.000,1,3,4 16 | 2,9,46,0,3,25.000,1,8,0.000,1,2,1 17 | 1,29,38,1,12,75.000,5,1,0.000,0,4,2 18 | 3,60,57,0,38,162.000,2,30,0.000,0,1,3 19 | 3,34,48,0,3,49.000,2,6,0.000,1,3,3 20 | 2,1,24,0,3,20.000,1,3,0.000,0,1,1 21 | 1,26,29,1,3,77.000,4,2,0.000,0,4,4 22 | 3,6,30,0,7,16.000,3,1,0.000,1,1,2 23 | 1,68,52,1,17,120.000,1,24,0.000,0,2,1 24 | 3,53,33,0,10,101.000,5,4,0.000,1,2,4 25 | 3,55,48,1,19,67.000,1,25,0.000,0,3,1 26 | 3,14,43,1,18,36.000,1,5,0.000,0,5,3 27 | 2,1,21,0,0,33.000,2,0,0.000,1,3,3 28 | 2,42,40,0,7,37.000,2,8,0.000,1,1,4 29 | 3,25,33,1,11,31.000,1,5,0.000,0,4,3 30 | 1,9,21,1,1,17.000,2,2,0.000,1,3,1 31 | 2,13,33,1,9,19.000,4,0,0.000,1,2,2 32 | 1,56,37,1,6,36.000,1,13,0.000,1,2,2 33 | 1,71,53,1,27,155.000,5,12,0.000,0,2,4 34 | 1,35,50,1,26,140.000,2,21,0.000,1,4,3 35 | 1,11,27,1,8,55.000,5,0,0.000,0,3,2 36 | 2,60,46,1,13,163.000,3,24,0.000,0,2,4 37 | 3,20,35,1,11,52.000,4,0,0.000,0,2,2 38 | 2,54,60,0,38,211.000,4,25,0.000,0,1,4 39 | 1,44,57,1,1,186.000,2,17,0.000,0,2,3 40 | 1,11,41,1,0,39.000,1,1,0.000,1,2,3 41 | 2,72,57,0,34,22.000,2,35,1.000,1,1,3 42 | 3,10,41,0,7,30.000,1,7,0.000,0,1,3 43 | 2,15,28,0,0,29.000,2,4,0.000,1,1,3 44 | 2,27,28,1,4,23.000,2,8,0.000,0,5,1 45 | 1,9,36,1,14,62.000,4,10,0.000,0,6,4 46 | 1,64,43,1,20,76.000,4,20,0.000,1,4,3 47 | 1,65,41,1,3,74.000,4,16,0.000,0,2,2 48 | 1,49,51,1,27,63.000,4,19,0.000,0,5,2 49 | 3,47,41,1,0,36.000,4,8,0.000,0,2,4 50 | 3,9,34,1,9,33.000,2,8,0.000,1,4,1 51 | 1,5,36,0,14,29.000,2,9,0.000,1,1,3 52 | 2,30,34,1,4,27.000,2,1,0.000,0,5,1 53 | 1,56,52,1,28,49.000,2,12,0.000,0,4,2 54 | 3,10,22,0,0,24.000,4,0,0.000,0,1,4 55 | 1,7,26,1,3,26.000,2,2,0.000,0,5,1 56 | 1,52,27,0,6,47.000,3,5,0.000,0,2,1 57 | 2,36,45,1,1,94.000,2,18,0.000,1,5,1 58 | 3,27,34,0,8,21.000,3,4,0.000,1,1,2 59 | 1,58,62,1,36,27.000,1,0,0.000,0,2,1 60 | 2,41,52,0,27,30.000,2,2,0.000,1,1,3 61 | 2,47,40,1,16,127.000,4,12,0.000,1,4,2 62 | 2,55,39,1,15,137.000,2,20,0.000,1,2,3 63 | 2,56,50,1,1,80.000,2,24,0.000,1,4,4 64 | 3,35,55,0,24,30.000,2,2,0.000,0,1,1 65 | 2,69,51,1,11,438.000,4,23,0.000,1,2,4 66 | 2,44,39,0,16,79.000,2,16,0.000,0,1,4 67 | 3,36,47,1,16,63.000,1,23,0.000,0,2,1 68 | 3,28,51,1,22,40.000,3,10,0.000,1,6,3 69 | 1,72,67,1,44,51.000,1,34,1.000,0,2,3 70 | 1,16,27,0,5,37.000,3,5,0.000,0,4,1 71 | 3,3,43,0,19,61.000,4,11,0.000,1,1,1 72 | 1,40,57,1,15,22.000,2,9,0.000,0,2,1 73 | 3,16,39,0,6,44.000,4,3,0.000,1,1,2 74 | 1,20,42,0,4,17.000,3,5,0.000,0,1,1 75 | 2,48,43,0,13,110.000,5,15,0.000,1,3,2 76 | 2,64,48,1,13,91.000,2,24,0.000,0,2,3 77 | 1,8,46,0,3,46.000,2,8,0.000,0,1,1 78 | 1,40,38,1,10,85.000,3,12,0.000,0,2,3 79 | 2,38,71,0,27,10.000,2,12,1.000,0,1,1 80 | 1,67,68,0,28,244.000,1,47,0.000,1,1,3 81 | 1,26,42,0,0,80.000,4,15,0.000,1,1,4 82 | 3,41,34,1,15,83.000,5,1,0.000,1,2,4 83 | 2,2,31,0,2,21.000,1,6,0.000,0,1,1 84 | 3,20,48,1,29,24.000,2,3,0.000,0,2,4 85 | 2,31,53,0,2,351.000,3,31,0.000,1,1,3 86 | 2,45,52,1,4,169.000,4,29,0.000,1,4,2 87 | 2,33,27,1,7,46.000,2,7,0.000,0,4,1 88 | 1,10,26,0,6,34.000,3,3,0.000,0,1,2 89 | 2,62,54,1,2,50.000,4,3,0.000,0,2,4 90 | 2,4,35,0,16,161.000,5,6,0.000,1,1,4 91 | 1,42,47,1,17,212.000,4,17,0.000,0,2,3 92 | 3,27,51,1,3,80.000,5,11,0.000,0,3,2 93 | 2,38,42,0,1,30.000,4,7,0.000,1,1,1 94 | 3,52,61,1,3,53.000,5,1,0.000,1,2,2 95 | 3,35,33,0,9,73.000,5,4,0.000,1,1,4 96 | 3,3,20,1,1,17.000,2,0,0.000,1,4,1 97 | 2,17,33,0,9,23.000,5,3,0.000,0,1,4 98 | 1,55,53,1,21,34.000,1,8,0.000,0,2,3 99 | 3,43,36,1,5,107.000,1,19,0.000,1,3,2 100 | 2,47,25,1,5,21.000,1,1,0.000,1,2,3 101 | 3,65,58,0,30,83.000,2,16,0.000,1,1,2 102 | 1,13,20,0,1,17.000,3,0,0.000,0,5,4 103 | 3,64,25,1,4,76.000,3,2,0.000,1,6,3 104 | 2,12,24,0,2,19.000,1,0,0.000,0,1,3 105 | 1,29,32,1,0,49.000,4,3,0.000,1,3,2 106 | 2,23,47,0,21,173.000,3,22,0.000,1,1,2 107 | 2,14,35,0,4,47.000,3,10,0.000,1,1,2 108 | 1,35,61,0,23,41.000,2,11,0.000,0,1,1 109 | 3,26,39,0,8,105.000,3,13,0.000,1,1,4 110 | 1,13,54,0,2,31.000,4,2,0.000,0,1,1 111 | 2,69,40,1,13,41.000,2,17,0.000,0,3,2 112 | 3,61,50,1,30,102.000,4,9,0.000,1,3,2 113 | 2,45,22,1,2,36.000,4,0,0.000,1,5,2 114 | 2,3,37,0,13,24.000,1,3,0.000,1,1,1 115 | 2,53,22,0,1,25.000,4,0,0.000,0,2,2 116 | 1,59,42,0,1,68.000,2,21,0.000,1,1,3 117 | 1,13,34,0,11,20.000,3,0,0.000,1,1,1 118 | 3,31,31,0,4,48.000,1,15,0.000,0,1,3 119 | 1,40,29,1,6,40.000,2,8,0.000,1,5,3 120 | 1,48,55,0,15,79.000,1,25,0.000,0,1,3 121 | 3,3,31,0,1,28.000,1,0,0.000,0,1,1 122 | 2,51,48,0,12,64.000,2,22,0.000,1,1,1 123 | 3,33,41,0,15,37.000,2,11,0.000,0,1,1 124 | 3,14,47,1,7,48.000,1,13,0.000,0,2,1 125 | 1,17,42,1,6,131.000,5,6,0.000,0,3,2 126 | 3,19,29,1,9,18.000,2,2,0.000,0,3,1 127 | 2,58,36,0,10,38.000,2,14,0.000,1,1,2 128 | 3,22,23,0,3,37.000,2,4,0.000,1,3,4 129 | 2,29,31,1,1,60.000,4,6,0.000,0,5,2 130 | 3,14,22,1,2,13.000,3,0,0.000,1,4,2 131 | 2,70,64,1,18,98.000,1,31,0.000,1,2,4 132 | 1,62,52,1,2,195.000,1,23,0.000,0,2,3 133 | 2,7,35,1,0,47.000,1,14,0.000,0,5,1 134 | 1,56,47,1,19,65.000,2,22,0.000,0,3,4 135 | 1,50,50,1,22,150.000,5,5,0.000,1,4,2 136 | 2,57,39,0,6,106.000,5,8,0.000,0,1,3 137 | 3,2,40,0,19,51.000,4,14,0.000,1,1,1 138 | 2,40,38,0,17,96.000,4,0,0.000,1,1,1 139 | 2,6,43,0,4,33.000,5,7,0.000,0,2,3 140 | 3,23,25,1,6,38.000,2,1,0.000,1,3,3 141 | 3,11,32,0,6,125.000,4,5,0.000,0,1,4 142 | 1,29,37,1,13,145.000,5,0,0.000,1,4,4 143 | 1,42,44,1,2,99.000,2,21,0.000,0,3,3 144 | 1,20,34,1,2,22.000,4,0,0.000,1,3,4 145 | 2,72,60,1,21,31.000,1,38,1.000,0,2,2 146 | 1,2,36,0,3,25.000,1,6,0.000,1,1,3 147 | 3,46,25,0,5,57.000,5,0,0.000,1,2,1 148 | 2,39,51,1,21,41.000,5,20,0.000,0,6,4 149 | 3,24,25,1,1,20.000,3,1,0.000,1,2,2 150 | 3,59,43,1,4,101.000,2,22,0.000,1,5,2 151 | 1,65,37,0,8,56.000,2,15,0.000,0,1,2 152 | 2,50,27,1,5,22.000,2,5,0.000,1,3,1 153 | 1,57,37,0,11,108.000,4,9,0.000,1,1,3 154 | 3,61,48,0,14,115.000,4,20,0.000,1,1,4 155 | 3,34,54,0,31,53.000,4,22,0.000,0,1,4 156 | 3,38,53,0,13,242.000,4,12,0.000,0,1,4 157 | 1,3,24,1,2,20.000,2,3,0.000,1,5,3 158 | 2,4,47,0,5,123.000,4,11,0.000,0,1,1 159 | 2,15,35,0,0,34.000,1,12,0.000,0,1,1 160 | 1,25,29,1,8,31.000,4,3,0.000,1,5,2 161 | 3,21,44,0,25,47.000,1,12,0.000,1,2,3 162 | 3,50,37,1,11,48.000,3,8,0.000,1,2,3 163 | 2,65,64,1,22,13.000,2,5,1.000,1,3,3 164 | 2,59,52,1,18,78.000,1,25,0.000,0,2,1 165 | 3,29,26,0,7,34.000,1,7,0.000,1,2,3 166 | 1,60,46,0,17,81.000,5,9,0.000,1,1,2 167 | 1,71,45,1,14,86.000,2,25,0.000,0,2,2 168 | 3,38,27,1,0,26.000,4,2,0.000,0,4,4 169 | 3,16,34,1,5,51.000,2,8,0.000,1,5,1 170 | 3,7,31,0,10,59.000,3,10,0.000,0,1,1 171 | 1,59,53,0,21,112.000,4,16,0.000,0,1,2 172 | 2,22,56,1,14,57.000,4,28,0.000,0,2,4 173 | 1,18,48,1,20,41.000,1,2,0.000,1,2,1 174 | 2,64,46,1,19,168.000,2,26,0.000,1,2,4 175 | 2,7,33,0,4,29.000,4,4,0.000,1,3,1 176 | 3,65,58,0,13,167.000,2,14,0.000,1,2,2 177 | 3,7,24,0,5,29.000,2,3,0.000,0,3,1 178 | 1,64,55,0,28,104.000,1,26,0.000,1,1,3 179 | 3,42,22,1,0,46.000,4,0,0.000,0,2,4 180 | 3,33,26,0,5,46.000,4,1,0.000,1,3,4 181 | 1,3,33,0,8,51.000,4,0,0.000,1,1,1 182 | 2,69,42,1,11,65.000,2,18,0.000,0,2,4 183 | 2,9,33,0,1,48.000,2,13,0.000,0,2,1 184 | 3,22,34,1,1,46.000,3,1,0.000,0,4,2 185 | 2,53,63,1,15,43.000,2,18,0.000,0,2,2 186 | 3,71,53,0,30,110.000,1,34,0.000,0,1,2 187 | 3,24,26,0,2,32.000,3,2,0.000,0,3,1 188 | 3,12,22,1,1,18.000,3,0,0.000,1,2,1 189 | 1,25,39,1,1,26.000,4,0,0.000,0,4,2 190 | 3,63,28,1,9,34.000,2,10,0.000,1,6,3 191 | 1,33,54,1,18,57.000,4,4,0.000,1,3,3 192 | 2,57,54,1,20,21.000,2,0,0.000,1,3,1 193 | 3,13,33,0,7,38.000,3,10,0.000,1,1,4 194 | 1,13,21,0,2,19.000,3,0,0.000,1,1,2 195 | 1,71,39,0,2,40.000,3,17,0.000,0,1,3 196 | 3,18,69,1,11,58.000,3,8,0.000,0,2,4 197 | 1,17,51,1,10,95.000,2,15,0.000,1,2,1 198 | 2,22,43,0,4,114.000,2,19,0.000,0,1,1 199 | 2,65,65,1,27,128.000,3,24,0.000,0,2,3 200 | 3,7,40,0,9,33.000,4,2,0.000,1,1,1 201 | 3,72,66,0,30,460.000,4,41,0.000,1,1,4 202 | 1,35,43,0,12,224.000,3,17,0.000,0,1,1 203 | 1,24,39,0,2,122.000,4,12,0.000,0,3,4 204 | 1,64,24,1,0,23.000,3,1,0.000,1,5,2 205 | 2,63,63,1,19,191.000,3,27,0.000,0,2,2 206 | 1,38,31,1,7,187.000,4,6,0.000,0,3,4 207 | 2,29,37,1,9,119.000,3,12,0.000,0,3,1 208 | 1,72,69,1,49,44.000,2,44,1.000,1,2,3 209 | 1,10,27,1,2,15.000,3,2,0.000,1,2,4 210 | 3,72,64,0,41,674.000,4,37,0.000,1,1,3 211 | 3,52,23,1,4,33.000,4,0,0.000,1,2,3 212 | 3,13,55,0,3,250.000,2,35,0.000,1,2,1 213 | 1,3,33,0,6,64.000,4,0,0.000,0,2,1 214 | 3,39,24,1,2,26.000,2,4,0.000,0,4,1 215 | 2,28,29,0,4,23.000,3,5,0.000,0,2,2 216 | 2,46,42,0,9,52.000,4,7,0.000,0,3,2 217 | 2,62,46,0,27,200.000,3,9,0.000,1,1,1 218 | 2,23,33,1,7,28.000,1,4,0.000,0,2,3 219 | 3,23,25,1,5,19.000,5,0,0.000,0,3,4 220 | 2,72,61,1,39,76.000,3,21,0.000,0,2,4 221 | 3,56,60,0,26,70.000,2,20,0.000,0,1,1 222 | 1,8,30,0,1,34.000,2,9,0.000,1,1,3 223 | 3,3,36,0,7,34.000,5,3,0.000,1,3,4 224 | 3,37,37,0,7,172.000,3,13,0.000,0,1,1 225 | 3,37,53,1,13,48.000,1,10,0.000,0,3,3 226 | 2,6,30,0,9,45.000,2,2,0.000,1,1,1 227 | 1,48,47,0,9,72.000,3,13,0.000,0,5,4 228 | 2,43,43,1,3,55.000,4,18,0.000,1,4,4 229 | 2,21,29,0,7,40.000,4,2,0.000,1,4,1 230 | 3,6,33,0,12,26.000,3,0,0.000,0,1,3 231 | 2,69,51,0,18,96.000,1,33,0.000,0,4,3 232 | 1,22,33,0,9,54.000,1,10,0.000,0,1,3 233 | 1,28,40,1,1,47.000,2,9,0.000,1,3,3 234 | 1,71,47,1,23,142.000,1,30,0.000,0,2,2 235 | 2,7,37,0,5,57.000,4,1,0.000,0,1,1 236 | 1,63,29,0,10,20.000,3,7,0.000,0,2,4 237 | 2,53,57,0,25,37.000,1,7,0.000,0,1,2 238 | 2,50,52,1,17,36.000,4,16,0.000,0,4,4 239 | 2,39,31,1,6,24.000,4,2,0.000,0,2,4 240 | 3,16,19,0,0,21.000,2,0,0.000,1,1,1 241 | 3,43,51,1,15,186.000,3,25,0.000,0,2,4 242 | 3,44,31,0,12,30.000,1,7,0.000,1,3,1 243 | 2,72,66,1,43,96.000,2,16,0.000,1,2,4 244 | 3,61,76,0,39,15.000,1,29,1.000,0,1,3 245 | 3,43,21,1,1,25.000,1,4,0.000,0,4,2 246 | 3,24,39,0,11,49.000,3,7,0.000,1,1,1 247 | 3,64,45,0,4,38.000,2,7,0.000,1,1,3 248 | 2,64,38,0,0,29.000,2,2,0.000,0,2,2 249 | 1,24,26,0,7,55.000,2,7,0.000,1,1,1 250 | 3,33,55,1,21,64.000,3,10,0.000,1,3,1 251 | 2,33,33,1,12,42.000,4,7,0.000,1,5,2 252 | 1,2,40,0,9,134.000,4,2,0.000,0,1,1 253 | 2,42,48,1,22,82.000,1,21,0.000,1,5,4 254 | 1,52,54,1,5,153.000,5,10,0.000,0,2,2 255 | 1,45,66,0,43,144.000,2,13,0.000,1,1,2 256 | 3,3,25,0,4,22.000,4,0,0.000,0,2,4 257 | 2,37,53,1,16,35.000,1,16,0.000,1,4,3 258 | 2,28,57,0,33,82.000,4,22,0.000,1,1,2 259 | 3,40,63,1,9,61.000,1,22,0.000,0,2,1 260 | 2,37,33,0,1,102.000,2,12,0.000,0,1,4 261 | 3,7,27,0,5,31.000,3,4,0.000,0,5,4 262 | 1,71,56,0,23,170.000,1,30,0.000,1,1,4 263 | 2,19,46,0,11,26.000,2,0,0.000,1,1,1 264 | 2,61,40,0,15,85.000,3,15,0.000,1,1,3 265 | 3,70,53,1,16,183.000,2,35,0.000,1,2,2 266 | 2,60,55,1,13,67.000,1,18,0.000,1,2,1 267 | 3,48,45,1,5,42.000,1,11,0.000,1,2,1 268 | 1,8,63,1,1,31.000,1,9,0.000,0,2,3 269 | 2,58,58,1,10,96.000,2,17,0.000,0,4,3 270 | 1,16,24,1,5,24.000,3,2,0.000,0,5,3 271 | 2,33,33,0,4,53.000,2,14,0.000,1,1,1 272 | 2,39,25,0,4,18.000,1,0,0.000,1,1,3 273 | 1,62,51,0,10,43.000,4,19,0.000,0,2,2 274 | 1,50,34,0,10,104.000,3,9,0.000,0,1,4 275 | 2,48,55,0,31,26.000,1,1,0.000,0,1,1 276 | 3,31,62,0,5,56.000,2,26,0.000,1,1,3 277 | 2,65,33,1,9,49.000,2,6,0.000,0,5,2 278 | 1,36,58,0,34,80.000,1,21,0.000,1,1,2 279 | 1,61,46,0,5,318.000,3,18,0.000,1,1,3 280 | 2,48,42,0,21,35.000,4,5,0.000,0,1,3 281 | 3,35,45,0,17,29.000,1,6,0.000,0,1,1 282 | 1,51,28,0,8,43.000,5,1,0.000,0,5,4 283 | 2,49,51,1,5,80.000,5,3,0.000,0,2,4 284 | 1,35,39,1,11,30.000,3,2,0.000,1,3,3 285 | 1,58,70,0,2,9.000,3,24,1.000,1,1,2 286 | 1,23,35,1,0,23.000,2,1,0.000,0,5,3 287 | 2,26,39,1,15,58.000,4,9,0.000,1,2,3 288 | 1,38,37,1,1,52.000,1,20,0.000,1,4,4 289 | 1,35,66,1,44,14.000,3,12,1.000,1,2,3 290 | 2,37,50,0,9,110.000,2,21,0.000,1,1,3 291 | 2,48,43,1,1,60.000,2,12,0.000,0,2,2 292 | 3,13,43,1,1,123.000,3,21,0.000,0,5,4 293 | 1,72,63,1,29,15.000,5,31,1.000,0,2,3 294 | 1,30,27,0,4,47.000,4,2,0.000,1,2,2 295 | 1,24,43,0,14,138.000,4,11,0.000,1,1,3 296 | 2,25,51,1,20,359.000,3,29,0.000,0,2,4 297 | 2,50,52,0,30,214.000,3,22,0.000,0,1,4 298 | 2,28,40,0,7,64.000,1,19,0.000,0,1,3 299 | 3,34,51,0,8,50.000,4,14,0.000,0,1,1 300 | 1,48,32,0,2,88.000,3,9,0.000,0,1,4 301 | 2,42,36,1,8,86.000,2,15,0.000,1,2,2 302 | 3,67,65,1,30,17.000,1,29,1.000,1,2,3 303 | 1,17,41,1,7,25.000,2,17,0.000,0,3,3 304 | 1,29,38,1,19,46.000,2,6,0.000,0,2,2 305 | 3,66,61,0,26,361.000,1,40,0.000,1,1,2 306 | 2,1,21,0,1,18.000,3,0,0.000,1,1,1 307 | 1,28,33,1,3,14.000,4,1,0.000,0,3,4 308 | 1,15,35,1,5,34.000,3,8,0.000,0,2,2 309 | 2,12,64,0,13,9.000,2,6,1.000,1,1,3 310 | 3,48,49,0,18,39.000,2,5,0.000,1,1,4 311 | 2,59,25,1,4,18.000,3,2,0.000,0,4,1 312 | 3,8,26,1,1,25.000,4,1,0.000,0,2,1 313 | 1,45,52,0,15,46.000,5,8,0.000,1,1,2 314 | 1,25,39,1,8,94.000,4,11,0.000,1,4,2 315 | 1,21,44,1,4,26.000,3,2,0.000,0,2,4 316 | 2,17,68,1,1,335.000,1,35,0.000,1,3,4 317 | 2,41,45,0,14,25.000,3,6,0.000,0,1,1 318 | 1,13,45,1,3,99.000,3,8,0.000,1,2,3 319 | 2,1,26,1,3,30.000,2,7,0.000,1,5,1 320 | 3,3,31,1,4,35.000,2,2,0.000,1,3,4 321 | 1,23,27,1,2,51.000,5,1,0.000,1,2,1 322 | 3,26,42,0,20,49.000,3,13,0.000,0,1,4 323 | 1,37,51,1,15,54.000,1,15,0.000,0,3,3 324 | 3,5,30,0,3,46.000,4,7,0.000,0,1,2 325 | 1,32,27,1,3,91.000,4,1,0.000,1,3,4 326 | 3,12,22,0,1,23.000,4,0,0.000,0,2,1 327 | 1,72,54,1,6,345.000,1,32,0.000,0,2,3 328 | 3,3,25,1,2,45.000,4,0,0.000,1,2,1 329 | 1,54,43,0,12,53.000,2,12,0.000,0,1,3 330 | 2,26,26,0,2,14.000,2,6,0.000,1,1,4 331 | 3,14,36,0,13,67.000,5,4,0.000,1,1,1 332 | 3,22,51,0,27,49.000,3,1,0.000,0,1,1 333 | 3,65,47,1,18,37.000,1,15,0.000,1,4,1 334 | 3,26,45,0,18,33.000,1,9,0.000,0,1,1 335 | 1,20,37,0,5,46.000,3,10,0.000,1,4,4 336 | 2,67,60,1,32,93.000,1,21,0.000,0,4,2 337 | 1,34,55,0,2,48.000,2,11,0.000,0,1,1 338 | 1,37,54,1,35,183.000,2,22,0.000,0,3,3 339 | 2,22,23,1,3,16.000,1,5,0.000,0,4,1 340 | 2,5,69,0,0,11.000,2,14,1.000,0,1,1 341 | 1,28,31,1,0,42.000,4,5,0.000,1,3,3 342 | 1,20,41,1,7,67.000,4,14,0.000,1,4,1 343 | 3,72,41,0,20,181.000,4,12,0.000,0,1,4 344 | 2,30,43,0,18,53.000,4,6,0.000,0,1,1 345 | 3,71,52,0,17,172.000,2,32,0.000,0,1,4 346 | 2,56,44,0,24,87.000,2,19,0.000,0,1,3 347 | 1,8,29,1,2,21.000,5,0,0.000,0,2,1 348 | 2,32,65,0,6,9.000,2,30,1.000,1,1,2 349 | 2,46,47,0,22,144.000,3,22,0.000,1,2,3 350 | 3,60,45,1,23,117.000,4,11,0.000,0,3,4 351 | 2,42,32,1,0,59.000,4,4,0.000,1,6,4 352 | 3,71,60,1,39,508.000,4,35,0.000,0,4,4 353 | 2,72,63,1,40,222.000,2,43,1.000,1,2,3 354 | 3,45,66,0,11,87.000,3,25,1.000,1,1,1 355 | 3,5,31,0,4,34.000,1,9,0.000,0,1,3 356 | 2,17,38,1,19,18.000,4,0,0.000,1,4,2 357 | 2,70,55,0,12,65.000,3,24,0.000,0,1,3 358 | 3,57,57,1,19,130.000,3,27,0.000,1,2,2 359 | 1,20,33,0,13,44.000,3,8,0.000,1,1,2 360 | 1,72,52,1,21,63.000,1,24,0.000,0,3,3 361 | 3,32,50,0,28,57.000,4,20,0.000,0,1,3 362 | 3,22,57,1,7,264.000,1,35,0.000,1,2,3 363 | 2,6,24,0,2,28.000,1,5,0.000,0,2,3 364 | 3,32,23,1,2,26.000,3,1,0.000,1,6,4 365 | 1,38,33,0,3,38.000,4,0,0.000,0,1,2 366 | 1,53,35,0,15,59.000,3,5,0.000,1,1,3 367 | 3,37,76,0,38,117.000,4,21,0.000,1,1,1 368 | 1,47,35,1,13,70.000,4,9,0.000,1,2,4 369 | 3,69,59,0,37,104.000,2,13,0.000,0,1,3 370 | 3,63,56,0,17,71.000,3,18,0.000,1,1,1 371 | 1,61,34,1,0,70.000,3,7,0.000,1,3,4 372 | 1,56,38,1,8,56.000,4,10,0.000,0,2,4 373 | 3,40,57,1,18,68.000,1,23,0.000,0,2,3 374 | 2,61,45,0,21,80.000,2,13,0.000,1,1,2 375 | 2,16,26,0,7,21.000,4,1,0.000,0,6,4 376 | 2,7,43,0,4,69.000,3,5,0.000,1,1,1 377 | 3,20,31,1,10,39.000,2,3,0.000,1,2,1 378 | 2,35,38,0,7,90.000,2,18,0.000,0,4,2 379 | 1,60,37,1,12,64.000,5,1,0.000,1,2,2 380 | 2,47,25,0,2,28.000,2,6,0.000,0,1,4 381 | 2,33,28,1,3,24.000,2,0,0.000,1,6,1 382 | 3,12,34,0,2,44.000,2,7,0.000,0,1,1 383 | 1,10,31,0,0,32.000,4,7,0.000,1,2,2 384 | 2,69,33,1,9,52.000,4,9,0.000,1,3,3 385 | 1,23,20,0,1,15.000,3,0,0.000,1,1,2 386 | 1,53,31,0,10,46.000,3,6,0.000,0,2,2 387 | 2,60,57,1,14,107.000,2,33,0.000,1,2,3 388 | 2,33,33,0,7,56.000,2,11,0.000,0,1,1 389 | 3,67,40,1,21,40.000,4,7,0.000,1,4,4 390 | 3,15,38,1,5,56.000,3,13,0.000,0,3,3 391 | 3,64,31,1,5,46.000,2,7,0.000,1,4,2 392 | 2,46,49,1,13,51.000,2,21,0.000,0,2,1 393 | 2,72,60,1,33,12.000,1,20,1.000,0,3,4 394 | 1,61,43,1,6,34.000,5,6,0.000,0,3,4 395 | 3,54,27,0,3,27.000,2,6,0.000,1,5,4 396 | 3,28,36,0,3,42.000,3,7,0.000,1,1,3 397 | 2,28,38,0,8,119.000,5,2,0.000,0,3,4 398 | 2,32,43,0,13,146.000,1,27,0.000,1,1,3 399 | 2,52,46,0,17,51.000,2,13,0.000,1,3,2 400 | 1,48,62,0,33,86.000,1,22,1.000,1,1,3 401 | 3,52,42,1,17,27.000,3,8,0.000,1,2,3 402 | 1,37,43,0,8,38.000,3,9,0.000,0,1,2 403 | 1,41,52,0,26,928.000,3,29,0.000,0,3,3 404 | 2,43,27,1,3,21.000,2,1,0.000,1,3,2 405 | 3,67,60,0,24,24.000,1,6,0.000,1,1,3 406 | 3,61,52,1,30,91.000,1,25,0.000,1,2,1 407 | 3,70,39,1,20,38.000,2,8,0.000,1,4,3 408 | 3,44,32,1,1,96.000,4,5,0.000,1,4,4 409 | 3,46,45,0,12,96.000,3,17,0.000,0,1,2 410 | 3,39,34,0,2,54.000,3,5,0.000,0,1,1 411 | 2,39,59,0,20,1668.000,4,27,0.000,1,1,2 412 | 2,50,29,1,10,53.000,4,0,0.000,1,4,4 413 | 3,5,31,0,1,21.000,3,1,0.000,1,2,1 414 | 2,10,34,0,1,52.000,5,3,0.000,1,1,2 415 | 1,72,69,0,45,17.000,3,36,1.000,1,1,2 416 | 2,68,60,0,40,262.000,2,39,0.000,1,1,3 417 | 3,58,28,0,5,34.000,5,1,0.000,1,1,2 418 | 3,18,44,1,10,107.000,1,19,0.000,1,7,1 419 | 1,65,45,1,25,67.000,5,13,0.000,1,2,3 420 | 3,8,45,0,1,37.000,4,0,0.000,0,1,2 421 | 2,1,20,0,1,21.000,2,0,0.000,1,2,1 422 | 3,42,58,0,7,22.000,1,4,0.000,1,1,2 423 | 1,4,25,1,3,71.000,3,3,0.000,1,5,1 424 | 3,17,35,0,4,77.000,3,8,0.000,1,1,3 425 | 2,8,24,1,1,18.000,4,0,0.000,1,5,4 426 | 1,49,37,1,17,50.000,2,12,0.000,1,2,2 427 | 1,56,46,1,21,33.000,4,7,0.000,0,2,2 428 | 1,62,57,1,21,94.000,1,22,0.000,0,2,4 429 | 2,42,57,1,25,209.000,1,40,0.000,1,4,1 430 | 1,13,32,0,7,31.000,4,5,0.000,0,2,3 431 | 1,69,46,1,18,66.000,2,19,0.000,0,4,2 432 | 2,45,59,0,14,51.000,1,15,0.000,0,2,1 433 | 2,66,42,1,18,118.000,2,20,0.000,0,2,2 434 | 2,15,35,0,10,25.000,2,3,0.000,0,1,1 435 | 1,41,44,1,1,59.000,3,21,0.000,1,3,2 436 | 2,11,20,0,1,20.000,2,0,0.000,1,1,4 437 | 3,45,30,1,0,63.000,5,4,0.000,0,4,4 438 | 1,52,62,0,23,36.000,4,17,0.000,0,1,1 439 | 1,42,56,0,24,72.000,4,18,0.000,1,1,4 440 | 2,48,35,0,11,50.000,1,16,0.000,0,3,3 441 | 3,4,31,0,5,39.000,2,5,0.000,0,2,4 442 | 3,53,64,1,10,54.000,2,20,0.000,1,2,4 443 | 3,1,59,0,4,9.000,1,4,1.000,0,1,3 444 | 3,19,30,1,10,33.000,4,1,0.000,0,3,4 445 | 2,33,30,0,11,28.000,1,9,0.000,0,1,1 446 | 3,42,36,1,14,44.000,2,11,0.000,0,3,3 447 | 1,60,32,0,12,34.000,4,4,0.000,1,2,2 448 | 2,57,51,1,7,68.000,4,18,0.000,1,3,3 449 | 1,64,39,0,5,37.000,1,9,0.000,0,1,1 450 | 2,28,20,1,1,18.000,3,0,0.000,0,3,4 451 | 1,29,35,1,10,55.000,2,7,0.000,0,4,3 452 | 1,46,31,1,12,21.000,1,5,0.000,0,5,3 453 | 1,22,41,1,9,29.000,3,11,0.000,0,4,1 454 | 2,10,56,0,26,9.000,2,6,1.000,0,2,1 455 | 3,50,41,0,16,138.000,2,18,0.000,1,1,2 456 | 1,55,65,0,34,28.000,4,12,1.000,1,1,4 457 | 2,15,54,0,7,62.000,1,11,0.000,0,1,1 458 | 1,31,32,1,13,37.000,3,8,0.000,1,3,4 459 | 3,5,44,0,5,83.000,1,16,0.000,1,1,2 460 | 2,69,63,0,28,168.000,1,43,0.000,1,2,3 461 | 2,26,27,1,4,33.000,4,1,0.000,0,6,4 462 | 3,10,33,1,2,66.000,3,9,0.000,1,4,1 463 | 1,26,30,0,9,18.000,4,1,0.000,0,3,4 464 | 3,25,30,0,0,20.000,1,4,0.000,1,1,3 465 | 3,30,23,1,4,19.000,3,1,0.000,0,4,2 466 | 1,21,39,1,7,25.000,2,1,0.000,0,4,3 467 | 1,71,46,0,27,119.000,5,7,0.000,1,1,4 468 | 3,11,48,1,5,323.000,4,25,0.000,0,4,3 469 | 1,24,30,1,7,45.000,1,11,0.000,1,2,1 470 | 1,65,59,1,27,197.000,4,26,0.000,0,2,2 471 | 1,16,34,1,13,56.000,3,10,0.000,1,3,1 472 | 3,37,51,1,26,24.000,4,4,0.000,0,2,2 473 | 1,5,30,0,4,25.000,3,6,0.000,0,1,3 474 | 1,3,55,1,9,26.000,5,0,0.000,1,2,2 475 | 1,5,32,0,6,33.000,4,3,0.000,0,2,1 476 | 1,60,44,1,4,150.000,2,24,0.000,0,2,4 477 | 2,19,37,1,4,99.000,2,15,0.000,1,3,2 478 | 3,54,40,0,19,50.000,4,12,0.000,1,2,4 479 | 2,25,41,0,3,81.000,3,20,0.000,1,4,3 480 | 2,1,36,0,6,21.000,1,1,0.000,0,2,2 481 | 3,54,68,0,7,273.000,3,27,0.000,1,1,1 482 | 1,14,31,1,9,39.000,3,4,0.000,1,7,1 483 | 3,14,28,1,7,17.000,2,3,0.000,0,4,1 484 | 2,10,45,1,8,115.000,3,17,0.000,1,4,2 485 | 1,7,23,0,3,27.000,2,1,0.000,1,3,1 486 | 2,33,45,0,24,35.000,1,6,0.000,0,1,3 487 | 1,3,23,0,2,20.000,2,4,0.000,0,1,1 488 | 2,56,58,1,3,92.000,1,25,0.000,0,3,1 489 | 2,54,56,0,8,207.000,2,35,0.000,1,1,3 490 | 1,72,55,1,28,40.000,1,11,0.000,0,5,2 491 | 1,18,29,0,4,40.000,2,1,0.000,0,1,3 492 | 3,25,29,0,9,55.000,4,1,0.000,0,1,1 493 | 1,42,62,0,26,26.000,1,35,1.000,1,1,1 494 | 3,4,37,0,2,41.000,2,8,0.000,0,1,1 495 | 2,12,50,1,16,203.000,4,25,0.000,0,2,1 496 | 2,53,64,1,17,380.000,2,36,0.000,1,2,3 497 | 2,13,38,1,8,104.000,4,6,0.000,0,7,4 498 | 3,18,31,1,12,24.000,3,4,0.000,0,4,2 499 | 3,67,42,1,20,81.000,3,20,0.000,1,5,4 500 | 2,6,24,0,2,17.000,1,1,0.000,0,1,3 501 | 1,25,46,0,13,45.000,1,12,0.000,1,1,3 502 | 2,15,39,0,19,32.000,2,2,0.000,0,1,2 503 | 3,32,35,0,12,57.000,4,5,0.000,0,1,2 504 | 2,22,39,0,0,63.000,4,9,0.000,0,1,2 505 | 2,8,22,0,3,25.000,4,0,0.000,0,1,3 506 | 1,3,31,1,4,22.000,1,1,0.000,0,2,1 507 | 2,32,54,0,6,155.000,2,20,0.000,0,1,1 508 | 1,49,54,0,24,30.000,2,5,0.000,0,1,2 509 | 2,2,28,0,7,46.000,3,0,0.000,1,1,4 510 | 1,39,47,0,1,68.000,4,10,0.000,1,2,2 511 | 1,42,33,0,2,67.000,2,5,0.000,0,2,2 512 | 1,28,36,0,3,69.000,3,2,0.000,0,1,4 513 | 2,41,46,1,14,47.000,2,11,0.000,0,5,4 514 | 3,67,50,1,31,83.000,2,12,0.000,0,2,2 515 | 1,38,40,0,15,34.000,2,11,0.000,1,1,2 516 | 2,18,38,0,8,45.000,2,2,0.000,1,4,4 517 | 2,6,42,1,17,29.000,2,2,0.000,1,2,4 518 | 2,4,36,0,0,21.000,2,1,0.000,1,3,1 519 | 1,13,23,1,3,30.000,3,0,0.000,1,4,4 520 | 1,20,35,0,0,78.000,4,7,0.000,0,1,4 521 | 1,34,49,1,27,61.000,3,15,0.000,1,2,3 522 | 1,46,29,1,4,90.000,5,3,0.000,0,4,4 523 | 1,48,41,0,21,43.000,2,7,0.000,0,1,2 524 | 3,69,61,1,12,301.000,2,37,0.000,0,2,3 525 | 1,32,34,1,7,32.000,2,15,0.000,0,4,1 526 | 1,29,29,0,9,41.000,2,9,0.000,0,2,2 527 | 3,55,52,0,22,127.000,1,28,0.000,1,3,2 528 | 3,72,60,1,36,271.000,1,43,0.000,1,2,3 529 | 3,29,26,0,1,32.000,3,2,0.000,1,1,1 530 | 2,50,25,1,1,36.000,4,2,0.000,1,5,4 531 | 3,35,50,1,11,293.000,3,21,0.000,0,2,1 532 | 3,11,63,0,9,41.000,3,3,0.000,0,1,1 533 | 1,9,29,0,9,39.000,3,5,0.000,1,1,1 534 | 2,51,48,0,27,58.000,1,18,0.000,0,1,3 535 | 1,33,34,1,2,28.000,1,3,0.000,1,3,2 536 | 1,25,20,0,0,15.000,2,0,0.000,0,2,3 537 | 3,31,34,0,9,105.000,4,7,0.000,1,4,3 538 | 2,67,40,1,10,80.000,4,8,0.000,1,2,4 539 | 3,55,45,0,8,36.000,2,9,0.000,0,3,3 540 | 3,19,50,0,7,112.000,4,3,0.000,1,1,4 541 | 1,39,34,1,11,55.000,4,10,0.000,1,4,4 542 | 2,52,35,1,8,40.000,4,1,0.000,0,2,3 543 | 3,55,37,1,15,23.000,2,0,0.000,1,5,3 544 | 2,25,62,0,27,28.000,4,33,1.000,1,1,4 545 | 3,33,58,0,12,21.000,2,23,1.000,1,1,1 546 | 2,48,35,0,5,63.000,4,12,0.000,0,2,3 547 | 2,48,31,1,12,52.000,4,3,0.000,0,3,4 548 | 2,49,57,0,3,189.000,1,31,0.000,1,3,1 549 | 3,54,32,1,0,50.000,1,15,0.000,0,3,3 550 | 1,45,32,1,9,23.000,1,15,0.000,0,4,1 551 | 3,4,37,1,1,33.000,2,15,0.000,1,5,1 552 | 3,49,27,1,3,29.000,3,6,0.000,1,3,2 553 | 2,23,35,0,2,96.000,3,4,0.000,0,1,4 554 | 2,31,44,0,23,80.000,5,15,0.000,0,1,2 555 | 2,55,44,1,2,68.000,2,15,0.000,0,5,3 556 | 1,9,28,0,7,30.000,1,4,0.000,1,6,1 557 | 2,55,57,1,25,34.000,2,12,0.000,0,3,1 558 | 1,34,30,0,3,35.000,2,2,0.000,1,3,3 559 | 1,10,23,0,4,36.000,2,1,0.000,0,3,3 560 | 2,62,76,0,20,35.000,3,18,1.000,1,1,2 561 | 2,51,36,0,13,62.000,3,9,0.000,0,2,4 562 | 1,65,48,0,28,94.000,1,25,0.000,0,1,2 563 | 3,53,33,0,1,60.000,1,6,0.000,0,2,4 564 | 1,23,35,0,4,118.000,2,11,0.000,0,1,1 565 | 3,1,30,0,3,135.000,4,3,0.000,0,3,1 566 | 3,66,55,0,20,80.000,2,24,0.000,1,1,3 567 | 3,8,27,1,0,21.000,3,3,0.000,1,2,2 568 | 3,18,28,1,3,22.000,1,2,0.000,1,5,1 569 | 3,36,32,1,3,63.000,4,7,0.000,0,4,2 570 | 1,13,40,0,16,142.000,2,18,0.000,1,1,1 571 | 3,4,44,0,15,32.000,3,0,0.000,1,1,3 572 | 3,20,32,0,10,19.000,3,5,0.000,0,1,2 573 | 1,66,39,1,12,60.000,4,10,0.000,0,3,4 574 | 3,67,47,1,7,97.000,1,23,0.000,0,4,3 575 | 1,11,24,1,0,31.000,2,3,0.000,1,4,4 576 | 1,6,26,0,7,30.000,3,1,0.000,1,2,1 577 | 2,13,23,1,4,31.000,1,5,0.000,1,4,1 578 | 2,2,29,1,3,44.000,2,9,0.000,1,6,4 579 | 1,4,40,0,12,38.000,2,5,0.000,0,1,1 580 | 1,34,63,1,10,23.000,2,0,0.000,1,2,4 581 | 1,13,34,1,6,37.000,3,6,0.000,0,4,3 582 | 2,37,40,1,14,26.000,2,0,0.000,0,5,2 583 | 1,45,30,1,8,38.000,2,5,0.000,0,6,3 584 | 2,71,70,0,30,153.000,3,34,1.000,1,1,3 585 | 1,35,40,0,5,49.000,1,20,0.000,1,1,1 586 | 3,44,41,0,21,51.000,3,7,0.000,0,1,1 587 | 3,44,45,1,19,88.000,1,21,0.000,0,2,3 588 | 1,26,41,1,20,19.000,1,0,0.000,1,6,1 589 | 2,42,46,1,9,31.000,1,12,0.000,1,3,1 590 | 2,41,47,1,6,90.000,2,19,0.000,0,3,1 591 | 1,31,24,1,3,14.000,1,0,0.000,0,4,1 592 | 3,62,63,0,43,341.000,2,30,0.000,0,1,3 593 | 1,12,23,1,2,24.000,4,0,0.000,1,8,4 594 | 2,45,49,1,27,81.000,2,7,0.000,1,4,3 595 | 1,47,34,1,4,63.000,2,13,0.000,1,4,1 596 | 2,23,22,1,1,27.000,1,4,0.000,0,5,3 597 | 3,39,34,0,4,20.000,5,3,0.000,1,1,4 598 | 2,19,26,0,2,48.000,3,0,0.000,0,2,3 599 | 1,13,54,0,12,121.000,4,23,0.000,0,1,3 600 | 2,5,33,0,6,39.000,3,4,0.000,0,3,4 601 | 2,11,19,0,0,21.000,2,0,0.000,0,1,1 602 | 3,4,32,0,2,53.000,4,5,0.000,0,1,1 603 | 3,17,42,0,8,58.000,1,23,0.000,0,1,2 604 | 2,7,27,0,3,39.000,3,2,0.000,0,1,1 605 | 1,19,65,0,3,108.000,1,33,0.000,0,3,3 606 | 2,32,34,1,0,38.000,1,10,0.000,0,4,2 607 | 2,53,51,0,31,245.000,4,0,0.000,0,1,2 608 | 1,51,38,0,19,51.000,1,18,0.000,1,1,3 609 | 1,35,34,0,7,78.000,4,10,0.000,1,1,1 610 | 1,3,31,1,4,23.000,4,4,0.000,0,5,4 611 | 1,22,48,0,7,88.000,4,12,0.000,0,1,1 612 | 3,17,21,1,0,25.000,2,1,0.000,1,3,3 613 | 2,68,39,0,20,73.000,2,16,0.000,1,1,2 614 | 2,27,41,1,7,62.000,4,15,0.000,1,2,3 615 | 3,68,52,1,8,456.000,3,30,0.000,1,4,3 616 | 3,56,42,1,10,24.000,2,5,0.000,0,2,3 617 | 3,11,32,0,8,23.000,2,0,0.000,1,1,1 618 | 3,5,47,0,7,46.000,1,6,0.000,1,1,1 619 | 3,16,50,0,5,263.000,2,29,0.000,1,3,3 620 | 2,59,30,0,2,34.000,4,2,0.000,1,1,2 621 | 1,60,64,0,14,33.000,2,13,1.000,0,1,4 622 | 3,55,47,0,11,37.000,1,11,0.000,0,1,3 623 | 1,18,56,0,22,296.000,2,25,0.000,0,1,1 624 | 2,16,22,0,0,26.000,3,0,0.000,1,3,2 625 | 1,35,57,1,32,47.000,4,12,0.000,0,2,2 626 | 3,31,23,1,1,31.000,3,1,0.000,1,2,4 627 | 2,20,49,1,5,264.000,2,29,0.000,1,2,1 628 | 2,16,27,0,5,41.000,4,2,0.000,1,1,4 629 | 2,69,48,1,27,55.000,2,4,0.000,0,2,3 630 | 3,69,66,1,31,49.000,2,15,0.000,0,2,1 631 | 2,27,24,1,3,26.000,1,7,0.000,1,4,1 632 | 3,48,39,0,20,110.000,4,9,0.000,0,1,3 633 | 1,18,52,1,20,66.000,1,19,0.000,1,2,1 634 | 2,24,40,1,4,39.000,3,5,0.000,1,2,3 635 | 2,17,42,0,6,31.000,2,2,0.000,0,1,4 636 | 2,9,24,1,3,26.000,4,1,0.000,1,3,1 637 | 2,15,46,0,5,232.000,4,15,0.000,1,2,4 638 | 1,62,37,1,10,43.000,4,1,0.000,1,3,2 639 | 1,9,41,0,12,39.000,4,3,0.000,0,1,1 640 | 2,71,41,1,10,73.000,2,23,0.000,1,2,3 641 | 2,62,59,1,37,74.000,1,20,0.000,0,2,3 642 | 2,20,38,0,19,237.000,4,13,0.000,0,1,4 643 | 1,19,33,1,11,22.000,1,3,0.000,0,5,3 644 | 2,49,61,1,28,21.000,1,8,0.000,0,2,3 645 | 3,3,64,0,3,9.000,2,6,1.000,0,1,1 646 | 1,33,36,0,2,41.000,5,10,0.000,0,1,1 647 | 2,38,44,0,1,50.000,4,7,0.000,1,1,2 648 | 3,42,48,1,14,27.000,3,5,0.000,1,3,4 649 | 1,11,26,0,2,53.000,3,3,0.000,0,1,4 650 | 3,27,28,1,0,58.000,3,0,0.000,0,2,3 651 | 3,23,50,0,1,151.000,4,8,0.000,1,1,1 652 | 2,4,38,1,13,54.000,2,4,0.000,0,2,3 653 | 3,36,42,0,5,31.000,1,4,0.000,1,1,1 654 | 2,37,42,0,10,115.000,1,22,0.000,1,1,3 655 | 3,24,58,1,30,24.000,1,5,0.000,0,2,1 656 | 3,4,24,0,1,17.000,2,2,0.000,0,3,1 657 | 3,54,33,0,5,47.000,2,13,0.000,1,2,3 658 | 3,7,49,0,8,36.000,1,0,0.000,1,1,3 659 | 1,10,28,0,9,75.000,4,1,0.000,0,1,4 660 | 3,24,35,1,10,41.000,5,6,0.000,1,2,2 661 | 3,12,31,0,8,18.000,4,4,0.000,0,1,1 662 | 2,45,54,0,25,171.000,3,33,0.000,1,1,3 663 | 3,59,55,1,29,42.000,3,21,0.000,1,2,2 664 | 1,3,21,1,1,36.000,3,0,0.000,1,4,1 665 | 3,8,35,0,6,31.000,5,7,0.000,0,1,4 666 | 1,16,59,0,5,207.000,2,37,0.000,1,2,4 667 | 3,32,33,0,1,38.000,1,12,0.000,1,1,1 668 | 1,8,49,0,14,134.000,4,9,0.000,1,1,4 669 | 1,9,28,1,8,28.000,3,4,0.000,0,3,2 670 | 2,12,19,1,0,15.000,2,0,0.000,1,4,2 671 | 2,24,43,1,15,48.000,4,4,0.000,0,6,4 672 | 3,72,75,0,48,14.000,2,6,1.000,1,1,2 673 | 3,58,61,0,42,101.000,4,18,0.000,1,1,2 674 | 1,70,44,1,24,87.000,2,18,0.000,0,3,3 675 | 3,9,25,0,3,27.000,1,4,0.000,1,1,1 676 | 1,49,25,0,1,27.000,5,0,0.000,1,1,2 677 | 3,52,54,0,7,59.000,3,22,0.000,0,1,3 678 | 1,10,42,1,10,294.000,4,9,0.000,1,6,4 679 | 1,14,52,1,2,44.000,3,5,0.000,0,2,3 680 | 1,19,27,1,3,35.000,2,4,0.000,0,6,4 681 | 3,12,36,1,4,205.000,4,10,0.000,1,4,2 682 | 1,65,59,0,27,732.000,3,31,0.000,1,1,2 683 | 1,67,40,1,14,59.000,3,11,0.000,0,2,3 684 | 2,29,24,1,3,23.000,1,7,0.000,0,2,3 685 | 3,55,46,0,8,87.000,2,19,0.000,1,1,1 686 | 3,71,67,1,25,110.000,1,23,0.000,1,2,3 687 | 3,5,44,1,2,83.000,3,14,0.000,1,3,3 688 | 3,60,58,0,0,63.000,4,21,0.000,1,3,2 689 | 2,10,40,0,6,22.000,3,6,0.000,1,1,1 690 | 3,52,64,0,44,43.000,3,7,0.000,0,1,4 691 | 2,61,43,1,22,35.000,2,11,0.000,0,3,2 692 | 1,61,29,1,5,48.000,2,11,0.000,0,4,2 693 | 2,67,47,1,19,69.000,3,12,0.000,0,3,3 694 | 3,41,42,1,12,26.000,4,5,0.000,0,4,2 695 | 1,30,28,0,1,20.000,1,8,0.000,1,1,1 696 | 1,24,46,0,12,43.000,2,6,0.000,0,1,4 697 | 1,72,75,0,37,33.000,1,44,1.000,1,1,2 698 | 2,17,35,0,7,66.000,2,11,0.000,0,1,4 699 | 2,65,72,0,46,12.000,2,0,1.000,0,1,3 700 | 1,15,30,1,8,90.000,2,11,0.000,0,5,4 701 | 1,34,33,0,8,35.000,3,2,0.000,1,1,2 702 | 1,26,43,0,23,51.000,5,4,0.000,1,1,2 703 | 2,36,45,0,22,117.000,3,15,0.000,1,1,2 704 | 1,60,48,0,29,100.000,2,26,0.000,1,1,3 705 | 1,16,54,0,20,147.000,1,29,0.000,0,4,3 706 | 2,1,33,0,5,53.000,4,6,0.000,1,1,1 707 | 3,56,30,1,9,57.000,5,2,0.000,0,4,2 708 | 2,6,50,0,5,37.000,2,0,0.000,0,1,2 709 | 2,48,34,0,7,42.000,3,2,0.000,1,1,2 710 | 2,34,36,1,17,50.000,1,4,0.000,0,2,3 711 | 2,54,42,1,0,55.000,4,2,0.000,0,5,4 712 | 3,38,31,1,2,42.000,2,7,0.000,1,4,4 713 | 2,25,55,0,15,37.000,1,7,0.000,1,1,1 714 | 1,47,69,1,17,228.000,3,39,0.000,1,4,4 715 | 1,72,62,1,35,163.000,5,31,0.000,0,2,4 716 | 2,6,35,1,6,28.000,4,3,0.000,1,3,4 717 | 1,30,51,1,9,118.000,5,21,0.000,1,2,1 718 | 1,65,70,1,9,115.000,2,39,1.000,1,2,4 719 | 1,22,44,1,19,37.000,4,1,0.000,0,2,3 720 | 1,60,53,0,32,93.000,1,28,0.000,0,1,3 721 | 2,19,32,0,12,71.000,4,5,0.000,1,1,4 722 | 3,18,25,0,4,26.000,2,7,0.000,1,3,1 723 | 1,9,37,1,10,14.000,4,5,0.000,1,6,1 724 | 1,1,34,1,6,18.000,1,0,0.000,1,2,1 725 | 3,10,28,1,2,36.000,4,0,0.000,1,3,4 726 | 3,45,34,1,14,43.000,4,0,0.000,0,4,2 727 | 1,25,28,1,6,41.000,4,4,0.000,1,4,3 728 | 3,66,39,0,1,66.000,2,15,0.000,1,1,2 729 | 1,27,67,0,1,33.000,4,19,1.000,1,1,3 730 | 1,19,41,0,5,53.000,4,6,0.000,0,1,1 731 | 2,17,41,1,9,28.000,4,3,0.000,0,6,1 732 | 2,16,36,0,13,58.000,4,4,0.000,0,1,1 733 | 1,8,42,1,2,129.000,4,17,0.000,1,3,1 734 | 1,3,25,0,0,65.000,4,0,0.000,1,1,4 735 | 3,59,57,0,26,311.000,3,36,0.000,1,1,1 736 | 2,45,37,0,2,57.000,3,14,0.000,1,1,2 737 | 1,66,62,0,31,47.000,2,4,0.000,1,1,2 738 | 3,28,50,1,7,25.000,1,3,0.000,0,2,1 739 | 1,15,38,1,11,46.000,5,11,0.000,0,3,1 740 | 3,39,34,1,10,51.000,3,13,0.000,1,4,4 741 | 3,38,33,0,4,19.000,2,5,0.000,1,2,3 742 | 3,34,29,0,5,26.000,3,4,0.000,1,2,2 743 | 3,60,57,1,18,72.000,5,30,1.000,0,2,4 744 | 3,72,65,1,33,71.000,1,41,1.000,1,4,3 745 | 1,42,42,1,9,41.000,2,13,0.000,0,8,3 746 | 3,32,37,0,9,44.000,2,7,0.000,0,1,1 747 | 3,48,50,1,4,208.000,4,19,0.000,1,2,4 748 | 3,63,37,1,1,45.000,4,9,0.000,1,6,2 749 | 3,45,27,1,3,39.000,3,2,0.000,0,4,2 750 | 2,68,42,0,16,89.000,4,12,0.000,0,1,2 751 | 1,6,29,1,4,19.000,2,5,0.000,1,2,1 752 | 1,68,57,1,33,15.000,2,0,0.000,1,2,4 753 | 3,72,68,1,26,59.000,2,36,1.000,1,2,4 754 | 3,53,38,1,15,20.000,3,6,0.000,1,4,2 755 | 3,20,51,1,11,27.000,4,6,0.000,0,3,3 756 | 2,70,35,1,4,48.000,2,9,0.000,0,3,2 757 | 3,32,47,0,25,135.000,2,6,0.000,1,1,1 758 | 2,61,52,1,23,19.000,1,0,0.000,0,2,3 759 | 1,24,25,1,3,28.000,4,0,0.000,0,3,2 760 | 2,56,61,1,14,12.000,1,16,1.000,1,2,1 761 | 3,54,59,0,28,28.000,2,15,0.000,0,1,2 762 | 3,43,38,1,7,18.000,3,8,0.000,1,3,4 763 | 1,19,35,0,7,58.000,3,5,0.000,1,1,4 764 | 3,5,43,0,16,72.000,3,17,0.000,1,1,3 765 | 1,24,61,1,6,112.000,4,3,0.000,1,4,1 766 | 3,62,35,1,2,28.000,2,8,0.000,1,2,2 767 | 2,14,40,0,13,398.000,5,11,0.000,1,1,4 768 | 3,60,48,1,22,106.000,3,21,0.000,1,3,4 769 | 2,30,74,1,12,10.000,1,3,1.000,1,2,3 770 | 1,40,42,1,16,108.000,3,17,0.000,0,4,2 771 | 2,42,50,1,17,85.000,2,15,0.000,1,4,1 772 | 1,20,34,0,14,51.000,3,9,0.000,1,1,2 773 | 1,4,22,1,1,22.000,2,2,0.000,1,3,3 774 | 2,44,49,1,2,66.000,2,8,0.000,1,4,4 775 | 3,49,63,0,18,10.000,2,2,1.000,0,1,3 776 | 3,5,36,1,2,34.000,1,4,0.000,1,2,3 777 | 3,70,50,1,29,67.000,1,22,0.000,0,4,2 778 | 1,30,57,1,16,19.000,1,1,0.000,0,4,1 779 | 2,28,48,0,20,128.000,4,4,0.000,1,1,4 780 | 1,5,28,1,3,39.000,5,1,0.000,0,6,4 781 | 2,27,35,0,12,56.000,1,3,0.000,1,1,4 782 | 2,17,39,0,12,45.000,4,10,0.000,1,1,1 783 | 1,4,37,1,8,25.000,1,1,0.000,1,5,1 784 | 1,30,23,1,0,24.000,2,1,0.000,1,4,3 785 | 3,71,40,1,19,53.000,4,14,0.000,1,2,4 786 | 3,67,77,0,55,49.000,1,45,1.000,1,1,1 787 | 3,24,48,1,3,38.000,1,16,0.000,1,2,1 788 | 1,24,54,1,7,84.000,3,10,0.000,1,2,1 789 | 2,59,54,0,32,126.000,1,22,0.000,1,1,3 790 | 3,59,32,0,9,73.000,4,5,0.000,0,1,2 791 | 3,44,37,1,1,41.000,4,8,0.000,1,6,2 792 | 1,19,31,1,0,22.000,4,2,0.000,0,3,2 793 | 2,58,46,1,23,23.000,1,2,0.000,1,2,1 794 | 1,41,53,1,22,94.000,2,25,0.000,0,2,2 795 | 2,16,33,0,1,30.000,3,7,0.000,0,4,3 796 | 3,13,36,0,11,31.000,2,0,0.000,0,1,1 797 | 1,60,53,0,22,171.000,1,37,0.000,1,1,3 798 | 2,72,49,1,23,102.000,1,27,0.000,1,6,2 799 | 2,33,39,0,17,27.000,4,3,0.000,1,1,4 800 | 2,72,45,1,25,98.000,4,20,0.000,1,2,4 801 | 1,66,54,1,8,591.000,4,25,0.000,1,2,4 802 | 3,53,37,1,7,25.000,1,2,0.000,1,6,3 803 | 2,22,28,1,0,36.000,4,0,0.000,0,4,4 804 | 2,14,26,1,1,25.000,2,0,0.000,1,3,3 805 | 1,52,49,0,18,69.000,2,13,0.000,1,2,3 806 | 3,33,30,1,5,22.000,1,0,0.000,0,2,3 807 | 3,72,66,0,26,67.000,2,37,1.000,1,1,3 808 | 1,40,40,1,3,73.000,4,12,0.000,1,4,3 809 | 2,29,26,0,1,22.000,4,2,0.000,1,1,4 810 | 3,63,67,0,22,9.000,2,12,1.000,0,1,3 811 | 2,56,54,0,25,147.000,3,31,0.000,1,1,2 812 | 3,60,50,1,26,239.000,2,19,0.000,1,2,4 813 | 3,28,22,1,3,23.000,3,1,0.000,0,5,1 814 | 2,39,39,0,19,29.000,4,6,0.000,0,1,2 815 | 3,65,39,1,18,32.000,2,15,0.000,0,4,3 816 | 3,29,31,0,7,30.000,3,7,0.000,1,1,3 817 | 3,28,36,0,8,57.000,2,13,0.000,1,2,3 818 | 1,2,18,0,0,20.000,2,0,0.000,0,1,3 819 | 3,67,52,0,13,248.000,2,20,0.000,1,3,3 820 | 3,5,35,1,9,59.000,3,8,0.000,1,5,4 821 | 3,48,35,0,10,24.000,2,6,0.000,0,1,3 822 | 2,10,26,1,4,25.000,1,4,0.000,1,3,1 823 | 2,17,28,0,3,37.000,3,2,0.000,1,1,3 824 | 1,21,24,1,0,30.000,4,0,0.000,1,5,4 825 | 2,25,51,0,9,66.000,4,13,0.000,0,2,4 826 | 3,44,31,0,8,16.000,1,3,0.000,0,1,1 827 | 2,59,49,0,28,429.000,4,23,0.000,1,2,4 828 | 2,11,26,0,7,20.000,2,4,0.000,0,1,1 829 | 2,50,68,1,32,262.000,3,28,0.000,0,2,3 830 | 3,23,25,1,2,28.000,3,1,0.000,1,5,2 831 | 1,17,19,0,0,18.000,2,0,0.000,1,2,2 832 | 1,15,62,1,3,144.000,3,21,0.000,0,2,3 833 | 2,68,57,1,38,33.000,1,9,0.000,1,2,2 834 | 1,69,42,1,23,19.000,3,0,0.000,1,2,1 835 | 1,30,24,1,3,30.000,1,7,0.000,1,5,1 836 | 3,22,30,0,8,41.000,2,8,0.000,0,1,3 837 | 2,10,20,1,1,20.000,2,0,0.000,1,4,1 838 | 1,72,59,1,12,65.000,3,21,0.000,1,6,2 839 | 2,34,24,1,5,17.000,4,0,0.000,1,2,3 840 | 2,43,49,1,18,66.000,1,22,0.000,0,4,3 841 | 3,31,37,1,15,18.000,4,5,0.000,0,3,4 842 | 2,6,27,1,5,26.000,4,2,0.000,0,5,1 843 | 2,48,42,0,21,46.000,4,4,0.000,0,1,3 844 | 2,50,25,0,6,19.000,1,5,0.000,0,3,1 845 | 3,20,41,1,19,58.000,2,6,0.000,1,2,2 846 | 3,61,73,0,10,25.000,1,21,1.000,0,1,1 847 | 2,6,21,0,0,15.000,2,1,0.000,1,1,1 848 | 2,1,22,0,2,35.000,2,2,0.000,0,1,1 849 | 1,21,43,0,13,41.000,2,1,0.000,1,1,4 850 | 3,7,32,0,1,71.000,4,3,0.000,0,2,4 851 | 3,65,56,1,19,608.000,3,34,0.000,1,2,4 852 | 1,64,37,0,10,44.000,4,9,0.000,1,1,2 853 | 3,4,30,0,0,41.000,2,0,0.000,0,2,3 854 | 1,67,45,1,22,83.000,3,11,0.000,1,2,2 855 | 2,22,35,0,12,28.000,2,3,0.000,0,2,2 856 | 3,51,37,1,17,101.000,2,10,0.000,1,2,3 857 | 1,50,28,0,6,28.000,1,10,0.000,1,1,3 858 | 1,31,47,1,18,35.000,2,0,0.000,0,2,1 859 | 3,4,28,0,0,39.000,2,3,0.000,1,1,3 860 | 3,60,56,0,19,51.000,4,11,0.000,0,1,4 861 | 3,7,31,1,12,31.000,2,7,0.000,1,6,1 862 | 3,20,40,0,2,209.000,4,15,0.000,1,1,2 863 | 2,19,23,1,4,37.000,3,1,0.000,1,4,3 864 | 3,58,37,1,5,64.000,4,8,0.000,1,2,4 865 | 3,65,64,0,20,88.000,3,26,0.000,1,1,4 866 | 3,72,61,1,34,61.000,1,8,0.000,0,3,2 867 | 3,8,42,0,3,66.000,3,10,0.000,0,1,4 868 | 2,33,40,1,14,19.000,3,5,0.000,0,2,1 869 | 3,18,34,1,4,42.000,2,14,0.000,1,5,4 870 | 2,24,31,1,6,41.000,2,13,0.000,0,5,3 871 | 3,70,36,0,8,50.000,1,15,0.000,1,1,2 872 | 1,7,38,0,4,70.000,4,4,0.000,0,1,4 873 | 3,70,59,0,27,85.000,1,19,0.000,1,1,3 874 | 2,20,48,1,21,45.000,3,2,0.000,1,2,3 875 | 3,54,32,1,12,27.000,1,9,0.000,0,2,2 876 | 3,36,48,0,28,29.000,1,2,0.000,0,1,1 877 | 3,71,53,1,29,48.000,4,0,0.000,1,2,3 878 | 1,17,41,0,4,32.000,2,3,0.000,0,1,3 879 | 1,18,45,1,17,43.000,1,9,0.000,0,3,3 880 | 1,16,27,0,8,16.000,5,0,0.000,1,1,4 881 | 3,38,30,1,6,22.000,2,3,0.000,1,2,2 882 | 1,52,45,1,19,40.000,3,9,0.000,1,3,4 883 | 1,46,48,1,12,256.000,3,25,0.000,0,6,4 884 | 2,31,46,0,23,144.000,4,13,0.000,0,1,3 885 | 2,23,31,0,10,45.000,4,0,0.000,0,2,2 886 | 2,38,56,1,34,34.000,2,0,0.000,1,3,3 887 | 2,50,54,0,15,248.000,3,28,0.000,1,1,3 888 | 2,16,49,0,17,41.000,2,5,0.000,0,1,4 889 | 2,14,34,1,4,27.000,4,1,0.000,1,2,4 890 | 2,30,52,0,23,17.000,3,3,0.000,1,1,2 891 | 3,22,47,1,0,44.000,4,11,0.000,1,3,4 892 | 1,38,55,1,12,135.000,2,24,0.000,0,4,4 893 | 3,59,26,1,3,41.000,4,1,0.000,1,3,2 894 | 2,54,55,0,1,587.000,3,33,0.000,0,1,3 895 | 3,9,40,0,13,38.000,4,7,0.000,1,1,4 896 | 1,67,67,1,38,49.000,2,10,0.000,1,2,2 897 | 2,24,44,0,19,33.000,3,0,0.000,1,2,1 898 | 2,12,55,1,13,36.000,1,5,0.000,1,2,2 899 | 1,26,47,0,13,54.000,3,0,0.000,0,1,2 900 | 2,3,32,0,4,58.000,2,11,0.000,0,4,4 901 | 2,20,46,0,2,23.000,5,4,0.000,1,2,4 902 | 3,5,52,1,0,97.000,5,9,0.000,0,2,4 903 | 3,58,53,0,19,207.000,3,12,0.000,1,1,4 904 | 1,54,28,1,2,21.000,3,3,0.000,0,4,1 905 | 2,52,39,0,6,119.000,3,18,0.000,1,1,1 906 | 2,38,30,1,10,25.000,1,5,0.000,1,4,1 907 | 2,19,46,0,5,59.000,3,17,0.000,0,1,1 908 | 2,22,33,1,2,24.000,1,3,0.000,0,4,1 909 | 1,49,35,1,15,113.000,4,4,0.000,0,5,2 910 | 3,5,39,0,0,22.000,1,0,0.000,1,2,3 911 | 2,18,69,0,28,11.000,1,17,1.000,0,1,1 912 | 3,43,29,0,4,33.000,1,13,0.000,1,4,3 913 | 3,17,50,0,2,205.000,5,19,0.000,0,1,4 914 | 2,37,33,0,4,41.000,3,8,0.000,1,3,2 915 | 2,8,39,0,0,39.000,1,7,0.000,1,1,1 916 | 2,51,46,1,8,107.000,2,21,0.000,1,6,3 917 | 1,71,55,1,11,198.000,1,31,0.000,1,3,3 918 | 1,56,53,1,23,100.000,5,14,0.000,1,2,2 919 | 3,70,68,0,21,1131.000,2,45,0.000,0,1,3 920 | 1,62,53,0,14,360.000,3,26,0.000,1,2,3 921 | 1,50,52,1,22,162.000,2,27,0.000,1,2,4 922 | 3,1,31,0,3,29.000,5,2,0.000,1,3,1 923 | 3,15,26,0,1,38.000,4,1,0.000,0,2,4 924 | 3,72,55,1,24,82.000,3,25,0.000,1,2,2 925 | 2,69,47,1,10,159.000,2,24,0.000,0,4,3 926 | 2,32,44,0,10,201.000,2,24,0.000,0,1,3 927 | 1,19,25,1,6,27.000,4,0,0.000,1,2,3 928 | 2,42,54,0,19,57.000,3,10,0.000,1,1,4 929 | 3,51,49,0,29,45.000,1,16,0.000,0,1,1 930 | 2,32,22,1,2,17.000,2,2,0.000,1,4,3 931 | 3,26,55,1,13,61.000,1,26,0.000,0,2,3 932 | 2,39,44,1,14,418.000,4,18,0.000,0,2,4 933 | 2,72,53,0,12,152.000,2,32,0.000,1,1,4 934 | 2,4,39,0,11,38.000,4,1,0.000,0,2,4 935 | 2,54,50,1,5,151.000,4,20,0.000,0,3,3 936 | 1,34,40,1,21,23.000,4,9,0.000,1,3,4 937 | 1,20,25,1,4,33.000,4,0,0.000,1,3,1 938 | 3,14,49,0,4,26.000,1,8,0.000,0,1,1 939 | 1,61,59,0,26,89.000,1,40,1.000,1,1,3 940 | 2,4,24,0,2,25.000,4,0,0.000,0,1,1 941 | 3,16,48,0,25,59.000,2,13,0.000,0,2,3 942 | 1,13,41,0,9,55.000,2,12,0.000,1,3,1 943 | 2,53,37,1,18,25.000,3,6,0.000,1,2,2 944 | 2,58,36,1,13,39.000,2,8,0.000,1,7,4 945 | 2,25,38,1,19,56.000,1,19,0.000,1,2,3 946 | 1,71,61,1,19,155.000,3,30,0.000,0,2,3 947 | 1,22,33,1,13,119.000,5,0,0.000,1,4,4 948 | 1,46,48,1,15,64.000,3,20,0.000,0,2,3 949 | 3,66,50,1,2,333.000,5,24,0.000,0,2,2 950 | 1,69,58,1,29,26.000,2,7,0.000,1,3,4 951 | 2,38,49,1,3,33.000,4,11,0.000,0,4,2 952 | 3,16,35,0,7,46.000,3,3,0.000,0,1,1 953 | 3,44,39,1,8,41.000,2,8,0.000,0,4,4 954 | 1,50,50,0,24,31.000,1,8,0.000,0,1,3 955 | 3,10,23,0,1,29.000,4,0,0.000,1,1,4 956 | 2,7,41,0,6,94.000,2,16,0.000,0,1,4 957 | 3,71,48,0,25,288.000,3,19,0.000,0,1,1 958 | 3,16,39,1,5,31.000,1,4,0.000,1,5,1 959 | 3,6,20,0,0,25.000,2,0,0.000,1,2,3 960 | 2,46,55,0,24,269.000,2,35,0.000,0,1,3 961 | 3,51,49,1,13,203.000,3,27,0.000,0,4,3 962 | 3,5,34,0,12,22.000,2,2,0.000,0,2,1 963 | 2,49,54,1,23,25.000,2,1,0.000,1,4,1 964 | 3,49,51,1,14,222.000,5,16,0.000,0,2,2 965 | 1,26,30,1,4,76.000,3,7,0.000,0,2,1 966 | 3,22,39,0,4,55.000,2,17,0.000,1,1,4 967 | 3,51,45,1,4,46.000,3,6,0.000,0,2,4 968 | 1,19,27,1,7,56.000,4,1,0.000,0,4,4 969 | 1,9,36,0,1,79.000,3,11,0.000,1,5,3 970 | 3,15,25,1,0,51.000,3,2,0.000,1,2,1 971 | 2,57,63,0,19,61.000,1,27,0.000,0,1,3 972 | 3,48,45,0,15,41.000,1,10,0.000,1,2,1 973 | 2,9,53,1,20,50.000,2,13,0.000,1,2,1 974 | 1,61,52,0,21,82.000,1,18,0.000,0,1,3 975 | 3,72,53,0,34,108.000,1,23,0.000,0,1,3 976 | 3,57,60,0,20,14.000,2,27,1.000,1,1,3 977 | 2,57,51,0,10,46.000,1,13,0.000,0,1,2 978 | 1,37,60,1,0,46.000,1,20,0.000,0,3,3 979 | 3,45,51,0,13,146.000,4,16,0.000,0,2,1 980 | 3,45,39,1,15,36.000,1,11,0.000,1,5,1 981 | 2,55,44,0,24,83.000,1,23,0.000,1,1,3 982 | 2,24,27,0,2,17.000,3,3,0.000,0,5,2 983 | 3,1,20,0,1,18.000,3,0,0.000,0,1,1 984 | 2,34,23,1,3,24.000,1,7,0.000,1,4,3 985 | 3,14,37,1,5,48.000,3,1,0.000,1,4,1 986 | 3,6,32,0,10,47.000,1,10,0.000,0,1,3 987 | 3,24,30,0,0,25.000,4,5,0.000,0,2,4 988 | 2,3,26,1,6,59.000,4,0,0.000,1,3,4 989 | 1,4,30,0,1,45.000,4,6,0.000,0,3,4 990 | 1,72,40,1,19,163.000,4,15,0.000,0,2,2 991 | 1,44,56,0,4,63.000,2,6,0.000,1,1,3 992 | 1,50,43,0,6,27.000,3,4,0.000,0,1,3 993 | 2,61,50,1,16,190.000,2,22,0.000,1,2,2 994 | 1,34,52,1,2,106.000,2,19,0.000,0,2,3 995 | 3,26,54,1,30,26.000,3,1,0.000,1,2,4 996 | 1,15,46,1,17,63.000,5,1,0.000,0,2,4 997 | 3,10,39,0,0,27.000,3,0,0.000,1,3,1 998 | 1,7,34,0,2,22.000,5,5,0.000,1,1,1 999 | 3,67,59,0,40,944.000,5,33,0.000,1,1,4 1000 | 3,70,49,0,18,87.000,2,22,0.000,1,1,3 1001 | 3,50,36,1,7,39.000,3,3,0.000,1,3,2 1002 | -------------------------------------------------------------------------------- /Kmeans/Cust_Segmentation.csv: -------------------------------------------------------------------------------- 1 | Customer Id,Age,Edu,Years Employed,Income,Card Debt,Other Debt,Defaulted,Address,DebtIncomeRatio 2 | 1,41,2,6,19,0.124,1.073,0,NBA001,6.3 3 | 2,47,1,26,100,4.582,8.218,0,NBA021,12.8 4 | 3,33,2,10,57,6.111,5.802,1,NBA013,20.9 5 | 4,29,2,4,19,0.681,0.516,0,NBA009,6.3 6 | 5,47,1,31,253,9.308,8.908,0,NBA008,7.2 7 | 6,40,1,23,81,0.998,7.831,,NBA016,10.9 8 | 7,38,2,4,56,0.442,0.454,0,NBA013,1.6 9 | 8,42,3,0,64,0.279,3.945,0,NBA009,6.6 10 | 9,26,1,5,18,0.575,2.215,,NBA006,15.5 11 | 10,47,3,23,115,0.653,3.947,0,NBA011,4 12 | 11,44,3,8,88,0.285,5.083,1,NBA010,6.1 13 | 12,34,2,9,40,0.374,0.266,,NBA003,1.6 14 | 13,24,1,7,18,0.526,0.643,0,NBA000,6.5 15 | 14,46,1,6,30,1.415,3.865,,NBA019,17.6 16 | 15,28,3,2,20,0.233,1.647,1,NBA000,9.4 17 | 16,24,1,1,16,0.185,1.287,,NBA005,9.2 18 | 17,29,1,1,17,0.132,0.293,0,NBA004,2.5 19 | 18,43,4,1,26,1.519,1.237,0,NBA005,10.6 20 | 19,44,1,18,61,2.806,3.782,,NBA000,10.8 21 | 20,36,1,16,32,0.544,2.944,,NBA013,10.9 22 | 21,29,2,6,25,0.585,0.465,0,NBA009,4.2 23 | 22,36,3,10,43,0.961,4.629,0,NBA004,13 24 | 23,28,3,6,47,5.574,3.732,1,NBA008,19.8 25 | 24,45,1,19,77,2.303,4.165,0,NBA022,8.4 26 | 25,37,4,10,123,3.022,18.257,0,NBA018,17.3 27 | 26,43,1,9,66,2.341,3.467,,NBA008,8.8 28 | 27,24,1,4,21,0.099,0.447,0,NBA002,2.6 29 | 28,37,1,19,38,2.591,2.539,,NBA007,13.5 30 | 29,38,2,13,59,0.408,1.008,0,NBA000,2.4 31 | 30,34,2,9,45,1.118,3.427,0,NBA013,10.1 32 | 31,40,3,18,100,1.485,1.015,0,NBA006,2.5 33 | 32,42,2,12,55,2.533,5.717,0,NBA009,15 34 | 33,23,2,0,42,1.019,0.619,1,NBA001,3.9 35 | 34,40,3,5,28,1.263,1.537,,NBA018,10 36 | 35,28,1,12,45,1.854,1.611,0,NBA007,7.7 37 | 36,33,2,5,37,1.029,2.079,,NBA002,8.4 38 | 37,35,3,5,37,0.581,1.417,1,NBA003,5.4 39 | 38,37,1,0,18,1.584,0.738,1,NBA018,12.9 40 | 39,22,1,1,18,0.27,0.522,0,NBA000,4.4 41 | 40,39,3,16,126,1.405,7.163,,NBA010,6.8 42 | 41,20,1,4,14,0.201,1.157,1,NBA000,9.7 43 | 42,48,3,17,113,3.376,10.184,0,NBA026,12 44 | 43,28,2,5,34,3.099,4.993,0,NBA009,23.8 45 | 44,37,5,9,177,0.888,9.555,0,NBA016,5.9 46 | 45,48,1,3,27,1.403,4.348,0,NBA011,21.3 47 | 46,45,3,9,84,1.276,9.728,,NBA000,13.1 48 | 47,22,1,4,14,0.225,2.225,0,NBA003,17.5 49 | 48,30,1,4,21,0.229,0.506,,NBA002,3.5 50 | 49,28,1,3,19,0.261,0.518,0,NBA007,4.1 51 | 50,29,2,10,61,0.798,2.984,,NBA003,6.2 52 | 51,47,1,22,81,1.506,2.949,0,NBA019,5.5 53 | 52,36,1,11,33,1.266,9.459,0,NBA002,32.5 54 | 53,24,1,3,19,1.358,3.278,1,NBA004,24.4 55 | 54,56,1,19,66,0.847,1.331,,NBA026,3.3 56 | 55,29,3,5,70,3.176,10.754,1,NBA006,19.9 57 | 56,34,1,2,25,0.573,2.577,1,NBA011,12.6 58 | 57,32,1,1,20,0.315,0.645,1,NBA000,4.8 59 | 58,27,3,2,26,0.722,0.838,0,NBA007,6 60 | 59,40,1,9,64,0.79,3.434,0,NBA004,6.6 61 | 60,50,1,11,36,0.835,0.893,0,NBA005,4.8 62 | 61,39,1,17,60,2.45,5.77,0,NBA020,13.7 63 | 62,34,1,18,34,2.1,3.136,0,NBA010,15.4 64 | 63,36,1,18,67,5.245,2.259,0,NBA009,11.2 65 | 64,44,2,18,74,4.522,5.394,0,NBA004,13.4 66 | 65,24,2,4,20,0.324,0.416,0,NBA004,3.7 67 | 66,31,1,8,44,1.721,1.799,0,NBA004,8 68 | 67,34,1,16,79,1.735,1.425,0,NBA009,4 69 | 68,26,1,4,27,2.472,0.363,1,NBA003,10.5 70 | 69,38,1,3,23,0.832,2.549,0,NBA019,14.7 71 | 70,24,1,3,14,0.838,1.416,1,NBA000,16.1 72 | 71,30,3,8,61,0.55,1.158,0,NBA007,2.8 73 | 72,40,4,5,75,0.885,0.54,0,NBA006,1.9 74 | 73,30,2,12,98,2.935,4.121,0,NBA009,7.2 75 | 74,25,2,5,42,3.366,3.144,,NBA003,15.5 76 | 75,29,1,9,33,0.694,0.89,0,NBA002,4.8 77 | 76,33,1,5,18,0.985,1.337,0,NBA010,12.9 78 | 77,43,1,8,45,0.677,0.808,0,NBA011,3.3 79 | 78,35,2,1,21,0.37,1.289,0,NBA001,7.9 80 | 79,41,2,21,145,3.237,14.453,0,NBA022,12.2 81 | 80,46,2,18,55,0.419,2.001,,NBA018,4.4 82 | 81,33,1,12,68,1.366,5.978,0,NBA012,10.8 83 | 82,30,3,0,65,3.9,15.405,1,NBA008,29.7 84 | 83,40,3,18,157,3.326,7.036,0,NBA014,6.6 85 | 84,38,1,1,42,0.891,2.931,,NBA009,9.1 86 | 85,49,2,11,51,0.543,7.107,0,NBA001,15 87 | 86,26,1,0,15,0.386,0.424,,NBA002,5.4 88 | 87,22,4,0,25,1.491,1.559,,NBA001,12.2 89 | 88,29,2,2,16,0.433,1.247,,NBA004,10.5 90 | 89,28,2,8,31,1.492,1.05,1,NBA003,8.2 91 | 90,23,4,0,32,0.818,5.07,1,NBA004,18.4 92 | 91,37,2,11,75,0.633,1.992,0,NBA004,3.5 93 | 92,35,2,5,33,0.103,0.821,0,NBA001,2.8 94 | 93,31,1,1,21,0.277,0.248,1,NBA006,2.5 95 | 94,29,1,1,20,0.569,3.151,1,NBA002,18.6 96 | 95,34,3,7,27,0.125,0.469,0,NBA000,2.2 97 | 96,47,2,7,49,0.254,0.187,,NBA024,0.9 98 | 97,37,1,5,27,0.419,2.119,,NBA011,9.4 99 | 98,38,1,10,34,0.036,0.372,0,NBA008,1.2 100 | 99,32,2,5,28,2.594,1.55,1,NBA000,14.8 101 | 100,22,3,0,20,0.219,0.721,0,NBA002,4.7 102 | 101,30,1,7,33,1.165,7.217,1,NBA002,25.4 103 | 102,38,4,13,126,7.613,9.649,,NBA002,13.7 104 | 103,36,1,20,60,1.056,2.184,0,NBA015,5.4 105 | 104,44,1,5,48,1.97,2.35,1,NBA003,9 106 | 105,27,2,4,22,1.086,1.818,1,NBA003,13.2 107 | 106,47,1,19,50,3.176,11.874,0,NBA007,30.1 108 | 107,41,1,7,32,0.072,0.856,0,NBA022,2.9 109 | 108,25,1,8,27,1.019,2.869,0,NBA004,14.4 110 | 109,33,2,10,54,0.12,1.5,,NBA002,3 111 | 110,42,3,13,82,1.315,1.391,0,NBA000,3.3 112 | 111,36,1,14,53,1.888,1.133,0,NBA017,5.7 113 | 112,25,4,0,32,2.14,3.492,0,NBA002,17.6 114 | 113,30,1,10,39,2.056,1.649,0,NBA011,9.5 115 | 114,24,1,0,16,1.009,1.551,1,NBA002,16 116 | 115,32,1,7,23,0.861,1.393,,NBA010,9.8 117 | 116,39,2,9,32,1.43,0.682,0,NBA012,6.6 118 | 117,30,1,4,18,0.227,1.699,0,NBA002,10.7 119 | 118,33,1,16,46,2.4,1.326,,NBA012,8.1 120 | 119,32,2,12,63,5.55,5.916,0,NBA003,18.2 121 | 120,33,1,7,26,0.543,1.017,0,NBA006,6 122 | 121,29,2,6,21,0.369,2.046,0,NBA004,11.5 123 | 122,28,2,5,37,1.124,1.947,0,NBA002,8.3 124 | 123,33,4,9,32,0.496,1.264,0,NBA008,5.5 125 | 124,39,2,16,53,0.694,1.267,0,NBA012,3.7 126 | 125,32,1,10,42,1.54,2.282,0,NBA011,9.1 127 | 126,46,1,9,72,0.828,2.772,,NBA006,5 128 | 127,32,2,3,47,0.429,0.464,0,NBA013,1.9 129 | 128,28,2,5,25,0.339,0.386,0,NBA006,2.9 130 | 129,26,1,2,21,1.023,3.135,0,NBA006,19.8 131 | 130,27,1,0,16,0.183,0.089,0,NBA001,1.7 132 | 131,35,1,16,36,0.179,1.045,0,NBA015,3.4 133 | 132,35,1,16,57,1.143,4.842,0,NBA010,10.5 134 | 133,31,2,6,25,2.332,0.543,0,NBA005,11.5 135 | 134,32,3,5,23,0.326,0.709,0,NBA009,4.5 136 | 135,29,1,9,25,1.07,2.405,0,NBA000,13.9 137 | 136,35,4,4,29,1.844,1.346,0,NBA016,11 138 | 137,48,2,21,86,0.146,0.886,,NBA014,1.2 139 | 138,24,1,4,19,1.185,0.905,,NBA000,11 140 | 139,44,1,12,31,0.358,2.649,0,NBA003,9.7 141 | 140,40,2,5,35,1.584,4.156,1,NBA012,16.4 142 | 141,36,2,13,41,2.918,3.806,1,NBA006,16.4 143 | 142,23,2,0,21,0.455,1.372,0,NBA004,8.7 144 | 143,26,2,8,40,0.444,4.276,0,NBA001,11.8 145 | 144,30,1,8,23,0.762,1.998,0,NBA006,12 146 | 145,34,1,12,68,7.817,9.251,1,NBA005,25.1 147 | 146,28,3,2,30,1.522,4.448,0,NBA005,19.9 148 | 147,37,1,18,58,2.868,3.106,,NBA009,10.3 149 | 148,46,3,3,43,3.042,2.634,1,NBA013,13.2 150 | 149,24,2,1,42,0.838,1.556,0,NBA002,5.7 151 | 150,25,2,6,26,0.07,1.1,0,NBA002,4.5 152 | 151,33,1,14,37,0.199,0.763,0,NBA002,2.6 153 | 152,46,1,7,41,0.585,9.009,,NBA006,23.4 154 | 153,38,3,4,31,1.332,1.551,1,NBA006,9.3 155 | 154,47,1,3,21,0.068,3.166,,NBA001,15.4 156 | 155,34,2,9,65,2.108,1.922,0,NBA009,6.2 157 | 156,35,1,16,37,4.213,2.151,,NBA000,17.2 158 | 157,39,1,22,113,0.987,1.951,0,NBA009,2.6 159 | 158,44,1,24,101,2.992,2.664,0,NBA010,5.6 160 | 159,28,2,1,24,1.338,2.766,1,NBA008,17.1 161 | 160,31,3,9,59,3.11,5.504,1,NBA007,14.6 162 | 161,24,1,1,20,0.325,0.495,,NBA000,4.1 163 | 162,36,3,13,39,2.801,4.687,0,NBA003,19.2 164 | 163,35,2,14,82,0.468,0.188,0,NBA003,0.8 165 | 164,31,1,11,47,2.864,2.259,0,NBA012,10.9 166 | 165,28,2,6,22,0.65,1.88,0,NBA009,11.5 167 | 166,36,4,8,32,0.025,0.487,0,NBA000,1.6 168 | 167,31,3,6,54,0.402,1.11,0,NBA008,2.8 169 | 168,30,1,11,27,0.074,0.169,0,NBA009,0.9 170 | 169,26,1,10,24,0.311,0.337,0,NBA006,2.7 171 | 170,48,4,3,45,0.975,3.435,0,NBA023,9.8 172 | 171,39,2,16,89,5.002,7.725,0,NBA009,14.3 173 | 172,31,1,7,20,0.111,2.349,0,NBA002,12.3 174 | 173,31,3,9,28,0.122,0.606,0,NBA002,2.6 175 | 174,27,3,3,45,0.292,4.658,1,NBA004,11 176 | 175,31,1,13,27,0.238,1.112,0,NBA012,5 177 | 176,53,1,0,27,2.754,5.049,1,NBA026,28.9 178 | 177,40,1,15,55,0.856,2.169,0,NBA014,5.5 179 | 178,26,1,7,22,0.209,0.913,0,NBA003,5.1 180 | 179,29,2,5,36,3.491,0.973,1,NBA004,12.4 181 | 180,52,1,19,89,0.397,3.786,0,NBA025,4.7 182 | 181,39,2,2,46,4.004,3.356,1,NBA012,16 183 | 182,33,2,13,40,0.181,2.699,0,NBA002,7.2 184 | 183,24,1,7,21,0.52,0.803,0,NBA002,6.3 185 | 184,47,1,29,129,20.561,12.076,1,NBA018,25.3 186 | 185,41,3,2,15,1.961,1.984,1,NBA000,26.3 187 | 186,26,2,6,45,6.049,5.651,0,NBA007,26 188 | 187,48,1,3,24,0.764,0.916,0,NBA007,7 189 | 188,37,1,13,24,0.602,1.534,0,NBA005,8.9 190 | 189,37,1,20,41,0.899,4.39,,NBA013,12.9 191 | 190,46,4,7,73,0.556,0.247,,NBA016,1.1 192 | 191,30,1,8,27,3.744,1.737,,NBA011,20.3 193 | 192,47,1,15,30,2.212,5.138,0,NBA001,24.5 194 | 193,37,3,16,75,0.022,0.053,,NBA008,0.1 195 | 194,37,2,0,31,0.118,0.161,1,NBA013,0.9 196 | 195,40,2,15,73,1.033,1.449,0,NBA016,3.4 197 | 196,35,3,5,30,1.526,1.654,,NBA013,10.6 198 | 197,21,2,0,16,0.15,0.938,0,NBA001,6.8 199 | 198,52,1,24,64,3.93,2.47,0,NBA014,10 200 | 199,47,3,16,221,15.792,23.104,1,NBA026,17.6 201 | 200,41,3,0,26,0.099,0.343,0,NBA021,1.7 202 | 201,47,1,27,113,2.381,8.015,0,NBA008,9.2 203 | 202,38,2,0,21,0.612,0.354,,NBA018,4.6 204 | 203,22,3,1,25,1.977,1.473,1,NBA003,13.8 205 | 204,39,1,8,21,0.276,0.564,0,NBA000,4 206 | 205,45,1,8,27,0.416,0.286,,NBA025,2.6 207 | 206,51,1,10,44,4.96,0.848,1,NBA001,13.2 208 | 207,33,2,10,26,0.031,0.775,0,NBA004,3.1 209 | 208,43,1,25,242,1.636,4.656,0,NBA016,2.6 210 | 209,40,1,22,95,0.633,2.787,0,NBA004,3.6 211 | 210,27,4,2,23,0.32,0.991,1,NBA008,5.7 212 | 211,31,1,11,25,0.141,2.134,0,NBA008,9.1 213 | 212,34,1,10,48,0.264,2.184,,NBA012,5.1 214 | 213,37,1,9,57,1.357,1.778,0,NBA008,5.5 215 | 214,44,4,18,78,0.565,0.215,,NBA023,1 216 | 215,42,1,3,24,0.667,1.877,0,NBA010,10.6 217 | 216,25,1,3,16,1.011,1.837,1,NBA002,17.8 218 | 217,39,2,7,68,1.017,7.823,1,NBA002,13 219 | 218,26,4,1,64,7.754,7.158,,NBA006,23.3 220 | 219,36,1,19,45,0.92,4.525,,NBA008,12.1 221 | 220,48,1,15,60,0.321,0.939,0,NBA005,2.1 222 | 221,21,2,0,21,0.488,2.137,1,NBA002,12.5 223 | 222,40,3,17,116,2.561,4.399,0,NBA011,6 224 | 223,31,2,9,30,0.602,1.138,0,NBA001,5.8 225 | 224,23,4,0,23,0.468,1.073,0,NBA002,6.7 226 | 225,31,1,6,21,0.729,1.014,0,NBA009,8.3 227 | 226,31,1,7,23,0.225,0.902,0,NBA010,4.9 228 | 227,50,3,18,102,5.782,8.6,0,NBA027,14.1 229 | 228,29,1,1,31,0.156,2.324,0,NBA008,8 230 | 229,37,3,5,29,1.405,5.729,0,NBA017,24.6 231 | 230,46,1,16,52,3.032,3.676,1,NBA018,12.9 232 | 231,39,1,9,35,0.233,1.517,0,NBA006,5 233 | 232,34,1,14,28,1.138,3.622,0,NBA008,17 234 | 233,27,1,11,56,4.637,4.827,0,NBA008,16.9 235 | 234,23,1,6,15,1.375,0.53,,NBA002,12.7 236 | 235,41,1,24,83,6.589,5.031,0,NBA012,14 237 | 236,29,1,1,18,0.03,0.168,0,NBA001,1.1 238 | 237,44,1,19,40,0.344,4.016,,NBA009,10.9 239 | 238,24,2,0,15,0.321,2.094,1,NBA002,16.1 240 | 239,32,3,9,51,2.686,9.044,0,NBA010,23 241 | 240,23,2,3,34,0.262,1.234,0,NBA004,4.4 242 | 241,31,1,4,28,0.291,2.873,0,NBA010,11.3 243 | 242,36,1,15,39,0.686,0.562,0,NBA010,3.2 244 | 243,25,1,3,31,0.881,1.63,,NBA005,8.1 245 | 244,26,1,10,32,0.126,1.378,0,NBA002,4.7 246 | 245,43,1,4,26,0.751,1.095,,NBA011,7.1 247 | 246,47,1,29,169,0.349,3.369,,NBA020,2.2 248 | 247,43,1,10,69,0.411,4.005,0,NBA009,6.4 249 | 248,37,1,18,54,4.593,1.941,0,NBA008,12.1 250 | 249,35,4,10,45,1.04,2.785,,NBA012,8.5 251 | 250,28,4,1,26,0.377,2.847,0,NBA003,12.4 252 | 251,28,1,8,30,0.613,1.547,0,NBA006,7.2 253 | 252,26,3,2,37,0.205,5.049,0,NBA001,14.2 254 | 253,39,1,10,31,0.185,1.303,0,NBA004,4.8 255 | 254,34,2,3,21,1.402,3.449,0,NBA008,23.1 256 | 255,51,3,16,82,2.605,1.003,0,NBA025,4.4 257 | 256,28,2,3,41,5.897,4.394,1,NBA004,25.1 258 | 257,39,1,20,39,0.774,2.424,0,NBA012,8.2 259 | 258,23,1,3,19,0.279,1.317,1,NBA000,8.4 260 | 259,23,1,3,13,0.046,0.357,,NBA004,3.1 261 | 260,21,1,4,26,1.421,0.893,0,NBA000,8.9 262 | 261,40,1,3,23,0.409,1.178,0,NBA013,6.9 263 | 262,26,4,1,92,6.506,5.454,,NBA005,13 264 | 263,32,2,12,54,3.196,4.58,0,NBA001,14.4 265 | 264,29,3,3,50,0.259,0.941,1,NBA003,2.4 266 | 265,43,1,25,64,0.951,9.737,0,NBA021,16.7 267 | 266,55,1,3,40,0.563,2.637,1,NBA011,8 268 | 267,30,1,12,40,1.249,1.551,,NBA004,7 269 | 268,38,3,15,63,5.091,3.351,0,NBA018,13.4 270 | 269,40,1,2,28,0.185,1.803,0,NBA003,7.1 271 | 270,43,1,12,38,0.129,1.239,0,NBA011,3.6 272 | 271,35,2,1,24,0.591,2.409,1,NBA008,12.5 273 | 272,24,1,8,24,0.382,0.818,0,NBA004,5 274 | 273,35,1,11,40,0.572,0.588,,NBA009,2.9 275 | 274,39,2,18,47,1.565,2.007,,NBA018,7.6 276 | 275,41,2,5,25,0.393,2.157,0,NBA005,10.2 277 | 276,28,1,2,17,0.199,0.226,1,NBA002,2.5 278 | 277,24,2,2,28,1.787,3.057,1,NBA000,17.3 279 | 278,40,3,3,55,1.751,7.324,1,NBA015,16.5 280 | 279,29,1,8,27,0.402,2.244,0,NBA006,9.8 281 | 280,26,2,4,28,0.226,2.434,0,NBA003,9.5 282 | 281,27,1,5,26,0.13,0.182,,NBA005,1.2 283 | 282,50,1,30,150,13.553,35.197,,NBA008,32.5 284 | 283,41,1,15,120,2.659,0.821,0,NBA014,2.9 285 | 284,26,1,0,14,0.256,1.214,0,NBA000,10.5 286 | 285,48,1,22,100,3.704,5.396,0,NBA015,9.1 287 | 286,35,2,1,24,0.417,1.455,,NBA011,7.8 288 | 287,35,1,15,77,5.501,2.661,0,NBA001,10.6 289 | 288,23,1,7,31,0.338,1.708,0,NBA002,6.6 290 | 289,39,2,20,101,0.341,1.073,0,NBA017,1.4 291 | 290,38,1,21,58,4.584,4.986,0,NBA016,16.5 292 | 291,30,1,4,33,0.5,0.886,0,NBA000,4.2 293 | 292,44,2,8,43,0.555,1.595,,NBA018,5 294 | 293,33,1,0,23,0.077,0.13,0,NBA012,0.9 295 | 294,34,1,15,39,4.373,5.26,1,NBA002,24.7 296 | 295,27,2,6,34,0.238,1.258,0,NBA001,4.4 297 | 296,30,1,1,22,1.139,1.171,0,NBA010,10.5 298 | 297,27,2,1,20,0.377,1.683,1,NBA000,10.3 299 | 298,29,1,9,30,0.155,2.305,0,NBA008,8.2 300 | 299,25,4,1,34,0.133,2.757,1,NBA001,8.5 301 | 300,41,1,23,91,0.573,11.894,0,NBA002,13.7 302 | 301,42,2,11,73,2.869,2.095,0,NBA005,6.8 303 | 302,38,3,12,64,0.895,2.817,0,NBA018,5.8 304 | 303,23,1,5,17,0.302,1.041,0,NBA002,7.9 305 | 304,33,1,2,24,1.041,1.263,0,NBA010,9.6 306 | 305,31,1,2,22,1.739,1.869,1,NBA002,16.4 307 | 306,37,2,6,62,1.186,4.084,0,NBA013,8.5 308 | 307,40,1,10,45,2.013,6.447,0,NBA015,18.8 309 | 308,31,1,3,16,3.065,2.103,,NBA005,32.3 310 | 309,35,2,3,40,1.803,5.077,1,NBA011,17.2 311 | 310,38,1,6,41,0.266,3.383,0,NBA000,8.9 312 | 311,30,2,8,56,0.333,3.251,0,NBA004,6.4 313 | 312,30,1,1,17,0.746,1.124,0,NBA008,11 314 | 313,44,1,2,22,1.092,1.02,,NBA015,9.6 315 | 314,29,2,3,15,0.147,0.243,0,NBA008,2.6 316 | 315,39,1,19,45,0.916,0.749,0,NBA016,3.7 317 | 316,26,1,0,17,0.142,0.708,,NBA005,5 318 | 317,36,1,5,27,0.724,1.166,,NBA002,7 319 | 318,31,2,0,27,1.089,2.367,1,NBA004,12.8 320 | 319,38,2,12,40,0.898,1.142,,NBA014,5.1 321 | 320,38,1,0,23,0.898,0.965,1,NBA008,8.1 322 | 321,41,3,13,91,2.316,20.616,1,NBA013,25.2 323 | 322,41,2,2,30,0.113,1.897,0,NBA022,6.7 324 | 323,45,3,5,94,3.612,6.258,1,NBA017,10.5 325 | 324,24,1,4,23,0.242,0.885,,NBA001,4.9 326 | 325,22,1,4,16,0.084,0.108,0,NBA001,1.2 327 | 326,34,1,13,56,0.864,2.552,0,NBA008,6.1 328 | 327,25,1,0,18,0.514,2.888,1,NBA002,18.9 329 | 328,29,2,6,18,0.45,0.468,1,NBA007,5.1 330 | 329,35,2,13,105,1.418,0.577,0,NBA003,1.9 331 | 330,39,1,19,48,1.93,4.358,0,NBA005,13.1 332 | 331,34,1,17,59,1.808,2.912,0,NBA003,8 333 | 332,44,2,21,113,3.192,11.724,0,NBA014,13.2 334 | 333,23,2,0,21,0.776,1.618,1,NBA002,11.4 335 | 334,46,1,6,31,1.986,1.796,0,NBA019,12.2 336 | 335,41,4,9,47,0.437,1.913,0,NBA013,5 337 | 336,52,1,9,24,0.802,3.806,1,NBA011,19.2 338 | 337,23,1,1,17,0.012,0.26,0,NBA000,1.6 339 | 338,48,1,28,70,4.334,4.066,0,NBA025,12 340 | 339,35,1,10,24,1.084,4.028,,NBA003,21.3 341 | 340,53,2,16,44,1.333,1.131,0,NBA031,5.6 342 | 341,39,3,11,39,2.561,2.47,0,NBA017,12.9 343 | 342,37,1,20,56,0.543,0.521,0,NBA002,1.9 344 | 343,34,1,12,32,0.239,0.625,,NBA015,2.7 345 | 344,49,2,2,20,0.379,3.001,1,NBA013,16.9 346 | 345,39,1,23,75,3.133,11.042,0,NBA001,18.9 347 | 346,42,2,7,41,0.941,0.945,0,NBA023,4.6 348 | 347,28,2,1,16,0.574,1.778,0,NBA004,14.7 349 | 348,27,3,5,75,2.207,2.518,0,NBA001,6.3 350 | 349,24,1,2,21,0.025,0.101,0,NBA001,0.6 351 | 350,34,1,5,33,0.778,1.631,0,NBA003,7.3 352 | 351,47,1,31,136,14.231,17.185,1,NBA009,23.1 353 | 352,26,4,1,27,0.312,0.471,0,NBA007,2.9 354 | 353,35,1,17,42,0.093,1.167,0,NBA004,3 355 | 354,31,1,6,33,0.146,1.24,0,NBA012,4.2 356 | 355,29,1,13,27,0.069,0.552,0,NBA007,2.3 357 | 356,44,1,9,33,0.546,1.17,0,NBA020,5.2 358 | 357,40,2,13,102,6.227,13.051,1,NBA011,18.9 359 | 358,36,4,1,30,0.324,3.126,0,NBA017,11.5 360 | 359,32,3,6,22,0.385,1.089,,NBA012,6.7 361 | 360,21,1,1,18,0.159,2.955,0,NBA001,17.3 362 | 361,42,4,15,186,2.404,4.292,0,NBA008,3.6 363 | 362,37,2,4,20,0.174,4.166,1,NBA011,21.7 364 | 363,24,1,6,21,0.108,0.984,0,NBA002,5.2 365 | 364,24,4,0,29,0.59,1.324,1,NBA003,6.6 366 | 365,46,3,4,73,2.223,1.719,0,NBA017,5.4 367 | 366,28,1,2,16,0.871,1.561,0,NBA009,15.2 368 | 367,41,1,16,68,0.448,3.224,0,NBA017,5.4 369 | 368,46,5,15,126,0.477,3.429,0,NBA000,3.1 370 | 369,51,2,22,120,4.14,4.98,0,NBA023,7.6 371 | 370,36,1,17,54,0.727,1.649,0,NBA006,4.4 372 | 371,25,1,1,20,0.472,0.868,1,NBA000,6.7 373 | 372,27,2,3,33,1.633,2.723,0,NBA005,13.2 374 | 373,43,1,23,72,1.182,4.29,0,NBA019,7.6 375 | 374,53,1,6,27,0.594,1.134,1,NBA007,6.4 376 | 375,32,1,4,16,0.894,3.666,1,NBA003,28.5 377 | 376,36,1,0,25,2.778,2.147,0,NBA013,19.7 378 | 377,29,2,9,30,3.646,2.864,,NBA008,21.7 379 | 378,35,1,18,42,0.205,2.903,0,NBA002,7.4 380 | 379,32,2,0,18,0.296,0.748,1,NBA006,5.8 381 | 380,25,2,1,14,0.233,1.153,,NBA003,9.9 382 | 381,42,1,2,30,1.472,1.438,0,NBA016,9.7 383 | 382,25,1,9,20,0.105,0.595,0,NBA002,3.5 384 | 383,20,3,0,17,0.044,0.347,0,NBA001,2.3 385 | 384,50,1,4,23,0.141,2.734,0,NBA004,12.5 386 | 385,46,2,22,144,3.325,12.659,0,NBA017,11.1 387 | 386,33,1,15,32,0.491,1.941,0,NBA003,7.6 388 | 387,43,1,19,79,0.782,5.143,0,NBA011,7.5 389 | 388,30,2,4,21,0.492,3.351,0,NBA004,18.3 390 | 389,34,1,3,36,0.624,1.068,0,NBA014,4.7 391 | 390,37,1,3,25,0.276,0.649,0,NBA003,3.7 392 | 391,38,1,4,19,1.245,1.339,0,NBA009,13.6 393 | 392,32,2,11,57,5.44,8.126,1,NBA004,23.8 394 | 393,39,1,15,39,0.14,0.562,0,NBA016,1.8 395 | 394,31,1,7,41,2.996,9.591,1,NBA011,30.7 396 | 395,25,1,5,22,0.403,1.995,0,NBA005,10.9 397 | 396,31,2,12,44,1.867,2.357,,NBA008,9.6 398 | 397,29,2,0,23,1.242,0.598,,NBA007,8 399 | 398,45,2,21,132,2.558,1.402,0,NBA026,3 400 | 399,39,1,4,38,1.178,1.292,0,NBA009,6.5 401 | 400,34,1,4,23,0.617,0.671,,NBA006,5.6 402 | 401,24,2,3,24,2.173,2.411,0,NBA001,19.1 403 | 402,30,1,10,19,0.336,0.5,0,NBA002,4.4 404 | 403,30,1,0,19,0.624,2.53,1,NBA008,16.6 405 | 404,27,1,0,18,0.583,1.721,0,NBA007,12.8 406 | 405,31,1,5,25,1.177,2.348,0,NBA003,14.1 407 | 406,47,3,9,52,2.54,7.912,1,NBA001,20.1 408 | 407,47,2,4,33,1.195,1.148,1,NBA009,7.1 409 | 408,45,2,15,51,1.551,7.68,0,NBA014,18.1 410 | 409,31,4,7,97,1.827,3.993,,NBA012,6 411 | 410,45,1,7,29,0.739,1.03,,NBA017,6.1 412 | 411,29,1,12,40,1.632,1.048,0,NBA002,6.7 413 | 412,36,2,12,60,1.721,3.019,0,NBA013,7.9 414 | 413,29,2,10,44,3.703,4.261,0,NBA008,18.1 415 | 414,39,1,2,22,1.916,3.166,1,NBA015,23.1 416 | 415,22,2,0,35,0.781,2.369,1,NBA001,9 417 | 416,46,1,14,35,1.349,2.221,0,NBA025,10.2 418 | 417,38,1,21,65,2.533,8.387,,NBA017,16.8 419 | 418,37,1,1,24,1.801,1.823,1,NBA003,15.1 420 | 419,42,1,12,51,2.412,8.502,0,NBA011,21.4 421 | 420,36,1,4,25,0.144,0.856,,NBA017,4 422 | 421,34,1,18,53,0.84,4.725,0,NBA010,10.5 423 | 422,38,3,12,63,5.715,4.365,1,NBA014,16 424 | 423,27,1,11,21,0.915,1.479,,NBA001,11.4 425 | 424,35,2,9,30,0.65,1.63,0,NBA016,7.6 426 | 425,48,1,10,70,10.679,9.061,,NBA000,28.2 427 | 426,45,2,18,35,0.548,5.542,0,NBA019,17.4 428 | 427,43,1,4,29,0.053,1.281,0,NBA002,4.6 429 | 428,54,1,9,28,1.558,2.586,1,NBA002,14.8 430 | 429,35,3,7,38,1.383,4.355,0,NBA005,15.1 431 | 430,28,1,4,19,0.287,0.492,0,NBA007,4.1 432 | 431,39,1,16,52,4.16,6.24,0,NBA004,20 433 | 432,43,1,8,32,1.234,4.846,,NBA000,19 434 | 433,32,2,2,15,0.119,1.246,1,NBA011,9.1 435 | 434,22,3,0,18,0.478,0.908,0,NBA001,7.7 436 | 435,41,3,18,71,6.566,4.581,1,NBA006,15.7 437 | 436,34,2,4,25,0.57,0.88,0,NBA010,5.8 438 | 437,34,2,9,27,0.295,0.164,0,NBA007,1.7 439 | 438,31,1,1,24,0.217,0.863,0,NBA001,4.5 440 | 439,55,1,19,78,4.405,7.763,0,NBA002,15.6 441 | 440,40,1,21,46,0.967,1.885,0,NBA021,6.2 442 | 441,41,1,13,44,0.071,0.677,0,NBA010,1.7 443 | 442,43,2,10,37,0.676,2.469,0,NBA024,8.5 444 | 443,36,1,6,26,0.03,2.128,0,NBA009,8.3 445 | 444,51,2,31,249,4.273,15.149,0,NBA014,7.8 446 | 445,40,1,17,55,2.126,4.144,,NBA005,11.4 447 | 446,27,3,5,42,2.708,5.65,1,NBA004,19.9 448 | 447,36,2,11,49,0.273,0.364,0,NBA008,1.3 449 | 448,38,1,5,54,1.5,4.224,0,NBA005,10.6 450 | 449,47,1,17,43,0.588,1.82,0,NBA021,5.6 451 | 450,33,1,13,42,0.228,1.284,0,NBA013,3.6 452 | 451,41,3,12,86,4.991,17.799,1,NBA011,26.5 453 | 452,33,1,9,29,1.349,2.653,1,NBA004,13.8 454 | 453,21,3,0,26,1.53,0.472,0,NBA001,7.7 455 | 454,34,1,0,21,0.187,1.325,0,NBA001,7.2 456 | 455,51,2,27,166,0.821,18.269,0,NBA021,11.5 457 | 456,36,2,0,30,0.329,2.581,0,NBA017,9.7 458 | 457,23,2,0,17,2.044,2.665,1,NBA001,27.7 459 | 458,34,1,4,28,1.058,1.574,,NBA003,9.4 460 | 459,29,1,7,22,0.551,1.583,0,NBA009,9.7 461 | 460,46,3,9,60,1.607,7.574,0,NBA010,15.3 462 | 461,45,3,2,39,0.459,1.452,0,NBA005,4.9 463 | 462,41,3,17,176,11.359,5.009,1,NBA012,9.3 464 | 463,28,1,4,16,1.085,2.723,0,NBA001,23.8 465 | 464,37,1,6,29,1.716,3.011,0,NBA009,16.3 466 | 465,25,1,8,35,0.077,0.938,0,NBA001,2.9 467 | 466,48,3,0,19,0.85,1.202,0,NBA027,10.8 468 | 467,37,3,12,83,0.32,3.083,0,NBA008,4.1 469 | 468,32,1,3,23,0.053,1.373,0,NBA010,6.2 470 | 469,31,1,15,60,0.804,2.076,0,NBA002,4.8 471 | 470,48,4,14,59,4.936,4.799,1,NBA010,16.5 472 | 471,45,1,22,91,1.256,9.391,0,NBA024,11.7 473 | 472,41,2,6,36,1.486,3.518,,NBA007,13.9 474 | 473,39,1,19,46,2.516,1.67,0,NBA008,9.1 475 | 474,41,1,19,96,2.254,5.234,0,NBA001,7.8 476 | 475,50,2,15,60,0.468,0.192,0,NBA031,1.1 477 | 476,22,3,0,23,0.373,1.559,1,NBA000,8.4 478 | 477,43,2,6,54,0.622,4.562,0,NBA024,9.6 479 | 478,33,1,8,27,0.777,0.789,0,NBA007,5.8 480 | 479,29,1,11,32,0.927,0.993,0,NBA007,6 481 | 480,24,1,3,21,2.682,3.387,1,NBA003,28.9 482 | 481,27,1,6,52,1.902,5.274,,NBA002,13.8 483 | 482,37,4,1,33,2.694,2.619,1,NBA014,16.1 484 | 483,34,2,8,78,2.157,2.055,0,NBA006,5.4 485 | 484,22,1,1,17,0.806,2.356,0,NBA003,18.6 486 | 485,41,1,4,25,1.06,1.464,0,NBA019,10.1 487 | 486,32,2,7,32,0.433,0.943,0,NBA010,4.3 488 | 487,35,1,15,40,1.214,6.186,0,NBA011,18.5 489 | 488,41,1,24,100,5.06,6.44,0,NBA015,11.5 490 | 489,21,2,1,16,0.242,2.638,1,NBA002,18 491 | 490,23,1,2,25,0.124,1.001,1,NBA001,4.5 492 | 491,25,1,6,22,2.262,2.182,0,NBA006,20.2 493 | 492,40,1,12,89,2.772,12.714,0,NBA006,17.4 494 | 493,38,1,11,35,1.266,1.534,0,NBA013,8 495 | 494,49,2,14,63,0.936,9.018,,NBA026,15.8 496 | 495,40,1,13,28,0.965,0.687,0,NBA002,5.9 497 | 496,29,2,1,25,0.569,2.031,0,NBA007,10.4 498 | 497,25,1,3,18,0.352,2.582,1,NBA002,16.3 499 | 498,33,1,14,72,15.017,14.719,1,NBA008,41.3 500 | 499,37,2,11,47,1.597,2.915,,NBA011,9.6 501 | 500,24,1,1,18,0.239,0.823,1,NBA000,5.9 502 | 501,29,2,4,37,1.16,1.282,1,NBA003,6.6 503 | 502,28,2,0,29,2.148,1.709,1,NBA007,13.3 504 | 503,37,2,15,108,5.251,7.493,,NBA011,11.8 505 | 504,47,2,27,107,1.638,4.889,,NBA007,6.1 506 | 505,31,2,2,26,0.283,1.485,1,NBA001,6.8 507 | 506,28,3,5,44,0.773,3.055,0,NBA009,8.7 508 | 507,39,2,15,32,0.422,1.146,0,NBA019,4.9 509 | 508,33,2,2,35,2.124,5.891,1,NBA014,22.9 510 | 509,41,2,19,68,1.507,2.437,0,NBA002,5.8 511 | 510,29,2,1,29,2.33,3.818,1,NBA007,21.2 512 | 511,31,1,2,22,1.092,1.702,1,NBA004,12.7 513 | 512,28,4,0,38,0.636,3.316,0,NBA005,10.4 514 | 513,35,1,0,34,1.37,2.404,1,NBA005,11.1 515 | 514,54,1,25,120,14.596,17.204,1,NBA012,26.5 516 | 515,39,1,19,53,0.19,1.771,,NBA016,3.7 517 | 516,46,1,9,31,1.617,2.661,0,NBA023,13.8 518 | 517,23,2,3,22,1.274,1.234,1,NBA003,11.4 519 | 518,28,2,6,38,0.774,1.962,0,NBA007,7.2 520 | 519,24,1,3,15,0.073,1.622,1,NBA005,11.3 521 | 520,41,2,7,63,4.638,5.064,0,NBA017,15.4 522 | 521,43,2,9,60,4.039,8.201,1,NBA005,20.4 523 | 522,27,3,3,35,1.601,3.054,0,NBA004,13.3 524 | 523,33,4,4,22,0.566,0.644,,NBA012,5.5 525 | 524,28,1,2,18,1.939,0.725,1,NBA000,14.8 526 | 525,48,1,13,38,0.722,3.382,,NBA011,10.8 527 | 526,35,3,1,20,0.853,0.727,0,NBA004,7.9 528 | 527,31,1,11,28,0.345,0.999,1,NBA006,4.8 529 | 528,42,2,14,48,1.201,2.927,1,NBA001,8.6 530 | 529,29,1,1,18,1.981,2.627,1,NBA003,25.6 531 | 530,36,1,4,17,0.521,2.42,0,NBA015,17.3 532 | 531,29,1,4,24,0.867,1.005,,NBA000,7.8 533 | 532,31,1,12,31,2.057,5.29,0,NBA004,23.7 534 | 533,43,4,18,446,16.031,12.959,1,NBA014,6.5 535 | 534,41,2,13,93,9.542,4.129,,NBA001,14.7 536 | 535,31,1,9,28,0.048,0.624,0,NBA010,2.4 537 | 536,31,1,6,45,0.585,2.7,,NBA001,7.3 538 | 537,28,2,2,23,0.187,1.285,1,NBA003,6.4 539 | 538,35,1,7,29,0.264,1.157,0,NBA014,4.9 540 | 539,38,1,5,21,0.45,1.44,0,NBA013,9 541 | 540,40,1,14,43,2.793,3.657,1,NBA021,15 542 | 541,39,1,20,116,2.24,2.516,,NBA008,4.1 543 | 542,38,2,8,38,0.029,0.199,0,NBA016,0.6 544 | 543,27,3,4,40,0.284,0.956,1,NBA001,3.1 545 | 544,49,1,11,39,2.458,1.481,0,NBA005,10.1 546 | 545,38,1,18,45,0.535,2.615,0,NBA007,7 547 | 546,43,1,16,89,0.159,0.197,0,NBA007,0.4 548 | 547,36,2,9,49,0.818,3.396,1,NBA006,8.6 549 | 548,37,1,4,23,0.87,1.982,0,NBA000,12.4 550 | 549,46,3,22,99,0.827,1.45,0,NBA004,2.3 551 | 550,22,2,0,20,0.207,0.913,0,NBA003,5.6 552 | 551,47,1,13,51,3.739,8.399,0,NBA025,23.8 553 | 552,47,3,16,266,2.192,3.128,,NBA007,2 554 | 553,30,4,2,25,1.77,0.73,0,NBA008,10 555 | 554,51,2,19,159,1.068,3.384,0,NBA025,2.8 556 | 555,28,1,10,42,1.047,2.565,,NBA001,8.6 557 | 556,50,1,6,21,1.317,1.392,,NBA027,12.9 558 | 557,43,1,24,254,7.002,12.556,,NBA022,7.7 559 | 558,46,2,9,15,0.015,0.63,0,NBA026,4.3 560 | 559,29,1,7,20,1.006,2.954,1,NBA005,19.8 561 | 560,26,1,1,21,0.608,0.421,0,NBA000,4.9 562 | 561,45,1,14,46,1.262,1.084,,NBA026,5.1 563 | 562,39,1,22,52,1.155,0.509,0,NBA003,3.2 564 | 563,30,2,10,22,1.41,2.132,0,NBA004,16.1 565 | 564,41,3,7,56,0.374,7.746,1,NBA006,14.5 566 | 565,25,2,4,27,0.549,0.801,1,NBA005,5 567 | 566,34,1,16,48,1.474,2.174,0,NBA009,7.6 568 | 567,44,3,7,78,3.153,5.037,,NBA016,10.5 569 | 568,25,1,4,23,0.252,0.944,0,NBA000,5.2 570 | 569,39,1,20,67,3.834,16.668,0,NBA009,30.6 571 | 570,26,1,4,20,0.319,0.761,0,NBA003,5.4 572 | 571,31,1,8,41,0.134,5.032,0,NBA003,12.6 573 | 572,40,3,5,220,8.166,27.034,1,NBA003,16 574 | 573,42,1,6,31,1.025,2.571,0,NBA009,11.6 575 | 574,23,1,1,21,1.21,0.554,1,NBA000,8.4 576 | 575,34,1,9,48,0.42,4.044,0,NBA008,9.3 577 | 576,28,4,0,29,1.425,5.593,0,NBA007,24.2 578 | 577,29,1,6,21,1.11,2.691,0,NBA007,18.1 579 | 578,29,2,3,26,0.538,1.048,1,NBA006,6.1 580 | 579,24,2,2,26,0.888,1.634,1,NBA003,9.7 581 | 580,53,1,9,50,2.016,3.584,1,NBA018,11.2 582 | 581,39,1,19,60,9.593,6.667,1,NBA015,27.1 583 | 582,28,4,2,41,0.459,0.566,1,NBA004,2.5 584 | 583,38,1,16,37,0.083,3.099,,NBA008,8.6 585 | 584,39,1,12,46,0.561,1.739,0,NBA010,5 586 | 585,38,1,18,44,0.606,1.374,0,NBA019,4.5 587 | 586,34,4,6,27,1.982,7.549,1,NBA003,35.3 588 | 587,31,1,11,45,2.739,4.911,0,NBA012,17 589 | 588,40,1,2,32,0.854,0.33,0,NBA004,3.7 590 | 589,32,1,8,26,0.326,0.74,0,NBA006,4.1 591 | 590,35,2,12,64,1.45,8.15,,NBA012,15 592 | 591,29,2,3,32,1.071,1.809,,NBA009,9 593 | 592,44,2,12,86,1.476,9.704,0,NBA005,13 594 | 593,48,2,9,44,2.134,0.462,,NBA022,5.9 595 | 594,41,2,6,36,4.514,2.506,1,NBA000,19.5 596 | 595,28,1,0,28,2.284,7.04,1,NBA002,33.3 597 | 596,29,1,3,23,1.104,1.035,,NBA010,9.3 598 | 597,24,1,1,16,0.878,0.274,,NBA004,7.2 599 | 598,25,1,9,18,0.066,0.15,0,NBA004,1.2 600 | 599,43,2,16,83,0.259,3.144,0,NBA010,4.1 601 | 600,45,1,12,37,1.343,2.172,0,NBA019,9.5 602 | 601,35,1,13,35,0.432,1.143,0,NBA015,4.5 603 | 602,34,1,13,51,1.875,3.021,0,NBA011,9.6 604 | 603,29,1,2,14,0.587,1.429,1,NBA000,14.4 605 | 604,32,2,11,75,7.759,9.716,1,NBA006,23.3 606 | 605,27,3,6,26,0.93,1.774,0,NBA004,10.4 607 | 606,39,1,16,57,1.559,9.499,0,NBA020,19.4 608 | 607,37,4,2,29,2.782,1.684,,NBA001,15.4 609 | 608,31,4,1,29,1.065,2.154,0,NBA006,11.1 610 | 609,28,1,4,28,0.437,3.539,1,NBA002,14.2 611 | 610,27,2,8,38,0.364,2.258,0,NBA004,6.9 612 | 611,31,1,8,27,0.674,0.784,,NBA004,5.4 613 | 612,31,1,9,26,0.31,0.86,0,NBA005,4.5 614 | 613,32,1,0,20,0.263,0.337,1,NBA004,3 615 | 614,29,2,9,36,0.127,0.377,0,NBA003,1.4 616 | 615,31,2,9,53,1.195,5.96,0,NBA008,13.5 617 | 616,32,2,5,43,1.806,4.644,1,NBA005,15 618 | 617,39,1,6,42,2.541,5.817,1,NBA019,19.9 619 | 618,27,2,7,30,0.448,0.752,1,NBA008,4 620 | 619,29,1,13,42,1.458,1.65,1,NBA001,7.4 621 | 620,31,1,11,34,0.298,2.014,0,NBA006,6.8 622 | 621,41,2,22,75,9.877,7.823,0,NBA017,23.6 623 | 622,30,1,1,27,1.275,2.046,,NBA001,12.3 624 | 623,35,2,0,22,1.97,1.33,1,NBA016,15 625 | 624,38,2,2,22,1.209,3.785,1,NBA016,22.7 626 | 625,29,1,1,17,0.157,0.982,0,NBA009,6.7 627 | 626,37,1,7,32,2.697,3.575,0,NBA003,19.6 628 | 627,26,1,8,25,0.21,0.965,0,NBA004,4.7 629 | 628,27,1,0,16,0.13,0.046,0,NBA005,1.1 630 | 629,42,2,18,66,1.641,4.695,0,NBA023,9.6 631 | 630,45,3,8,140,4.184,15.276,,NBA001,13.9 632 | 631,51,4,15,26,2.012,1.524,,NBA030,13.6 633 | 632,36,1,10,28,0.815,2.321,1,NBA000,11.2 634 | 633,48,1,17,66,1.468,0.644,0,NBA022,3.2 635 | 634,45,1,10,52,0.932,1.876,0,NBA014,5.4 636 | 635,47,1,16,110,5.821,8.039,,NBA019,12.6 637 | 636,26,1,6,22,0.721,1.545,0,NBA000,10.3 638 | 637,35,1,12,30,1.899,2.721,,NBA003,15.4 639 | 638,50,3,10,80,2.479,7.281,0,NBA020,12.2 640 | 639,30,1,11,33,0.682,2.09,0,NBA001,8.4 641 | 640,49,2,22,79,0.288,5.479,0,NBA004,7.3 642 | 641,36,1,1,16,0.433,1.055,0,NBA004,9.3 643 | 642,24,2,3,19,0.359,0.458,0,NBA000,4.3 644 | 643,42,5,6,190,3.157,11.663,0,NBA023,7.8 645 | 644,28,2,0,30,0.121,1.319,1,NBA003,4.8 646 | 645,52,2,12,76,0.995,4.857,0,NBA016,7.7 647 | 646,39,2,9,56,4.765,5.707,0,NBA016,18.7 648 | 647,40,2,15,138,1.222,4.436,,NBA019,4.1 649 | 648,29,2,0,22,1.039,1.799,1,NBA008,12.9 650 | 649,40,1,8,28,0.534,0.474,0,NBA017,3.6 651 | 650,27,1,10,34,0.851,1.359,0,NBA006,6.5 652 | 651,24,3,2,16,0.27,0.754,1,NBA005,6.4 653 | 652,26,2,5,28,1.024,1.16,0,NBA007,7.8 654 | 653,40,3,16,116,1.614,12.422,0,NBA006,12.1 655 | 654,50,2,16,71,1.663,6.218,0,NBA014,11.1 656 | 655,30,1,0,20,0.622,0.458,,NBA002,5.4 657 | 656,26,2,6,30,0.144,0.156,0,NBA006,1 658 | 657,35,1,10,28,0.11,0.254,,NBA008,1.3 659 | 658,37,3,16,50,7.32,10.98,1,NBA014,36.6 660 | 659,52,5,9,70,1.329,5.251,1,NBA000,9.4 661 | 660,24,1,5,46,0.53,1.218,,NBA001,3.8 662 | 661,28,1,3,26,0.432,2.168,0,NBA006,10 663 | 662,29,2,6,57,0.453,0.801,0,NBA007,2.2 664 | 663,35,1,11,77,1.1,2.519,,NBA012,4.7 665 | 664,54,1,18,114,3.295,6.395,0,NBA034,8.5 666 | 665,32,1,10,32,1.168,2.448,0,NBA013,11.3 667 | 666,32,1,11,53,0.089,1.66,0,NBA002,3.3 668 | 667,36,1,14,81,1.785,4.047,0,NBA011,7.2 669 | 668,31,1,12,24,0.442,1.142,0,NBA009,6.6 670 | 669,28,1,11,24,0.107,0.469,0,NBA006,2.4 671 | 670,34,1,10,33,2.502,0.897,1,NBA001,10.3 672 | 671,53,4,7,61,1.875,2.944,0,NBA011,7.9 673 | 672,50,1,8,47,0.399,2.844,0,NBA027,6.9 674 | 673,52,4,13,234,7.387,10.631,0,NBA017,7.7 675 | 674,30,2,4,15,0.35,0.625,0,NBA008,6.5 676 | 675,26,2,0,28,1.271,1.333,0,NBA007,9.3 677 | 676,42,2,21,121,1.365,2.386,0,NBA011,3.1 678 | 677,27,1,6,43,0.883,1.224,0,NBA006,4.9 679 | 678,41,1,14,52,0.926,2.87,0,NBA008,7.3 680 | 679,27,1,10,31,1.362,4.001,0,NBA006,17.3 681 | 680,32,1,12,33,0.795,2.901,,NBA010,11.2 682 | 681,37,1,12,44,2.995,3.473,0,NBA014,14.7 683 | 682,29,1,3,17,0.47,1.06,0,NBA001,9 684 | 683,27,1,9,45,2.852,5.158,1,NBA006,17.8 685 | 684,50,3,25,94,1.734,3.248,,NBA007,5.3 686 | 685,21,3,0,24,0.833,1.015,0,NBA002,7.7 687 | 686,41,4,14,44,0.353,0.395,0,NBA003,1.7 688 | 687,23,2,1,18,0.885,2.481,1,NBA003,18.7 689 | 688,45,2,9,69,0.707,3.916,0,NBA026,6.7 690 | 689,45,2,2,29,0.443,1.123,0,NBA019,5.4 691 | 690,33,1,6,21,0.567,1.008,0,NBA014,7.5 692 | 691,21,1,1,16,0.141,0.867,0,NBA001,6.3 693 | 692,40,1,6,36,0.391,0.365,1,NBA009,2.1 694 | 693,40,2,3,28,0.553,2.107,0,NBA002,9.5 695 | 694,56,1,11,59,4.673,4.177,0,NBA020,15 696 | 695,26,1,2,22,0.237,1.171,,NBA005,6.4 697 | 696,29,1,6,46,1.17,1.314,0,NBA004,5.4 698 | 697,29,2,6,65,1.985,9.975,0,NBA010,18.4 699 | 698,39,1,13,27,0.966,1.194,0,NBA009,8 700 | 699,27,2,1,23,0.285,2.567,0,NBA000,12.4 701 | 700,25,1,1,15,0.371,1.339,1,NBA006,11.4 702 | 701,36,5,5,20,0.729,0.891,0,NBA012,8.1 703 | 702,35,1,7,39,1.702,4.577,1,NBA005,16.1 704 | 703,25,2,4,33,0.553,1.163,0,NBA006,5.2 705 | 704,21,2,2,20,0.291,0.609,1,NBA000,4.5 706 | 705,23,1,2,16,0.805,0.731,1,NBA002,9.6 707 | 706,32,1,14,73,3.752,2.672,0,NBA007,8.8 708 | 707,34,1,0,20,2.049,1.371,0,NBA010,17.1 709 | 708,38,1,5,20,0.091,0.208,,NBA004,1.5 710 | 709,39,1,22,73,3.727,6.712,0,NBA009,14.3 711 | 710,46,1,1,21,1.377,1.731,1,NBA015,14.8 712 | 711,35,2,10,98,4.875,8.159,0,NBA016,13.3 713 | 712,33,4,9,28,0.377,0.827,0,NBA010,4.3 714 | 713,30,2,12,68,2.857,10.811,0,NBA009,20.1 715 | 714,54,3,21,118,9.6,7.392,0,NBA020,14.4 716 | 715,42,2,5,41,0.355,1.039,0,NBA003,3.4 717 | 716,28,1,9,22,5.283,0.811,1,NBA000,27.7 718 | 717,35,1,9,34,0.398,1.302,0,NBA001,5 719 | 718,41,3,16,135,1.277,4.663,0,NBA022,4.4 720 | 719,41,1,16,49,0.431,0.353,0,NBA022,1.6 721 | 720,53,4,5,78,6.936,6.558,1,NBA029,17.3 722 | 721,28,1,4,26,1.295,2.215,0,NBA007,13.5 723 | 722,35,1,5,32,0.175,3.985,0,NBA005,13 724 | 723,42,1,7,25,1.629,0.921,0,NBA012,10.2 725 | 724,33,1,11,35,0.506,5.059,,NBA006,15.9 726 | 725,27,3,0,50,1.044,6.306,,NBA004,14.7 727 | 726,48,2,30,148,3.975,6.681,0,NBA014,7.2 728 | 727,33,1,2,44,0.525,0.839,0,NBA014,3.1 729 | 728,37,1,6,31,1.701,1.926,1,NBA010,11.7 730 | 729,31,1,5,23,0.046,0.414,,NBA007,2 731 | 730,35,3,10,39,1.602,2.454,0,NBA015,10.4 732 | 731,48,2,6,66,2.316,5.67,0,NBA001,12.1 733 | 732,34,1,16,75,3.955,3.845,0,NBA003,10.4 734 | 733,23,2,2,48,0.511,3.089,,NBA002,7.5 735 | 734,41,2,8,43,0.086,0.215,0,NBA021,0.7 736 | 735,52,1,33,139,2.288,5.496,,NBA023,5.6 737 | 736,39,1,6,61,0.563,2.914,0,NBA009,5.7 738 | 737,46,1,1,20,0.809,1.991,0,NBA012,14 739 | 738,33,2,4,55,2.16,1.69,0,NBA009,7 740 | 739,34,3,2,39,1.143,4.005,0,NBA014,13.2 741 | 740,34,3,12,47,1.301,1.848,0,NBA008,6.7 742 | 741,40,1,9,33,4.881,0.729,,NBA009,17 743 | 742,45,3,16,80,0.912,1.488,0,NBA021,3 744 | 743,25,1,1,19,0.19,1.121,0,NBA001,6.9 745 | 744,31,2,6,32,3.341,4.051,1,NBA006,23.1 746 | 745,34,4,7,40,0.95,1.61,0,NBA015,6.4 747 | 746,45,1,23,50,0.559,1.541,0,NBA005,4.2 748 | 747,44,2,17,129,0.551,2.674,0,NBA000,2.5 749 | 748,35,1,13,42,0.769,6.623,,NBA004,17.6 750 | 749,22,2,0,14,0.242,2.152,,NBA000,17.1 751 | 750,26,3,3,40,1.896,2.424,,NBA001,10.8 752 | 751,38,1,6,18,0.131,2.209,0,NBA017,13 753 | 752,27,1,7,26,0.641,1.725,0,NBA004,9.1 754 | 753,29,1,11,36,0.709,1.487,0,NBA005,6.1 755 | 754,29,2,5,28,2.126,3.11,0,NBA007,18.7 756 | 755,22,1,4,24,1.636,2.108,1,NBA002,15.6 757 | 756,47,1,4,26,0.122,2.582,0,NBA002,10.4 758 | 757,27,2,5,25,0.706,0.995,0,NBA006,6.8 759 | 758,24,1,2,18,0.528,0.552,0,NBA005,6 760 | 759,52,1,17,73,1.054,5.297,,NBA004,8.7 761 | 760,26,1,3,29,2.527,4.926,1,NBA005,25.7 762 | 761,40,1,22,100,5.402,9.198,0,NBA001,14.6 763 | 762,33,2,12,58,3.084,7.588,0,NBA008,18.4 764 | 763,30,1,0,17,0.304,1.736,0,NBA011,12 765 | 764,39,2,16,69,1.061,0.595,0,NBA013,2.4 766 | 765,24,2,0,16,0.025,1.143,0,NBA005,7.3 767 | 766,27,4,0,70,1.618,3.982,1,NBA006,8 768 | 767,38,2,7,64,0.651,1.269,0,NBA004,3 769 | 768,36,1,9,40,1.976,1.944,0,NBA001,9.8 770 | 769,39,1,4,33,0.397,1.913,0,NBA015,7 771 | 770,43,1,13,76,2.151,2.485,1,NBA023,6.1 772 | 771,24,1,8,17,0.569,0.383,1,NBA000,5.6 773 | 772,21,1,5,25,0.367,1.883,0,NBA001,9 774 | 773,26,2,2,24,1.577,1.687,1,NBA006,13.6 775 | 774,43,1,11,37,0.18,0.301,0,NBA017,1.3 776 | 775,36,3,11,44,0.446,6.022,0,NBA012,14.7 777 | 776,43,1,15,62,0.694,5.134,,NBA021,9.4 778 | 777,35,2,11,62,9.703,10.385,,NBA001,32.4 779 | 778,34,2,10,33,0.574,2.561,0,NBA002,9.5 780 | 779,47,2,17,41,0.456,3.439,0,NBA023,9.5 781 | 780,48,1,0,30,2.663,4.027,0,NBA023,22.3 782 | 781,29,2,9,46,0.241,0.495,0,NBA000,1.6 783 | 782,36,1,7,43,1.04,1.626,0,NBA002,6.2 784 | 783,35,1,10,39,0.218,2.005,0,NBA016,5.7 785 | 784,39,1,13,38,1.897,1.485,0,NBA001,8.9 786 | 785,41,1,21,76,6.949,8.631,0,NBA002,20.5 787 | 786,29,3,1,30,0.279,1.071,0,NBA010,4.5 788 | 787,39,2,18,44,0.368,0.556,0,NBA009,2.1 789 | 788,33,1,12,32,2.105,1.511,0,NBA011,11.3 790 | 789,33,2,8,27,1.646,1,,NBA013,9.8 791 | 790,39,1,0,39,1.066,2.015,0,NBA008,7.9 792 | 791,40,2,8,57,0.878,2.314,0,NBA019,5.6 793 | 792,53,1,33,324,7.053,15.627,,NBA025,7 794 | 793,34,1,15,67,3.741,5.103,0,NBA000,13.2 795 | 794,29,3,7,84,6.912,4.512,1,NBA002,13.6 796 | 795,30,1,0,17,0.447,0.182,1,NBA011,3.7 797 | 796,38,3,3,25,0.312,0.613,0,NBA018,3.7 798 | 797,25,4,0,24,1.597,1.307,1,NBA006,12.1 799 | 798,26,2,6,35,0.086,0.579,0,NBA000,1.9 800 | 799,25,2,5,35,1.969,3.806,,NBA005,16.5 801 | 800,29,1,8,24,0.47,1.138,0,NBA010,6.7 802 | 801,28,1,5,22,0.14,0.476,0,NBA009,2.8 803 | 802,48,1,30,101,1.875,4.589,0,NBA008,6.4 804 | 803,26,1,0,14,0.302,0.748,1,NBA000,7.5 805 | 804,36,1,4,23,1.327,2.422,0,NBA010,16.3 806 | 805,30,1,12,38,0.171,3.059,0,NBA010,8.5 807 | 806,41,1,1,19,0.242,0.594,0,NBA016,4.4 808 | 807,34,3,13,52,1.497,3.287,0,NBA007,9.2 809 | 808,25,3,3,54,1.163,2.833,,NBA002,7.4 810 | 809,45,1,17,62,2.437,6.863,0,NBA000,15 811 | 810,22,1,4,19,1.887,2.502,1,NBA003,23.1 812 | 811,32,1,13,25,0.596,1.279,,NBA011,7.5 813 | 812,21,2,1,17,0.555,1.23,,NBA000,10.5 814 | 813,34,1,6,20,0.042,0.198,0,NBA001,1.2 815 | 814,31,1,10,49,3.237,1.565,1,NBA012,9.8 816 | 815,33,1,7,22,0.631,0.777,0,NBA000,6.4 817 | 816,40,2,2,36,0.278,1.09,0,NBA001,3.8 818 | 817,36,2,6,27,0.262,0.98,1,NBA015,4.6 819 | 818,30,2,2,26,0.249,0.739,,NBA002,3.8 820 | 819,35,2,0,35,2.383,1.957,,NBA006,12.4 821 | 820,36,1,7,40,1.695,2.265,0,NBA017,9.9 822 | 821,37,1,4,24,0.419,2.989,,NBA010,14.2 823 | 822,32,1,16,38,0.694,7.286,0,NBA010,21 824 | 823,45,1,3,20,0.105,0.315,0,NBA015,2.1 825 | 824,27,4,0,25,1.419,1.756,1,NBA000,12.7 826 | 825,41,2,4,26,1.473,3.519,1,NBA014,19.2 827 | 826,32,2,12,116,4.027,2.585,,NBA011,5.7 828 | 827,48,1,13,50,6.114,9.286,1,NBA020,30.8 829 | 828,50,1,1,26,1.852,1.866,0,NBA026,14.3 830 | 829,45,3,0,22,0.03,0.894,0,NBA019,4.2 831 | 830,33,2,2,37,0.834,0.831,0,NBA009,4.5 832 | 831,33,1,13,52,2.714,8.362,1,NBA003,21.3 833 | 832,27,2,8,18,0.401,1.741,0,NBA007,11.9 834 | 833,36,2,7,43,2.649,3.973,0,NBA016,15.4 835 | 834,30,4,7,30,0.264,4.446,0,NBA010,15.7 836 | 835,28,2,3,36,0.384,2.712,0,NBA001,8.6 837 | 836,21,3,0,41,2.367,5.628,,NBA001,19.5 838 | 837,23,2,3,24,0.552,0.96,0,NBA004,6.3 839 | 838,23,1,7,22,0.849,2.319,0,NBA003,14.4 840 | 839,26,1,10,25,1.306,0.469,0,NBA001,7.1 841 | 840,31,1,8,22,0.37,1.104,0,NBA001,6.7 842 | 841,38,3,13,25,0.343,1.082,0,NBA018,5.7 843 | 842,29,3,7,63,0.572,2.893,0,NBA001,5.5 844 | 843,32,1,14,36,0.273,0.591,0,NBA000,2.4 845 | 844,32,2,8,45,0.982,0.683,0,NBA002,3.7 846 | 845,41,1,7,43,0.694,1.198,0,NBA011,4.4 847 | 846,27,1,5,26,0.548,1.22,,NBA007,6.8 848 | 847,28,2,7,34,0.359,2.021,0,NBA002,7 849 | 848,25,4,0,18,2.802,3.21,1,NBA001,33.4 850 | 849,32,1,12,28,0.116,0.696,0,NBA012,2.9 851 | 850,52,1,16,64,1.866,3.638,0,NBA025,8.6 -------------------------------------------------------------------------------- /Kmeans/readme.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Kmeans/teleCust1000t.csv: -------------------------------------------------------------------------------- 1 | region,tenure,age,marital,address,income,ed,employ,retire,gender,reside,custcat 2 | 2,13,44,1,9,64.000,4,5,0.000,0,2,1 3 | 3,11,33,1,7,136.000,5,5,0.000,0,6,4 4 | 3,68,52,1,24,116.000,1,29,0.000,1,2,3 5 | 2,33,33,0,12,33.000,2,0,0.000,1,1,1 6 | 2,23,30,1,9,30.000,1,2,0.000,0,4,3 7 | 2,41,39,0,17,78.000,2,16,0.000,1,1,3 8 | 3,45,22,1,2,19.000,2,4,0.000,1,5,2 9 | 2,38,35,0,5,76.000,2,10,0.000,0,3,4 10 | 3,45,59,1,7,166.000,4,31,0.000,0,5,3 11 | 1,68,41,1,21,72.000,1,22,0.000,0,3,2 12 | 2,5,33,0,10,125.000,4,5,0.000,1,1,1 13 | 3,7,35,0,14,80.000,2,15,0.000,1,1,3 14 | 1,41,38,1,8,37.000,2,9,0.000,1,3,1 15 | 2,57,54,1,30,115.000,4,23,0.000,1,3,4 16 | 2,9,46,0,3,25.000,1,8,0.000,1,2,1 17 | 1,29,38,1,12,75.000,5,1,0.000,0,4,2 18 | 3,60,57,0,38,162.000,2,30,0.000,0,1,3 19 | 3,34,48,0,3,49.000,2,6,0.000,1,3,3 20 | 2,1,24,0,3,20.000,1,3,0.000,0,1,1 21 | 1,26,29,1,3,77.000,4,2,0.000,0,4,4 22 | 3,6,30,0,7,16.000,3,1,0.000,1,1,2 23 | 1,68,52,1,17,120.000,1,24,0.000,0,2,1 24 | 3,53,33,0,10,101.000,5,4,0.000,1,2,4 25 | 3,55,48,1,19,67.000,1,25,0.000,0,3,1 26 | 3,14,43,1,18,36.000,1,5,0.000,0,5,3 27 | 2,1,21,0,0,33.000,2,0,0.000,1,3,3 28 | 2,42,40,0,7,37.000,2,8,0.000,1,1,4 29 | 3,25,33,1,11,31.000,1,5,0.000,0,4,3 30 | 1,9,21,1,1,17.000,2,2,0.000,1,3,1 31 | 2,13,33,1,9,19.000,4,0,0.000,1,2,2 32 | 1,56,37,1,6,36.000,1,13,0.000,1,2,2 33 | 1,71,53,1,27,155.000,5,12,0.000,0,2,4 34 | 1,35,50,1,26,140.000,2,21,0.000,1,4,3 35 | 1,11,27,1,8,55.000,5,0,0.000,0,3,2 36 | 2,60,46,1,13,163.000,3,24,0.000,0,2,4 37 | 3,20,35,1,11,52.000,4,0,0.000,0,2,2 38 | 2,54,60,0,38,211.000,4,25,0.000,0,1,4 39 | 1,44,57,1,1,186.000,2,17,0.000,0,2,3 40 | 1,11,41,1,0,39.000,1,1,0.000,1,2,3 41 | 2,72,57,0,34,22.000,2,35,1.000,1,1,3 42 | 3,10,41,0,7,30.000,1,7,0.000,0,1,3 43 | 2,15,28,0,0,29.000,2,4,0.000,1,1,3 44 | 2,27,28,1,4,23.000,2,8,0.000,0,5,1 45 | 1,9,36,1,14,62.000,4,10,0.000,0,6,4 46 | 1,64,43,1,20,76.000,4,20,0.000,1,4,3 47 | 1,65,41,1,3,74.000,4,16,0.000,0,2,2 48 | 1,49,51,1,27,63.000,4,19,0.000,0,5,2 49 | 3,47,41,1,0,36.000,4,8,0.000,0,2,4 50 | 3,9,34,1,9,33.000,2,8,0.000,1,4,1 51 | 1,5,36,0,14,29.000,2,9,0.000,1,1,3 52 | 2,30,34,1,4,27.000,2,1,0.000,0,5,1 53 | 1,56,52,1,28,49.000,2,12,0.000,0,4,2 54 | 3,10,22,0,0,24.000,4,0,0.000,0,1,4 55 | 1,7,26,1,3,26.000,2,2,0.000,0,5,1 56 | 1,52,27,0,6,47.000,3,5,0.000,0,2,1 57 | 2,36,45,1,1,94.000,2,18,0.000,1,5,1 58 | 3,27,34,0,8,21.000,3,4,0.000,1,1,2 59 | 1,58,62,1,36,27.000,1,0,0.000,0,2,1 60 | 2,41,52,0,27,30.000,2,2,0.000,1,1,3 61 | 2,47,40,1,16,127.000,4,12,0.000,1,4,2 62 | 2,55,39,1,15,137.000,2,20,0.000,1,2,3 63 | 2,56,50,1,1,80.000,2,24,0.000,1,4,4 64 | 3,35,55,0,24,30.000,2,2,0.000,0,1,1 65 | 2,69,51,1,11,438.000,4,23,0.000,1,2,4 66 | 2,44,39,0,16,79.000,2,16,0.000,0,1,4 67 | 3,36,47,1,16,63.000,1,23,0.000,0,2,1 68 | 3,28,51,1,22,40.000,3,10,0.000,1,6,3 69 | 1,72,67,1,44,51.000,1,34,1.000,0,2,3 70 | 1,16,27,0,5,37.000,3,5,0.000,0,4,1 71 | 3,3,43,0,19,61.000,4,11,0.000,1,1,1 72 | 1,40,57,1,15,22.000,2,9,0.000,0,2,1 73 | 3,16,39,0,6,44.000,4,3,0.000,1,1,2 74 | 1,20,42,0,4,17.000,3,5,0.000,0,1,1 75 | 2,48,43,0,13,110.000,5,15,0.000,1,3,2 76 | 2,64,48,1,13,91.000,2,24,0.000,0,2,3 77 | 1,8,46,0,3,46.000,2,8,0.000,0,1,1 78 | 1,40,38,1,10,85.000,3,12,0.000,0,2,3 79 | 2,38,71,0,27,10.000,2,12,1.000,0,1,1 80 | 1,67,68,0,28,244.000,1,47,0.000,1,1,3 81 | 1,26,42,0,0,80.000,4,15,0.000,1,1,4 82 | 3,41,34,1,15,83.000,5,1,0.000,1,2,4 83 | 2,2,31,0,2,21.000,1,6,0.000,0,1,1 84 | 3,20,48,1,29,24.000,2,3,0.000,0,2,4 85 | 2,31,53,0,2,351.000,3,31,0.000,1,1,3 86 | 2,45,52,1,4,169.000,4,29,0.000,1,4,2 87 | 2,33,27,1,7,46.000,2,7,0.000,0,4,1 88 | 1,10,26,0,6,34.000,3,3,0.000,0,1,2 89 | 2,62,54,1,2,50.000,4,3,0.000,0,2,4 90 | 2,4,35,0,16,161.000,5,6,0.000,1,1,4 91 | 1,42,47,1,17,212.000,4,17,0.000,0,2,3 92 | 3,27,51,1,3,80.000,5,11,0.000,0,3,2 93 | 2,38,42,0,1,30.000,4,7,0.000,1,1,1 94 | 3,52,61,1,3,53.000,5,1,0.000,1,2,2 95 | 3,35,33,0,9,73.000,5,4,0.000,1,1,4 96 | 3,3,20,1,1,17.000,2,0,0.000,1,4,1 97 | 2,17,33,0,9,23.000,5,3,0.000,0,1,4 98 | 1,55,53,1,21,34.000,1,8,0.000,0,2,3 99 | 3,43,36,1,5,107.000,1,19,0.000,1,3,2 100 | 2,47,25,1,5,21.000,1,1,0.000,1,2,3 101 | 3,65,58,0,30,83.000,2,16,0.000,1,1,2 102 | 1,13,20,0,1,17.000,3,0,0.000,0,5,4 103 | 3,64,25,1,4,76.000,3,2,0.000,1,6,3 104 | 2,12,24,0,2,19.000,1,0,0.000,0,1,3 105 | 1,29,32,1,0,49.000,4,3,0.000,1,3,2 106 | 2,23,47,0,21,173.000,3,22,0.000,1,1,2 107 | 2,14,35,0,4,47.000,3,10,0.000,1,1,2 108 | 1,35,61,0,23,41.000,2,11,0.000,0,1,1 109 | 3,26,39,0,8,105.000,3,13,0.000,1,1,4 110 | 1,13,54,0,2,31.000,4,2,0.000,0,1,1 111 | 2,69,40,1,13,41.000,2,17,0.000,0,3,2 112 | 3,61,50,1,30,102.000,4,9,0.000,1,3,2 113 | 2,45,22,1,2,36.000,4,0,0.000,1,5,2 114 | 2,3,37,0,13,24.000,1,3,0.000,1,1,1 115 | 2,53,22,0,1,25.000,4,0,0.000,0,2,2 116 | 1,59,42,0,1,68.000,2,21,0.000,1,1,3 117 | 1,13,34,0,11,20.000,3,0,0.000,1,1,1 118 | 3,31,31,0,4,48.000,1,15,0.000,0,1,3 119 | 1,40,29,1,6,40.000,2,8,0.000,1,5,3 120 | 1,48,55,0,15,79.000,1,25,0.000,0,1,3 121 | 3,3,31,0,1,28.000,1,0,0.000,0,1,1 122 | 2,51,48,0,12,64.000,2,22,0.000,1,1,1 123 | 3,33,41,0,15,37.000,2,11,0.000,0,1,1 124 | 3,14,47,1,7,48.000,1,13,0.000,0,2,1 125 | 1,17,42,1,6,131.000,5,6,0.000,0,3,2 126 | 3,19,29,1,9,18.000,2,2,0.000,0,3,1 127 | 2,58,36,0,10,38.000,2,14,0.000,1,1,2 128 | 3,22,23,0,3,37.000,2,4,0.000,1,3,4 129 | 2,29,31,1,1,60.000,4,6,0.000,0,5,2 130 | 3,14,22,1,2,13.000,3,0,0.000,1,4,2 131 | 2,70,64,1,18,98.000,1,31,0.000,1,2,4 132 | 1,62,52,1,2,195.000,1,23,0.000,0,2,3 133 | 2,7,35,1,0,47.000,1,14,0.000,0,5,1 134 | 1,56,47,1,19,65.000,2,22,0.000,0,3,4 135 | 1,50,50,1,22,150.000,5,5,0.000,1,4,2 136 | 2,57,39,0,6,106.000,5,8,0.000,0,1,3 137 | 3,2,40,0,19,51.000,4,14,0.000,1,1,1 138 | 2,40,38,0,17,96.000,4,0,0.000,1,1,1 139 | 2,6,43,0,4,33.000,5,7,0.000,0,2,3 140 | 3,23,25,1,6,38.000,2,1,0.000,1,3,3 141 | 3,11,32,0,6,125.000,4,5,0.000,0,1,4 142 | 1,29,37,1,13,145.000,5,0,0.000,1,4,4 143 | 1,42,44,1,2,99.000,2,21,0.000,0,3,3 144 | 1,20,34,1,2,22.000,4,0,0.000,1,3,4 145 | 2,72,60,1,21,31.000,1,38,1.000,0,2,2 146 | 1,2,36,0,3,25.000,1,6,0.000,1,1,3 147 | 3,46,25,0,5,57.000,5,0,0.000,1,2,1 148 | 2,39,51,1,21,41.000,5,20,0.000,0,6,4 149 | 3,24,25,1,1,20.000,3,1,0.000,1,2,2 150 | 3,59,43,1,4,101.000,2,22,0.000,1,5,2 151 | 1,65,37,0,8,56.000,2,15,0.000,0,1,2 152 | 2,50,27,1,5,22.000,2,5,0.000,1,3,1 153 | 1,57,37,0,11,108.000,4,9,0.000,1,1,3 154 | 3,61,48,0,14,115.000,4,20,0.000,1,1,4 155 | 3,34,54,0,31,53.000,4,22,0.000,0,1,4 156 | 3,38,53,0,13,242.000,4,12,0.000,0,1,4 157 | 1,3,24,1,2,20.000,2,3,0.000,1,5,3 158 | 2,4,47,0,5,123.000,4,11,0.000,0,1,1 159 | 2,15,35,0,0,34.000,1,12,0.000,0,1,1 160 | 1,25,29,1,8,31.000,4,3,0.000,1,5,2 161 | 3,21,44,0,25,47.000,1,12,0.000,1,2,3 162 | 3,50,37,1,11,48.000,3,8,0.000,1,2,3 163 | 2,65,64,1,22,13.000,2,5,1.000,1,3,3 164 | 2,59,52,1,18,78.000,1,25,0.000,0,2,1 165 | 3,29,26,0,7,34.000,1,7,0.000,1,2,3 166 | 1,60,46,0,17,81.000,5,9,0.000,1,1,2 167 | 1,71,45,1,14,86.000,2,25,0.000,0,2,2 168 | 3,38,27,1,0,26.000,4,2,0.000,0,4,4 169 | 3,16,34,1,5,51.000,2,8,0.000,1,5,1 170 | 3,7,31,0,10,59.000,3,10,0.000,0,1,1 171 | 1,59,53,0,21,112.000,4,16,0.000,0,1,2 172 | 2,22,56,1,14,57.000,4,28,0.000,0,2,4 173 | 1,18,48,1,20,41.000,1,2,0.000,1,2,1 174 | 2,64,46,1,19,168.000,2,26,0.000,1,2,4 175 | 2,7,33,0,4,29.000,4,4,0.000,1,3,1 176 | 3,65,58,0,13,167.000,2,14,0.000,1,2,2 177 | 3,7,24,0,5,29.000,2,3,0.000,0,3,1 178 | 1,64,55,0,28,104.000,1,26,0.000,1,1,3 179 | 3,42,22,1,0,46.000,4,0,0.000,0,2,4 180 | 3,33,26,0,5,46.000,4,1,0.000,1,3,4 181 | 1,3,33,0,8,51.000,4,0,0.000,1,1,1 182 | 2,69,42,1,11,65.000,2,18,0.000,0,2,4 183 | 2,9,33,0,1,48.000,2,13,0.000,0,2,1 184 | 3,22,34,1,1,46.000,3,1,0.000,0,4,2 185 | 2,53,63,1,15,43.000,2,18,0.000,0,2,2 186 | 3,71,53,0,30,110.000,1,34,0.000,0,1,2 187 | 3,24,26,0,2,32.000,3,2,0.000,0,3,1 188 | 3,12,22,1,1,18.000,3,0,0.000,1,2,1 189 | 1,25,39,1,1,26.000,4,0,0.000,0,4,2 190 | 3,63,28,1,9,34.000,2,10,0.000,1,6,3 191 | 1,33,54,1,18,57.000,4,4,0.000,1,3,3 192 | 2,57,54,1,20,21.000,2,0,0.000,1,3,1 193 | 3,13,33,0,7,38.000,3,10,0.000,1,1,4 194 | 1,13,21,0,2,19.000,3,0,0.000,1,1,2 195 | 1,71,39,0,2,40.000,3,17,0.000,0,1,3 196 | 3,18,69,1,11,58.000,3,8,0.000,0,2,4 197 | 1,17,51,1,10,95.000,2,15,0.000,1,2,1 198 | 2,22,43,0,4,114.000,2,19,0.000,0,1,1 199 | 2,65,65,1,27,128.000,3,24,0.000,0,2,3 200 | 3,7,40,0,9,33.000,4,2,0.000,1,1,1 201 | 3,72,66,0,30,460.000,4,41,0.000,1,1,4 202 | 1,35,43,0,12,224.000,3,17,0.000,0,1,1 203 | 1,24,39,0,2,122.000,4,12,0.000,0,3,4 204 | 1,64,24,1,0,23.000,3,1,0.000,1,5,2 205 | 2,63,63,1,19,191.000,3,27,0.000,0,2,2 206 | 1,38,31,1,7,187.000,4,6,0.000,0,3,4 207 | 2,29,37,1,9,119.000,3,12,0.000,0,3,1 208 | 1,72,69,1,49,44.000,2,44,1.000,1,2,3 209 | 1,10,27,1,2,15.000,3,2,0.000,1,2,4 210 | 3,72,64,0,41,674.000,4,37,0.000,1,1,3 211 | 3,52,23,1,4,33.000,4,0,0.000,1,2,3 212 | 3,13,55,0,3,250.000,2,35,0.000,1,2,1 213 | 1,3,33,0,6,64.000,4,0,0.000,0,2,1 214 | 3,39,24,1,2,26.000,2,4,0.000,0,4,1 215 | 2,28,29,0,4,23.000,3,5,0.000,0,2,2 216 | 2,46,42,0,9,52.000,4,7,0.000,0,3,2 217 | 2,62,46,0,27,200.000,3,9,0.000,1,1,1 218 | 2,23,33,1,7,28.000,1,4,0.000,0,2,3 219 | 3,23,25,1,5,19.000,5,0,0.000,0,3,4 220 | 2,72,61,1,39,76.000,3,21,0.000,0,2,4 221 | 3,56,60,0,26,70.000,2,20,0.000,0,1,1 222 | 1,8,30,0,1,34.000,2,9,0.000,1,1,3 223 | 3,3,36,0,7,34.000,5,3,0.000,1,3,4 224 | 3,37,37,0,7,172.000,3,13,0.000,0,1,1 225 | 3,37,53,1,13,48.000,1,10,0.000,0,3,3 226 | 2,6,30,0,9,45.000,2,2,0.000,1,1,1 227 | 1,48,47,0,9,72.000,3,13,0.000,0,5,4 228 | 2,43,43,1,3,55.000,4,18,0.000,1,4,4 229 | 2,21,29,0,7,40.000,4,2,0.000,1,4,1 230 | 3,6,33,0,12,26.000,3,0,0.000,0,1,3 231 | 2,69,51,0,18,96.000,1,33,0.000,0,4,3 232 | 1,22,33,0,9,54.000,1,10,0.000,0,1,3 233 | 1,28,40,1,1,47.000,2,9,0.000,1,3,3 234 | 1,71,47,1,23,142.000,1,30,0.000,0,2,2 235 | 2,7,37,0,5,57.000,4,1,0.000,0,1,1 236 | 1,63,29,0,10,20.000,3,7,0.000,0,2,4 237 | 2,53,57,0,25,37.000,1,7,0.000,0,1,2 238 | 2,50,52,1,17,36.000,4,16,0.000,0,4,4 239 | 2,39,31,1,6,24.000,4,2,0.000,0,2,4 240 | 3,16,19,0,0,21.000,2,0,0.000,1,1,1 241 | 3,43,51,1,15,186.000,3,25,0.000,0,2,4 242 | 3,44,31,0,12,30.000,1,7,0.000,1,3,1 243 | 2,72,66,1,43,96.000,2,16,0.000,1,2,4 244 | 3,61,76,0,39,15.000,1,29,1.000,0,1,3 245 | 3,43,21,1,1,25.000,1,4,0.000,0,4,2 246 | 3,24,39,0,11,49.000,3,7,0.000,1,1,1 247 | 3,64,45,0,4,38.000,2,7,0.000,1,1,3 248 | 2,64,38,0,0,29.000,2,2,0.000,0,2,2 249 | 1,24,26,0,7,55.000,2,7,0.000,1,1,1 250 | 3,33,55,1,21,64.000,3,10,0.000,1,3,1 251 | 2,33,33,1,12,42.000,4,7,0.000,1,5,2 252 | 1,2,40,0,9,134.000,4,2,0.000,0,1,1 253 | 2,42,48,1,22,82.000,1,21,0.000,1,5,4 254 | 1,52,54,1,5,153.000,5,10,0.000,0,2,2 255 | 1,45,66,0,43,144.000,2,13,0.000,1,1,2 256 | 3,3,25,0,4,22.000,4,0,0.000,0,2,4 257 | 2,37,53,1,16,35.000,1,16,0.000,1,4,3 258 | 2,28,57,0,33,82.000,4,22,0.000,1,1,2 259 | 3,40,63,1,9,61.000,1,22,0.000,0,2,1 260 | 2,37,33,0,1,102.000,2,12,0.000,0,1,4 261 | 3,7,27,0,5,31.000,3,4,0.000,0,5,4 262 | 1,71,56,0,23,170.000,1,30,0.000,1,1,4 263 | 2,19,46,0,11,26.000,2,0,0.000,1,1,1 264 | 2,61,40,0,15,85.000,3,15,0.000,1,1,3 265 | 3,70,53,1,16,183.000,2,35,0.000,1,2,2 266 | 2,60,55,1,13,67.000,1,18,0.000,1,2,1 267 | 3,48,45,1,5,42.000,1,11,0.000,1,2,1 268 | 1,8,63,1,1,31.000,1,9,0.000,0,2,3 269 | 2,58,58,1,10,96.000,2,17,0.000,0,4,3 270 | 1,16,24,1,5,24.000,3,2,0.000,0,5,3 271 | 2,33,33,0,4,53.000,2,14,0.000,1,1,1 272 | 2,39,25,0,4,18.000,1,0,0.000,1,1,3 273 | 1,62,51,0,10,43.000,4,19,0.000,0,2,2 274 | 1,50,34,0,10,104.000,3,9,0.000,0,1,4 275 | 2,48,55,0,31,26.000,1,1,0.000,0,1,1 276 | 3,31,62,0,5,56.000,2,26,0.000,1,1,3 277 | 2,65,33,1,9,49.000,2,6,0.000,0,5,2 278 | 1,36,58,0,34,80.000,1,21,0.000,1,1,2 279 | 1,61,46,0,5,318.000,3,18,0.000,1,1,3 280 | 2,48,42,0,21,35.000,4,5,0.000,0,1,3 281 | 3,35,45,0,17,29.000,1,6,0.000,0,1,1 282 | 1,51,28,0,8,43.000,5,1,0.000,0,5,4 283 | 2,49,51,1,5,80.000,5,3,0.000,0,2,4 284 | 1,35,39,1,11,30.000,3,2,0.000,1,3,3 285 | 1,58,70,0,2,9.000,3,24,1.000,1,1,2 286 | 1,23,35,1,0,23.000,2,1,0.000,0,5,3 287 | 2,26,39,1,15,58.000,4,9,0.000,1,2,3 288 | 1,38,37,1,1,52.000,1,20,0.000,1,4,4 289 | 1,35,66,1,44,14.000,3,12,1.000,1,2,3 290 | 2,37,50,0,9,110.000,2,21,0.000,1,1,3 291 | 2,48,43,1,1,60.000,2,12,0.000,0,2,2 292 | 3,13,43,1,1,123.000,3,21,0.000,0,5,4 293 | 1,72,63,1,29,15.000,5,31,1.000,0,2,3 294 | 1,30,27,0,4,47.000,4,2,0.000,1,2,2 295 | 1,24,43,0,14,138.000,4,11,0.000,1,1,3 296 | 2,25,51,1,20,359.000,3,29,0.000,0,2,4 297 | 2,50,52,0,30,214.000,3,22,0.000,0,1,4 298 | 2,28,40,0,7,64.000,1,19,0.000,0,1,3 299 | 3,34,51,0,8,50.000,4,14,0.000,0,1,1 300 | 1,48,32,0,2,88.000,3,9,0.000,0,1,4 301 | 2,42,36,1,8,86.000,2,15,0.000,1,2,2 302 | 3,67,65,1,30,17.000,1,29,1.000,1,2,3 303 | 1,17,41,1,7,25.000,2,17,0.000,0,3,3 304 | 1,29,38,1,19,46.000,2,6,0.000,0,2,2 305 | 3,66,61,0,26,361.000,1,40,0.000,1,1,2 306 | 2,1,21,0,1,18.000,3,0,0.000,1,1,1 307 | 1,28,33,1,3,14.000,4,1,0.000,0,3,4 308 | 1,15,35,1,5,34.000,3,8,0.000,0,2,2 309 | 2,12,64,0,13,9.000,2,6,1.000,1,1,3 310 | 3,48,49,0,18,39.000,2,5,0.000,1,1,4 311 | 2,59,25,1,4,18.000,3,2,0.000,0,4,1 312 | 3,8,26,1,1,25.000,4,1,0.000,0,2,1 313 | 1,45,52,0,15,46.000,5,8,0.000,1,1,2 314 | 1,25,39,1,8,94.000,4,11,0.000,1,4,2 315 | 1,21,44,1,4,26.000,3,2,0.000,0,2,4 316 | 2,17,68,1,1,335.000,1,35,0.000,1,3,4 317 | 2,41,45,0,14,25.000,3,6,0.000,0,1,1 318 | 1,13,45,1,3,99.000,3,8,0.000,1,2,3 319 | 2,1,26,1,3,30.000,2,7,0.000,1,5,1 320 | 3,3,31,1,4,35.000,2,2,0.000,1,3,4 321 | 1,23,27,1,2,51.000,5,1,0.000,1,2,1 322 | 3,26,42,0,20,49.000,3,13,0.000,0,1,4 323 | 1,37,51,1,15,54.000,1,15,0.000,0,3,3 324 | 3,5,30,0,3,46.000,4,7,0.000,0,1,2 325 | 1,32,27,1,3,91.000,4,1,0.000,1,3,4 326 | 3,12,22,0,1,23.000,4,0,0.000,0,2,1 327 | 1,72,54,1,6,345.000,1,32,0.000,0,2,3 328 | 3,3,25,1,2,45.000,4,0,0.000,1,2,1 329 | 1,54,43,0,12,53.000,2,12,0.000,0,1,3 330 | 2,26,26,0,2,14.000,2,6,0.000,1,1,4 331 | 3,14,36,0,13,67.000,5,4,0.000,1,1,1 332 | 3,22,51,0,27,49.000,3,1,0.000,0,1,1 333 | 3,65,47,1,18,37.000,1,15,0.000,1,4,1 334 | 3,26,45,0,18,33.000,1,9,0.000,0,1,1 335 | 1,20,37,0,5,46.000,3,10,0.000,1,4,4 336 | 2,67,60,1,32,93.000,1,21,0.000,0,4,2 337 | 1,34,55,0,2,48.000,2,11,0.000,0,1,1 338 | 1,37,54,1,35,183.000,2,22,0.000,0,3,3 339 | 2,22,23,1,3,16.000,1,5,0.000,0,4,1 340 | 2,5,69,0,0,11.000,2,14,1.000,0,1,1 341 | 1,28,31,1,0,42.000,4,5,0.000,1,3,3 342 | 1,20,41,1,7,67.000,4,14,0.000,1,4,1 343 | 3,72,41,0,20,181.000,4,12,0.000,0,1,4 344 | 2,30,43,0,18,53.000,4,6,0.000,0,1,1 345 | 3,71,52,0,17,172.000,2,32,0.000,0,1,4 346 | 2,56,44,0,24,87.000,2,19,0.000,0,1,3 347 | 1,8,29,1,2,21.000,5,0,0.000,0,2,1 348 | 2,32,65,0,6,9.000,2,30,1.000,1,1,2 349 | 2,46,47,0,22,144.000,3,22,0.000,1,2,3 350 | 3,60,45,1,23,117.000,4,11,0.000,0,3,4 351 | 2,42,32,1,0,59.000,4,4,0.000,1,6,4 352 | 3,71,60,1,39,508.000,4,35,0.000,0,4,4 353 | 2,72,63,1,40,222.000,2,43,1.000,1,2,3 354 | 3,45,66,0,11,87.000,3,25,1.000,1,1,1 355 | 3,5,31,0,4,34.000,1,9,0.000,0,1,3 356 | 2,17,38,1,19,18.000,4,0,0.000,1,4,2 357 | 2,70,55,0,12,65.000,3,24,0.000,0,1,3 358 | 3,57,57,1,19,130.000,3,27,0.000,1,2,2 359 | 1,20,33,0,13,44.000,3,8,0.000,1,1,2 360 | 1,72,52,1,21,63.000,1,24,0.000,0,3,3 361 | 3,32,50,0,28,57.000,4,20,0.000,0,1,3 362 | 3,22,57,1,7,264.000,1,35,0.000,1,2,3 363 | 2,6,24,0,2,28.000,1,5,0.000,0,2,3 364 | 3,32,23,1,2,26.000,3,1,0.000,1,6,4 365 | 1,38,33,0,3,38.000,4,0,0.000,0,1,2 366 | 1,53,35,0,15,59.000,3,5,0.000,1,1,3 367 | 3,37,76,0,38,117.000,4,21,0.000,1,1,1 368 | 1,47,35,1,13,70.000,4,9,0.000,1,2,4 369 | 3,69,59,0,37,104.000,2,13,0.000,0,1,3 370 | 3,63,56,0,17,71.000,3,18,0.000,1,1,1 371 | 1,61,34,1,0,70.000,3,7,0.000,1,3,4 372 | 1,56,38,1,8,56.000,4,10,0.000,0,2,4 373 | 3,40,57,1,18,68.000,1,23,0.000,0,2,3 374 | 2,61,45,0,21,80.000,2,13,0.000,1,1,2 375 | 2,16,26,0,7,21.000,4,1,0.000,0,6,4 376 | 2,7,43,0,4,69.000,3,5,0.000,1,1,1 377 | 3,20,31,1,10,39.000,2,3,0.000,1,2,1 378 | 2,35,38,0,7,90.000,2,18,0.000,0,4,2 379 | 1,60,37,1,12,64.000,5,1,0.000,1,2,2 380 | 2,47,25,0,2,28.000,2,6,0.000,0,1,4 381 | 2,33,28,1,3,24.000,2,0,0.000,1,6,1 382 | 3,12,34,0,2,44.000,2,7,0.000,0,1,1 383 | 1,10,31,0,0,32.000,4,7,0.000,1,2,2 384 | 2,69,33,1,9,52.000,4,9,0.000,1,3,3 385 | 1,23,20,0,1,15.000,3,0,0.000,1,1,2 386 | 1,53,31,0,10,46.000,3,6,0.000,0,2,2 387 | 2,60,57,1,14,107.000,2,33,0.000,1,2,3 388 | 2,33,33,0,7,56.000,2,11,0.000,0,1,1 389 | 3,67,40,1,21,40.000,4,7,0.000,1,4,4 390 | 3,15,38,1,5,56.000,3,13,0.000,0,3,3 391 | 3,64,31,1,5,46.000,2,7,0.000,1,4,2 392 | 2,46,49,1,13,51.000,2,21,0.000,0,2,1 393 | 2,72,60,1,33,12.000,1,20,1.000,0,3,4 394 | 1,61,43,1,6,34.000,5,6,0.000,0,3,4 395 | 3,54,27,0,3,27.000,2,6,0.000,1,5,4 396 | 3,28,36,0,3,42.000,3,7,0.000,1,1,3 397 | 2,28,38,0,8,119.000,5,2,0.000,0,3,4 398 | 2,32,43,0,13,146.000,1,27,0.000,1,1,3 399 | 2,52,46,0,17,51.000,2,13,0.000,1,3,2 400 | 1,48,62,0,33,86.000,1,22,1.000,1,1,3 401 | 3,52,42,1,17,27.000,3,8,0.000,1,2,3 402 | 1,37,43,0,8,38.000,3,9,0.000,0,1,2 403 | 1,41,52,0,26,928.000,3,29,0.000,0,3,3 404 | 2,43,27,1,3,21.000,2,1,0.000,1,3,2 405 | 3,67,60,0,24,24.000,1,6,0.000,1,1,3 406 | 3,61,52,1,30,91.000,1,25,0.000,1,2,1 407 | 3,70,39,1,20,38.000,2,8,0.000,1,4,3 408 | 3,44,32,1,1,96.000,4,5,0.000,1,4,4 409 | 3,46,45,0,12,96.000,3,17,0.000,0,1,2 410 | 3,39,34,0,2,54.000,3,5,0.000,0,1,1 411 | 2,39,59,0,20,1668.000,4,27,0.000,1,1,2 412 | 2,50,29,1,10,53.000,4,0,0.000,1,4,4 413 | 3,5,31,0,1,21.000,3,1,0.000,1,2,1 414 | 2,10,34,0,1,52.000,5,3,0.000,1,1,2 415 | 1,72,69,0,45,17.000,3,36,1.000,1,1,2 416 | 2,68,60,0,40,262.000,2,39,0.000,1,1,3 417 | 3,58,28,0,5,34.000,5,1,0.000,1,1,2 418 | 3,18,44,1,10,107.000,1,19,0.000,1,7,1 419 | 1,65,45,1,25,67.000,5,13,0.000,1,2,3 420 | 3,8,45,0,1,37.000,4,0,0.000,0,1,2 421 | 2,1,20,0,1,21.000,2,0,0.000,1,2,1 422 | 3,42,58,0,7,22.000,1,4,0.000,1,1,2 423 | 1,4,25,1,3,71.000,3,3,0.000,1,5,1 424 | 3,17,35,0,4,77.000,3,8,0.000,1,1,3 425 | 2,8,24,1,1,18.000,4,0,0.000,1,5,4 426 | 1,49,37,1,17,50.000,2,12,0.000,1,2,2 427 | 1,56,46,1,21,33.000,4,7,0.000,0,2,2 428 | 1,62,57,1,21,94.000,1,22,0.000,0,2,4 429 | 2,42,57,1,25,209.000,1,40,0.000,1,4,1 430 | 1,13,32,0,7,31.000,4,5,0.000,0,2,3 431 | 1,69,46,1,18,66.000,2,19,0.000,0,4,2 432 | 2,45,59,0,14,51.000,1,15,0.000,0,2,1 433 | 2,66,42,1,18,118.000,2,20,0.000,0,2,2 434 | 2,15,35,0,10,25.000,2,3,0.000,0,1,1 435 | 1,41,44,1,1,59.000,3,21,0.000,1,3,2 436 | 2,11,20,0,1,20.000,2,0,0.000,1,1,4 437 | 3,45,30,1,0,63.000,5,4,0.000,0,4,4 438 | 1,52,62,0,23,36.000,4,17,0.000,0,1,1 439 | 1,42,56,0,24,72.000,4,18,0.000,1,1,4 440 | 2,48,35,0,11,50.000,1,16,0.000,0,3,3 441 | 3,4,31,0,5,39.000,2,5,0.000,0,2,4 442 | 3,53,64,1,10,54.000,2,20,0.000,1,2,4 443 | 3,1,59,0,4,9.000,1,4,1.000,0,1,3 444 | 3,19,30,1,10,33.000,4,1,0.000,0,3,4 445 | 2,33,30,0,11,28.000,1,9,0.000,0,1,1 446 | 3,42,36,1,14,44.000,2,11,0.000,0,3,3 447 | 1,60,32,0,12,34.000,4,4,0.000,1,2,2 448 | 2,57,51,1,7,68.000,4,18,0.000,1,3,3 449 | 1,64,39,0,5,37.000,1,9,0.000,0,1,1 450 | 2,28,20,1,1,18.000,3,0,0.000,0,3,4 451 | 1,29,35,1,10,55.000,2,7,0.000,0,4,3 452 | 1,46,31,1,12,21.000,1,5,0.000,0,5,3 453 | 1,22,41,1,9,29.000,3,11,0.000,0,4,1 454 | 2,10,56,0,26,9.000,2,6,1.000,0,2,1 455 | 3,50,41,0,16,138.000,2,18,0.000,1,1,2 456 | 1,55,65,0,34,28.000,4,12,1.000,1,1,4 457 | 2,15,54,0,7,62.000,1,11,0.000,0,1,1 458 | 1,31,32,1,13,37.000,3,8,0.000,1,3,4 459 | 3,5,44,0,5,83.000,1,16,0.000,1,1,2 460 | 2,69,63,0,28,168.000,1,43,0.000,1,2,3 461 | 2,26,27,1,4,33.000,4,1,0.000,0,6,4 462 | 3,10,33,1,2,66.000,3,9,0.000,1,4,1 463 | 1,26,30,0,9,18.000,4,1,0.000,0,3,4 464 | 3,25,30,0,0,20.000,1,4,0.000,1,1,3 465 | 3,30,23,1,4,19.000,3,1,0.000,0,4,2 466 | 1,21,39,1,7,25.000,2,1,0.000,0,4,3 467 | 1,71,46,0,27,119.000,5,7,0.000,1,1,4 468 | 3,11,48,1,5,323.000,4,25,0.000,0,4,3 469 | 1,24,30,1,7,45.000,1,11,0.000,1,2,1 470 | 1,65,59,1,27,197.000,4,26,0.000,0,2,2 471 | 1,16,34,1,13,56.000,3,10,0.000,1,3,1 472 | 3,37,51,1,26,24.000,4,4,0.000,0,2,2 473 | 1,5,30,0,4,25.000,3,6,0.000,0,1,3 474 | 1,3,55,1,9,26.000,5,0,0.000,1,2,2 475 | 1,5,32,0,6,33.000,4,3,0.000,0,2,1 476 | 1,60,44,1,4,150.000,2,24,0.000,0,2,4 477 | 2,19,37,1,4,99.000,2,15,0.000,1,3,2 478 | 3,54,40,0,19,50.000,4,12,0.000,1,2,4 479 | 2,25,41,0,3,81.000,3,20,0.000,1,4,3 480 | 2,1,36,0,6,21.000,1,1,0.000,0,2,2 481 | 3,54,68,0,7,273.000,3,27,0.000,1,1,1 482 | 1,14,31,1,9,39.000,3,4,0.000,1,7,1 483 | 3,14,28,1,7,17.000,2,3,0.000,0,4,1 484 | 2,10,45,1,8,115.000,3,17,0.000,1,4,2 485 | 1,7,23,0,3,27.000,2,1,0.000,1,3,1 486 | 2,33,45,0,24,35.000,1,6,0.000,0,1,3 487 | 1,3,23,0,2,20.000,2,4,0.000,0,1,1 488 | 2,56,58,1,3,92.000,1,25,0.000,0,3,1 489 | 2,54,56,0,8,207.000,2,35,0.000,1,1,3 490 | 1,72,55,1,28,40.000,1,11,0.000,0,5,2 491 | 1,18,29,0,4,40.000,2,1,0.000,0,1,3 492 | 3,25,29,0,9,55.000,4,1,0.000,0,1,1 493 | 1,42,62,0,26,26.000,1,35,1.000,1,1,1 494 | 3,4,37,0,2,41.000,2,8,0.000,0,1,1 495 | 2,12,50,1,16,203.000,4,25,0.000,0,2,1 496 | 2,53,64,1,17,380.000,2,36,0.000,1,2,3 497 | 2,13,38,1,8,104.000,4,6,0.000,0,7,4 498 | 3,18,31,1,12,24.000,3,4,0.000,0,4,2 499 | 3,67,42,1,20,81.000,3,20,0.000,1,5,4 500 | 2,6,24,0,2,17.000,1,1,0.000,0,1,3 501 | 1,25,46,0,13,45.000,1,12,0.000,1,1,3 502 | 2,15,39,0,19,32.000,2,2,0.000,0,1,2 503 | 3,32,35,0,12,57.000,4,5,0.000,0,1,2 504 | 2,22,39,0,0,63.000,4,9,0.000,0,1,2 505 | 2,8,22,0,3,25.000,4,0,0.000,0,1,3 506 | 1,3,31,1,4,22.000,1,1,0.000,0,2,1 507 | 2,32,54,0,6,155.000,2,20,0.000,0,1,1 508 | 1,49,54,0,24,30.000,2,5,0.000,0,1,2 509 | 2,2,28,0,7,46.000,3,0,0.000,1,1,4 510 | 1,39,47,0,1,68.000,4,10,0.000,1,2,2 511 | 1,42,33,0,2,67.000,2,5,0.000,0,2,2 512 | 1,28,36,0,3,69.000,3,2,0.000,0,1,4 513 | 2,41,46,1,14,47.000,2,11,0.000,0,5,4 514 | 3,67,50,1,31,83.000,2,12,0.000,0,2,2 515 | 1,38,40,0,15,34.000,2,11,0.000,1,1,2 516 | 2,18,38,0,8,45.000,2,2,0.000,1,4,4 517 | 2,6,42,1,17,29.000,2,2,0.000,1,2,4 518 | 2,4,36,0,0,21.000,2,1,0.000,1,3,1 519 | 1,13,23,1,3,30.000,3,0,0.000,1,4,4 520 | 1,20,35,0,0,78.000,4,7,0.000,0,1,4 521 | 1,34,49,1,27,61.000,3,15,0.000,1,2,3 522 | 1,46,29,1,4,90.000,5,3,0.000,0,4,4 523 | 1,48,41,0,21,43.000,2,7,0.000,0,1,2 524 | 3,69,61,1,12,301.000,2,37,0.000,0,2,3 525 | 1,32,34,1,7,32.000,2,15,0.000,0,4,1 526 | 1,29,29,0,9,41.000,2,9,0.000,0,2,2 527 | 3,55,52,0,22,127.000,1,28,0.000,1,3,2 528 | 3,72,60,1,36,271.000,1,43,0.000,1,2,3 529 | 3,29,26,0,1,32.000,3,2,0.000,1,1,1 530 | 2,50,25,1,1,36.000,4,2,0.000,1,5,4 531 | 3,35,50,1,11,293.000,3,21,0.000,0,2,1 532 | 3,11,63,0,9,41.000,3,3,0.000,0,1,1 533 | 1,9,29,0,9,39.000,3,5,0.000,1,1,1 534 | 2,51,48,0,27,58.000,1,18,0.000,0,1,3 535 | 1,33,34,1,2,28.000,1,3,0.000,1,3,2 536 | 1,25,20,0,0,15.000,2,0,0.000,0,2,3 537 | 3,31,34,0,9,105.000,4,7,0.000,1,4,3 538 | 2,67,40,1,10,80.000,4,8,0.000,1,2,4 539 | 3,55,45,0,8,36.000,2,9,0.000,0,3,3 540 | 3,19,50,0,7,112.000,4,3,0.000,1,1,4 541 | 1,39,34,1,11,55.000,4,10,0.000,1,4,4 542 | 2,52,35,1,8,40.000,4,1,0.000,0,2,3 543 | 3,55,37,1,15,23.000,2,0,0.000,1,5,3 544 | 2,25,62,0,27,28.000,4,33,1.000,1,1,4 545 | 3,33,58,0,12,21.000,2,23,1.000,1,1,1 546 | 2,48,35,0,5,63.000,4,12,0.000,0,2,3 547 | 2,48,31,1,12,52.000,4,3,0.000,0,3,4 548 | 2,49,57,0,3,189.000,1,31,0.000,1,3,1 549 | 3,54,32,1,0,50.000,1,15,0.000,0,3,3 550 | 1,45,32,1,9,23.000,1,15,0.000,0,4,1 551 | 3,4,37,1,1,33.000,2,15,0.000,1,5,1 552 | 3,49,27,1,3,29.000,3,6,0.000,1,3,2 553 | 2,23,35,0,2,96.000,3,4,0.000,0,1,4 554 | 2,31,44,0,23,80.000,5,15,0.000,0,1,2 555 | 2,55,44,1,2,68.000,2,15,0.000,0,5,3 556 | 1,9,28,0,7,30.000,1,4,0.000,1,6,1 557 | 2,55,57,1,25,34.000,2,12,0.000,0,3,1 558 | 1,34,30,0,3,35.000,2,2,0.000,1,3,3 559 | 1,10,23,0,4,36.000,2,1,0.000,0,3,3 560 | 2,62,76,0,20,35.000,3,18,1.000,1,1,2 561 | 2,51,36,0,13,62.000,3,9,0.000,0,2,4 562 | 1,65,48,0,28,94.000,1,25,0.000,0,1,2 563 | 3,53,33,0,1,60.000,1,6,0.000,0,2,4 564 | 1,23,35,0,4,118.000,2,11,0.000,0,1,1 565 | 3,1,30,0,3,135.000,4,3,0.000,0,3,1 566 | 3,66,55,0,20,80.000,2,24,0.000,1,1,3 567 | 3,8,27,1,0,21.000,3,3,0.000,1,2,2 568 | 3,18,28,1,3,22.000,1,2,0.000,1,5,1 569 | 3,36,32,1,3,63.000,4,7,0.000,0,4,2 570 | 1,13,40,0,16,142.000,2,18,0.000,1,1,1 571 | 3,4,44,0,15,32.000,3,0,0.000,1,1,3 572 | 3,20,32,0,10,19.000,3,5,0.000,0,1,2 573 | 1,66,39,1,12,60.000,4,10,0.000,0,3,4 574 | 3,67,47,1,7,97.000,1,23,0.000,0,4,3 575 | 1,11,24,1,0,31.000,2,3,0.000,1,4,4 576 | 1,6,26,0,7,30.000,3,1,0.000,1,2,1 577 | 2,13,23,1,4,31.000,1,5,0.000,1,4,1 578 | 2,2,29,1,3,44.000,2,9,0.000,1,6,4 579 | 1,4,40,0,12,38.000,2,5,0.000,0,1,1 580 | 1,34,63,1,10,23.000,2,0,0.000,1,2,4 581 | 1,13,34,1,6,37.000,3,6,0.000,0,4,3 582 | 2,37,40,1,14,26.000,2,0,0.000,0,5,2 583 | 1,45,30,1,8,38.000,2,5,0.000,0,6,3 584 | 2,71,70,0,30,153.000,3,34,1.000,1,1,3 585 | 1,35,40,0,5,49.000,1,20,0.000,1,1,1 586 | 3,44,41,0,21,51.000,3,7,0.000,0,1,1 587 | 3,44,45,1,19,88.000,1,21,0.000,0,2,3 588 | 1,26,41,1,20,19.000,1,0,0.000,1,6,1 589 | 2,42,46,1,9,31.000,1,12,0.000,1,3,1 590 | 2,41,47,1,6,90.000,2,19,0.000,0,3,1 591 | 1,31,24,1,3,14.000,1,0,0.000,0,4,1 592 | 3,62,63,0,43,341.000,2,30,0.000,0,1,3 593 | 1,12,23,1,2,24.000,4,0,0.000,1,8,4 594 | 2,45,49,1,27,81.000,2,7,0.000,1,4,3 595 | 1,47,34,1,4,63.000,2,13,0.000,1,4,1 596 | 2,23,22,1,1,27.000,1,4,0.000,0,5,3 597 | 3,39,34,0,4,20.000,5,3,0.000,1,1,4 598 | 2,19,26,0,2,48.000,3,0,0.000,0,2,3 599 | 1,13,54,0,12,121.000,4,23,0.000,0,1,3 600 | 2,5,33,0,6,39.000,3,4,0.000,0,3,4 601 | 2,11,19,0,0,21.000,2,0,0.000,0,1,1 602 | 3,4,32,0,2,53.000,4,5,0.000,0,1,1 603 | 3,17,42,0,8,58.000,1,23,0.000,0,1,2 604 | 2,7,27,0,3,39.000,3,2,0.000,0,1,1 605 | 1,19,65,0,3,108.000,1,33,0.000,0,3,3 606 | 2,32,34,1,0,38.000,1,10,0.000,0,4,2 607 | 2,53,51,0,31,245.000,4,0,0.000,0,1,2 608 | 1,51,38,0,19,51.000,1,18,0.000,1,1,3 609 | 1,35,34,0,7,78.000,4,10,0.000,1,1,1 610 | 1,3,31,1,4,23.000,4,4,0.000,0,5,4 611 | 1,22,48,0,7,88.000,4,12,0.000,0,1,1 612 | 3,17,21,1,0,25.000,2,1,0.000,1,3,3 613 | 2,68,39,0,20,73.000,2,16,0.000,1,1,2 614 | 2,27,41,1,7,62.000,4,15,0.000,1,2,3 615 | 3,68,52,1,8,456.000,3,30,0.000,1,4,3 616 | 3,56,42,1,10,24.000,2,5,0.000,0,2,3 617 | 3,11,32,0,8,23.000,2,0,0.000,1,1,1 618 | 3,5,47,0,7,46.000,1,6,0.000,1,1,1 619 | 3,16,50,0,5,263.000,2,29,0.000,1,3,3 620 | 2,59,30,0,2,34.000,4,2,0.000,1,1,2 621 | 1,60,64,0,14,33.000,2,13,1.000,0,1,4 622 | 3,55,47,0,11,37.000,1,11,0.000,0,1,3 623 | 1,18,56,0,22,296.000,2,25,0.000,0,1,1 624 | 2,16,22,0,0,26.000,3,0,0.000,1,3,2 625 | 1,35,57,1,32,47.000,4,12,0.000,0,2,2 626 | 3,31,23,1,1,31.000,3,1,0.000,1,2,4 627 | 2,20,49,1,5,264.000,2,29,0.000,1,2,1 628 | 2,16,27,0,5,41.000,4,2,0.000,1,1,4 629 | 2,69,48,1,27,55.000,2,4,0.000,0,2,3 630 | 3,69,66,1,31,49.000,2,15,0.000,0,2,1 631 | 2,27,24,1,3,26.000,1,7,0.000,1,4,1 632 | 3,48,39,0,20,110.000,4,9,0.000,0,1,3 633 | 1,18,52,1,20,66.000,1,19,0.000,1,2,1 634 | 2,24,40,1,4,39.000,3,5,0.000,1,2,3 635 | 2,17,42,0,6,31.000,2,2,0.000,0,1,4 636 | 2,9,24,1,3,26.000,4,1,0.000,1,3,1 637 | 2,15,46,0,5,232.000,4,15,0.000,1,2,4 638 | 1,62,37,1,10,43.000,4,1,0.000,1,3,2 639 | 1,9,41,0,12,39.000,4,3,0.000,0,1,1 640 | 2,71,41,1,10,73.000,2,23,0.000,1,2,3 641 | 2,62,59,1,37,74.000,1,20,0.000,0,2,3 642 | 2,20,38,0,19,237.000,4,13,0.000,0,1,4 643 | 1,19,33,1,11,22.000,1,3,0.000,0,5,3 644 | 2,49,61,1,28,21.000,1,8,0.000,0,2,3 645 | 3,3,64,0,3,9.000,2,6,1.000,0,1,1 646 | 1,33,36,0,2,41.000,5,10,0.000,0,1,1 647 | 2,38,44,0,1,50.000,4,7,0.000,1,1,2 648 | 3,42,48,1,14,27.000,3,5,0.000,1,3,4 649 | 1,11,26,0,2,53.000,3,3,0.000,0,1,4 650 | 3,27,28,1,0,58.000,3,0,0.000,0,2,3 651 | 3,23,50,0,1,151.000,4,8,0.000,1,1,1 652 | 2,4,38,1,13,54.000,2,4,0.000,0,2,3 653 | 3,36,42,0,5,31.000,1,4,0.000,1,1,1 654 | 2,37,42,0,10,115.000,1,22,0.000,1,1,3 655 | 3,24,58,1,30,24.000,1,5,0.000,0,2,1 656 | 3,4,24,0,1,17.000,2,2,0.000,0,3,1 657 | 3,54,33,0,5,47.000,2,13,0.000,1,2,3 658 | 3,7,49,0,8,36.000,1,0,0.000,1,1,3 659 | 1,10,28,0,9,75.000,4,1,0.000,0,1,4 660 | 3,24,35,1,10,41.000,5,6,0.000,1,2,2 661 | 3,12,31,0,8,18.000,4,4,0.000,0,1,1 662 | 2,45,54,0,25,171.000,3,33,0.000,1,1,3 663 | 3,59,55,1,29,42.000,3,21,0.000,1,2,2 664 | 1,3,21,1,1,36.000,3,0,0.000,1,4,1 665 | 3,8,35,0,6,31.000,5,7,0.000,0,1,4 666 | 1,16,59,0,5,207.000,2,37,0.000,1,2,4 667 | 3,32,33,0,1,38.000,1,12,0.000,1,1,1 668 | 1,8,49,0,14,134.000,4,9,0.000,1,1,4 669 | 1,9,28,1,8,28.000,3,4,0.000,0,3,2 670 | 2,12,19,1,0,15.000,2,0,0.000,1,4,2 671 | 2,24,43,1,15,48.000,4,4,0.000,0,6,4 672 | 3,72,75,0,48,14.000,2,6,1.000,1,1,2 673 | 3,58,61,0,42,101.000,4,18,0.000,1,1,2 674 | 1,70,44,1,24,87.000,2,18,0.000,0,3,3 675 | 3,9,25,0,3,27.000,1,4,0.000,1,1,1 676 | 1,49,25,0,1,27.000,5,0,0.000,1,1,2 677 | 3,52,54,0,7,59.000,3,22,0.000,0,1,3 678 | 1,10,42,1,10,294.000,4,9,0.000,1,6,4 679 | 1,14,52,1,2,44.000,3,5,0.000,0,2,3 680 | 1,19,27,1,3,35.000,2,4,0.000,0,6,4 681 | 3,12,36,1,4,205.000,4,10,0.000,1,4,2 682 | 1,65,59,0,27,732.000,3,31,0.000,1,1,2 683 | 1,67,40,1,14,59.000,3,11,0.000,0,2,3 684 | 2,29,24,1,3,23.000,1,7,0.000,0,2,3 685 | 3,55,46,0,8,87.000,2,19,0.000,1,1,1 686 | 3,71,67,1,25,110.000,1,23,0.000,1,2,3 687 | 3,5,44,1,2,83.000,3,14,0.000,1,3,3 688 | 3,60,58,0,0,63.000,4,21,0.000,1,3,2 689 | 2,10,40,0,6,22.000,3,6,0.000,1,1,1 690 | 3,52,64,0,44,43.000,3,7,0.000,0,1,4 691 | 2,61,43,1,22,35.000,2,11,0.000,0,3,2 692 | 1,61,29,1,5,48.000,2,11,0.000,0,4,2 693 | 2,67,47,1,19,69.000,3,12,0.000,0,3,3 694 | 3,41,42,1,12,26.000,4,5,0.000,0,4,2 695 | 1,30,28,0,1,20.000,1,8,0.000,1,1,1 696 | 1,24,46,0,12,43.000,2,6,0.000,0,1,4 697 | 1,72,75,0,37,33.000,1,44,1.000,1,1,2 698 | 2,17,35,0,7,66.000,2,11,0.000,0,1,4 699 | 2,65,72,0,46,12.000,2,0,1.000,0,1,3 700 | 1,15,30,1,8,90.000,2,11,0.000,0,5,4 701 | 1,34,33,0,8,35.000,3,2,0.000,1,1,2 702 | 1,26,43,0,23,51.000,5,4,0.000,1,1,2 703 | 2,36,45,0,22,117.000,3,15,0.000,1,1,2 704 | 1,60,48,0,29,100.000,2,26,0.000,1,1,3 705 | 1,16,54,0,20,147.000,1,29,0.000,0,4,3 706 | 2,1,33,0,5,53.000,4,6,0.000,1,1,1 707 | 3,56,30,1,9,57.000,5,2,0.000,0,4,2 708 | 2,6,50,0,5,37.000,2,0,0.000,0,1,2 709 | 2,48,34,0,7,42.000,3,2,0.000,1,1,2 710 | 2,34,36,1,17,50.000,1,4,0.000,0,2,3 711 | 2,54,42,1,0,55.000,4,2,0.000,0,5,4 712 | 3,38,31,1,2,42.000,2,7,0.000,1,4,4 713 | 2,25,55,0,15,37.000,1,7,0.000,1,1,1 714 | 1,47,69,1,17,228.000,3,39,0.000,1,4,4 715 | 1,72,62,1,35,163.000,5,31,0.000,0,2,4 716 | 2,6,35,1,6,28.000,4,3,0.000,1,3,4 717 | 1,30,51,1,9,118.000,5,21,0.000,1,2,1 718 | 1,65,70,1,9,115.000,2,39,1.000,1,2,4 719 | 1,22,44,1,19,37.000,4,1,0.000,0,2,3 720 | 1,60,53,0,32,93.000,1,28,0.000,0,1,3 721 | 2,19,32,0,12,71.000,4,5,0.000,1,1,4 722 | 3,18,25,0,4,26.000,2,7,0.000,1,3,1 723 | 1,9,37,1,10,14.000,4,5,0.000,1,6,1 724 | 1,1,34,1,6,18.000,1,0,0.000,1,2,1 725 | 3,10,28,1,2,36.000,4,0,0.000,1,3,4 726 | 3,45,34,1,14,43.000,4,0,0.000,0,4,2 727 | 1,25,28,1,6,41.000,4,4,0.000,1,4,3 728 | 3,66,39,0,1,66.000,2,15,0.000,1,1,2 729 | 1,27,67,0,1,33.000,4,19,1.000,1,1,3 730 | 1,19,41,0,5,53.000,4,6,0.000,0,1,1 731 | 2,17,41,1,9,28.000,4,3,0.000,0,6,1 732 | 2,16,36,0,13,58.000,4,4,0.000,0,1,1 733 | 1,8,42,1,2,129.000,4,17,0.000,1,3,1 734 | 1,3,25,0,0,65.000,4,0,0.000,1,1,4 735 | 3,59,57,0,26,311.000,3,36,0.000,1,1,1 736 | 2,45,37,0,2,57.000,3,14,0.000,1,1,2 737 | 1,66,62,0,31,47.000,2,4,0.000,1,1,2 738 | 3,28,50,1,7,25.000,1,3,0.000,0,2,1 739 | 1,15,38,1,11,46.000,5,11,0.000,0,3,1 740 | 3,39,34,1,10,51.000,3,13,0.000,1,4,4 741 | 3,38,33,0,4,19.000,2,5,0.000,1,2,3 742 | 3,34,29,0,5,26.000,3,4,0.000,1,2,2 743 | 3,60,57,1,18,72.000,5,30,1.000,0,2,4 744 | 3,72,65,1,33,71.000,1,41,1.000,1,4,3 745 | 1,42,42,1,9,41.000,2,13,0.000,0,8,3 746 | 3,32,37,0,9,44.000,2,7,0.000,0,1,1 747 | 3,48,50,1,4,208.000,4,19,0.000,1,2,4 748 | 3,63,37,1,1,45.000,4,9,0.000,1,6,2 749 | 3,45,27,1,3,39.000,3,2,0.000,0,4,2 750 | 2,68,42,0,16,89.000,4,12,0.000,0,1,2 751 | 1,6,29,1,4,19.000,2,5,0.000,1,2,1 752 | 1,68,57,1,33,15.000,2,0,0.000,1,2,4 753 | 3,72,68,1,26,59.000,2,36,1.000,1,2,4 754 | 3,53,38,1,15,20.000,3,6,0.000,1,4,2 755 | 3,20,51,1,11,27.000,4,6,0.000,0,3,3 756 | 2,70,35,1,4,48.000,2,9,0.000,0,3,2 757 | 3,32,47,0,25,135.000,2,6,0.000,1,1,1 758 | 2,61,52,1,23,19.000,1,0,0.000,0,2,3 759 | 1,24,25,1,3,28.000,4,0,0.000,0,3,2 760 | 2,56,61,1,14,12.000,1,16,1.000,1,2,1 761 | 3,54,59,0,28,28.000,2,15,0.000,0,1,2 762 | 3,43,38,1,7,18.000,3,8,0.000,1,3,4 763 | 1,19,35,0,7,58.000,3,5,0.000,1,1,4 764 | 3,5,43,0,16,72.000,3,17,0.000,1,1,3 765 | 1,24,61,1,6,112.000,4,3,0.000,1,4,1 766 | 3,62,35,1,2,28.000,2,8,0.000,1,2,2 767 | 2,14,40,0,13,398.000,5,11,0.000,1,1,4 768 | 3,60,48,1,22,106.000,3,21,0.000,1,3,4 769 | 2,30,74,1,12,10.000,1,3,1.000,1,2,3 770 | 1,40,42,1,16,108.000,3,17,0.000,0,4,2 771 | 2,42,50,1,17,85.000,2,15,0.000,1,4,1 772 | 1,20,34,0,14,51.000,3,9,0.000,1,1,2 773 | 1,4,22,1,1,22.000,2,2,0.000,1,3,3 774 | 2,44,49,1,2,66.000,2,8,0.000,1,4,4 775 | 3,49,63,0,18,10.000,2,2,1.000,0,1,3 776 | 3,5,36,1,2,34.000,1,4,0.000,1,2,3 777 | 3,70,50,1,29,67.000,1,22,0.000,0,4,2 778 | 1,30,57,1,16,19.000,1,1,0.000,0,4,1 779 | 2,28,48,0,20,128.000,4,4,0.000,1,1,4 780 | 1,5,28,1,3,39.000,5,1,0.000,0,6,4 781 | 2,27,35,0,12,56.000,1,3,0.000,1,1,4 782 | 2,17,39,0,12,45.000,4,10,0.000,1,1,1 783 | 1,4,37,1,8,25.000,1,1,0.000,1,5,1 784 | 1,30,23,1,0,24.000,2,1,0.000,1,4,3 785 | 3,71,40,1,19,53.000,4,14,0.000,1,2,4 786 | 3,67,77,0,55,49.000,1,45,1.000,1,1,1 787 | 3,24,48,1,3,38.000,1,16,0.000,1,2,1 788 | 1,24,54,1,7,84.000,3,10,0.000,1,2,1 789 | 2,59,54,0,32,126.000,1,22,0.000,1,1,3 790 | 3,59,32,0,9,73.000,4,5,0.000,0,1,2 791 | 3,44,37,1,1,41.000,4,8,0.000,1,6,2 792 | 1,19,31,1,0,22.000,4,2,0.000,0,3,2 793 | 2,58,46,1,23,23.000,1,2,0.000,1,2,1 794 | 1,41,53,1,22,94.000,2,25,0.000,0,2,2 795 | 2,16,33,0,1,30.000,3,7,0.000,0,4,3 796 | 3,13,36,0,11,31.000,2,0,0.000,0,1,1 797 | 1,60,53,0,22,171.000,1,37,0.000,1,1,3 798 | 2,72,49,1,23,102.000,1,27,0.000,1,6,2 799 | 2,33,39,0,17,27.000,4,3,0.000,1,1,4 800 | 2,72,45,1,25,98.000,4,20,0.000,1,2,4 801 | 1,66,54,1,8,591.000,4,25,0.000,1,2,4 802 | 3,53,37,1,7,25.000,1,2,0.000,1,6,3 803 | 2,22,28,1,0,36.000,4,0,0.000,0,4,4 804 | 2,14,26,1,1,25.000,2,0,0.000,1,3,3 805 | 1,52,49,0,18,69.000,2,13,0.000,1,2,3 806 | 3,33,30,1,5,22.000,1,0,0.000,0,2,3 807 | 3,72,66,0,26,67.000,2,37,1.000,1,1,3 808 | 1,40,40,1,3,73.000,4,12,0.000,1,4,3 809 | 2,29,26,0,1,22.000,4,2,0.000,1,1,4 810 | 3,63,67,0,22,9.000,2,12,1.000,0,1,3 811 | 2,56,54,0,25,147.000,3,31,0.000,1,1,2 812 | 3,60,50,1,26,239.000,2,19,0.000,1,2,4 813 | 3,28,22,1,3,23.000,3,1,0.000,0,5,1 814 | 2,39,39,0,19,29.000,4,6,0.000,0,1,2 815 | 3,65,39,1,18,32.000,2,15,0.000,0,4,3 816 | 3,29,31,0,7,30.000,3,7,0.000,1,1,3 817 | 3,28,36,0,8,57.000,2,13,0.000,1,2,3 818 | 1,2,18,0,0,20.000,2,0,0.000,0,1,3 819 | 3,67,52,0,13,248.000,2,20,0.000,1,3,3 820 | 3,5,35,1,9,59.000,3,8,0.000,1,5,4 821 | 3,48,35,0,10,24.000,2,6,0.000,0,1,3 822 | 2,10,26,1,4,25.000,1,4,0.000,1,3,1 823 | 2,17,28,0,3,37.000,3,2,0.000,1,1,3 824 | 1,21,24,1,0,30.000,4,0,0.000,1,5,4 825 | 2,25,51,0,9,66.000,4,13,0.000,0,2,4 826 | 3,44,31,0,8,16.000,1,3,0.000,0,1,1 827 | 2,59,49,0,28,429.000,4,23,0.000,1,2,4 828 | 2,11,26,0,7,20.000,2,4,0.000,0,1,1 829 | 2,50,68,1,32,262.000,3,28,0.000,0,2,3 830 | 3,23,25,1,2,28.000,3,1,0.000,1,5,2 831 | 1,17,19,0,0,18.000,2,0,0.000,1,2,2 832 | 1,15,62,1,3,144.000,3,21,0.000,0,2,3 833 | 2,68,57,1,38,33.000,1,9,0.000,1,2,2 834 | 1,69,42,1,23,19.000,3,0,0.000,1,2,1 835 | 1,30,24,1,3,30.000,1,7,0.000,1,5,1 836 | 3,22,30,0,8,41.000,2,8,0.000,0,1,3 837 | 2,10,20,1,1,20.000,2,0,0.000,1,4,1 838 | 1,72,59,1,12,65.000,3,21,0.000,1,6,2 839 | 2,34,24,1,5,17.000,4,0,0.000,1,2,3 840 | 2,43,49,1,18,66.000,1,22,0.000,0,4,3 841 | 3,31,37,1,15,18.000,4,5,0.000,0,3,4 842 | 2,6,27,1,5,26.000,4,2,0.000,0,5,1 843 | 2,48,42,0,21,46.000,4,4,0.000,0,1,3 844 | 2,50,25,0,6,19.000,1,5,0.000,0,3,1 845 | 3,20,41,1,19,58.000,2,6,0.000,1,2,2 846 | 3,61,73,0,10,25.000,1,21,1.000,0,1,1 847 | 2,6,21,0,0,15.000,2,1,0.000,1,1,1 848 | 2,1,22,0,2,35.000,2,2,0.000,0,1,1 849 | 1,21,43,0,13,41.000,2,1,0.000,1,1,4 850 | 3,7,32,0,1,71.000,4,3,0.000,0,2,4 851 | 3,65,56,1,19,608.000,3,34,0.000,1,2,4 852 | 1,64,37,0,10,44.000,4,9,0.000,1,1,2 853 | 3,4,30,0,0,41.000,2,0,0.000,0,2,3 854 | 1,67,45,1,22,83.000,3,11,0.000,1,2,2 855 | 2,22,35,0,12,28.000,2,3,0.000,0,2,2 856 | 3,51,37,1,17,101.000,2,10,0.000,1,2,3 857 | 1,50,28,0,6,28.000,1,10,0.000,1,1,3 858 | 1,31,47,1,18,35.000,2,0,0.000,0,2,1 859 | 3,4,28,0,0,39.000,2,3,0.000,1,1,3 860 | 3,60,56,0,19,51.000,4,11,0.000,0,1,4 861 | 3,7,31,1,12,31.000,2,7,0.000,1,6,1 862 | 3,20,40,0,2,209.000,4,15,0.000,1,1,2 863 | 2,19,23,1,4,37.000,3,1,0.000,1,4,3 864 | 3,58,37,1,5,64.000,4,8,0.000,1,2,4 865 | 3,65,64,0,20,88.000,3,26,0.000,1,1,4 866 | 3,72,61,1,34,61.000,1,8,0.000,0,3,2 867 | 3,8,42,0,3,66.000,3,10,0.000,0,1,4 868 | 2,33,40,1,14,19.000,3,5,0.000,0,2,1 869 | 3,18,34,1,4,42.000,2,14,0.000,1,5,4 870 | 2,24,31,1,6,41.000,2,13,0.000,0,5,3 871 | 3,70,36,0,8,50.000,1,15,0.000,1,1,2 872 | 1,7,38,0,4,70.000,4,4,0.000,0,1,4 873 | 3,70,59,0,27,85.000,1,19,0.000,1,1,3 874 | 2,20,48,1,21,45.000,3,2,0.000,1,2,3 875 | 3,54,32,1,12,27.000,1,9,0.000,0,2,2 876 | 3,36,48,0,28,29.000,1,2,0.000,0,1,1 877 | 3,71,53,1,29,48.000,4,0,0.000,1,2,3 878 | 1,17,41,0,4,32.000,2,3,0.000,0,1,3 879 | 1,18,45,1,17,43.000,1,9,0.000,0,3,3 880 | 1,16,27,0,8,16.000,5,0,0.000,1,1,4 881 | 3,38,30,1,6,22.000,2,3,0.000,1,2,2 882 | 1,52,45,1,19,40.000,3,9,0.000,1,3,4 883 | 1,46,48,1,12,256.000,3,25,0.000,0,6,4 884 | 2,31,46,0,23,144.000,4,13,0.000,0,1,3 885 | 2,23,31,0,10,45.000,4,0,0.000,0,2,2 886 | 2,38,56,1,34,34.000,2,0,0.000,1,3,3 887 | 2,50,54,0,15,248.000,3,28,0.000,1,1,3 888 | 2,16,49,0,17,41.000,2,5,0.000,0,1,4 889 | 2,14,34,1,4,27.000,4,1,0.000,1,2,4 890 | 2,30,52,0,23,17.000,3,3,0.000,1,1,2 891 | 3,22,47,1,0,44.000,4,11,0.000,1,3,4 892 | 1,38,55,1,12,135.000,2,24,0.000,0,4,4 893 | 3,59,26,1,3,41.000,4,1,0.000,1,3,2 894 | 2,54,55,0,1,587.000,3,33,0.000,0,1,3 895 | 3,9,40,0,13,38.000,4,7,0.000,1,1,4 896 | 1,67,67,1,38,49.000,2,10,0.000,1,2,2 897 | 2,24,44,0,19,33.000,3,0,0.000,1,2,1 898 | 2,12,55,1,13,36.000,1,5,0.000,1,2,2 899 | 1,26,47,0,13,54.000,3,0,0.000,0,1,2 900 | 2,3,32,0,4,58.000,2,11,0.000,0,4,4 901 | 2,20,46,0,2,23.000,5,4,0.000,1,2,4 902 | 3,5,52,1,0,97.000,5,9,0.000,0,2,4 903 | 3,58,53,0,19,207.000,3,12,0.000,1,1,4 904 | 1,54,28,1,2,21.000,3,3,0.000,0,4,1 905 | 2,52,39,0,6,119.000,3,18,0.000,1,1,1 906 | 2,38,30,1,10,25.000,1,5,0.000,1,4,1 907 | 2,19,46,0,5,59.000,3,17,0.000,0,1,1 908 | 2,22,33,1,2,24.000,1,3,0.000,0,4,1 909 | 1,49,35,1,15,113.000,4,4,0.000,0,5,2 910 | 3,5,39,0,0,22.000,1,0,0.000,1,2,3 911 | 2,18,69,0,28,11.000,1,17,1.000,0,1,1 912 | 3,43,29,0,4,33.000,1,13,0.000,1,4,3 913 | 3,17,50,0,2,205.000,5,19,0.000,0,1,4 914 | 2,37,33,0,4,41.000,3,8,0.000,1,3,2 915 | 2,8,39,0,0,39.000,1,7,0.000,1,1,1 916 | 2,51,46,1,8,107.000,2,21,0.000,1,6,3 917 | 1,71,55,1,11,198.000,1,31,0.000,1,3,3 918 | 1,56,53,1,23,100.000,5,14,0.000,1,2,2 919 | 3,70,68,0,21,1131.000,2,45,0.000,0,1,3 920 | 1,62,53,0,14,360.000,3,26,0.000,1,2,3 921 | 1,50,52,1,22,162.000,2,27,0.000,1,2,4 922 | 3,1,31,0,3,29.000,5,2,0.000,1,3,1 923 | 3,15,26,0,1,38.000,4,1,0.000,0,2,4 924 | 3,72,55,1,24,82.000,3,25,0.000,1,2,2 925 | 2,69,47,1,10,159.000,2,24,0.000,0,4,3 926 | 2,32,44,0,10,201.000,2,24,0.000,0,1,3 927 | 1,19,25,1,6,27.000,4,0,0.000,1,2,3 928 | 2,42,54,0,19,57.000,3,10,0.000,1,1,4 929 | 3,51,49,0,29,45.000,1,16,0.000,0,1,1 930 | 2,32,22,1,2,17.000,2,2,0.000,1,4,3 931 | 3,26,55,1,13,61.000,1,26,0.000,0,2,3 932 | 2,39,44,1,14,418.000,4,18,0.000,0,2,4 933 | 2,72,53,0,12,152.000,2,32,0.000,1,1,4 934 | 2,4,39,0,11,38.000,4,1,0.000,0,2,4 935 | 2,54,50,1,5,151.000,4,20,0.000,0,3,3 936 | 1,34,40,1,21,23.000,4,9,0.000,1,3,4 937 | 1,20,25,1,4,33.000,4,0,0.000,1,3,1 938 | 3,14,49,0,4,26.000,1,8,0.000,0,1,1 939 | 1,61,59,0,26,89.000,1,40,1.000,1,1,3 940 | 2,4,24,0,2,25.000,4,0,0.000,0,1,1 941 | 3,16,48,0,25,59.000,2,13,0.000,0,2,3 942 | 1,13,41,0,9,55.000,2,12,0.000,1,3,1 943 | 2,53,37,1,18,25.000,3,6,0.000,1,2,2 944 | 2,58,36,1,13,39.000,2,8,0.000,1,7,4 945 | 2,25,38,1,19,56.000,1,19,0.000,1,2,3 946 | 1,71,61,1,19,155.000,3,30,0.000,0,2,3 947 | 1,22,33,1,13,119.000,5,0,0.000,1,4,4 948 | 1,46,48,1,15,64.000,3,20,0.000,0,2,3 949 | 3,66,50,1,2,333.000,5,24,0.000,0,2,2 950 | 1,69,58,1,29,26.000,2,7,0.000,1,3,4 951 | 2,38,49,1,3,33.000,4,11,0.000,0,4,2 952 | 3,16,35,0,7,46.000,3,3,0.000,0,1,1 953 | 3,44,39,1,8,41.000,2,8,0.000,0,4,4 954 | 1,50,50,0,24,31.000,1,8,0.000,0,1,3 955 | 3,10,23,0,1,29.000,4,0,0.000,1,1,4 956 | 2,7,41,0,6,94.000,2,16,0.000,0,1,4 957 | 3,71,48,0,25,288.000,3,19,0.000,0,1,1 958 | 3,16,39,1,5,31.000,1,4,0.000,1,5,1 959 | 3,6,20,0,0,25.000,2,0,0.000,1,2,3 960 | 2,46,55,0,24,269.000,2,35,0.000,0,1,3 961 | 3,51,49,1,13,203.000,3,27,0.000,0,4,3 962 | 3,5,34,0,12,22.000,2,2,0.000,0,2,1 963 | 2,49,54,1,23,25.000,2,1,0.000,1,4,1 964 | 3,49,51,1,14,222.000,5,16,0.000,0,2,2 965 | 1,26,30,1,4,76.000,3,7,0.000,0,2,1 966 | 3,22,39,0,4,55.000,2,17,0.000,1,1,4 967 | 3,51,45,1,4,46.000,3,6,0.000,0,2,4 968 | 1,19,27,1,7,56.000,4,1,0.000,0,4,4 969 | 1,9,36,0,1,79.000,3,11,0.000,1,5,3 970 | 3,15,25,1,0,51.000,3,2,0.000,1,2,1 971 | 2,57,63,0,19,61.000,1,27,0.000,0,1,3 972 | 3,48,45,0,15,41.000,1,10,0.000,1,2,1 973 | 2,9,53,1,20,50.000,2,13,0.000,1,2,1 974 | 1,61,52,0,21,82.000,1,18,0.000,0,1,3 975 | 3,72,53,0,34,108.000,1,23,0.000,0,1,3 976 | 3,57,60,0,20,14.000,2,27,1.000,1,1,3 977 | 2,57,51,0,10,46.000,1,13,0.000,0,1,2 978 | 1,37,60,1,0,46.000,1,20,0.000,0,3,3 979 | 3,45,51,0,13,146.000,4,16,0.000,0,2,1 980 | 3,45,39,1,15,36.000,1,11,0.000,1,5,1 981 | 2,55,44,0,24,83.000,1,23,0.000,1,1,3 982 | 2,24,27,0,2,17.000,3,3,0.000,0,5,2 983 | 3,1,20,0,1,18.000,3,0,0.000,0,1,1 984 | 2,34,23,1,3,24.000,1,7,0.000,1,4,3 985 | 3,14,37,1,5,48.000,3,1,0.000,1,4,1 986 | 3,6,32,0,10,47.000,1,10,0.000,0,1,3 987 | 3,24,30,0,0,25.000,4,5,0.000,0,2,4 988 | 2,3,26,1,6,59.000,4,0,0.000,1,3,4 989 | 1,4,30,0,1,45.000,4,6,0.000,0,3,4 990 | 1,72,40,1,19,163.000,4,15,0.000,0,2,2 991 | 1,44,56,0,4,63.000,2,6,0.000,1,1,3 992 | 1,50,43,0,6,27.000,3,4,0.000,0,1,3 993 | 2,61,50,1,16,190.000,2,22,0.000,1,2,2 994 | 1,34,52,1,2,106.000,2,19,0.000,0,2,3 995 | 3,26,54,1,30,26.000,3,1,0.000,1,2,4 996 | 1,15,46,1,17,63.000,5,1,0.000,0,2,4 997 | 3,10,39,0,0,27.000,3,0,0.000,1,3,1 998 | 1,7,34,0,2,22.000,5,5,0.000,1,1,1 999 | 3,67,59,0,40,944.000,5,33,0.000,1,1,4 1000 | 3,70,49,0,18,87.000,2,22,0.000,1,1,3 1001 | 3,50,36,1,7,39.000,3,3,0.000,1,3,2 1002 | -------------------------------------------------------------------------------- /RegressaoLinear/crashcourse_regressão_linear.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """CrashCourse - Regressão Linear.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1VWUpP4Ut3UGQJ6m8IsfKpHgqjXxih4BA 8 | 9 | # Crash Course IA - Aula 2 - Regressão Linear 10 | Este é o primeiro exercício prático do Crash Course de IA, 11 | uma iniciativa em adaptar a disciplina de IA do TIDD/PUC-SP para uma versão online 12 | e acessível para todos. 13 | 14 | Neste tutorial, vamos treinar um modelo de regressão linear para predição de valores contínuos. 15 | 16 | As aulas estão disponíveis no YouTube: https://youtu.be/Ze-Q6ZNWpco 17 | 18 | Qualquer dúvida, entre em contato: 19 | https://twitter.com/diogocortiz 20 | https://instagram.com/diogocortiz 21 | """ 22 | 23 | from matplotlib import pyplot as plt 24 | import pandas as pd 25 | import pylab as pl 26 | import numpy as np 27 | from sklearn import linear_model 28 | from sklearn.metrics import r2_score, mean_squared_error,mean_absolute_error 29 | from sklearn.model_selection import train_test_split 30 | from math import sqrt 31 | 32 | 33 | # Cria um dataset chamado 'df' que carregará os dados do csv 34 | df = pd.read_csv("FuelConsumptionCo2.csv") 35 | 36 | #EXIBE A ESTRUTURA DO DATAFRAME 37 | print(df.head()) 38 | 39 | """# Exibe o resumo do Dataset""" 40 | 41 | print(df.describe()) 42 | 43 | """# Selecionar apenas as features do Motor e CO2""" 44 | 45 | motores = df[['ENGINESIZE']] 46 | co2 = df[['CO2EMISSIONS']] 47 | print(motores.head()) 48 | 49 | """#Dividir o dataset em dados de treinamento e dados de teste 50 | neste casos vamos usar o train_test_split do scikitlearn 51 | """ 52 | 53 | motores_treino, motores_test, co2_treino, co2_teste = train_test_split(motores, co2, test_size=0.2, random_state=42) 54 | print(type(motores_treino)) 55 | 56 | """#Exibir a correlação entre as features do dataset de treinamento""" 57 | 58 | plt.scatter(motores_treino, co2_treino, color='blue') 59 | plt.xlabel("Motor") 60 | plt.ylabel("Emissão de CO2") 61 | plt.show() 62 | 63 | """# Vamos treinar o modelo de regressão linear""" 64 | 65 | # CRIAR UM MODELO DE TIPO DE REGRESSÃO LINEAR 66 | modelo = linear_model.LinearRegression() 67 | 68 | # TREINAR O MODELO USANDO O DATASET DE TESTE 69 | # PARA ENCONTRAR O VALOR DE A E B (Y = A + B.X) 70 | modelo.fit(motores_treino, co2_treino) 71 | 72 | """#Exibir os coeficientes (A e B)""" 73 | 74 | print('(A) Intercepto: ', modelo.intercept_) 75 | print('(B) Inclinação: ', modelo.coef_) 76 | 77 | """# Vamos exibir a nossa reta de regressão no dataset de treino""" 78 | 79 | plt.scatter(motores_treino, co2_treino, color='blue') 80 | plt.plot(motores_treino, modelo.coef_[0][0]*motores_treino + modelo.intercept_[0], '-r') 81 | plt.ylabel("Emissão de C02") 82 | plt.xlabel("Motores") 83 | plt.show() 84 | 85 | """# Vamos executar o nosso modelo no dataset de teste""" 86 | 87 | #Primeiro a gente tem que fazer as predições usando o modelo e base de teste 88 | predicoesCo2 = modelo.predict(motores_test) 89 | 90 | """# Vamos exibir a nossa reta de regressão no dataset de teste""" 91 | 92 | plt.scatter(motores_test, co2_teste, color='blue') 93 | plt.plot(motores_test, modelo.coef_[0][0]*motores_test + modelo.intercept_[0], '-r') 94 | plt.ylabel("Emissão de C02") 95 | plt.xlabel("Motores") 96 | plt.show() 97 | 98 | """# Vamos avaliar o modelo""" 99 | 100 | #Agora é mostrar as métricas 101 | print("Soma dos Erros ao Quadrado (SSE): %.2f " % np.sum((predicoesCo2 - co2_teste)**2)) 102 | print("Erro Quadrático Médio (MSE): %.2f" % mean_squared_error(co2_teste, predicoesCo2)) 103 | print("Erro Médio Absoluto (MAE): %.2f" % mean_absolute_error(co2_teste, predicoesCo2)) 104 | print ("Raiz do Erro Quadrático Médio (RMSE): %.2f " % sqrt(mean_squared_error(co2_teste, predicoesCo2))) 105 | print("R2-score: %.2f" % r2_score(co2_teste, predicoesCo2) ) 106 | -------------------------------------------------------------------------------- /RegressaoLogistica/Logistic_Regression_Pytorch.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """PyTorch - Regressão Logística.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/106atii-O68UdB_2d34m0nWA0jwdX_dQ5 8 | 9 | #REGRESSÃO LOGÍSTICA 10 | """ 11 | 12 | # RECEITA DE TREINAMENTO 13 | # 1 - DESIGN DO MODELO (INPUT, OUTPUT, FORWARD PASS) 14 | # 2 - DEFINIÇAO DA FUNÇÃO DE CUSTO E OTIMIZADOR 15 | # 3 - LOOP DE TREINAMENTO: 16 | # - FORWARD PASS: CALCULAR A PREDIÇÃO E O CUSTO 17 | # - BACKWARPASS: CALCULAR OS GRADIENTES 18 | # - ATUALIZAR OS PESOS 19 | 20 | import torch 21 | import torch.nn as nn 22 | import numpy as np 23 | import matplotlib.pyplot as plt 24 | 25 | # PREPARAÇÃO DA DATA 26 | x_numpy = np.array([5,7,2,9,4,10,9,4,6,1]) 27 | y_numpy = np.array([1,1,0,1,0,1,1,0,1,0]) 28 | 29 | 30 | x = torch.from_numpy(x_numpy.astype(np.float32)) 31 | y = torch.from_numpy(y_numpy.astype(np.float32)) 32 | y = y.view(y.shape[0], 1) 33 | x = x.view(x.shape[0], 1) 34 | 35 | print(x.shape) 36 | print(y.shape) 37 | 38 | plt.plot(x_numpy, y_numpy, 'ro') 39 | 40 | # CLASS DE REGRESSÃO LOGÍSTICA 41 | 42 | class RegressaoLogistica(nn.Module): 43 | def __init__(self, n_input, n_output): 44 | super(RegressaoLogistica, self).__init__() 45 | self.Linear = nn.Linear(n_input, 1) 46 | 47 | def forward(self, x): 48 | y_hat = torch.sigmoid(self.Linear(x)) 49 | return y_hat 50 | 51 | 52 | # DEFINICIÇÃO DE MODELO 53 | input_size = 1 54 | output_size = 1 55 | model = RegressaoLogistica(1,1) 56 | 57 | # DEFINIÇÃO DA FUNÇAO DE CUSTO E OTIMIZADOR 58 | learning_rate = 0.01 59 | criterion = nn.BCELoss() 60 | optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) 61 | print (model.parameters()) 62 | 63 | # LOOP DE TREINAMENTO 64 | num_epochs = 2000 65 | contador_custo = [] 66 | for epoch in range(num_epochs): 67 | #forward pass and loos 68 | y_hat = model(x) 69 | loss = criterion(y_hat, y) 70 | contador_custo.append(loss) 71 | #print(y_hat) 72 | 73 | 74 | #backward pass (calcular gradientes) 75 | loss.backward() 76 | 77 | #update (atualizar os pesos) 78 | optimizer.step() 79 | 80 | if (epoch+1)%10 == 0: 81 | print("===============================") 82 | print('Epoch: ', epoch) 83 | print('Custo: {:.20f}'.format(loss.item())) 84 | print('m: {:.5f}'.format(model.Linear.weight.data.detach().item())) 85 | print('m (gradiente): {:.5f}'.format(model.Linear.weight.grad.detach().item())) 86 | print('b: {:.5f}'.format(model.Linear.bias.data.detach().item())) 87 | print('b (gradiente): {:.5f}'.format(model.Linear.bias.grad.detach().item())) 88 | 89 | #limpar o otimizador 90 | optimizer.zero_grad() 91 | 92 | 93 | 94 | # PLOTANDO O GRÁFICO DA FUNÇÃO DE CUSTO 95 | print("GRÁFICO DA FUNÇÃO DE CUSTO") 96 | plt.plot(contador_custo, 'b') 97 | plt.show() 98 | 99 | """#Fazer a predição""" 100 | 101 | # fazer predição de teste 102 | teste = np.array([2, 3, 6, 7, 8]) 103 | t_teste = torch.from_numpy(teste.astype(np.float32)) 104 | t_teste = t_teste.view(t_teste.shape[0], 1) 105 | 106 | with torch.no_grad(): 107 | predicoes = model(t_teste) 108 | for x, y in zip(t_teste, predicoes): 109 | #definindo o cutoff / threshold 110 | status = "" 111 | if (y >= 0.7): 112 | status = "aprovado" 113 | else: 114 | status = "reprovado" 115 | print ('x: {:.2f} | ŷ: {:.2f} | '.format(x.detach().item(),y.detach().item()), status) -------------------------------------------------------------------------------- /RegressaoLogistica/readme.txt: -------------------------------------------------------------------------------- 1 | Implmentação de Regressão Logística usando o framework Pytorch 2 | -------------------------------------------------------------------------------- /RegressaoSoftmax/pytorch_regressão_softmax.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """PyTorch - Regressão Logística MULTICLASS.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1LcOaVibVBl6jO-m0qsNKlhsIhYrS6b9E 8 | 9 | #REGRESSÃO LOGÍSTICA MULTICLASS (SOFTMAX REGRESSION) 10 | """ 11 | 12 | # RECEITA DE TREINAMENTO 13 | # 1 - DESIGN DO MODELO (INPUT, OUTPUT, FORWARD PASS) 14 | # 2 - DEFINIÇAO DA FUNÇÃO DE CUSTO E OTIMIZADOR 15 | # 3 - LOOP DE TREINAMENTO: 16 | # - FORWARD PASS: CALCULAR A PREDIÇÃO E O CUSTO 17 | # - BACKWARPASS: CALCULAR OS GRADIENTES 18 | # - ATUALIZAR OS PESOS 19 | 20 | import torch 21 | import torch.nn as nn 22 | import numpy as np 23 | import matplotlib.pyplot as plt 24 | 25 | # PREPARAÇÃO DOS DADOS 26 | 27 | # Datapoints (NOTAS) 28 | x_numpy = np.array([2,4,3,5,7,2,9,4,10,9,4,6,1,5,6,9,8,3, 1, 5, 6, 5,2, 7, 8, 2,5,9, 10,2,1,3,8,5,6,5]) 29 | # 3 classes (0=reprovados, 1=recuperação, 2=aprovados) 30 | y_numpy = np.array([0,0,0,1,2,0,2,0,2,2,0,1,0,1,1,2,1,0,0,1,1,1,0,2,2,0,1,2,2,0,0,0,2,1,1,1]) 31 | 32 | x = torch.from_numpy(x_numpy.astype(np.float32)) 33 | y = torch.from_numpy(y_numpy.astype(np.float32)) 34 | y = y.long() 35 | x = x.view(x.shape[0], 1) 36 | 37 | print(x.shape) 38 | print(y.shape) 39 | print(y) 40 | 41 | # CLASS DE REGRESSÃO LOGÍSTICA 42 | 43 | class RegressaoSoftmax(nn.Module): 44 | def __init__(self, n_input, n_output): 45 | super(RegressaoSoftmax, self).__init__() 46 | self.Linear = nn.Linear(n_input, n_output) 47 | 48 | def forward(self, x): 49 | return self.Linear(x) 50 | 51 | 52 | # DEFINICIÇÃO DE MODELO 53 | input_size = 1 54 | output_size = 3 55 | model = RegressaoSoftmax(input_size, output_size) 56 | 57 | # DEFINIÇÃO DA FUNÇAO DE CUSTO E OTIMIZADOR 58 | learning_rate = 0.05 59 | criterion = nn.CrossEntropyLoss() 60 | optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) 61 | 62 | # LOOP DE TREINAMENTO 63 | num_epochs = 1000 64 | contador_custo = [] 65 | for epoch in range(num_epochs): 66 | #forward pass and loos 67 | y_hat = model(x) 68 | loss = criterion(y_hat, y) 69 | contador_custo.append(loss) 70 | #print(y_hat) 71 | 72 | #backward pass (calcular gradientes) 73 | loss.backward() 74 | 75 | #update (atualizar os pesos) 76 | optimizer.step() 77 | 78 | 79 | #limpar o otimizador 80 | optimizer.zero_grad() 81 | 82 | 83 | 84 | # PLOTANDO O GRÁFICO DA FUNÇÃO DE CUSTO 85 | print("GRÁFICO DA FUNÇÃO DE CUSTO") 86 | plt.plot(contador_custo, 'b') 87 | plt.show() 88 | 89 | """#Fazer a predição""" 90 | 91 | # fazer predição de teste 92 | teste = np.array([4, 9, 7, 2,6]) 93 | t_teste = torch.from_numpy(teste.astype(np.float32)) 94 | t_teste = t_teste.view(t_teste.shape[0], 1) 95 | 96 | with torch.no_grad(): 97 | predicoes = model(t_teste) 98 | print (np.argmax(predicoes, axis=1).flatten()) -------------------------------------------------------------------------------- /RegressaoSoftmax/readme.txt: -------------------------------------------------------------------------------- 1 | Implementação em Pytorch da Regressão Logística Multinomial, também chamada de Regressão Softmax e Regressão Logístic 2 | --------------------------------------------------------------------------------