├── 1.Webscraping_Early_EDA_and_Cleaning.ipynb ├── 2.Further_EDA_and_Preprocessing.ipynb ├── 3.Modelling_and_Hyperparameter_Tuning.ipynb ├── 4.Evaluation.ipynb ├── 5.Neural_Network_Modelling.ipynb ├── 6.Revaluation_and_Deployment.ipynb ├── Classification.py ├── Data ├── .ipynb_checkpoints │ ├── 1.tripadvisor_scraped_hotel_reviews-checkpoint.csv │ ├── 2.hotel_reviews_structured-checkpoint.csv │ └── 4.all_models-checkpoint.csv ├── 1.tripadvisor_scraped_hotel_reviews.csv ├── 2.hotel_reviews_structured.csv ├── 3.x_train_data.csv ├── 3.y_train_data.csv ├── 4.all_models.csv ├── 4.x_test_data.csv ├── 4.y_test_data.csv ├── 6.x_test.npy ├── 6.y_test_nn_df.csv ├── 6.y_test_ohe.csv ├── 6.y_test_predicted_array.npy ├── Neural_Network.h5 └── word_index_dict.pkl ├── Ensemble.py ├── Helpers_NN.py ├── Hilton_Hotel_App.py ├── Hilton_Hotel_Presentation.pdf ├── Images ├── .ipynb_checkpoints │ ├── all_models-checkpoint.png │ ├── five_stars-checkpoint.png │ ├── freq_dist_review-checkpoint.png │ ├── freq_dist_review_sum-checkpoint.png │ ├── nn_conf_matrix-checkpoint.png │ ├── test_conf_matrix-checkpoint.png │ ├── validation_conf_matrix-checkpoint.png │ ├── word_cloud_review-checkpoint.png │ └── word_cloud_review_sum-checkpoint.png ├── Adjacent_Score_Image.png ├── NN_architecture.png ├── Tripadvisor_Review_Example.png ├── all_models.png ├── five_stars.png ├── freq_dist_review.png ├── freq_dist_review_sum.png ├── histogram_of_scores_for_all_hotels.png ├── histogram_of_scores_for_all_hotels_after_balancing.png ├── histogram_of_scores_for_each_hotel.png ├── lemm_stemm_ex.png ├── nn_conf_matrix.png ├── test_conf_matrix.png ├── test_results.png ├── twitter.jpg ├── validation_conf_matrix.png ├── word_cloud_review.png └── word_cloud_review_sum.png ├── Models ├── AdaBoost.pkl ├── Decision Tree.pkl ├── KNN.pkl ├── Logistic Regression.pkl ├── Naive Bayes.pkl ├── Neural_Network.h5 ├── Neural_Network.pkl ├── Random Forest.pkl ├── SVM.pkl ├── Stacking.pkl ├── Voting.pkl └── XGBoost.pkl ├── Procfile ├── README.md ├── Tripadvisor_Webscrape ├── .ipynb_checkpoints │ └── scrapy-checkpoint.cfg ├── Tripadvisor │ ├── .ipynb_checkpoints │ │ ├── __init__-checkpoint.py │ │ ├── items-checkpoint.py │ │ ├── middlewares-checkpoint.py │ │ ├── pipelines-checkpoint.py │ │ └── settings-checkpoint.py │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── settings.cpython-37.pyc │ ├── items.py │ ├── middlewares.py │ ├── pipelines.py │ ├── settings.py │ └── spiders │ │ ├── .ipynb_checkpoints │ │ ├── hotels-checkpoint.py │ │ └── tripadvisor_scraped_hotel_reviews-checkpoint.csv │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── hotels.cpython-37.pyc │ │ ├── hotels.py │ │ └── tripadvisor_scraped_hotel_reviews.csv └── scrapy.cfg └── requirements.txt /Classification.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import seaborn as sns 5 | from sklearn.svm import SVC 6 | from sklearn.metrics import confusion_matrix, classification_report 7 | from sklearn.ensemble import RandomForestClassifier 8 | from sklearn.model_selection import GridSearchCV, StratifiedKFold 9 | from sklearn.tree import DecisionTreeClassifier 10 | from sklearn.linear_model import LogisticRegression 11 | from sklearn.neighbors import KNeighborsClassifier 12 | from sklearn.naive_bayes import GaussianNB 13 | from yellowbrick.model_selection import feature_importances 14 | 15 | #===============================================================================================# 16 | 17 | # Classification Models Class 18 | 19 | #===============================================================================================# 20 | 21 | class Classification(): 22 | 23 | """ 24 | This class is for performing classifcation algorithms such as Logistic Regression, Decision Tree, Random Forest, and SVM. 25 | 26 | Parameters 27 | ---------- 28 | model_type: 'Logistic Regression', 'Decision Tree', 'Random Forest', 'SVM' 29 | the type of classifcation algorithm you would like to apply 30 | 31 | x_train: dataframe 32 | the independant variables of the training data 33 | 34 | x_val: dataframe 35 | the independant variables of the validation data 36 | 37 | y_train: series 38 | the target variable of the training data 39 | 40 | y_val: series 41 | the target variable of the validation data 42 | 43 | """ 44 | 45 | def __init__(self,model_type,x_train,x_val,y_train,y_val): 46 | 47 | self.model_type = model_type 48 | self.x_train = x_train 49 | self.y_train = y_train 50 | self.x_val = x_val 51 | self.y_val = y_val 52 | self.scores_table = pd.DataFrame() 53 | self.feature_importances = pd.DataFrame() 54 | self.name = self 55 | 56 | if self.model_type == 'Logistic Regression': 57 | self.technique = LogisticRegression(fit_intercept=False) 58 | elif self.model_type == 'Decision Tree': 59 | self.technique = DecisionTreeClassifier(random_state=42) 60 | elif self.model_type == 'Random Forest': 61 | self.technique = RandomForestClassifier(n_estimators=20,n_jobs=-1,random_state=42) 62 | elif self.model_type == 'SVM': 63 | self.technique = SVC() 64 | elif self.model_type == 'Naive Bayes': 65 | self.technique = GaussianNB() 66 | elif self.model_type == 'KNN': 67 | self.technique = KNeighborsClassifier(n_jobs=-1) 68 | 69 | #===============================================================================================# 70 | 71 | # Score Function 72 | 73 | #===============================================================================================# 74 | 75 | def scores(self,model,x_train,x_val,y_train,y_val): 76 | 77 | """ 78 | Gets the accuracy for the given data and creates a dataframe containing scores. 79 | 80 | Parameters 81 | ---------- 82 | model: 'Logistic Regression', 'Decision Tree', 'Random Forest', 'SVM' 83 | the type of classifcation applied 84 | 85 | x_train: dataframe 86 | the independant variables of the training data 87 | 88 | x_val: dataframe 89 | the independant variables of the validation data 90 | 91 | y_train: series 92 | the target variable of the training data 93 | 94 | y_val: series 95 | the target variable of the validation data 96 | 97 | Returns 98 | ---------- 99 | scores_table: a dataframe with the model used, the train accuracy and validation accuracy 100 | 101 | """ 102 | 103 | self.acc_train = self.best_model.score(x_train,y_train) 104 | self.acc_val = self.best_model.score(x_val,y_val) 105 | 106 | d = {'Model Name': [self.model_type], 107 | 'Train Accuracy': [self.acc_train], 108 | 'Validation Accuracy': [self.acc_val], 109 | 'Accuracy Difference':[self.acc_train-self.acc_val]} 110 | self.scores_table = pd.DataFrame(data=d) 111 | 112 | return self.scores_table 113 | 114 | 115 | #===============================================================================================# 116 | 117 | # Get Scores Function 118 | 119 | #===============================================================================================# 120 | 121 | def get_scores(self,params,cv_type): 122 | 123 | """ 124 | Performs a gridsearch cross validation with given hyperparameters and data. 125 | Gets the accuracy for the given data and creates a dataframe containing scores. 126 | 127 | Parameters 128 | ---------- 129 | param_grid: dictionary 130 | specified hyperparameters for chosen classification algorithm to be passed through gridsearch cross validation 131 | 132 | cv_type: 'skf' 133 | the type of cross validation split to be used for gridsearch 134 | 135 | """ 136 | 137 | classifier = self.technique 138 | fit_classifier = classifier.fit(self.x_train,self.y_train) 139 | opt_model = GridSearchCV(fit_classifier, 140 | params, 141 | cv=cv_type, 142 | scoring='accuracy', 143 | return_train_score=True, 144 | n_jobs=-1) 145 | self.opt_model = opt_model.fit(self.x_train,self.y_train) 146 | self.best_model = opt_model.best_estimator_ 147 | self.scores = Classification.scores(self,self.best_model,self.x_train,self.x_val,self.y_train,self.y_val) 148 | self.best_params = opt_model.best_params_ 149 | display(self.scores_table) 150 | if params == {}: 151 | pass 152 | else: 153 | print("The best hyperparameters are: ", self.best_params,'\n') 154 | self.y_validated = self.best_model.predict(self.x_val) 155 | self.classification_report = pd.DataFrame.from_dict(classification_report(self.y_val,self.y_validated,output_dict=True)).iloc[0:3,0:5] 156 | return self.classification_report 157 | 158 | #===============================================================================================# 159 | 160 | # Feature Importance Function 161 | 162 | #===============================================================================================# 163 | 164 | def get_feature_importances(self): 165 | 166 | """ 167 | Create a confusion matrix. 168 | 169 | 170 | Returns 171 | ---------- 172 | feature_importances_bar : a bar chart with feature importance of given model 173 | 174 | """ 175 | if (self.model_type == 'Decision Tree') or (self.model_type == 'Random Forest') or (self.model_type == 'SVM'): 176 | self.feature_importances_table = pd.DataFrame(self.best_model.feature_importances_, 177 | index = self.x_train.columns, 178 | columns=['Importance']).sort_values('Importance',ascending =False) 179 | plt.figure(figsize=(9,7.5)) 180 | self.feature_importances_bar = sns.barplot(y= self.feature_importances_table.index[:15], x= self.feature_importances_table['Importance'][:15]) 181 | plt.show() 182 | return self.feature_importances_bar 183 | 184 | else: 185 | return print('This classification method does not have the attribute feature importance.') 186 | 187 | #===============================================================================================# 188 | 189 | # Confusion Matrix Function 190 | 191 | #===============================================================================================# 192 | 193 | def conf_matrix(self): 194 | 195 | """ 196 | Create a confusion matrix. 197 | 198 | 199 | Returns 200 | ---------- 201 | scores_table: a confusion matrix 202 | 203 | """ 204 | 205 | plt.figure(figsize=(9,9)) 206 | ax = sns.heatmap(confusion_matrix(self.y_val, self.y_validated), 207 | annot= True, 208 | fmt = '.4g', 209 | cbar=0, 210 | xticklabels=[1,2,3,4,5], 211 | yticklabels=[1,2,3,4,5]) 212 | ax.set(xlabel='Predicted', ylabel='True') 213 | plt.show() 214 | 215 | 216 | 217 | #===============================================================================================# 218 | 219 | # Test Score Function 220 | 221 | #===============================================================================================# 222 | 223 | def get_test_scores(self,X_test,y_test): 224 | 225 | """ 226 | Gets a ROC AUC score for given data and creates a dataframe containing scores. 227 | Creates a ROC plot. 228 | 229 | 230 | Parameters 231 | ---------- 232 | x_test: dataframe 233 | independant variables of the test data 234 | 235 | y_test: dataframe 236 | target variable of the test data 237 | 238 | """ 239 | 240 | self.y_test = y_test 241 | self.x_test = X_test 242 | self.scores_table = pd.DataFrame() 243 | self.test_scores = Classification.scores(self,self.best_model,self.x_train,self.x_test,self.y_train,self.y_test) 244 | display(self.scores_table) 245 | self.y_tested = self.best_model.predict(self.x_test) 246 | self.test_classification_report = pd.DataFrame.from_dict(classification_report(self.y_test,self.y_tested,output_dict=True)).iloc[0:3,0:5] 247 | 248 | return self.test_classification_report 249 | 250 | #===============================================================================================# 251 | 252 | # Show Test Confusion Matrix Function 253 | 254 | #===============================================================================================# 255 | 256 | def test_conf_matrix(self): 257 | 258 | """ 259 | Create a confusion matrix for the test data. 260 | 261 | 262 | Returns 263 | ---------- 264 | scores_table: a confusion matrix 265 | 266 | """ 267 | plt.figure(figsize=(9,9)) 268 | ax = sns.heatmap(confusion_matrix(self.y_test, self.y_tested), 269 | annot= True, 270 | fmt = '.4g', 271 | cbar=0, 272 | xticklabels=[1,2,3,4,5], 273 | yticklabels=[1,2,3,4,5]) 274 | ax.set(xlabel='Predicted', ylabel='True') 275 | plt.show() 276 | 277 | -------------------------------------------------------------------------------- /Data/.ipynb_checkpoints/4.all_models-checkpoint.csv: -------------------------------------------------------------------------------- 1 | Model Name,Train Accuracy,Validation Accuracy,Accuracy Difference 2 | Decision Tree,0.4329257487152224,0.35353535353535354,0.07939039517986884 3 | Random Forest,0.6473506999822789,0.45773524720893144,0.18961545277334746 4 | Logistic Regression,0.5621123515860358,0.5093035619351409,0.05280878965089486 5 | SVM,0.5599858231437179,0.5114300903774588,0.04855573276625913 6 | Naive Bayes,0.3721424774056353,0.3870281765018607,-0.014885699096225447 7 | KNN,0.5069998227892965,0.5013290802764487,0.005670742512847715 8 | AdaBoost,0.5137338295233032,0.5039872408293461,0.009746588693957059 9 | XGBoost,0.8536239588871167,0.48484848484848486,0.36877547403863187 10 | Voting,0.5578592947013999,0.5156831472620946,0.04217614743930531 11 | Stacking,0.5546695020379231,0.519404572036151,0.03526493000177211 12 | -------------------------------------------------------------------------------- /Data/4.all_models.csv: -------------------------------------------------------------------------------- 1 | Model Name,Train Accuracy,Validation Accuracy,Accuracy Difference 2 | Decision Tree,0.4329257487152224,0.35353535353535354,0.07939039517986884 3 | Random Forest,0.6473506999822789,0.45773524720893144,0.18961545277334746 4 | Logistic Regression,0.5621123515860358,0.5093035619351409,0.05280878965089486 5 | SVM,0.5599858231437179,0.5114300903774588,0.04855573276625913 6 | Naive Bayes,0.4986709197235513,0.48484848484848486,0.013822434875066458 7 | KNN,0.5069998227892965,0.5013290802764487,0.005670742512847715 8 | AdaBoost,0.5137338295233032,0.5039872408293461,0.009746588693957059 9 | XGBoost,0.8536239588871167,0.48484848484848486,0.36877547403863187 10 | Voting,0.5578592947013999,0.5156831472620946,0.04217614743930531 11 | Stacking,0.5546695020379231,0.519404572036151,0.03526493000177211 12 | -------------------------------------------------------------------------------- /Data/4.y_test_data.csv: -------------------------------------------------------------------------------- 1 | score 2 | 3 3 | 1 4 | 3 5 | 2 6 | 2 7 | 4 8 | 2 9 | 5 10 | 4 11 | 5 12 | 1 13 | 1 14 | 4 15 | 5 16 | 2 17 | 4 18 | 4 19 | 1 20 | 1 21 | 5 22 | 3 23 | 5 24 | 3 25 | 5 26 | 3 27 | 3 28 | 3 29 | 1 30 | 4 31 | 3 32 | 4 33 | 2 34 | 5 35 | 5 36 | 4 37 | 2 38 | 1 39 | 5 40 | 2 41 | 3 42 | 4 43 | 4 44 | 1 45 | 3 46 | 2 47 | 2 48 | 1 49 | 1 50 | 3 51 | 5 52 | 3 53 | 5 54 | 1 55 | 3 56 | 2 57 | 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0.0,1.0,0.0,0.0,0.0,2 323 | 0.0,0.0,1.0,0.0,0.0,3 324 | 0.0,1.0,0.0,0.0,0.0,2 325 | 1.0,0.0,0.0,0.0,0.0,1 326 | 1.0,0.0,0.0,0.0,0.0,1 327 | 1.0,0.0,0.0,0.0,0.0,1 328 | 0.0,0.0,1.0,0.0,0.0,3 329 | 0.0,1.0,0.0,0.0,0.0,2 330 | 0.0,0.0,0.0,0.0,1.0,5 331 | 0.0,0.0,1.0,0.0,0.0,3 332 | 0.0,1.0,0.0,0.0,0.0,2 333 | 1.0,0.0,0.0,0.0,0.0,1 334 | 0.0,1.0,0.0,0.0,0.0,2 335 | 0.0,1.0,0.0,0.0,0.0,2 336 | 0.0,0.0,0.0,0.0,1.0,5 337 | 0.0,1.0,0.0,0.0,0.0,2 338 | 1.0,0.0,0.0,0.0,0.0,1 339 | 0.0,0.0,0.0,0.0,1.0,5 340 | 0.0,0.0,1.0,0.0,0.0,3 341 | 0.0,0.0,0.0,1.0,0.0,4 342 | 1.0,0.0,0.0,0.0,0.0,1 343 | 0.0,1.0,0.0,0.0,0.0,2 344 | 0.0,0.0,1.0,0.0,0.0,3 345 | 0.0,0.0,0.0,1.0,0.0,4 346 | 1.0,0.0,0.0,0.0,0.0,1 347 | 1.0,0.0,0.0,0.0,0.0,1 348 | 0.0,0.0,0.0,0.0,1.0,5 349 | 1.0,0.0,0.0,0.0,0.0,1 350 | 0.0,0.0,0.0,1.0,0.0,4 351 | 0.0,0.0,1.0,0.0,0.0,3 352 | 0.0,1.0,0.0,0.0,0.0,2 353 | 0.0,0.0,1.0,0.0,0.0,3 354 | 0.0,0.0,0.0,1.0,0.0,4 355 | 0.0,0.0,0.0,0.0,1.0,5 356 | 0.0,0.0,1.0,0.0,0.0,3 357 | 0.0,0.0,1.0,0.0,0.0,3 358 | 0.0,0.0,0.0,0.0,1.0,5 359 | 0.0,0.0,0.0,0.0,1.0,5 360 | 0.0,0.0,0.0,1.0,0.0,4 361 | 0.0,1.0,0.0,0.0,0.0,2 362 | 0.0,0.0,0.0,1.0,0.0,4 363 | 0.0,0.0,0.0,0.0,1.0,5 364 | 0.0,1.0,0.0,0.0,0.0,2 365 | 0.0,0.0,0.0,1.0,0.0,4 366 | 0.0,1.0,0.0,0.0,0.0,2 367 | 0.0,0.0,0.0,0.0,1.0,5 368 | 0.0,1.0,0.0,0.0,0.0,2 369 | 0.0,1.0,0.0,0.0,0.0,2 370 | 0.0,0.0,0.0,1.0,0.0,4 371 | 0.0,1.0,0.0,0.0,0.0,2 372 | 0.0,0.0,0.0,0.0,1.0,5 373 | 0.0,0.0,1.0,0.0,0.0,3 374 | 0.0,0.0,1.0,0.0,0.0,3 375 | 0.0,0.0,0.0,1.0,0.0,4 376 | 0.0,0.0,0.0,1.0,0.0,4 377 | 0.0,1.0,0.0,0.0,0.0,2 378 | 0.0,0.0,0.0,0.0,1.0,5 379 | 1.0,0.0,0.0,0.0,0.0,1 380 | 0.0,0.0,1.0,0.0,0.0,3 381 | 0.0,0.0,0.0,0.0,1.0,5 382 | 0.0,1.0,0.0,0.0,0.0,2 383 | 1.0,0.0,0.0,0.0,0.0,1 384 | 0.0,0.0,1.0,0.0,0.0,3 385 | 1.0,0.0,0.0,0.0,0.0,1 386 | 0.0,0.0,0.0,0.0,1.0,5 387 | 0.0,1.0,0.0,0.0,0.0,2 388 | 0.0,0.0,0.0,1.0,0.0,4 389 | 0.0,0.0,0.0,1.0,0.0,4 390 | 0.0,0.0,1.0,0.0,0.0,3 391 | 0.0,0.0,1.0,0.0,0.0,3 392 | 0.0,0.0,0.0,1.0,0.0,4 393 | 0.0,0.0,0.0,1.0,0.0,4 394 | 0.0,0.0,0.0,0.0,1.0,5 395 | 0.0,0.0,1.0,0.0,0.0,3 396 | 0.0,0.0,0.0,0.0,1.0,5 397 | 1.0,0.0,0.0,0.0,0.0,1 398 | 1.0,0.0,0.0,0.0,0.0,1 399 | 0.0,0.0,0.0,0.0,1.0,5 400 | 1.0,0.0,0.0,0.0,0.0,1 401 | 0.0,1.0,0.0,0.0,0.0,2 402 | 0.0,1.0,0.0,0.0,0.0,2 403 | 1.0,0.0,0.0,0.0,0.0,1 404 | 0.0,1.0,0.0,0.0,0.0,2 405 | 0.0,0.0,0.0,1.0,0.0,4 406 | 1.0,0.0,0.0,0.0,0.0,1 407 | 1.0,0.0,0.0,0.0,0.0,1 408 | 0.0,0.0,0.0,0.0,1.0,5 409 | 0.0,1.0,0.0,0.0,0.0,2 410 | 0.0,0.0,1.0,0.0,0.0,3 411 | 1.0,0.0,0.0,0.0,0.0,1 412 | 1.0,0.0,0.0,0.0,0.0,1 413 | 1.0,0.0,0.0,0.0,0.0,1 414 | 0.0,0.0,0.0,1.0,0.0,4 415 | 1.0,0.0,0.0,0.0,0.0,1 416 | 0.0,0.0,0.0,1.0,0.0,4 417 | 0.0,0.0,0.0,0.0,1.0,5 418 | 0.0,0.0,1.0,0.0,0.0,3 419 | 0.0,0.0,0.0,1.0,0.0,4 420 | 0.0,0.0,0.0,0.0,1.0,5 421 | 1.0,0.0,0.0,0.0,0.0,1 422 | 0.0,0.0,1.0,0.0,0.0,3 423 | 0.0,0.0,1.0,0.0,0.0,3 424 | 1.0,0.0,0.0,0.0,0.0,1 425 | 0.0,0.0,1.0,0.0,0.0,3 426 | 1.0,0.0,0.0,0.0,0.0,1 427 | 1.0,0.0,0.0,0.0,0.0,1 428 | 1.0,0.0,0.0,0.0,0.0,1 429 | 0.0,0.0,0.0,1.0,0.0,4 430 | 0.0,0.0,0.0,0.0,1.0,5 431 | 1.0,0.0,0.0,0.0,0.0,1 432 | 0.0,0.0,1.0,0.0,0.0,3 433 | 0.0,0.0,0.0,0.0,1.0,5 434 | 0.0,1.0,0.0,0.0,0.0,2 435 | 0.0,0.0,0.0,1.0,0.0,4 436 | 1.0,0.0,0.0,0.0,0.0,1 437 | 0.0,1.0,0.0,0.0,0.0,2 438 | 0.0,0.0,1.0,0.0,0.0,3 439 | 0.0,1.0,0.0,0.0,0.0,2 440 | 0.0,0.0,0.0,0.0,1.0,5 441 | 0.0,1.0,0.0,0.0,0.0,2 442 | 0.0,0.0,0.0,1.0,0.0,4 443 | 1.0,0.0,0.0,0.0,0.0,1 444 | 1.0,0.0,0.0,0.0,0.0,1 445 | 0.0,1.0,0.0,0.0,0.0,2 446 | 1.0,0.0,0.0,0.0,0.0,1 447 | 0.0,1.0,0.0,0.0,0.0,2 448 | 0.0,0.0,1.0,0.0,0.0,3 449 | 0.0,0.0,0.0,1.0,0.0,4 450 | 0.0,0.0,0.0,1.0,0.0,4 451 | 1.0,0.0,0.0,0.0,0.0,1 452 | 0.0,1.0,0.0,0.0,0.0,2 453 | 1.0,0.0,0.0,0.0,0.0,1 454 | 1.0,0.0,0.0,0.0,0.0,1 455 | 0.0,0.0,1.0,0.0,0.0,3 456 | 0.0,1.0,0.0,0.0,0.0,2 457 | 0.0,0.0,1.0,0.0,0.0,3 458 | 0.0,0.0,0.0,1.0,0.0,4 459 | 0.0,0.0,0.0,1.0,0.0,4 460 | 0.0,0.0,0.0,0.0,1.0,5 461 | 0.0,0.0,0.0,0.0,1.0,5 462 | 1.0,0.0,0.0,0.0,0.0,1 463 | 1.0,0.0,0.0,0.0,0.0,1 464 | 0.0,0.0,0.0,0.0,1.0,5 465 | 0.0,0.0,0.0,0.0,1.0,5 466 | 0.0,0.0,1.0,0.0,0.0,3 467 | 0.0,1.0,0.0,0.0,0.0,2 468 | 0.0,0.0,0.0,0.0,1.0,5 469 | 1.0,0.0,0.0,0.0,0.0,1 470 | 1.0,0.0,0.0,0.0,0.0,1 471 | 0.0,0.0,0.0,0.0,1.0,5 472 | 0.0,0.0,0.0,1.0,0.0,4 473 | 0.0,1.0,0.0,0.0,0.0,2 474 | 0.0,0.0,1.0,0.0,0.0,3 475 | 0.0,0.0,1.0,0.0,0.0,3 476 | 0.0,0.0,0.0,0.0,1.0,5 477 | 0.0,0.0,0.0,0.0,1.0,5 478 | 0.0,0.0,1.0,0.0,0.0,3 479 | 0.0,0.0,1.0,0.0,0.0,3 480 | 1.0,0.0,0.0,0.0,0.0,1 481 | 0.0,0.0,0.0,0.0,1.0,5 482 | 0.0,0.0,0.0,0.0,1.0,5 483 | 0.0,0.0,0.0,0.0,1.0,5 484 | 1.0,0.0,0.0,0.0,0.0,1 485 | 1.0,0.0,0.0,0.0,0.0,1 486 | 0.0,0.0,0.0,0.0,1.0,5 487 | 1.0,0.0,0.0,0.0,0.0,1 488 | 0.0,0.0,0.0,1.0,0.0,4 489 | 0.0,0.0,0.0,1.0,0.0,4 490 | 0.0,0.0,0.0,0.0,1.0,5 491 | 0.0,0.0,0.0,1.0,0.0,4 492 | 0.0,0.0,0.0,0.0,1.0,5 493 | 0.0,1.0,0.0,0.0,0.0,2 494 | 0.0,0.0,0.0,1.0,0.0,4 495 | 0.0,0.0,0.0,1.0,0.0,4 496 | 0.0,0.0,1.0,0.0,0.0,3 497 | 0.0,1.0,0.0,0.0,0.0,2 498 | 0.0,0.0,1.0,0.0,0.0,3 499 | 0.0,0.0,0.0,1.0,0.0,4 500 | 1.0,0.0,0.0,0.0,0.0,1 501 | 0.0,0.0,0.0,0.0,1.0,5 502 | 0.0,0.0,0.0,0.0,1.0,5 503 | 0.0,0.0,0.0,0.0,1.0,5 504 | 1.0,0.0,0.0,0.0,0.0,1 505 | 0.0,1.0,0.0,0.0,0.0,2 506 | 1.0,0.0,0.0,0.0,0.0,1 507 | 0.0,1.0,0.0,0.0,0.0,2 508 | 0.0,0.0,1.0,0.0,0.0,3 509 | 1.0,0.0,0.0,0.0,0.0,1 510 | 0.0,1.0,0.0,0.0,0.0,2 511 | 1.0,0.0,0.0,0.0,0.0,1 512 | 0.0,0.0,1.0,0.0,0.0,3 513 | 1.0,0.0,0.0,0.0,0.0,1 514 | 0.0,0.0,0.0,0.0,1.0,5 515 | 0.0,0.0,0.0,1.0,0.0,4 516 | 1.0,0.0,0.0,0.0,0.0,1 517 | 0.0,1.0,0.0,0.0,0.0,2 518 | 1.0,0.0,0.0,0.0,0.0,1 519 | 0.0,0.0,0.0,0.0,1.0,5 520 | 0.0,0.0,0.0,0.0,1.0,5 521 | 0.0,0.0,0.0,1.0,0.0,4 522 | 0.0,0.0,1.0,0.0,0.0,3 523 | 0.0,0.0,1.0,0.0,0.0,3 524 | 0.0,0.0,1.0,0.0,0.0,3 525 | 0.0,1.0,0.0,0.0,0.0,2 526 | 0.0,0.0,0.0,0.0,1.0,5 527 | 1.0,0.0,0.0,0.0,0.0,1 528 | 0.0,0.0,0.0,1.0,0.0,4 529 | 0.0,0.0,0.0,0.0,1.0,5 530 | 1.0,0.0,0.0,0.0,0.0,1 531 | 0.0,0.0,0.0,1.0,0.0,4 532 | 0.0,0.0,0.0,0.0,1.0,5 533 | 0.0,1.0,0.0,0.0,0.0,2 534 | 0.0,0.0,0.0,0.0,1.0,5 535 | 0.0,1.0,0.0,0.0,0.0,2 536 | 0.0,0.0,0.0,1.0,0.0,4 537 | 0.0,0.0,0.0,1.0,0.0,4 538 | 0.0,0.0,0.0,1.0,0.0,4 539 | 0.0,1.0,0.0,0.0,0.0,2 540 | 1.0,0.0,0.0,0.0,0.0,1 541 | 0.0,0.0,1.0,0.0,0.0,3 542 | 0.0,0.0,0.0,0.0,1.0,5 543 | 0.0,0.0,0.0,1.0,0.0,4 544 | 0.0,0.0,0.0,1.0,0.0,4 545 | 0.0,0.0,1.0,0.0,0.0,3 546 | 0.0,0.0,1.0,0.0,0.0,3 547 | 0.0,0.0,1.0,0.0,0.0,3 548 | 0.0,1.0,0.0,0.0,0.0,2 549 | 0.0,0.0,0.0,0.0,1.0,5 550 | 1.0,0.0,0.0,0.0,0.0,1 551 | 0.0,1.0,0.0,0.0,0.0,2 552 | 0.0,0.0,0.0,0.0,1.0,5 553 | 1.0,0.0,0.0,0.0,0.0,1 554 | 0.0,0.0,0.0,0.0,1.0,5 555 | 0.0,0.0,0.0,1.0,0.0,4 556 | 0.0,0.0,1.0,0.0,0.0,3 557 | 0.0,1.0,0.0,0.0,0.0,2 558 | 0.0,0.0,0.0,1.0,0.0,4 559 | 0.0,0.0,1.0,0.0,0.0,3 560 | 0.0,0.0,0.0,1.0,0.0,4 561 | 0.0,0.0,0.0,1.0,0.0,4 562 | 0.0,0.0,1.0,0.0,0.0,3 563 | 0.0,0.0,1.0,0.0,0.0,3 564 | 0.0,0.0,0.0,1.0,0.0,4 565 | 0.0,0.0,1.0,0.0,0.0,3 566 | 0.0,0.0,0.0,1.0,0.0,4 567 | 0.0,1.0,0.0,0.0,0.0,2 568 | 0.0,0.0,1.0,0.0,0.0,3 569 | 0.0,0.0,0.0,0.0,1.0,5 570 | 0.0,0.0,1.0,0.0,0.0,3 571 | 0.0,0.0,0.0,0.0,1.0,5 572 | 0.0,0.0,0.0,0.0,1.0,5 573 | 0.0,1.0,0.0,0.0,0.0,2 574 | 1.0,0.0,0.0,0.0,0.0,1 575 | 0.0,0.0,1.0,0.0,0.0,3 576 | 0.0,1.0,0.0,0.0,0.0,2 577 | 0.0,0.0,0.0,0.0,1.0,5 578 | 0.0,0.0,1.0,0.0,0.0,3 579 | 0.0,0.0,0.0,0.0,1.0,5 580 | 0.0,0.0,0.0,1.0,0.0,4 581 | 0.0,0.0,0.0,0.0,1.0,5 582 | 0.0,0.0,0.0,0.0,1.0,5 583 | 0.0,0.0,0.0,1.0,0.0,4 584 | 1.0,0.0,0.0,0.0,0.0,1 585 | 0.0,1.0,0.0,0.0,0.0,2 586 | 0.0,0.0,0.0,0.0,1.0,5 587 | 0.0,0.0,1.0,0.0,0.0,3 588 | 1.0,0.0,0.0,0.0,0.0,1 589 | 0.0,0.0,0.0,0.0,1.0,5 590 | 1.0,0.0,0.0,0.0,0.0,1 591 | 0.0,1.0,0.0,0.0,0.0,2 592 | 0.0,0.0,1.0,0.0,0.0,3 593 | 1.0,0.0,0.0,0.0,0.0,1 594 | 0.0,0.0,0.0,0.0,1.0,5 595 | 0.0,0.0,0.0,0.0,1.0,5 596 | 0.0,0.0,0.0,0.0,1.0,5 597 | 0.0,0.0,1.0,0.0,0.0,3 598 | 0.0,0.0,1.0,0.0,0.0,3 599 | 0.0,1.0,0.0,0.0,0.0,2 600 | 0.0,0.0,1.0,0.0,0.0,3 601 | 0.0,1.0,0.0,0.0,0.0,2 602 | 1.0,0.0,0.0,0.0,0.0,1 603 | 0.0,0.0,0.0,1.0,0.0,4 604 | 0.0,0.0,0.0,1.0,0.0,4 605 | 0.0,0.0,1.0,0.0,0.0,3 606 | 0.0,1.0,0.0,0.0,0.0,2 607 | 0.0,0.0,0.0,1.0,0.0,4 608 | 0.0,1.0,0.0,0.0,0.0,2 609 | 0.0,0.0,0.0,0.0,1.0,5 610 | 0.0,0.0,0.0,1.0,0.0,4 611 | 0.0,0.0,0.0,1.0,0.0,4 612 | 0.0,0.0,0.0,0.0,1.0,5 613 | 0.0,0.0,0.0,0.0,1.0,5 614 | 0.0,0.0,0.0,0.0,1.0,5 615 | 0.0,0.0,1.0,0.0,0.0,3 616 | 0.0,0.0,0.0,1.0,0.0,4 617 | 0.0,0.0,0.0,1.0,0.0,4 618 | 0.0,0.0,0.0,0.0,1.0,5 619 | 0.0,1.0,0.0,0.0,0.0,2 620 | 0.0,0.0,0.0,1.0,0.0,4 621 | 0.0,0.0,0.0,0.0,1.0,5 622 | 0.0,1.0,0.0,0.0,0.0,2 623 | 1.0,0.0,0.0,0.0,0.0,1 624 | 1.0,0.0,0.0,0.0,0.0,1 625 | 1.0,0.0,0.0,0.0,0.0,1 626 | 0.0,0.0,0.0,0.0,1.0,5 627 | 0.0,0.0,1.0,0.0,0.0,3 628 | 0.0,0.0,0.0,1.0,0.0,4 629 | 0.0,0.0,0.0,0.0,1.0,5 630 | 0.0,0.0,1.0,0.0,0.0,3 631 | 0.0,0.0,1.0,0.0,0.0,3 632 | 0.0,1.0,0.0,0.0,0.0,2 633 | 0.0,1.0,0.0,0.0,0.0,2 634 | 0.0,1.0,0.0,0.0,0.0,2 635 | 0.0,1.0,0.0,0.0,0.0,2 636 | 0.0,1.0,0.0,0.0,0.0,2 637 | 0.0,0.0,0.0,1.0,0.0,4 638 | 0.0,1.0,0.0,0.0,0.0,2 639 | 1.0,0.0,0.0,0.0,0.0,1 640 | 0.0,0.0,0.0,1.0,0.0,4 641 | 0.0,1.0,0.0,0.0,0.0,2 642 | 0.0,0.0,0.0,0.0,1.0,5 643 | 0.0,0.0,0.0,1.0,0.0,4 644 | 0.0,0.0,0.0,0.0,1.0,5 645 | 0.0,1.0,0.0,0.0,0.0,2 646 | 0.0,0.0,1.0,0.0,0.0,3 647 | 1.0,0.0,0.0,0.0,0.0,1 648 | 0.0,0.0,1.0,0.0,0.0,3 649 | 0.0,0.0,0.0,1.0,0.0,4 650 | 0.0,0.0,0.0,1.0,0.0,4 651 | 0.0,1.0,0.0,0.0,0.0,2 652 | 1.0,0.0,0.0,0.0,0.0,1 653 | 0.0,0.0,0.0,0.0,1.0,5 654 | 1.0,0.0,0.0,0.0,0.0,1 655 | 0.0,1.0,0.0,0.0,0.0,2 656 | 1.0,0.0,0.0,0.0,0.0,1 657 | 0.0,0.0,1.0,0.0,0.0,3 658 | 0.0,1.0,0.0,0.0,0.0,2 659 | 0.0,0.0,0.0,0.0,1.0,5 660 | 0.0,0.0,0.0,1.0,0.0,4 661 | 1.0,0.0,0.0,0.0,0.0,1 662 | 0.0,1.0,0.0,0.0,0.0,2 663 | 0.0,0.0,0.0,0.0,1.0,5 664 | 0.0,0.0,0.0,0.0,1.0,5 665 | 0.0,0.0,0.0,1.0,0.0,4 666 | 0.0,0.0,1.0,0.0,0.0,3 667 | 0.0,0.0,0.0,1.0,0.0,4 668 | 0.0,0.0,1.0,0.0,0.0,3 669 | 0.0,0.0,0.0,0.0,1.0,5 670 | 1.0,0.0,0.0,0.0,0.0,1 671 | 0.0,1.0,0.0,0.0,0.0,2 672 | 0.0,0.0,0.0,0.0,1.0,5 673 | 1.0,0.0,0.0,0.0,0.0,1 674 | 0.0,0.0,0.0,0.0,1.0,5 675 | 0.0,0.0,0.0,0.0,1.0,5 676 | 0.0,0.0,0.0,1.0,0.0,4 677 | 0.0,0.0,1.0,0.0,0.0,3 678 | 1.0,0.0,0.0,0.0,0.0,1 679 | 0.0,0.0,1.0,0.0,0.0,3 680 | 0.0,0.0,1.0,0.0,0.0,3 681 | 0.0,0.0,0.0,1.0,0.0,4 682 | 0.0,0.0,0.0,0.0,1.0,5 683 | 0.0,0.0,1.0,0.0,0.0,3 684 | 0.0,1.0,0.0,0.0,0.0,2 685 | 1.0,0.0,0.0,0.0,0.0,1 686 | 0.0,0.0,0.0,1.0,0.0,4 687 | 0.0,0.0,0.0,1.0,0.0,4 688 | 0.0,0.0,0.0,0.0,1.0,5 689 | 0.0,0.0,0.0,1.0,0.0,4 690 | 0.0,0.0,0.0,0.0,1.0,5 691 | 0.0,0.0,0.0,0.0,1.0,5 692 | 0.0,0.0,1.0,0.0,0.0,3 693 | 0.0,0.0,0.0,0.0,1.0,5 694 | 0.0,0.0,1.0,0.0,0.0,3 695 | 0.0,0.0,0.0,1.0,0.0,4 696 | 0.0,0.0,1.0,0.0,0.0,3 697 | 0.0,1.0,0.0,0.0,0.0,2 698 | 0.0,0.0,0.0,1.0,0.0,4 699 | 0.0,0.0,0.0,0.0,1.0,5 700 | 0.0,1.0,0.0,0.0,0.0,2 701 | 0.0,1.0,0.0,0.0,0.0,2 702 | 0.0,0.0,0.0,1.0,0.0,4 703 | 0.0,1.0,0.0,0.0,0.0,2 704 | 1.0,0.0,0.0,0.0,0.0,1 705 | 0.0,0.0,0.0,1.0,0.0,4 706 | 0.0,1.0,0.0,0.0,0.0,2 707 | 0.0,1.0,0.0,0.0,0.0,2 708 | 1.0,0.0,0.0,0.0,0.0,1 709 | 0.0,0.0,1.0,0.0,0.0,3 710 | 0.0,0.0,1.0,0.0,0.0,3 711 | 0.0,0.0,1.0,0.0,0.0,3 712 | 0.0,1.0,0.0,0.0,0.0,2 713 | 1.0,0.0,0.0,0.0,0.0,1 714 | 0.0,0.0,0.0,1.0,0.0,4 715 | 0.0,0.0,0.0,1.0,0.0,4 716 | 0.0,1.0,0.0,0.0,0.0,2 717 | 0.0,0.0,1.0,0.0,0.0,3 718 | 0.0,0.0,1.0,0.0,0.0,3 719 | 0.0,0.0,0.0,1.0,0.0,4 720 | 1.0,0.0,0.0,0.0,0.0,1 721 | 0.0,0.0,0.0,1.0,0.0,4 722 | 0.0,0.0,1.0,0.0,0.0,3 723 | 0.0,1.0,0.0,0.0,0.0,2 724 | 0.0,0.0,1.0,0.0,0.0,3 725 | 0.0,0.0,0.0,1.0,0.0,4 726 | 0.0,0.0,0.0,0.0,1.0,5 727 | 0.0,0.0,0.0,1.0,0.0,4 728 | 1.0,0.0,0.0,0.0,0.0,1 729 | 1.0,0.0,0.0,0.0,0.0,1 730 | 1.0,0.0,0.0,0.0,0.0,1 731 | 0.0,1.0,0.0,0.0,0.0,2 732 | 0.0,0.0,0.0,0.0,1.0,5 733 | 0.0,0.0,0.0,0.0,1.0,5 734 | 1.0,0.0,0.0,0.0,0.0,1 735 | 0.0,0.0,0.0,0.0,1.0,5 736 | 0.0,0.0,1.0,0.0,0.0,3 737 | 1.0,0.0,0.0,0.0,0.0,1 738 | 1.0,0.0,0.0,0.0,0.0,1 739 | 0.0,0.0,0.0,1.0,0.0,4 740 | 0.0,0.0,0.0,1.0,0.0,4 741 | 1.0,0.0,0.0,0.0,0.0,1 742 | 0.0,0.0,0.0,0.0,1.0,5 743 | 0.0,0.0,1.0,0.0,0.0,3 744 | 0.0,0.0,1.0,0.0,0.0,3 745 | 0.0,0.0,0.0,0.0,1.0,5 746 | 0.0,0.0,0.0,1.0,0.0,4 747 | 0.0,1.0,0.0,0.0,0.0,2 748 | 1.0,0.0,0.0,0.0,0.0,1 749 | 0.0,0.0,0.0,0.0,1.0,5 750 | 0.0,1.0,0.0,0.0,0.0,2 751 | 0.0,0.0,1.0,0.0,0.0,3 752 | 0.0,0.0,1.0,0.0,0.0,3 753 | 1.0,0.0,0.0,0.0,0.0,1 754 | 0.0,0.0,0.0,1.0,0.0,4 755 | 1.0,0.0,0.0,0.0,0.0,1 756 | 0.0,0.0,1.0,0.0,0.0,3 757 | 0.0,1.0,0.0,0.0,0.0,2 758 | 1.0,0.0,0.0,0.0,0.0,1 759 | 0.0,1.0,0.0,0.0,0.0,2 760 | 0.0,0.0,1.0,0.0,0.0,3 761 | 0.0,0.0,1.0,0.0,0.0,3 762 | 0.0,0.0,0.0,1.0,0.0,4 763 | 0.0,1.0,0.0,0.0,0.0,2 764 | 0.0,0.0,0.0,0.0,1.0,5 765 | 0.0,0.0,0.0,1.0,0.0,4 766 | 0.0,0.0,0.0,0.0,1.0,5 767 | 0.0,0.0,0.0,0.0,1.0,5 768 | 0.0,0.0,0.0,1.0,0.0,4 769 | 0.0,0.0,0.0,1.0,0.0,4 770 | 0.0,0.0,0.0,0.0,1.0,5 771 | 0.0,0.0,0.0,0.0,1.0,5 772 | 0.0,0.0,0.0,1.0,0.0,4 773 | 0.0,0.0,0.0,0.0,1.0,5 774 | 0.0,1.0,0.0,0.0,0.0,2 775 | 0.0,0.0,1.0,0.0,0.0,3 776 | 0.0,0.0,0.0,0.0,1.0,5 777 | 0.0,0.0,0.0,0.0,1.0,5 778 | 0.0,0.0,0.0,0.0,1.0,5 779 | 1.0,0.0,0.0,0.0,0.0,1 780 | 0.0,1.0,0.0,0.0,0.0,2 781 | 0.0,0.0,0.0,0.0,1.0,5 782 | 0.0,0.0,0.0,1.0,0.0,4 783 | 1.0,0.0,0.0,0.0,0.0,1 784 | 0.0,0.0,0.0,0.0,1.0,5 785 | 0.0,0.0,0.0,0.0,1.0,5 786 | 1.0,0.0,0.0,0.0,0.0,1 787 | 0.0,1.0,0.0,0.0,0.0,2 788 | 1.0,0.0,0.0,0.0,0.0,1 789 | 0.0,0.0,0.0,1.0,0.0,4 790 | 0.0,1.0,0.0,0.0,0.0,2 791 | 0.0,0.0,1.0,0.0,0.0,3 792 | 0.0,0.0,0.0,1.0,0.0,4 793 | 0.0,1.0,0.0,0.0,0.0,2 794 | 1.0,0.0,0.0,0.0,0.0,1 795 | 0.0,0.0,0.0,0.0,1.0,5 796 | 0.0,1.0,0.0,0.0,0.0,2 797 | 1.0,0.0,0.0,0.0,0.0,1 798 | 0.0,0.0,0.0,1.0,0.0,4 799 | 0.0,0.0,0.0,1.0,0.0,4 800 | 1.0,0.0,0.0,0.0,0.0,1 801 | 0.0,1.0,0.0,0.0,0.0,2 802 | 0.0,1.0,0.0,0.0,0.0,2 803 | 0.0,0.0,0.0,1.0,0.0,4 804 | 1.0,0.0,0.0,0.0,0.0,1 805 | 1.0,0.0,0.0,0.0,0.0,1 806 | 0.0,1.0,0.0,0.0,0.0,2 807 | 1.0,0.0,0.0,0.0,0.0,1 808 | 0.0,0.0,0.0,1.0,0.0,4 809 | 0.0,0.0,0.0,1.0,0.0,4 810 | 0.0,1.0,0.0,0.0,0.0,2 811 | 0.0,1.0,0.0,0.0,0.0,2 812 | 0.0,1.0,0.0,0.0,0.0,2 813 | 0.0,0.0,0.0,1.0,0.0,4 814 | 0.0,0.0,1.0,0.0,0.0,3 815 | 0.0,0.0,0.0,1.0,0.0,4 816 | 0.0,1.0,0.0,0.0,0.0,2 817 | 0.0,0.0,1.0,0.0,0.0,3 818 | 0.0,0.0,0.0,0.0,1.0,5 819 | 0.0,1.0,0.0,0.0,0.0,2 820 | 0.0,1.0,0.0,0.0,0.0,2 821 | 0.0,0.0,0.0,1.0,0.0,4 822 | 1.0,0.0,0.0,0.0,0.0,1 823 | 0.0,0.0,0.0,1.0,0.0,4 824 | 0.0,0.0,1.0,0.0,0.0,3 825 | 0.0,0.0,0.0,0.0,1.0,5 826 | 0.0,0.0,0.0,1.0,0.0,4 827 | 1.0,0.0,0.0,0.0,0.0,1 828 | 0.0,1.0,0.0,0.0,0.0,2 829 | 0.0,0.0,1.0,0.0,0.0,3 830 | 0.0,1.0,0.0,0.0,0.0,2 831 | 0.0,0.0,0.0,0.0,1.0,5 832 | 1.0,0.0,0.0,0.0,0.0,1 833 | 0.0,0.0,0.0,0.0,1.0,5 834 | 0.0,0.0,1.0,0.0,0.0,3 835 | 0.0,0.0,0.0,1.0,0.0,4 836 | 0.0,1.0,0.0,0.0,0.0,2 837 | 0.0,0.0,0.0,1.0,0.0,4 838 | 1.0,0.0,0.0,0.0,0.0,1 839 | 0.0,0.0,1.0,0.0,0.0,3 840 | 0.0,0.0,1.0,0.0,0.0,3 841 | 0.0,1.0,0.0,0.0,0.0,2 842 | 0.0,1.0,0.0,0.0,0.0,2 843 | 0.0,0.0,1.0,0.0,0.0,3 844 | 0.0,1.0,0.0,0.0,0.0,2 845 | 0.0,0.0,1.0,0.0,0.0,3 846 | 1.0,0.0,0.0,0.0,0.0,1 847 | 0.0,1.0,0.0,0.0,0.0,2 848 | 1.0,0.0,0.0,0.0,0.0,1 849 | 0.0,0.0,1.0,0.0,0.0,3 850 | 0.0,1.0,0.0,0.0,0.0,2 851 | 0.0,1.0,0.0,0.0,0.0,2 852 | 0.0,0.0,1.0,0.0,0.0,3 853 | 0.0,0.0,0.0,0.0,1.0,5 854 | 1.0,0.0,0.0,0.0,0.0,1 855 | 0.0,0.0,0.0,0.0,1.0,5 856 | 0.0,0.0,0.0,1.0,0.0,4 857 | 0.0,0.0,1.0,0.0,0.0,3 858 | 0.0,0.0,0.0,0.0,1.0,5 859 | 0.0,0.0,0.0,0.0,1.0,5 860 | 0.0,0.0,0.0,0.0,1.0,5 861 | 0.0,0.0,1.0,0.0,0.0,3 862 | 0.0,0.0,0.0,0.0,1.0,5 863 | 0.0,0.0,0.0,1.0,0.0,4 864 | 0.0,0.0,0.0,0.0,1.0,5 865 | 0.0,0.0,1.0,0.0,0.0,3 866 | 0.0,0.0,1.0,0.0,0.0,3 867 | 0.0,0.0,0.0,0.0,1.0,5 868 | 0.0,0.0,0.0,1.0,0.0,4 869 | 1.0,0.0,0.0,0.0,0.0,1 870 | 0.0,0.0,1.0,0.0,0.0,3 871 | 0.0,0.0,1.0,0.0,0.0,3 872 | 0.0,0.0,0.0,1.0,0.0,4 873 | 0.0,0.0,1.0,0.0,0.0,3 874 | 0.0,1.0,0.0,0.0,0.0,2 875 | 0.0,0.0,0.0,1.0,0.0,4 876 | 0.0,0.0,1.0,0.0,0.0,3 877 | 0.0,0.0,0.0,1.0,0.0,4 878 | 0.0,0.0,0.0,0.0,1.0,5 879 | 0.0,1.0,0.0,0.0,0.0,2 880 | 0.0,0.0,1.0,0.0,0.0,3 881 | 0.0,0.0,0.0,1.0,0.0,4 882 | 1.0,0.0,0.0,0.0,0.0,1 883 | 0.0,1.0,0.0,0.0,0.0,2 884 | 0.0,0.0,1.0,0.0,0.0,3 885 | 0.0,0.0,0.0,0.0,1.0,5 886 | 0.0,1.0,0.0,0.0,0.0,2 887 | 1.0,0.0,0.0,0.0,0.0,1 888 | 1.0,0.0,0.0,0.0,0.0,1 889 | 0.0,1.0,0.0,0.0,0.0,2 890 | 0.0,0.0,1.0,0.0,0.0,3 891 | 0.0,0.0,1.0,0.0,0.0,3 892 | 0.0,0.0,0.0,1.0,0.0,4 893 | 0.0,1.0,0.0,0.0,0.0,2 894 | 0.0,0.0,0.0,0.0,1.0,5 895 | 0.0,0.0,0.0,0.0,1.0,5 896 | 0.0,0.0,0.0,0.0,1.0,5 897 | 0.0,1.0,0.0,0.0,0.0,2 898 | 1.0,0.0,0.0,0.0,0.0,1 899 | 1.0,0.0,0.0,0.0,0.0,1 900 | 0.0,0.0,0.0,1.0,0.0,4 901 | 0.0,0.0,1.0,0.0,0.0,3 902 | 1.0,0.0,0.0,0.0,0.0,1 903 | 0.0,0.0,0.0,1.0,0.0,4 904 | 0.0,0.0,0.0,0.0,1.0,5 905 | 0.0,0.0,1.0,0.0,0.0,3 906 | 1.0,0.0,0.0,0.0,0.0,1 907 | 1.0,0.0,0.0,0.0,0.0,1 908 | 0.0,0.0,0.0,0.0,1.0,5 909 | 0.0,0.0,0.0,0.0,1.0,5 910 | 0.0,0.0,0.0,0.0,1.0,5 911 | 0.0,1.0,0.0,0.0,0.0,2 912 | 0.0,0.0,0.0,0.0,1.0,5 913 | 0.0,1.0,0.0,0.0,0.0,2 914 | 0.0,0.0,0.0,0.0,1.0,5 915 | 0.0,1.0,0.0,0.0,0.0,2 916 | 0.0,0.0,1.0,0.0,0.0,3 917 | 1.0,0.0,0.0,0.0,0.0,1 918 | 0.0,0.0,0.0,0.0,1.0,5 919 | 0.0,0.0,0.0,0.0,1.0,5 920 | 1.0,0.0,0.0,0.0,0.0,1 921 | 0.0,1.0,0.0,0.0,0.0,2 922 | 1.0,0.0,0.0,0.0,0.0,1 923 | 1.0,0.0,0.0,0.0,0.0,1 924 | 1.0,0.0,0.0,0.0,0.0,1 925 | 1.0,0.0,0.0,0.0,0.0,1 926 | 0.0,0.0,0.0,0.0,1.0,5 927 | 0.0,0.0,0.0,0.0,1.0,5 928 | 0.0,0.0,0.0,1.0,0.0,4 929 | 0.0,1.0,0.0,0.0,0.0,2 930 | 1.0,0.0,0.0,0.0,0.0,1 931 | 0.0,0.0,0.0,1.0,0.0,4 932 | 0.0,0.0,0.0,1.0,0.0,4 933 | 0.0,0.0,0.0,0.0,1.0,5 934 | 0.0,0.0,0.0,0.0,1.0,5 935 | 0.0,0.0,1.0,0.0,0.0,3 936 | 0.0,0.0,0.0,0.0,1.0,5 937 | 0.0,0.0,0.0,0.0,1.0,5 938 | 0.0,0.0,0.0,1.0,0.0,4 939 | 1.0,0.0,0.0,0.0,0.0,1 940 | 0.0,0.0,0.0,1.0,0.0,4 941 | 1.0,0.0,0.0,0.0,0.0,1 942 | 0.0,1.0,0.0,0.0,0.0,2 943 | 1.0,0.0,0.0,0.0,0.0,1 944 | 1.0,0.0,0.0,0.0,0.0,1 945 | 0.0,0.0,1.0,0.0,0.0,3 946 | 1.0,0.0,0.0,0.0,0.0,1 947 | 0.0,0.0,0.0,1.0,0.0,4 948 | 0.0,0.0,1.0,0.0,0.0,3 949 | 0.0,0.0,0.0,1.0,0.0,4 950 | 1.0,0.0,0.0,0.0,0.0,1 951 | 0.0,1.0,0.0,0.0,0.0,2 952 | 0.0,0.0,1.0,0.0,0.0,3 953 | 0.0,0.0,0.0,1.0,0.0,4 954 | 0.0,1.0,0.0,0.0,0.0,2 955 | 0.0,0.0,0.0,1.0,0.0,4 956 | 0.0,0.0,1.0,0.0,0.0,3 957 | 0.0,1.0,0.0,0.0,0.0,2 958 | 0.0,0.0,1.0,0.0,0.0,3 959 | 0.0,0.0,0.0,1.0,0.0,4 960 | 0.0,0.0,0.0,0.0,1.0,5 961 | 0.0,1.0,0.0,0.0,0.0,2 962 | 0.0,0.0,1.0,0.0,0.0,3 963 | 0.0,0.0,0.0,0.0,1.0,5 964 | 0.0,0.0,0.0,1.0,0.0,4 965 | 0.0,0.0,0.0,1.0,0.0,4 966 | 1.0,0.0,0.0,0.0,0.0,1 967 | 1.0,0.0,0.0,0.0,0.0,1 968 | 0.0,0.0,1.0,0.0,0.0,3 969 | 0.0,0.0,1.0,0.0,0.0,3 970 | 0.0,0.0,0.0,0.0,1.0,5 971 | 1.0,0.0,0.0,0.0,0.0,1 972 | 0.0,0.0,0.0,1.0,0.0,4 973 | 0.0,0.0,1.0,0.0,0.0,3 974 | 0.0,0.0,0.0,1.0,0.0,4 975 | 0.0,0.0,0.0,0.0,1.0,5 976 | 0.0,1.0,0.0,0.0,0.0,2 977 | 0.0,0.0,1.0,0.0,0.0,3 978 | 0.0,1.0,0.0,0.0,0.0,2 979 | 1.0,0.0,0.0,0.0,0.0,1 980 | 0.0,1.0,0.0,0.0,0.0,2 981 | 0.0,0.0,0.0,1.0,0.0,4 982 | 0.0,0.0,0.0,1.0,0.0,4 983 | 0.0,1.0,0.0,0.0,0.0,2 984 | 1.0,0.0,0.0,0.0,0.0,1 985 | 0.0,0.0,1.0,0.0,0.0,3 986 | 0.0,0.0,0.0,0.0,1.0,5 987 | 0.0,0.0,0.0,1.0,0.0,4 988 | 1.0,0.0,0.0,0.0,0.0,1 989 | 0.0,0.0,0.0,1.0,0.0,4 990 | 0.0,0.0,0.0,1.0,0.0,4 991 | 0.0,1.0,0.0,0.0,0.0,2 992 | 0.0,0.0,0.0,1.0,0.0,4 993 | 1.0,0.0,0.0,0.0,0.0,1 994 | 0.0,0.0,0.0,1.0,0.0,4 995 | 0.0,1.0,0.0,0.0,0.0,2 996 | 0.0,0.0,1.0,0.0,0.0,3 997 | 0.0,0.0,1.0,0.0,0.0,3 998 | 1.0,0.0,0.0,0.0,0.0,1 999 | 1.0,0.0,0.0,0.0,0.0,1 1000 | 0.0,0.0,0.0,1.0,0.0,4 1001 | 0.0,1.0,0.0,0.0,0.0,2 1002 | 0.0,0.0,0.0,1.0,0.0,4 1003 | 0.0,0.0,0.0,1.0,0.0,4 1004 | 0.0,1.0,0.0,0.0,0.0,2 1005 | 0.0,0.0,1.0,0.0,0.0,3 1006 | 1.0,0.0,0.0,0.0,0.0,1 1007 | 1.0,0.0,0.0,0.0,0.0,1 1008 | 0.0,0.0,1.0,0.0,0.0,3 1009 | 1.0,0.0,0.0,0.0,0.0,1 1010 | 0.0,1.0,0.0,0.0,0.0,2 1011 | 1.0,0.0,0.0,0.0,0.0,1 1012 | 0.0,0.0,0.0,0.0,1.0,5 1013 | 0.0,0.0,1.0,0.0,0.0,3 1014 | 1.0,0.0,0.0,0.0,0.0,1 1015 | 0.0,0.0,0.0,0.0,1.0,5 1016 | 0.0,0.0,0.0,0.0,1.0,5 1017 | 0.0,0.0,0.0,1.0,0.0,4 1018 | 0.0,0.0,0.0,1.0,0.0,4 1019 | 1.0,0.0,0.0,0.0,0.0,1 1020 | 0.0,0.0,0.0,1.0,0.0,4 1021 | 0.0,1.0,0.0,0.0,0.0,2 1022 | 0.0,1.0,0.0,0.0,0.0,2 1023 | 0.0,1.0,0.0,0.0,0.0,2 1024 | 0.0,0.0,0.0,1.0,0.0,4 1025 | 0.0,0.0,0.0,1.0,0.0,4 1026 | 0.0,1.0,0.0,0.0,0.0,2 1027 | 1.0,0.0,0.0,0.0,0.0,1 1028 | 0.0,1.0,0.0,0.0,0.0,2 1029 | 0.0,0.0,0.0,0.0,1.0,5 1030 | 0.0,1.0,0.0,0.0,0.0,2 1031 | 0.0,0.0,1.0,0.0,0.0,3 1032 | 0.0,1.0,0.0,0.0,0.0,2 1033 | 0.0,1.0,0.0,0.0,0.0,2 1034 | 1.0,0.0,0.0,0.0,0.0,1 1035 | 0.0,0.0,1.0,0.0,0.0,3 1036 | 0.0,0.0,0.0,0.0,1.0,5 1037 | 1.0,0.0,0.0,0.0,0.0,1 1038 | 1.0,0.0,0.0,0.0,0.0,1 1039 | 0.0,0.0,0.0,0.0,1.0,5 1040 | 0.0,0.0,1.0,0.0,0.0,3 1041 | 0.0,0.0,0.0,1.0,0.0,4 1042 | 0.0,0.0,0.0,0.0,1.0,5 1043 | 0.0,0.0,1.0,0.0,0.0,3 1044 | 0.0,0.0,1.0,0.0,0.0,3 1045 | 0.0,0.0,0.0,0.0,1.0,5 1046 | 1.0,0.0,0.0,0.0,0.0,1 1047 | 0.0,0.0,0.0,0.0,1.0,5 1048 | 0.0,0.0,1.0,0.0,0.0,3 1049 | 0.0,0.0,1.0,0.0,0.0,3 1050 | 0.0,0.0,0.0,1.0,0.0,4 1051 | 0.0,0.0,1.0,0.0,0.0,3 1052 | 1.0,0.0,0.0,0.0,0.0,1 1053 | 0.0,0.0,1.0,0.0,0.0,3 1054 | 1.0,0.0,0.0,0.0,0.0,1 1055 | 0.0,0.0,0.0,1.0,0.0,4 1056 | 0.0,1.0,0.0,0.0,0.0,2 1057 | 1.0,0.0,0.0,0.0,0.0,1 1058 | 0.0,0.0,0.0,0.0,1.0,5 1059 | 0.0,0.0,0.0,1.0,0.0,4 1060 | 0.0,0.0,0.0,1.0,0.0,4 1061 | 0.0,0.0,1.0,0.0,0.0,3 1062 | 0.0,0.0,1.0,0.0,0.0,3 1063 | 0.0,0.0,1.0,0.0,0.0,3 1064 | 0.0,0.0,1.0,0.0,0.0,3 1065 | 0.0,0.0,0.0,0.0,1.0,5 1066 | 0.0,0.0,1.0,0.0,0.0,3 1067 | 0.0,0.0,0.0,0.0,1.0,5 1068 | 0.0,0.0,1.0,0.0,0.0,3 1069 | 0.0,0.0,0.0,0.0,1.0,5 1070 | 0.0,0.0,0.0,0.0,1.0,5 1071 | 0.0,0.0,0.0,1.0,0.0,4 1072 | 1.0,0.0,0.0,0.0,0.0,1 1073 | 1.0,0.0,0.0,0.0,0.0,1 1074 | 0.0,0.0,0.0,0.0,1.0,5 1075 | 0.0,0.0,1.0,0.0,0.0,3 1076 | 1.0,0.0,0.0,0.0,0.0,1 1077 | 1.0,0.0,0.0,0.0,0.0,1 1078 | 0.0,0.0,0.0,0.0,1.0,5 1079 | 1.0,0.0,0.0,0.0,0.0,1 1080 | 0.0,0.0,1.0,0.0,0.0,3 1081 | 1.0,0.0,0.0,0.0,0.0,1 1082 | 0.0,1.0,0.0,0.0,0.0,2 1083 | 0.0,0.0,0.0,0.0,1.0,5 1084 | 0.0,0.0,0.0,0.0,1.0,5 1085 | 1.0,0.0,0.0,0.0,0.0,1 1086 | 0.0,0.0,0.0,1.0,0.0,4 1087 | 0.0,0.0,0.0,1.0,0.0,4 1088 | 0.0,1.0,0.0,0.0,0.0,2 1089 | 0.0,0.0,0.0,1.0,0.0,4 1090 | 0.0,0.0,0.0,1.0,0.0,4 1091 | 1.0,0.0,0.0,0.0,0.0,1 1092 | 1.0,0.0,0.0,0.0,0.0,1 1093 | 0.0,1.0,0.0,0.0,0.0,2 1094 | 0.0,0.0,0.0,1.0,0.0,4 1095 | 0.0,1.0,0.0,0.0,0.0,2 1096 | 0.0,1.0,0.0,0.0,0.0,2 1097 | 0.0,0.0,0.0,0.0,1.0,5 1098 | 0.0,0.0,0.0,1.0,0.0,4 1099 | 0.0,0.0,1.0,0.0,0.0,3 1100 | 0.0,1.0,0.0,0.0,0.0,2 1101 | 0.0,0.0,1.0,0.0,0.0,3 1102 | 0.0,0.0,1.0,0.0,0.0,3 1103 | 0.0,0.0,1.0,0.0,0.0,3 1104 | 0.0,0.0,0.0,0.0,1.0,5 1105 | 0.0,1.0,0.0,0.0,0.0,2 1106 | 0.0,0.0,1.0,0.0,0.0,3 1107 | 0.0,0.0,1.0,0.0,0.0,3 1108 | 0.0,0.0,1.0,0.0,0.0,3 1109 | 0.0,0.0,0.0,1.0,0.0,4 1110 | 0.0,0.0,0.0,0.0,1.0,5 1111 | 0.0,1.0,0.0,0.0,0.0,2 1112 | 0.0,0.0,1.0,0.0,0.0,3 1113 | 0.0,0.0,0.0,1.0,0.0,4 1114 | 1.0,0.0,0.0,0.0,0.0,1 1115 | 0.0,0.0,1.0,0.0,0.0,3 1116 | 1.0,0.0,0.0,0.0,0.0,1 1117 | 0.0,0.0,0.0,1.0,0.0,4 1118 | 0.0,1.0,0.0,0.0,0.0,2 1119 | 1.0,0.0,0.0,0.0,0.0,1 1120 | 0.0,0.0,0.0,1.0,0.0,4 1121 | 0.0,0.0,0.0,0.0,1.0,5 1122 | 0.0,0.0,0.0,1.0,0.0,4 1123 | 0.0,0.0,0.0,0.0,1.0,5 1124 | 0.0,1.0,0.0,0.0,0.0,2 1125 | 0.0,0.0,0.0,1.0,0.0,4 1126 | 0.0,0.0,1.0,0.0,0.0,3 1127 | 1.0,0.0,0.0,0.0,0.0,1 1128 | 0.0,0.0,1.0,0.0,0.0,3 1129 | 0.0,1.0,0.0,0.0,0.0,2 1130 | 0.0,0.0,0.0,0.0,1.0,5 1131 | 0.0,0.0,0.0,0.0,1.0,5 1132 | 0.0,0.0,0.0,0.0,1.0,5 1133 | 0.0,0.0,1.0,0.0,0.0,3 1134 | 0.0,0.0,0.0,1.0,0.0,4 1135 | 0.0,1.0,0.0,0.0,0.0,2 1136 | 0.0,0.0,0.0,1.0,0.0,4 1137 | 0.0,0.0,0.0,1.0,0.0,4 1138 | 0.0,1.0,0.0,0.0,0.0,2 1139 | 0.0,1.0,0.0,0.0,0.0,2 1140 | 0.0,0.0,0.0,0.0,1.0,5 1141 | 0.0,0.0,0.0,0.0,1.0,5 1142 | 0.0,0.0,0.0,0.0,1.0,5 1143 | 0.0,0.0,0.0,1.0,0.0,4 1144 | 0.0,0.0,1.0,0.0,0.0,3 1145 | 0.0,0.0,0.0,1.0,0.0,4 1146 | 0.0,0.0,0.0,1.0,0.0,4 1147 | 0.0,0.0,0.0,0.0,1.0,5 1148 | 0.0,0.0,0.0,1.0,0.0,4 1149 | 0.0,0.0,0.0,1.0,0.0,4 1150 | 0.0,1.0,0.0,0.0,0.0,2 1151 | 0.0,0.0,0.0,1.0,0.0,4 1152 | 0.0,1.0,0.0,0.0,0.0,2 1153 | 0.0,0.0,1.0,0.0,0.0,3 1154 | 0.0,0.0,0.0,0.0,1.0,5 1155 | 0.0,0.0,1.0,0.0,0.0,3 1156 | 0.0,0.0,0.0,1.0,0.0,4 1157 | 0.0,0.0,0.0,0.0,1.0,5 1158 | 0.0,0.0,0.0,0.0,1.0,5 1159 | 0.0,0.0,1.0,0.0,0.0,3 1160 | 0.0,0.0,0.0,0.0,1.0,5 1161 | 0.0,1.0,0.0,0.0,0.0,2 1162 | 1.0,0.0,0.0,0.0,0.0,1 1163 | 0.0,0.0,0.0,0.0,1.0,5 1164 | 0.0,0.0,0.0,1.0,0.0,4 1165 | 0.0,1.0,0.0,0.0,0.0,2 1166 | 1.0,0.0,0.0,0.0,0.0,1 1167 | 0.0,0.0,1.0,0.0,0.0,3 1168 | 1.0,0.0,0.0,0.0,0.0,1 1169 | 0.0,1.0,0.0,0.0,0.0,2 1170 | 0.0,1.0,0.0,0.0,0.0,2 1171 | 1.0,0.0,0.0,0.0,0.0,1 1172 | 0.0,0.0,0.0,0.0,1.0,5 1173 | 0.0,0.0,0.0,0.0,1.0,5 1174 | 0.0,0.0,1.0,0.0,0.0,3 1175 | 1.0,0.0,0.0,0.0,0.0,1 1176 | 0.0,0.0,0.0,0.0,1.0,5 1177 | 0.0,0.0,1.0,0.0,0.0,3 1178 | 0.0,0.0,0.0,1.0,0.0,4 1179 | 0.0,1.0,0.0,0.0,0.0,2 1180 | 0.0,1.0,0.0,0.0,0.0,2 1181 | 0.0,0.0,0.0,0.0,1.0,5 1182 | 0.0,0.0,1.0,0.0,0.0,3 1183 | 0.0,1.0,0.0,0.0,0.0,2 1184 | 0.0,0.0,1.0,0.0,0.0,3 1185 | 0.0,1.0,0.0,0.0,0.0,2 1186 | 0.0,0.0,1.0,0.0,0.0,3 1187 | 0.0,1.0,0.0,0.0,0.0,2 1188 | 0.0,0.0,0.0,0.0,1.0,5 1189 | 1.0,0.0,0.0,0.0,0.0,1 1190 | 1.0,0.0,0.0,0.0,0.0,1 1191 | 0.0,0.0,0.0,1.0,0.0,4 1192 | 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0.0,1.0,0.0,0.0,0.0,2 1227 | 0.0,0.0,1.0,0.0,0.0,3 1228 | 0.0,0.0,1.0,0.0,0.0,3 1229 | 1.0,0.0,0.0,0.0,0.0,1 1230 | 0.0,0.0,1.0,0.0,0.0,3 1231 | 0.0,0.0,0.0,0.0,1.0,5 1232 | 1.0,0.0,0.0,0.0,0.0,1 1233 | 0.0,0.0,0.0,0.0,1.0,5 1234 | 0.0,0.0,0.0,0.0,1.0,5 1235 | 0.0,0.0,1.0,0.0,0.0,3 1236 | 0.0,0.0,0.0,0.0,1.0,5 1237 | 0.0,0.0,0.0,1.0,0.0,4 1238 | 0.0,0.0,1.0,0.0,0.0,3 1239 | 1.0,0.0,0.0,0.0,0.0,1 1240 | 1.0,0.0,0.0,0.0,0.0,1 1241 | 0.0,0.0,1.0,0.0,0.0,3 1242 | 1.0,0.0,0.0,0.0,0.0,1 1243 | 0.0,0.0,0.0,0.0,1.0,5 1244 | 0.0,0.0,0.0,1.0,0.0,4 1245 | 1.0,0.0,0.0,0.0,0.0,1 1246 | 0.0,0.0,0.0,1.0,0.0,4 1247 | 0.0,0.0,0.0,0.0,1.0,5 1248 | 0.0,1.0,0.0,0.0,0.0,2 1249 | 0.0,1.0,0.0,0.0,0.0,2 1250 | 0.0,0.0,1.0,0.0,0.0,3 1251 | 0.0,0.0,0.0,0.0,1.0,5 1252 | 1.0,0.0,0.0,0.0,0.0,1 1253 | 0.0,1.0,0.0,0.0,0.0,2 1254 | 0.0,1.0,0.0,0.0,0.0,2 1255 | 0.0,0.0,0.0,1.0,0.0,4 1256 | 0.0,0.0,1.0,0.0,0.0,3 1257 | 0.0,1.0,0.0,0.0,0.0,2 1258 | 0.0,0.0,1.0,0.0,0.0,3 1259 | 0.0,1.0,0.0,0.0,0.0,2 1260 | 0.0,0.0,0.0,0.0,1.0,5 1261 | 0.0,0.0,0.0,1.0,0.0,4 1262 | 0.0,0.0,0.0,1.0,0.0,4 1263 | 0.0,0.0,0.0,1.0,0.0,4 1264 | 0.0,0.0,0.0,0.0,1.0,5 1265 | 0.0,1.0,0.0,0.0,0.0,2 1266 | 0.0,0.0,0.0,1.0,0.0,4 1267 | 0.0,0.0,0.0,1.0,0.0,4 1268 | 0.0,0.0,0.0,0.0,1.0,5 1269 | 0.0,0.0,0.0,1.0,0.0,4 1270 | 0.0,0.0,0.0,1.0,0.0,4 1271 | 0.0,0.0,0.0,1.0,0.0,4 1272 | 0.0,0.0,0.0,1.0,0.0,4 1273 | 1.0,0.0,0.0,0.0,0.0,1 1274 | 0.0,0.0,0.0,0.0,1.0,5 1275 | 0.0,0.0,0.0,0.0,1.0,5 1276 | 0.0,0.0,0.0,1.0,0.0,4 1277 | 0.0,0.0,0.0,1.0,0.0,4 1278 | 0.0,1.0,0.0,0.0,0.0,2 1279 | 1.0,0.0,0.0,0.0,0.0,1 1280 | 0.0,0.0,1.0,0.0,0.0,3 1281 | 0.0,1.0,0.0,0.0,0.0,2 1282 | 0.0,1.0,0.0,0.0,0.0,2 1283 | 0.0,0.0,1.0,0.0,0.0,3 1284 | 0.0,1.0,0.0,0.0,0.0,2 1285 | 0.0,0.0,1.0,0.0,0.0,3 1286 | 0.0,0.0,0.0,0.0,1.0,5 1287 | 0.0,0.0,1.0,0.0,0.0,3 1288 | 1.0,0.0,0.0,0.0,0.0,1 1289 | 0.0,0.0,0.0,1.0,0.0,4 1290 | 0.0,0.0,1.0,0.0,0.0,3 1291 | 0.0,0.0,0.0,0.0,1.0,5 1292 | 0.0,0.0,1.0,0.0,0.0,3 1293 | 0.0,0.0,0.0,1.0,0.0,4 1294 | 0.0,0.0,1.0,0.0,0.0,3 1295 | 1.0,0.0,0.0,0.0,0.0,1 1296 | 0.0,0.0,0.0,1.0,0.0,4 1297 | 0.0,1.0,0.0,0.0,0.0,2 1298 | 1.0,0.0,0.0,0.0,0.0,1 1299 | 0.0,0.0,1.0,0.0,0.0,3 1300 | 0.0,0.0,0.0,1.0,0.0,4 1301 | 1.0,0.0,0.0,0.0,0.0,1 1302 | 0.0,1.0,0.0,0.0,0.0,2 1303 | 0.0,0.0,1.0,0.0,0.0,3 1304 | 0.0,1.0,0.0,0.0,0.0,2 1305 | 1.0,0.0,0.0,0.0,0.0,1 1306 | 0.0,0.0,0.0,1.0,0.0,4 1307 | 0.0,0.0,1.0,0.0,0.0,3 1308 | 0.0,1.0,0.0,0.0,0.0,2 1309 | 0.0,0.0,1.0,0.0,0.0,3 1310 | 0.0,0.0,0.0,1.0,0.0,4 1311 | 0.0,0.0,0.0,1.0,0.0,4 1312 | 1.0,0.0,0.0,0.0,0.0,1 1313 | 1.0,0.0,0.0,0.0,0.0,1 1314 | 0.0,1.0,0.0,0.0,0.0,2 1315 | 0.0,0.0,0.0,0.0,1.0,5 1316 | 0.0,1.0,0.0,0.0,0.0,2 1317 | 0.0,0.0,0.0,1.0,0.0,4 1318 | 0.0,0.0,1.0,0.0,0.0,3 1319 | 1.0,0.0,0.0,0.0,0.0,1 1320 | 0.0,0.0,0.0,1.0,0.0,4 1321 | 0.0,0.0,0.0,0.0,1.0,5 1322 | 0.0,0.0,0.0,0.0,1.0,5 1323 | 0.0,0.0,0.0,1.0,0.0,4 1324 | 0.0,0.0,0.0,0.0,1.0,5 1325 | 0.0,0.0,0.0,0.0,1.0,5 1326 | 0.0,0.0,1.0,0.0,0.0,3 1327 | 0.0,1.0,0.0,0.0,0.0,2 1328 | 1.0,0.0,0.0,0.0,0.0,1 1329 | 0.0,0.0,1.0,0.0,0.0,3 1330 | 1.0,0.0,0.0,0.0,0.0,1 1331 | 0.0,0.0,0.0,0.0,1.0,5 1332 | 0.0,0.0,0.0,0.0,1.0,5 1333 | 1.0,0.0,0.0,0.0,0.0,1 1334 | 1.0,0.0,0.0,0.0,0.0,1 1335 | 0.0,0.0,1.0,0.0,0.0,3 1336 | 0.0,0.0,0.0,1.0,0.0,4 1337 | 1.0,0.0,0.0,0.0,0.0,1 1338 | 0.0,0.0,1.0,0.0,0.0,3 1339 | 0.0,0.0,0.0,0.0,1.0,5 1340 | 0.0,0.0,0.0,0.0,1.0,5 1341 | 0.0,0.0,0.0,1.0,0.0,4 1342 | 1.0,0.0,0.0,0.0,0.0,1 1343 | 0.0,0.0,0.0,1.0,0.0,4 1344 | 0.0,0.0,0.0,0.0,1.0,5 1345 | 0.0,0.0,1.0,0.0,0.0,3 1346 | 1.0,0.0,0.0,0.0,0.0,1 1347 | 0.0,0.0,1.0,0.0,0.0,3 1348 | 0.0,0.0,0.0,1.0,0.0,4 1349 | 0.0,0.0,0.0,1.0,0.0,4 1350 | 0.0,0.0,0.0,0.0,1.0,5 1351 | 0.0,1.0,0.0,0.0,0.0,2 1352 | 0.0,0.0,1.0,0.0,0.0,3 1353 | 1.0,0.0,0.0,0.0,0.0,1 1354 | 1.0,0.0,0.0,0.0,0.0,1 1355 | 1.0,0.0,0.0,0.0,0.0,1 1356 | 1.0,0.0,0.0,0.0,0.0,1 1357 | 1.0,0.0,0.0,0.0,0.0,1 1358 | 1.0,0.0,0.0,0.0,0.0,1 1359 | 0.0,0.0,1.0,0.0,0.0,3 1360 | 0.0,0.0,0.0,1.0,0.0,4 1361 | 0.0,1.0,0.0,0.0,0.0,2 1362 | 0.0,0.0,0.0,0.0,1.0,5 1363 | 0.0,0.0,0.0,0.0,1.0,5 1364 | 0.0,0.0,0.0,0.0,1.0,5 1365 | 1.0,0.0,0.0,0.0,0.0,1 1366 | 0.0,1.0,0.0,0.0,0.0,2 1367 | 0.0,0.0,0.0,0.0,1.0,5 1368 | 0.0,0.0,1.0,0.0,0.0,3 1369 | 0.0,0.0,0.0,1.0,0.0,4 1370 | 0.0,0.0,1.0,0.0,0.0,3 1371 | 1.0,0.0,0.0,0.0,0.0,1 1372 | 0.0,0.0,0.0,1.0,0.0,4 1373 | 0.0,0.0,1.0,0.0,0.0,3 1374 | 0.0,1.0,0.0,0.0,0.0,2 1375 | 0.0,0.0,0.0,1.0,0.0,4 1376 | 0.0,0.0,0.0,0.0,1.0,5 1377 | 0.0,0.0,0.0,0.0,1.0,5 1378 | 0.0,0.0,1.0,0.0,0.0,3 1379 | 0.0,0.0,0.0,1.0,0.0,4 1380 | 0.0,1.0,0.0,0.0,0.0,2 1381 | 0.0,0.0,0.0,0.0,1.0,5 1382 | 0.0,0.0,1.0,0.0,0.0,3 1383 | 0.0,0.0,0.0,0.0,1.0,5 1384 | 0.0,0.0,1.0,0.0,0.0,3 1385 | 1.0,0.0,0.0,0.0,0.0,1 1386 | 0.0,0.0,0.0,0.0,1.0,5 1387 | 0.0,0.0,0.0,1.0,0.0,4 1388 | 0.0,1.0,0.0,0.0,0.0,2 1389 | 0.0,0.0,0.0,0.0,1.0,5 1390 | 0.0,0.0,1.0,0.0,0.0,3 1391 | 0.0,0.0,0.0,0.0,1.0,5 1392 | 0.0,0.0,0.0,0.0,1.0,5 1393 | 0.0,0.0,1.0,0.0,0.0,3 1394 | 0.0,1.0,0.0,0.0,0.0,2 1395 | 0.0,1.0,0.0,0.0,0.0,2 1396 | 1.0,0.0,0.0,0.0,0.0,1 1397 | 0.0,1.0,0.0,0.0,0.0,2 1398 | 0.0,0.0,0.0,0.0,1.0,5 1399 | 0.0,0.0,0.0,1.0,0.0,4 1400 | 0.0,1.0,0.0,0.0,0.0,2 1401 | 1.0,0.0,0.0,0.0,0.0,1 1402 | 0.0,0.0,0.0,0.0,1.0,5 1403 | 0.0,1.0,0.0,0.0,0.0,2 1404 | 0.0,0.0,0.0,1.0,0.0,4 1405 | 0.0,0.0,0.0,1.0,0.0,4 1406 | 0.0,1.0,0.0,0.0,0.0,2 1407 | 1.0,0.0,0.0,0.0,0.0,1 1408 | 0.0,1.0,0.0,0.0,0.0,2 1409 | 0.0,0.0,0.0,0.0,1.0,5 1410 | 0.0,0.0,1.0,0.0,0.0,3 1411 | 0.0,0.0,0.0,0.0,1.0,5 1412 | 0.0,0.0,0.0,1.0,0.0,4 1413 | 0.0,0.0,1.0,0.0,0.0,3 1414 | 1.0,0.0,0.0,0.0,0.0,1 1415 | 0.0,0.0,0.0,0.0,1.0,5 1416 | 0.0,0.0,1.0,0.0,0.0,3 1417 | 1.0,0.0,0.0,0.0,0.0,1 1418 | 1.0,0.0,0.0,0.0,0.0,1 1419 | 0.0,0.0,0.0,0.0,1.0,5 1420 | 0.0,0.0,0.0,0.0,1.0,5 1421 | 0.0,0.0,0.0,1.0,0.0,4 1422 | 0.0,0.0,0.0,1.0,0.0,4 1423 | 0.0,0.0,0.0,0.0,1.0,5 1424 | 0.0,0.0,1.0,0.0,0.0,3 1425 | 0.0,0.0,0.0,1.0,0.0,4 1426 | 0.0,0.0,0.0,0.0,1.0,5 1427 | 0.0,0.0,1.0,0.0,0.0,3 1428 | 0.0,0.0,0.0,1.0,0.0,4 1429 | 0.0,0.0,0.0,1.0,0.0,4 1430 | 0.0,0.0,1.0,0.0,0.0,3 1431 | 0.0,0.0,1.0,0.0,0.0,3 1432 | 0.0,0.0,0.0,1.0,0.0,4 1433 | 0.0,1.0,0.0,0.0,0.0,2 1434 | 0.0,0.0,1.0,0.0,0.0,3 1435 | 0.0,0.0,0.0,0.0,1.0,5 1436 | 0.0,0.0,1.0,0.0,0.0,3 1437 | 0.0,0.0,0.0,1.0,0.0,4 1438 | 0.0,0.0,0.0,0.0,1.0,5 1439 | 0.0,0.0,0.0,0.0,1.0,5 1440 | 0.0,1.0,0.0,0.0,0.0,2 1441 | 0.0,0.0,1.0,0.0,0.0,3 1442 | 0.0,0.0,0.0,0.0,1.0,5 1443 | 0.0,0.0,1.0,0.0,0.0,3 1444 | 0.0,1.0,0.0,0.0,0.0,2 1445 | 0.0,0.0,0.0,1.0,0.0,4 1446 | 0.0,0.0,0.0,0.0,1.0,5 1447 | 0.0,0.0,1.0,0.0,0.0,3 1448 | 0.0,0.0,1.0,0.0,0.0,3 1449 | 1.0,0.0,0.0,0.0,0.0,1 1450 | 0.0,0.0,1.0,0.0,0.0,3 1451 | 1.0,0.0,0.0,0.0,0.0,1 1452 | 0.0,0.0,1.0,0.0,0.0,3 1453 | 0.0,0.0,0.0,1.0,0.0,4 1454 | 0.0,0.0,0.0,0.0,1.0,5 1455 | 1.0,0.0,0.0,0.0,0.0,1 1456 | 0.0,1.0,0.0,0.0,0.0,2 1457 | 0.0,0.0,0.0,0.0,1.0,5 1458 | 0.0,0.0,0.0,1.0,0.0,4 1459 | 0.0,1.0,0.0,0.0,0.0,2 1460 | 0.0,0.0,0.0,1.0,0.0,4 1461 | 0.0,0.0,0.0,0.0,1.0,5 1462 | 1.0,0.0,0.0,0.0,0.0,1 1463 | 0.0,0.0,1.0,0.0,0.0,3 1464 | 0.0,0.0,0.0,1.0,0.0,4 1465 | 1.0,0.0,0.0,0.0,0.0,1 1466 | 0.0,0.0,1.0,0.0,0.0,3 1467 | 0.0,0.0,0.0,0.0,1.0,5 1468 | 0.0,0.0,0.0,1.0,0.0,4 1469 | 0.0,0.0,1.0,0.0,0.0,3 1470 | 0.0,0.0,1.0,0.0,0.0,3 1471 | 0.0,0.0,1.0,0.0,0.0,3 1472 | 0.0,1.0,0.0,0.0,0.0,2 1473 | 1.0,0.0,0.0,0.0,0.0,1 1474 | 0.0,0.0,0.0,0.0,1.0,5 1475 | 0.0,1.0,0.0,0.0,0.0,2 1476 | 0.0,1.0,0.0,0.0,0.0,2 1477 | 0.0,0.0,0.0,0.0,1.0,5 1478 | 1.0,0.0,0.0,0.0,0.0,1 1479 | 0.0,0.0,0.0,1.0,0.0,4 1480 | 0.0,1.0,0.0,0.0,0.0,2 1481 | 1.0,0.0,0.0,0.0,0.0,1 1482 | 1.0,0.0,0.0,0.0,0.0,1 1483 | 0.0,0.0,0.0,0.0,1.0,5 1484 | 0.0,0.0,0.0,1.0,0.0,4 1485 | 0.0,0.0,1.0,0.0,0.0,3 1486 | 0.0,0.0,1.0,0.0,0.0,3 1487 | 0.0,0.0,0.0,1.0,0.0,4 1488 | 0.0,0.0,0.0,0.0,1.0,5 1489 | 0.0,0.0,1.0,0.0,0.0,3 1490 | 0.0,0.0,1.0,0.0,0.0,3 1491 | 0.0,0.0,1.0,0.0,0.0,3 1492 | 1.0,0.0,0.0,0.0,0.0,1 1493 | 0.0,0.0,1.0,0.0,0.0,3 1494 | 0.0,0.0,0.0,1.0,0.0,4 1495 | 0.0,0.0,0.0,0.0,1.0,5 1496 | 0.0,0.0,1.0,0.0,0.0,3 1497 | 0.0,0.0,0.0,0.0,1.0,5 1498 | 1.0,0.0,0.0,0.0,0.0,1 1499 | 1.0,0.0,0.0,0.0,0.0,1 1500 | 0.0,0.0,0.0,1.0,0.0,4 1501 | 1.0,0.0,0.0,0.0,0.0,1 1502 | 0.0,0.0,0.0,0.0,1.0,5 1503 | 0.0,0.0,0.0,0.0,1.0,5 1504 | 0.0,0.0,0.0,0.0,1.0,5 1505 | 0.0,0.0,0.0,1.0,0.0,4 1506 | 1.0,0.0,0.0,0.0,0.0,1 1507 | 1.0,0.0,0.0,0.0,0.0,1 1508 | 0.0,0.0,0.0,0.0,1.0,5 1509 | 1.0,0.0,0.0,0.0,0.0,1 1510 | 1.0,0.0,0.0,0.0,0.0,1 1511 | 0.0,0.0,0.0,1.0,0.0,4 1512 | 0.0,0.0,0.0,1.0,0.0,4 1513 | 0.0,0.0,0.0,1.0,0.0,4 1514 | 0.0,0.0,1.0,0.0,0.0,3 1515 | 0.0,0.0,0.0,1.0,0.0,4 1516 | 0.0,0.0,1.0,0.0,0.0,3 1517 | 0.0,0.0,1.0,0.0,0.0,3 1518 | 1.0,0.0,0.0,0.0,0.0,1 1519 | 0.0,0.0,1.0,0.0,0.0,3 1520 | 0.0,1.0,0.0,0.0,0.0,2 1521 | 0.0,0.0,1.0,0.0,0.0,3 1522 | 1.0,0.0,0.0,0.0,0.0,1 1523 | 0.0,0.0,0.0,0.0,1.0,5 1524 | 0.0,0.0,0.0,0.0,1.0,5 1525 | 0.0,1.0,0.0,0.0,0.0,2 1526 | 0.0,0.0,0.0,0.0,1.0,5 1527 | 0.0,0.0,0.0,1.0,0.0,4 1528 | 0.0,0.0,0.0,0.0,1.0,5 1529 | 0.0,0.0,0.0,1.0,0.0,4 1530 | 1.0,0.0,0.0,0.0,0.0,1 1531 | 1.0,0.0,0.0,0.0,0.0,1 1532 | 0.0,1.0,0.0,0.0,0.0,2 1533 | 0.0,0.0,0.0,0.0,1.0,5 1534 | 0.0,0.0,0.0,1.0,0.0,4 1535 | 1.0,0.0,0.0,0.0,0.0,1 1536 | 0.0,0.0,0.0,1.0,0.0,4 1537 | 0.0,0.0,0.0,0.0,1.0,5 1538 | 0.0,0.0,1.0,0.0,0.0,3 1539 | 0.0,0.0,0.0,1.0,0.0,4 1540 | 0.0,0.0,0.0,0.0,1.0,5 1541 | 0.0,0.0,0.0,0.0,1.0,5 1542 | 1.0,0.0,0.0,0.0,0.0,1 1543 | 1.0,0.0,0.0,0.0,0.0,1 1544 | 0.0,0.0,1.0,0.0,0.0,3 1545 | 1.0,0.0,0.0,0.0,0.0,1 1546 | 0.0,1.0,0.0,0.0,0.0,2 1547 | 0.0,0.0,0.0,0.0,1.0,5 1548 | 1.0,0.0,0.0,0.0,0.0,1 1549 | 0.0,0.0,0.0,1.0,0.0,4 1550 | 0.0,0.0,0.0,0.0,1.0,5 1551 | 1.0,0.0,0.0,0.0,0.0,1 1552 | 0.0,0.0,0.0,1.0,0.0,4 1553 | 1.0,0.0,0.0,0.0,0.0,1 1554 | 0.0,0.0,0.0,0.0,1.0,5 1555 | 1.0,0.0,0.0,0.0,0.0,1 1556 | 0.0,0.0,0.0,0.0,1.0,5 1557 | 0.0,0.0,1.0,0.0,0.0,3 1558 | 1.0,0.0,0.0,0.0,0.0,1 1559 | 1.0,0.0,0.0,0.0,0.0,1 1560 | 1.0,0.0,0.0,0.0,0.0,1 1561 | 0.0,1.0,0.0,0.0,0.0,2 1562 | 1.0,0.0,0.0,0.0,0.0,1 1563 | 0.0,0.0,0.0,1.0,0.0,4 1564 | 0.0,0.0,0.0,0.0,1.0,5 1565 | 1.0,0.0,0.0,0.0,0.0,1 1566 | 0.0,0.0,0.0,0.0,1.0,5 1567 | 1.0,0.0,0.0,0.0,0.0,1 1568 | 0.0,0.0,0.0,0.0,1.0,5 1569 | 0.0,1.0,0.0,0.0,0.0,2 1570 | 0.0,0.0,0.0,1.0,0.0,4 1571 | 0.0,0.0,1.0,0.0,0.0,3 1572 | 0.0,0.0,0.0,1.0,0.0,4 1573 | 0.0,0.0,1.0,0.0,0.0,3 1574 | 0.0,0.0,1.0,0.0,0.0,3 1575 | 0.0,0.0,1.0,0.0,0.0,3 1576 | 0.0,0.0,0.0,0.0,1.0,5 1577 | 0.0,1.0,0.0,0.0,0.0,2 1578 | 1.0,0.0,0.0,0.0,0.0,1 1579 | 1.0,0.0,0.0,0.0,0.0,1 1580 | 1.0,0.0,0.0,0.0,0.0,1 1581 | 1.0,0.0,0.0,0.0,0.0,1 1582 | 0.0,1.0,0.0,0.0,0.0,2 1583 | 0.0,0.0,0.0,0.0,1.0,5 1584 | 0.0,1.0,0.0,0.0,0.0,2 1585 | 0.0,0.0,0.0,1.0,0.0,4 1586 | 0.0,0.0,0.0,1.0,0.0,4 1587 | 0.0,0.0,0.0,1.0,0.0,4 1588 | 0.0,1.0,0.0,0.0,0.0,2 1589 | 0.0,0.0,0.0,0.0,1.0,5 1590 | 0.0,1.0,0.0,0.0,0.0,2 1591 | 1.0,0.0,0.0,0.0,0.0,1 1592 | 1.0,0.0,0.0,0.0,0.0,1 1593 | 0.0,1.0,0.0,0.0,0.0,2 1594 | 1.0,0.0,0.0,0.0,0.0,1 1595 | 1.0,0.0,0.0,0.0,0.0,1 1596 | 0.0,1.0,0.0,0.0,0.0,2 1597 | 1.0,0.0,0.0,0.0,0.0,1 1598 | 0.0,0.0,1.0,0.0,0.0,3 1599 | 0.0,0.0,0.0,0.0,1.0,5 1600 | 0.0,0.0,0.0,0.0,1.0,5 1601 | 0.0,0.0,1.0,0.0,0.0,3 1602 | 0.0,0.0,1.0,0.0,0.0,3 1603 | 0.0,1.0,0.0,0.0,0.0,2 1604 | 0.0,0.0,1.0,0.0,0.0,3 1605 | 0.0,0.0,0.0,0.0,1.0,5 1606 | 1.0,0.0,0.0,0.0,0.0,1 1607 | 0.0,0.0,0.0,1.0,0.0,4 1608 | 0.0,0.0,0.0,1.0,0.0,4 1609 | 0.0,0.0,0.0,1.0,0.0,4 1610 | 1.0,0.0,0.0,0.0,0.0,1 1611 | 0.0,0.0,0.0,1.0,0.0,4 1612 | 1.0,0.0,0.0,0.0,0.0,1 1613 | 0.0,0.0,1.0,0.0,0.0,3 1614 | 0.0,0.0,0.0,0.0,1.0,5 1615 | 0.0,0.0,0.0,0.0,1.0,5 1616 | 0.0,0.0,0.0,1.0,0.0,4 1617 | 0.0,0.0,0.0,1.0,0.0,4 1618 | 0.0,1.0,0.0,0.0,0.0,2 1619 | 0.0,1.0,0.0,0.0,0.0,2 1620 | 0.0,0.0,0.0,1.0,0.0,4 1621 | 0.0,0.0,0.0,0.0,1.0,5 1622 | 1.0,0.0,0.0,0.0,0.0,1 1623 | 0.0,0.0,1.0,0.0,0.0,3 1624 | 0.0,0.0,1.0,0.0,0.0,3 1625 | 0.0,0.0,0.0,1.0,0.0,4 1626 | 0.0,0.0,0.0,0.0,1.0,5 1627 | 0.0,0.0,0.0,0.0,1.0,5 1628 | 0.0,0.0,0.0,0.0,1.0,5 1629 | 0.0,1.0,0.0,0.0,0.0,2 1630 | 0.0,0.0,1.0,0.0,0.0,3 1631 | 0.0,0.0,0.0,0.0,1.0,5 1632 | 0.0,0.0,0.0,1.0,0.0,4 1633 | 1.0,0.0,0.0,0.0,0.0,1 1634 | 0.0,1.0,0.0,0.0,0.0,2 1635 | 0.0,0.0,1.0,0.0,0.0,3 1636 | 0.0,1.0,0.0,0.0,0.0,2 1637 | 1.0,0.0,0.0,0.0,0.0,1 1638 | 0.0,0.0,0.0,0.0,1.0,5 1639 | 0.0,0.0,0.0,0.0,1.0,5 1640 | 0.0,0.0,1.0,0.0,0.0,3 1641 | 0.0,1.0,0.0,0.0,0.0,2 1642 | 0.0,1.0,0.0,0.0,0.0,2 1643 | 0.0,0.0,0.0,1.0,0.0,4 1644 | 0.0,0.0,0.0,0.0,1.0,5 1645 | 1.0,0.0,0.0,0.0,0.0,1 1646 | 0.0,0.0,0.0,1.0,0.0,4 1647 | 0.0,0.0,0.0,0.0,1.0,5 1648 | 0.0,0.0,1.0,0.0,0.0,3 1649 | 1.0,0.0,0.0,0.0,0.0,1 1650 | 0.0,1.0,0.0,0.0,0.0,2 1651 | 1.0,0.0,0.0,0.0,0.0,1 1652 | 0.0,0.0,1.0,0.0,0.0,3 1653 | 0.0,1.0,0.0,0.0,0.0,2 1654 | 0.0,1.0,0.0,0.0,0.0,2 1655 | 0.0,1.0,0.0,0.0,0.0,2 1656 | 0.0,0.0,0.0,0.0,1.0,5 1657 | 0.0,0.0,0.0,1.0,0.0,4 1658 | 0.0,1.0,0.0,0.0,0.0,2 1659 | 0.0,1.0,0.0,0.0,0.0,2 1660 | 0.0,0.0,0.0,0.0,1.0,5 1661 | 0.0,1.0,0.0,0.0,0.0,2 1662 | 1.0,0.0,0.0,0.0,0.0,1 1663 | 0.0,0.0,1.0,0.0,0.0,3 1664 | 0.0,1.0,0.0,0.0,0.0,2 1665 | 0.0,0.0,1.0,0.0,0.0,3 1666 | 0.0,0.0,1.0,0.0,0.0,3 1667 | 0.0,0.0,0.0,0.0,1.0,5 1668 | 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0.0,0.0,1.0,0.0,0.0 139 | 0.0,0.0,0.0,0.0,1.0 140 | 1.0,0.0,0.0,0.0,0.0 141 | 0.0,0.0,1.0,0.0,0.0 142 | 0.0,0.0,0.0,0.0,1.0 143 | 0.0,1.0,0.0,0.0,0.0 144 | 0.0,0.0,0.0,0.0,1.0 145 | 1.0,0.0,0.0,0.0,0.0 146 | 0.0,0.0,1.0,0.0,0.0 147 | 0.0,1.0,0.0,0.0,0.0 148 | 0.0,1.0,0.0,0.0,0.0 149 | 0.0,1.0,0.0,0.0,0.0 150 | 0.0,0.0,0.0,1.0,0.0 151 | 0.0,0.0,0.0,1.0,0.0 152 | 1.0,0.0,0.0,0.0,0.0 153 | 0.0,0.0,0.0,0.0,1.0 154 | 0.0,1.0,0.0,0.0,0.0 155 | 0.0,0.0,1.0,0.0,0.0 156 | 0.0,0.0,0.0,0.0,1.0 157 | 0.0,0.0,0.0,1.0,0.0 158 | 1.0,0.0,0.0,0.0,0.0 159 | 1.0,0.0,0.0,0.0,0.0 160 | 0.0,0.0,0.0,1.0,0.0 161 | 0.0,0.0,0.0,0.0,1.0 162 | 0.0,0.0,0.0,1.0,0.0 163 | 0.0,0.0,0.0,1.0,0.0 164 | 0.0,1.0,0.0,0.0,0.0 165 | 0.0,0.0,0.0,0.0,1.0 166 | 0.0,0.0,0.0,1.0,0.0 167 | 1.0,0.0,0.0,0.0,0.0 168 | 1.0,0.0,0.0,0.0,0.0 169 | 0.0,0.0,0.0,0.0,1.0 170 | 0.0,1.0,0.0,0.0,0.0 171 | 0.0,0.0,1.0,0.0,0.0 172 | 0.0,0.0,0.0,0.0,1.0 173 | 1.0,0.0,0.0,0.0,0.0 174 | 0.0,0.0,0.0,1.0,0.0 175 | 0.0,0.0,1.0,0.0,0.0 176 | 1.0,0.0,0.0,0.0,0.0 177 | 0.0,1.0,0.0,0.0,0.0 178 | 1.0,0.0,0.0,0.0,0.0 179 | 1.0,0.0,0.0,0.0,0.0 180 | 0.0,1.0,0.0,0.0,0.0 181 | 0.0,0.0,1.0,0.0,0.0 182 | 0.0,1.0,0.0,0.0,0.0 183 | 0.0,0.0,0.0,1.0,0.0 184 | 1.0,0.0,0.0,0.0,0.0 185 | 0.0,0.0,0.0,0.0,1.0 186 | 1.0,0.0,0.0,0.0,0.0 187 | 1.0,0.0,0.0,0.0,0.0 188 | 0.0,1.0,0.0,0.0,0.0 189 | 1.0,0.0,0.0,0.0,0.0 190 | 0.0,0.0,1.0,0.0,0.0 191 | 0.0,0.0,0.0,0.0,1.0 192 | 0.0,0.0,0.0,1.0,0.0 193 | 0.0,1.0,0.0,0.0,0.0 194 | 0.0,0.0,0.0,1.0,0.0 195 | 0.0,0.0,0.0,1.0,0.0 196 | 0.0,0.0,0.0,0.0,1.0 197 | 1.0,0.0,0.0,0.0,0.0 198 | 0.0,0.0,0.0,1.0,0.0 199 | 0.0,0.0,1.0,0.0,0.0 200 | 0.0,0.0,1.0,0.0,0.0 201 | 0.0,0.0,0.0,1.0,0.0 202 | 0.0,0.0,0.0,0.0,1.0 203 | 0.0,0.0,0.0,1.0,0.0 204 | 0.0,0.0,0.0,0.0,1.0 205 | 1.0,0.0,0.0,0.0,0.0 206 | 0.0,0.0,1.0,0.0,0.0 207 | 0.0,0.0,0.0,0.0,1.0 208 | 0.0,0.0,0.0,0.0,1.0 209 | 0.0,0.0,0.0,1.0,0.0 210 | 0.0,0.0,0.0,0.0,1.0 211 | 1.0,0.0,0.0,0.0,0.0 212 | 0.0,0.0,0.0,1.0,0.0 213 | 0.0,0.0,0.0,1.0,0.0 214 | 0.0,0.0,1.0,0.0,0.0 215 | 0.0,1.0,0.0,0.0,0.0 216 | 0.0,0.0,0.0,1.0,0.0 217 | 0.0,0.0,1.0,0.0,0.0 218 | 0.0,0.0,1.0,0.0,0.0 219 | 0.0,0.0,0.0,1.0,0.0 220 | 1.0,0.0,0.0,0.0,0.0 221 | 0.0,1.0,0.0,0.0,0.0 222 | 0.0,1.0,0.0,0.0,0.0 223 | 1.0,0.0,0.0,0.0,0.0 224 | 0.0,0.0,0.0,0.0,1.0 225 | 0.0,0.0,0.0,1.0,0.0 226 | 0.0,0.0,1.0,0.0,0.0 227 | 0.0,1.0,0.0,0.0,0.0 228 | 0.0,0.0,1.0,0.0,0.0 229 | 0.0,1.0,0.0,0.0,0.0 230 | 0.0,1.0,0.0,0.0,0.0 231 | 1.0,0.0,0.0,0.0,0.0 232 | 1.0,0.0,0.0,0.0,0.0 233 | 0.0,1.0,0.0,0.0,0.0 234 | 0.0,0.0,0.0,0.0,1.0 235 | 0.0,1.0,0.0,0.0,0.0 236 | 1.0,0.0,0.0,0.0,0.0 237 | 0.0,0.0,0.0,0.0,1.0 238 | 0.0,0.0,0.0,1.0,0.0 239 | 0.0,0.0,0.0,0.0,1.0 240 | 0.0,0.0,1.0,0.0,0.0 241 | 1.0,0.0,0.0,0.0,0.0 242 | 0.0,0.0,1.0,0.0,0.0 243 | 0.0,0.0,0.0,0.0,1.0 244 | 1.0,0.0,0.0,0.0,0.0 245 | 1.0,0.0,0.0,0.0,0.0 246 | 0.0,1.0,0.0,0.0,0.0 247 | 0.0,0.0,1.0,0.0,0.0 248 | 0.0,0.0,0.0,1.0,0.0 249 | 0.0,0.0,1.0,0.0,0.0 250 | 0.0,0.0,0.0,1.0,0.0 251 | 0.0,1.0,0.0,0.0,0.0 252 | 0.0,0.0,0.0,0.0,1.0 253 | 0.0,0.0,0.0,1.0,0.0 254 | 0.0,0.0,1.0,0.0,0.0 255 | 0.0,0.0,1.0,0.0,0.0 256 | 1.0,0.0,0.0,0.0,0.0 257 | 0.0,0.0,1.0,0.0,0.0 258 | 0.0,0.0,1.0,0.0,0.0 259 | 0.0,0.0,0.0,1.0,0.0 260 | 0.0,0.0,0.0,1.0,0.0 261 | 1.0,0.0,0.0,0.0,0.0 262 | 1.0,0.0,0.0,0.0,0.0 263 | 0.0,0.0,1.0,0.0,0.0 264 | 1.0,0.0,0.0,0.0,0.0 265 | 0.0,0.0,0.0,0.0,1.0 266 | 0.0,1.0,0.0,0.0,0.0 267 | 0.0,0.0,0.0,0.0,1.0 268 | 0.0,0.0,1.0,0.0,0.0 269 | 0.0,0.0,0.0,1.0,0.0 270 | 0.0,0.0,0.0,1.0,0.0 271 | 1.0,0.0,0.0,0.0,0.0 272 | 0.0,0.0,0.0,1.0,0.0 273 | 0.0,1.0,0.0,0.0,0.0 274 | 0.0,0.0,0.0,1.0,0.0 275 | 0.0,1.0,0.0,0.0,0.0 276 | 0.0,0.0,0.0,1.0,0.0 277 | 0.0,1.0,0.0,0.0,0.0 278 | 0.0,0.0,0.0,0.0,1.0 279 | 0.0,0.0,1.0,0.0,0.0 280 | 1.0,0.0,0.0,0.0,0.0 281 | 0.0,0.0,0.0,1.0,0.0 282 | 0.0,1.0,0.0,0.0,0.0 283 | 1.0,0.0,0.0,0.0,0.0 284 | 0.0,0.0,0.0,0.0,1.0 285 | 0.0,1.0,0.0,0.0,0.0 286 | 0.0,0.0,1.0,0.0,0.0 287 | 0.0,0.0,0.0,0.0,1.0 288 | 1.0,0.0,0.0,0.0,0.0 289 | 0.0,0.0,0.0,0.0,1.0 290 | 0.0,0.0,1.0,0.0,0.0 291 | 1.0,0.0,0.0,0.0,0.0 292 | 0.0,1.0,0.0,0.0,0.0 293 | 0.0,0.0,0.0,1.0,0.0 294 | 0.0,0.0,0.0,1.0,0.0 295 | 0.0,0.0,0.0,1.0,0.0 296 | 1.0,0.0,0.0,0.0,0.0 297 | 0.0,0.0,0.0,0.0,1.0 298 | 1.0,0.0,0.0,0.0,0.0 299 | 0.0,1.0,0.0,0.0,0.0 300 | 0.0,1.0,0.0,0.0,0.0 301 | 0.0,1.0,0.0,0.0,0.0 302 | 0.0,1.0,0.0,0.0,0.0 303 | 1.0,0.0,0.0,0.0,0.0 304 | 0.0,1.0,0.0,0.0,0.0 305 | 0.0,0.0,0.0,0.0,1.0 306 | 0.0,0.0,0.0,1.0,0.0 307 | 0.0,0.0,1.0,0.0,0.0 308 | 0.0,0.0,1.0,0.0,0.0 309 | 0.0,0.0,0.0,0.0,1.0 310 | 0.0,0.0,0.0,1.0,0.0 311 | 0.0,0.0,1.0,0.0,0.0 312 | 0.0,0.0,0.0,0.0,1.0 313 | 0.0,0.0,1.0,0.0,0.0 314 | 0.0,0.0,0.0,1.0,0.0 315 | 0.0,0.0,0.0,0.0,1.0 316 | 1.0,0.0,0.0,0.0,0.0 317 | 1.0,0.0,0.0,0.0,0.0 318 | 1.0,0.0,0.0,0.0,0.0 319 | 0.0,1.0,0.0,0.0,0.0 320 | 0.0,1.0,0.0,0.0,0.0 321 | 1.0,0.0,0.0,0.0,0.0 322 | 0.0,1.0,0.0,0.0,0.0 323 | 0.0,0.0,1.0,0.0,0.0 324 | 0.0,1.0,0.0,0.0,0.0 325 | 1.0,0.0,0.0,0.0,0.0 326 | 1.0,0.0,0.0,0.0,0.0 327 | 1.0,0.0,0.0,0.0,0.0 328 | 0.0,0.0,1.0,0.0,0.0 329 | 0.0,1.0,0.0,0.0,0.0 330 | 0.0,0.0,0.0,0.0,1.0 331 | 0.0,0.0,1.0,0.0,0.0 332 | 0.0,1.0,0.0,0.0,0.0 333 | 1.0,0.0,0.0,0.0,0.0 334 | 0.0,1.0,0.0,0.0,0.0 335 | 0.0,1.0,0.0,0.0,0.0 336 | 0.0,0.0,0.0,0.0,1.0 337 | 0.0,1.0,0.0,0.0,0.0 338 | 1.0,0.0,0.0,0.0,0.0 339 | 0.0,0.0,0.0,0.0,1.0 340 | 0.0,0.0,1.0,0.0,0.0 341 | 0.0,0.0,0.0,1.0,0.0 342 | 1.0,0.0,0.0,0.0,0.0 343 | 0.0,1.0,0.0,0.0,0.0 344 | 0.0,0.0,1.0,0.0,0.0 345 | 0.0,0.0,0.0,1.0,0.0 346 | 1.0,0.0,0.0,0.0,0.0 347 | 1.0,0.0,0.0,0.0,0.0 348 | 0.0,0.0,0.0,0.0,1.0 349 | 1.0,0.0,0.0,0.0,0.0 350 | 0.0,0.0,0.0,1.0,0.0 351 | 0.0,0.0,1.0,0.0,0.0 352 | 0.0,1.0,0.0,0.0,0.0 353 | 0.0,0.0,1.0,0.0,0.0 354 | 0.0,0.0,0.0,1.0,0.0 355 | 0.0,0.0,0.0,0.0,1.0 356 | 0.0,0.0,1.0,0.0,0.0 357 | 0.0,0.0,1.0,0.0,0.0 358 | 0.0,0.0,0.0,0.0,1.0 359 | 0.0,0.0,0.0,0.0,1.0 360 | 0.0,0.0,0.0,1.0,0.0 361 | 0.0,1.0,0.0,0.0,0.0 362 | 0.0,0.0,0.0,1.0,0.0 363 | 0.0,0.0,0.0,0.0,1.0 364 | 0.0,1.0,0.0,0.0,0.0 365 | 0.0,0.0,0.0,1.0,0.0 366 | 0.0,1.0,0.0,0.0,0.0 367 | 0.0,0.0,0.0,0.0,1.0 368 | 0.0,1.0,0.0,0.0,0.0 369 | 0.0,1.0,0.0,0.0,0.0 370 | 0.0,0.0,0.0,1.0,0.0 371 | 0.0,1.0,0.0,0.0,0.0 372 | 0.0,0.0,0.0,0.0,1.0 373 | 0.0,0.0,1.0,0.0,0.0 374 | 0.0,0.0,1.0,0.0,0.0 375 | 0.0,0.0,0.0,1.0,0.0 376 | 0.0,0.0,0.0,1.0,0.0 377 | 0.0,1.0,0.0,0.0,0.0 378 | 0.0,0.0,0.0,0.0,1.0 379 | 1.0,0.0,0.0,0.0,0.0 380 | 0.0,0.0,1.0,0.0,0.0 381 | 0.0,0.0,0.0,0.0,1.0 382 | 0.0,1.0,0.0,0.0,0.0 383 | 1.0,0.0,0.0,0.0,0.0 384 | 0.0,0.0,1.0,0.0,0.0 385 | 1.0,0.0,0.0,0.0,0.0 386 | 0.0,0.0,0.0,0.0,1.0 387 | 0.0,1.0,0.0,0.0,0.0 388 | 0.0,0.0,0.0,1.0,0.0 389 | 0.0,0.0,0.0,1.0,0.0 390 | 0.0,0.0,1.0,0.0,0.0 391 | 0.0,0.0,1.0,0.0,0.0 392 | 0.0,0.0,0.0,1.0,0.0 393 | 0.0,0.0,0.0,1.0,0.0 394 | 0.0,0.0,0.0,0.0,1.0 395 | 0.0,0.0,1.0,0.0,0.0 396 | 0.0,0.0,0.0,0.0,1.0 397 | 1.0,0.0,0.0,0.0,0.0 398 | 1.0,0.0,0.0,0.0,0.0 399 | 0.0,0.0,0.0,0.0,1.0 400 | 1.0,0.0,0.0,0.0,0.0 401 | 0.0,1.0,0.0,0.0,0.0 402 | 0.0,1.0,0.0,0.0,0.0 403 | 1.0,0.0,0.0,0.0,0.0 404 | 0.0,1.0,0.0,0.0,0.0 405 | 0.0,0.0,0.0,1.0,0.0 406 | 1.0,0.0,0.0,0.0,0.0 407 | 1.0,0.0,0.0,0.0,0.0 408 | 0.0,0.0,0.0,0.0,1.0 409 | 0.0,1.0,0.0,0.0,0.0 410 | 0.0,0.0,1.0,0.0,0.0 411 | 1.0,0.0,0.0,0.0,0.0 412 | 1.0,0.0,0.0,0.0,0.0 413 | 1.0,0.0,0.0,0.0,0.0 414 | 0.0,0.0,0.0,1.0,0.0 415 | 1.0,0.0,0.0,0.0,0.0 416 | 0.0,0.0,0.0,1.0,0.0 417 | 0.0,0.0,0.0,0.0,1.0 418 | 0.0,0.0,1.0,0.0,0.0 419 | 0.0,0.0,0.0,1.0,0.0 420 | 0.0,0.0,0.0,0.0,1.0 421 | 1.0,0.0,0.0,0.0,0.0 422 | 0.0,0.0,1.0,0.0,0.0 423 | 0.0,0.0,1.0,0.0,0.0 424 | 1.0,0.0,0.0,0.0,0.0 425 | 0.0,0.0,1.0,0.0,0.0 426 | 1.0,0.0,0.0,0.0,0.0 427 | 1.0,0.0,0.0,0.0,0.0 428 | 1.0,0.0,0.0,0.0,0.0 429 | 0.0,0.0,0.0,1.0,0.0 430 | 0.0,0.0,0.0,0.0,1.0 431 | 1.0,0.0,0.0,0.0,0.0 432 | 0.0,0.0,1.0,0.0,0.0 433 | 0.0,0.0,0.0,0.0,1.0 434 | 0.0,1.0,0.0,0.0,0.0 435 | 0.0,0.0,0.0,1.0,0.0 436 | 1.0,0.0,0.0,0.0,0.0 437 | 0.0,1.0,0.0,0.0,0.0 438 | 0.0,0.0,1.0,0.0,0.0 439 | 0.0,1.0,0.0,0.0,0.0 440 | 0.0,0.0,0.0,0.0,1.0 441 | 0.0,1.0,0.0,0.0,0.0 442 | 0.0,0.0,0.0,1.0,0.0 443 | 1.0,0.0,0.0,0.0,0.0 444 | 1.0,0.0,0.0,0.0,0.0 445 | 0.0,1.0,0.0,0.0,0.0 446 | 1.0,0.0,0.0,0.0,0.0 447 | 0.0,1.0,0.0,0.0,0.0 448 | 0.0,0.0,1.0,0.0,0.0 449 | 0.0,0.0,0.0,1.0,0.0 450 | 0.0,0.0,0.0,1.0,0.0 451 | 1.0,0.0,0.0,0.0,0.0 452 | 0.0,1.0,0.0,0.0,0.0 453 | 1.0,0.0,0.0,0.0,0.0 454 | 1.0,0.0,0.0,0.0,0.0 455 | 0.0,0.0,1.0,0.0,0.0 456 | 0.0,1.0,0.0,0.0,0.0 457 | 0.0,0.0,1.0,0.0,0.0 458 | 0.0,0.0,0.0,1.0,0.0 459 | 0.0,0.0,0.0,1.0,0.0 460 | 0.0,0.0,0.0,0.0,1.0 461 | 0.0,0.0,0.0,0.0,1.0 462 | 1.0,0.0,0.0,0.0,0.0 463 | 1.0,0.0,0.0,0.0,0.0 464 | 0.0,0.0,0.0,0.0,1.0 465 | 0.0,0.0,0.0,0.0,1.0 466 | 0.0,0.0,1.0,0.0,0.0 467 | 0.0,1.0,0.0,0.0,0.0 468 | 0.0,0.0,0.0,0.0,1.0 469 | 1.0,0.0,0.0,0.0,0.0 470 | 1.0,0.0,0.0,0.0,0.0 471 | 0.0,0.0,0.0,0.0,1.0 472 | 0.0,0.0,0.0,1.0,0.0 473 | 0.0,1.0,0.0,0.0,0.0 474 | 0.0,0.0,1.0,0.0,0.0 475 | 0.0,0.0,1.0,0.0,0.0 476 | 0.0,0.0,0.0,0.0,1.0 477 | 0.0,0.0,0.0,0.0,1.0 478 | 0.0,0.0,1.0,0.0,0.0 479 | 0.0,0.0,1.0,0.0,0.0 480 | 1.0,0.0,0.0,0.0,0.0 481 | 0.0,0.0,0.0,0.0,1.0 482 | 0.0,0.0,0.0,0.0,1.0 483 | 0.0,0.0,0.0,0.0,1.0 484 | 1.0,0.0,0.0,0.0,0.0 485 | 1.0,0.0,0.0,0.0,0.0 486 | 0.0,0.0,0.0,0.0,1.0 487 | 1.0,0.0,0.0,0.0,0.0 488 | 0.0,0.0,0.0,1.0,0.0 489 | 0.0,0.0,0.0,1.0,0.0 490 | 0.0,0.0,0.0,0.0,1.0 491 | 0.0,0.0,0.0,1.0,0.0 492 | 0.0,0.0,0.0,0.0,1.0 493 | 0.0,1.0,0.0,0.0,0.0 494 | 0.0,0.0,0.0,1.0,0.0 495 | 0.0,0.0,0.0,1.0,0.0 496 | 0.0,0.0,1.0,0.0,0.0 497 | 0.0,1.0,0.0,0.0,0.0 498 | 0.0,0.0,1.0,0.0,0.0 499 | 0.0,0.0,0.0,1.0,0.0 500 | 1.0,0.0,0.0,0.0,0.0 501 | 0.0,0.0,0.0,0.0,1.0 502 | 0.0,0.0,0.0,0.0,1.0 503 | 0.0,0.0,0.0,0.0,1.0 504 | 1.0,0.0,0.0,0.0,0.0 505 | 0.0,1.0,0.0,0.0,0.0 506 | 1.0,0.0,0.0,0.0,0.0 507 | 0.0,1.0,0.0,0.0,0.0 508 | 0.0,0.0,1.0,0.0,0.0 509 | 1.0,0.0,0.0,0.0,0.0 510 | 0.0,1.0,0.0,0.0,0.0 511 | 1.0,0.0,0.0,0.0,0.0 512 | 0.0,0.0,1.0,0.0,0.0 513 | 1.0,0.0,0.0,0.0,0.0 514 | 0.0,0.0,0.0,0.0,1.0 515 | 0.0,0.0,0.0,1.0,0.0 516 | 1.0,0.0,0.0,0.0,0.0 517 | 0.0,1.0,0.0,0.0,0.0 518 | 1.0,0.0,0.0,0.0,0.0 519 | 0.0,0.0,0.0,0.0,1.0 520 | 0.0,0.0,0.0,0.0,1.0 521 | 0.0,0.0,0.0,1.0,0.0 522 | 0.0,0.0,1.0,0.0,0.0 523 | 0.0,0.0,1.0,0.0,0.0 524 | 0.0,0.0,1.0,0.0,0.0 525 | 0.0,1.0,0.0,0.0,0.0 526 | 0.0,0.0,0.0,0.0,1.0 527 | 1.0,0.0,0.0,0.0,0.0 528 | 0.0,0.0,0.0,1.0,0.0 529 | 0.0,0.0,0.0,0.0,1.0 530 | 1.0,0.0,0.0,0.0,0.0 531 | 0.0,0.0,0.0,1.0,0.0 532 | 0.0,0.0,0.0,0.0,1.0 533 | 0.0,1.0,0.0,0.0,0.0 534 | 0.0,0.0,0.0,0.0,1.0 535 | 0.0,1.0,0.0,0.0,0.0 536 | 0.0,0.0,0.0,1.0,0.0 537 | 0.0,0.0,0.0,1.0,0.0 538 | 0.0,0.0,0.0,1.0,0.0 539 | 0.0,1.0,0.0,0.0,0.0 540 | 1.0,0.0,0.0,0.0,0.0 541 | 0.0,0.0,1.0,0.0,0.0 542 | 0.0,0.0,0.0,0.0,1.0 543 | 0.0,0.0,0.0,1.0,0.0 544 | 0.0,0.0,0.0,1.0,0.0 545 | 0.0,0.0,1.0,0.0,0.0 546 | 0.0,0.0,1.0,0.0,0.0 547 | 0.0,0.0,1.0,0.0,0.0 548 | 0.0,1.0,0.0,0.0,0.0 549 | 0.0,0.0,0.0,0.0,1.0 550 | 1.0,0.0,0.0,0.0,0.0 551 | 0.0,1.0,0.0,0.0,0.0 552 | 0.0,0.0,0.0,0.0,1.0 553 | 1.0,0.0,0.0,0.0,0.0 554 | 0.0,0.0,0.0,0.0,1.0 555 | 0.0,0.0,0.0,1.0,0.0 556 | 0.0,0.0,1.0,0.0,0.0 557 | 0.0,1.0,0.0,0.0,0.0 558 | 0.0,0.0,0.0,1.0,0.0 559 | 0.0,0.0,1.0,0.0,0.0 560 | 0.0,0.0,0.0,1.0,0.0 561 | 0.0,0.0,0.0,1.0,0.0 562 | 0.0,0.0,1.0,0.0,0.0 563 | 0.0,0.0,1.0,0.0,0.0 564 | 0.0,0.0,0.0,1.0,0.0 565 | 0.0,0.0,1.0,0.0,0.0 566 | 0.0,0.0,0.0,1.0,0.0 567 | 0.0,1.0,0.0,0.0,0.0 568 | 0.0,0.0,1.0,0.0,0.0 569 | 0.0,0.0,0.0,0.0,1.0 570 | 0.0,0.0,1.0,0.0,0.0 571 | 0.0,0.0,0.0,0.0,1.0 572 | 0.0,0.0,0.0,0.0,1.0 573 | 0.0,1.0,0.0,0.0,0.0 574 | 1.0,0.0,0.0,0.0,0.0 575 | 0.0,0.0,1.0,0.0,0.0 576 | 0.0,1.0,0.0,0.0,0.0 577 | 0.0,0.0,0.0,0.0,1.0 578 | 0.0,0.0,1.0,0.0,0.0 579 | 0.0,0.0,0.0,0.0,1.0 580 | 0.0,0.0,0.0,1.0,0.0 581 | 0.0,0.0,0.0,0.0,1.0 582 | 0.0,0.0,0.0,0.0,1.0 583 | 0.0,0.0,0.0,1.0,0.0 584 | 1.0,0.0,0.0,0.0,0.0 585 | 0.0,1.0,0.0,0.0,0.0 586 | 0.0,0.0,0.0,0.0,1.0 587 | 0.0,0.0,1.0,0.0,0.0 588 | 1.0,0.0,0.0,0.0,0.0 589 | 0.0,0.0,0.0,0.0,1.0 590 | 1.0,0.0,0.0,0.0,0.0 591 | 0.0,1.0,0.0,0.0,0.0 592 | 0.0,0.0,1.0,0.0,0.0 593 | 1.0,0.0,0.0,0.0,0.0 594 | 0.0,0.0,0.0,0.0,1.0 595 | 0.0,0.0,0.0,0.0,1.0 596 | 0.0,0.0,0.0,0.0,1.0 597 | 0.0,0.0,1.0,0.0,0.0 598 | 0.0,0.0,1.0,0.0,0.0 599 | 0.0,1.0,0.0,0.0,0.0 600 | 0.0,0.0,1.0,0.0,0.0 601 | 0.0,1.0,0.0,0.0,0.0 602 | 1.0,0.0,0.0,0.0,0.0 603 | 0.0,0.0,0.0,1.0,0.0 604 | 0.0,0.0,0.0,1.0,0.0 605 | 0.0,0.0,1.0,0.0,0.0 606 | 0.0,1.0,0.0,0.0,0.0 607 | 0.0,0.0,0.0,1.0,0.0 608 | 0.0,1.0,0.0,0.0,0.0 609 | 0.0,0.0,0.0,0.0,1.0 610 | 0.0,0.0,0.0,1.0,0.0 611 | 0.0,0.0,0.0,1.0,0.0 612 | 0.0,0.0,0.0,0.0,1.0 613 | 0.0,0.0,0.0,0.0,1.0 614 | 0.0,0.0,0.0,0.0,1.0 615 | 0.0,0.0,1.0,0.0,0.0 616 | 0.0,0.0,0.0,1.0,0.0 617 | 0.0,0.0,0.0,1.0,0.0 618 | 0.0,0.0,0.0,0.0,1.0 619 | 0.0,1.0,0.0,0.0,0.0 620 | 0.0,0.0,0.0,1.0,0.0 621 | 0.0,0.0,0.0,0.0,1.0 622 | 0.0,1.0,0.0,0.0,0.0 623 | 1.0,0.0,0.0,0.0,0.0 624 | 1.0,0.0,0.0,0.0,0.0 625 | 1.0,0.0,0.0,0.0,0.0 626 | 0.0,0.0,0.0,0.0,1.0 627 | 0.0,0.0,1.0,0.0,0.0 628 | 0.0,0.0,0.0,1.0,0.0 629 | 0.0,0.0,0.0,0.0,1.0 630 | 0.0,0.0,1.0,0.0,0.0 631 | 0.0,0.0,1.0,0.0,0.0 632 | 0.0,1.0,0.0,0.0,0.0 633 | 0.0,1.0,0.0,0.0,0.0 634 | 0.0,1.0,0.0,0.0,0.0 635 | 0.0,1.0,0.0,0.0,0.0 636 | 0.0,1.0,0.0,0.0,0.0 637 | 0.0,0.0,0.0,1.0,0.0 638 | 0.0,1.0,0.0,0.0,0.0 639 | 1.0,0.0,0.0,0.0,0.0 640 | 0.0,0.0,0.0,1.0,0.0 641 | 0.0,1.0,0.0,0.0,0.0 642 | 0.0,0.0,0.0,0.0,1.0 643 | 0.0,0.0,0.0,1.0,0.0 644 | 0.0,0.0,0.0,0.0,1.0 645 | 0.0,1.0,0.0,0.0,0.0 646 | 0.0,0.0,1.0,0.0,0.0 647 | 1.0,0.0,0.0,0.0,0.0 648 | 0.0,0.0,1.0,0.0,0.0 649 | 0.0,0.0,0.0,1.0,0.0 650 | 0.0,0.0,0.0,1.0,0.0 651 | 0.0,1.0,0.0,0.0,0.0 652 | 1.0,0.0,0.0,0.0,0.0 653 | 0.0,0.0,0.0,0.0,1.0 654 | 1.0,0.0,0.0,0.0,0.0 655 | 0.0,1.0,0.0,0.0,0.0 656 | 1.0,0.0,0.0,0.0,0.0 657 | 0.0,0.0,1.0,0.0,0.0 658 | 0.0,1.0,0.0,0.0,0.0 659 | 0.0,0.0,0.0,0.0,1.0 660 | 0.0,0.0,0.0,1.0,0.0 661 | 1.0,0.0,0.0,0.0,0.0 662 | 0.0,1.0,0.0,0.0,0.0 663 | 0.0,0.0,0.0,0.0,1.0 664 | 0.0,0.0,0.0,0.0,1.0 665 | 0.0,0.0,0.0,1.0,0.0 666 | 0.0,0.0,1.0,0.0,0.0 667 | 0.0,0.0,0.0,1.0,0.0 668 | 0.0,0.0,1.0,0.0,0.0 669 | 0.0,0.0,0.0,0.0,1.0 670 | 1.0,0.0,0.0,0.0,0.0 671 | 0.0,1.0,0.0,0.0,0.0 672 | 0.0,0.0,0.0,0.0,1.0 673 | 1.0,0.0,0.0,0.0,0.0 674 | 0.0,0.0,0.0,0.0,1.0 675 | 0.0,0.0,0.0,0.0,1.0 676 | 0.0,0.0,0.0,1.0,0.0 677 | 0.0,0.0,1.0,0.0,0.0 678 | 1.0,0.0,0.0,0.0,0.0 679 | 0.0,0.0,1.0,0.0,0.0 680 | 0.0,0.0,1.0,0.0,0.0 681 | 0.0,0.0,0.0,1.0,0.0 682 | 0.0,0.0,0.0,0.0,1.0 683 | 0.0,0.0,1.0,0.0,0.0 684 | 0.0,1.0,0.0,0.0,0.0 685 | 1.0,0.0,0.0,0.0,0.0 686 | 0.0,0.0,0.0,1.0,0.0 687 | 0.0,0.0,0.0,1.0,0.0 688 | 0.0,0.0,0.0,0.0,1.0 689 | 0.0,0.0,0.0,1.0,0.0 690 | 0.0,0.0,0.0,0.0,1.0 691 | 0.0,0.0,0.0,0.0,1.0 692 | 0.0,0.0,1.0,0.0,0.0 693 | 0.0,0.0,0.0,0.0,1.0 694 | 0.0,0.0,1.0,0.0,0.0 695 | 0.0,0.0,0.0,1.0,0.0 696 | 0.0,0.0,1.0,0.0,0.0 697 | 0.0,1.0,0.0,0.0,0.0 698 | 0.0,0.0,0.0,1.0,0.0 699 | 0.0,0.0,0.0,0.0,1.0 700 | 0.0,1.0,0.0,0.0,0.0 701 | 0.0,1.0,0.0,0.0,0.0 702 | 0.0,0.0,0.0,1.0,0.0 703 | 0.0,1.0,0.0,0.0,0.0 704 | 1.0,0.0,0.0,0.0,0.0 705 | 0.0,0.0,0.0,1.0,0.0 706 | 0.0,1.0,0.0,0.0,0.0 707 | 0.0,1.0,0.0,0.0,0.0 708 | 1.0,0.0,0.0,0.0,0.0 709 | 0.0,0.0,1.0,0.0,0.0 710 | 0.0,0.0,1.0,0.0,0.0 711 | 0.0,0.0,1.0,0.0,0.0 712 | 0.0,1.0,0.0,0.0,0.0 713 | 1.0,0.0,0.0,0.0,0.0 714 | 0.0,0.0,0.0,1.0,0.0 715 | 0.0,0.0,0.0,1.0,0.0 716 | 0.0,1.0,0.0,0.0,0.0 717 | 0.0,0.0,1.0,0.0,0.0 718 | 0.0,0.0,1.0,0.0,0.0 719 | 0.0,0.0,0.0,1.0,0.0 720 | 1.0,0.0,0.0,0.0,0.0 721 | 0.0,0.0,0.0,1.0,0.0 722 | 0.0,0.0,1.0,0.0,0.0 723 | 0.0,1.0,0.0,0.0,0.0 724 | 0.0,0.0,1.0,0.0,0.0 725 | 0.0,0.0,0.0,1.0,0.0 726 | 0.0,0.0,0.0,0.0,1.0 727 | 0.0,0.0,0.0,1.0,0.0 728 | 1.0,0.0,0.0,0.0,0.0 729 | 1.0,0.0,0.0,0.0,0.0 730 | 1.0,0.0,0.0,0.0,0.0 731 | 0.0,1.0,0.0,0.0,0.0 732 | 0.0,0.0,0.0,0.0,1.0 733 | 0.0,0.0,0.0,0.0,1.0 734 | 1.0,0.0,0.0,0.0,0.0 735 | 0.0,0.0,0.0,0.0,1.0 736 | 0.0,0.0,1.0,0.0,0.0 737 | 1.0,0.0,0.0,0.0,0.0 738 | 1.0,0.0,0.0,0.0,0.0 739 | 0.0,0.0,0.0,1.0,0.0 740 | 0.0,0.0,0.0,1.0,0.0 741 | 1.0,0.0,0.0,0.0,0.0 742 | 0.0,0.0,0.0,0.0,1.0 743 | 0.0,0.0,1.0,0.0,0.0 744 | 0.0,0.0,1.0,0.0,0.0 745 | 0.0,0.0,0.0,0.0,1.0 746 | 0.0,0.0,0.0,1.0,0.0 747 | 0.0,1.0,0.0,0.0,0.0 748 | 1.0,0.0,0.0,0.0,0.0 749 | 0.0,0.0,0.0,0.0,1.0 750 | 0.0,1.0,0.0,0.0,0.0 751 | 0.0,0.0,1.0,0.0,0.0 752 | 0.0,0.0,1.0,0.0,0.0 753 | 1.0,0.0,0.0,0.0,0.0 754 | 0.0,0.0,0.0,1.0,0.0 755 | 1.0,0.0,0.0,0.0,0.0 756 | 0.0,0.0,1.0,0.0,0.0 757 | 0.0,1.0,0.0,0.0,0.0 758 | 1.0,0.0,0.0,0.0,0.0 759 | 0.0,1.0,0.0,0.0,0.0 760 | 0.0,0.0,1.0,0.0,0.0 761 | 0.0,0.0,1.0,0.0,0.0 762 | 0.0,0.0,0.0,1.0,0.0 763 | 0.0,1.0,0.0,0.0,0.0 764 | 0.0,0.0,0.0,0.0,1.0 765 | 0.0,0.0,0.0,1.0,0.0 766 | 0.0,0.0,0.0,0.0,1.0 767 | 0.0,0.0,0.0,0.0,1.0 768 | 0.0,0.0,0.0,1.0,0.0 769 | 0.0,0.0,0.0,1.0,0.0 770 | 0.0,0.0,0.0,0.0,1.0 771 | 0.0,0.0,0.0,0.0,1.0 772 | 0.0,0.0,0.0,1.0,0.0 773 | 0.0,0.0,0.0,0.0,1.0 774 | 0.0,1.0,0.0,0.0,0.0 775 | 0.0,0.0,1.0,0.0,0.0 776 | 0.0,0.0,0.0,0.0,1.0 777 | 0.0,0.0,0.0,0.0,1.0 778 | 0.0,0.0,0.0,0.0,1.0 779 | 1.0,0.0,0.0,0.0,0.0 780 | 0.0,1.0,0.0,0.0,0.0 781 | 0.0,0.0,0.0,0.0,1.0 782 | 0.0,0.0,0.0,1.0,0.0 783 | 1.0,0.0,0.0,0.0,0.0 784 | 0.0,0.0,0.0,0.0,1.0 785 | 0.0,0.0,0.0,0.0,1.0 786 | 1.0,0.0,0.0,0.0,0.0 787 | 0.0,1.0,0.0,0.0,0.0 788 | 1.0,0.0,0.0,0.0,0.0 789 | 0.0,0.0,0.0,1.0,0.0 790 | 0.0,1.0,0.0,0.0,0.0 791 | 0.0,0.0,1.0,0.0,0.0 792 | 0.0,0.0,0.0,1.0,0.0 793 | 0.0,1.0,0.0,0.0,0.0 794 | 1.0,0.0,0.0,0.0,0.0 795 | 0.0,0.0,0.0,0.0,1.0 796 | 0.0,1.0,0.0,0.0,0.0 797 | 1.0,0.0,0.0,0.0,0.0 798 | 0.0,0.0,0.0,1.0,0.0 799 | 0.0,0.0,0.0,1.0,0.0 800 | 1.0,0.0,0.0,0.0,0.0 801 | 0.0,1.0,0.0,0.0,0.0 802 | 0.0,1.0,0.0,0.0,0.0 803 | 0.0,0.0,0.0,1.0,0.0 804 | 1.0,0.0,0.0,0.0,0.0 805 | 1.0,0.0,0.0,0.0,0.0 806 | 0.0,1.0,0.0,0.0,0.0 807 | 1.0,0.0,0.0,0.0,0.0 808 | 0.0,0.0,0.0,1.0,0.0 809 | 0.0,0.0,0.0,1.0,0.0 810 | 0.0,1.0,0.0,0.0,0.0 811 | 0.0,1.0,0.0,0.0,0.0 812 | 0.0,1.0,0.0,0.0,0.0 813 | 0.0,0.0,0.0,1.0,0.0 814 | 0.0,0.0,1.0,0.0,0.0 815 | 0.0,0.0,0.0,1.0,0.0 816 | 0.0,1.0,0.0,0.0,0.0 817 | 0.0,0.0,1.0,0.0,0.0 818 | 0.0,0.0,0.0,0.0,1.0 819 | 0.0,1.0,0.0,0.0,0.0 820 | 0.0,1.0,0.0,0.0,0.0 821 | 0.0,0.0,0.0,1.0,0.0 822 | 1.0,0.0,0.0,0.0,0.0 823 | 0.0,0.0,0.0,1.0,0.0 824 | 0.0,0.0,1.0,0.0,0.0 825 | 0.0,0.0,0.0,0.0,1.0 826 | 0.0,0.0,0.0,1.0,0.0 827 | 1.0,0.0,0.0,0.0,0.0 828 | 0.0,1.0,0.0,0.0,0.0 829 | 0.0,0.0,1.0,0.0,0.0 830 | 0.0,1.0,0.0,0.0,0.0 831 | 0.0,0.0,0.0,0.0,1.0 832 | 1.0,0.0,0.0,0.0,0.0 833 | 0.0,0.0,0.0,0.0,1.0 834 | 0.0,0.0,1.0,0.0,0.0 835 | 0.0,0.0,0.0,1.0,0.0 836 | 0.0,1.0,0.0,0.0,0.0 837 | 0.0,0.0,0.0,1.0,0.0 838 | 1.0,0.0,0.0,0.0,0.0 839 | 0.0,0.0,1.0,0.0,0.0 840 | 0.0,0.0,1.0,0.0,0.0 841 | 0.0,1.0,0.0,0.0,0.0 842 | 0.0,1.0,0.0,0.0,0.0 843 | 0.0,0.0,1.0,0.0,0.0 844 | 0.0,1.0,0.0,0.0,0.0 845 | 0.0,0.0,1.0,0.0,0.0 846 | 1.0,0.0,0.0,0.0,0.0 847 | 0.0,1.0,0.0,0.0,0.0 848 | 1.0,0.0,0.0,0.0,0.0 849 | 0.0,0.0,1.0,0.0,0.0 850 | 0.0,1.0,0.0,0.0,0.0 851 | 0.0,1.0,0.0,0.0,0.0 852 | 0.0,0.0,1.0,0.0,0.0 853 | 0.0,0.0,0.0,0.0,1.0 854 | 1.0,0.0,0.0,0.0,0.0 855 | 0.0,0.0,0.0,0.0,1.0 856 | 0.0,0.0,0.0,1.0,0.0 857 | 0.0,0.0,1.0,0.0,0.0 858 | 0.0,0.0,0.0,0.0,1.0 859 | 0.0,0.0,0.0,0.0,1.0 860 | 0.0,0.0,0.0,0.0,1.0 861 | 0.0,0.0,1.0,0.0,0.0 862 | 0.0,0.0,0.0,0.0,1.0 863 | 0.0,0.0,0.0,1.0,0.0 864 | 0.0,0.0,0.0,0.0,1.0 865 | 0.0,0.0,1.0,0.0,0.0 866 | 0.0,0.0,1.0,0.0,0.0 867 | 0.0,0.0,0.0,0.0,1.0 868 | 0.0,0.0,0.0,1.0,0.0 869 | 1.0,0.0,0.0,0.0,0.0 870 | 0.0,0.0,1.0,0.0,0.0 871 | 0.0,0.0,1.0,0.0,0.0 872 | 0.0,0.0,0.0,1.0,0.0 873 | 0.0,0.0,1.0,0.0,0.0 874 | 0.0,1.0,0.0,0.0,0.0 875 | 0.0,0.0,0.0,1.0,0.0 876 | 0.0,0.0,1.0,0.0,0.0 877 | 0.0,0.0,0.0,1.0,0.0 878 | 0.0,0.0,0.0,0.0,1.0 879 | 0.0,1.0,0.0,0.0,0.0 880 | 0.0,0.0,1.0,0.0,0.0 881 | 0.0,0.0,0.0,1.0,0.0 882 | 1.0,0.0,0.0,0.0,0.0 883 | 0.0,1.0,0.0,0.0,0.0 884 | 0.0,0.0,1.0,0.0,0.0 885 | 0.0,0.0,0.0,0.0,1.0 886 | 0.0,1.0,0.0,0.0,0.0 887 | 1.0,0.0,0.0,0.0,0.0 888 | 1.0,0.0,0.0,0.0,0.0 889 | 0.0,1.0,0.0,0.0,0.0 890 | 0.0,0.0,1.0,0.0,0.0 891 | 0.0,0.0,1.0,0.0,0.0 892 | 0.0,0.0,0.0,1.0,0.0 893 | 0.0,1.0,0.0,0.0,0.0 894 | 0.0,0.0,0.0,0.0,1.0 895 | 0.0,0.0,0.0,0.0,1.0 896 | 0.0,0.0,0.0,0.0,1.0 897 | 0.0,1.0,0.0,0.0,0.0 898 | 1.0,0.0,0.0,0.0,0.0 899 | 1.0,0.0,0.0,0.0,0.0 900 | 0.0,0.0,0.0,1.0,0.0 901 | 0.0,0.0,1.0,0.0,0.0 902 | 1.0,0.0,0.0,0.0,0.0 903 | 0.0,0.0,0.0,1.0,0.0 904 | 0.0,0.0,0.0,0.0,1.0 905 | 0.0,0.0,1.0,0.0,0.0 906 | 1.0,0.0,0.0,0.0,0.0 907 | 1.0,0.0,0.0,0.0,0.0 908 | 0.0,0.0,0.0,0.0,1.0 909 | 0.0,0.0,0.0,0.0,1.0 910 | 0.0,0.0,0.0,0.0,1.0 911 | 0.0,1.0,0.0,0.0,0.0 912 | 0.0,0.0,0.0,0.0,1.0 913 | 0.0,1.0,0.0,0.0,0.0 914 | 0.0,0.0,0.0,0.0,1.0 915 | 0.0,1.0,0.0,0.0,0.0 916 | 0.0,0.0,1.0,0.0,0.0 917 | 1.0,0.0,0.0,0.0,0.0 918 | 0.0,0.0,0.0,0.0,1.0 919 | 0.0,0.0,0.0,0.0,1.0 920 | 1.0,0.0,0.0,0.0,0.0 921 | 0.0,1.0,0.0,0.0,0.0 922 | 1.0,0.0,0.0,0.0,0.0 923 | 1.0,0.0,0.0,0.0,0.0 924 | 1.0,0.0,0.0,0.0,0.0 925 | 1.0,0.0,0.0,0.0,0.0 926 | 0.0,0.0,0.0,0.0,1.0 927 | 0.0,0.0,0.0,0.0,1.0 928 | 0.0,0.0,0.0,1.0,0.0 929 | 0.0,1.0,0.0,0.0,0.0 930 | 1.0,0.0,0.0,0.0,0.0 931 | 0.0,0.0,0.0,1.0,0.0 932 | 0.0,0.0,0.0,1.0,0.0 933 | 0.0,0.0,0.0,0.0,1.0 934 | 0.0,0.0,0.0,0.0,1.0 935 | 0.0,0.0,1.0,0.0,0.0 936 | 0.0,0.0,0.0,0.0,1.0 937 | 0.0,0.0,0.0,0.0,1.0 938 | 0.0,0.0,0.0,1.0,0.0 939 | 1.0,0.0,0.0,0.0,0.0 940 | 0.0,0.0,0.0,1.0,0.0 941 | 1.0,0.0,0.0,0.0,0.0 942 | 0.0,1.0,0.0,0.0,0.0 943 | 1.0,0.0,0.0,0.0,0.0 944 | 1.0,0.0,0.0,0.0,0.0 945 | 0.0,0.0,1.0,0.0,0.0 946 | 1.0,0.0,0.0,0.0,0.0 947 | 0.0,0.0,0.0,1.0,0.0 948 | 0.0,0.0,1.0,0.0,0.0 949 | 0.0,0.0,0.0,1.0,0.0 950 | 1.0,0.0,0.0,0.0,0.0 951 | 0.0,1.0,0.0,0.0,0.0 952 | 0.0,0.0,1.0,0.0,0.0 953 | 0.0,0.0,0.0,1.0,0.0 954 | 0.0,1.0,0.0,0.0,0.0 955 | 0.0,0.0,0.0,1.0,0.0 956 | 0.0,0.0,1.0,0.0,0.0 957 | 0.0,1.0,0.0,0.0,0.0 958 | 0.0,0.0,1.0,0.0,0.0 959 | 0.0,0.0,0.0,1.0,0.0 960 | 0.0,0.0,0.0,0.0,1.0 961 | 0.0,1.0,0.0,0.0,0.0 962 | 0.0,0.0,1.0,0.0,0.0 963 | 0.0,0.0,0.0,0.0,1.0 964 | 0.0,0.0,0.0,1.0,0.0 965 | 0.0,0.0,0.0,1.0,0.0 966 | 1.0,0.0,0.0,0.0,0.0 967 | 1.0,0.0,0.0,0.0,0.0 968 | 0.0,0.0,1.0,0.0,0.0 969 | 0.0,0.0,1.0,0.0,0.0 970 | 0.0,0.0,0.0,0.0,1.0 971 | 1.0,0.0,0.0,0.0,0.0 972 | 0.0,0.0,0.0,1.0,0.0 973 | 0.0,0.0,1.0,0.0,0.0 974 | 0.0,0.0,0.0,1.0,0.0 975 | 0.0,0.0,0.0,0.0,1.0 976 | 0.0,1.0,0.0,0.0,0.0 977 | 0.0,0.0,1.0,0.0,0.0 978 | 0.0,1.0,0.0,0.0,0.0 979 | 1.0,0.0,0.0,0.0,0.0 980 | 0.0,1.0,0.0,0.0,0.0 981 | 0.0,0.0,0.0,1.0,0.0 982 | 0.0,0.0,0.0,1.0,0.0 983 | 0.0,1.0,0.0,0.0,0.0 984 | 1.0,0.0,0.0,0.0,0.0 985 | 0.0,0.0,1.0,0.0,0.0 986 | 0.0,0.0,0.0,0.0,1.0 987 | 0.0,0.0,0.0,1.0,0.0 988 | 1.0,0.0,0.0,0.0,0.0 989 | 0.0,0.0,0.0,1.0,0.0 990 | 0.0,0.0,0.0,1.0,0.0 991 | 0.0,1.0,0.0,0.0,0.0 992 | 0.0,0.0,0.0,1.0,0.0 993 | 1.0,0.0,0.0,0.0,0.0 994 | 0.0,0.0,0.0,1.0,0.0 995 | 0.0,1.0,0.0,0.0,0.0 996 | 0.0,0.0,1.0,0.0,0.0 997 | 0.0,0.0,1.0,0.0,0.0 998 | 1.0,0.0,0.0,0.0,0.0 999 | 1.0,0.0,0.0,0.0,0.0 1000 | 0.0,0.0,0.0,1.0,0.0 1001 | 0.0,1.0,0.0,0.0,0.0 1002 | 0.0,0.0,0.0,1.0,0.0 1003 | 0.0,0.0,0.0,1.0,0.0 1004 | 0.0,1.0,0.0,0.0,0.0 1005 | 0.0,0.0,1.0,0.0,0.0 1006 | 1.0,0.0,0.0,0.0,0.0 1007 | 1.0,0.0,0.0,0.0,0.0 1008 | 0.0,0.0,1.0,0.0,0.0 1009 | 1.0,0.0,0.0,0.0,0.0 1010 | 0.0,1.0,0.0,0.0,0.0 1011 | 1.0,0.0,0.0,0.0,0.0 1012 | 0.0,0.0,0.0,0.0,1.0 1013 | 0.0,0.0,1.0,0.0,0.0 1014 | 1.0,0.0,0.0,0.0,0.0 1015 | 0.0,0.0,0.0,0.0,1.0 1016 | 0.0,0.0,0.0,0.0,1.0 1017 | 0.0,0.0,0.0,1.0,0.0 1018 | 0.0,0.0,0.0,1.0,0.0 1019 | 1.0,0.0,0.0,0.0,0.0 1020 | 0.0,0.0,0.0,1.0,0.0 1021 | 0.0,1.0,0.0,0.0,0.0 1022 | 0.0,1.0,0.0,0.0,0.0 1023 | 0.0,1.0,0.0,0.0,0.0 1024 | 0.0,0.0,0.0,1.0,0.0 1025 | 0.0,0.0,0.0,1.0,0.0 1026 | 0.0,1.0,0.0,0.0,0.0 1027 | 1.0,0.0,0.0,0.0,0.0 1028 | 0.0,1.0,0.0,0.0,0.0 1029 | 0.0,0.0,0.0,0.0,1.0 1030 | 0.0,1.0,0.0,0.0,0.0 1031 | 0.0,0.0,1.0,0.0,0.0 1032 | 0.0,1.0,0.0,0.0,0.0 1033 | 0.0,1.0,0.0,0.0,0.0 1034 | 1.0,0.0,0.0,0.0,0.0 1035 | 0.0,0.0,1.0,0.0,0.0 1036 | 0.0,0.0,0.0,0.0,1.0 1037 | 1.0,0.0,0.0,0.0,0.0 1038 | 1.0,0.0,0.0,0.0,0.0 1039 | 0.0,0.0,0.0,0.0,1.0 1040 | 0.0,0.0,1.0,0.0,0.0 1041 | 0.0,0.0,0.0,1.0,0.0 1042 | 0.0,0.0,0.0,0.0,1.0 1043 | 0.0,0.0,1.0,0.0,0.0 1044 | 0.0,0.0,1.0,0.0,0.0 1045 | 0.0,0.0,0.0,0.0,1.0 1046 | 1.0,0.0,0.0,0.0,0.0 1047 | 0.0,0.0,0.0,0.0,1.0 1048 | 0.0,0.0,1.0,0.0,0.0 1049 | 0.0,0.0,1.0,0.0,0.0 1050 | 0.0,0.0,0.0,1.0,0.0 1051 | 0.0,0.0,1.0,0.0,0.0 1052 | 1.0,0.0,0.0,0.0,0.0 1053 | 0.0,0.0,1.0,0.0,0.0 1054 | 1.0,0.0,0.0,0.0,0.0 1055 | 0.0,0.0,0.0,1.0,0.0 1056 | 0.0,1.0,0.0,0.0,0.0 1057 | 1.0,0.0,0.0,0.0,0.0 1058 | 0.0,0.0,0.0,0.0,1.0 1059 | 0.0,0.0,0.0,1.0,0.0 1060 | 0.0,0.0,0.0,1.0,0.0 1061 | 0.0,0.0,1.0,0.0,0.0 1062 | 0.0,0.0,1.0,0.0,0.0 1063 | 0.0,0.0,1.0,0.0,0.0 1064 | 0.0,0.0,1.0,0.0,0.0 1065 | 0.0,0.0,0.0,0.0,1.0 1066 | 0.0,0.0,1.0,0.0,0.0 1067 | 0.0,0.0,0.0,0.0,1.0 1068 | 0.0,0.0,1.0,0.0,0.0 1069 | 0.0,0.0,0.0,0.0,1.0 1070 | 0.0,0.0,0.0,0.0,1.0 1071 | 0.0,0.0,0.0,1.0,0.0 1072 | 1.0,0.0,0.0,0.0,0.0 1073 | 1.0,0.0,0.0,0.0,0.0 1074 | 0.0,0.0,0.0,0.0,1.0 1075 | 0.0,0.0,1.0,0.0,0.0 1076 | 1.0,0.0,0.0,0.0,0.0 1077 | 1.0,0.0,0.0,0.0,0.0 1078 | 0.0,0.0,0.0,0.0,1.0 1079 | 1.0,0.0,0.0,0.0,0.0 1080 | 0.0,0.0,1.0,0.0,0.0 1081 | 1.0,0.0,0.0,0.0,0.0 1082 | 0.0,1.0,0.0,0.0,0.0 1083 | 0.0,0.0,0.0,0.0,1.0 1084 | 0.0,0.0,0.0,0.0,1.0 1085 | 1.0,0.0,0.0,0.0,0.0 1086 | 0.0,0.0,0.0,1.0,0.0 1087 | 0.0,0.0,0.0,1.0,0.0 1088 | 0.0,1.0,0.0,0.0,0.0 1089 | 0.0,0.0,0.0,1.0,0.0 1090 | 0.0,0.0,0.0,1.0,0.0 1091 | 1.0,0.0,0.0,0.0,0.0 1092 | 1.0,0.0,0.0,0.0,0.0 1093 | 0.0,1.0,0.0,0.0,0.0 1094 | 0.0,0.0,0.0,1.0,0.0 1095 | 0.0,1.0,0.0,0.0,0.0 1096 | 0.0,1.0,0.0,0.0,0.0 1097 | 0.0,0.0,0.0,0.0,1.0 1098 | 0.0,0.0,0.0,1.0,0.0 1099 | 0.0,0.0,1.0,0.0,0.0 1100 | 0.0,1.0,0.0,0.0,0.0 1101 | 0.0,0.0,1.0,0.0,0.0 1102 | 0.0,0.0,1.0,0.0,0.0 1103 | 0.0,0.0,1.0,0.0,0.0 1104 | 0.0,0.0,0.0,0.0,1.0 1105 | 0.0,1.0,0.0,0.0,0.0 1106 | 0.0,0.0,1.0,0.0,0.0 1107 | 0.0,0.0,1.0,0.0,0.0 1108 | 0.0,0.0,1.0,0.0,0.0 1109 | 0.0,0.0,0.0,1.0,0.0 1110 | 0.0,0.0,0.0,0.0,1.0 1111 | 0.0,1.0,0.0,0.0,0.0 1112 | 0.0,0.0,1.0,0.0,0.0 1113 | 0.0,0.0,0.0,1.0,0.0 1114 | 1.0,0.0,0.0,0.0,0.0 1115 | 0.0,0.0,1.0,0.0,0.0 1116 | 1.0,0.0,0.0,0.0,0.0 1117 | 0.0,0.0,0.0,1.0,0.0 1118 | 0.0,1.0,0.0,0.0,0.0 1119 | 1.0,0.0,0.0,0.0,0.0 1120 | 0.0,0.0,0.0,1.0,0.0 1121 | 0.0,0.0,0.0,0.0,1.0 1122 | 0.0,0.0,0.0,1.0,0.0 1123 | 0.0,0.0,0.0,0.0,1.0 1124 | 0.0,1.0,0.0,0.0,0.0 1125 | 0.0,0.0,0.0,1.0,0.0 1126 | 0.0,0.0,1.0,0.0,0.0 1127 | 1.0,0.0,0.0,0.0,0.0 1128 | 0.0,0.0,1.0,0.0,0.0 1129 | 0.0,1.0,0.0,0.0,0.0 1130 | 0.0,0.0,0.0,0.0,1.0 1131 | 0.0,0.0,0.0,0.0,1.0 1132 | 0.0,0.0,0.0,0.0,1.0 1133 | 0.0,0.0,1.0,0.0,0.0 1134 | 0.0,0.0,0.0,1.0,0.0 1135 | 0.0,1.0,0.0,0.0,0.0 1136 | 0.0,0.0,0.0,1.0,0.0 1137 | 0.0,0.0,0.0,1.0,0.0 1138 | 0.0,1.0,0.0,0.0,0.0 1139 | 0.0,1.0,0.0,0.0,0.0 1140 | 0.0,0.0,0.0,0.0,1.0 1141 | 0.0,0.0,0.0,0.0,1.0 1142 | 0.0,0.0,0.0,0.0,1.0 1143 | 0.0,0.0,0.0,1.0,0.0 1144 | 0.0,0.0,1.0,0.0,0.0 1145 | 0.0,0.0,0.0,1.0,0.0 1146 | 0.0,0.0,0.0,1.0,0.0 1147 | 0.0,0.0,0.0,0.0,1.0 1148 | 0.0,0.0,0.0,1.0,0.0 1149 | 0.0,0.0,0.0,1.0,0.0 1150 | 0.0,1.0,0.0,0.0,0.0 1151 | 0.0,0.0,0.0,1.0,0.0 1152 | 0.0,1.0,0.0,0.0,0.0 1153 | 0.0,0.0,1.0,0.0,0.0 1154 | 0.0,0.0,0.0,0.0,1.0 1155 | 0.0,0.0,1.0,0.0,0.0 1156 | 0.0,0.0,0.0,1.0,0.0 1157 | 0.0,0.0,0.0,0.0,1.0 1158 | 0.0,0.0,0.0,0.0,1.0 1159 | 0.0,0.0,1.0,0.0,0.0 1160 | 0.0,0.0,0.0,0.0,1.0 1161 | 0.0,1.0,0.0,0.0,0.0 1162 | 1.0,0.0,0.0,0.0,0.0 1163 | 0.0,0.0,0.0,0.0,1.0 1164 | 0.0,0.0,0.0,1.0,0.0 1165 | 0.0,1.0,0.0,0.0,0.0 1166 | 1.0,0.0,0.0,0.0,0.0 1167 | 0.0,0.0,1.0,0.0,0.0 1168 | 1.0,0.0,0.0,0.0,0.0 1169 | 0.0,1.0,0.0,0.0,0.0 1170 | 0.0,1.0,0.0,0.0,0.0 1171 | 1.0,0.0,0.0,0.0,0.0 1172 | 0.0,0.0,0.0,0.0,1.0 1173 | 0.0,0.0,0.0,0.0,1.0 1174 | 0.0,0.0,1.0,0.0,0.0 1175 | 1.0,0.0,0.0,0.0,0.0 1176 | 0.0,0.0,0.0,0.0,1.0 1177 | 0.0,0.0,1.0,0.0,0.0 1178 | 0.0,0.0,0.0,1.0,0.0 1179 | 0.0,1.0,0.0,0.0,0.0 1180 | 0.0,1.0,0.0,0.0,0.0 1181 | 0.0,0.0,0.0,0.0,1.0 1182 | 0.0,0.0,1.0,0.0,0.0 1183 | 0.0,1.0,0.0,0.0,0.0 1184 | 0.0,0.0,1.0,0.0,0.0 1185 | 0.0,1.0,0.0,0.0,0.0 1186 | 0.0,0.0,1.0,0.0,0.0 1187 | 0.0,1.0,0.0,0.0,0.0 1188 | 0.0,0.0,0.0,0.0,1.0 1189 | 1.0,0.0,0.0,0.0,0.0 1190 | 1.0,0.0,0.0,0.0,0.0 1191 | 0.0,0.0,0.0,1.0,0.0 1192 | 0.0,1.0,0.0,0.0,0.0 1193 | 0.0,0.0,0.0,1.0,0.0 1194 | 1.0,0.0,0.0,0.0,0.0 1195 | 0.0,0.0,0.0,1.0,0.0 1196 | 0.0,0.0,1.0,0.0,0.0 1197 | 0.0,1.0,0.0,0.0,0.0 1198 | 1.0,0.0,0.0,0.0,0.0 1199 | 0.0,0.0,0.0,0.0,1.0 1200 | 0.0,0.0,0.0,1.0,0.0 1201 | 1.0,0.0,0.0,0.0,0.0 1202 | 0.0,1.0,0.0,0.0,0.0 1203 | 0.0,0.0,1.0,0.0,0.0 1204 | 0.0,0.0,0.0,1.0,0.0 1205 | 0.0,0.0,1.0,0.0,0.0 1206 | 0.0,0.0,0.0,0.0,1.0 1207 | 0.0,0.0,0.0,1.0,0.0 1208 | 0.0,1.0,0.0,0.0,0.0 1209 | 1.0,0.0,0.0,0.0,0.0 1210 | 0.0,1.0,0.0,0.0,0.0 1211 | 0.0,0.0,1.0,0.0,0.0 1212 | 0.0,1.0,0.0,0.0,0.0 1213 | 1.0,0.0,0.0,0.0,0.0 1214 | 0.0,1.0,0.0,0.0,0.0 1215 | 0.0,0.0,0.0,1.0,0.0 1216 | 0.0,0.0,0.0,1.0,0.0 1217 | 0.0,0.0,1.0,0.0,0.0 1218 | 1.0,0.0,0.0,0.0,0.0 1219 | 0.0,0.0,1.0,0.0,0.0 1220 | 0.0,0.0,0.0,1.0,0.0 1221 | 0.0,0.0,0.0,1.0,0.0 1222 | 0.0,0.0,0.0,1.0,0.0 1223 | 1.0,0.0,0.0,0.0,0.0 1224 | 0.0,0.0,0.0,0.0,1.0 1225 | 0.0,1.0,0.0,0.0,0.0 1226 | 0.0,1.0,0.0,0.0,0.0 1227 | 0.0,0.0,1.0,0.0,0.0 1228 | 0.0,0.0,1.0,0.0,0.0 1229 | 1.0,0.0,0.0,0.0,0.0 1230 | 0.0,0.0,1.0,0.0,0.0 1231 | 0.0,0.0,0.0,0.0,1.0 1232 | 1.0,0.0,0.0,0.0,0.0 1233 | 0.0,0.0,0.0,0.0,1.0 1234 | 0.0,0.0,0.0,0.0,1.0 1235 | 0.0,0.0,1.0,0.0,0.0 1236 | 0.0,0.0,0.0,0.0,1.0 1237 | 0.0,0.0,0.0,1.0,0.0 1238 | 0.0,0.0,1.0,0.0,0.0 1239 | 1.0,0.0,0.0,0.0,0.0 1240 | 1.0,0.0,0.0,0.0,0.0 1241 | 0.0,0.0,1.0,0.0,0.0 1242 | 1.0,0.0,0.0,0.0,0.0 1243 | 0.0,0.0,0.0,0.0,1.0 1244 | 0.0,0.0,0.0,1.0,0.0 1245 | 1.0,0.0,0.0,0.0,0.0 1246 | 0.0,0.0,0.0,1.0,0.0 1247 | 0.0,0.0,0.0,0.0,1.0 1248 | 0.0,1.0,0.0,0.0,0.0 1249 | 0.0,1.0,0.0,0.0,0.0 1250 | 0.0,0.0,1.0,0.0,0.0 1251 | 0.0,0.0,0.0,0.0,1.0 1252 | 1.0,0.0,0.0,0.0,0.0 1253 | 0.0,1.0,0.0,0.0,0.0 1254 | 0.0,1.0,0.0,0.0,0.0 1255 | 0.0,0.0,0.0,1.0,0.0 1256 | 0.0,0.0,1.0,0.0,0.0 1257 | 0.0,1.0,0.0,0.0,0.0 1258 | 0.0,0.0,1.0,0.0,0.0 1259 | 0.0,1.0,0.0,0.0,0.0 1260 | 0.0,0.0,0.0,0.0,1.0 1261 | 0.0,0.0,0.0,1.0,0.0 1262 | 0.0,0.0,0.0,1.0,0.0 1263 | 0.0,0.0,0.0,1.0,0.0 1264 | 0.0,0.0,0.0,0.0,1.0 1265 | 0.0,1.0,0.0,0.0,0.0 1266 | 0.0,0.0,0.0,1.0,0.0 1267 | 0.0,0.0,0.0,1.0,0.0 1268 | 0.0,0.0,0.0,0.0,1.0 1269 | 0.0,0.0,0.0,1.0,0.0 1270 | 0.0,0.0,0.0,1.0,0.0 1271 | 0.0,0.0,0.0,1.0,0.0 1272 | 0.0,0.0,0.0,1.0,0.0 1273 | 1.0,0.0,0.0,0.0,0.0 1274 | 0.0,0.0,0.0,0.0,1.0 1275 | 0.0,0.0,0.0,0.0,1.0 1276 | 0.0,0.0,0.0,1.0,0.0 1277 | 0.0,0.0,0.0,1.0,0.0 1278 | 0.0,1.0,0.0,0.0,0.0 1279 | 1.0,0.0,0.0,0.0,0.0 1280 | 0.0,0.0,1.0,0.0,0.0 1281 | 0.0,1.0,0.0,0.0,0.0 1282 | 0.0,1.0,0.0,0.0,0.0 1283 | 0.0,0.0,1.0,0.0,0.0 1284 | 0.0,1.0,0.0,0.0,0.0 1285 | 0.0,0.0,1.0,0.0,0.0 1286 | 0.0,0.0,0.0,0.0,1.0 1287 | 0.0,0.0,1.0,0.0,0.0 1288 | 1.0,0.0,0.0,0.0,0.0 1289 | 0.0,0.0,0.0,1.0,0.0 1290 | 0.0,0.0,1.0,0.0,0.0 1291 | 0.0,0.0,0.0,0.0,1.0 1292 | 0.0,0.0,1.0,0.0,0.0 1293 | 0.0,0.0,0.0,1.0,0.0 1294 | 0.0,0.0,1.0,0.0,0.0 1295 | 1.0,0.0,0.0,0.0,0.0 1296 | 0.0,0.0,0.0,1.0,0.0 1297 | 0.0,1.0,0.0,0.0,0.0 1298 | 1.0,0.0,0.0,0.0,0.0 1299 | 0.0,0.0,1.0,0.0,0.0 1300 | 0.0,0.0,0.0,1.0,0.0 1301 | 1.0,0.0,0.0,0.0,0.0 1302 | 0.0,1.0,0.0,0.0,0.0 1303 | 0.0,0.0,1.0,0.0,0.0 1304 | 0.0,1.0,0.0,0.0,0.0 1305 | 1.0,0.0,0.0,0.0,0.0 1306 | 0.0,0.0,0.0,1.0,0.0 1307 | 0.0,0.0,1.0,0.0,0.0 1308 | 0.0,1.0,0.0,0.0,0.0 1309 | 0.0,0.0,1.0,0.0,0.0 1310 | 0.0,0.0,0.0,1.0,0.0 1311 | 0.0,0.0,0.0,1.0,0.0 1312 | 1.0,0.0,0.0,0.0,0.0 1313 | 1.0,0.0,0.0,0.0,0.0 1314 | 0.0,1.0,0.0,0.0,0.0 1315 | 0.0,0.0,0.0,0.0,1.0 1316 | 0.0,1.0,0.0,0.0,0.0 1317 | 0.0,0.0,0.0,1.0,0.0 1318 | 0.0,0.0,1.0,0.0,0.0 1319 | 1.0,0.0,0.0,0.0,0.0 1320 | 0.0,0.0,0.0,1.0,0.0 1321 | 0.0,0.0,0.0,0.0,1.0 1322 | 0.0,0.0,0.0,0.0,1.0 1323 | 0.0,0.0,0.0,1.0,0.0 1324 | 0.0,0.0,0.0,0.0,1.0 1325 | 0.0,0.0,0.0,0.0,1.0 1326 | 0.0,0.0,1.0,0.0,0.0 1327 | 0.0,1.0,0.0,0.0,0.0 1328 | 1.0,0.0,0.0,0.0,0.0 1329 | 0.0,0.0,1.0,0.0,0.0 1330 | 1.0,0.0,0.0,0.0,0.0 1331 | 0.0,0.0,0.0,0.0,1.0 1332 | 0.0,0.0,0.0,0.0,1.0 1333 | 1.0,0.0,0.0,0.0,0.0 1334 | 1.0,0.0,0.0,0.0,0.0 1335 | 0.0,0.0,1.0,0.0,0.0 1336 | 0.0,0.0,0.0,1.0,0.0 1337 | 1.0,0.0,0.0,0.0,0.0 1338 | 0.0,0.0,1.0,0.0,0.0 1339 | 0.0,0.0,0.0,0.0,1.0 1340 | 0.0,0.0,0.0,0.0,1.0 1341 | 0.0,0.0,0.0,1.0,0.0 1342 | 1.0,0.0,0.0,0.0,0.0 1343 | 0.0,0.0,0.0,1.0,0.0 1344 | 0.0,0.0,0.0,0.0,1.0 1345 | 0.0,0.0,1.0,0.0,0.0 1346 | 1.0,0.0,0.0,0.0,0.0 1347 | 0.0,0.0,1.0,0.0,0.0 1348 | 0.0,0.0,0.0,1.0,0.0 1349 | 0.0,0.0,0.0,1.0,0.0 1350 | 0.0,0.0,0.0,0.0,1.0 1351 | 0.0,1.0,0.0,0.0,0.0 1352 | 0.0,0.0,1.0,0.0,0.0 1353 | 1.0,0.0,0.0,0.0,0.0 1354 | 1.0,0.0,0.0,0.0,0.0 1355 | 1.0,0.0,0.0,0.0,0.0 1356 | 1.0,0.0,0.0,0.0,0.0 1357 | 1.0,0.0,0.0,0.0,0.0 1358 | 1.0,0.0,0.0,0.0,0.0 1359 | 0.0,0.0,1.0,0.0,0.0 1360 | 0.0,0.0,0.0,1.0,0.0 1361 | 0.0,1.0,0.0,0.0,0.0 1362 | 0.0,0.0,0.0,0.0,1.0 1363 | 0.0,0.0,0.0,0.0,1.0 1364 | 0.0,0.0,0.0,0.0,1.0 1365 | 1.0,0.0,0.0,0.0,0.0 1366 | 0.0,1.0,0.0,0.0,0.0 1367 | 0.0,0.0,0.0,0.0,1.0 1368 | 0.0,0.0,1.0,0.0,0.0 1369 | 0.0,0.0,0.0,1.0,0.0 1370 | 0.0,0.0,1.0,0.0,0.0 1371 | 1.0,0.0,0.0,0.0,0.0 1372 | 0.0,0.0,0.0,1.0,0.0 1373 | 0.0,0.0,1.0,0.0,0.0 1374 | 0.0,1.0,0.0,0.0,0.0 1375 | 0.0,0.0,0.0,1.0,0.0 1376 | 0.0,0.0,0.0,0.0,1.0 1377 | 0.0,0.0,0.0,0.0,1.0 1378 | 0.0,0.0,1.0,0.0,0.0 1379 | 0.0,0.0,0.0,1.0,0.0 1380 | 0.0,1.0,0.0,0.0,0.0 1381 | 0.0,0.0,0.0,0.0,1.0 1382 | 0.0,0.0,1.0,0.0,0.0 1383 | 0.0,0.0,0.0,0.0,1.0 1384 | 0.0,0.0,1.0,0.0,0.0 1385 | 1.0,0.0,0.0,0.0,0.0 1386 | 0.0,0.0,0.0,0.0,1.0 1387 | 0.0,0.0,0.0,1.0,0.0 1388 | 0.0,1.0,0.0,0.0,0.0 1389 | 0.0,0.0,0.0,0.0,1.0 1390 | 0.0,0.0,1.0,0.0,0.0 1391 | 0.0,0.0,0.0,0.0,1.0 1392 | 0.0,0.0,0.0,0.0,1.0 1393 | 0.0,0.0,1.0,0.0,0.0 1394 | 0.0,1.0,0.0,0.0,0.0 1395 | 0.0,1.0,0.0,0.0,0.0 1396 | 1.0,0.0,0.0,0.0,0.0 1397 | 0.0,1.0,0.0,0.0,0.0 1398 | 0.0,0.0,0.0,0.0,1.0 1399 | 0.0,0.0,0.0,1.0,0.0 1400 | 0.0,1.0,0.0,0.0,0.0 1401 | 1.0,0.0,0.0,0.0,0.0 1402 | 0.0,0.0,0.0,0.0,1.0 1403 | 0.0,1.0,0.0,0.0,0.0 1404 | 0.0,0.0,0.0,1.0,0.0 1405 | 0.0,0.0,0.0,1.0,0.0 1406 | 0.0,1.0,0.0,0.0,0.0 1407 | 1.0,0.0,0.0,0.0,0.0 1408 | 0.0,1.0,0.0,0.0,0.0 1409 | 0.0,0.0,0.0,0.0,1.0 1410 | 0.0,0.0,1.0,0.0,0.0 1411 | 0.0,0.0,0.0,0.0,1.0 1412 | 0.0,0.0,0.0,1.0,0.0 1413 | 0.0,0.0,1.0,0.0,0.0 1414 | 1.0,0.0,0.0,0.0,0.0 1415 | 0.0,0.0,0.0,0.0,1.0 1416 | 0.0,0.0,1.0,0.0,0.0 1417 | 1.0,0.0,0.0,0.0,0.0 1418 | 1.0,0.0,0.0,0.0,0.0 1419 | 0.0,0.0,0.0,0.0,1.0 1420 | 0.0,0.0,0.0,0.0,1.0 1421 | 0.0,0.0,0.0,1.0,0.0 1422 | 0.0,0.0,0.0,1.0,0.0 1423 | 0.0,0.0,0.0,0.0,1.0 1424 | 0.0,0.0,1.0,0.0,0.0 1425 | 0.0,0.0,0.0,1.0,0.0 1426 | 0.0,0.0,0.0,0.0,1.0 1427 | 0.0,0.0,1.0,0.0,0.0 1428 | 0.0,0.0,0.0,1.0,0.0 1429 | 0.0,0.0,0.0,1.0,0.0 1430 | 0.0,0.0,1.0,0.0,0.0 1431 | 0.0,0.0,1.0,0.0,0.0 1432 | 0.0,0.0,0.0,1.0,0.0 1433 | 0.0,1.0,0.0,0.0,0.0 1434 | 0.0,0.0,1.0,0.0,0.0 1435 | 0.0,0.0,0.0,0.0,1.0 1436 | 0.0,0.0,1.0,0.0,0.0 1437 | 0.0,0.0,0.0,1.0,0.0 1438 | 0.0,0.0,0.0,0.0,1.0 1439 | 0.0,0.0,0.0,0.0,1.0 1440 | 0.0,1.0,0.0,0.0,0.0 1441 | 0.0,0.0,1.0,0.0,0.0 1442 | 0.0,0.0,0.0,0.0,1.0 1443 | 0.0,0.0,1.0,0.0,0.0 1444 | 0.0,1.0,0.0,0.0,0.0 1445 | 0.0,0.0,0.0,1.0,0.0 1446 | 0.0,0.0,0.0,0.0,1.0 1447 | 0.0,0.0,1.0,0.0,0.0 1448 | 0.0,0.0,1.0,0.0,0.0 1449 | 1.0,0.0,0.0,0.0,0.0 1450 | 0.0,0.0,1.0,0.0,0.0 1451 | 1.0,0.0,0.0,0.0,0.0 1452 | 0.0,0.0,1.0,0.0,0.0 1453 | 0.0,0.0,0.0,1.0,0.0 1454 | 0.0,0.0,0.0,0.0,1.0 1455 | 1.0,0.0,0.0,0.0,0.0 1456 | 0.0,1.0,0.0,0.0,0.0 1457 | 0.0,0.0,0.0,0.0,1.0 1458 | 0.0,0.0,0.0,1.0,0.0 1459 | 0.0,1.0,0.0,0.0,0.0 1460 | 0.0,0.0,0.0,1.0,0.0 1461 | 0.0,0.0,0.0,0.0,1.0 1462 | 1.0,0.0,0.0,0.0,0.0 1463 | 0.0,0.0,1.0,0.0,0.0 1464 | 0.0,0.0,0.0,1.0,0.0 1465 | 1.0,0.0,0.0,0.0,0.0 1466 | 0.0,0.0,1.0,0.0,0.0 1467 | 0.0,0.0,0.0,0.0,1.0 1468 | 0.0,0.0,0.0,1.0,0.0 1469 | 0.0,0.0,1.0,0.0,0.0 1470 | 0.0,0.0,1.0,0.0,0.0 1471 | 0.0,0.0,1.0,0.0,0.0 1472 | 0.0,1.0,0.0,0.0,0.0 1473 | 1.0,0.0,0.0,0.0,0.0 1474 | 0.0,0.0,0.0,0.0,1.0 1475 | 0.0,1.0,0.0,0.0,0.0 1476 | 0.0,1.0,0.0,0.0,0.0 1477 | 0.0,0.0,0.0,0.0,1.0 1478 | 1.0,0.0,0.0,0.0,0.0 1479 | 0.0,0.0,0.0,1.0,0.0 1480 | 0.0,1.0,0.0,0.0,0.0 1481 | 1.0,0.0,0.0,0.0,0.0 1482 | 1.0,0.0,0.0,0.0,0.0 1483 | 0.0,0.0,0.0,0.0,1.0 1484 | 0.0,0.0,0.0,1.0,0.0 1485 | 0.0,0.0,1.0,0.0,0.0 1486 | 0.0,0.0,1.0,0.0,0.0 1487 | 0.0,0.0,0.0,1.0,0.0 1488 | 0.0,0.0,0.0,0.0,1.0 1489 | 0.0,0.0,1.0,0.0,0.0 1490 | 0.0,0.0,1.0,0.0,0.0 1491 | 0.0,0.0,1.0,0.0,0.0 1492 | 1.0,0.0,0.0,0.0,0.0 1493 | 0.0,0.0,1.0,0.0,0.0 1494 | 0.0,0.0,0.0,1.0,0.0 1495 | 0.0,0.0,0.0,0.0,1.0 1496 | 0.0,0.0,1.0,0.0,0.0 1497 | 0.0,0.0,0.0,0.0,1.0 1498 | 1.0,0.0,0.0,0.0,0.0 1499 | 1.0,0.0,0.0,0.0,0.0 1500 | 0.0,0.0,0.0,1.0,0.0 1501 | 1.0,0.0,0.0,0.0,0.0 1502 | 0.0,0.0,0.0,0.0,1.0 1503 | 0.0,0.0,0.0,0.0,1.0 1504 | 0.0,0.0,0.0,0.0,1.0 1505 | 0.0,0.0,0.0,1.0,0.0 1506 | 1.0,0.0,0.0,0.0,0.0 1507 | 1.0,0.0,0.0,0.0,0.0 1508 | 0.0,0.0,0.0,0.0,1.0 1509 | 1.0,0.0,0.0,0.0,0.0 1510 | 1.0,0.0,0.0,0.0,0.0 1511 | 0.0,0.0,0.0,1.0,0.0 1512 | 0.0,0.0,0.0,1.0,0.0 1513 | 0.0,0.0,0.0,1.0,0.0 1514 | 0.0,0.0,1.0,0.0,0.0 1515 | 0.0,0.0,0.0,1.0,0.0 1516 | 0.0,0.0,1.0,0.0,0.0 1517 | 0.0,0.0,1.0,0.0,0.0 1518 | 1.0,0.0,0.0,0.0,0.0 1519 | 0.0,0.0,1.0,0.0,0.0 1520 | 0.0,1.0,0.0,0.0,0.0 1521 | 0.0,0.0,1.0,0.0,0.0 1522 | 1.0,0.0,0.0,0.0,0.0 1523 | 0.0,0.0,0.0,0.0,1.0 1524 | 0.0,0.0,0.0,0.0,1.0 1525 | 0.0,1.0,0.0,0.0,0.0 1526 | 0.0,0.0,0.0,0.0,1.0 1527 | 0.0,0.0,0.0,1.0,0.0 1528 | 0.0,0.0,0.0,0.0,1.0 1529 | 0.0,0.0,0.0,1.0,0.0 1530 | 1.0,0.0,0.0,0.0,0.0 1531 | 1.0,0.0,0.0,0.0,0.0 1532 | 0.0,1.0,0.0,0.0,0.0 1533 | 0.0,0.0,0.0,0.0,1.0 1534 | 0.0,0.0,0.0,1.0,0.0 1535 | 1.0,0.0,0.0,0.0,0.0 1536 | 0.0,0.0,0.0,1.0,0.0 1537 | 0.0,0.0,0.0,0.0,1.0 1538 | 0.0,0.0,1.0,0.0,0.0 1539 | 0.0,0.0,0.0,1.0,0.0 1540 | 0.0,0.0,0.0,0.0,1.0 1541 | 0.0,0.0,0.0,0.0,1.0 1542 | 1.0,0.0,0.0,0.0,0.0 1543 | 1.0,0.0,0.0,0.0,0.0 1544 | 0.0,0.0,1.0,0.0,0.0 1545 | 1.0,0.0,0.0,0.0,0.0 1546 | 0.0,1.0,0.0,0.0,0.0 1547 | 0.0,0.0,0.0,0.0,1.0 1548 | 1.0,0.0,0.0,0.0,0.0 1549 | 0.0,0.0,0.0,1.0,0.0 1550 | 0.0,0.0,0.0,0.0,1.0 1551 | 1.0,0.0,0.0,0.0,0.0 1552 | 0.0,0.0,0.0,1.0,0.0 1553 | 1.0,0.0,0.0,0.0,0.0 1554 | 0.0,0.0,0.0,0.0,1.0 1555 | 1.0,0.0,0.0,0.0,0.0 1556 | 0.0,0.0,0.0,0.0,1.0 1557 | 0.0,0.0,1.0,0.0,0.0 1558 | 1.0,0.0,0.0,0.0,0.0 1559 | 1.0,0.0,0.0,0.0,0.0 1560 | 1.0,0.0,0.0,0.0,0.0 1561 | 0.0,1.0,0.0,0.0,0.0 1562 | 1.0,0.0,0.0,0.0,0.0 1563 | 0.0,0.0,0.0,1.0,0.0 1564 | 0.0,0.0,0.0,0.0,1.0 1565 | 1.0,0.0,0.0,0.0,0.0 1566 | 0.0,0.0,0.0,0.0,1.0 1567 | 1.0,0.0,0.0,0.0,0.0 1568 | 0.0,0.0,0.0,0.0,1.0 1569 | 0.0,1.0,0.0,0.0,0.0 1570 | 0.0,0.0,0.0,1.0,0.0 1571 | 0.0,0.0,1.0,0.0,0.0 1572 | 0.0,0.0,0.0,1.0,0.0 1573 | 0.0,0.0,1.0,0.0,0.0 1574 | 0.0,0.0,1.0,0.0,0.0 1575 | 0.0,0.0,1.0,0.0,0.0 1576 | 0.0,0.0,0.0,0.0,1.0 1577 | 0.0,1.0,0.0,0.0,0.0 1578 | 1.0,0.0,0.0,0.0,0.0 1579 | 1.0,0.0,0.0,0.0,0.0 1580 | 1.0,0.0,0.0,0.0,0.0 1581 | 1.0,0.0,0.0,0.0,0.0 1582 | 0.0,1.0,0.0,0.0,0.0 1583 | 0.0,0.0,0.0,0.0,1.0 1584 | 0.0,1.0,0.0,0.0,0.0 1585 | 0.0,0.0,0.0,1.0,0.0 1586 | 0.0,0.0,0.0,1.0,0.0 1587 | 0.0,0.0,0.0,1.0,0.0 1588 | 0.0,1.0,0.0,0.0,0.0 1589 | 0.0,0.0,0.0,0.0,1.0 1590 | 0.0,1.0,0.0,0.0,0.0 1591 | 1.0,0.0,0.0,0.0,0.0 1592 | 1.0,0.0,0.0,0.0,0.0 1593 | 0.0,1.0,0.0,0.0,0.0 1594 | 1.0,0.0,0.0,0.0,0.0 1595 | 1.0,0.0,0.0,0.0,0.0 1596 | 0.0,1.0,0.0,0.0,0.0 1597 | 1.0,0.0,0.0,0.0,0.0 1598 | 0.0,0.0,1.0,0.0,0.0 1599 | 0.0,0.0,0.0,0.0,1.0 1600 | 0.0,0.0,0.0,0.0,1.0 1601 | 0.0,0.0,1.0,0.0,0.0 1602 | 0.0,0.0,1.0,0.0,0.0 1603 | 0.0,1.0,0.0,0.0,0.0 1604 | 0.0,0.0,1.0,0.0,0.0 1605 | 0.0,0.0,0.0,0.0,1.0 1606 | 1.0,0.0,0.0,0.0,0.0 1607 | 0.0,0.0,0.0,1.0,0.0 1608 | 0.0,0.0,0.0,1.0,0.0 1609 | 0.0,0.0,0.0,1.0,0.0 1610 | 1.0,0.0,0.0,0.0,0.0 1611 | 0.0,0.0,0.0,1.0,0.0 1612 | 1.0,0.0,0.0,0.0,0.0 1613 | 0.0,0.0,1.0,0.0,0.0 1614 | 0.0,0.0,0.0,0.0,1.0 1615 | 0.0,0.0,0.0,0.0,1.0 1616 | 0.0,0.0,0.0,1.0,0.0 1617 | 0.0,0.0,0.0,1.0,0.0 1618 | 0.0,1.0,0.0,0.0,0.0 1619 | 0.0,1.0,0.0,0.0,0.0 1620 | 0.0,0.0,0.0,1.0,0.0 1621 | 0.0,0.0,0.0,0.0,1.0 1622 | 1.0,0.0,0.0,0.0,0.0 1623 | 0.0,0.0,1.0,0.0,0.0 1624 | 0.0,0.0,1.0,0.0,0.0 1625 | 0.0,0.0,0.0,1.0,0.0 1626 | 0.0,0.0,0.0,0.0,1.0 1627 | 0.0,0.0,0.0,0.0,1.0 1628 | 0.0,0.0,0.0,0.0,1.0 1629 | 0.0,1.0,0.0,0.0,0.0 1630 | 0.0,0.0,1.0,0.0,0.0 1631 | 0.0,0.0,0.0,0.0,1.0 1632 | 0.0,0.0,0.0,1.0,0.0 1633 | 1.0,0.0,0.0,0.0,0.0 1634 | 0.0,1.0,0.0,0.0,0.0 1635 | 0.0,0.0,1.0,0.0,0.0 1636 | 0.0,1.0,0.0,0.0,0.0 1637 | 1.0,0.0,0.0,0.0,0.0 1638 | 0.0,0.0,0.0,0.0,1.0 1639 | 0.0,0.0,0.0,0.0,1.0 1640 | 0.0,0.0,1.0,0.0,0.0 1641 | 0.0,1.0,0.0,0.0,0.0 1642 | 0.0,1.0,0.0,0.0,0.0 1643 | 0.0,0.0,0.0,1.0,0.0 1644 | 0.0,0.0,0.0,0.0,1.0 1645 | 1.0,0.0,0.0,0.0,0.0 1646 | 0.0,0.0,0.0,1.0,0.0 1647 | 0.0,0.0,0.0,0.0,1.0 1648 | 0.0,0.0,1.0,0.0,0.0 1649 | 1.0,0.0,0.0,0.0,0.0 1650 | 0.0,1.0,0.0,0.0,0.0 1651 | 1.0,0.0,0.0,0.0,0.0 1652 | 0.0,0.0,1.0,0.0,0.0 1653 | 0.0,1.0,0.0,0.0,0.0 1654 | 0.0,1.0,0.0,0.0,0.0 1655 | 0.0,1.0,0.0,0.0,0.0 1656 | 0.0,0.0,0.0,0.0,1.0 1657 | 0.0,0.0,0.0,1.0,0.0 1658 | 0.0,1.0,0.0,0.0,0.0 1659 | 0.0,1.0,0.0,0.0,0.0 1660 | 0.0,0.0,0.0,0.0,1.0 1661 | 0.0,1.0,0.0,0.0,0.0 1662 | 1.0,0.0,0.0,0.0,0.0 1663 | 0.0,0.0,1.0,0.0,0.0 1664 | 0.0,1.0,0.0,0.0,0.0 1665 | 0.0,0.0,1.0,0.0,0.0 1666 | 0.0,0.0,1.0,0.0,0.0 1667 | 0.0,0.0,0.0,0.0,1.0 1668 | 1.0,0.0,0.0,0.0,0.0 1669 | 0.0,0.0,0.0,1.0,0.0 1670 | 0.0,0.0,0.0,1.0,0.0 1671 | 0.0,1.0,0.0,0.0,0.0 1672 | 0.0,0.0,0.0,1.0,0.0 1673 | 0.0,0.0,1.0,0.0,0.0 1674 | 0.0,0.0,0.0,0.0,1.0 1675 | 0.0,0.0,0.0,0.0,1.0 1676 | 0.0,1.0,0.0,0.0,0.0 1677 | 0.0,0.0,0.0,1.0,0.0 1678 | 1.0,0.0,0.0,0.0,0.0 1679 | 1.0,0.0,0.0,0.0,0.0 1680 | 0.0,0.0,0.0,1.0,0.0 1681 | 0.0,1.0,0.0,0.0,0.0 1682 | 0.0,0.0,1.0,0.0,0.0 1683 | 0.0,0.0,1.0,0.0,0.0 1684 | 0.0,0.0,0.0,1.0,0.0 1685 | 0.0,0.0,1.0,0.0,0.0 1686 | 0.0,0.0,0.0,0.0,1.0 1687 | 0.0,0.0,1.0,0.0,0.0 1688 | 0.0,0.0,0.0,1.0,0.0 1689 | 0.0,0.0,0.0,0.0,1.0 1690 | 1.0,0.0,0.0,0.0,0.0 1691 | 1.0,0.0,0.0,0.0,0.0 1692 | 1.0,0.0,0.0,0.0,0.0 1693 | 0.0,0.0,1.0,0.0,0.0 1694 | 0.0,0.0,0.0,0.0,1.0 1695 | 0.0,0.0,0.0,1.0,0.0 1696 | 1.0,0.0,0.0,0.0,0.0 1697 | 1.0,0.0,0.0,0.0,0.0 1698 | 1.0,0.0,0.0,0.0,0.0 1699 | 0.0,1.0,0.0,0.0,0.0 1700 | 0.0,0.0,1.0,0.0,0.0 1701 | 0.0,0.0,1.0,0.0,0.0 1702 | 0.0,1.0,0.0,0.0,0.0 1703 | 0.0,0.0,1.0,0.0,0.0 1704 | 0.0,0.0,1.0,0.0,0.0 1705 | 0.0,0.0,0.0,0.0,1.0 1706 | 1.0,0.0,0.0,0.0,0.0 1707 | 1.0,0.0,0.0,0.0,0.0 1708 | 0.0,0.0,0.0,1.0,0.0 1709 | 0.0,0.0,0.0,1.0,0.0 1710 | 0.0,1.0,0.0,0.0,0.0 1711 | 1.0,0.0,0.0,0.0,0.0 1712 | 0.0,0.0,0.0,0.0,1.0 1713 | 1.0,0.0,0.0,0.0,0.0 1714 | 0.0,0.0,0.0,1.0,0.0 1715 | 1.0,0.0,0.0,0.0,0.0 1716 | 0.0,0.0,1.0,0.0,0.0 1717 | 0.0,0.0,0.0,0.0,1.0 1718 | 0.0,0.0,0.0,0.0,1.0 1719 | 0.0,0.0,0.0,1.0,0.0 1720 | 0.0,1.0,0.0,0.0,0.0 1721 | 0.0,1.0,0.0,0.0,0.0 1722 | 0.0,0.0,1.0,0.0,0.0 1723 | 1.0,0.0,0.0,0.0,0.0 1724 | 0.0,0.0,1.0,0.0,0.0 1725 | 0.0,0.0,0.0,1.0,0.0 1726 | 0.0,1.0,0.0,0.0,0.0 1727 | 0.0,0.0,0.0,0.0,1.0 1728 | 0.0,0.0,1.0,0.0,0.0 1729 | 0.0,0.0,1.0,0.0,0.0 1730 | 0.0,0.0,1.0,0.0,0.0 1731 | 0.0,0.0,0.0,1.0,0.0 1732 | 0.0,0.0,1.0,0.0,0.0 1733 | 0.0,0.0,1.0,0.0,0.0 1734 | 0.0,0.0,1.0,0.0,0.0 1735 | 0.0,1.0,0.0,0.0,0.0 1736 | 0.0,0.0,0.0,0.0,1.0 1737 | 0.0,0.0,1.0,0.0,0.0 1738 | 0.0,0.0,0.0,1.0,0.0 1739 | 0.0,0.0,0.0,0.0,1.0 1740 | 0.0,0.0,1.0,0.0,0.0 1741 | 0.0,1.0,0.0,0.0,0.0 1742 | 1.0,0.0,0.0,0.0,0.0 1743 | 1.0,0.0,0.0,0.0,0.0 1744 | 1.0,0.0,0.0,0.0,0.0 1745 | 0.0,0.0,0.0,1.0,0.0 1746 | 0.0,1.0,0.0,0.0,0.0 1747 | 0.0,0.0,0.0,1.0,0.0 1748 | 0.0,0.0,0.0,1.0,0.0 1749 | 0.0,1.0,0.0,0.0,0.0 1750 | 0.0,1.0,0.0,0.0,0.0 1751 | 0.0,0.0,1.0,0.0,0.0 1752 | 0.0,1.0,0.0,0.0,0.0 1753 | 0.0,1.0,0.0,0.0,0.0 1754 | 0.0,1.0,0.0,0.0,0.0 1755 | 0.0,0.0,1.0,0.0,0.0 1756 | 0.0,0.0,0.0,1.0,0.0 1757 | 0.0,0.0,0.0,0.0,1.0 1758 | 0.0,1.0,0.0,0.0,0.0 1759 | 0.0,0.0,0.0,0.0,1.0 1760 | 1.0,0.0,0.0,0.0,0.0 1761 | 0.0,0.0,0.0,0.0,1.0 1762 | 0.0,1.0,0.0,0.0,0.0 1763 | 0.0,1.0,0.0,0.0,0.0 1764 | 0.0,0.0,1.0,0.0,0.0 1765 | 0.0,0.0,1.0,0.0,0.0 1766 | 0.0,0.0,1.0,0.0,0.0 1767 | 0.0,0.0,0.0,1.0,0.0 1768 | 1.0,0.0,0.0,0.0,0.0 1769 | 1.0,0.0,0.0,0.0,0.0 1770 | 0.0,0.0,0.0,1.0,0.0 1771 | 0.0,0.0,0.0,1.0,0.0 1772 | 1.0,0.0,0.0,0.0,0.0 1773 | 0.0,0.0,0.0,1.0,0.0 1774 | 0.0,0.0,0.0,0.0,1.0 1775 | 0.0,0.0,1.0,0.0,0.0 1776 | 0.0,0.0,0.0,1.0,0.0 1777 | 0.0,0.0,0.0,0.0,1.0 1778 | 0.0,0.0,0.0,0.0,1.0 1779 | 0.0,1.0,0.0,0.0,0.0 1780 | 0.0,0.0,0.0,1.0,0.0 1781 | 0.0,0.0,1.0,0.0,0.0 1782 | 0.0,0.0,0.0,1.0,0.0 1783 | 0.0,0.0,1.0,0.0,0.0 1784 | 0.0,0.0,1.0,0.0,0.0 1785 | 0.0,0.0,0.0,0.0,1.0 1786 | 0.0,0.0,0.0,1.0,0.0 1787 | 0.0,0.0,0.0,0.0,1.0 1788 | 0.0,0.0,1.0,0.0,0.0 1789 | 0.0,1.0,0.0,0.0,0.0 1790 | 0.0,0.0,0.0,0.0,1.0 1791 | 0.0,0.0,1.0,0.0,0.0 1792 | 0.0,0.0,0.0,0.0,1.0 1793 | 0.0,0.0,0.0,1.0,0.0 1794 | 0.0,0.0,0.0,1.0,0.0 1795 | 1.0,0.0,0.0,0.0,0.0 1796 | 1.0,0.0,0.0,0.0,0.0 1797 | 0.0,0.0,0.0,1.0,0.0 1798 | 0.0,0.0,1.0,0.0,0.0 1799 | 0.0,0.0,1.0,0.0,0.0 1800 | 1.0,0.0,0.0,0.0,0.0 1801 | 0.0,1.0,0.0,0.0,0.0 1802 | 0.0,1.0,0.0,0.0,0.0 1803 | 0.0,0.0,0.0,0.0,1.0 1804 | 0.0,1.0,0.0,0.0,0.0 1805 | 1.0,0.0,0.0,0.0,0.0 1806 | 0.0,1.0,0.0,0.0,0.0 1807 | 0.0,1.0,0.0,0.0,0.0 1808 | 0.0,1.0,0.0,0.0,0.0 1809 | 0.0,1.0,0.0,0.0,0.0 1810 | 0.0,0.0,0.0,1.0,0.0 1811 | 0.0,1.0,0.0,0.0,0.0 1812 | 0.0,0.0,0.0,1.0,0.0 1813 | 0.0,0.0,0.0,0.0,1.0 1814 | 0.0,0.0,0.0,1.0,0.0 1815 | 0.0,0.0,1.0,0.0,0.0 1816 | 0.0,0.0,0.0,0.0,1.0 1817 | 1.0,0.0,0.0,0.0,0.0 1818 | 0.0,0.0,0.0,1.0,0.0 1819 | 0.0,0.0,0.0,0.0,1.0 1820 | 0.0,0.0,0.0,0.0,1.0 1821 | 0.0,0.0,1.0,0.0,0.0 1822 | 0.0,0.0,1.0,0.0,0.0 1823 | 1.0,0.0,0.0,0.0,0.0 1824 | 0.0,0.0,1.0,0.0,0.0 1825 | 1.0,0.0,0.0,0.0,0.0 1826 | 0.0,1.0,0.0,0.0,0.0 1827 | 1.0,0.0,0.0,0.0,0.0 1828 | 0.0,0.0,0.0,1.0,0.0 1829 | 0.0,0.0,1.0,0.0,0.0 1830 | 0.0,0.0,0.0,1.0,0.0 1831 | 0.0,0.0,0.0,1.0,0.0 1832 | 0.0,1.0,0.0,0.0,0.0 1833 | 0.0,1.0,0.0,0.0,0.0 1834 | 0.0,1.0,0.0,0.0,0.0 1835 | 0.0,0.0,0.0,0.0,1.0 1836 | 0.0,1.0,0.0,0.0,0.0 1837 | 0.0,0.0,0.0,0.0,1.0 1838 | 0.0,0.0,1.0,0.0,0.0 1839 | 0.0,0.0,1.0,0.0,0.0 1840 | 0.0,1.0,0.0,0.0,0.0 1841 | 1.0,0.0,0.0,0.0,0.0 1842 | 0.0,0.0,0.0,1.0,0.0 1843 | 1.0,0.0,0.0,0.0,0.0 1844 | 0.0,0.0,0.0,0.0,1.0 1845 | 0.0,0.0,0.0,0.0,1.0 1846 | 1.0,0.0,0.0,0.0,0.0 1847 | 0.0,0.0,0.0,0.0,1.0 1848 | 0.0,0.0,0.0,0.0,1.0 1849 | 0.0,0.0,0.0,1.0,0.0 1850 | 0.0,1.0,0.0,0.0,0.0 1851 | 0.0,0.0,0.0,0.0,1.0 1852 | 0.0,0.0,0.0,1.0,0.0 1853 | 0.0,0.0,0.0,0.0,1.0 1854 | 0.0,0.0,0.0,0.0,1.0 1855 | 0.0,0.0,0.0,1.0,0.0 1856 | 0.0,0.0,1.0,0.0,0.0 1857 | 0.0,0.0,0.0,1.0,0.0 1858 | 0.0,0.0,0.0,0.0,1.0 1859 | 1.0,0.0,0.0,0.0,0.0 1860 | 0.0,1.0,0.0,0.0,0.0 1861 | 0.0,0.0,0.0,1.0,0.0 1862 | 0.0,0.0,0.0,1.0,0.0 1863 | 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#===============================================================================================# 7 | 8 | # Ensemble Models Class 9 | 10 | #===============================================================================================# 11 | 12 | class Ensemble(Classification): 13 | 14 | """ 15 | This class is for performing ensemble algorithms such as voting, adaboost, xgboost, or stacking. 16 | 17 | Parameters 18 | ---------- 19 | ensemble_method: 'Voting', 'AdaBoost', 'XGBoost', 'Stacking' 20 | the type of ensemble algorithm you would like to apply 21 | 22 | estimators: list 23 | the classifcation models to be used by the ensemble algorithm 24 | 25 | x_train: dataframe 26 | the independant variables of the training data 27 | 28 | x_val: dataframe 29 | the independant variables of the validation data 30 | 31 | y_train: series 32 | the target variable of the training data 33 | 34 | y_val: series 35 | the target variable of the validation data 36 | 37 | """ 38 | 39 | def __init__(self, ensemble_method, estimators, X_train, X_val, y_train, y_val): 40 | 41 | self.ensemble_method = ensemble_method 42 | self.x_train = X_train 43 | self.y_train = y_train 44 | self.x_val = X_val 45 | self.y_val = y_val 46 | self.model_type = ensemble_method 47 | self.scores_table = pd.DataFrame() 48 | 49 | if self.ensemble_method == "Voting": 50 | self.technique = VotingClassifier(estimators=estimators, voting='soft', n_jobs=-1) 51 | elif self.ensemble_method == "AdaBoost": 52 | self.technique = AdaBoostClassifier(estimators, algorithm='SAMME') 53 | elif self.ensemble_method == "XGBoost": 54 | self.technique = XGBClassifier(n_jobs=-1) 55 | elif self.ensemble_method == "Stacking": 56 | self.technique = StackingClassifier(estimators) 57 | -------------------------------------------------------------------------------- /Helpers_NN.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import seaborn as sns 4 | import matplotlib.pyplot as plt 5 | from nltk.tokenize import RegexpTokenizer 6 | from sklearn.metrics import confusion_matrix 7 | 8 | tokenizer = RegexpTokenizer(r'[a-zA-Z]+') 9 | 10 | 11 | #===============================================================================================# 12 | 13 | # Method to Add Suffix '_sum' to Review Summary 14 | 15 | #===============================================================================================# 16 | 17 | 18 | def add_sum_suffix(text): 19 | 20 | token_list = tokenizer.tokenize(text.lower()) 21 | new_text = '' 22 | for word in token_list: 23 | word = word + '_sum' 24 | new_text += word + ' ' 25 | 26 | return new_text 27 | 28 | 29 | #===============================================================================================# 30 | 31 | # Method to Tokenize Review Column 32 | 33 | #===============================================================================================# 34 | 35 | 36 | def text_cleanup(text): 37 | 38 | token_list = tokenizer.tokenize(text.lower()) 39 | new_text = '' 40 | for word in token_list: 41 | new_text += word + ' ' 42 | 43 | return new_text 44 | 45 | 46 | #===============================================================================================# 47 | 48 | # Method to Reverse Encode 'Score' Column 49 | 50 | #===============================================================================================# 51 | 52 | 53 | def reverse_encode(y_df): 54 | 55 | y_df['score'] = (y_df.iloc[:, 0:] == 1).idxmax(1)['score'] = (y_df.iloc[:, 0:] == 1).idxmax(1) 56 | for i in range(0,len(y_df)): 57 | if y_df.iloc[i,-1] == 'score_1': 58 | y_df.iloc[i,-1] = 1 59 | elif y_df.iloc[i,-1] == 'score_2': 60 | y_df.iloc[i,-1] = 2 61 | elif y_df.iloc[i,-1]== 'score_3': 62 | y_df.iloc[i,-1] = 3 63 | elif y_df.iloc[i,-1]== 'score_4': 64 | y_df.iloc[i,-1] = 4 65 | elif y_df.iloc[i,-1] == 'score_5': 66 | y_df.iloc[i,-1]= 5 67 | 68 | 69 | #===============================================================================================# 70 | 71 | # Method to Adjust Argmax of Score to a 1-5 Scale 72 | 73 | #===============================================================================================# 74 | 75 | 76 | def add_one_argmax_score(x): 77 | 78 | x = x+1 79 | 80 | return x 81 | 82 | 83 | #===============================================================================================# 84 | 85 | # Method to Create a Confusion Matriz 86 | 87 | #===============================================================================================# 88 | 89 | 90 | def conf_matrix(cm): 91 | 92 | plt.figure(figsize=(9,9)) 93 | ax = sns.heatmap(cm, 94 | annot= True, 95 | fmt = '.4g', 96 | cbar=0, 97 | xticklabels=[1,2,3,4,5], 98 | yticklabels=[1,2,3,4,5]) 99 | ax.set(xlabel='Predicted', ylabel='True') 100 | plt.show() -------------------------------------------------------------------------------- /Hilton_Hotel_App.py: -------------------------------------------------------------------------------- 1 | #===============================================================================================# 2 | 3 | # Imports 4 | 5 | #===============================================================================================# 6 | 7 | import streamlit as sl 8 | 9 | import pandas as pd 10 | import numpy as np 11 | 12 | import pickle 13 | 14 | from nltk.tokenize import RegexpTokenizer 15 | 16 | from tensorflow.keras.models import load_model 17 | from keras.preprocessing import sequence 18 | 19 | #===============================================================================================# 20 | 21 | # Functions and Models Prepared 22 | 23 | #===============================================================================================# 24 | 25 | word_index_dict = pickle.load(open('Data/word_index_dict.pkl','rb')) 26 | 27 | neural_net_model = load_model('Models/Neural_Network.h5') 28 | 29 | tokenizer = RegexpTokenizer(r'[a-zA-Z]+') 30 | 31 | def index_review_words(text): 32 | review_word_list = [] 33 | for word in text.lower().split(): 34 | if word in word_index_dict.keys(): 35 | review_word_list.append(word_index_dict[word]) 36 | else: 37 | review_word_list.append(word_index_dict['']) 38 | 39 | return review_word_list 40 | 41 | def add_sum_suffix(text): 42 | 43 | token_list = tokenizer.tokenize(text.lower()) 44 | new_text = '' 45 | for word in token_list: 46 | word = word + '_sum' 47 | new_text += word + ' ' 48 | 49 | return new_text 50 | 51 | def text_cleanup(text): 52 | tokenizer = RegexpTokenizer(r'[a-zA-Z]+') 53 | token_list = tokenizer.tokenize(text.lower()) 54 | new_text = '' 55 | for word in token_list: 56 | new_text += word + ' ' 57 | 58 | return new_text 59 | #===============================================================================================# 60 | 61 | # Streamlit 62 | 63 | #===============================================================================================# 64 | 65 | sl.title("Hotel Review Classifier Application") 66 | 67 | 68 | review_summary_text = sl.text_input('Enter Your Review Summary Here') 69 | review_text = sl.text_area('Enter Your Review Here') 70 | 71 | if sl.button('Predict'): 72 | 73 | result_review_sum = review_summary_text.title() 74 | 75 | result_review = review_text.title() 76 | 77 | review_summary_text = add_sum_suffix(review_summary_text) 78 | 79 | review_text = text_cleanup(review_text) 80 | 81 | review_text = index_review_words(review_text) 82 | 83 | review_summary_text = index_review_words(review_summary_text) 84 | 85 | all_review_text = review_text + review_summary_text 86 | 87 | all_review_text = sequence.pad_sequences([all_review_text],value=word_index_dict[''],padding='post',maxlen=250) 88 | 89 | prediction = neural_net_model.predict(all_review_text) 90 | 91 | prediction = np.argmax(prediction) 92 | 93 | 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HILTON HOTEL LONDON REVIEWS CLASSIFIER

2 | 3 |

4 | 5 |

6 | 7 | ## Business Case 8 | 9 | In the modern day, public discussion and critiquing of products and services occurs beyond dedicated mediums, and now also takes place in the realm of social media, too. 10 | 11 | Online Hilton Hotel reviews are currently found on tripadvisor, trustpilot, and expedia. The majority of reviewers gave a score between 3 and 5, so if a new customer browses online reviews on any of the previously mentioned review sites, they may consider booking a room at the Hilton. 12 | 13 | What if they already made up their mind from hearing what a friend had to say? 14 | Potential customers, could have their hotel choice be influenced by a tweet. Opinions are shared constantly on social media platforms, and are read by their followers. The knowledge, of what these followers think about our hotel, from reading these online posts, could help us better understand the general public's perception of our hotel. 15 | 16 | By using sentiment analysis, on existing hotel reviews from Tripadvisor.com, I created a model that can quantify on a scale of 1-5, how the author of a tweet on twitter, or a post on a reddit thread, feels about our hotel, and as a result, also how the readers think about us. If a review classifies to be less than a score of 3, this post/tweet could be looked into, find out why they had a negative opinion of our hotel, and in return fix the problem. 17 | 18 | Email: candyahs@gmail.com
19 | LinkedIn: www.linkedin.com/in/ahilan-srivishnumohan/
20 | Medium: www.medium.com/@candyahs
21 | 22 | ## Table of Contents 23 |
24 | Show/Hide 25 |
26 | 27 | 1. [ File Descriptions ](#File_Description) 28 | 2. [ Technologies Used ](#Technologies_Used) 29 | 3. [ Structure ](#Structure) 30 | 4. [ Executive Summary ](#Executive_Summary) 31 | * [ 1. Webscraping, Early EDA, and Cleaning ](#Webscraping_Early_EDA_and_Cleaning) 32 | * [ Webscraping ](#Webscraping) 33 | * [ Early EDA and Cleaning](#Early_EDA_and_Cleaning) 34 | * [ 2. Further EDA and Preprocessing ](#Further_EDA_and_Preprocessing) 35 | * [ 3. Modelling and Hyperparameter Tuning ](#Modelling) 36 | * [ 4. Evaluation ](#Evaluation) 37 | * [ Future Improvements ](#Future_Improvements) 38 | * [ 5. Neural Network Modelling ](#Neural_Network_Modelling) 39 | * [ 6. Revaluation and Deployment ](#Revaluation) 40 |
41 | 42 | ## File Descriptions 43 |
44 | 45 | Show/Hide 46 |
47 | 48 | * [ Data ](https://github.com/awesomeahi95/Hotel_Review_NLP/tree/master/Data): folder containing all data files 49 | * 1.tripadvisor_scraped_hotel_reviews.csv: webscraped data before any changes 50 | * 2.hotel_reviews_structured.csv: data after balancing and cleaning 51 | * 3.x_train_data.csv: training data with x values from preprocessed dataset 52 | * 3.y_train_data.csv: training data with y values from preprocessed dataset 53 | * 4.x_test_data.csv: test data with x values from preprocessed dataset 54 | * 4.y_test_data.csv: test data with y values from preprocessed dataset 55 | * [ Images ](https://github.com/awesomeahi95/Hotel_Review_NLP/tree/master/Images): folder containing images used for README and presentation pdf 56 | * [ Models ](https://github.com/awesomeahi95/Hotel_Review_NLP/tree/master/Models): folder containing trained models saved with pickle 57 | * Adabooost.pkl, Decision Tree.pkl, KNN.pkl, Logistic Regression.pkl, Naive Bayes.pkl, Neural Network.pkl, Random Forest.pkl, Stacking.pkl, SVM.pkl, Voting.pkl, XGBoost.pkl 58 | * [ Tripadvisor_Webscrape ](https://github.com/awesomeahi95/Hotel_Review_NLP/tree/master/Tripadvisor_Webscrape): folder containing all webscraping files 59 | * Tripadvisor: folder containing .py files and spiders used 60 | * spiders: folder containing spider files and datasets 61 | * hotels.py: main spider .py file for scraping hotel reviews from Tripadvisor 62 | * tripadvisor_scraped_hotel_reviews.csv: csv file with data to be used for project 63 | * _init_.py, items.py, middlewares.py, pipelines.py, settings.py: default scrapy files used for webscrape 64 | * scrapy.cfg: scrap config file 65 | * [ 1.Webscraping_Early_EDA_and_Cleaning.ipynb ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/1.Webscraping_Early_EDA_and_Cleaning.ipynb): notebook with early data exploration and data manipulation 66 | * [ 2.Further_EDA_and_Preprocessing.ipynb ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/2.Further_EDA_and_Preprocessing.ipynb): notebook with feature engineering and nlp preprocessing 67 | * [ 3.Modelling_and_Hyperparameter_Tuning.ipynb ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/3.Modelling_and_Hyperparameter_Tuning.ipynb): notebook with all the models created 68 | * [ 4.Evaluation ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/4.Evaluation.ipynb): notebook with final model selection, testing, and model evaluation 69 | * [ 5.Neural_Network_Modelling ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/5.Neural_Network_Modelling.ipynb): notebook with an extra model training using neural networks 70 | * [ 6.Revaluation_and_Deployment ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/6.Revaluation_and_Deployment.ipynb): notebook comparing old best model and NN model, and deployment 71 | * [ Classification.py ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/Classification.py): contains classes with classifcation methods 72 | * [ Ensemble.py ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/Ensemble.py): contains classes with ensemble methods 73 | * [ Helpers_NN.py ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/Helpers_NN.py): contains methods used to help with neural network processes 74 | * [ Hilton_Hotel_App.py ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/Hilton_Hotel_App.py): contains script to run app 75 | * [ Hilton__Hotel_Presentation.pdf ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/Hilton_Hotel_Presentation.pdf): presentation summarising project case, processese, and findings 76 | * [ Procfile ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/Procfile): file supporting Heroku application 77 | * [ requirements.txt ](https://github.com/awesomeahi95/Hotel_Review_NLP/blob/master/requirements.txt): dependencies for heroku application 78 |
79 | 80 | ## Tecnologies Used: 81 |
82 | 83 | Show/Hide 84 |
85 | 86 | * Python 87 | * Pandas 88 | * Numpy 89 | * Matplotlib 90 | * Seaborn 91 | * NLTK 92 | * Scrapy 93 | * Scikit-Learn 94 | * Keras 95 | * Tensorflow 96 | * Streamlit 97 | * Heroku 98 |
99 | 100 | ## Structure of Notebooks: 101 |
102 | 103 | Show/Hide 104 |
105 | 106 | 1. Early EDA and Cleaning 107 | * 1.1 Webscrape Process Explained 108 | * 1.2 Imports 109 | * 1.3 Checking for Nulls 110 | * 1.4 Converting Score Column 111 | * 1.5 Adjusting Class Imbalance for Scores 112 | * 1.6 Joining Review Part 1 with Review Part 2 in New Column Review 113 | * 1.7 Removing Review Part 1 and Review Part 2 Columns 114 | * 1.8 Saving Structured Dataset as a CSV 115 | 116 | 2. Further EDA and Preprocessing 117 | * 2.1 Imports 118 | * 2.2 Checking Frequency of Words and Phrases in Review Summaries 119 | * 2.3 Checking Frequency of Words and Phrases in Reviews 120 | * 2.4 Stemming and Lemming 121 | * 2.5 Train Test Split 122 | * 2.6 TF-IDF Vectorisation for Reviews 123 | * 2.7 TF-IDF Vectorisation for Review Summaries 124 | * 2.8 Joining Reviews With Review Summaries 125 | * 2.9 Saving Preprocessed Dataset as CSVs 126 | 127 | 3. Modelling and Hyperparameter Tuning 128 | * 3.1 Imports 129 | * 3.2 Train and Validation Split 130 | * 3.3 Decision Tree (Baseline) 131 | * 3.4 Random Forest 132 | * 3.5 Logistic Regression 133 | * 3.6 Support Vector Machines 134 | * 3.7 Guassian Naive Bayes 135 | * 3.8 KNN 136 | * 3.9 Adaboost (Logistic Regression) 137 | * 3.10 XGBoost (Logistic Regression) 138 | * 3.11 Voting 139 | * 3.12 Stacking 140 | * 3.13 All Models Compared 141 | * 3.14 Best Model (Logistic Regression) - Deeper Look 142 | * 3.15 Saving Best Model 143 | 144 | 4. Evaluation 145 | * 4.1 Imports 146 | * 4.2 Best Model Selection 147 | * 4.3 Best Model Tested 148 | * 4.4 Deeper Diver Into Best Model 149 | * 4.5 Application Deployability 150 | 151 | 5. Neural Network Modelling 152 | * 5.1 Imports 153 | * 5.2 One Hot Encoding Score Column 154 | * 5.3 Train Test Split 155 | * 5.4 Add Suffix to the Review Summary to Distinguish the Difference 156 | * 5.5 Removing Punctuation and Tokenizing Review Column 157 | * 5.6 Creating a Dictionary With Words That Appear in Reviews and an Index 158 | * 5.7 Indexing Words in Reviews Using Dictionary 159 | * 5.8 Combining Indexed Review Summary and Indexed Review Into a Single Column Called All Preprocessed Review 160 | * 5.9 Modelling 161 | * 5.10 Testing Model 162 | * 5.11 Test Confusion Matrix 163 | * 5.12 Saving Model 164 | 165 | 6. Revaluation and Deployment 166 | * 6.1 Imports 167 | * 6.2 Comparing Stacking Model with Neural Network Model 168 | * 6.3 Deployment 169 |
170 | 171 | 172 | ## Executive Summary 173 | 174 | 175 | 176 | ### Webscraping, Early EDA, and Cleaning: 177 |
178 | Show/Hide 179 |
180 | 181 | 182 | #### Webscraping 183 | 184 | I set a goal of a minimum of 5000 reviews to scrape, before choosing the specific hotels. I then chose the 5 Hilton hotels with the highest number of reviews, to scrape; London Gatwick Airport, London Metropole, London Euston, London Croydon, and London - West End. 185 | Between these 5 hotels there were 17538 reviews, I had plenty room to filter or drop reviews and retain at least my minimum of 5000. 186 | 187 |
Tripadvisor Review Example
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189 | 190 |

191 | 192 | The structure of each review consisted of a 1-5 scale score rating in bubble form, a review summary, and a detailed review split into p1 and p2 (depending on if there was a read more option). Each page on tripadvisor had 5 reviews per page, so I had to navigate between pages using tripadvisor's next page function. 193 | 194 | The root URL I used was 'www.tripadvisor.co.uk' 195 | 196 | The 5 starting URL extensions I used were: 197 | - '/Hotel_Review-g187051-d239658-Reviews-Hotel_Hilton_London_Gatwick_Airport-Crawley_West_Sussex_England.html/' 198 | - '/Hotel_Review-g186338-d193089-Reviews-Hilton_London_Metropole-London_England.html/' 199 | - '/Hotel_Review-g186338-d192048-Reviews-Hilton_London_Euston-London_England.html/' 200 | - '/Hotel_Review-g186338-d193102-Reviews-DoubleTree_by_Hilton_Hotel_London_West_End-London_England.html/' 201 | - '/Hotel_Review-g504167-d192599-Reviews-Hilton_London_Croydon-Croydon_Greater_London_England.html' 202 | 203 | From these pages I chose to extract 5 different features: 204 | - hotel_name 205 | - review_summary 206 | - review_p1 207 | - review_p2 208 | - score 209 | 210 | I used a scrapy spider to crawl the website to scrape the requested data. Scrapy proved the be efficient and fast at extracting the data. I ran the spider script (hotels.py) for around 20 minutes, on the 13th May 2020. 211 | 212 |
Histogram of Scores for Each Hotel
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214 | 215 |

216 | 217 | 218 | 219 | #### Early EDA and Cleaning: 220 | 221 | The initial shape of the dataset was (35078,5). The 5 columns was as expected, but there were double the number of rows as the number of reviews scraped. There were null rows with only hotel_name and no other values, so I removed those rows, bringing us back to the expected 17538. 222 | 223 | This project entailed the use of classification models, and for reliable results, I had to remove reviews to undo class imbalance. Using this visualisation I saw that were much less reviews with a score of 1 compared to reviews with a score of 3, 4, and 5. To combat this imbalance, I randomly removed reviews with scores of 2, 3, 4, and 5, to match with 1 (1881 reviews). 224 | 225 |
Histogram of Scores for All Hotels (With Class Imbalance (Left) vs Without Class Imbalance (Right))
226 |
227 | 228 | I combined the review p1 and review p2 column into one to make future vectorisation much easier, then I saved the cleaned dataset as a csv, for the next stage. 229 |
230 | 231 | 232 | ### Further EDA and Preprocessing 233 |
234 | Show/Hide 235 |
236 | 237 | The cleaned dataset had a shape of (9405,4). I started with some analysis on the text columns; review and review summary. 238 | 239 | Using the FreqDist function in the ntlk library I plotted a graph with the most frequent words and phrases in both columns. Stopwords were removed to capture the more meaningful words. 240 | 241 |
Distribution Plot of Frequent Words and Phrases in Text ( Review Summary (Left) and Review (Right) )
242 |
243 | 244 | I had noticed a lot of the most frequent words in the review text happened to be words with no sentimental impact, so I iteratively removed unmeaningful words such as 'room', 'hotel', 'hilton' etc. I did this as a precaution, as some of these words may impact my model accuracies. 245 | 246 |
World Cloud of Frequent Words and Phrases in Text After Removing Unmeaningful Words ( Review Summary (Left) and Review (Right) )
247 |
248 | 249 | To narrow down the feature words I applied stemmation and lemmitisation to both the reviews and review summaries. 250 | 251 |
Example of Lemmatisation and Stemmation Applied to a Review and Review Summary
252 |

253 | 254 |

255 | 256 | Stemmation had broken down some words into words that don't exist, whereas lemmitisation had simplified adjectives and verbs to their root form. I chose to continue with the lemmitised version of the texts for further processing. 257 | 258 | Prior to vectorising the current dataset, I did a train, test split to save the test data for after modelling. 259 | 260 | Using the lemmed texts for review and review summary I used TF-IDF vectorisation with an ngram range of 2, leaving me with a vectorised dataset with 138 words and phrases (112 from reviews and 26 from review summaries). I then saved the x and y train data in separate csv files for modelling. 261 |
262 | 263 | 264 | ### Modelling: 265 |
266 | Show/Hide 267 |
268 | 269 | I have created .py files; Classifiction.py and Ensemble.py with classes, that contain functions to simplify the modelling process, and to neaten up the modelling notebook. 270 | 271 | I did another data split into Train and Validation data in preparation for using GridSearch Cross Validation. I also chose Stratified 5-fold has a my choice for cross validating. 272 | 273 | For the majority of models I created, I applied hyperparameter tuning, where I started with a broad range of hyperparameters, and tuned for optimal train accuracy and validation accuracy. 274 | 275 | 276 |
Table Comparing Best Models
277 |

278 | 279 |

280 | 281 | Initially, I thought the validation accuracy was low for most of the models I created, but when considering these models were attempting to classify for 5 different classes, 0.45 and greater seems very reasonable (where 0.2 = randomly guessing correctly). 282 | 283 | I have saved all the models using the pickle library's dump function and stored them in the Models folder. 284 |
285 | 286 | 287 | ### Evaluation 288 |
289 | Show/Hide 290 |
291 | 292 | I focused on 3 factors of defining a good model: 293 | 294 | 1. Good Validation Accuracy 295 | 2. Good Training Accuracy 296 | 3. Small Difference between Training and Validation Accuracy 297 | 298 | I chose the Stacking ensemble model ( (Adaboost with log_reg_2) stacked with log_reg_2 ) as my best model, because it has the highest validation accuracy with only around 3.5% drop from train to validation in accuracy. I wanted to minimise overfitting and make the model as reusable as possible. Stacking achieved a reasonable training accuracy as well, although it did not reach the level of some of the other ensemble techniques. 299 | 300 | I next tested the best model with the earlier saved test data. The model managed to get a high test accuracy, similar to the validation data from the model training stage. This is very good, proving that prioritising a high validation score, and minimising the difference between train and validation accuracy, has helped it classify new review texts very well. 301 | 302 |
Test Results
303 |

304 | 305 |

306 | 307 | Looking at the precision, recall, and f1 score, I also noticed the scores were higher around scores of 1 and 5, lower for 2, 3, and 4. This shows that the models performs well on more extreme opinions on reviews than mixed opinions. 308 | 309 | Looking into different metrics and deeper into my best model; Stacking, I learnt that most the False Postives came from close misses (e.g. predicting a score of 4 for a true score of 5). This is best shown by these two confusion matrixes (validation and test). 310 | 311 |
Confusion Matrix for Validation and Test Data Predictions ( Validation (Left) and Test (Right) )
312 |
313 | 314 | The adjacent squares of the diagonal going across the confusion matrix, shows that the model's second highest prediction for a given class (review score) is always a review score that is +-1 the true score. 315 | Very few reviews that have a score of 5, have been predicted to have a score of 1 or 2. This is very relieving to know, the majority of the error for the model, is no different to the error a human may make classifying a review to a score with a scale of 1-5. 316 | 317 | - most errors were near misses (e.g. 5 predicted as 4) 318 | - extreme scores (1 and 5) were relatively accurate 319 | - comparable to human prediction 320 | - reusable and consistent 321 | 322 | 323 | Given the classifcation problem is 5 way multi-class one and the adjacent classes can have overlap in the english language even to humans, this model I have created can be deployed. 324 | 325 | Applying this model will address the problem of not having a full understanding of public opinion of our hotel. We can apply this to new sources for opinions on our hotel and yield more feedback then we did had before. 326 | 327 | 328 | #### Future Improvements 329 | 330 | - Model using neural networks - see if better accuracy can be achieved 331 | - Create a working application to test new reviews written by people 332 | - Try a different pre-processing approach and see if model performances change 333 | - Bring in new sources of data to see if there are significant differences on frequent words used 334 | 335 |
336 | 337 | 338 | ### Neural Network Modelling: 339 |
340 | Show/Hide 341 |
342 | 343 | I experimented with different classifcation and ensemble methods to help classify hotel review scores. Some performed well, but there was definitely room for improvement, so I wanted to explore a deep learning approach. 344 | 345 | 346 |
Neural Network Architecture
347 |

348 | 349 |

350 | 351 | * Input Layer: 17317 Nodes (one for each word in training data + 4 extra for padding, unknown words, start of review, and unused words) 352 | * Embedding Layer: takes 17317 unique items and maps them into a 16 dimensional vector space 353 | * Global Average 1D Pooling Layer: scales down 16 dimensional layer 354 | * Dense Hidden Layer: 16 Nodes (using relu activation function) 355 | * Dense Output Layer: 5 nodes for each score (using sigmoid activation function) 356 | 357 |
358 | 359 | 360 | ### Revaluation and Deployment: 361 |
362 | Show/Hide 363 |
364 | 365 | I tested the neural network model using the test data and achieved an accuracy of 0.5710 which is better than the stacking model accuracy of 0.5077, by over 5%. 366 | 367 | I wanted to look at the confusion matrix, as this gives a better idea of how the model is performing over all 5 classes. 368 | 369 |
Neural Network Model Test Confusion Matrix
370 |

371 | 372 |

373 | 374 | The error is more contained within adjacent scores with the neural network model. Almost zero confusion between extreme scores 1 and 5, and minimal confusion with scores 2 and 4. Although a score of 3 can be harder to predict, there is definitely an improvement from the Stacking model. Around 97% of the time the model predicts at least the adjacent score to the actual score. 375 | 376 | #### Deployment and Application 377 | 378 | After seeing the improvements from the Stacking model, I was more confident about deploying the model for actionable use. 379 | 380 | I planned on future improvements being the addition of the neural network model and then creating an application for the model, so as a next step I decided to make a working application to test out new reviews using streamlit. I have deployed the app using Heroku: https://hilton-hotel-app.herokuapp.com/. 381 | 382 | Using this model, we will learn more about our new and old customers, then we can improve Hilton Hotel's guest satisfaction, and as a result increase customer retention and bring in new travelers. 383 | 384 | #### Future Development 385 | 386 | * Create a webscraper spider for twitter, reddit, etc for further model assessment 387 | 388 |
389 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/.ipynb_checkpoints/scrapy-checkpoint.cfg: -------------------------------------------------------------------------------- 1 | # Automatically created by: scrapy startproject 2 | # 3 | # For more information about the [deploy] section see: 4 | # https://scrapyd.readthedocs.io/en/latest/deploy.html 5 | 6 | [settings] 7 | default = Tripadvisor.settings 8 | 9 | [deploy] 10 | #url = http://localhost:6800/ 11 | project = Tripadvisor 12 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/.ipynb_checkpoints/__init__-checkpoint.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awesomeahi95/Hotel_Review_NLP/6c98a71ec91ba8770171909b0e59ae563c6d48c4/Tripadvisor_Webscrape/Tripadvisor/.ipynb_checkpoints/__init__-checkpoint.py -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/.ipynb_checkpoints/items-checkpoint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Define here the models for your scraped items 4 | # 5 | # See documentation in: 6 | # https://docs.scrapy.org/en/latest/topics/items.html 7 | 8 | import scrapy 9 | 10 | 11 | class TripadvisorItem(scrapy.Item): 12 | # define the fields for your item here like: 13 | # name = scrapy.Field() 14 | pass 15 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/.ipynb_checkpoints/middlewares-checkpoint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Define here the models for your spider middleware 4 | # 5 | # See documentation in: 6 | # https://docs.scrapy.org/en/latest/topics/spider-middleware.html 7 | 8 | from scrapy import signals 9 | 10 | 11 | class TripadvisorSpiderMiddleware: 12 | # Not all methods need to be defined. If a method is not defined, 13 | # scrapy acts as if the spider middleware does not modify the 14 | # passed objects. 15 | 16 | @classmethod 17 | def from_crawler(cls, crawler): 18 | # This method is used by Scrapy to create your spiders. 19 | s = cls() 20 | crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) 21 | return s 22 | 23 | def process_spider_input(self, response, spider): 24 | # Called for each response that goes through the spider 25 | # middleware and into the spider. 26 | 27 | # Should return None or raise an exception. 28 | return None 29 | 30 | def process_spider_output(self, response, result, spider): 31 | # Called with the results returned from the Spider, after 32 | # it has processed the response. 33 | 34 | # Must return an iterable of Request, dict or Item objects. 35 | for i in result: 36 | yield i 37 | 38 | def process_spider_exception(self, response, exception, spider): 39 | # Called when a spider or process_spider_input() method 40 | # (from other spider middleware) raises an exception. 41 | 42 | # Should return either None or an iterable of Request, dict 43 | # or Item objects. 44 | pass 45 | 46 | def process_start_requests(self, start_requests, spider): 47 | # Called with the start requests of the spider, and works 48 | # similarly to the process_spider_output() method, except 49 | # that it doesn’t have a response associated. 50 | 51 | # Must return only requests (not items). 52 | for r in start_requests: 53 | yield r 54 | 55 | def spider_opened(self, spider): 56 | spider.logger.info('Spider opened: %s' % spider.name) 57 | 58 | 59 | class TripadvisorDownloaderMiddleware: 60 | # Not all methods need to be defined. If a method is not defined, 61 | # scrapy acts as if the downloader middleware does not modify the 62 | # passed objects. 63 | 64 | @classmethod 65 | def from_crawler(cls, crawler): 66 | # This method is used by Scrapy to create your spiders. 67 | s = cls() 68 | crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) 69 | return s 70 | 71 | def process_request(self, request, spider): 72 | # Called for each request that goes through the downloader 73 | # middleware. 74 | 75 | # Must either: 76 | # - return None: continue processing this request 77 | # - or return a Response object 78 | # - or return a Request object 79 | # - or raise IgnoreRequest: process_exception() methods of 80 | # installed downloader middleware will be called 81 | return None 82 | 83 | def process_response(self, request, response, spider): 84 | # Called with the response returned from the downloader. 85 | 86 | # Must either; 87 | # - return a Response object 88 | # - return a Request object 89 | # - or raise IgnoreRequest 90 | return response 91 | 92 | def process_exception(self, request, exception, spider): 93 | # Called when a download handler or a process_request() 94 | # (from other downloader middleware) raises an exception. 95 | 96 | # Must either: 97 | # - return None: continue processing this exception 98 | # - return a Response object: stops process_exception() chain 99 | # - return a Request object: stops process_exception() chain 100 | pass 101 | 102 | def spider_opened(self, spider): 103 | spider.logger.info('Spider opened: %s' % spider.name) 104 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/.ipynb_checkpoints/pipelines-checkpoint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Define your item pipelines here 4 | # 5 | # Don't forget to add your pipeline to the ITEM_PIPELINES setting 6 | # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html 7 | 8 | 9 | class HotelWebsitesPipeline: 10 | def process_item(self, item, spider): 11 | return item 12 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/.ipynb_checkpoints/settings-checkpoint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Scrapy settings for Tripadvisor project 4 | # 5 | # For simplicity, this file contains only settings considered important or 6 | # commonly used. You can find more settings consulting the documentation: 7 | # 8 | # https://docs.scrapy.org/en/latest/topics/settings.html 9 | # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html 10 | # https://docs.scrapy.org/en/latest/topics/spider-middleware.html 11 | 12 | BOT_NAME = 'Tripadvisor' 13 | 14 | SPIDER_MODULES = ['Tripadvisor.spiders'] 15 | NEWSPIDER_MODULE = 'Tripadvisor.spiders' 16 | 17 | 18 | # Crawl responsibly by identifying yourself (and your website) on the user-agent 19 | #USER_AGENT = 'Tripadvisor (+http://www.yourdomain.com)' 20 | 21 | # Obey robots.txt rules 22 | ROBOTSTXT_OBEY = True 23 | 24 | # Configure maximum concurrent requests performed by Scrapy (default: 16) 25 | #CONCURRENT_REQUESTS = 32 26 | 27 | # Configure a delay for requests for the same website (default: 0) 28 | # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay 29 | # See also autothrottle settings and docs 30 | #DOWNLOAD_DELAY = 3 31 | # The download delay setting will honor only one of: 32 | #CONCURRENT_REQUESTS_PER_DOMAIN = 16 33 | #CONCURRENT_REQUESTS_PER_IP = 16 34 | 35 | # Disable cookies (enabled by default) 36 | #COOKIES_ENABLED = False 37 | 38 | # Disable Telnet Console (enabled by default) 39 | #TELNETCONSOLE_ENABLED = False 40 | 41 | # Override the default request headers: 42 | #DEFAULT_REQUEST_HEADERS = { 43 | # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 44 | # 'Accept-Language': 'en', 45 | #} 46 | 47 | # Enable or disable spider middlewares 48 | # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html 49 | #SPIDER_MIDDLEWARES = { 50 | # 'Tripadvisor.middlewares.TripadvisorSpiderMiddleware': 543, 51 | #} 52 | 53 | # Enable or disable downloader middlewares 54 | # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html 55 | #DOWNLOADER_MIDDLEWARES = { 56 | # 'Tripadvisor.middlewares.TripadvisorDownloaderMiddleware': 543, 57 | #} 58 | 59 | # Enable or disable extensions 60 | # See https://docs.scrapy.org/en/latest/topics/extensions.html 61 | #EXTENSIONS = { 62 | # 'scrapy.extensions.telnet.TelnetConsole': None, 63 | #} 64 | 65 | # Configure item pipelines 66 | # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html 67 | #ITEM_PIPELINES = { 68 | # 'Tripadvisor.pipelines.TripadvisorPipeline': 300, 69 | #} 70 | 71 | # Enable and configure the AutoThrottle extension (disabled by default) 72 | # See https://docs.scrapy.org/en/latest/topics/autothrottle.html 73 | #AUTOTHROTTLE_ENABLED = True 74 | # The initial download delay 75 | #AUTOTHROTTLE_START_DELAY = 5 76 | # The maximum download delay to be set in case of high latencies 77 | #AUTOTHROTTLE_MAX_DELAY = 60 78 | # The average number of requests Scrapy should be sending in parallel to 79 | # each remote server 80 | #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 81 | # Enable showing throttling stats for every response received: 82 | #AUTOTHROTTLE_DEBUG = False 83 | 84 | # Enable and configure HTTP caching (disabled by default) 85 | # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings 86 | #HTTPCACHE_ENABLED = True 87 | #HTTPCACHE_EXPIRATION_SECS = 0 88 | #HTTPCACHE_DIR = 'httpcache' 89 | #HTTPCACHE_IGNORE_HTTP_CODES = [] 90 | #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage' 91 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awesomeahi95/Hotel_Review_NLP/6c98a71ec91ba8770171909b0e59ae563c6d48c4/Tripadvisor_Webscrape/Tripadvisor/__init__.py -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awesomeahi95/Hotel_Review_NLP/6c98a71ec91ba8770171909b0e59ae563c6d48c4/Tripadvisor_Webscrape/Tripadvisor/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/__pycache__/settings.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awesomeahi95/Hotel_Review_NLP/6c98a71ec91ba8770171909b0e59ae563c6d48c4/Tripadvisor_Webscrape/Tripadvisor/__pycache__/settings.cpython-37.pyc -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/items.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Define here the models for your scraped items 4 | # 5 | # See documentation in: 6 | # https://docs.scrapy.org/en/latest/topics/items.html 7 | 8 | import scrapy 9 | 10 | 11 | class TripadvisorItem(scrapy.Item): 12 | # define the fields for your item here like: 13 | # name = scrapy.Field() 14 | pass 15 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/middlewares.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Define here the models for your spider middleware 4 | # 5 | # See documentation in: 6 | # https://docs.scrapy.org/en/latest/topics/spider-middleware.html 7 | 8 | from scrapy import signals 9 | 10 | 11 | class TripadvisorSpiderMiddleware: 12 | # Not all methods need to be defined. If a method is not defined, 13 | # scrapy acts as if the spider middleware does not modify the 14 | # passed objects. 15 | 16 | @classmethod 17 | def from_crawler(cls, crawler): 18 | # This method is used by Scrapy to create your spiders. 19 | s = cls() 20 | crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) 21 | return s 22 | 23 | def process_spider_input(self, response, spider): 24 | # Called for each response that goes through the spider 25 | # middleware and into the spider. 26 | 27 | # Should return None or raise an exception. 28 | return None 29 | 30 | def process_spider_output(self, response, result, spider): 31 | # Called with the results returned from the Spider, after 32 | # it has processed the response. 33 | 34 | # Must return an iterable of Request, dict or Item objects. 35 | for i in result: 36 | yield i 37 | 38 | def process_spider_exception(self, response, exception, spider): 39 | # Called when a spider or process_spider_input() method 40 | # (from other spider middleware) raises an exception. 41 | 42 | # Should return either None or an iterable of Request, dict 43 | # or Item objects. 44 | pass 45 | 46 | def process_start_requests(self, start_requests, spider): 47 | # Called with the start requests of the spider, and works 48 | # similarly to the process_spider_output() method, except 49 | # that it doesn’t have a response associated. 50 | 51 | # Must return only requests (not items). 52 | for r in start_requests: 53 | yield r 54 | 55 | def spider_opened(self, spider): 56 | spider.logger.info('Spider opened: %s' % spider.name) 57 | 58 | 59 | class TripadvisorDownloaderMiddleware: 60 | # Not all methods need to be defined. If a method is not defined, 61 | # scrapy acts as if the downloader middleware does not modify the 62 | # passed objects. 63 | 64 | @classmethod 65 | def from_crawler(cls, crawler): 66 | # This method is used by Scrapy to create your spiders. 67 | s = cls() 68 | crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) 69 | return s 70 | 71 | def process_request(self, request, spider): 72 | # Called for each request that goes through the downloader 73 | # middleware. 74 | 75 | # Must either: 76 | # - return None: continue processing this request 77 | # - or return a Response object 78 | # - or return a Request object 79 | # - or raise IgnoreRequest: process_exception() methods of 80 | # installed downloader middleware will be called 81 | return None 82 | 83 | def process_response(self, request, response, spider): 84 | # Called with the response returned from the downloader. 85 | 86 | # Must either; 87 | # - return a Response object 88 | # - return a Request object 89 | # - or raise IgnoreRequest 90 | return response 91 | 92 | def process_exception(self, request, exception, spider): 93 | # Called when a download handler or a process_request() 94 | # (from other downloader middleware) raises an exception. 95 | 96 | # Must either: 97 | # - return None: continue processing this exception 98 | # - return a Response object: stops process_exception() chain 99 | # - return a Request object: stops process_exception() chain 100 | pass 101 | 102 | def spider_opened(self, spider): 103 | spider.logger.info('Spider opened: %s' % spider.name) 104 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/pipelines.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Define your item pipelines here 4 | # 5 | # Don't forget to add your pipeline to the ITEM_PIPELINES setting 6 | # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html 7 | 8 | 9 | class HotelWebsitesPipeline: 10 | def process_item(self, item, spider): 11 | return item 12 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/settings.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Scrapy settings for Tripadvisor project 4 | # 5 | # For simplicity, this file contains only settings considered important or 6 | # commonly used. You can find more settings consulting the documentation: 7 | # 8 | # https://docs.scrapy.org/en/latest/topics/settings.html 9 | # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html 10 | # https://docs.scrapy.org/en/latest/topics/spider-middleware.html 11 | 12 | BOT_NAME = 'Tripadvisor' 13 | 14 | SPIDER_MODULES = ['Tripadvisor.spiders'] 15 | NEWSPIDER_MODULE = 'Tripadvisor.spiders' 16 | 17 | 18 | # Crawl responsibly by identifying yourself (and your website) on the user-agent 19 | #USER_AGENT = 'Tripadvisor (+http://www.yourdomain.com)' 20 | 21 | # Obey robots.txt rules 22 | ROBOTSTXT_OBEY = True 23 | 24 | # Configure maximum concurrent requests performed by Scrapy (default: 16) 25 | #CONCURRENT_REQUESTS = 32 26 | 27 | # Configure a delay for requests for the same website (default: 0) 28 | # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay 29 | # See also autothrottle settings and docs 30 | #DOWNLOAD_DELAY = 3 31 | # The download delay setting will honor only one of: 32 | #CONCURRENT_REQUESTS_PER_DOMAIN = 16 33 | #CONCURRENT_REQUESTS_PER_IP = 16 34 | 35 | # Disable cookies (enabled by default) 36 | #COOKIES_ENABLED = False 37 | 38 | # Disable Telnet Console (enabled by default) 39 | #TELNETCONSOLE_ENABLED = False 40 | 41 | # Override the default request headers: 42 | #DEFAULT_REQUEST_HEADERS = { 43 | # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 44 | # 'Accept-Language': 'en', 45 | #} 46 | 47 | # Enable or disable spider middlewares 48 | # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html 49 | #SPIDER_MIDDLEWARES = { 50 | # 'Tripadvisor.middlewares.TripadvisorSpiderMiddleware': 543, 51 | #} 52 | 53 | # Enable or disable downloader middlewares 54 | # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html 55 | #DOWNLOADER_MIDDLEWARES = { 56 | # 'Tripadvisor.middlewares.TripadvisorDownloaderMiddleware': 543, 57 | #} 58 | 59 | # Enable or disable extensions 60 | # See https://docs.scrapy.org/en/latest/topics/extensions.html 61 | #EXTENSIONS = { 62 | # 'scrapy.extensions.telnet.TelnetConsole': None, 63 | #} 64 | 65 | # Configure item pipelines 66 | # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html 67 | #ITEM_PIPELINES = { 68 | # 'Tripadvisor.pipelines.TripadvisorPipeline': 300, 69 | #} 70 | 71 | # Enable and configure the AutoThrottle extension (disabled by default) 72 | # See https://docs.scrapy.org/en/latest/topics/autothrottle.html 73 | #AUTOTHROTTLE_ENABLED = True 74 | # The initial download delay 75 | #AUTOTHROTTLE_START_DELAY = 5 76 | # The maximum download delay to be set in case of high latencies 77 | #AUTOTHROTTLE_MAX_DELAY = 60 78 | # The average number of requests Scrapy should be sending in parallel to 79 | # each remote server 80 | #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 81 | # Enable showing throttling stats for every response received: 82 | #AUTOTHROTTLE_DEBUG = False 83 | 84 | # Enable and configure HTTP caching (disabled by default) 85 | # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings 86 | #HTTPCACHE_ENABLED = True 87 | #HTTPCACHE_EXPIRATION_SECS = 0 88 | #HTTPCACHE_DIR = 'httpcache' 89 | #HTTPCACHE_IGNORE_HTTP_CODES = [] 90 | #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage' 91 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/spiders/.ipynb_checkpoints/hotels-checkpoint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import scrapy 3 | 4 | 5 | class HotelsSpider(scrapy.Spider): 6 | name = 'hotels' 7 | allowed_domains = ['www.tripadvisor.co.uk'] 8 | start_urls = ['https://www.tripadvisor.co.uk/Hotel_Review-g187051-d239658-Reviews-Hotel_Hilton_London_Gatwick_Airport-Crawley_West_Sussex_England.html/', 9 | 'https://www.tripadvisor.co.uk/Hotel_Review-g186338-d193089-Reviews-Hilton_London_Metropole-London_England.html/', 10 | 'https://www.tripadvisor.co.uk/Hotel_Review-g186338-d192048-Reviews-Hilton_London_Euston-London_England.html/', 11 | 'https://www.tripadvisor.co.uk/Hotel_Review-g186338-d193102-Reviews-DoubleTree_by_Hilton_Hotel_London_West_End-London_England.html/', 12 | 'https://www.tripadvisor.co.uk/Hotel_Review-g504167-d192599-Reviews-Hilton_London_Croydon-Croydon_Greater_London_England.html'] 13 | 14 | def parse(self, response): 15 | for review in response.xpath('//div[@class="hotels-community-tab-common-Card__card--ihfZB hotels-community-tab-common-Card__section--4r93H"]'): 16 | yield { 17 | 'hotel_name': review.xpath('//div[@class="ui_column is-12-tablet is-9-mobile hotels-hotel-review-atf-info-parts-ATFInfo__description--1njly"]/div[1]/h1/text()').get(), 18 | 'review_summary': review.xpath('.//a[@class="location-review-review-list-parts-ReviewTitle__reviewTitleText--2tFRT"]/span[1]/span/text()').get(), 19 | 'review_p1': review.xpath('.//q[@class="location-review-review-list-parts-ExpandableReview__reviewText--gOmRC"]/span[1]/text()').get(), 20 | 'review_p2': review.xpath('.//q[@class="location-review-review-list-parts-ExpandableReview__reviewText--gOmRC"]/span[2]/text()').get(), 21 | 'score': review.xpath('.//div[@class="location-review-review-list-parts-RatingLine__bubbles--GcJvM"]/span').get(), 22 | } 23 | 24 | href = response.xpath('//a[@class="ui_button nav next primary "]/@href').get() 25 | 26 | next_page_url = 'https://www.tripadvisor.co.uk' + href 27 | 28 | yield scrapy.Request(url=next_page_url, callback = self.parse) 29 | 30 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/spiders/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awesomeahi95/Hotel_Review_NLP/6c98a71ec91ba8770171909b0e59ae563c6d48c4/Tripadvisor_Webscrape/Tripadvisor/spiders/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/spiders/__pycache__/hotels.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awesomeahi95/Hotel_Review_NLP/6c98a71ec91ba8770171909b0e59ae563c6d48c4/Tripadvisor_Webscrape/Tripadvisor/spiders/__pycache__/hotels.cpython-37.pyc -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/Tripadvisor/spiders/hotels.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import scrapy 3 | 4 | 5 | class HotelsSpider(scrapy.Spider): 6 | name = 'hotels' 7 | allowed_domains = ['www.tripadvisor.co.uk'] 8 | start_urls = ['https://www.tripadvisor.co.uk/Hotel_Review-g187051-d239658-Reviews-Hotel_Hilton_London_Gatwick_Airport-Crawley_West_Sussex_England.html/', 9 | 'https://www.tripadvisor.co.uk/Hotel_Review-g186338-d193089-Reviews-Hilton_London_Metropole-London_England.html/', 10 | 'https://www.tripadvisor.co.uk/Hotel_Review-g186338-d192048-Reviews-Hilton_London_Euston-London_England.html/', 11 | 'https://www.tripadvisor.co.uk/Hotel_Review-g186338-d193102-Reviews-DoubleTree_by_Hilton_Hotel_London_West_End-London_England.html/', 12 | 'https://www.tripadvisor.co.uk/Hotel_Review-g504167-d192599-Reviews-Hilton_London_Croydon-Croydon_Greater_London_England.html'] 13 | 14 | def parse(self, response): 15 | for review in response.xpath('//div[@class="hotels-community-tab-common-Card__card--ihfZB hotels-community-tab-common-Card__section--4r93H"]'): 16 | yield { 17 | 'hotel_name': review.xpath('//div[@class="ui_column is-12-tablet is-9-mobile hotels-hotel-review-atf-info-parts-ATFInfo__description--1njly"]/div[1]/h1/text()').get(), 18 | 'review_summary': review.xpath('.//a[@class="location-review-review-list-parts-ReviewTitle__reviewTitleText--2tFRT"]/span[1]/span/text()').get(), 19 | 'review_p1': review.xpath('.//q[@class="location-review-review-list-parts-ExpandableReview__reviewText--gOmRC"]/span[1]/text()').get(), 20 | 'review_p2': review.xpath('.//q[@class="location-review-review-list-parts-ExpandableReview__reviewText--gOmRC"]/span[2]/text()').get(), 21 | 'score': review.xpath('.//div[@class="location-review-review-list-parts-RatingLine__bubbles--GcJvM"]/span').get(), 22 | } 23 | 24 | href = response.xpath('//a[@class="ui_button nav next primary "]/@href').get() 25 | 26 | next_page_url = 'https://www.tripadvisor.co.uk' + href 27 | 28 | yield scrapy.Request(url=next_page_url, callback = self.parse) 29 | 30 | -------------------------------------------------------------------------------- /Tripadvisor_Webscrape/scrapy.cfg: -------------------------------------------------------------------------------- 1 | # Automatically created by: scrapy startproject 2 | # 3 | # For more information about the [deploy] section see: 4 | # https://scrapyd.readthedocs.io/en/latest/deploy.html 5 | 6 | [settings] 7 | default = Tripadvisor.settings 8 | 9 | [deploy] 10 | #url = http://localhost:6800/ 11 | project = Tripadvisor 12 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.16.5 2 | pandas==0.25.1 3 | streamlit==0.57.3 4 | keras==2.3.0 5 | tensorflow==2.0.1 6 | matplotlib==3.1.0 7 | nltk==3.4.5 8 | scikit-learn==0.22.2.post1 9 | seaborn==0.9.0 --------------------------------------------------------------------------------