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Repository: khanhnamle1994/applied-machine-learning
Branch: master
Commit: 09eeba715e43
Files: 2346
Total size: 37.3 MB
Directory structure:
gitextract_ye72zr3b/
├── Algorithms-From-Scratch/
│ ├── algorithm-test-harness.py
│ ├── backpropagation.py
│ ├── bagging.py
│ ├── data_banknote_authentication.csv
│ ├── decision-tree.py
│ ├── insurance.csv
│ ├── ionosphere.csv
│ ├── iris.data.csv
│ ├── knn.py
│ ├── learning-vector-quantization.py
│ ├── linear-reg-SGD.py
│ ├── logistic-reg-SGD.py
│ ├── naive-bayes.py
│ ├── perceptron.py
│ ├── performance-metrics.py
│ ├── pima-indians-diabetes.data.csv
│ ├── random-forest.py
│ ├── resampling.py
│ ├── simple-linear-regression.py
│ ├── sonar.all-data.csv
│ ├── stack-generalization.py
│ ├── wheat-seeds.csv
│ └── winequality-white.csv
├── Deep-Learning/
│ ├── .ipynb_checkpoints/
│ │ ├── 5_step_life_cycle_neural_network_models_in_keras-checkpoint.ipynb
│ │ ├── crash_course_in_convolutional_neural_networks_for_machine_learning-checkpoint.ipynb
│ │ ├── crash_course_on_multilayer_perceptron_neural_networks-checkpoint.ipynb
│ │ ├── crash_course_recurrent_neural_networks_for_deep_learning-checkpoint.ipynb
│ │ ├── display_deep_learning_model_training_history_keras-checkpoint.ipynb
│ │ ├── dropout_regularization_in_deep_learning_models_with_keras-checkpoint.ipynb
│ │ ├── grid_search_hyperparameters_for_deep_learning_models_in_python_with_keras-checkpoint.ipynb
│ │ ├── handwritten_digit_recognition_using_CNN_Python_Keras-checkpoint.ipynb
│ │ ├── object_recognition_with_CNN_in_Keras_deep_learning_library-checkpoint.ipynb
│ │ ├── predict_sentiment_from_movies_using_deep_learning-checkpoint.ipynb
│ │ ├── save_and_load_keras_deep_learning_models-checkpoint.ipynb
│ │ ├── text_generation_with_LSTM_recurrent_neural_nets_python_keras-checkpoint.ipynb
│ │ └── understanding_stateful_LSTM_recurrent_neural_nets_python_keras-checkpoint.ipynb
│ ├── 5_step_life_cycle_neural_network_models_in_keras.ipynb
│ ├── README.md
│ ├── crash_course_in_convolutional_neural_networks_for_machine_learning.ipynb
│ ├── crash_course_on_multilayer_perceptron_neural_networks.ipynb
│ ├── crash_course_recurrent_neural_networks_for_deep_learning.ipynb
│ ├── display_deep_learning_model_training_history_keras.ipynb
│ ├── dropout_regularization_in_deep_learning_models_with_keras.ipynb
│ ├── grid_search_hyperparameters_for_deep_learning_models_in_python_with_keras.ipynb
│ ├── handwritten_digit_recognition_using_CNN_Python_Keras.ipynb
│ ├── model.h5
│ ├── model.json
│ ├── model.yaml
│ ├── object_recognition_with_CNN_in_Keras_deep_learning_library.ipynb
│ ├── pima-indians-diabetes.csv
│ ├── predict_sentiment_from_movies_using_deep_learning.ipynb
│ ├── save_and_load_keras_deep_learning_models.ipynb
│ ├── sonar.csv
│ ├── text_generation_with_LSTM_recurrent_neural_nets_python_keras.ipynb
│ ├── understanding_stateful_LSTM_recurrent_neural_nets_python_keras.ipynb
│ ├── weights-improvement-20-2.0518.hdf5
│ └── wonderland.txt
├── Linear-Algebra/
│ ├── .ipynb_checkpoints/
│ │ ├── 10_Examples_of_Linear_Algebra_in_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Expected_Value_Variance_Covariance_with_NumPy-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Matrix_Operations_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_N-Dimensional_Arrays_in_Python_with_NumPy-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_SVD_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Tensors_for_ML_with_Numpy-checkpoint.ipynb
│ │ ├── Basic_of_Mathematical_Notation_for_ML-checkpoint.ipynb
│ │ ├── Broadcasting_with_Numpy_Arrays-checkpoint.ipynb
│ │ ├── Calculate_PCA_from_Scratch_in_Python-checkpoint.ipynb
│ │ ├── Gentle_Introduction_to_Vector_Norms_in_ML-checkpoint.ipynb
│ │ ├── Gentle_Introduction_to_Vectors_for_ML-checkpoint.ipynb
│ │ ├── Index_Slice_Reshape_NumPy_Arrays_for_ML_in_Python-checkpoint.ipynb
│ │ ├── Introduction_to_Eigendecomposition_Eigenvalues_Eigenvectors-checkpoint.ipynb
│ │ ├── Introduction_to_Matrices_and_Matrix_Arithmetic_for_ML-checkpoint.ipynb
│ │ ├── Introduction_to_Matrix_Factorization-checkpoint.ipynb
│ │ ├── Introduction_to_Matrix_Types_in_Linear_Algebra_for_ML-checkpoint.ipynb
│ │ ├── Linear_Algebra_Cheat_Sheet-checkpoint.ipynb
│ │ ├── Solve_Linear_Regression_using_Linear_Algebra-checkpoint.ipynb
│ │ └── Sparse_Matrices_for_Machine_Learning-checkpoint.ipynb
│ ├── 10_Examples_of_Linear_Algebra_in_ML.ipynb
│ ├── A_Gentle_Introduction_to_Expected_Value_Variance_Covariance_with_NumPy.ipynb
│ ├── A_Gentle_Introduction_to_Matrix_Operations_for_ML.ipynb
│ ├── A_Gentle_Introduction_to_N-Dimensional_Arrays_in_Python_with_NumPy.ipynb
│ ├── A_Gentle_Introduction_to_SVD_for_ML.ipynb
│ ├── A_Gentle_Introduction_to_Tensors_for_ML_with_Numpy.ipynb
│ ├── Basic_of_Mathematical_Notation_for_ML.ipynb
│ ├── Broadcasting_with_Numpy_Arrays.ipynb
│ ├── Calculate_PCA_from_Scratch_in_Python.ipynb
│ ├── Gentle_Introduction_to_Vector_Norms_in_ML.ipynb
│ ├── Gentle_Introduction_to_Vectors_for_ML.ipynb
│ ├── Index_Slice_Reshape_NumPy_Arrays_for_ML_in_Python.ipynb
│ ├── Introduction_to_Eigendecomposition_Eigenvalues_Eigenvectors.ipynb
│ ├── Introduction_to_Matrices_and_Matrix_Arithmetic_for_ML.ipynb
│ ├── Introduction_to_Matrix_Factorization.ipynb
│ ├── Introduction_to_Matrix_Types_in_Linear_Algebra_for_ML.ipynb
│ ├── Linear_Algebra_Cheat_Sheet.ipynb
│ ├── README.md
│ ├── Solve_Linear_Regression_using_Linear_Algebra.ipynb
│ └── Sparse_Matrices_for_Machine_Learning.ipynb
├── Long-Short-Term-Memory/
│ ├── .ipynb_checkpoints/
│ │ ├── Use_TimeDistributed_Layer_for_LSTM_networks_in_Python-checkpoint.ipynb
│ │ ├── a_gentle_introduction_to_backpropagation_through_time-checkpoint.ipynb
│ │ ├── attention_in_LSTM_Recurrent_Neural_Nets-checkpoint.ipynb
│ │ ├── cnn_LSTM_networks-checkpoint.ipynb
│ │ ├── demo_of_memory_with_LSTM_in_Python-checkpoint.ipynb
│ │ ├── diagnose_overfitting_and_underfitting_of_LSTM_models-checkpoint.ipynb
│ │ ├── encoder_decoder_LSTM_networks-checkpoint.ipynb
│ │ ├── handle_missing_timesteps_in_sequence_prediction_problems_with_Python-checkpoint.ipynb
│ │ ├── intro_to_generative_LSTM_networks-checkpoint.ipynb
│ │ ├── make_predictions_with_LSTM_models_keras-checkpoint.ipynb
│ │ ├── multi_time_series_forecasting_with_LSTM_networks_in_Python-checkpoint.ipynb
│ │ ├── multivariate_time_series_forecasting_with_LSTM_in_Keras-checkpoint.ipynb
│ │ ├── one-hot-encode-sequence-data-in-python-checkpoint.ipynb
│ │ ├── prepare_sequence_prediction_for_truncated_backpropagation_through_time_in_keras-checkpoint.ipynb
│ │ ├── remove_trends_and_seasonality_with_a_difference_transform_in_python-checkpoint.ipynb
│ │ ├── reshape_input_data_LSTM_in_Keras-checkpoint.ipynb
│ │ ├── scale_data_for_LSTM_in_Python-checkpoint.ipynb
│ │ ├── stacked_LSTM_networks-checkpoint.ipynb
│ │ ├── suitability_of_LSTM_for_Time_Series_Forecasting-checkpoint.ipynb
│ │ ├── time_series_forecasting_with_LSTM_network_in_Python-checkpoint.ipynb
│ │ └── use_an_encoder_decoder_LSTM_to_echo_sequences_of_random_integers-checkpoint.ipynb
│ ├── README.md
│ ├── Use_TimeDistributed_Layer_for_LSTM_networks_in_Python.ipynb
│ ├── a_gentle_introduction_to_backpropagation_through_time.ipynb
│ ├── attention_in_LSTM_Recurrent_Neural_Nets.ipynb
│ ├── cnn_LSTM_networks.ipynb
│ ├── demo_of_memory_with_LSTM_in_Python.ipynb
│ ├── diagnose_overfitting_and_underfitting_of_LSTM_models.ipynb
│ ├── encoder_decoder_LSTM_networks.ipynb
│ ├── handle_missing_timesteps_in_sequence_prediction_problems_with_Python.ipynb
│ ├── intro_to_generative_LSTM_networks.ipynb
│ ├── make_predictions_with_LSTM_models_keras.ipynb
│ ├── multi_time_series_forecasting_with_LSTM_networks_in_Python.ipynb
│ ├── multivariate_time_series_forecasting_with_LSTM_in_Keras.ipynb
│ ├── one-hot-encode-sequence-data-in-python.ipynb
│ ├── pollution.csv
│ ├── prepare_sequence_prediction_for_truncated_backpropagation_through_time_in_keras.ipynb
│ ├── raw.csv
│ ├── remove_trends_and_seasonality_with_a_difference_transform_in_python.ipynb
│ ├── reshape_input_data_LSTM_in_Keras.ipynb
│ ├── scale_data_for_LSTM_in_Python.ipynb
│ ├── shampoo-sales.csv
│ ├── stacked_LSTM_networks.ipynb
│ ├── suitability_of_LSTM_for_Time_Series_Forecasting.ipynb
│ ├── time_series_forecasting_with_LSTM_network_in_Python.ipynb
│ └── use_an_encoder_decoder_LSTM_to_echo_sequences_of_random_integers.ipynb
├── Machine-Learning-Python/
│ ├── .ipynb_checkpoints/
│ │ ├── automate_ml_workflows_with_pipelines_in_Python_and_scikit_learn-checkpoint.ipynb
│ │ ├── compare_ml_algorithms_in_python_scikit_learn-checkpoint.ipynb
│ │ ├── ensemble_ml_algorithms_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── evaluate_performance_ml_algoritms_python_resampling-checkpoint.ipynb
│ │ ├── feature_selection_ml_python-checkpoint.ipynb
│ │ ├── how_to_generate_test_datasets_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── how_to_handle_missing_data_with_Python-checkpoint.ipynb
│ │ ├── how_to_make_predictions_with_scikit_learn-checkpoint.ipynb
│ │ ├── how_to_tune_algorithm_parameters_with_scikit_learn-checkpoint.ipynb
│ │ ├── load_data_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── load_ml_data_python-checkpoint.ipynb
│ │ ├── machine_learning-algorithm-recipes-in-scikit-learn-checkpoint.ipynb
│ │ ├── metrics_to_evaluate_ml_algorithms_python-checkpoint.ipynb
│ │ ├── prepare_data_for_ml_in_python_scikit_learn-checkpoint.ipynb
│ │ ├── quick_and_dirty_data_analysis_with_Pandas-checkpoint.ipynb
│ │ ├── rescaling_data_for_ML_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── spot_check_classification_ml_algorithms_python_scikit_learn-checkpoint.ipynb
│ │ ├── spot_check_regression_ml_algorithms_in_python_scikit_learn-checkpoint.ipynb
│ │ ├── understand_ml_data_descriptive_statistics_python-checkpoint.ipynb
│ │ └── visual_ml_data_in_python_with_pandas-checkpoint.ipynb
│ ├── README.md
│ ├── automate_ml_workflows_with_pipelines_in_Python_and_scikit_learn.ipynb
│ ├── compare_ml_algorithms_in_python_scikit_learn.ipynb
│ ├── ensemble_ml_algorithms_in_Python_with_scikit_learn.ipynb
│ ├── evaluate_performance_ml_algoritms_python_resampling.ipynb
│ ├── feature_selection_ml_python.ipynb
│ ├── how_to_generate_test_datasets_in_Python_with_scikit_learn.ipynb
│ ├── how_to_handle_missing_data_with_Python.ipynb
│ ├── how_to_make_predictions_with_scikit_learn.ipynb
│ ├── how_to_tune_algorithm_parameters_with_scikit_learn.ipynb
│ ├── load_data_in_Python_with_scikit_learn.ipynb
│ ├── load_ml_data_python.ipynb
│ ├── machine_learning-algorithm-recipes-in-scikit-learn.ipynb
│ ├── metrics_to_evaluate_ml_algorithms_python.ipynb
│ ├── pima-indians-diabetes.data.csv
│ ├── prepare_data_for_ml_in_python_scikit_learn.ipynb
│ ├── quick_and_dirty_data_analysis_with_Pandas.ipynb
│ ├── rescaling_data_for_ML_in_Python_with_scikit_learn.ipynb
│ ├── spot_check_classification_ml_algorithms_python_scikit_learn.ipynb
│ ├── spot_check_regression_ml_algorithms_in_python_scikit_learn.ipynb
│ ├── understand_ml_data_descriptive_statistics_python.ipynb
│ └── visual_ml_data_in_python_with_pandas.ipynb
├── Machine-Learning-R/
│ ├── .RData
│ ├── .Rhistory
│ ├── README.md
│ ├── build_an_ensemble_of_ml_algorithms_in_R.Rmd
│ ├── compare_models_and_select_the_best_using_the_Caret_R_package.Rmd
│ ├── compare_performance_ml_algorithms_in_R.Rmd
│ ├── convex_optimization_in_R.Rmd
│ ├── data_visualization_with_the_Caret_R_package.Rmd
│ ├── evaluate_ml_algorithms_with_R.Rmd
│ ├── feature_selection_with_caret_r_package.Rmd
│ ├── final_model.rds
│ ├── get_data_ready_for_ml_in_R_pre_processing.Rmd
│ ├── how_to_estimate_model_accuracy_in_R_using_Caret_package.Rmd
│ ├── linear_classification_in_R.Rmd
│ ├── linear_regression_in_R.Rmd
│ ├── load_ml_data_into_r.Rmd
│ ├── ml_evaluation_metrics.Rmd
│ ├── ml_project_template_in_R.Rmd
│ ├── non_linear_classification_in_R.Rmd
│ ├── non_linear_classification_in_R_with_decision_trees.Rmd
│ ├── non_linear_regression_in_R.Rmd
│ ├── non_linear_regression_in_R_with_decision_trees.Rmd
│ ├── penalized_regression_in_R.Rmd
│ ├── save_and_finalize_ml_model_in_R.Rmd
│ ├── spot_check_ml_algorithms_in_R.Rmd
│ ├── tune_ml_algorithms_in_R.Rmd
│ ├── tuning_ml_models_using_the_Caret_R_package.Rmd
│ ├── understand_data_in_R_descriptive_statistics.Rmd
│ └── understand_data_in_R_visualization.Rmd
├── Natural-Language-Processing/
│ ├── .ipynb_checkpoints/
│ │ ├── develop_a_deep_learning_bag_of_words_model_for_predicting_movie_review_sentiment-checkpoint.ipynb
│ │ ├── develop_word_embeddings_in_python_with_gensim-checkpoint.ipynb
│ │ ├── how_to_use_word_embedding_layers_for_deep_learning_with_keras-checkpoint.ipynb
│ │ ├── introduction_to_bag_of_words_model-checkpoint.ipynb
│ │ ├── prepare_text_data_for_machine_learning_with_scikit_learn-checkpoint.ipynb
│ │ └── word_embeddings_for_text-checkpoint.ipynb
│ ├── README.md
│ ├── develop_a_deep_learning_bag_of_words_model_for_predicting_movie_review_sentiment.ipynb
│ ├── develop_word_embeddings_in_python_with_gensim.ipynb
│ ├── how_to_use_word_embedding_layers_for_deep_learning_with_keras.ipynb
│ ├── introduction_to_bag_of_words_model.ipynb
│ ├── prepare_text_data_for_machine_learning_with_scikit_learn.ipynb
│ ├── txt_sentoken/
│ │ ├── neg/
│ │ │ ├── cv000_29416.txt
│ │ │ ├── cv001_19502.txt
│ │ │ ├── cv002_17424.txt
│ │ │ ├── cv003_12683.txt
│ │ │ ├── cv004_12641.txt
│ │ │ ├── cv005_29357.txt
│ │ │ ├── cv006_17022.txt
│ │ │ ├── cv007_4992.txt
│ │ │ ├── cv008_29326.txt
│ │ │ ├── cv009_29417.txt
│ │ │ ├── cv010_29063.txt
│ │ │ ├── cv011_13044.txt
│ │ │ ├── cv012_29411.txt
│ │ │ ├── cv013_10494.txt
│ │ │ ├── cv014_15600.txt
│ │ │ ├── cv015_29356.txt
│ │ │ ├── cv016_4348.txt
│ │ │ ├── cv017_23487.txt
│ │ │ ├── cv018_21672.txt
│ │ │ ├── cv019_16117.txt
│ │ │ ├── cv020_9234.txt
│ │ │ ├── cv021_17313.txt
│ │ │ ├── cv022_14227.txt
│ │ │ ├── cv023_13847.txt
│ │ │ ├── cv024_7033.txt
│ │ │ ├── cv025_29825.txt
│ │ │ ├── cv026_29229.txt
│ │ │ ├── cv027_26270.txt
│ │ │ ├── cv028_26964.txt
│ │ │ ├── cv029_19943.txt
│ │ │ ├── cv030_22893.txt
│ │ │ ├── cv031_19540.txt
│ │ │ ├── cv032_23718.txt
│ │ │ ├── cv033_25680.txt
│ │ │ ├── cv034_29446.txt
│ │ │ ├── cv035_3343.txt
│ │ │ ├── cv036_18385.txt
│ │ │ ├── cv037_19798.txt
│ │ │ ├── cv038_9781.txt
│ │ │ ├── cv039_5963.txt
│ │ │ ├── cv040_8829.txt
│ │ │ ├── cv041_22364.txt
│ │ │ ├── cv042_11927.txt
│ │ │ ├── cv043_16808.txt
│ │ │ ├── cv044_18429.txt
│ │ │ ├── cv045_25077.txt
│ │ │ ├── cv046_10613.txt
│ │ │ ├── cv047_18725.txt
│ │ │ ├── cv048_18380.txt
│ │ │ ├── cv049_21917.txt
│ │ │ ├── cv050_12128.txt
│ │ │ ├── cv051_10751.txt
│ │ │ ├── cv052_29318.txt
│ │ │ ├── cv053_23117.txt
│ │ │ ├── cv054_4101.txt
│ │ │ ├── cv055_8926.txt
│ │ │ ├── cv056_14663.txt
│ │ │ ├── cv057_7962.txt
│ │ │ ├── cv058_8469.txt
│ │ │ ├── cv059_28723.txt
│ │ │ ├── cv060_11754.txt
│ │ │ ├── cv061_9321.txt
│ │ │ ├── cv062_24556.txt
│ │ │ ├── cv063_28852.txt
│ │ │ ├── cv064_25842.txt
│ │ │ ├── cv065_16909.txt
│ │ │ ├── cv066_11668.txt
│ │ │ ├── cv067_21192.txt
│ │ │ ├── cv068_14810.txt
│ │ │ ├── cv069_11613.txt
│ │ │ ├── cv070_13249.txt
│ │ │ ├── cv071_12969.txt
│ │ │ ├── cv072_5928.txt
│ │ │ ├── cv073_23039.txt
│ │ │ ├── cv074_7188.txt
│ │ │ ├── cv075_6250.txt
│ │ │ ├── cv076_26009.txt
│ │ │ ├── cv077_23172.txt
│ │ │ ├── cv078_16506.txt
│ │ │ ├── cv079_12766.txt
│ │ │ ├── cv080_14899.txt
│ │ │ ├── cv081_18241.txt
│ │ │ ├── cv082_11979.txt
│ │ │ ├── cv083_25491.txt
│ │ │ ├── cv084_15183.txt
│ │ │ ├── cv085_15286.txt
│ │ │ ├── cv086_19488.txt
│ │ │ ├── cv087_2145.txt
│ │ │ ├── cv088_25274.txt
│ │ │ ├── cv089_12222.txt
│ │ │ ├── cv090_0049.txt
│ │ │ ├── cv091_7899.txt
│ │ │ ├── cv092_27987.txt
│ │ │ ├── cv093_15606.txt
│ │ │ ├── cv094_27868.txt
│ │ │ ├── cv095_28730.txt
│ │ │ ├── cv096_12262.txt
│ │ │ ├── cv097_26081.txt
│ │ │ ├── cv098_17021.txt
│ │ │ ├── cv099_11189.txt
│ │ │ ├── cv100_12406.txt
│ │ │ ├── cv101_10537.txt
│ │ │ ├── cv102_8306.txt
│ │ │ ├── cv103_11943.txt
│ │ │ ├── cv104_19176.txt
│ │ │ ├── cv105_19135.txt
│ │ │ ├── cv106_18379.txt
│ │ │ ├── cv107_25639.txt
│ │ │ ├── cv108_17064.txt
│ │ │ ├── cv109_22599.txt
│ │ │ ├── cv110_27832.txt
│ │ │ ├── cv111_12253.txt
│ │ │ ├── cv112_12178.txt
│ │ │ ├── cv113_24354.txt
│ │ │ ├── cv114_19501.txt
│ │ │ ├── cv115_26443.txt
│ │ │ ├── cv116_28734.txt
│ │ │ ├── cv117_25625.txt
│ │ │ ├── cv118_28837.txt
│ │ │ ├── cv119_9909.txt
│ │ │ ├── cv120_3793.txt
│ │ │ ├── cv121_18621.txt
│ │ │ ├── cv122_7891.txt
│ │ │ ├── cv123_12165.txt
│ │ │ ├── cv124_3903.txt
│ │ │ ├── cv125_9636.txt
│ │ │ ├── cv126_28821.txt
│ │ │ ├── cv127_16451.txt
│ │ │ ├── cv128_29444.txt
│ │ │ ├── cv129_18373.txt
│ │ │ ├── cv130_18521.txt
│ │ │ ├── cv131_11568.txt
│ │ │ ├── cv132_5423.txt
│ │ │ ├── cv133_18065.txt
│ │ │ ├── cv134_23300.txt
│ │ │ ├── cv135_12506.txt
│ │ │ ├── cv136_12384.txt
│ │ │ ├── cv137_17020.txt
│ │ │ ├── cv138_13903.txt
│ │ │ ├── cv139_14236.txt
│ │ │ ├── cv140_7963.txt
│ │ │ ├── cv141_17179.txt
│ │ │ ├── cv142_23657.txt
│ │ │ ├── cv143_21158.txt
│ │ │ ├── cv144_5010.txt
│ │ │ ├── cv145_12239.txt
│ │ │ ├── cv146_19587.txt
│ │ │ ├── cv147_22625.txt
│ │ │ ├── cv148_18084.txt
│ │ │ ├── cv149_17084.txt
│ │ │ ├── cv150_14279.txt
│ │ │ ├── cv151_17231.txt
│ │ │ ├── cv152_9052.txt
│ │ │ ├── cv153_11607.txt
│ │ │ ├── cv154_9562.txt
│ │ │ ├── cv155_7845.txt
│ │ │ ├── cv156_11119.txt
│ │ │ ├── cv157_29302.txt
│ │ │ ├── cv158_10914.txt
│ │ │ ├── cv159_29374.txt
│ │ │ ├── cv160_10848.txt
│ │ │ ├── cv161_12224.txt
│ │ │ ├── cv162_10977.txt
│ │ │ ├── cv163_10110.txt
│ │ │ ├── cv164_23451.txt
│ │ │ ├── cv165_2389.txt
│ │ │ ├── cv166_11959.txt
│ │ │ ├── cv167_18094.txt
│ │ │ ├── cv168_7435.txt
│ │ │ ├── cv169_24973.txt
│ │ │ ├── cv170_29808.txt
│ │ │ ├── cv171_15164.txt
│ │ │ ├── cv172_12037.txt
│ │ │ ├── cv173_4295.txt
│ │ │ ├── cv174_9735.txt
│ │ │ ├── cv175_7375.txt
│ │ │ ├── cv176_14196.txt
│ │ │ ├── cv177_10904.txt
│ │ │ ├── cv178_14380.txt
│ │ │ ├── cv179_9533.txt
│ │ │ ├── cv180_17823.txt
│ │ │ ├── cv181_16083.txt
│ │ │ ├── cv182_7791.txt
│ │ │ ├── cv183_19826.txt
│ │ │ ├── cv184_26935.txt
│ │ │ ├── cv185_28372.txt
│ │ │ ├── cv186_2396.txt
│ │ │ ├── cv187_14112.txt
│ │ │ ├── cv188_20687.txt
│ │ │ ├── cv189_24248.txt
│ │ │ ├── cv190_27176.txt
│ │ │ ├── cv191_29539.txt
│ │ │ ├── cv192_16079.txt
│ │ │ ├── cv193_5393.txt
│ │ │ ├── cv194_12855.txt
│ │ │ ├── cv195_16146.txt
│ │ │ ├── cv196_28898.txt
│ │ │ ├── cv197_29271.txt
│ │ │ ├── cv198_19313.txt
│ │ │ ├── cv199_9721.txt
│ │ │ ├── cv200_29006.txt
│ │ │ ├── cv201_7421.txt
│ │ │ ├── cv202_11382.txt
│ │ │ ├── cv203_19052.txt
│ │ │ ├── cv204_8930.txt
│ │ │ ├── cv205_9676.txt
│ │ │ ├── cv206_15893.txt
│ │ │ ├── cv207_29141.txt
│ │ │ ├── cv208_9475.txt
│ │ │ ├── cv209_28973.txt
│ │ │ ├── cv210_9557.txt
│ │ │ ├── cv211_9955.txt
│ │ │ ├── cv212_10054.txt
│ │ │ ├── cv213_20300.txt
│ │ │ ├── cv214_13285.txt
│ │ │ ├── cv215_23246.txt
│ │ │ ├── cv216_20165.txt
│ │ │ ├── cv217_28707.txt
│ │ │ ├── cv218_25651.txt
│ │ │ ├── cv219_19874.txt
│ │ │ ├── cv220_28906.txt
│ │ │ ├── cv221_27081.txt
│ │ │ ├── cv222_18720.txt
│ │ │ ├── cv223_28923.txt
│ │ │ ├── cv224_18875.txt
│ │ │ ├── cv225_29083.txt
│ │ │ ├── cv226_26692.txt
│ │ │ ├── cv227_25406.txt
│ │ │ ├── cv228_5644.txt
│ │ │ ├── cv229_15200.txt
│ │ │ ├── cv230_7913.txt
│ │ │ ├── cv231_11028.txt
│ │ │ ├── cv232_16768.txt
│ │ │ ├── cv233_17614.txt
│ │ │ ├── cv234_22123.txt
│ │ │ ├── cv235_10704.txt
│ │ │ ├── cv236_12427.txt
│ │ │ ├── cv237_20635.txt
│ │ │ ├── cv238_14285.txt
│ │ │ ├── cv239_29828.txt
│ │ │ ├── cv240_15948.txt
│ │ │ ├── cv241_24602.txt
│ │ │ ├── cv242_11354.txt
│ │ │ ├── cv243_22164.txt
│ │ │ ├── cv244_22935.txt
│ │ │ ├── cv245_8938.txt
│ │ │ ├── cv246_28668.txt
│ │ │ ├── cv247_14668.txt
│ │ │ ├── cv248_15672.txt
│ │ │ ├── cv249_12674.txt
│ │ │ ├── cv250_26462.txt
│ │ │ ├── cv251_23901.txt
│ │ │ ├── cv252_24974.txt
│ │ │ ├── cv253_10190.txt
│ │ │ ├── cv254_5870.txt
│ │ │ ├── cv255_15267.txt
│ │ │ ├── cv256_16529.txt
│ │ │ ├── cv257_11856.txt
│ │ │ ├── cv258_5627.txt
│ │ │ ├── cv259_11827.txt
│ │ │ ├── cv260_15652.txt
│ │ │ ├── cv261_11855.txt
│ │ │ ├── cv262_13812.txt
│ │ │ ├── cv263_20693.txt
│ │ │ ├── cv264_14108.txt
│ │ │ ├── cv265_11625.txt
│ │ │ ├── cv266_26644.txt
│ │ │ ├── cv267_16618.txt
│ │ │ ├── cv268_20288.txt
│ │ │ ├── cv269_23018.txt
│ │ │ ├── cv270_5873.txt
│ │ │ ├── cv271_15364.txt
│ │ │ ├── cv272_20313.txt
│ │ │ ├── cv273_28961.txt
│ │ │ ├── cv274_26379.txt
│ │ │ ├── cv275_28725.txt
│ │ │ ├── cv276_17126.txt
│ │ │ ├── cv277_20467.txt
│ │ │ ├── cv278_14533.txt
│ │ │ ├── cv279_19452.txt
│ │ │ ├── cv280_8651.txt
│ │ │ ├── cv281_24711.txt
│ │ │ ├── cv282_6833.txt
│ │ │ ├── cv283_11963.txt
│ │ │ ├── cv284_20530.txt
│ │ │ ├── cv285_18186.txt
│ │ │ ├── cv286_26156.txt
│ │ │ ├── cv287_17410.txt
│ │ │ ├── cv288_20212.txt
│ │ │ ├── cv289_6239.txt
│ │ │ ├── cv290_11981.txt
│ │ │ ├── cv291_26844.txt
│ │ │ ├── cv292_7804.txt
│ │ │ ├── cv293_29731.txt
│ │ │ ├── cv294_12695.txt
│ │ │ ├── cv295_17060.txt
│ │ │ ├── cv296_13146.txt
│ │ │ ├── cv297_10104.txt
│ │ │ ├── cv298_24487.txt
│ │ │ ├── cv299_17950.txt
│ │ │ ├── cv300_23302.txt
│ │ │ ├── cv301_13010.txt
│ │ │ ├── cv302_26481.txt
│ │ │ ├── cv303_27366.txt
│ │ │ ├── cv304_28489.txt
│ │ │ ├── cv305_9937.txt
│ │ │ ├── cv306_10859.txt
│ │ │ ├── cv307_26382.txt
│ │ │ ├── cv308_5079.txt
│ │ │ ├── cv309_23737.txt
│ │ │ ├── cv310_14568.txt
│ │ │ ├── cv311_17708.txt
│ │ │ ├── cv312_29308.txt
│ │ │ ├── cv313_19337.txt
│ │ │ ├── cv314_16095.txt
│ │ │ ├── cv315_12638.txt
│ │ │ ├── cv316_5972.txt
│ │ │ ├── cv317_25111.txt
│ │ │ ├── cv318_11146.txt
│ │ │ ├── cv319_16459.txt
│ │ │ ├── cv320_9693.txt
│ │ │ ├── cv321_14191.txt
│ │ │ ├── cv322_21820.txt
│ │ │ ├── cv323_29633.txt
│ │ │ ├── cv324_7502.txt
│ │ │ ├── cv325_18330.txt
│ │ │ ├── cv326_14777.txt
│ │ │ ├── cv327_21743.txt
│ │ │ ├── cv328_10908.txt
│ │ │ ├── cv329_29293.txt
│ │ │ ├── cv330_29675.txt
│ │ │ ├── cv331_8656.txt
│ │ │ ├── cv332_17997.txt
│ │ │ ├── cv333_9443.txt
│ │ │ ├── cv334_0074.txt
│ │ │ ├── cv335_16299.txt
│ │ │ ├── cv336_10363.txt
│ │ │ ├── cv337_29061.txt
│ │ │ ├── cv338_9183.txt
│ │ │ ├── cv339_22452.txt
│ │ │ ├── cv340_14776.txt
│ │ │ ├── cv341_25667.txt
│ │ │ ├── cv342_20917.txt
│ │ │ ├── cv343_10906.txt
│ │ │ ├── cv344_5376.txt
│ │ │ ├── cv345_9966.txt
│ │ │ ├── cv346_19198.txt
│ │ │ ├── cv347_14722.txt
│ │ │ ├── cv348_19207.txt
│ │ │ ├── cv349_15032.txt
│ │ │ ├── cv350_22139.txt
│ │ │ ├── cv351_17029.txt
│ │ │ ├── cv352_5414.txt
│ │ │ ├── cv353_19197.txt
│ │ │ ├── cv354_8573.txt
│ │ │ ├── cv355_18174.txt
│ │ │ ├── cv356_26170.txt
│ │ │ ├── cv357_14710.txt
│ │ │ ├── cv358_11557.txt
│ │ │ ├── cv359_6751.txt
│ │ │ ├── cv360_8927.txt
│ │ │ ├── cv361_28738.txt
│ │ │ ├── cv362_16985.txt
│ │ │ ├── cv363_29273.txt
│ │ │ ├── cv364_14254.txt
│ │ │ ├── cv365_12442.txt
│ │ │ ├── cv366_10709.txt
│ │ │ ├── cv367_24065.txt
│ │ │ ├── cv368_11090.txt
│ │ │ ├── cv369_14245.txt
│ │ │ ├── cv370_5338.txt
│ │ │ ├── cv371_8197.txt
│ │ │ ├── cv372_6654.txt
│ │ │ ├── cv373_21872.txt
│ │ │ ├── cv374_26455.txt
│ │ │ ├── cv375_9932.txt
│ │ │ ├── cv376_20883.txt
│ │ │ ├── cv377_8440.txt
│ │ │ ├── cv378_21982.txt
│ │ │ ├── cv379_23167.txt
│ │ │ ├── cv380_8164.txt
│ │ │ ├── cv381_21673.txt
│ │ │ ├── cv382_8393.txt
│ │ │ ├── cv383_14662.txt
│ │ │ ├── cv384_18536.txt
│ │ │ ├── cv385_29621.txt
│ │ │ ├── cv386_10229.txt
│ │ │ ├── cv387_12391.txt
│ │ │ ├── cv388_12810.txt
│ │ │ ├── cv389_9611.txt
│ │ │ ├── cv390_12187.txt
│ │ │ ├── cv391_11615.txt
│ │ │ ├── cv392_12238.txt
│ │ │ ├── cv393_29234.txt
│ │ │ ├── cv394_5311.txt
│ │ │ ├── cv395_11761.txt
│ │ │ ├── cv396_19127.txt
│ │ │ ├── cv397_28890.txt
│ │ │ ├── cv398_17047.txt
│ │ │ ├── cv399_28593.txt
│ │ │ ├── cv400_20631.txt
│ │ │ ├── cv401_13758.txt
│ │ │ ├── cv402_16097.txt
│ │ │ ├── cv403_6721.txt
│ │ │ ├── cv404_21805.txt
│ │ │ ├── cv405_21868.txt
│ │ │ ├── cv406_22199.txt
│ │ │ ├── cv407_23928.txt
│ │ │ ├── cv408_5367.txt
│ │ │ ├── cv409_29625.txt
│ │ │ ├── cv410_25624.txt
│ │ │ ├── cv411_16799.txt
│ │ │ ├── cv412_25254.txt
│ │ │ ├── cv413_7893.txt
│ │ │ ├── cv414_11161.txt
│ │ │ ├── cv415_23674.txt
│ │ │ ├── cv416_12048.txt
│ │ │ ├── cv417_14653.txt
│ │ │ ├── cv418_16562.txt
│ │ │ ├── cv419_14799.txt
│ │ │ ├── cv420_28631.txt
│ │ │ ├── cv421_9752.txt
│ │ │ ├── cv422_9632.txt
│ │ │ ├── cv423_12089.txt
│ │ │ ├── cv424_9268.txt
│ │ │ ├── cv425_8603.txt
│ │ │ ├── cv426_10976.txt
│ │ │ ├── cv427_11693.txt
│ │ │ ├── cv428_12202.txt
│ │ │ ├── cv429_7937.txt
│ │ │ ├── cv430_18662.txt
│ │ │ ├── cv431_7538.txt
│ │ │ ├── cv432_15873.txt
│ │ │ ├── cv433_10443.txt
│ │ │ ├── cv434_5641.txt
│ │ │ ├── cv435_24355.txt
│ │ │ ├── cv436_20564.txt
│ │ │ ├── cv437_24070.txt
│ │ │ ├── cv438_8500.txt
│ │ │ ├── cv439_17633.txt
│ │ │ ├── cv440_16891.txt
│ │ │ ├── cv441_15276.txt
│ │ │ ├── cv442_15499.txt
│ │ │ ├── cv443_22367.txt
│ │ │ ├── cv444_9975.txt
│ │ │ ├── cv445_26683.txt
│ │ │ ├── cv446_12209.txt
│ │ │ ├── cv447_27334.txt
│ │ │ ├── cv448_16409.txt
│ │ │ ├── cv449_9126.txt
│ │ │ ├── cv450_8319.txt
│ │ │ ├── cv451_11502.txt
│ │ │ ├── cv452_5179.txt
│ │ │ ├── cv453_10911.txt
│ │ │ ├── cv454_21961.txt
│ │ │ ├── cv455_28866.txt
│ │ │ ├── cv456_20370.txt
│ │ │ ├── cv457_19546.txt
│ │ │ ├── cv458_9000.txt
│ │ │ ├── cv459_21834.txt
│ │ │ ├── cv460_11723.txt
│ │ │ ├── cv461_21124.txt
│ │ │ ├── cv462_20788.txt
│ │ │ ├── cv463_10846.txt
│ │ │ ├── cv464_17076.txt
│ │ │ ├── cv465_23401.txt
│ │ │ ├── cv466_20092.txt
│ │ │ ├── cv467_26610.txt
│ │ │ ├── cv468_16844.txt
│ │ │ ├── cv469_21998.txt
│ │ │ ├── cv470_17444.txt
│ │ │ ├── cv471_18405.txt
│ │ │ ├── cv472_29140.txt
│ │ │ ├── cv473_7869.txt
│ │ │ ├── cv474_10682.txt
│ │ │ ├── cv475_22978.txt
│ │ │ ├── cv476_18402.txt
│ │ │ ├── cv477_23530.txt
│ │ │ ├── cv478_15921.txt
│ │ │ ├── cv479_5450.txt
│ │ │ ├── cv480_21195.txt
│ │ │ ├── cv481_7930.txt
│ │ │ ├── cv482_11233.txt
│ │ │ ├── cv483_18103.txt
│ │ │ ├── cv484_26169.txt
│ │ │ ├── cv485_26879.txt
│ │ │ ├── cv486_9788.txt
│ │ │ ├── cv487_11058.txt
│ │ │ ├── cv488_21453.txt
│ │ │ ├── cv489_19046.txt
│ │ │ ├── cv490_18986.txt
│ │ │ ├── cv491_12992.txt
│ │ │ ├── cv492_19370.txt
│ │ │ ├── cv493_14135.txt
│ │ │ ├── cv494_18689.txt
│ │ │ ├── cv495_16121.txt
│ │ │ ├── cv496_11185.txt
│ │ │ ├── cv497_27086.txt
│ │ │ ├── cv498_9288.txt
│ │ │ ├── cv499_11407.txt
│ │ │ ├── cv500_10722.txt
│ │ │ ├── cv501_12675.txt
│ │ │ ├── cv502_10970.txt
│ │ │ ├── cv503_11196.txt
│ │ │ ├── cv504_29120.txt
│ │ │ ├── cv505_12926.txt
│ │ │ ├── cv506_17521.txt
│ │ │ ├── cv507_9509.txt
│ │ │ ├── cv508_17742.txt
│ │ │ ├── cv509_17354.txt
│ │ │ ├── cv510_24758.txt
│ │ │ ├── cv511_10360.txt
│ │ │ ├── cv512_17618.txt
│ │ │ ├── cv513_7236.txt
│ │ │ ├── cv514_12173.txt
│ │ │ ├── cv515_18484.txt
│ │ │ ├── cv516_12117.txt
│ │ │ ├── cv517_20616.txt
│ │ │ ├── cv518_14798.txt
│ │ │ ├── cv519_16239.txt
│ │ │ ├── cv520_13297.txt
│ │ │ ├── cv521_1730.txt
│ │ │ ├── cv522_5418.txt
│ │ │ ├── cv523_18285.txt
│ │ │ ├── cv524_24885.txt
│ │ │ ├── cv525_17930.txt
│ │ │ ├── cv526_12868.txt
│ │ │ ├── cv527_10338.txt
│ │ │ ├── cv528_11669.txt
│ │ │ ├── cv529_10972.txt
│ │ │ ├── cv530_17949.txt
│ │ │ ├── cv531_26838.txt
│ │ │ ├── cv532_6495.txt
│ │ │ ├── cv533_9843.txt
│ │ │ ├── cv534_15683.txt
│ │ │ ├── cv535_21183.txt
│ │ │ ├── cv536_27221.txt
│ │ │ ├── cv537_13516.txt
│ │ │ ├── cv538_28485.txt
│ │ │ ├── cv539_21865.txt
│ │ │ ├── cv540_3092.txt
│ │ │ ├── cv541_28683.txt
│ │ │ ├── cv542_20359.txt
│ │ │ ├── cv543_5107.txt
│ │ │ ├── cv544_5301.txt
│ │ │ ├── cv545_12848.txt
│ │ │ ├── cv546_12723.txt
│ │ │ ├── cv547_18043.txt
│ │ │ ├── cv548_18944.txt
│ │ │ ├── cv549_22771.txt
│ │ │ ├── cv550_23226.txt
│ │ │ ├── cv551_11214.txt
│ │ │ ├── cv552_0150.txt
│ │ │ ├── cv553_26965.txt
│ │ │ ├── cv554_14678.txt
│ │ │ ├── cv555_25047.txt
│ │ │ ├── cv556_16563.txt
│ │ │ ├── cv557_12237.txt
│ │ │ ├── cv558_29376.txt
│ │ │ ├── cv559_0057.txt
│ │ │ ├── cv560_18608.txt
│ │ │ ├── cv561_9484.txt
│ │ │ ├── cv562_10847.txt
│ │ │ ├── cv563_18610.txt
│ │ │ ├── cv564_12011.txt
│ │ │ ├── cv565_29403.txt
│ │ │ ├── cv566_8967.txt
│ │ │ ├── cv567_29420.txt
│ │ │ ├── cv568_17065.txt
│ │ │ ├── cv569_26750.txt
│ │ │ ├── cv570_28960.txt
│ │ │ ├── cv571_29292.txt
│ │ │ ├── cv572_20053.txt
│ │ │ ├── cv573_29384.txt
│ │ │ ├── cv574_23191.txt
│ │ │ ├── cv575_22598.txt
│ │ │ ├── cv576_15688.txt
│ │ │ ├── cv577_28220.txt
│ │ │ ├── cv578_16825.txt
│ │ │ ├── cv579_12542.txt
│ │ │ ├── cv580_15681.txt
│ │ │ ├── cv581_20790.txt
│ │ │ ├── cv582_6678.txt
│ │ │ ├── cv583_29465.txt
│ │ │ ├── cv584_29549.txt
│ │ │ ├── cv585_23576.txt
│ │ │ ├── cv586_8048.txt
│ │ │ ├── cv587_20532.txt
│ │ │ ├── cv588_14467.txt
│ │ │ ├── cv589_12853.txt
│ │ │ ├── cv590_20712.txt
│ │ │ ├── cv591_24887.txt
│ │ │ ├── cv592_23391.txt
│ │ │ ├── cv593_11931.txt
│ │ │ ├── cv594_11945.txt
│ │ │ ├── cv595_26420.txt
│ │ │ ├── cv596_4367.txt
│ │ │ ├── cv597_26744.txt
│ │ │ ├── cv598_18184.txt
│ │ │ ├── cv599_22197.txt
│ │ │ ├── cv600_25043.txt
│ │ │ ├── cv601_24759.txt
│ │ │ ├── cv602_8830.txt
│ │ │ ├── cv603_18885.txt
│ │ │ ├── cv604_23339.txt
│ │ │ ├── cv605_12730.txt
│ │ │ ├── cv606_17672.txt
│ │ │ ├── cv607_8235.txt
│ │ │ ├── cv608_24647.txt
│ │ │ ├── cv609_25038.txt
│ │ │ ├── cv610_24153.txt
│ │ │ ├── cv611_2253.txt
│ │ │ ├── cv612_5396.txt
│ │ │ ├── cv613_23104.txt
│ │ │ ├── cv614_11320.txt
│ │ │ ├── cv615_15734.txt
│ │ │ ├── cv616_29187.txt
│ │ │ ├── cv617_9561.txt
│ │ │ ├── cv618_9469.txt
│ │ │ ├── cv619_13677.txt
│ │ │ ├── cv620_2556.txt
│ │ │ ├── cv621_15984.txt
│ │ │ ├── cv622_8583.txt
│ │ │ ├── cv623_16988.txt
│ │ │ ├── cv624_11601.txt
│ │ │ ├── cv625_13518.txt
│ │ │ ├── cv626_7907.txt
│ │ │ ├── cv627_12603.txt
│ │ │ ├── cv628_20758.txt
│ │ │ ├── cv629_16604.txt
│ │ │ ├── cv630_10152.txt
│ │ │ ├── cv631_4782.txt
│ │ │ ├── cv632_9704.txt
│ │ │ ├── cv633_29730.txt
│ │ │ ├── cv634_11989.txt
│ │ │ ├── cv635_0984.txt
│ │ │ ├── cv636_16954.txt
│ │ │ ├── cv637_13682.txt
│ │ │ ├── cv638_29394.txt
│ │ │ ├── cv639_10797.txt
│ │ │ ├── cv640_5380.txt
│ │ │ ├── cv641_13412.txt
│ │ │ ├── cv642_29788.txt
│ │ │ ├── cv643_29282.txt
│ │ │ ├── cv644_18551.txt
│ │ │ ├── cv645_17078.txt
│ │ │ ├── cv646_16817.txt
│ │ │ ├── cv647_15275.txt
│ │ │ ├── cv648_17277.txt
│ │ │ ├── cv649_13947.txt
│ │ │ ├── cv650_15974.txt
│ │ │ ├── cv651_11120.txt
│ │ │ ├── cv652_15653.txt
│ │ │ ├── cv653_2107.txt
│ │ │ ├── cv654_19345.txt
│ │ │ ├── cv655_12055.txt
│ │ │ ├── cv656_25395.txt
│ │ │ ├── cv657_25835.txt
│ │ │ ├── cv658_11186.txt
│ │ │ ├── cv659_21483.txt
│ │ │ ├── cv660_23140.txt
│ │ │ ├── cv661_25780.txt
│ │ │ ├── cv662_14791.txt
│ │ │ ├── cv663_14484.txt
│ │ │ ├── cv664_4264.txt
│ │ │ ├── cv665_29386.txt
│ │ │ ├── cv666_20301.txt
│ │ │ ├── cv667_19672.txt
│ │ │ ├── cv668_18848.txt
│ │ │ ├── cv669_24318.txt
│ │ │ ├── cv670_2666.txt
│ │ │ ├── cv671_5164.txt
│ │ │ ├── cv672_27988.txt
│ │ │ ├── cv673_25874.txt
│ │ │ ├── cv674_11593.txt
│ │ │ ├── cv675_22871.txt
│ │ │ ├── cv676_22202.txt
│ │ │ ├── cv677_18938.txt
│ │ │ ├── cv678_14887.txt
│ │ │ ├── cv679_28221.txt
│ │ │ ├── cv680_10533.txt
│ │ │ ├── cv681_9744.txt
│ │ │ ├── cv682_17947.txt
│ │ │ ├── cv683_13047.txt
│ │ │ ├── cv684_12727.txt
│ │ │ ├── cv685_5710.txt
│ │ │ ├── cv686_15553.txt
│ │ │ ├── cv687_22207.txt
│ │ │ ├── cv688_7884.txt
│ │ │ ├── cv689_13701.txt
│ │ │ ├── cv690_5425.txt
│ │ │ ├── cv691_5090.txt
│ │ │ ├── cv692_17026.txt
│ │ │ ├── cv693_19147.txt
│ │ │ ├── cv694_4526.txt
│ │ │ ├── cv695_22268.txt
│ │ │ ├── cv696_29619.txt
│ │ │ ├── cv697_12106.txt
│ │ │ ├── cv698_16930.txt
│ │ │ ├── cv699_7773.txt
│ │ │ ├── cv700_23163.txt
│ │ │ ├── cv701_15880.txt
│ │ │ ├── cv702_12371.txt
│ │ │ ├── cv703_17948.txt
│ │ │ ├── cv704_17622.txt
│ │ │ ├── cv705_11973.txt
│ │ │ ├── cv706_25883.txt
│ │ │ ├── cv707_11421.txt
│ │ │ ├── cv708_28539.txt
│ │ │ ├── cv709_11173.txt
│ │ │ ├── cv710_23745.txt
│ │ │ ├── cv711_12687.txt
│ │ │ ├── cv712_24217.txt
│ │ │ ├── cv713_29002.txt
│ │ │ ├── cv714_19704.txt
│ │ │ ├── cv715_19246.txt
│ │ │ ├── cv716_11153.txt
│ │ │ ├── cv717_17472.txt
│ │ │ ├── cv718_12227.txt
│ │ │ ├── cv719_5581.txt
│ │ │ ├── cv720_5383.txt
│ │ │ ├── cv721_28993.txt
│ │ │ ├── cv722_7571.txt
│ │ │ ├── cv723_9002.txt
│ │ │ ├── cv724_15265.txt
│ │ │ ├── cv725_10266.txt
│ │ │ ├── cv726_4365.txt
│ │ │ ├── cv727_5006.txt
│ │ │ ├── cv728_17931.txt
│ │ │ ├── cv729_10475.txt
│ │ │ ├── cv730_10729.txt
│ │ │ ├── cv731_3968.txt
│ │ │ ├── cv732_13092.txt
│ │ │ ├── cv733_9891.txt
│ │ │ ├── cv734_22821.txt
│ │ │ ├── cv735_20218.txt
│ │ │ ├── cv736_24947.txt
│ │ │ ├── cv737_28733.txt
│ │ │ ├── cv738_10287.txt
│ │ │ ├── cv739_12179.txt
│ │ │ ├── cv740_13643.txt
│ │ │ ├── cv741_12765.txt
│ │ │ ├── cv742_8279.txt
│ │ │ ├── cv743_17023.txt
│ │ │ ├── cv744_10091.txt
│ │ │ ├── cv745_14009.txt
│ │ │ ├── cv746_10471.txt
│ │ │ ├── cv747_18189.txt
│ │ │ ├── cv748_14044.txt
│ │ │ ├── cv749_18960.txt
│ │ │ ├── cv750_10606.txt
│ │ │ ├── cv751_17208.txt
│ │ │ ├── cv752_25330.txt
│ │ │ ├── cv753_11812.txt
│ │ │ ├── cv754_7709.txt
│ │ │ ├── cv755_24881.txt
│ │ │ ├── cv756_23676.txt
│ │ │ ├── cv757_10668.txt
│ │ │ ├── cv758_9740.txt
│ │ │ ├── cv759_15091.txt
│ │ │ ├── cv760_8977.txt
│ │ │ ├── cv761_13769.txt
│ │ │ ├── cv762_15604.txt
│ │ │ ├── cv763_16486.txt
│ │ │ ├── cv764_12701.txt
│ │ │ ├── cv765_20429.txt
│ │ │ ├── cv766_7983.txt
│ │ │ ├── cv767_15673.txt
│ │ │ ├── cv768_12709.txt
│ │ │ ├── cv769_8565.txt
│ │ │ ├── cv770_11061.txt
│ │ │ ├── cv771_28466.txt
│ │ │ ├── cv772_12971.txt
│ │ │ ├── cv773_20264.txt
│ │ │ ├── cv774_15488.txt
│ │ │ ├── cv775_17966.txt
│ │ │ ├── cv776_21934.txt
│ │ │ ├── cv777_10247.txt
│ │ │ ├── cv778_18629.txt
│ │ │ ├── cv779_18989.txt
│ │ │ ├── cv780_8467.txt
│ │ │ ├── cv781_5358.txt
│ │ │ ├── cv782_21078.txt
│ │ │ ├── cv783_14724.txt
│ │ │ ├── cv784_16077.txt
│ │ │ ├── cv785_23748.txt
│ │ │ ├── cv786_23608.txt
│ │ │ ├── cv787_15277.txt
│ │ │ ├── cv788_26409.txt
│ │ │ ├── cv789_12991.txt
│ │ │ ├── cv790_16202.txt
│ │ │ ├── cv791_17995.txt
│ │ │ ├── cv792_3257.txt
│ │ │ ├── cv793_15235.txt
│ │ │ ├── cv794_17353.txt
│ │ │ ├── cv795_10291.txt
│ │ │ ├── cv796_17243.txt
│ │ │ ├── cv797_7245.txt
│ │ │ ├── cv798_24779.txt
│ │ │ ├── cv799_19812.txt
│ │ │ ├── cv800_13494.txt
│ │ │ ├── cv801_26335.txt
│ │ │ ├── cv802_28381.txt
│ │ │ ├── cv803_8584.txt
│ │ │ ├── cv804_11763.txt
│ │ │ ├── cv805_21128.txt
│ │ │ ├── cv806_9405.txt
│ │ │ ├── cv807_23024.txt
│ │ │ ├── cv808_13773.txt
│ │ │ ├── cv809_5012.txt
│ │ │ ├── cv810_13660.txt
│ │ │ ├── cv811_22646.txt
│ │ │ ├── cv812_19051.txt
│ │ │ ├── cv813_6649.txt
│ │ │ ├── cv814_20316.txt
│ │ │ ├── cv815_23466.txt
│ │ │ ├── cv816_15257.txt
│ │ │ ├── cv817_3675.txt
│ │ │ ├── cv818_10698.txt
│ │ │ ├── cv819_9567.txt
│ │ │ ├── cv820_24157.txt
│ │ │ ├── cv821_29283.txt
│ │ │ ├── cv822_21545.txt
│ │ │ ├── cv823_17055.txt
│ │ │ ├── cv824_9335.txt
│ │ │ ├── cv825_5168.txt
│ │ │ ├── cv826_12761.txt
│ │ │ ├── cv827_19479.txt
│ │ │ ├── cv828_21392.txt
│ │ │ ├── cv829_21725.txt
│ │ │ ├── cv830_5778.txt
│ │ │ ├── cv831_16325.txt
│ │ │ ├── cv832_24713.txt
│ │ │ ├── cv833_11961.txt
│ │ │ ├── cv834_23192.txt
│ │ │ ├── cv835_20531.txt
│ │ │ ├── cv836_14311.txt
│ │ │ ├── cv837_27232.txt
│ │ │ ├── cv838_25886.txt
│ │ │ ├── cv839_22807.txt
│ │ │ ├── cv840_18033.txt
│ │ │ ├── cv841_3367.txt
│ │ │ ├── cv842_5702.txt
│ │ │ ├── cv843_17054.txt
│ │ │ ├── cv844_13890.txt
│ │ │ ├── cv845_15886.txt
│ │ │ ├── cv846_29359.txt
│ │ │ ├── cv847_20855.txt
│ │ │ ├── cv848_10061.txt
│ │ │ ├── cv849_17215.txt
│ │ │ ├── cv850_18185.txt
│ │ │ ├── cv851_21895.txt
│ │ │ ├── cv852_27512.txt
│ │ │ ├── cv853_29119.txt
│ │ │ ├── cv854_18955.txt
│ │ │ ├── cv855_22134.txt
│ │ │ ├── cv856_28882.txt
│ │ │ ├── cv857_17527.txt
│ │ │ ├── cv858_20266.txt
│ │ │ ├── cv859_15689.txt
│ │ │ ├── cv860_15520.txt
│ │ │ ├── cv861_12809.txt
│ │ │ ├── cv862_15924.txt
│ │ │ ├── cv863_7912.txt
│ │ │ ├── cv864_3087.txt
│ │ │ ├── cv865_28796.txt
│ │ │ ├── cv866_29447.txt
│ │ │ ├── cv867_18362.txt
│ │ │ ├── cv868_12799.txt
│ │ │ ├── cv869_24782.txt
│ │ │ ├── cv870_18090.txt
│ │ │ ├── cv871_25971.txt
│ │ │ ├── cv872_13710.txt
│ │ │ ├── cv873_19937.txt
│ │ │ ├── cv874_12182.txt
│ │ │ ├── cv875_5622.txt
│ │ │ ├── cv876_9633.txt
│ │ │ ├── cv877_29132.txt
│ │ │ ├── cv878_17204.txt
│ │ │ ├── cv879_16585.txt
│ │ │ ├── cv880_29629.txt
│ │ │ ├── cv881_14767.txt
│ │ │ ├── cv882_10042.txt
│ │ │ ├── cv883_27621.txt
│ │ │ ├── cv884_15230.txt
│ │ │ ├── cv885_13390.txt
│ │ │ ├── cv886_19210.txt
│ │ │ ├── cv887_5306.txt
│ │ │ ├── cv888_25678.txt
│ │ │ ├── cv889_22670.txt
│ │ │ ├── cv890_3515.txt
│ │ │ ├── cv891_6035.txt
│ │ │ ├── cv892_18788.txt
│ │ │ ├── cv893_26731.txt
│ │ │ ├── cv894_22140.txt
│ │ │ ├── cv895_22200.txt
│ │ │ ├── cv896_17819.txt
│ │ │ ├── cv897_11703.txt
│ │ │ ├── cv898_1576.txt
│ │ │ ├── cv899_17812.txt
│ │ │ ├── cv900_10800.txt
│ │ │ ├── cv901_11934.txt
│ │ │ ├── cv902_13217.txt
│ │ │ ├── cv903_18981.txt
│ │ │ ├── cv904_25663.txt
│ │ │ ├── cv905_28965.txt
│ │ │ ├── cv906_12332.txt
│ │ │ ├── cv907_3193.txt
│ │ │ ├── cv908_17779.txt
│ │ │ ├── cv909_9973.txt
│ │ │ ├── cv910_21930.txt
│ │ │ ├── cv911_21695.txt
│ │ │ ├── cv912_5562.txt
│ │ │ ├── cv913_29127.txt
│ │ │ ├── cv914_2856.txt
│ │ │ ├── cv915_9342.txt
│ │ │ ├── cv916_17034.txt
│ │ │ ├── cv917_29484.txt
│ │ │ ├── cv918_27080.txt
│ │ │ ├── cv919_18155.txt
│ │ │ ├── cv920_29423.txt
│ │ │ ├── cv921_13988.txt
│ │ │ ├── cv922_10185.txt
│ │ │ ├── cv923_11951.txt
│ │ │ ├── cv924_29397.txt
│ │ │ ├── cv925_9459.txt
│ │ │ ├── cv926_18471.txt
│ │ │ ├── cv927_11471.txt
│ │ │ ├── cv928_9478.txt
│ │ │ ├── cv929_1841.txt
│ │ │ ├── cv930_14949.txt
│ │ │ ├── cv931_18783.txt
│ │ │ ├── cv932_14854.txt
│ │ │ ├── cv933_24953.txt
│ │ │ ├── cv934_20426.txt
│ │ │ ├── cv935_24977.txt
│ │ │ ├── cv936_17473.txt
│ │ │ ├── cv937_9816.txt
│ │ │ ├── cv938_10706.txt
│ │ │ ├── cv939_11247.txt
│ │ │ ├── cv940_18935.txt
│ │ │ ├── cv941_10718.txt
│ │ │ ├── cv942_18509.txt
│ │ │ ├── cv943_23547.txt
│ │ │ ├── cv944_15042.txt
│ │ │ ├── cv945_13012.txt
│ │ │ ├── cv946_20084.txt
│ │ │ ├── cv947_11316.txt
│ │ │ ├── cv948_25870.txt
│ │ │ ├── cv949_21565.txt
│ │ │ ├── cv950_13478.txt
│ │ │ ├── cv951_11816.txt
│ │ │ ├── cv952_26375.txt
│ │ │ ├── cv953_7078.txt
│ │ │ ├── cv954_19932.txt
│ │ │ ├── cv955_26154.txt
│ │ │ ├── cv956_12547.txt
│ │ │ ├── cv957_9059.txt
│ │ │ ├── cv958_13020.txt
│ │ │ ├── cv959_16218.txt
│ │ │ ├── cv960_28877.txt
│ │ │ ├── cv961_5578.txt
│ │ │ ├── cv962_9813.txt
│ │ │ ├── cv963_7208.txt
│ │ │ ├── cv964_5794.txt
│ │ │ ├── cv965_26688.txt
│ │ │ ├── cv966_28671.txt
│ │ │ ├── cv967_5626.txt
│ │ │ ├── cv968_25413.txt
│ │ │ ├── cv969_14760.txt
│ │ │ ├── cv970_19532.txt
│ │ │ ├── cv971_11790.txt
│ │ │ ├── cv972_26837.txt
│ │ │ ├── cv973_10171.txt
│ │ │ ├── cv974_24303.txt
│ │ │ ├── cv975_11920.txt
│ │ │ ├── cv976_10724.txt
│ │ │ ├── cv977_4776.txt
│ │ │ ├── cv978_22192.txt
│ │ │ ├── cv979_2029.txt
│ │ │ ├── cv980_11851.txt
│ │ │ ├── cv981_16679.txt
│ │ │ ├── cv982_22209.txt
│ │ │ ├── cv983_24219.txt
│ │ │ ├── cv984_14006.txt
│ │ │ ├── cv985_5964.txt
│ │ │ ├── cv986_15092.txt
│ │ │ ├── cv987_7394.txt
│ │ │ ├── cv988_20168.txt
│ │ │ ├── cv989_17297.txt
│ │ │ ├── cv990_12443.txt
│ │ │ ├── cv991_19973.txt
│ │ │ ├── cv992_12806.txt
│ │ │ ├── cv993_29565.txt
│ │ │ ├── cv994_13229.txt
│ │ │ ├── cv995_23113.txt
│ │ │ ├── cv996_12447.txt
│ │ │ ├── cv997_5152.txt
│ │ │ ├── cv998_15691.txt
│ │ │ └── cv999_14636.txt
│ │ └── pos/
│ │ ├── cv000_29590.txt
│ │ ├── cv001_18431.txt
│ │ ├── cv002_15918.txt
│ │ ├── cv003_11664.txt
│ │ ├── cv004_11636.txt
│ │ ├── cv005_29443.txt
│ │ ├── cv006_15448.txt
│ │ ├── cv007_4968.txt
│ │ ├── cv008_29435.txt
│ │ ├── cv009_29592.txt
│ │ ├── cv010_29198.txt
│ │ ├── cv011_12166.txt
│ │ ├── cv012_29576.txt
│ │ ├── cv013_10159.txt
│ │ ├── cv014_13924.txt
│ │ ├── cv015_29439.txt
│ │ ├── cv016_4659.txt
│ │ ├── cv017_22464.txt
│ │ ├── cv018_20137.txt
│ │ ├── cv019_14482.txt
│ │ ├── cv020_8825.txt
│ │ ├── cv021_15838.txt
│ │ ├── cv022_12864.txt
│ │ ├── cv023_12672.txt
│ │ ├── cv024_6778.txt
│ │ ├── cv025_3108.txt
│ │ ├── cv026_29325.txt
│ │ ├── cv027_25219.txt
│ │ ├── cv028_26746.txt
│ │ ├── cv029_18643.txt
│ │ ├── cv030_21593.txt
│ │ ├── cv031_18452.txt
│ │ ├── cv032_22550.txt
│ │ ├── cv033_24444.txt
│ │ ├── cv034_29647.txt
│ │ ├── cv035_3954.txt
│ │ ├── cv036_16831.txt
│ │ ├── cv037_18510.txt
│ │ ├── cv038_9749.txt
│ │ ├── cv039_6170.txt
│ │ ├── cv040_8276.txt
│ │ ├── cv041_21113.txt
│ │ ├── cv042_10982.txt
│ │ ├── cv043_15013.txt
│ │ ├── cv044_16969.txt
│ │ ├── cv045_23923.txt
│ │ ├── cv046_10188.txt
│ │ ├── cv047_1754.txt
│ │ ├── cv048_16828.txt
│ │ ├── cv049_20471.txt
│ │ ├── cv050_11175.txt
│ │ ├── cv051_10306.txt
│ │ ├── cv052_29378.txt
│ │ ├── cv053_21822.txt
│ │ ├── cv054_4230.txt
│ │ ├── cv055_8338.txt
│ │ ├── cv056_13133.txt
│ │ ├── cv057_7453.txt
│ │ ├── cv058_8025.txt
│ │ ├── cv059_28885.txt
│ │ ├── cv060_10844.txt
│ │ ├── cv061_8837.txt
│ │ ├── cv062_23115.txt
│ │ ├── cv063_28997.txt
│ │ ├── cv064_24576.txt
│ │ ├── cv065_15248.txt
│ │ ├── cv066_10821.txt
│ │ ├── cv067_19774.txt
│ │ ├── cv068_13400.txt
│ │ ├── cv069_10801.txt
│ │ ├── cv070_12289.txt
│ │ ├── cv071_12095.txt
│ │ ├── cv072_6169.txt
│ │ ├── cv073_21785.txt
│ │ ├── cv074_6875.txt
│ │ ├── cv075_6500.txt
│ │ ├── cv076_24945.txt
│ │ ├── cv077_22138.txt
│ │ ├── cv078_14730.txt
│ │ ├── cv079_11933.txt
│ │ ├── cv080_13465.txt
│ │ ├── cv081_16582.txt
│ │ ├── cv082_11080.txt
│ │ ├── cv083_24234.txt
│ │ ├── cv084_13566.txt
│ │ ├── cv085_1381.txt
│ │ ├── cv086_18371.txt
│ │ ├── cv087_1989.txt
│ │ ├── cv088_24113.txt
│ │ ├── cv089_11418.txt
│ │ ├── cv090_0042.txt
│ │ ├── cv091_7400.txt
│ │ ├── cv092_28017.txt
│ │ ├── cv093_13951.txt
│ │ ├── cv094_27889.txt
│ │ ├── cv095_28892.txt
│ │ ├── cv096_11474.txt
│ │ ├── cv097_24970.txt
│ │ ├── cv098_15435.txt
│ │ ├── cv099_10534.txt
│ │ ├── cv100_11528.txt
│ │ ├── cv101_10175.txt
│ │ ├── cv102_7846.txt
│ │ ├── cv103_11021.txt
│ │ ├── cv104_18134.txt
│ │ ├── cv105_17990.txt
│ │ ├── cv106_16807.txt
│ │ ├── cv107_24319.txt
│ │ ├── cv108_15571.txt
│ │ ├── cv109_21172.txt
│ │ ├── cv110_27788.txt
│ │ ├── cv111_11473.txt
│ │ ├── cv112_11193.txt
│ │ ├── cv113_23102.txt
│ │ ├── cv114_18398.txt
│ │ ├── cv115_25396.txt
│ │ ├── cv116_28942.txt
│ │ ├── cv117_24295.txt
│ │ ├── cv118_28980.txt
│ │ ├── cv119_9867.txt
│ │ ├── cv120_4111.txt
│ │ ├── cv121_17302.txt
│ │ ├── cv122_7392.txt
│ │ ├── cv123_11182.txt
│ │ ├── cv124_4122.txt
│ │ ├── cv125_9391.txt
│ │ ├── cv126_28971.txt
│ │ ├── cv127_14711.txt
│ │ ├── cv128_29627.txt
│ │ ├── cv129_16741.txt
│ │ ├── cv130_17083.txt
│ │ ├── cv131_10713.txt
│ │ ├── cv132_5618.txt
│ │ ├── cv133_16336.txt
│ │ ├── cv134_22246.txt
│ │ ├── cv135_11603.txt
│ │ ├── cv136_11505.txt
│ │ ├── cv137_15422.txt
│ │ ├── cv138_12721.txt
│ │ ├── cv139_12873.txt
│ │ ├── cv140_7479.txt
│ │ ├── cv141_15686.txt
│ │ ├── cv142_22516.txt
│ │ ├── cv143_19666.txt
│ │ ├── cv144_5007.txt
│ │ ├── cv145_11472.txt
│ │ ├── cv146_18458.txt
│ │ ├── cv147_21193.txt
│ │ ├── cv148_16345.txt
│ │ ├── cv149_15670.txt
│ │ ├── cv150_12916.txt
│ │ ├── cv151_15771.txt
│ │ ├── cv152_8736.txt
│ │ ├── cv153_10779.txt
│ │ ├── cv154_9328.txt
│ │ ├── cv155_7308.txt
│ │ ├── cv156_10481.txt
│ │ ├── cv157_29372.txt
│ │ ├── cv158_10390.txt
│ │ ├── cv159_29505.txt
│ │ ├── cv160_10362.txt
│ │ ├── cv161_11425.txt
│ │ ├── cv162_10424.txt
│ │ ├── cv163_10052.txt
│ │ ├── cv164_22447.txt
│ │ ├── cv165_22619.txt
│ │ ├── cv166_11052.txt
│ │ ├── cv167_16376.txt
│ │ ├── cv168_7050.txt
│ │ ├── cv169_23778.txt
│ │ ├── cv170_3006.txt
│ │ ├── cv171_13537.txt
│ │ ├── cv172_11131.txt
│ │ ├── cv173_4471.txt
│ │ ├── cv174_9659.txt
│ │ ├── cv175_6964.txt
│ │ ├── cv176_12857.txt
│ │ ├── cv177_10367.txt
│ │ ├── cv178_12972.txt
│ │ ├── cv179_9228.txt
│ │ ├── cv180_16113.txt
│ │ ├── cv181_14401.txt
│ │ ├── cv182_7281.txt
│ │ ├── cv183_18612.txt
│ │ ├── cv184_2673.txt
│ │ ├── cv185_28654.txt
│ │ ├── cv186_2269.txt
│ │ ├── cv187_12829.txt
│ │ ├── cv188_19226.txt
│ │ ├── cv189_22934.txt
│ │ ├── cv190_27052.txt
│ │ ├── cv191_29719.txt
│ │ ├── cv192_14395.txt
│ │ ├── cv193_5416.txt
│ │ ├── cv194_12079.txt
│ │ ├── cv195_14528.txt
│ │ ├── cv196_29027.txt
│ │ ├── cv197_29328.txt
│ │ ├── cv198_18180.txt
│ │ ├── cv199_9629.txt
│ │ ├── cv200_2915.txt
│ │ ├── cv201_6997.txt
│ │ ├── cv202_10654.txt
│ │ ├── cv203_17986.txt
│ │ ├── cv204_8451.txt
│ │ ├── cv205_9457.txt
│ │ ├── cv206_14293.txt
│ │ ├── cv207_29284.txt
│ │ ├── cv208_9020.txt
│ │ ├── cv209_29118.txt
│ │ ├── cv210_9312.txt
│ │ ├── cv211_9953.txt
│ │ ├── cv212_10027.txt
│ │ ├── cv213_18934.txt
│ │ ├── cv214_12294.txt
│ │ ├── cv215_22240.txt
│ │ ├── cv216_18738.txt
│ │ ├── cv217_28842.txt
│ │ ├── cv218_24352.txt
│ │ ├── cv219_18626.txt
│ │ ├── cv220_29059.txt
│ │ ├── cv221_2695.txt
│ │ ├── cv222_17395.txt
│ │ ├── cv223_29066.txt
│ │ ├── cv224_17661.txt
│ │ ├── cv225_29224.txt
│ │ ├── cv226_2618.txt
│ │ ├── cv227_24215.txt
│ │ ├── cv228_5806.txt
│ │ ├── cv229_13611.txt
│ │ ├── cv230_7428.txt
│ │ ├── cv231_10425.txt
│ │ ├── cv232_14991.txt
│ │ ├── cv233_15964.txt
│ │ ├── cv234_20643.txt
│ │ ├── cv235_10217.txt
│ │ ├── cv236_11565.txt
│ │ ├── cv237_19221.txt
│ │ ├── cv238_12931.txt
│ │ ├── cv239_3385.txt
│ │ ├── cv240_14336.txt
│ │ ├── cv241_23130.txt
│ │ ├── cv242_10638.txt
│ │ ├── cv243_20728.txt
│ │ ├── cv244_21649.txt
│ │ ├── cv245_8569.txt
│ │ ├── cv246_28807.txt
│ │ ├── cv247_13142.txt
│ │ ├── cv248_13987.txt
│ │ ├── cv249_11640.txt
│ │ ├── cv250_25616.txt
│ │ ├── cv251_22636.txt
│ │ ├── cv252_23779.txt
│ │ ├── cv253_10077.txt
│ │ ├── cv254_6027.txt
│ │ ├── cv255_13683.txt
│ │ ├── cv256_14740.txt
│ │ ├── cv257_10975.txt
│ │ ├── cv258_5792.txt
│ │ ├── cv259_10934.txt
│ │ ├── cv260_13959.txt
│ │ ├── cv261_10954.txt
│ │ ├── cv262_12649.txt
│ │ ├── cv263_19259.txt
│ │ ├── cv264_12801.txt
│ │ ├── cv265_10814.txt
│ │ ├── cv266_25779.txt
│ │ ├── cv267_14952.txt
│ │ ├── cv268_18834.txt
│ │ ├── cv269_21732.txt
│ │ ├── cv270_6079.txt
│ │ ├── cv271_13837.txt
│ │ ├── cv272_18974.txt
│ │ ├── cv273_29112.txt
│ │ ├── cv274_25253.txt
│ │ ├── cv275_28887.txt
│ │ ├── cv276_15684.txt
│ │ ├── cv277_19091.txt
│ │ ├── cv278_13041.txt
│ │ ├── cv279_18329.txt
│ │ ├── cv280_8267.txt
│ │ ├── cv281_23253.txt
│ │ ├── cv282_6653.txt
│ │ ├── cv283_11055.txt
│ │ ├── cv284_19119.txt
│ │ ├── cv285_16494.txt
│ │ ├── cv286_25050.txt
│ │ ├── cv287_15900.txt
│ │ ├── cv288_18791.txt
│ │ ├── cv289_6463.txt
│ │ ├── cv290_11084.txt
│ │ ├── cv291_26635.txt
│ │ ├── cv292_7282.txt
│ │ ├── cv293_29856.txt
│ │ ├── cv294_11684.txt
│ │ ├── cv295_15570.txt
│ │ ├── cv296_12251.txt
│ │ ├── cv297_10047.txt
│ │ ├── cv298_23111.txt
│ │ ├── cv299_16214.txt
│ │ ├── cv300_22284.txt
│ │ ├── cv301_12146.txt
│ │ ├── cv302_25649.txt
│ │ ├── cv303_27520.txt
│ │ ├── cv304_28706.txt
│ │ ├── cv305_9946.txt
│ │ ├── cv306_10364.txt
│ │ ├── cv307_25270.txt
│ │ ├── cv308_5016.txt
│ │ ├── cv309_22571.txt
│ │ ├── cv310_13091.txt
│ │ ├── cv311_16002.txt
│ │ ├── cv312_29377.txt
│ │ ├── cv313_18198.txt
│ │ ├── cv314_14422.txt
│ │ ├── cv315_11629.txt
│ │ ├── cv316_6370.txt
│ │ ├── cv317_24049.txt
│ │ ├── cv318_10493.txt
│ │ ├── cv319_14727.txt
│ │ ├── cv320_9530.txt
│ │ ├── cv321_12843.txt
│ │ ├── cv322_20318.txt
│ │ ├── cv323_29805.txt
│ │ ├── cv324_7082.txt
│ │ ├── cv325_16629.txt
│ │ ├── cv326_13295.txt
│ │ ├── cv327_20292.txt
│ │ ├── cv328_10373.txt
│ │ ├── cv329_29370.txt
│ │ ├── cv330_29809.txt
│ │ ├── cv331_8273.txt
│ │ ├── cv332_16307.txt
│ │ ├── cv333_8916.txt
│ │ ├── cv334_10001.txt
│ │ ├── cv335_14665.txt
│ │ ├── cv336_10143.txt
│ │ ├── cv337_29181.txt
│ │ ├── cv338_8821.txt
│ │ ├── cv339_21119.txt
│ │ ├── cv340_13287.txt
│ │ ├── cv341_24430.txt
│ │ ├── cv342_19456.txt
│ │ ├── cv343_10368.txt
│ │ ├── cv344_5312.txt
│ │ ├── cv345_9954.txt
│ │ ├── cv346_18168.txt
│ │ ├── cv347_13194.txt
│ │ ├── cv348_18176.txt
│ │ ├── cv349_13507.txt
│ │ ├── cv350_20670.txt
│ │ ├── cv351_15458.txt
│ │ ├── cv352_5524.txt
│ │ ├── cv353_18159.txt
│ │ ├── cv354_8132.txt
│ │ ├── cv355_16413.txt
│ │ ├── cv356_25163.txt
│ │ ├── cv357_13156.txt
│ │ ├── cv358_10691.txt
│ │ ├── cv359_6647.txt
│ │ ├── cv360_8398.txt
│ │ ├── cv361_28944.txt
│ │ ├── cv362_15341.txt
│ │ ├── cv363_29332.txt
│ │ ├── cv364_12901.txt
│ │ ├── cv365_11576.txt
│ │ ├── cv366_10221.txt
│ │ ├── cv367_22792.txt
│ │ ├── cv368_10466.txt
│ │ ├── cv369_12886.txt
│ │ ├── cv370_5221.txt
│ │ ├── cv371_7630.txt
│ │ ├── cv372_6552.txt
│ │ ├── cv373_20404.txt
│ │ ├── cv374_25436.txt
│ │ ├── cv375_9929.txt
│ │ ├── cv376_19435.txt
│ │ ├── cv377_7946.txt
│ │ ├── cv378_20629.txt
│ │ ├── cv379_21963.txt
│ │ ├── cv380_7574.txt
│ │ ├── cv381_20172.txt
│ │ ├── cv382_7897.txt
│ │ ├── cv383_13116.txt
│ │ ├── cv384_17140.txt
│ │ ├── cv385_29741.txt
│ │ ├── cv386_10080.txt
│ │ ├── cv387_11507.txt
│ │ ├── cv388_12009.txt
│ │ ├── cv389_9369.txt
│ │ ├── cv390_11345.txt
│ │ ├── cv391_10802.txt
│ │ ├── cv392_11458.txt
│ │ ├── cv393_29327.txt
│ │ ├── cv394_5137.txt
│ │ ├── cv395_10849.txt
│ │ ├── cv396_17989.txt
│ │ ├── cv397_29023.txt
│ │ ├── cv398_15537.txt
│ │ ├── cv399_2877.txt
│ │ ├── cv400_19220.txt
│ │ ├── cv401_12605.txt
│ │ ├── cv402_14425.txt
│ │ ├── cv403_6621.txt
│ │ ├── cv404_20315.txt
│ │ ├── cv405_20399.txt
│ │ ├── cv406_21020.txt
│ │ ├── cv407_22637.txt
│ │ ├── cv408_5297.txt
│ │ ├── cv409_29786.txt
│ │ ├── cv410_24266.txt
│ │ ├── cv411_15007.txt
│ │ ├── cv412_24095.txt
│ │ ├── cv413_7398.txt
│ │ ├── cv414_10518.txt
│ │ ├── cv415_22517.txt
│ │ ├── cv416_11136.txt
│ │ ├── cv417_13115.txt
│ │ ├── cv418_14774.txt
│ │ ├── cv419_13394.txt
│ │ ├── cv420_28795.txt
│ │ ├── cv421_9709.txt
│ │ ├── cv422_9381.txt
│ │ ├── cv423_11155.txt
│ │ ├── cv424_8831.txt
│ │ ├── cv425_8250.txt
│ │ ├── cv426_10421.txt
│ │ ├── cv427_10825.txt
│ │ ├── cv428_11347.txt
│ │ ├── cv429_7439.txt
│ │ ├── cv430_17351.txt
│ │ ├── cv431_7085.txt
│ │ ├── cv432_14224.txt
│ │ ├── cv433_10144.txt
│ │ ├── cv434_5793.txt
│ │ ├── cv435_23110.txt
│ │ ├── cv436_19179.txt
│ │ ├── cv437_22849.txt
│ │ ├── cv438_8043.txt
│ │ ├── cv439_15970.txt
│ │ ├── cv440_15243.txt
│ │ ├── cv441_13711.txt
│ │ ├── cv442_13846.txt
│ │ ├── cv443_21118.txt
│ │ ├── cv444_9974.txt
│ │ ├── cv445_25882.txt
│ │ ├── cv446_11353.txt
│ │ ├── cv447_27332.txt
│ │ ├── cv448_14695.txt
│ │ ├── cv449_8785.txt
│ │ ├── cv450_7890.txt
│ │ ├── cv451_10690.txt
│ │ ├── cv452_5088.txt
│ │ ├── cv453_10379.txt
│ │ ├── cv454_2053.txt
│ │ ├── cv455_29000.txt
│ │ ├── cv456_18985.txt
│ │ ├── cv457_18453.txt
│ │ ├── cv458_8604.txt
│ │ ├── cv459_20319.txt
│ │ ├── cv460_10842.txt
│ │ ├── cv461_19600.txt
│ │ ├── cv462_19350.txt
│ │ ├── cv463_10343.txt
│ │ ├── cv464_15650.txt
│ │ ├── cv465_22431.txt
│ │ ├── cv466_18722.txt
│ │ ├── cv467_25773.txt
│ │ ├── cv468_15228.txt
│ │ ├── cv469_20630.txt
│ │ ├── cv470_15952.txt
│ │ ├── cv471_16858.txt
│ │ ├── cv472_29280.txt
│ │ ├── cv473_7367.txt
│ │ ├── cv474_10209.txt
│ │ ├── cv475_21692.txt
│ │ ├── cv476_16856.txt
│ │ ├── cv477_22479.txt
│ │ ├── cv478_14309.txt
│ │ ├── cv479_5649.txt
│ │ ├── cv480_19817.txt
│ │ ├── cv481_7436.txt
│ │ ├── cv482_10580.txt
│ │ ├── cv483_16378.txt
│ │ ├── cv484_25054.txt
│ │ ├── cv485_26649.txt
│ │ ├── cv486_9799.txt
│ │ ├── cv487_10446.txt
│ │ ├── cv488_19856.txt
│ │ ├── cv489_17906.txt
│ │ ├── cv490_17872.txt
│ │ ├── cv491_12145.txt
│ │ ├── cv492_18271.txt
│ │ ├── cv493_12839.txt
│ │ ├── cv494_17389.txt
│ │ ├── cv495_14518.txt
│ │ ├── cv496_10530.txt
│ │ ├── cv497_26980.txt
│ │ ├── cv498_8832.txt
│ │ ├── cv499_10658.txt
│ │ ├── cv500_10251.txt
│ │ ├── cv501_11657.txt
│ │ ├── cv502_10406.txt
│ │ ├── cv503_10558.txt
│ │ ├── cv504_29243.txt
│ │ ├── cv505_12090.txt
│ │ ├── cv506_15956.txt
│ │ ├── cv507_9220.txt
│ │ ├── cv508_16006.txt
│ │ ├── cv509_15888.txt
│ │ ├── cv510_23360.txt
│ │ ├── cv511_10132.txt
│ │ ├── cv512_15965.txt
│ │ ├── cv513_6923.txt
│ │ ├── cv514_11187.txt
│ │ ├── cv515_17069.txt
│ │ ├── cv516_11172.txt
│ │ ├── cv517_19219.txt
│ │ ├── cv518_13331.txt
│ │ ├── cv519_14661.txt
│ │ ├── cv520_12295.txt
│ │ ├── cv521_15828.txt
│ │ ├── cv522_5583.txt
│ │ ├── cv523_16615.txt
│ │ ├── cv524_23627.txt
│ │ ├── cv525_16122.txt
│ │ ├── cv526_12083.txt
│ │ ├── cv527_10123.txt
│ │ ├── cv528_10822.txt
│ │ ├── cv529_10420.txt
│ │ ├── cv530_16212.txt
│ │ ├── cv531_26486.txt
│ │ ├── cv532_6522.txt
│ │ ├── cv533_9821.txt
│ │ ├── cv534_14083.txt
│ │ ├── cv535_19728.txt
│ │ ├── cv536_27134.txt
│ │ ├── cv537_12370.txt
│ │ ├── cv538_28667.txt
│ │ ├── cv539_20347.txt
│ │ ├── cv540_3421.txt
│ │ ├── cv541_28835.txt
│ │ ├── cv542_18980.txt
│ │ ├── cv543_5045.txt
│ │ ├── cv544_5108.txt
│ │ ├── cv545_12014.txt
│ │ ├── cv546_11767.txt
│ │ ├── cv547_16324.txt
│ │ ├── cv548_17731.txt
│ │ ├── cv549_21443.txt
│ │ ├── cv550_22211.txt
│ │ ├── cv551_10565.txt
│ │ ├── cv552_10016.txt
│ │ ├── cv553_26915.txt
│ │ ├── cv554_13151.txt
│ │ ├── cv555_23922.txt
│ │ ├── cv556_14808.txt
│ │ ├── cv557_11449.txt
│ │ ├── cv558_29507.txt
│ │ ├── cv559_0050.txt
│ │ ├── cv560_17175.txt
│ │ ├── cv561_9201.txt
│ │ ├── cv562_10359.txt
│ │ ├── cv563_17257.txt
│ │ ├── cv564_11110.txt
│ │ ├── cv565_29572.txt
│ │ ├── cv566_8581.txt
│ │ ├── cv567_29611.txt
│ │ ├── cv568_15638.txt
│ │ ├── cv569_26381.txt
│ │ ├── cv570_29082.txt
│ │ ├── cv571_29366.txt
│ │ ├── cv572_18657.txt
│ │ ├── cv573_29525.txt
│ │ ├── cv574_22156.txt
│ │ ├── cv575_21150.txt
│ │ ├── cv576_14094.txt
│ │ ├── cv577_28549.txt
│ │ ├── cv578_15094.txt
│ │ ├── cv579_11605.txt
│ │ ├── cv580_14064.txt
│ │ ├── cv581_19381.txt
│ │ ├── cv582_6559.txt
│ │ ├── cv583_29692.txt
│ │ ├── cv584_29722.txt
│ │ ├── cv585_22496.txt
│ │ ├── cv586_7543.txt
│ │ ├── cv587_19162.txt
│ │ ├── cv588_13008.txt
│ │ ├── cv589_12064.txt
│ │ ├── cv590_19290.txt
│ │ ├── cv591_23640.txt
│ │ ├── cv592_22315.txt
│ │ ├── cv593_10987.txt
│ │ ├── cv594_11039.txt
│ │ ├── cv595_25335.txt
│ │ ├── cv596_28311.txt
│ │ ├── cv597_26360.txt
│ │ ├── cv598_16452.txt
│ │ ├── cv599_20988.txt
│ │ ├── cv600_23878.txt
│ │ ├── cv601_23453.txt
│ │ ├── cv602_8300.txt
│ │ ├── cv603_17694.txt
│ │ ├── cv604_2230.txt
│ │ ├── cv605_11800.txt
│ │ ├── cv606_15985.txt
│ │ ├── cv607_7717.txt
│ │ ├── cv608_23231.txt
│ │ ├── cv609_23877.txt
│ │ ├── cv610_2287.txt
│ │ ├── cv611_21120.txt
│ │ ├── cv612_5461.txt
│ │ ├── cv613_21796.txt
│ │ ├── cv614_10626.txt
│ │ ├── cv615_14182.txt
│ │ ├── cv616_29319.txt
│ │ ├── cv617_9322.txt
│ │ ├── cv618_8974.txt
│ │ ├── cv619_12462.txt
│ │ ├── cv620_24265.txt
│ │ ├── cv621_14368.txt
│ │ ├── cv622_8147.txt
│ │ ├── cv623_15356.txt
│ │ ├── cv624_10744.txt
│ │ ├── cv625_12440.txt
│ │ ├── cv626_7410.txt
│ │ ├── cv627_11620.txt
│ │ ├── cv628_19325.txt
│ │ ├── cv629_14909.txt
│ │ ├── cv630_10057.txt
│ │ ├── cv631_4967.txt
│ │ ├── cv632_9610.txt
│ │ ├── cv633_29837.txt
│ │ ├── cv634_11101.txt
│ │ ├── cv635_10022.txt
│ │ ├── cv636_15279.txt
│ │ ├── cv637_1250.txt
│ │ ├── cv638_2953.txt
│ │ ├── cv639_10308.txt
│ │ ├── cv640_5378.txt
│ │ ├── cv641_12349.txt
│ │ ├── cv642_29867.txt
│ │ ├── cv643_29349.txt
│ │ ├── cv644_17154.txt
│ │ ├── cv645_15668.txt
│ │ ├── cv646_15065.txt
│ │ ├── cv647_13691.txt
│ │ ├── cv648_15792.txt
│ │ ├── cv649_12735.txt
│ │ ├── cv650_14340.txt
│ │ ├── cv651_10492.txt
│ │ ├── cv652_13972.txt
│ │ ├── cv653_19583.txt
│ │ ├── cv654_18246.txt
│ │ ├── cv655_11154.txt
│ │ ├── cv656_24201.txt
│ │ ├── cv657_24513.txt
│ │ ├── cv658_10532.txt
│ │ ├── cv659_19944.txt
│ │ ├── cv660_21893.txt
│ │ ├── cv661_2450.txt
│ │ ├── cv662_13320.txt
│ │ ├── cv663_13019.txt
│ │ ├── cv664_4389.txt
│ │ ├── cv665_29538.txt
│ │ ├── cv666_18963.txt
│ │ ├── cv667_18467.txt
│ │ ├── cv668_17604.txt
│ │ ├── cv669_22995.txt
│ │ ├── cv670_25826.txt
│ │ ├── cv671_5054.txt
│ │ ├── cv672_28083.txt
│ │ ├── cv673_24714.txt
│ │ ├── cv674_10732.txt
│ │ ├── cv675_21588.txt
│ │ ├── cv676_21090.txt
│ │ ├── cv677_17715.txt
│ │ ├── cv678_13419.txt
│ │ ├── cv679_28559.txt
│ │ ├── cv680_10160.txt
│ │ ├── cv681_9692.txt
│ │ ├── cv682_16139.txt
│ │ ├── cv683_12167.txt
│ │ ├── cv684_11798.txt
│ │ ├── cv685_5947.txt
│ │ ├── cv686_13900.txt
│ │ ├── cv687_21100.txt
│ │ ├── cv688_7368.txt
│ │ ├── cv689_12587.txt
│ │ ├── cv690_5619.txt
│ │ ├── cv691_5043.txt
│ │ ├── cv692_15451.txt
│ │ ├── cv693_18063.txt
│ │ ├── cv694_4876.txt
│ │ ├── cv695_21108.txt
│ │ ├── cv696_29740.txt
│ │ ├── cv697_11162.txt
│ │ ├── cv698_15253.txt
│ │ ├── cv699_7223.txt
│ │ ├── cv700_21947.txt
│ │ ├── cv701_14252.txt
│ │ ├── cv702_11500.txt
│ │ ├── cv703_16143.txt
│ │ ├── cv704_15969.txt
│ │ ├── cv705_11059.txt
│ │ ├── cv706_24716.txt
│ │ ├── cv707_10678.txt
│ │ ├── cv708_28729.txt
│ │ ├── cv709_10529.txt
│ │ ├── cv710_22577.txt
│ │ ├── cv711_11665.txt
│ │ ├── cv712_22920.txt
│ │ ├── cv713_29155.txt
│ │ ├── cv714_18502.txt
│ │ ├── cv715_18179.txt
│ │ ├── cv716_10514.txt
│ │ ├── cv717_15953.txt
│ │ ├── cv718_11434.txt
│ │ ├── cv719_5713.txt
│ │ ├── cv720_5389.txt
│ │ ├── cv721_29121.txt
│ │ ├── cv722_7110.txt
│ │ ├── cv723_8648.txt
│ │ ├── cv724_13681.txt
│ │ ├── cv725_10103.txt
│ │ ├── cv726_4719.txt
│ │ ├── cv727_4978.txt
│ │ ├── cv728_16133.txt
│ │ ├── cv729_10154.txt
│ │ ├── cv730_10279.txt
│ │ ├── cv731_4136.txt
│ │ ├── cv732_12245.txt
│ │ ├── cv733_9839.txt
│ │ ├── cv734_21568.txt
│ │ ├── cv735_18801.txt
│ │ ├── cv736_23670.txt
│ │ ├── cv737_28907.txt
│ │ ├── cv738_10116.txt
│ │ ├── cv739_11209.txt
│ │ ├── cv740_12445.txt
│ │ ├── cv741_11890.txt
│ │ ├── cv742_7751.txt
│ │ ├── cv743_15449.txt
│ │ ├── cv744_10038.txt
│ │ ├── cv745_12773.txt
│ │ ├── cv746_10147.txt
│ │ ├── cv747_16556.txt
│ │ ├── cv748_12786.txt
│ │ ├── cv749_17765.txt
│ │ ├── cv750_10180.txt
│ │ ├── cv751_15719.txt
│ │ ├── cv752_24155.txt
│ │ ├── cv753_10875.txt
│ │ ├── cv754_7216.txt
│ │ ├── cv755_23616.txt
│ │ ├── cv756_22540.txt
│ │ ├── cv757_10189.txt
│ │ ├── cv758_9671.txt
│ │ ├── cv759_13522.txt
│ │ ├── cv760_8597.txt
│ │ ├── cv761_12620.txt
│ │ ├── cv762_13927.txt
│ │ ├── cv763_14729.txt
│ │ ├── cv764_11739.txt
│ │ ├── cv765_19037.txt
│ │ ├── cv766_7540.txt
│ │ ├── cv767_14062.txt
│ │ ├── cv768_11751.txt
│ │ ├── cv769_8123.txt
│ │ ├── cv770_10451.txt
│ │ ├── cv771_28665.txt
│ │ ├── cv772_12119.txt
│ │ ├── cv773_18817.txt
│ │ ├── cv774_13845.txt
│ │ ├── cv775_16237.txt
│ │ ├── cv776_20529.txt
│ │ ├── cv777_10094.txt
│ │ ├── cv778_17330.txt
│ │ ├── cv779_17881.txt
│ │ ├── cv780_7984.txt
│ │ ├── cv781_5262.txt
│ │ ├── cv782_19526.txt
│ │ ├── cv783_13227.txt
│ │ ├── cv784_14394.txt
│ │ ├── cv785_22600.txt
│ │ ├── cv786_22497.txt
│ │ ├── cv787_13743.txt
│ │ ├── cv788_25272.txt
│ │ ├── cv789_12136.txt
│ │ ├── cv790_14600.txt
│ │ ├── cv791_16302.txt
│ │ ├── cv792_3832.txt
│ │ ├── cv793_13650.txt
│ │ ├── cv794_15868.txt
│ │ ├── cv795_10122.txt
│ │ ├── cv796_15782.txt
│ │ ├── cv797_6957.txt
│ │ ├── cv798_23531.txt
│ │ ├── cv799_18543.txt
│ │ ├── cv800_12368.txt
│ │ ├── cv801_25228.txt
│ │ ├── cv802_28664.txt
│ │ ├── cv803_8207.txt
│ │ ├── cv804_10862.txt
│ │ ├── cv805_19601.txt
│ │ ├── cv806_8842.txt
│ │ ├── cv807_21740.txt
│ │ ├── cv808_12635.txt
│ │ ├── cv809_5009.txt
│ │ ├── cv810_12458.txt
│ │ ├── cv811_21386.txt
│ │ ├── cv812_17924.txt
│ │ ├── cv813_6534.txt
│ │ ├── cv814_18975.txt
│ │ ├── cv815_22456.txt
│ │ ├── cv816_13655.txt
│ │ ├── cv817_4041.txt
│ │ ├── cv818_10211.txt
│ │ ├── cv819_9364.txt
│ │ ├── cv820_22892.txt
│ │ ├── cv821_29364.txt
│ │ ├── cv822_20049.txt
│ │ ├── cv823_15569.txt
│ │ ├── cv824_8838.txt
│ │ ├── cv825_5063.txt
│ │ ├── cv826_11834.txt
│ │ ├── cv827_18331.txt
│ │ ├── cv828_19831.txt
│ │ ├── cv829_20289.txt
│ │ ├── cv830_6014.txt
│ │ ├── cv831_14689.txt
│ │ ├── cv832_23275.txt
│ │ ├── cv833_11053.txt
│ │ ├── cv834_22195.txt
│ │ ├── cv835_19159.txt
│ │ ├── cv836_12968.txt
│ │ ├── cv837_27325.txt
│ │ ├── cv838_24728.txt
│ │ ├── cv839_21467.txt
│ │ ├── cv840_16321.txt
│ │ ├── cv841_3967.txt
│ │ ├── cv842_5866.txt
│ │ ├── cv843_15544.txt
│ │ ├── cv844_12690.txt
│ │ ├── cv845_14290.txt
│ │ ├── cv846_29497.txt
│ │ ├── cv847_1941.txt
│ │ ├── cv848_10036.txt
│ │ ├── cv849_15729.txt
│ │ ├── cv850_16466.txt
│ │ ├── cv851_20469.txt
│ │ ├── cv852_27523.txt
│ │ ├── cv853_29233.txt
│ │ ├── cv854_17740.txt
│ │ ├── cv855_20661.txt
│ │ ├── cv856_29013.txt
│ │ ├── cv857_15958.txt
│ │ ├── cv858_18819.txt
│ │ ├── cv859_14107.txt
│ │ ├── cv860_13853.txt
│ │ ├── cv861_1198.txt
│ │ ├── cv862_14324.txt
│ │ ├── cv863_7424.txt
│ │ ├── cv864_3416.txt
│ │ ├── cv865_2895.txt
│ │ ├── cv866_29691.txt
│ │ ├── cv867_16661.txt
│ │ ├── cv868_11948.txt
│ │ ├── cv869_23611.txt
│ │ ├── cv870_16348.txt
│ │ ├── cv871_24888.txt
│ │ ├── cv872_12591.txt
│ │ ├── cv873_18636.txt
│ │ ├── cv874_11236.txt
│ │ ├── cv875_5754.txt
│ │ ├── cv876_9390.txt
│ │ ├── cv877_29274.txt
│ │ ├── cv878_15694.txt
│ │ ├── cv879_14903.txt
│ │ ├── cv880_29800.txt
│ │ ├── cv881_13254.txt
│ │ ├── cv882_10026.txt
│ │ ├── cv883_27751.txt
│ │ ├── cv884_13632.txt
│ │ ├── cv885_12318.txt
│ │ ├── cv886_18177.txt
│ │ ├── cv887_5126.txt
│ │ ├── cv888_24435.txt
│ │ ├── cv889_21430.txt
│ │ ├── cv890_3977.txt
│ │ ├── cv891_6385.txt
│ │ ├── cv892_17576.txt
│ │ ├── cv893_26269.txt
│ │ ├── cv894_2068.txt
│ │ ├── cv895_21022.txt
│ │ ├── cv896_16071.txt
│ │ ├── cv897_10837.txt
│ │ ├── cv898_14187.txt
│ │ ├── cv899_16014.txt
│ │ ├── cv900_10331.txt
│ │ ├── cv901_11017.txt
│ │ ├── cv902_12256.txt
│ │ ├── cv903_17822.txt
│ │ ├── cv904_24353.txt
│ │ ├── cv905_29114.txt
│ │ ├── cv906_11491.txt
│ │ ├── cv907_3541.txt
│ │ ├── cv908_16009.txt
│ │ ├── cv909_9960.txt
│ │ ├── cv910_20488.txt
│ │ ├── cv911_20260.txt
│ │ ├── cv912_5674.txt
│ │ ├── cv913_29252.txt
│ │ ├── cv914_28742.txt
│ │ ├── cv915_8841.txt
│ │ ├── cv916_15467.txt
│ │ ├── cv917_29715.txt
│ │ ├── cv918_2693.txt
│ │ ├── cv919_16380.txt
│ │ ├── cv920_29622.txt
│ │ ├── cv921_12747.txt
│ │ ├── cv922_10073.txt
│ │ ├── cv923_11051.txt
│ │ ├── cv924_29540.txt
│ │ ├── cv925_8969.txt
│ │ ├── cv926_17059.txt
│ │ ├── cv927_10681.txt
│ │ ├── cv928_9168.txt
│ │ ├── cv929_16908.txt
│ │ ├── cv930_13475.txt
│ │ ├── cv931_17563.txt
│ │ ├── cv932_13401.txt
│ │ ├── cv933_23776.txt
│ │ ├── cv934_19027.txt
│ │ ├── cv935_23841.txt
│ │ ├── cv936_15954.txt
│ │ ├── cv937_9811.txt
│ │ ├── cv938_10220.txt
│ │ ├── cv939_10583.txt
│ │ ├── cv940_17705.txt
│ │ ├── cv941_10246.txt
│ │ ├── cv942_17082.txt
│ │ ├── cv943_22488.txt
│ │ ├── cv944_13521.txt
│ │ ├── cv945_12160.txt
│ │ ├── cv946_18658.txt
│ │ ├── cv947_10601.txt
│ │ ├── cv948_24606.txt
│ │ ├── cv949_20112.txt
│ │ ├── cv950_12350.txt
│ │ ├── cv951_10926.txt
│ │ ├── cv952_25240.txt
│ │ ├── cv953_6836.txt
│ │ ├── cv954_18628.txt
│ │ ├── cv955_25001.txt
│ │ ├── cv956_11609.txt
│ │ ├── cv957_8737.txt
│ │ ├── cv958_12162.txt
│ │ ├── cv959_14611.txt
│ │ ├── cv960_29007.txt
│ │ ├── cv961_5682.txt
│ │ ├── cv962_9803.txt
│ │ ├── cv963_6895.txt
│ │ ├── cv964_6021.txt
│ │ ├── cv965_26071.txt
│ │ ├── cv966_28832.txt
│ │ ├── cv967_5788.txt
│ │ ├── cv968_24218.txt
│ │ ├── cv969_13250.txt
│ │ ├── cv970_18450.txt
│ │ ├── cv971_10874.txt
│ │ ├── cv972_26417.txt
│ │ ├── cv973_10066.txt
│ │ ├── cv974_22941.txt
│ │ ├── cv975_10981.txt
│ │ ├── cv976_10267.txt
│ │ ├── cv977_4938.txt
│ │ ├── cv978_20929.txt
│ │ ├── cv979_18921.txt
│ │ ├── cv980_10953.txt
│ │ ├── cv981_14989.txt
│ │ ├── cv982_21103.txt
│ │ ├── cv983_22928.txt
│ │ ├── cv984_12767.txt
│ │ ├── cv985_6359.txt
│ │ ├── cv986_13527.txt
│ │ ├── cv987_6965.txt
│ │ ├── cv988_18740.txt
│ │ ├── cv989_15824.txt
│ │ ├── cv990_11591.txt
│ │ ├── cv991_18645.txt
│ │ ├── cv992_11962.txt
│ │ ├── cv993_29737.txt
│ │ ├── cv994_12270.txt
│ │ ├── cv995_21821.txt
│ │ ├── cv996_11592.txt
│ │ ├── cv997_5046.txt
│ │ ├── cv998_14111.txt
│ │ └── cv999_13106.txt
│ ├── vocab.txt
│ └── word_embeddings_for_text.ipynb
├── README.md
├── Statistics/
│ ├── .ipynb_checkpoints/
│ │ ├── A_Gentle_Intro_to_Calculating_Normal_Summary_Stats-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Chi_Squared_Test_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Data_Visualization_Methods_in_Python-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Estimation_Stats_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Nonparametric_Stats-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Normality_Tests_in_Python-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Data_Distributions-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Hypothesis_Tests-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Sampling_and_Resampling-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Tolerance_Intervals_in_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_k_fold_cross_validation-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_the_Bootstrap_Method-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_the_Central_Limit_Theorem_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_the_Law_of_Large_Numbers_in_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Effect_Size_Measures_in_Python-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Statistical_Power_and_Power_Analysis_in_Python-checkpoint.ipynb
│ │ ├── Confidence_Intervals_for_ML-checkpoint.ipynb
│ │ ├── Critical_Values_for_Statistical_Hypothesis_Testing_in_Python-checkpoint.ipynb
│ │ ├── Estimate_the_Number_of_Experiment_Repeats_for_Stochastic_Machine_Learning_Algorithms-checkpoint.ipynb
│ │ ├── How_To_Generate_Random_Numbers_in_Python-checkpoint.ipynb
│ │ ├── How_to_Calculate_Bootstrap_Confidence_Interval_for_ML_Results_in_Python-checkpoint.ipynb
│ │ ├── How_to_Calculate_Nonparametric_Rank_Correlation_in_Python-checkpoint.ipynb
│ │ ├── How_to_Calculate_the_5_Number_Summary_for_your_data-checkpoint.ipynb
│ │ ├── How_to_Report_Classifier_Performance_with_Confidence_Intervals-checkpoint.ipynb
│ │ ├── How_to_Transform_Data_to_Better_Fit_the_Normal_Distribution-checkpoint.ipynb
│ │ ├── How_to_Use_Correlation_to_Understand_the_Relationship_Between_Variables-checkpoint.ipynb
│ │ ├── How_to_Use_Parametric_Statistical_Significance_Tests_in_Python-checkpoint.ipynb
│ │ ├── How_to_Use_Statistical_Significance_Tests_to_Interpret_ML_Results-checkpoint.ipynb
│ │ ├── How_to_Use_Stats_to_Identify_Outliers_in_Data-checkpoint.ipynb
│ │ ├── Intro_to_Nonparametric_Statistical_Significance_Tests_in_Python-checkpoint.ipynb
│ │ ├── Intro_to_Random_Number_Generators_for_ML_in_Python-checkpoint.ipynb
│ │ ├── Prediction_Intervals_for_ML-checkpoint.ipynb
│ │ └── how_to_code_t_test_from_scratch-checkpoint.ipynb
│ ├── A_Gentle_Intro_to_Calculating_Normal_Summary_Stats.ipynb
│ ├── A_Gentle_Intro_to_Chi_Squared_Test_for_ML.ipynb
│ ├── A_Gentle_Intro_to_Data_Visualization_Methods_in_Python.ipynb
│ ├── A_Gentle_Intro_to_Estimation_Stats_for_ML.ipynb
│ ├── A_Gentle_Intro_to_Nonparametric_Stats.ipynb
│ ├── A_Gentle_Intro_to_Normality_Tests_in_Python.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Data_Distributions.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Hypothesis_Tests.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Sampling_and_Resampling.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Tolerance_Intervals_in_ML.ipynb
│ ├── A_Gentle_Intro_to_k_fold_cross_validation.ipynb
│ ├── A_Gentle_Intro_to_the_Bootstrap_Method.ipynb
│ ├── A_Gentle_Intro_to_the_Central_Limit_Theorem_for_ML.ipynb
│ ├── A_Gentle_Intro_to_the_Law_of_Large_Numbers_in_ML.ipynb
│ ├── A_Gentle_Introduction_to_Effect_Size_Measures_in_Python.ipynb
│ ├── A_Gentle_Introduction_to_Statistical_Power_and_Power_Analysis_in_Python.ipynb
│ ├── Confidence_Intervals_for_ML.ipynb
│ ├── Critical_Values_for_Statistical_Hypothesis_Testing_in_Python.ipynb
│ ├── Estimate_the_Number_of_Experiment_Repeats_for_Stochastic_Machine_Learning_Algorithms.ipynb
│ ├── How_To_Generate_Random_Numbers_in_Python.ipynb
│ ├── How_to_Calculate_Bootstrap_Confidence_Interval_for_ML_Results_in_Python.ipynb
│ ├── How_to_Calculate_Nonparametric_Rank_Correlation_in_Python.ipynb
│ ├── How_to_Calculate_the_5_Number_Summary_for_your_data.ipynb
│ ├── How_to_Report_Classifier_Performance_with_Confidence_Intervals.ipynb
│ ├── How_to_Transform_Data_to_Better_Fit_the_Normal_Distribution.ipynb
│ ├── How_to_Use_Correlation_to_Understand_the_Relationship_Between_Variables.ipynb
│ ├── How_to_Use_Parametric_Statistical_Significance_Tests_in_Python.ipynb
│ ├── How_to_Use_Statistical_Significance_Tests_to_Interpret_ML_Results.ipynb
│ ├── How_to_Use_Stats_to_Identify_Outliers_in_Data.ipynb
│ ├── Intro_to_Nonparametric_Statistical_Significance_Tests_in_Python.ipynb
│ ├── Intro_to_Random_Number_Generators_for_ML_in_Python.ipynb
│ ├── Prediction_Intervals_for_ML.ipynb
│ ├── README.md
│ ├── how_to_code_t_test_from_scratch.ipynb
│ ├── pima-indians-diabetes.csv
│ ├── results.csv
│ ├── results1.csv
│ └── results2.csv
├── Time-Series-Forecasting/
│ ├── .ipynb_checkpoints/
│ │ ├── 7_time_series_dataset_for_machine_learning-checkpoint.ipynb
│ │ ├── backtest_machine_learning_models_for_time_series_forecasting-checkpoint.ipynb
│ │ ├── basic_feature_engineering_with_time_series_data_in_python-checkpoint.ipynb
│ │ ├── how_to_Grid_Search_ARIMA_model_hyperparameters_with_Python-checkpoint.ipynb
│ │ ├── how_to_check_if_time_series_data_is_stationary_with_Python-checkpoint.ipynb
│ │ ├── how_to_create_an_ARIMA_model_for_Time_Series_Forecasting_with_Python-checkpoint.ipynb
│ │ ├── how_to_make_baseline_predictions_for_time_series_forecasting_with_python-checkpoint.ipynb
│ │ ├── how_to_work_through_a_time_series_forecast_project-checkpoint.ipynb
│ │ └── load_and_explore_time_series_data_in_python-checkpoint.ipynb
│ ├── 7_time_series_dataset_for_machine_learning.ipynb
│ ├── README.md
│ ├── backtest_machine_learning_models_for_time_series_forecasting.ipynb
│ ├── basic_feature_engineering_with_time_series_data_in_python.ipynb
│ ├── daily-minimum-temperatures-in-me.csv
│ ├── daily-total-female-births.csv
│ ├── how_to_Grid_Search_ARIMA_model_hyperparameters_with_Python.ipynb
│ ├── how_to_check_if_time_series_data_is_stationary_with_Python.ipynb
│ ├── how_to_create_an_ARIMA_model_for_Time_Series_Forecasting_with_Python.ipynb
│ ├── how_to_make_baseline_predictions_for_time_series_forecasting_with_python.ipynb
│ ├── how_to_work_through_a_time_series_forecast_project.ipynb
│ ├── international-airline-passengers.csv
│ ├── load_and_explore_time_series_data_in_python.ipynb
│ ├── shampoo-sales.csv
│ └── sunspots.csv
└── XGBoost/
├── .ipynb_checkpoints/
│ ├── avoid_overfitting_by_early_stopping_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── data_preparation_for_gradient_boosting_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── feature_importance_and_feature_selection_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── how_to_best_tune_multithreading_support_for_XGBoost_in_Python-checkpoint.ipynb
│ ├── how_to_configure_the_gradient_boosting_algorithm-checkpoint.ipynb
│ ├── how_to_evaluate_gradient_boosting_models_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── how_to_tune_the_number_and_size_of_decision_trees_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── stochastic_gradient_boosting_with_XGBoost_and_Scikitlearn_in_Python-checkpoint.ipynb
│ └── tune_learning_rate_for_gradient_boosting_with_XGBoost_in_Python-checkpoint.ipynb
├── README.md
├── avoid_overfitting_by_early_stopping_with_XGBoost_in_Python.ipynb
├── data_preparation_for_gradient_boosting_with_XGBoost_in_Python.ipynb
├── feature_importance_and_feature_selection_with_XGBoost_in_Python.ipynb
├── horse-colic.csv
├── how_to_best_tune_multithreading_support_for_XGBoost_in_Python.ipynb
├── how_to_configure_the_gradient_boosting_algorithm.ipynb
├── how_to_evaluate_gradient_boosting_models_with_XGBoost_in_Python.ipynb
├── how_to_tune_the_number_and_size_of_decision_trees_with_XGBoost_in_Python.ipynb
├── iris.csv
├── pima-indians-diabetes.csv
├── stochastic_gradient_boosting_with_XGBoost_and_Scikitlearn_in_Python.ipynb
├── train.csv
└── tune_learning_rate_for_gradient_boosting_with_XGBoost_in_Python.ipynb
================================================
FILE CONTENTS
================================================
================================================
FILE: Algorithms-From-Scratch/algorithm-test-harness.py
================================================
# A test harness provides a consistent way to evaluate machine learning algorithms on a dataset.
# It involves 3 elements:
# 1 - The resampling method to split-up the dataset.
# 2 - The machine learning algorithm to evaluate.
# 3 - The performance measure by which to evaluate predictions.
# The loading and preparation of a dataset is a prerequisite step that must have been completed prior to using the test harness.
# The test harness must allow for different machine learning algorithms to be evaluated, whilst the dataset, resampling method and performance measures are kept constant.
# In this tutorial, we are going to demonstrate the test harnesses with a real dataset.
# The dataset used is the Pima Indians diabetes dataset. It contains 768 rows and 9 columns. All of the values in the file are numeric, specifically floating point values.
# The Zero Rule algorithm will be evaluated as part of the tutorial. The Zero Rule algorithm always predicts the class that has the most observations in the training dataset.
# 1. Train-Test Algorithm Test Harness
from random import seed
from random import randrange
from csv import reader
# Load a CSV file
def load_csv(filename):
file = open(filename, "rb")
lines = reader(file)
dataset = list(lines)
return dataset
# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
# Split a dataset into a train and test set
def train_test_split(dataset, split):
train = list()
train_size = split * len(dataset)
dataset_copy = list(dataset)
while len(train) < train_size:
index = randrange(len(dataset_copy))
train.append(dataset_copy.pop(index))
return train, dataset_copy
# Calculate accuracy percentage
def accuracy_metric(actual, predicted):
correct = 0
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / float(len(actual)) * 100.0
# Evaluate an algorithm using a train/test split
def evaluate_algorithm(dataset, algorithm, split, *args):
train, test = train_test_split(dataset, split)
test_set = list()
for row in test:
row_copy = list(row)
row_copy[-1] = None
test_set.append(row_copy)
predicted = algorithm(train, test_set, *args)
actual = [row[-1] for row in test]
accuracy = accuracy_metric(actual, predicted)
return accuracy
# zero rule algorithm for classification
def zero_rule_algorithm_classification(train, test):
output_values = [row[-1] for row in train]
prediction = max(set(output_values), key=output_values.count)
predicted = [prediction for i in range(len(test))]
return predicted
# Test the zero rule algorithm on the diabetes dataset
seed(1)
# load and prepare data
filename = 'pima-indians-diabetes.data.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])):
str_column_to_float(dataset, i)
# evaluate algorithm
split = 0.6
accuracy = evaluate_algorithm(dataset, zero_rule_algorithm_classification, split)
print('Accuracy: %.3f%%' % (accuracy))
# 2. Cross-Validation Algorithm Test Harness
# Split a dataset into k folds
def cross_validation_split(dataset, n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i in range(n_folds):
fold = list()
while len(fold) < fold_size:
index = randrange(len(dataset_copy))
fold.append(dataset_copy.pop(index))
dataset_split.append(fold)
return dataset_split
# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
folds = cross_validation_split(dataset, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
test_set = list()
for row in fold:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores
# zero rule algorithm for classification
def zero_rule_algorithm_classification(train, test):
output_values = [row[-1] for row in train]
prediction = max(set(output_values), key=output_values.count)
predicted = [prediction for i in range(len(test))]
return predicted
# Test the zero rule algorithm on the diabetes dataset
seed(1)
# load and prepare data
filename = 'pima-indians-diabetes.data.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])):
str_column_to_float(dataset, i)
# evaluate algorithm
n_folds = 5
scores = evaluate_algorithm(dataset, zero_rule_algorithm_classification, n_folds)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/len(scores)))
================================================
FILE: Algorithms-From-Scratch/backpropagation.py
================================================
# Backprop on the Seeds Dataset
from random import seed
from random import randrange
from random import random
from csv import reader
from math import exp
# Load a CSV file
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset
# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
# Convert string column to integer
def str_column_to_int(dataset, column):
class_values = [row[column] for row in dataset]
unique = set(class_values)
lookup = dict()
for i, value in enumerate(unique):
lookup[value] = i
for row in dataset:
row[column] = lookup[row[column]]
return lookup
# Find the min and max values for each column
def dataset_minmax(dataset):
minmax = list()
stats = [[min(column), max(column)] for column in zip(*dataset)]
return stats
# Rescale dataset columns to the range 0-1
def normalize_dataset(dataset, minmax):
for row in dataset:
for i in range(len(row)-1):
row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])
# Split a dataset into k folds
def cross_validation_split(dataset, n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i in range(n_folds):
fold = list()
while len(fold) < fold_size:
index = randrange(len(dataset_copy))
fold.append(dataset_copy.pop(index))
dataset_split.append(fold)
return dataset_split
# Calculate accuracy percentage
def accuracy_metric(actual, predicted):
correct = 0
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / float(len(actual)) * 100.0
# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
folds = cross_validation_split(dataset, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
test_set = list()
for row in fold:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores
# Calculate neuron activation for an input
def activate(weights, inputs):
activation = weights[-1]
for i in range(len(weights)-1):
activation += weights[i] * inputs[i]
return activation
# Transfer neuron activation
def transfer(activation):
return 1.0 / (1.0 + exp(-activation))
# Forward propagate input to a network output
def forward_propagate(network, row):
inputs = row
for layer in network:
new_inputs = []
for neuron in layer:
activation = activate(neuron['weights'], inputs)
neuron['output'] = transfer(activation)
new_inputs.append(neuron['output'])
inputs = new_inputs
return inputs
# Calculate the derivative of an neuron output
def transfer_derivative(output):
return output * (1.0 - output)
# Backpropagate error and store in neurons
def backward_propagate_error(network, expected):
for i in reversed(range(len(network))):
layer = network[i]
errors = list()
if i != len(network)-1:
for j in range(len(layer)):
error = 0.0
for neuron in network[i + 1]:
error += (neuron['weights'][j] * neuron['delta'])
errors.append(error)
else:
for j in range(len(layer)):
neuron = layer[j]
errors.append(expected[j] - neuron['output'])
for j in range(len(layer)):
neuron = layer[j]
neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])
# Update network weights with error
def update_weights(network, row, l_rate):
for i in range(len(network)):
inputs = row[:-1]
if i != 0:
inputs = [neuron['output'] for neuron in network[i - 1]]
for neuron in network[i]:
for j in range(len(inputs)):
neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j]
neuron['weights'][-1] += l_rate * neuron['delta']
# Train a network for a fixed number of epochs
def train_network(network, train, l_rate, n_epoch, n_outputs):
for epoch in range(n_epoch):
for row in train:
outputs = forward_propagate(network, row)
expected = [0 for i in range(n_outputs)]
expected[row[-1]] = 1
backward_propagate_error(network, expected)
update_weights(network, row, l_rate)
# Initialize a network
def initialize_network(n_inputs, n_hidden, n_outputs):
network = list()
hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)]
network.append(hidden_layer)
output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)]
network.append(output_layer)
return network
# Make a prediction with a network
def predict(network, row):
outputs = forward_propagate(network, row)
return outputs.index(max(outputs))
# Backpropagation Algorithm With Stochastic Gradient Descent
def back_propagation(train, test, l_rate, n_epoch, n_hidden):
n_inputs = len(train[0]) - 1
n_outputs = len(set([row[-1] for row in train]))
network = initialize_network(n_inputs, n_hidden, n_outputs)
train_network(network, train, l_rate, n_epoch, n_outputs)
predictions = list()
for row in test:
prediction = predict(network, row)
predictions.append(prediction)
return(predictions)
# Test Backprop on Seeds dataset
seed(1)
# load and prepare data
filename = 'wheat-seeds.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])-1):
str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# normalize input variables
minmax = dataset_minmax(dataset)
normalize_dataset(dataset, minmax)
# evaluate algorithm
n_folds = 5
l_rate = 0.3
n_epoch = 500
n_hidden = 5
scores = evaluate_algorithm(dataset, back_propagation, n_folds, l_rate, n_epoch, n_hidden)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
================================================
FILE: Algorithms-From-Scratch/bagging.py
================================================
# Bagging Algorithm on the Sonar dataset
from random import seed
from random import randrange
from csv import reader
# Load a CSV file
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset
# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
# Convert string column to integer
def str_column_to_int(dataset, column):
class_values = [row[column] for row in dataset]
unique = set(class_values)
lookup = dict()
for i, value in enumerate(unique):
lookup[value] = i
for row in dataset:
row[column] = lookup[row[column]]
return lookup
# Split a dataset into k folds
def cross_validation_split(dataset, n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i in range(n_folds):
fold = list()
while len(fold) < fold_size:
index = randrange(len(dataset_copy))
fold.append(dataset_copy.pop(index))
dataset_split.append(fold)
return dataset_split
# Calculate accuracy percentage
def accuracy_metric(actual, predicted):
correct = 0
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / float(len(actual)) * 100.0
# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
folds = cross_validation_split(dataset, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
test_set = list()
for row in fold:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores
# Split a dataset based on an attribute and an attribute value
def test_split(index, value, dataset):
left, right = list(), list()
for row in dataset:
if row[index] < value:
left.append(row)
else:
right.append(row)
return left, right
# Calculate the Gini index for a split dataset
def gini_index(groups, classes):
# count all samples at split point
n_instances = float(sum([len(group) for group in groups]))
# sum weighted Gini index for each group
gini = 0.0
for group in groups:
size = float(len(group))
# avoid divide by zero
if size == 0:
continue
score = 0.0
# score the group based on the score for each class
for class_val in classes:
p = [row[-1] for row in group].count(class_val) / size
score += p * p
# weight the group score by its relative size
gini += (1.0 - score) * (size / n_instances)
return gini
# Select the best split point for a dataset
def get_split(dataset):
class_values = list(set(row[-1] for row in dataset))
b_index, b_value, b_score, b_groups = 999, 999, 999, None
for index in range(len(dataset[0])-1):
for row in dataset:
# for i in range(len(dataset)):
# row = dataset[randrange(len(dataset))]
groups = test_split(index, row[index], dataset)
gini = gini_index(groups, class_values)
if gini < b_score:
b_index, b_value, b_score, b_groups = index, row[index], gini, groups
return {'index':b_index, 'value':b_value, 'groups':b_groups}
# Create a terminal node value
def to_terminal(group):
outcomes = [row[-1] for row in group]
return max(set(outcomes), key=outcomes.count)
# Create child splits for a node or make terminal
def split(node, max_depth, min_size, depth):
left, right = node['groups']
del(node['groups'])
# check for a no split
if not left or not right:
node['left'] = node['right'] = to_terminal(left + right)
return
# check for max depth
if depth >= max_depth:
node['left'], node['right'] = to_terminal(left), to_terminal(right)
return
# process left child
if len(left) <= min_size:
node['left'] = to_terminal(left)
else:
node['left'] = get_split(left)
split(node['left'], max_depth, min_size, depth+1)
# process right child
if len(right) <= min_size:
node['right'] = to_terminal(right)
else:
node['right'] = get_split(right)
split(node['right'], max_depth, min_size, depth+1)
# Build a decision tree
def build_tree(train, max_depth, min_size):
root = get_split(train)
split(root, max_depth, min_size, 1)
return root
# Make a prediction with a decision tree
def predict(node, row):
if row[node['index']] < node['value']:
if isinstance(node['left'], dict):
return predict(node['left'], row)
else:
return node['left']
else:
if isinstance(node['right'], dict):
return predict(node['right'], row)
else:
return node['right']
# Create a random subsample from the dataset with replacement
def subsample(dataset, ratio):
sample = list()
n_sample = round(len(dataset) * ratio)
while len(sample) < n_sample:
index = randrange(len(dataset))
sample.append(dataset[index])
return sample
# Make a prediction with a list of bagged trees
def bagging_predict(trees, row):
predictions = [predict(tree, row) for tree in trees]
return max(set(predictions), key=predictions.count)
# Bootstrap Aggregation Algorithm
def bagging(train, test, max_depth, min_size, sample_size, n_trees):
trees = list()
for i in range(n_trees):
sample = subsample(train, sample_size)
tree = build_tree(sample, max_depth, min_size)
trees.append(tree)
predictions = [bagging_predict(trees, row) for row in test]
return(predictions)
# Test bagging on the sonar dataset
seed(1)
# load and prepare data
filename = 'sonar.all-data.csv'
dataset = load_csv(filename)
# convert string attributes to integers
for i in range(len(dataset[0])-1):
str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# evaluate algorithm
n_folds = 5
max_depth = 6
min_size = 2
sample_size = 0.50
for n_trees in [1, 5, 10, 50]:
scores = evaluate_algorithm(dataset, bagging, n_folds, max_depth, min_size, sample_size, n_trees)
print('Trees: %d' % n_trees)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
================================================
FILE: Algorithms-From-Scratch/data_banknote_authentication.csv
================================================
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================================================
FILE: Algorithms-From-Scratch/decision-tree.py
================================================
# CART on the Bank Note dataset
from random import seed
from random import randrange
from csv import reader
# Load a CSV file
def load_csv(filename):
file = open(filename, "rb")
lines = reader(file)
dataset = list(lines)
return dataset
# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
# Split a dataset into k folds
def cross_validation_split(dataset, n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i in range(n_folds):
fold = list()
while len(fold) < fold_size:
index = randrange(len(dataset_copy))
fold.append(dataset_copy.pop(index))
dataset_split.append(fold)
return dataset_split
# Calculate accuracy percentage
def accuracy_metric(actual, predicted):
correct = 0
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / float(len(actual)) * 100.0
# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
folds = cross_validation_split(dataset, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
test_set = list()
for row in fold:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores
# Split a dataset based on an attribute and an attribute value
def test_split(index, value, dataset):
left, right = list(), list()
for row in dataset:
if row[index] < value:
left.append(row)
else:
right.append(row)
return left, right
# Calculate the Gini index for a split dataset
def gini_index(groups, classes):
# count all samples at split point
n_instances = float(sum([len(group) for group in groups]))
# sum weighted Gini index for each group
gini = 0.0
for group in groups:
size = float(len(group))
# avoid divide by zero
if size == 0:
continue
score = 0.0
# score the group based on the score for each class
for class_val in classes:
p = [row[-1] for row in group].count(class_val) / size
score += p * p
# weight the group score by its relative size
gini += (1.0 - score) * (size / n_instances)
return gini
# Select the best split point for a dataset
def get_split(dataset):
class_values = list(set(row[-1] for row in dataset))
b_index, b_value, b_score, b_groups = 999, 999, 999, None
for index in range(len(dataset[0])-1):
for row in dataset:
groups = test_split(index, row[index], dataset)
gini = gini_index(groups, class_values)
if gini < b_score:
b_index, b_value, b_score, b_groups = index, row[index], gini, groups
return {'index':b_index, 'value':b_value, 'groups':b_groups}
# Create a terminal node value
def to_terminal(group):
outcomes = [row[-1] for row in group]
return max(set(outcomes), key=outcomes.count)
# Create child splits for a node or make terminal
def split(node, max_depth, min_size, depth):
left, right = node['groups']
del(node['groups'])
# check for a no split
if not left or not right:
node['left'] = node['right'] = to_terminal(left + right)
return
# check for max depth
if depth >= max_depth:
node['left'], node['right'] = to_terminal(left), to_terminal(right)
return
# process left child
if len(left) <= min_size:
node['left'] = to_terminal(left)
else:
node['left'] = get_split(left)
split(node['left'], max_depth, min_size, depth+1)
# process right child
if len(right) <= min_size:
node['right'] = to_terminal(right)
else:
node['right'] = get_split(right)
split(node['right'], max_depth, min_size, depth+1)
# Build a decision tree
def build_tree(train, max_depth, min_size):
root = get_split(train)
split(root, max_depth, min_size, 1)
return root
# Make a prediction with a decision tree
def predict(node, row):
if row[node['index']] < node['value']:
if isinstance(node['left'], dict):
return predict(node['left'], row)
else:
return node['left']
else:
if isinstance(node['right'], dict):
return predict(node['right'], row)
else:
return node['right']
# Classification and Regression Tree Algorithm
def decision_tree(train, test, max_depth, min_size):
tree = build_tree(train, max_depth, min_size)
predictions = list()
for row in test:
prediction = predict(tree, row)
predictions.append(prediction)
return(predictions)
# Test CART on Bank Note dataset
seed(1)
# load and prepare data
filename = 'data_banknote_authentication.csv'
dataset = load_csv(filename)
# convert string attributes to integers
for i in range(len(dataset[0])):
str_column_to_float(dataset, i)
# evaluate algorithm
n_folds = 5
max_depth = 5
min_size = 10
scores = evaluate_algorithm(dataset, decision_tree, n_folds, max_depth, min_size)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
================================================
FILE: Algorithms-From-Scratch/ionosphere.csv
================================================
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gitextract_ye72zr3b/
├── Algorithms-From-Scratch/
│ ├── algorithm-test-harness.py
│ ├── backpropagation.py
│ ├── bagging.py
│ ├── data_banknote_authentication.csv
│ ├── decision-tree.py
│ ├── insurance.csv
│ ├── ionosphere.csv
│ ├── iris.data.csv
│ ├── knn.py
│ ├── learning-vector-quantization.py
│ ├── linear-reg-SGD.py
│ ├── logistic-reg-SGD.py
│ ├── naive-bayes.py
│ ├── perceptron.py
│ ├── performance-metrics.py
│ ├── pima-indians-diabetes.data.csv
│ ├── random-forest.py
│ ├── resampling.py
│ ├── simple-linear-regression.py
│ ├── sonar.all-data.csv
│ ├── stack-generalization.py
│ ├── wheat-seeds.csv
│ └── winequality-white.csv
├── Deep-Learning/
│ ├── .ipynb_checkpoints/
│ │ ├── 5_step_life_cycle_neural_network_models_in_keras-checkpoint.ipynb
│ │ ├── crash_course_in_convolutional_neural_networks_for_machine_learning-checkpoint.ipynb
│ │ ├── crash_course_on_multilayer_perceptron_neural_networks-checkpoint.ipynb
│ │ ├── crash_course_recurrent_neural_networks_for_deep_learning-checkpoint.ipynb
│ │ ├── display_deep_learning_model_training_history_keras-checkpoint.ipynb
│ │ ├── dropout_regularization_in_deep_learning_models_with_keras-checkpoint.ipynb
│ │ ├── grid_search_hyperparameters_for_deep_learning_models_in_python_with_keras-checkpoint.ipynb
│ │ ├── handwritten_digit_recognition_using_CNN_Python_Keras-checkpoint.ipynb
│ │ ├── object_recognition_with_CNN_in_Keras_deep_learning_library-checkpoint.ipynb
│ │ ├── predict_sentiment_from_movies_using_deep_learning-checkpoint.ipynb
│ │ ├── save_and_load_keras_deep_learning_models-checkpoint.ipynb
│ │ ├── text_generation_with_LSTM_recurrent_neural_nets_python_keras-checkpoint.ipynb
│ │ └── understanding_stateful_LSTM_recurrent_neural_nets_python_keras-checkpoint.ipynb
│ ├── 5_step_life_cycle_neural_network_models_in_keras.ipynb
│ ├── README.md
│ ├── crash_course_in_convolutional_neural_networks_for_machine_learning.ipynb
│ ├── crash_course_on_multilayer_perceptron_neural_networks.ipynb
│ ├── crash_course_recurrent_neural_networks_for_deep_learning.ipynb
│ ├── display_deep_learning_model_training_history_keras.ipynb
│ ├── dropout_regularization_in_deep_learning_models_with_keras.ipynb
│ ├── grid_search_hyperparameters_for_deep_learning_models_in_python_with_keras.ipynb
│ ├── handwritten_digit_recognition_using_CNN_Python_Keras.ipynb
│ ├── model.h5
│ ├── model.json
│ ├── model.yaml
│ ├── object_recognition_with_CNN_in_Keras_deep_learning_library.ipynb
│ ├── pima-indians-diabetes.csv
│ ├── predict_sentiment_from_movies_using_deep_learning.ipynb
│ ├── save_and_load_keras_deep_learning_models.ipynb
│ ├── sonar.csv
│ ├── text_generation_with_LSTM_recurrent_neural_nets_python_keras.ipynb
│ ├── understanding_stateful_LSTM_recurrent_neural_nets_python_keras.ipynb
│ ├── weights-improvement-20-2.0518.hdf5
│ └── wonderland.txt
├── Linear-Algebra/
│ ├── .ipynb_checkpoints/
│ │ ├── 10_Examples_of_Linear_Algebra_in_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Expected_Value_Variance_Covariance_with_NumPy-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Matrix_Operations_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_N-Dimensional_Arrays_in_Python_with_NumPy-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_SVD_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Tensors_for_ML_with_Numpy-checkpoint.ipynb
│ │ ├── Basic_of_Mathematical_Notation_for_ML-checkpoint.ipynb
│ │ ├── Broadcasting_with_Numpy_Arrays-checkpoint.ipynb
│ │ ├── Calculate_PCA_from_Scratch_in_Python-checkpoint.ipynb
│ │ ├── Gentle_Introduction_to_Vector_Norms_in_ML-checkpoint.ipynb
│ │ ├── Gentle_Introduction_to_Vectors_for_ML-checkpoint.ipynb
│ │ ├── Index_Slice_Reshape_NumPy_Arrays_for_ML_in_Python-checkpoint.ipynb
│ │ ├── Introduction_to_Eigendecomposition_Eigenvalues_Eigenvectors-checkpoint.ipynb
│ │ ├── Introduction_to_Matrices_and_Matrix_Arithmetic_for_ML-checkpoint.ipynb
│ │ ├── Introduction_to_Matrix_Factorization-checkpoint.ipynb
│ │ ├── Introduction_to_Matrix_Types_in_Linear_Algebra_for_ML-checkpoint.ipynb
│ │ ├── Linear_Algebra_Cheat_Sheet-checkpoint.ipynb
│ │ ├── Solve_Linear_Regression_using_Linear_Algebra-checkpoint.ipynb
│ │ └── Sparse_Matrices_for_Machine_Learning-checkpoint.ipynb
│ ├── 10_Examples_of_Linear_Algebra_in_ML.ipynb
│ ├── A_Gentle_Introduction_to_Expected_Value_Variance_Covariance_with_NumPy.ipynb
│ ├── A_Gentle_Introduction_to_Matrix_Operations_for_ML.ipynb
│ ├── A_Gentle_Introduction_to_N-Dimensional_Arrays_in_Python_with_NumPy.ipynb
│ ├── A_Gentle_Introduction_to_SVD_for_ML.ipynb
│ ├── A_Gentle_Introduction_to_Tensors_for_ML_with_Numpy.ipynb
│ ├── Basic_of_Mathematical_Notation_for_ML.ipynb
│ ├── Broadcasting_with_Numpy_Arrays.ipynb
│ ├── Calculate_PCA_from_Scratch_in_Python.ipynb
│ ├── Gentle_Introduction_to_Vector_Norms_in_ML.ipynb
│ ├── Gentle_Introduction_to_Vectors_for_ML.ipynb
│ ├── Index_Slice_Reshape_NumPy_Arrays_for_ML_in_Python.ipynb
│ ├── Introduction_to_Eigendecomposition_Eigenvalues_Eigenvectors.ipynb
│ ├── Introduction_to_Matrices_and_Matrix_Arithmetic_for_ML.ipynb
│ ├── Introduction_to_Matrix_Factorization.ipynb
│ ├── Introduction_to_Matrix_Types_in_Linear_Algebra_for_ML.ipynb
│ ├── Linear_Algebra_Cheat_Sheet.ipynb
│ ├── README.md
│ ├── Solve_Linear_Regression_using_Linear_Algebra.ipynb
│ └── Sparse_Matrices_for_Machine_Learning.ipynb
├── Long-Short-Term-Memory/
│ ├── .ipynb_checkpoints/
│ │ ├── Use_TimeDistributed_Layer_for_LSTM_networks_in_Python-checkpoint.ipynb
│ │ ├── a_gentle_introduction_to_backpropagation_through_time-checkpoint.ipynb
│ │ ├── attention_in_LSTM_Recurrent_Neural_Nets-checkpoint.ipynb
│ │ ├── cnn_LSTM_networks-checkpoint.ipynb
│ │ ├── demo_of_memory_with_LSTM_in_Python-checkpoint.ipynb
│ │ ├── diagnose_overfitting_and_underfitting_of_LSTM_models-checkpoint.ipynb
│ │ ├── encoder_decoder_LSTM_networks-checkpoint.ipynb
│ │ ├── handle_missing_timesteps_in_sequence_prediction_problems_with_Python-checkpoint.ipynb
│ │ ├── intro_to_generative_LSTM_networks-checkpoint.ipynb
│ │ ├── make_predictions_with_LSTM_models_keras-checkpoint.ipynb
│ │ ├── multi_time_series_forecasting_with_LSTM_networks_in_Python-checkpoint.ipynb
│ │ ├── multivariate_time_series_forecasting_with_LSTM_in_Keras-checkpoint.ipynb
│ │ ├── one-hot-encode-sequence-data-in-python-checkpoint.ipynb
│ │ ├── prepare_sequence_prediction_for_truncated_backpropagation_through_time_in_keras-checkpoint.ipynb
│ │ ├── remove_trends_and_seasonality_with_a_difference_transform_in_python-checkpoint.ipynb
│ │ ├── reshape_input_data_LSTM_in_Keras-checkpoint.ipynb
│ │ ├── scale_data_for_LSTM_in_Python-checkpoint.ipynb
│ │ ├── stacked_LSTM_networks-checkpoint.ipynb
│ │ ├── suitability_of_LSTM_for_Time_Series_Forecasting-checkpoint.ipynb
│ │ ├── time_series_forecasting_with_LSTM_network_in_Python-checkpoint.ipynb
│ │ └── use_an_encoder_decoder_LSTM_to_echo_sequences_of_random_integers-checkpoint.ipynb
│ ├── README.md
│ ├── Use_TimeDistributed_Layer_for_LSTM_networks_in_Python.ipynb
│ ├── a_gentle_introduction_to_backpropagation_through_time.ipynb
│ ├── attention_in_LSTM_Recurrent_Neural_Nets.ipynb
│ ├── cnn_LSTM_networks.ipynb
│ ├── demo_of_memory_with_LSTM_in_Python.ipynb
│ ├── diagnose_overfitting_and_underfitting_of_LSTM_models.ipynb
│ ├── encoder_decoder_LSTM_networks.ipynb
│ ├── handle_missing_timesteps_in_sequence_prediction_problems_with_Python.ipynb
│ ├── intro_to_generative_LSTM_networks.ipynb
│ ├── make_predictions_with_LSTM_models_keras.ipynb
│ ├── multi_time_series_forecasting_with_LSTM_networks_in_Python.ipynb
│ ├── multivariate_time_series_forecasting_with_LSTM_in_Keras.ipynb
│ ├── one-hot-encode-sequence-data-in-python.ipynb
│ ├── pollution.csv
│ ├── prepare_sequence_prediction_for_truncated_backpropagation_through_time_in_keras.ipynb
│ ├── raw.csv
│ ├── remove_trends_and_seasonality_with_a_difference_transform_in_python.ipynb
│ ├── reshape_input_data_LSTM_in_Keras.ipynb
│ ├── scale_data_for_LSTM_in_Python.ipynb
│ ├── shampoo-sales.csv
│ ├── stacked_LSTM_networks.ipynb
│ ├── suitability_of_LSTM_for_Time_Series_Forecasting.ipynb
│ ├── time_series_forecasting_with_LSTM_network_in_Python.ipynb
│ └── use_an_encoder_decoder_LSTM_to_echo_sequences_of_random_integers.ipynb
├── Machine-Learning-Python/
│ ├── .ipynb_checkpoints/
│ │ ├── automate_ml_workflows_with_pipelines_in_Python_and_scikit_learn-checkpoint.ipynb
│ │ ├── compare_ml_algorithms_in_python_scikit_learn-checkpoint.ipynb
│ │ ├── ensemble_ml_algorithms_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── evaluate_performance_ml_algoritms_python_resampling-checkpoint.ipynb
│ │ ├── feature_selection_ml_python-checkpoint.ipynb
│ │ ├── how_to_generate_test_datasets_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── how_to_handle_missing_data_with_Python-checkpoint.ipynb
│ │ ├── how_to_make_predictions_with_scikit_learn-checkpoint.ipynb
│ │ ├── how_to_tune_algorithm_parameters_with_scikit_learn-checkpoint.ipynb
│ │ ├── load_data_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── load_ml_data_python-checkpoint.ipynb
│ │ ├── machine_learning-algorithm-recipes-in-scikit-learn-checkpoint.ipynb
│ │ ├── metrics_to_evaluate_ml_algorithms_python-checkpoint.ipynb
│ │ ├── prepare_data_for_ml_in_python_scikit_learn-checkpoint.ipynb
│ │ ├── quick_and_dirty_data_analysis_with_Pandas-checkpoint.ipynb
│ │ ├── rescaling_data_for_ML_in_Python_with_scikit_learn-checkpoint.ipynb
│ │ ├── spot_check_classification_ml_algorithms_python_scikit_learn-checkpoint.ipynb
│ │ ├── spot_check_regression_ml_algorithms_in_python_scikit_learn-checkpoint.ipynb
│ │ ├── understand_ml_data_descriptive_statistics_python-checkpoint.ipynb
│ │ └── visual_ml_data_in_python_with_pandas-checkpoint.ipynb
│ ├── README.md
│ ├── automate_ml_workflows_with_pipelines_in_Python_and_scikit_learn.ipynb
│ ├── compare_ml_algorithms_in_python_scikit_learn.ipynb
│ ├── ensemble_ml_algorithms_in_Python_with_scikit_learn.ipynb
│ ├── evaluate_performance_ml_algoritms_python_resampling.ipynb
│ ├── feature_selection_ml_python.ipynb
│ ├── how_to_generate_test_datasets_in_Python_with_scikit_learn.ipynb
│ ├── how_to_handle_missing_data_with_Python.ipynb
│ ├── how_to_make_predictions_with_scikit_learn.ipynb
│ ├── how_to_tune_algorithm_parameters_with_scikit_learn.ipynb
│ ├── load_data_in_Python_with_scikit_learn.ipynb
│ ├── load_ml_data_python.ipynb
│ ├── machine_learning-algorithm-recipes-in-scikit-learn.ipynb
│ ├── metrics_to_evaluate_ml_algorithms_python.ipynb
│ ├── pima-indians-diabetes.data.csv
│ ├── prepare_data_for_ml_in_python_scikit_learn.ipynb
│ ├── quick_and_dirty_data_analysis_with_Pandas.ipynb
│ ├── rescaling_data_for_ML_in_Python_with_scikit_learn.ipynb
│ ├── spot_check_classification_ml_algorithms_python_scikit_learn.ipynb
│ ├── spot_check_regression_ml_algorithms_in_python_scikit_learn.ipynb
│ ├── understand_ml_data_descriptive_statistics_python.ipynb
│ └── visual_ml_data_in_python_with_pandas.ipynb
├── Machine-Learning-R/
│ ├── .RData
│ ├── .Rhistory
│ ├── README.md
│ ├── build_an_ensemble_of_ml_algorithms_in_R.Rmd
│ ├── compare_models_and_select_the_best_using_the_Caret_R_package.Rmd
│ ├── compare_performance_ml_algorithms_in_R.Rmd
│ ├── convex_optimization_in_R.Rmd
│ ├── data_visualization_with_the_Caret_R_package.Rmd
│ ├── evaluate_ml_algorithms_with_R.Rmd
│ ├── feature_selection_with_caret_r_package.Rmd
│ ├── final_model.rds
│ ├── get_data_ready_for_ml_in_R_pre_processing.Rmd
│ ├── how_to_estimate_model_accuracy_in_R_using_Caret_package.Rmd
│ ├── linear_classification_in_R.Rmd
│ ├── linear_regression_in_R.Rmd
│ ├── load_ml_data_into_r.Rmd
│ ├── ml_evaluation_metrics.Rmd
│ ├── ml_project_template_in_R.Rmd
│ ├── non_linear_classification_in_R.Rmd
│ ├── non_linear_classification_in_R_with_decision_trees.Rmd
│ ├── non_linear_regression_in_R.Rmd
│ ├── non_linear_regression_in_R_with_decision_trees.Rmd
│ ├── penalized_regression_in_R.Rmd
│ ├── save_and_finalize_ml_model_in_R.Rmd
│ ├── spot_check_ml_algorithms_in_R.Rmd
│ ├── tune_ml_algorithms_in_R.Rmd
│ ├── tuning_ml_models_using_the_Caret_R_package.Rmd
│ ├── understand_data_in_R_descriptive_statistics.Rmd
│ └── understand_data_in_R_visualization.Rmd
├── Natural-Language-Processing/
│ ├── .ipynb_checkpoints/
│ │ ├── develop_a_deep_learning_bag_of_words_model_for_predicting_movie_review_sentiment-checkpoint.ipynb
│ │ ├── develop_word_embeddings_in_python_with_gensim-checkpoint.ipynb
│ │ ├── how_to_use_word_embedding_layers_for_deep_learning_with_keras-checkpoint.ipynb
│ │ ├── introduction_to_bag_of_words_model-checkpoint.ipynb
│ │ ├── prepare_text_data_for_machine_learning_with_scikit_learn-checkpoint.ipynb
│ │ └── word_embeddings_for_text-checkpoint.ipynb
│ ├── README.md
│ ├── develop_a_deep_learning_bag_of_words_model_for_predicting_movie_review_sentiment.ipynb
│ ├── develop_word_embeddings_in_python_with_gensim.ipynb
│ ├── how_to_use_word_embedding_layers_for_deep_learning_with_keras.ipynb
│ ├── introduction_to_bag_of_words_model.ipynb
│ ├── prepare_text_data_for_machine_learning_with_scikit_learn.ipynb
│ ├── txt_sentoken/
│ │ ├── neg/
│ │ │ ├── cv000_29416.txt
│ │ │ ├── cv001_19502.txt
│ │ │ ├── cv002_17424.txt
│ │ │ ├── cv003_12683.txt
│ │ │ ├── cv004_12641.txt
│ │ │ ├── cv005_29357.txt
│ │ │ ├── cv006_17022.txt
│ │ │ ├── cv007_4992.txt
│ │ │ ├── cv008_29326.txt
│ │ │ ├── cv009_29417.txt
│ │ │ ├── cv010_29063.txt
│ │ │ ├── cv011_13044.txt
│ │ │ ├── cv012_29411.txt
│ │ │ ├── cv013_10494.txt
│ │ │ ├── cv014_15600.txt
│ │ │ ├── cv015_29356.txt
│ │ │ ├── cv016_4348.txt
│ │ │ ├── cv017_23487.txt
│ │ │ ├── cv018_21672.txt
│ │ │ ├── cv019_16117.txt
│ │ │ ├── cv020_9234.txt
│ │ │ ├── cv021_17313.txt
│ │ │ ├── cv022_14227.txt
│ │ │ ├── cv023_13847.txt
│ │ │ ├── cv024_7033.txt
│ │ │ ├── cv025_29825.txt
│ │ │ ├── cv026_29229.txt
│ │ │ ├── cv027_26270.txt
│ │ │ ├── cv028_26964.txt
│ │ │ ├── cv029_19943.txt
│ │ │ ├── cv030_22893.txt
│ │ │ ├── cv031_19540.txt
│ │ │ ├── cv032_23718.txt
│ │ │ ├── cv033_25680.txt
│ │ │ ├── cv034_29446.txt
│ │ │ ├── cv035_3343.txt
│ │ │ ├── cv036_18385.txt
│ │ │ ├── cv037_19798.txt
│ │ │ ├── cv038_9781.txt
│ │ │ ├── cv039_5963.txt
│ │ │ ├── cv040_8829.txt
│ │ │ ├── cv041_22364.txt
│ │ │ ├── cv042_11927.txt
│ │ │ ├── cv043_16808.txt
│ │ │ ├── cv044_18429.txt
│ │ │ ├── cv045_25077.txt
│ │ │ ├── cv046_10613.txt
│ │ │ ├── cv047_18725.txt
│ │ │ ├── cv048_18380.txt
│ │ │ ├── cv049_21917.txt
│ │ │ ├── cv050_12128.txt
│ │ │ ├── cv051_10751.txt
│ │ │ ├── cv052_29318.txt
│ │ │ ├── cv053_23117.txt
│ │ │ ├── cv054_4101.txt
│ │ │ ├── cv055_8926.txt
│ │ │ ├── cv056_14663.txt
│ │ │ ├── cv057_7962.txt
│ │ │ ├── cv058_8469.txt
│ │ │ ├── cv059_28723.txt
│ │ │ ├── cv060_11754.txt
│ │ │ ├── cv061_9321.txt
│ │ │ ├── cv062_24556.txt
│ │ │ ├── cv063_28852.txt
│ │ │ ├── cv064_25842.txt
│ │ │ ├── cv065_16909.txt
│ │ │ ├── cv066_11668.txt
│ │ │ ├── cv067_21192.txt
│ │ │ ├── cv068_14810.txt
│ │ │ ├── cv069_11613.txt
│ │ │ ├── cv070_13249.txt
│ │ │ ├── cv071_12969.txt
│ │ │ ├── cv072_5928.txt
│ │ │ ├── cv073_23039.txt
│ │ │ ├── cv074_7188.txt
│ │ │ ├── cv075_6250.txt
│ │ │ ├── cv076_26009.txt
│ │ │ ├── cv077_23172.txt
│ │ │ ├── cv078_16506.txt
│ │ │ ├── cv079_12766.txt
│ │ │ ├── cv080_14899.txt
│ │ │ ├── cv081_18241.txt
│ │ │ ├── cv082_11979.txt
│ │ │ ├── cv083_25491.txt
│ │ │ ├── cv084_15183.txt
│ │ │ ├── cv085_15286.txt
│ │ │ ├── cv086_19488.txt
│ │ │ ├── cv087_2145.txt
│ │ │ ├── cv088_25274.txt
│ │ │ ├── cv089_12222.txt
│ │ │ ├── cv090_0049.txt
│ │ │ ├── cv091_7899.txt
│ │ │ ├── cv092_27987.txt
│ │ │ ├── cv093_15606.txt
│ │ │ ├── cv094_27868.txt
│ │ │ ├── cv095_28730.txt
│ │ │ ├── cv096_12262.txt
│ │ │ ├── cv097_26081.txt
│ │ │ ├── cv098_17021.txt
│ │ │ ├── cv099_11189.txt
│ │ │ ├── cv100_12406.txt
│ │ │ ├── cv101_10537.txt
│ │ │ ├── cv102_8306.txt
│ │ │ ├── cv103_11943.txt
│ │ │ ├── cv104_19176.txt
│ │ │ ├── cv105_19135.txt
│ │ │ ├── cv106_18379.txt
│ │ │ ├── cv107_25639.txt
│ │ │ ├── cv108_17064.txt
│ │ │ ├── cv109_22599.txt
│ │ │ ├── cv110_27832.txt
│ │ │ ├── cv111_12253.txt
│ │ │ ├── cv112_12178.txt
│ │ │ ├── cv113_24354.txt
│ │ │ ├── cv114_19501.txt
│ │ │ ├── cv115_26443.txt
│ │ │ ├── cv116_28734.txt
│ │ │ ├── cv117_25625.txt
│ │ │ ├── cv118_28837.txt
│ │ │ ├── cv119_9909.txt
│ │ │ ├── cv120_3793.txt
│ │ │ ├── cv121_18621.txt
│ │ │ ├── cv122_7891.txt
│ │ │ ├── cv123_12165.txt
│ │ │ ├── cv124_3903.txt
│ │ │ ├── cv125_9636.txt
│ │ │ ├── cv126_28821.txt
│ │ │ ├── cv127_16451.txt
│ │ │ ├── cv128_29444.txt
│ │ │ ├── cv129_18373.txt
│ │ │ ├── cv130_18521.txt
│ │ │ ├── cv131_11568.txt
│ │ │ ├── cv132_5423.txt
│ │ │ ├── cv133_18065.txt
│ │ │ ├── cv134_23300.txt
│ │ │ ├── cv135_12506.txt
│ │ │ ├── cv136_12384.txt
│ │ │ ├── cv137_17020.txt
│ │ │ ├── cv138_13903.txt
│ │ │ ├── cv139_14236.txt
│ │ │ ├── cv140_7963.txt
│ │ │ ├── cv141_17179.txt
│ │ │ ├── cv142_23657.txt
│ │ │ ├── cv143_21158.txt
│ │ │ ├── cv144_5010.txt
│ │ │ ├── cv145_12239.txt
│ │ │ ├── cv146_19587.txt
│ │ │ ├── cv147_22625.txt
│ │ │ ├── cv148_18084.txt
│ │ │ ├── cv149_17084.txt
│ │ │ ├── cv150_14279.txt
│ │ │ ├── cv151_17231.txt
│ │ │ ├── cv152_9052.txt
│ │ │ ├── cv153_11607.txt
│ │ │ ├── cv154_9562.txt
│ │ │ ├── cv155_7845.txt
│ │ │ ├── cv156_11119.txt
│ │ │ ├── cv157_29302.txt
│ │ │ ├── cv158_10914.txt
│ │ │ ├── cv159_29374.txt
│ │ │ ├── cv160_10848.txt
│ │ │ ├── cv161_12224.txt
│ │ │ ├── cv162_10977.txt
│ │ │ ├── cv163_10110.txt
│ │ │ ├── cv164_23451.txt
│ │ │ ├── cv165_2389.txt
│ │ │ ├── cv166_11959.txt
│ │ │ ├── cv167_18094.txt
│ │ │ ├── cv168_7435.txt
│ │ │ ├── cv169_24973.txt
│ │ │ ├── cv170_29808.txt
│ │ │ ├── cv171_15164.txt
│ │ │ ├── cv172_12037.txt
│ │ │ ├── cv173_4295.txt
│ │ │ ├── cv174_9735.txt
│ │ │ ├── cv175_7375.txt
│ │ │ ├── cv176_14196.txt
│ │ │ ├── cv177_10904.txt
│ │ │ ├── cv178_14380.txt
│ │ │ ├── cv179_9533.txt
│ │ │ ├── cv180_17823.txt
│ │ │ ├── cv181_16083.txt
│ │ │ ├── cv182_7791.txt
│ │ │ ├── cv183_19826.txt
│ │ │ ├── cv184_26935.txt
│ │ │ ├── cv185_28372.txt
│ │ │ ├── cv186_2396.txt
│ │ │ ├── cv187_14112.txt
│ │ │ ├── cv188_20687.txt
│ │ │ ├── cv189_24248.txt
│ │ │ ├── cv190_27176.txt
│ │ │ ├── cv191_29539.txt
│ │ │ ├── cv192_16079.txt
│ │ │ ├── cv193_5393.txt
│ │ │ ├── cv194_12855.txt
│ │ │ ├── cv195_16146.txt
│ │ │ ├── cv196_28898.txt
│ │ │ ├── cv197_29271.txt
│ │ │ ├── cv198_19313.txt
│ │ │ ├── cv199_9721.txt
│ │ │ ├── cv200_29006.txt
│ │ │ ├── cv201_7421.txt
│ │ │ ├── cv202_11382.txt
│ │ │ ├── cv203_19052.txt
│ │ │ ├── cv204_8930.txt
│ │ │ ├── cv205_9676.txt
│ │ │ ├── cv206_15893.txt
│ │ │ ├── cv207_29141.txt
│ │ │ ├── cv208_9475.txt
│ │ │ ├── cv209_28973.txt
│ │ │ ├── cv210_9557.txt
│ │ │ ├── cv211_9955.txt
│ │ │ ├── cv212_10054.txt
│ │ │ ├── cv213_20300.txt
│ │ │ ├── cv214_13285.txt
│ │ │ ├── cv215_23246.txt
│ │ │ ├── cv216_20165.txt
│ │ │ ├── cv217_28707.txt
│ │ │ ├── cv218_25651.txt
│ │ │ ├── cv219_19874.txt
│ │ │ ├── cv220_28906.txt
│ │ │ ├── cv221_27081.txt
│ │ │ ├── cv222_18720.txt
│ │ │ ├── cv223_28923.txt
│ │ │ ├── cv224_18875.txt
│ │ │ ├── cv225_29083.txt
│ │ │ ├── cv226_26692.txt
│ │ │ ├── cv227_25406.txt
│ │ │ ├── cv228_5644.txt
│ │ │ ├── cv229_15200.txt
│ │ │ ├── cv230_7913.txt
│ │ │ ├── cv231_11028.txt
│ │ │ ├── cv232_16768.txt
│ │ │ ├── cv233_17614.txt
│ │ │ ├── cv234_22123.txt
│ │ │ ├── cv235_10704.txt
│ │ │ ├── cv236_12427.txt
│ │ │ ├── cv237_20635.txt
│ │ │ ├── cv238_14285.txt
│ │ │ ├── cv239_29828.txt
│ │ │ ├── cv240_15948.txt
│ │ │ ├── cv241_24602.txt
│ │ │ ├── cv242_11354.txt
│ │ │ ├── cv243_22164.txt
│ │ │ ├── cv244_22935.txt
│ │ │ ├── cv245_8938.txt
│ │ │ ├── cv246_28668.txt
│ │ │ ├── cv247_14668.txt
│ │ │ ├── cv248_15672.txt
│ │ │ ├── cv249_12674.txt
│ │ │ ├── cv250_26462.txt
│ │ │ ├── cv251_23901.txt
│ │ │ ├── cv252_24974.txt
│ │ │ ├── cv253_10190.txt
│ │ │ ├── cv254_5870.txt
│ │ │ ├── cv255_15267.txt
│ │ │ ├── cv256_16529.txt
│ │ │ ├── cv257_11856.txt
│ │ │ ├── cv258_5627.txt
│ │ │ ├── cv259_11827.txt
│ │ │ ├── cv260_15652.txt
│ │ │ ├── cv261_11855.txt
│ │ │ ├── cv262_13812.txt
│ │ │ ├── cv263_20693.txt
│ │ │ ├── cv264_14108.txt
│ │ │ ├── cv265_11625.txt
│ │ │ ├── cv266_26644.txt
│ │ │ ├── cv267_16618.txt
│ │ │ ├── cv268_20288.txt
│ │ │ ├── cv269_23018.txt
│ │ │ ├── cv270_5873.txt
│ │ │ ├── cv271_15364.txt
│ │ │ ├── cv272_20313.txt
│ │ │ ├── cv273_28961.txt
│ │ │ ├── cv274_26379.txt
│ │ │ ├── cv275_28725.txt
│ │ │ ├── cv276_17126.txt
│ │ │ ├── cv277_20467.txt
│ │ │ ├── cv278_14533.txt
│ │ │ ├── cv279_19452.txt
│ │ │ ├── cv280_8651.txt
│ │ │ ├── cv281_24711.txt
│ │ │ ├── cv282_6833.txt
│ │ │ ├── cv283_11963.txt
│ │ │ ├── cv284_20530.txt
│ │ │ ├── cv285_18186.txt
│ │ │ ├── cv286_26156.txt
│ │ │ ├── cv287_17410.txt
│ │ │ ├── cv288_20212.txt
│ │ │ ├── cv289_6239.txt
│ │ │ ├── cv290_11981.txt
│ │ │ ├── cv291_26844.txt
│ │ │ ├── cv292_7804.txt
│ │ │ ├── cv293_29731.txt
│ │ │ ├── cv294_12695.txt
│ │ │ ├── cv295_17060.txt
│ │ │ ├── cv296_13146.txt
│ │ │ ├── cv297_10104.txt
│ │ │ ├── cv298_24487.txt
│ │ │ ├── cv299_17950.txt
│ │ │ ├── cv300_23302.txt
│ │ │ ├── cv301_13010.txt
│ │ │ ├── cv302_26481.txt
│ │ │ ├── cv303_27366.txt
│ │ │ ├── cv304_28489.txt
│ │ │ ├── cv305_9937.txt
│ │ │ ├── cv306_10859.txt
│ │ │ ├── cv307_26382.txt
│ │ │ ├── cv308_5079.txt
│ │ │ ├── cv309_23737.txt
│ │ │ ├── cv310_14568.txt
│ │ │ ├── cv311_17708.txt
│ │ │ ├── cv312_29308.txt
│ │ │ ├── cv313_19337.txt
│ │ │ ├── cv314_16095.txt
│ │ │ ├── cv315_12638.txt
│ │ │ ├── cv316_5972.txt
│ │ │ ├── cv317_25111.txt
│ │ │ ├── cv318_11146.txt
│ │ │ ├── cv319_16459.txt
│ │ │ ├── cv320_9693.txt
│ │ │ ├── cv321_14191.txt
│ │ │ ├── cv322_21820.txt
│ │ │ ├── cv323_29633.txt
│ │ │ ├── cv324_7502.txt
│ │ │ ├── cv325_18330.txt
│ │ │ ├── cv326_14777.txt
│ │ │ ├── cv327_21743.txt
│ │ │ ├── cv328_10908.txt
│ │ │ ├── cv329_29293.txt
│ │ │ ├── cv330_29675.txt
│ │ │ ├── cv331_8656.txt
│ │ │ ├── cv332_17997.txt
│ │ │ ├── cv333_9443.txt
│ │ │ ├── cv334_0074.txt
│ │ │ ├── cv335_16299.txt
│ │ │ ├── cv336_10363.txt
│ │ │ ├── cv337_29061.txt
│ │ │ ├── cv338_9183.txt
│ │ │ ├── cv339_22452.txt
│ │ │ ├── cv340_14776.txt
│ │ │ ├── cv341_25667.txt
│ │ │ ├── cv342_20917.txt
│ │ │ ├── cv343_10906.txt
│ │ │ ├── cv344_5376.txt
│ │ │ ├── cv345_9966.txt
│ │ │ ├── cv346_19198.txt
│ │ │ ├── cv347_14722.txt
│ │ │ ├── cv348_19207.txt
│ │ │ ├── cv349_15032.txt
│ │ │ ├── cv350_22139.txt
│ │ │ ├── cv351_17029.txt
│ │ │ ├── cv352_5414.txt
│ │ │ ├── cv353_19197.txt
│ │ │ ├── cv354_8573.txt
│ │ │ ├── cv355_18174.txt
│ │ │ ├── cv356_26170.txt
│ │ │ ├── cv357_14710.txt
│ │ │ ├── cv358_11557.txt
│ │ │ ├── cv359_6751.txt
│ │ │ ├── cv360_8927.txt
│ │ │ ├── cv361_28738.txt
│ │ │ ├── cv362_16985.txt
│ │ │ ├── cv363_29273.txt
│ │ │ ├── cv364_14254.txt
│ │ │ ├── cv365_12442.txt
│ │ │ ├── cv366_10709.txt
│ │ │ ├── cv367_24065.txt
│ │ │ ├── cv368_11090.txt
│ │ │ ├── cv369_14245.txt
│ │ │ ├── cv370_5338.txt
│ │ │ ├── cv371_8197.txt
│ │ │ ├── cv372_6654.txt
│ │ │ ├── cv373_21872.txt
│ │ │ ├── cv374_26455.txt
│ │ │ ├── cv375_9932.txt
│ │ │ ├── cv376_20883.txt
│ │ │ ├── cv377_8440.txt
│ │ │ ├── cv378_21982.txt
│ │ │ ├── cv379_23167.txt
│ │ │ ├── cv380_8164.txt
│ │ │ ├── cv381_21673.txt
│ │ │ ├── cv382_8393.txt
│ │ │ ├── cv383_14662.txt
│ │ │ ├── cv384_18536.txt
│ │ │ ├── cv385_29621.txt
│ │ │ ├── cv386_10229.txt
│ │ │ ├── cv387_12391.txt
│ │ │ ├── cv388_12810.txt
│ │ │ ├── cv389_9611.txt
│ │ │ ├── cv390_12187.txt
│ │ │ ├── cv391_11615.txt
│ │ │ ├── cv392_12238.txt
│ │ │ ├── cv393_29234.txt
│ │ │ ├── cv394_5311.txt
│ │ │ ├── cv395_11761.txt
│ │ │ ├── cv396_19127.txt
│ │ │ ├── cv397_28890.txt
│ │ │ ├── cv398_17047.txt
│ │ │ ├── cv399_28593.txt
│ │ │ ├── cv400_20631.txt
│ │ │ ├── cv401_13758.txt
│ │ │ ├── cv402_16097.txt
│ │ │ ├── cv403_6721.txt
│ │ │ ├── cv404_21805.txt
│ │ │ ├── cv405_21868.txt
│ │ │ ├── cv406_22199.txt
│ │ │ ├── cv407_23928.txt
│ │ │ ├── cv408_5367.txt
│ │ │ ├── cv409_29625.txt
│ │ │ ├── cv410_25624.txt
│ │ │ ├── cv411_16799.txt
│ │ │ ├── cv412_25254.txt
│ │ │ ├── cv413_7893.txt
│ │ │ ├── cv414_11161.txt
│ │ │ ├── cv415_23674.txt
│ │ │ ├── cv416_12048.txt
│ │ │ ├── cv417_14653.txt
│ │ │ ├── cv418_16562.txt
│ │ │ ├── cv419_14799.txt
│ │ │ ├── cv420_28631.txt
│ │ │ ├── cv421_9752.txt
│ │ │ ├── cv422_9632.txt
│ │ │ ├── cv423_12089.txt
│ │ │ ├── cv424_9268.txt
│ │ │ ├── cv425_8603.txt
│ │ │ ├── cv426_10976.txt
│ │ │ ├── cv427_11693.txt
│ │ │ ├── cv428_12202.txt
│ │ │ ├── cv429_7937.txt
│ │ │ ├── cv430_18662.txt
│ │ │ ├── cv431_7538.txt
│ │ │ ├── cv432_15873.txt
│ │ │ ├── cv433_10443.txt
│ │ │ ├── cv434_5641.txt
│ │ │ ├── cv435_24355.txt
│ │ │ ├── cv436_20564.txt
│ │ │ ├── cv437_24070.txt
│ │ │ ├── cv438_8500.txt
│ │ │ ├── cv439_17633.txt
│ │ │ ├── cv440_16891.txt
│ │ │ ├── cv441_15276.txt
│ │ │ ├── cv442_15499.txt
│ │ │ ├── cv443_22367.txt
│ │ │ ├── cv444_9975.txt
│ │ │ ├── cv445_26683.txt
│ │ │ ├── cv446_12209.txt
│ │ │ ├── cv447_27334.txt
│ │ │ ├── cv448_16409.txt
│ │ │ ├── cv449_9126.txt
│ │ │ ├── cv450_8319.txt
│ │ │ ├── cv451_11502.txt
│ │ │ ├── cv452_5179.txt
│ │ │ ├── cv453_10911.txt
│ │ │ ├── cv454_21961.txt
│ │ │ ├── cv455_28866.txt
│ │ │ ├── cv456_20370.txt
│ │ │ ├── cv457_19546.txt
│ │ │ ├── cv458_9000.txt
│ │ │ ├── cv459_21834.txt
│ │ │ ├── cv460_11723.txt
│ │ │ ├── cv461_21124.txt
│ │ │ ├── cv462_20788.txt
│ │ │ ├── cv463_10846.txt
│ │ │ ├── cv464_17076.txt
│ │ │ ├── cv465_23401.txt
│ │ │ ├── cv466_20092.txt
│ │ │ ├── cv467_26610.txt
│ │ │ ├── cv468_16844.txt
│ │ │ ├── cv469_21998.txt
│ │ │ ├── cv470_17444.txt
│ │ │ ├── cv471_18405.txt
│ │ │ ├── cv472_29140.txt
│ │ │ ├── cv473_7869.txt
│ │ │ ├── cv474_10682.txt
│ │ │ ├── cv475_22978.txt
│ │ │ ├── cv476_18402.txt
│ │ │ ├── cv477_23530.txt
│ │ │ ├── cv478_15921.txt
│ │ │ ├── cv479_5450.txt
│ │ │ ├── cv480_21195.txt
│ │ │ ├── cv481_7930.txt
│ │ │ ├── cv482_11233.txt
│ │ │ ├── cv483_18103.txt
│ │ │ ├── cv484_26169.txt
│ │ │ ├── cv485_26879.txt
│ │ │ ├── cv486_9788.txt
│ │ │ ├── cv487_11058.txt
│ │ │ ├── cv488_21453.txt
│ │ │ ├── cv489_19046.txt
│ │ │ ├── cv490_18986.txt
│ │ │ ├── cv491_12992.txt
│ │ │ ├── cv492_19370.txt
│ │ │ ├── cv493_14135.txt
│ │ │ ├── cv494_18689.txt
│ │ │ ├── cv495_16121.txt
│ │ │ ├── cv496_11185.txt
│ │ │ ├── cv497_27086.txt
│ │ │ ├── cv498_9288.txt
│ │ │ ├── cv499_11407.txt
│ │ │ ├── cv500_10722.txt
│ │ │ ├── cv501_12675.txt
│ │ │ ├── cv502_10970.txt
│ │ │ ├── cv503_11196.txt
│ │ │ ├── cv504_29120.txt
│ │ │ ├── cv505_12926.txt
│ │ │ ├── cv506_17521.txt
│ │ │ ├── cv507_9509.txt
│ │ │ ├── cv508_17742.txt
│ │ │ ├── cv509_17354.txt
│ │ │ ├── cv510_24758.txt
│ │ │ ├── cv511_10360.txt
│ │ │ ├── cv512_17618.txt
│ │ │ ├── cv513_7236.txt
│ │ │ ├── cv514_12173.txt
│ │ │ ├── cv515_18484.txt
│ │ │ ├── cv516_12117.txt
│ │ │ ├── cv517_20616.txt
│ │ │ ├── cv518_14798.txt
│ │ │ ├── cv519_16239.txt
│ │ │ ├── cv520_13297.txt
│ │ │ ├── cv521_1730.txt
│ │ │ ├── cv522_5418.txt
│ │ │ ├── cv523_18285.txt
│ │ │ ├── cv524_24885.txt
│ │ │ ├── cv525_17930.txt
│ │ │ ├── cv526_12868.txt
│ │ │ ├── cv527_10338.txt
│ │ │ ├── cv528_11669.txt
│ │ │ ├── cv529_10972.txt
│ │ │ ├── cv530_17949.txt
│ │ │ ├── cv531_26838.txt
│ │ │ ├── cv532_6495.txt
│ │ │ ├── cv533_9843.txt
│ │ │ ├── cv534_15683.txt
│ │ │ ├── cv535_21183.txt
│ │ │ ├── cv536_27221.txt
│ │ │ ├── cv537_13516.txt
│ │ │ ├── cv538_28485.txt
│ │ │ ├── cv539_21865.txt
│ │ │ ├── cv540_3092.txt
│ │ │ ├── cv541_28683.txt
│ │ │ ├── cv542_20359.txt
│ │ │ ├── cv543_5107.txt
│ │ │ ├── cv544_5301.txt
│ │ │ ├── cv545_12848.txt
│ │ │ ├── cv546_12723.txt
│ │ │ ├── cv547_18043.txt
│ │ │ ├── cv548_18944.txt
│ │ │ ├── cv549_22771.txt
│ │ │ ├── cv550_23226.txt
│ │ │ ├── cv551_11214.txt
│ │ │ ├── cv552_0150.txt
│ │ │ ├── cv553_26965.txt
│ │ │ ├── cv554_14678.txt
│ │ │ ├── cv555_25047.txt
│ │ │ ├── cv556_16563.txt
│ │ │ ├── cv557_12237.txt
│ │ │ ├── cv558_29376.txt
│ │ │ ├── cv559_0057.txt
│ │ │ ├── cv560_18608.txt
│ │ │ ├── cv561_9484.txt
│ │ │ ├── cv562_10847.txt
│ │ │ ├── cv563_18610.txt
│ │ │ ├── cv564_12011.txt
│ │ │ ├── cv565_29403.txt
│ │ │ ├── cv566_8967.txt
│ │ │ ├── cv567_29420.txt
│ │ │ ├── cv568_17065.txt
│ │ │ ├── cv569_26750.txt
│ │ │ ├── cv570_28960.txt
│ │ │ ├── cv571_29292.txt
│ │ │ ├── cv572_20053.txt
│ │ │ ├── cv573_29384.txt
│ │ │ ├── cv574_23191.txt
│ │ │ ├── cv575_22598.txt
│ │ │ ├── cv576_15688.txt
│ │ │ ├── cv577_28220.txt
│ │ │ ├── cv578_16825.txt
│ │ │ ├── cv579_12542.txt
│ │ │ ├── cv580_15681.txt
│ │ │ ├── cv581_20790.txt
│ │ │ ├── cv582_6678.txt
│ │ │ ├── cv583_29465.txt
│ │ │ ├── cv584_29549.txt
│ │ │ ├── cv585_23576.txt
│ │ │ ├── cv586_8048.txt
│ │ │ ├── cv587_20532.txt
│ │ │ ├── cv588_14467.txt
│ │ │ ├── cv589_12853.txt
│ │ │ ├── cv590_20712.txt
│ │ │ ├── cv591_24887.txt
│ │ │ ├── cv592_23391.txt
│ │ │ ├── cv593_11931.txt
│ │ │ ├── cv594_11945.txt
│ │ │ ├── cv595_26420.txt
│ │ │ ├── cv596_4367.txt
│ │ │ ├── cv597_26744.txt
│ │ │ ├── cv598_18184.txt
│ │ │ ├── cv599_22197.txt
│ │ │ ├── cv600_25043.txt
│ │ │ ├── cv601_24759.txt
│ │ │ ├── cv602_8830.txt
│ │ │ ├── cv603_18885.txt
│ │ │ ├── cv604_23339.txt
│ │ │ ├── cv605_12730.txt
│ │ │ ├── cv606_17672.txt
│ │ │ ├── cv607_8235.txt
│ │ │ ├── cv608_24647.txt
│ │ │ ├── cv609_25038.txt
│ │ │ ├── cv610_24153.txt
│ │ │ ├── cv611_2253.txt
│ │ │ ├── cv612_5396.txt
│ │ │ ├── cv613_23104.txt
│ │ │ ├── cv614_11320.txt
│ │ │ ├── cv615_15734.txt
│ │ │ ├── cv616_29187.txt
│ │ │ ├── cv617_9561.txt
│ │ │ ├── cv618_9469.txt
│ │ │ ├── cv619_13677.txt
│ │ │ ├── cv620_2556.txt
│ │ │ ├── cv621_15984.txt
│ │ │ ├── cv622_8583.txt
│ │ │ ├── cv623_16988.txt
│ │ │ ├── cv624_11601.txt
│ │ │ ├── cv625_13518.txt
│ │ │ ├── cv626_7907.txt
│ │ │ ├── cv627_12603.txt
│ │ │ ├── cv628_20758.txt
│ │ │ ├── cv629_16604.txt
│ │ │ ├── cv630_10152.txt
│ │ │ ├── cv631_4782.txt
│ │ │ ├── cv632_9704.txt
│ │ │ ├── cv633_29730.txt
│ │ │ ├── cv634_11989.txt
│ │ │ ├── cv635_0984.txt
│ │ │ ├── cv636_16954.txt
│ │ │ ├── cv637_13682.txt
│ │ │ ├── cv638_29394.txt
│ │ │ ├── cv639_10797.txt
│ │ │ ├── cv640_5380.txt
│ │ │ ├── cv641_13412.txt
│ │ │ ├── cv642_29788.txt
│ │ │ ├── cv643_29282.txt
│ │ │ ├── cv644_18551.txt
│ │ │ ├── cv645_17078.txt
│ │ │ ├── cv646_16817.txt
│ │ │ ├── cv647_15275.txt
│ │ │ ├── cv648_17277.txt
│ │ │ ├── cv649_13947.txt
│ │ │ ├── cv650_15974.txt
│ │ │ ├── cv651_11120.txt
│ │ │ ├── cv652_15653.txt
│ │ │ ├── cv653_2107.txt
│ │ │ ├── cv654_19345.txt
│ │ │ ├── cv655_12055.txt
│ │ │ ├── cv656_25395.txt
│ │ │ ├── cv657_25835.txt
│ │ │ ├── cv658_11186.txt
│ │ │ ├── cv659_21483.txt
│ │ │ ├── cv660_23140.txt
│ │ │ ├── cv661_25780.txt
│ │ │ ├── cv662_14791.txt
│ │ │ ├── cv663_14484.txt
│ │ │ ├── cv664_4264.txt
│ │ │ ├── cv665_29386.txt
│ │ │ ├── cv666_20301.txt
│ │ │ ├── cv667_19672.txt
│ │ │ ├── cv668_18848.txt
│ │ │ ├── cv669_24318.txt
│ │ │ ├── cv670_2666.txt
│ │ │ ├── cv671_5164.txt
│ │ │ ├── cv672_27988.txt
│ │ │ ├── cv673_25874.txt
│ │ │ ├── cv674_11593.txt
│ │ │ ├── cv675_22871.txt
│ │ │ ├── cv676_22202.txt
│ │ │ ├── cv677_18938.txt
│ │ │ ├── cv678_14887.txt
│ │ │ ├── cv679_28221.txt
│ │ │ ├── cv680_10533.txt
│ │ │ ├── cv681_9744.txt
│ │ │ ├── cv682_17947.txt
│ │ │ ├── cv683_13047.txt
│ │ │ ├── cv684_12727.txt
│ │ │ ├── cv685_5710.txt
│ │ │ ├── cv686_15553.txt
│ │ │ ├── cv687_22207.txt
│ │ │ ├── cv688_7884.txt
│ │ │ ├── cv689_13701.txt
│ │ │ ├── cv690_5425.txt
│ │ │ ├── cv691_5090.txt
│ │ │ ├── cv692_17026.txt
│ │ │ ├── cv693_19147.txt
│ │ │ ├── cv694_4526.txt
│ │ │ ├── cv695_22268.txt
│ │ │ ├── cv696_29619.txt
│ │ │ ├── cv697_12106.txt
│ │ │ ├── cv698_16930.txt
│ │ │ ├── cv699_7773.txt
│ │ │ ├── cv700_23163.txt
│ │ │ ├── cv701_15880.txt
│ │ │ ├── cv702_12371.txt
│ │ │ ├── cv703_17948.txt
│ │ │ ├── cv704_17622.txt
│ │ │ ├── cv705_11973.txt
│ │ │ ├── cv706_25883.txt
│ │ │ ├── cv707_11421.txt
│ │ │ ├── cv708_28539.txt
│ │ │ ├── cv709_11173.txt
│ │ │ ├── cv710_23745.txt
│ │ │ ├── cv711_12687.txt
│ │ │ ├── cv712_24217.txt
│ │ │ ├── cv713_29002.txt
│ │ │ ├── cv714_19704.txt
│ │ │ ├── cv715_19246.txt
│ │ │ ├── cv716_11153.txt
│ │ │ ├── cv717_17472.txt
│ │ │ ├── cv718_12227.txt
│ │ │ ├── cv719_5581.txt
│ │ │ ├── cv720_5383.txt
│ │ │ ├── cv721_28993.txt
│ │ │ ├── cv722_7571.txt
│ │ │ ├── cv723_9002.txt
│ │ │ ├── cv724_15265.txt
│ │ │ ├── cv725_10266.txt
│ │ │ ├── cv726_4365.txt
│ │ │ ├── cv727_5006.txt
│ │ │ ├── cv728_17931.txt
│ │ │ ├── cv729_10475.txt
│ │ │ ├── cv730_10729.txt
│ │ │ ├── cv731_3968.txt
│ │ │ ├── cv732_13092.txt
│ │ │ ├── cv733_9891.txt
│ │ │ ├── cv734_22821.txt
│ │ │ ├── cv735_20218.txt
│ │ │ ├── cv736_24947.txt
│ │ │ ├── cv737_28733.txt
│ │ │ ├── cv738_10287.txt
│ │ │ ├── cv739_12179.txt
│ │ │ ├── cv740_13643.txt
│ │ │ ├── cv741_12765.txt
│ │ │ ├── cv742_8279.txt
│ │ │ ├── cv743_17023.txt
│ │ │ ├── cv744_10091.txt
│ │ │ ├── cv745_14009.txt
│ │ │ ├── cv746_10471.txt
│ │ │ ├── cv747_18189.txt
│ │ │ ├── cv748_14044.txt
│ │ │ ├── cv749_18960.txt
│ │ │ ├── cv750_10606.txt
│ │ │ ├── cv751_17208.txt
│ │ │ ├── cv752_25330.txt
│ │ │ ├── cv753_11812.txt
│ │ │ ├── cv754_7709.txt
│ │ │ ├── cv755_24881.txt
│ │ │ ├── cv756_23676.txt
│ │ │ ├── cv757_10668.txt
│ │ │ ├── cv758_9740.txt
│ │ │ ├── cv759_15091.txt
│ │ │ ├── cv760_8977.txt
│ │ │ ├── cv761_13769.txt
│ │ │ ├── cv762_15604.txt
│ │ │ ├── cv763_16486.txt
│ │ │ ├── cv764_12701.txt
│ │ │ ├── cv765_20429.txt
│ │ │ ├── cv766_7983.txt
│ │ │ ├── cv767_15673.txt
│ │ │ ├── cv768_12709.txt
│ │ │ ├── cv769_8565.txt
│ │ │ ├── cv770_11061.txt
│ │ │ ├── cv771_28466.txt
│ │ │ ├── cv772_12971.txt
│ │ │ ├── cv773_20264.txt
│ │ │ ├── cv774_15488.txt
│ │ │ ├── cv775_17966.txt
│ │ │ ├── cv776_21934.txt
│ │ │ ├── cv777_10247.txt
│ │ │ ├── cv778_18629.txt
│ │ │ ├── cv779_18989.txt
│ │ │ ├── cv780_8467.txt
│ │ │ ├── cv781_5358.txt
│ │ │ ├── cv782_21078.txt
│ │ │ ├── cv783_14724.txt
│ │ │ ├── cv784_16077.txt
│ │ │ ├── cv785_23748.txt
│ │ │ ├── cv786_23608.txt
│ │ │ ├── cv787_15277.txt
│ │ │ ├── cv788_26409.txt
│ │ │ ├── cv789_12991.txt
│ │ │ ├── cv790_16202.txt
│ │ │ ├── cv791_17995.txt
│ │ │ ├── cv792_3257.txt
│ │ │ ├── cv793_15235.txt
│ │ │ ├── cv794_17353.txt
│ │ │ ├── cv795_10291.txt
│ │ │ ├── cv796_17243.txt
│ │ │ ├── cv797_7245.txt
│ │ │ ├── cv798_24779.txt
│ │ │ ├── cv799_19812.txt
│ │ │ ├── cv800_13494.txt
│ │ │ ├── cv801_26335.txt
│ │ │ ├── cv802_28381.txt
│ │ │ ├── cv803_8584.txt
│ │ │ ├── cv804_11763.txt
│ │ │ ├── cv805_21128.txt
│ │ │ ├── cv806_9405.txt
│ │ │ ├── cv807_23024.txt
│ │ │ ├── cv808_13773.txt
│ │ │ ├── cv809_5012.txt
│ │ │ ├── cv810_13660.txt
│ │ │ ├── cv811_22646.txt
│ │ │ ├── cv812_19051.txt
│ │ │ ├── cv813_6649.txt
│ │ │ ├── cv814_20316.txt
│ │ │ ├── cv815_23466.txt
│ │ │ ├── cv816_15257.txt
│ │ │ ├── cv817_3675.txt
│ │ │ ├── cv818_10698.txt
│ │ │ ├── cv819_9567.txt
│ │ │ ├── cv820_24157.txt
│ │ │ ├── cv821_29283.txt
│ │ │ ├── cv822_21545.txt
│ │ │ ├── cv823_17055.txt
│ │ │ ├── cv824_9335.txt
│ │ │ ├── cv825_5168.txt
│ │ │ ├── cv826_12761.txt
│ │ │ ├── cv827_19479.txt
│ │ │ ├── cv828_21392.txt
│ │ │ ├── cv829_21725.txt
│ │ │ ├── cv830_5778.txt
│ │ │ ├── cv831_16325.txt
│ │ │ ├── cv832_24713.txt
│ │ │ ├── cv833_11961.txt
│ │ │ ├── cv834_23192.txt
│ │ │ ├── cv835_20531.txt
│ │ │ ├── cv836_14311.txt
│ │ │ ├── cv837_27232.txt
│ │ │ ├── cv838_25886.txt
│ │ │ ├── cv839_22807.txt
│ │ │ ├── cv840_18033.txt
│ │ │ ├── cv841_3367.txt
│ │ │ ├── cv842_5702.txt
│ │ │ ├── cv843_17054.txt
│ │ │ ├── cv844_13890.txt
│ │ │ ├── cv845_15886.txt
│ │ │ ├── cv846_29359.txt
│ │ │ ├── cv847_20855.txt
│ │ │ ├── cv848_10061.txt
│ │ │ ├── cv849_17215.txt
│ │ │ ├── cv850_18185.txt
│ │ │ ├── cv851_21895.txt
│ │ │ ├── cv852_27512.txt
│ │ │ ├── cv853_29119.txt
│ │ │ ├── cv854_18955.txt
│ │ │ ├── cv855_22134.txt
│ │ │ ├── cv856_28882.txt
│ │ │ ├── cv857_17527.txt
│ │ │ ├── cv858_20266.txt
│ │ │ ├── cv859_15689.txt
│ │ │ ├── cv860_15520.txt
│ │ │ ├── cv861_12809.txt
│ │ │ ├── cv862_15924.txt
│ │ │ ├── cv863_7912.txt
│ │ │ ├── cv864_3087.txt
│ │ │ ├── cv865_28796.txt
│ │ │ ├── cv866_29447.txt
│ │ │ ├── cv867_18362.txt
│ │ │ ├── cv868_12799.txt
│ │ │ ├── cv869_24782.txt
│ │ │ ├── cv870_18090.txt
│ │ │ ├── cv871_25971.txt
│ │ │ ├── cv872_13710.txt
│ │ │ ├── cv873_19937.txt
│ │ │ ├── cv874_12182.txt
│ │ │ ├── cv875_5622.txt
│ │ │ ├── cv876_9633.txt
│ │ │ ├── cv877_29132.txt
│ │ │ ├── cv878_17204.txt
│ │ │ ├── cv879_16585.txt
│ │ │ ├── cv880_29629.txt
│ │ │ ├── cv881_14767.txt
│ │ │ ├── cv882_10042.txt
│ │ │ ├── cv883_27621.txt
│ │ │ ├── cv884_15230.txt
│ │ │ ├── cv885_13390.txt
│ │ │ ├── cv886_19210.txt
│ │ │ ├── cv887_5306.txt
│ │ │ ├── cv888_25678.txt
│ │ │ ├── cv889_22670.txt
│ │ │ ├── cv890_3515.txt
│ │ │ ├── cv891_6035.txt
│ │ │ ├── cv892_18788.txt
│ │ │ ├── cv893_26731.txt
│ │ │ ├── cv894_22140.txt
│ │ │ ├── cv895_22200.txt
│ │ │ ├── cv896_17819.txt
│ │ │ ├── cv897_11703.txt
│ │ │ ├── cv898_1576.txt
│ │ │ ├── cv899_17812.txt
│ │ │ ├── cv900_10800.txt
│ │ │ ├── cv901_11934.txt
│ │ │ ├── cv902_13217.txt
│ │ │ ├── cv903_18981.txt
│ │ │ ├── cv904_25663.txt
│ │ │ ├── cv905_28965.txt
│ │ │ ├── cv906_12332.txt
│ │ │ ├── cv907_3193.txt
│ │ │ ├── cv908_17779.txt
│ │ │ ├── cv909_9973.txt
│ │ │ ├── cv910_21930.txt
│ │ │ ├── cv911_21695.txt
│ │ │ ├── cv912_5562.txt
│ │ │ ├── cv913_29127.txt
│ │ │ ├── cv914_2856.txt
│ │ │ ├── cv915_9342.txt
│ │ │ ├── cv916_17034.txt
│ │ │ ├── cv917_29484.txt
│ │ │ ├── cv918_27080.txt
│ │ │ ├── cv919_18155.txt
│ │ │ ├── cv920_29423.txt
│ │ │ ├── cv921_13988.txt
│ │ │ ├── cv922_10185.txt
│ │ │ ├── cv923_11951.txt
│ │ │ ├── cv924_29397.txt
│ │ │ ├── cv925_9459.txt
│ │ │ ├── cv926_18471.txt
│ │ │ ├── cv927_11471.txt
│ │ │ ├── cv928_9478.txt
│ │ │ ├── cv929_1841.txt
│ │ │ ├── cv930_14949.txt
│ │ │ ├── cv931_18783.txt
│ │ │ ├── cv932_14854.txt
│ │ │ ├── cv933_24953.txt
│ │ │ ├── cv934_20426.txt
│ │ │ ├── cv935_24977.txt
│ │ │ ├── cv936_17473.txt
│ │ │ ├── cv937_9816.txt
│ │ │ ├── cv938_10706.txt
│ │ │ ├── cv939_11247.txt
│ │ │ ├── cv940_18935.txt
│ │ │ ├── cv941_10718.txt
│ │ │ ├── cv942_18509.txt
│ │ │ ├── cv943_23547.txt
│ │ │ ├── cv944_15042.txt
│ │ │ ├── cv945_13012.txt
│ │ │ ├── cv946_20084.txt
│ │ │ ├── cv947_11316.txt
│ │ │ ├── cv948_25870.txt
│ │ │ ├── cv949_21565.txt
│ │ │ ├── cv950_13478.txt
│ │ │ ├── cv951_11816.txt
│ │ │ ├── cv952_26375.txt
│ │ │ ├── cv953_7078.txt
│ │ │ ├── cv954_19932.txt
│ │ │ ├── cv955_26154.txt
│ │ │ ├── cv956_12547.txt
│ │ │ ├── cv957_9059.txt
│ │ │ ├── cv958_13020.txt
│ │ │ ├── cv959_16218.txt
│ │ │ ├── cv960_28877.txt
│ │ │ ├── cv961_5578.txt
│ │ │ ├── cv962_9813.txt
│ │ │ ├── cv963_7208.txt
│ │ │ ├── cv964_5794.txt
│ │ │ ├── cv965_26688.txt
│ │ │ ├── cv966_28671.txt
│ │ │ ├── cv967_5626.txt
│ │ │ ├── cv968_25413.txt
│ │ │ ├── cv969_14760.txt
│ │ │ ├── cv970_19532.txt
│ │ │ ├── cv971_11790.txt
│ │ │ ├── cv972_26837.txt
│ │ │ ├── cv973_10171.txt
│ │ │ ├── cv974_24303.txt
│ │ │ ├── cv975_11920.txt
│ │ │ ├── cv976_10724.txt
│ │ │ ├── cv977_4776.txt
│ │ │ ├── cv978_22192.txt
│ │ │ ├── cv979_2029.txt
│ │ │ ├── cv980_11851.txt
│ │ │ ├── cv981_16679.txt
│ │ │ ├── cv982_22209.txt
│ │ │ ├── cv983_24219.txt
│ │ │ ├── cv984_14006.txt
│ │ │ ├── cv985_5964.txt
│ │ │ ├── cv986_15092.txt
│ │ │ ├── cv987_7394.txt
│ │ │ ├── cv988_20168.txt
│ │ │ ├── cv989_17297.txt
│ │ │ ├── cv990_12443.txt
│ │ │ ├── cv991_19973.txt
│ │ │ ├── cv992_12806.txt
│ │ │ ├── cv993_29565.txt
│ │ │ ├── cv994_13229.txt
│ │ │ ├── cv995_23113.txt
│ │ │ ├── cv996_12447.txt
│ │ │ ├── cv997_5152.txt
│ │ │ ├── cv998_15691.txt
│ │ │ └── cv999_14636.txt
│ │ └── pos/
│ │ ├── cv000_29590.txt
│ │ ├── cv001_18431.txt
│ │ ├── cv002_15918.txt
│ │ ├── cv003_11664.txt
│ │ ├── cv004_11636.txt
│ │ ├── cv005_29443.txt
│ │ ├── cv006_15448.txt
│ │ ├── cv007_4968.txt
│ │ ├── cv008_29435.txt
│ │ ├── cv009_29592.txt
│ │ ├── cv010_29198.txt
│ │ ├── cv011_12166.txt
│ │ ├── cv012_29576.txt
│ │ ├── cv013_10159.txt
│ │ ├── cv014_13924.txt
│ │ ├── cv015_29439.txt
│ │ ├── cv016_4659.txt
│ │ ├── cv017_22464.txt
│ │ ├── cv018_20137.txt
│ │ ├── cv019_14482.txt
│ │ ├── cv020_8825.txt
│ │ ├── cv021_15838.txt
│ │ ├── cv022_12864.txt
│ │ ├── cv023_12672.txt
│ │ ├── cv024_6778.txt
│ │ ├── cv025_3108.txt
│ │ ├── cv026_29325.txt
│ │ ├── cv027_25219.txt
│ │ ├── cv028_26746.txt
│ │ ├── cv029_18643.txt
│ │ ├── cv030_21593.txt
│ │ ├── cv031_18452.txt
│ │ ├── cv032_22550.txt
│ │ ├── cv033_24444.txt
│ │ ├── cv034_29647.txt
│ │ ├── cv035_3954.txt
│ │ ├── cv036_16831.txt
│ │ ├── cv037_18510.txt
│ │ ├── cv038_9749.txt
│ │ ├── cv039_6170.txt
│ │ ├── cv040_8276.txt
│ │ ├── cv041_21113.txt
│ │ ├── cv042_10982.txt
│ │ ├── cv043_15013.txt
│ │ ├── cv044_16969.txt
│ │ ├── cv045_23923.txt
│ │ ├── cv046_10188.txt
│ │ ├── cv047_1754.txt
│ │ ├── cv048_16828.txt
│ │ ├── cv049_20471.txt
│ │ ├── cv050_11175.txt
│ │ ├── cv051_10306.txt
│ │ ├── cv052_29378.txt
│ │ ├── cv053_21822.txt
│ │ ├── cv054_4230.txt
│ │ ├── cv055_8338.txt
│ │ ├── cv056_13133.txt
│ │ ├── cv057_7453.txt
│ │ ├── cv058_8025.txt
│ │ ├── cv059_28885.txt
│ │ ├── cv060_10844.txt
│ │ ├── cv061_8837.txt
│ │ ├── cv062_23115.txt
│ │ ├── cv063_28997.txt
│ │ ├── cv064_24576.txt
│ │ ├── cv065_15248.txt
│ │ ├── cv066_10821.txt
│ │ ├── cv067_19774.txt
│ │ ├── cv068_13400.txt
│ │ ├── cv069_10801.txt
│ │ ├── cv070_12289.txt
│ │ ├── cv071_12095.txt
│ │ ├── cv072_6169.txt
│ │ ├── cv073_21785.txt
│ │ ├── cv074_6875.txt
│ │ ├── cv075_6500.txt
│ │ ├── cv076_24945.txt
│ │ ├── cv077_22138.txt
│ │ ├── cv078_14730.txt
│ │ ├── cv079_11933.txt
│ │ ├── cv080_13465.txt
│ │ ├── cv081_16582.txt
│ │ ├── cv082_11080.txt
│ │ ├── cv083_24234.txt
│ │ ├── cv084_13566.txt
│ │ ├── cv085_1381.txt
│ │ ├── cv086_18371.txt
│ │ ├── cv087_1989.txt
│ │ ├── cv088_24113.txt
│ │ ├── cv089_11418.txt
│ │ ├── cv090_0042.txt
│ │ ├── cv091_7400.txt
│ │ ├── cv092_28017.txt
│ │ ├── cv093_13951.txt
│ │ ├── cv094_27889.txt
│ │ ├── cv095_28892.txt
│ │ ├── cv096_11474.txt
│ │ ├── cv097_24970.txt
│ │ ├── cv098_15435.txt
│ │ ├── cv099_10534.txt
│ │ ├── cv100_11528.txt
│ │ ├── cv101_10175.txt
│ │ ├── cv102_7846.txt
│ │ ├── cv103_11021.txt
│ │ ├── cv104_18134.txt
│ │ ├── cv105_17990.txt
│ │ ├── cv106_16807.txt
│ │ ├── cv107_24319.txt
│ │ ├── cv108_15571.txt
│ │ ├── cv109_21172.txt
│ │ ├── cv110_27788.txt
│ │ ├── cv111_11473.txt
│ │ ├── cv112_11193.txt
│ │ ├── cv113_23102.txt
│ │ ├── cv114_18398.txt
│ │ ├── cv115_25396.txt
│ │ ├── cv116_28942.txt
│ │ ├── cv117_24295.txt
│ │ ├── cv118_28980.txt
│ │ ├── cv119_9867.txt
│ │ ├── cv120_4111.txt
│ │ ├── cv121_17302.txt
│ │ ├── cv122_7392.txt
│ │ ├── cv123_11182.txt
│ │ ├── cv124_4122.txt
│ │ ├── cv125_9391.txt
│ │ ├── cv126_28971.txt
│ │ ├── cv127_14711.txt
│ │ ├── cv128_29627.txt
│ │ ├── cv129_16741.txt
│ │ ├── cv130_17083.txt
│ │ ├── cv131_10713.txt
│ │ ├── cv132_5618.txt
│ │ ├── cv133_16336.txt
│ │ ├── cv134_22246.txt
│ │ ├── cv135_11603.txt
│ │ ├── cv136_11505.txt
│ │ ├── cv137_15422.txt
│ │ ├── cv138_12721.txt
│ │ ├── cv139_12873.txt
│ │ ├── cv140_7479.txt
│ │ ├── cv141_15686.txt
│ │ ├── cv142_22516.txt
│ │ ├── cv143_19666.txt
│ │ ├── cv144_5007.txt
│ │ ├── cv145_11472.txt
│ │ ├── cv146_18458.txt
│ │ ├── cv147_21193.txt
│ │ ├── cv148_16345.txt
│ │ ├── cv149_15670.txt
│ │ ├── cv150_12916.txt
│ │ ├── cv151_15771.txt
│ │ ├── cv152_8736.txt
│ │ ├── cv153_10779.txt
│ │ ├── cv154_9328.txt
│ │ ├── cv155_7308.txt
│ │ ├── cv156_10481.txt
│ │ ├── cv157_29372.txt
│ │ ├── cv158_10390.txt
│ │ ├── cv159_29505.txt
│ │ ├── cv160_10362.txt
│ │ ├── cv161_11425.txt
│ │ ├── cv162_10424.txt
│ │ ├── cv163_10052.txt
│ │ ├── cv164_22447.txt
│ │ ├── cv165_22619.txt
│ │ ├── cv166_11052.txt
│ │ ├── cv167_16376.txt
│ │ ├── cv168_7050.txt
│ │ ├── cv169_23778.txt
│ │ ├── cv170_3006.txt
│ │ ├── cv171_13537.txt
│ │ ├── cv172_11131.txt
│ │ ├── cv173_4471.txt
│ │ ├── cv174_9659.txt
│ │ ├── cv175_6964.txt
│ │ ├── cv176_12857.txt
│ │ ├── cv177_10367.txt
│ │ ├── cv178_12972.txt
│ │ ├── cv179_9228.txt
│ │ ├── cv180_16113.txt
│ │ ├── cv181_14401.txt
│ │ ├── cv182_7281.txt
│ │ ├── cv183_18612.txt
│ │ ├── cv184_2673.txt
│ │ ├── cv185_28654.txt
│ │ ├── cv186_2269.txt
│ │ ├── cv187_12829.txt
│ │ ├── cv188_19226.txt
│ │ ├── cv189_22934.txt
│ │ ├── cv190_27052.txt
│ │ ├── cv191_29719.txt
│ │ ├── cv192_14395.txt
│ │ ├── cv193_5416.txt
│ │ ├── cv194_12079.txt
│ │ ├── cv195_14528.txt
│ │ ├── cv196_29027.txt
│ │ ├── cv197_29328.txt
│ │ ├── cv198_18180.txt
│ │ ├── cv199_9629.txt
│ │ ├── cv200_2915.txt
│ │ ├── cv201_6997.txt
│ │ ├── cv202_10654.txt
│ │ ├── cv203_17986.txt
│ │ ├── cv204_8451.txt
│ │ ├── cv205_9457.txt
│ │ ├── cv206_14293.txt
│ │ ├── cv207_29284.txt
│ │ ├── cv208_9020.txt
│ │ ├── cv209_29118.txt
│ │ ├── cv210_9312.txt
│ │ ├── cv211_9953.txt
│ │ ├── cv212_10027.txt
│ │ ├── cv213_18934.txt
│ │ ├── cv214_12294.txt
│ │ ├── cv215_22240.txt
│ │ ├── cv216_18738.txt
│ │ ├── cv217_28842.txt
│ │ ├── cv218_24352.txt
│ │ ├── cv219_18626.txt
│ │ ├── cv220_29059.txt
│ │ ├── cv221_2695.txt
│ │ ├── cv222_17395.txt
│ │ ├── cv223_29066.txt
│ │ ├── cv224_17661.txt
│ │ ├── cv225_29224.txt
│ │ ├── cv226_2618.txt
│ │ ├── cv227_24215.txt
│ │ ├── cv228_5806.txt
│ │ ├── cv229_13611.txt
│ │ ├── cv230_7428.txt
│ │ ├── cv231_10425.txt
│ │ ├── cv232_14991.txt
│ │ ├── cv233_15964.txt
│ │ ├── cv234_20643.txt
│ │ ├── cv235_10217.txt
│ │ ├── cv236_11565.txt
│ │ ├── cv237_19221.txt
│ │ ├── cv238_12931.txt
│ │ ├── cv239_3385.txt
│ │ ├── cv240_14336.txt
│ │ ├── cv241_23130.txt
│ │ ├── cv242_10638.txt
│ │ ├── cv243_20728.txt
│ │ ├── cv244_21649.txt
│ │ ├── cv245_8569.txt
│ │ ├── cv246_28807.txt
│ │ ├── cv247_13142.txt
│ │ ├── cv248_13987.txt
│ │ ├── cv249_11640.txt
│ │ ├── cv250_25616.txt
│ │ ├── cv251_22636.txt
│ │ ├── cv252_23779.txt
│ │ ├── cv253_10077.txt
│ │ ├── cv254_6027.txt
│ │ ├── cv255_13683.txt
│ │ ├── cv256_14740.txt
│ │ ├── cv257_10975.txt
│ │ ├── cv258_5792.txt
│ │ ├── cv259_10934.txt
│ │ ├── cv260_13959.txt
│ │ ├── cv261_10954.txt
│ │ ├── cv262_12649.txt
│ │ ├── cv263_19259.txt
│ │ ├── cv264_12801.txt
│ │ ├── cv265_10814.txt
│ │ ├── cv266_25779.txt
│ │ ├── cv267_14952.txt
│ │ ├── cv268_18834.txt
│ │ ├── cv269_21732.txt
│ │ ├── cv270_6079.txt
│ │ ├── cv271_13837.txt
│ │ ├── cv272_18974.txt
│ │ ├── cv273_29112.txt
│ │ ├── cv274_25253.txt
│ │ ├── cv275_28887.txt
│ │ ├── cv276_15684.txt
│ │ ├── cv277_19091.txt
│ │ ├── cv278_13041.txt
│ │ ├── cv279_18329.txt
│ │ ├── cv280_8267.txt
│ │ ├── cv281_23253.txt
│ │ ├── cv282_6653.txt
│ │ ├── cv283_11055.txt
│ │ ├── cv284_19119.txt
│ │ ├── cv285_16494.txt
│ │ ├── cv286_25050.txt
│ │ ├── cv287_15900.txt
│ │ ├── cv288_18791.txt
│ │ ├── cv289_6463.txt
│ │ ├── cv290_11084.txt
│ │ ├── cv291_26635.txt
│ │ ├── cv292_7282.txt
│ │ ├── cv293_29856.txt
│ │ ├── cv294_11684.txt
│ │ ├── cv295_15570.txt
│ │ ├── cv296_12251.txt
│ │ ├── cv297_10047.txt
│ │ ├── cv298_23111.txt
│ │ ├── cv299_16214.txt
│ │ ├── cv300_22284.txt
│ │ ├── cv301_12146.txt
│ │ ├── cv302_25649.txt
│ │ ├── cv303_27520.txt
│ │ ├── cv304_28706.txt
│ │ ├── cv305_9946.txt
│ │ ├── cv306_10364.txt
│ │ ├── cv307_25270.txt
│ │ ├── cv308_5016.txt
│ │ ├── cv309_22571.txt
│ │ ├── cv310_13091.txt
│ │ ├── cv311_16002.txt
│ │ ├── cv312_29377.txt
│ │ ├── cv313_18198.txt
│ │ ├── cv314_14422.txt
│ │ ├── cv315_11629.txt
│ │ ├── cv316_6370.txt
│ │ ├── cv317_24049.txt
│ │ ├── cv318_10493.txt
│ │ ├── cv319_14727.txt
│ │ ├── cv320_9530.txt
│ │ ├── cv321_12843.txt
│ │ ├── cv322_20318.txt
│ │ ├── cv323_29805.txt
│ │ ├── cv324_7082.txt
│ │ ├── cv325_16629.txt
│ │ ├── cv326_13295.txt
│ │ ├── cv327_20292.txt
│ │ ├── cv328_10373.txt
│ │ ├── cv329_29370.txt
│ │ ├── cv330_29809.txt
│ │ ├── cv331_8273.txt
│ │ ├── cv332_16307.txt
│ │ ├── cv333_8916.txt
│ │ ├── cv334_10001.txt
│ │ ├── cv335_14665.txt
│ │ ├── cv336_10143.txt
│ │ ├── cv337_29181.txt
│ │ ├── cv338_8821.txt
│ │ ├── cv339_21119.txt
│ │ ├── cv340_13287.txt
│ │ ├── cv341_24430.txt
│ │ ├── cv342_19456.txt
│ │ ├── cv343_10368.txt
│ │ ├── cv344_5312.txt
│ │ ├── cv345_9954.txt
│ │ ├── cv346_18168.txt
│ │ ├── cv347_13194.txt
│ │ ├── cv348_18176.txt
│ │ ├── cv349_13507.txt
│ │ ├── cv350_20670.txt
│ │ ├── cv351_15458.txt
│ │ ├── cv352_5524.txt
│ │ ├── cv353_18159.txt
│ │ ├── cv354_8132.txt
│ │ ├── cv355_16413.txt
│ │ ├── cv356_25163.txt
│ │ ├── cv357_13156.txt
│ │ ├── cv358_10691.txt
│ │ ├── cv359_6647.txt
│ │ ├── cv360_8398.txt
│ │ ├── cv361_28944.txt
│ │ ├── cv362_15341.txt
│ │ ├── cv363_29332.txt
│ │ ├── cv364_12901.txt
│ │ ├── cv365_11576.txt
│ │ ├── cv366_10221.txt
│ │ ├── cv367_22792.txt
│ │ ├── cv368_10466.txt
│ │ ├── cv369_12886.txt
│ │ ├── cv370_5221.txt
│ │ ├── cv371_7630.txt
│ │ ├── cv372_6552.txt
│ │ ├── cv373_20404.txt
│ │ ├── cv374_25436.txt
│ │ ├── cv375_9929.txt
│ │ ├── cv376_19435.txt
│ │ ├── cv377_7946.txt
│ │ ├── cv378_20629.txt
│ │ ├── cv379_21963.txt
│ │ ├── cv380_7574.txt
│ │ ├── cv381_20172.txt
│ │ ├── cv382_7897.txt
│ │ ├── cv383_13116.txt
│ │ ├── cv384_17140.txt
│ │ ├── cv385_29741.txt
│ │ ├── cv386_10080.txt
│ │ ├── cv387_11507.txt
│ │ ├── cv388_12009.txt
│ │ ├── cv389_9369.txt
│ │ ├── cv390_11345.txt
│ │ ├── cv391_10802.txt
│ │ ├── cv392_11458.txt
│ │ ├── cv393_29327.txt
│ │ ├── cv394_5137.txt
│ │ ├── cv395_10849.txt
│ │ ├── cv396_17989.txt
│ │ ├── cv397_29023.txt
│ │ ├── cv398_15537.txt
│ │ ├── cv399_2877.txt
│ │ ├── cv400_19220.txt
│ │ ├── cv401_12605.txt
│ │ ├── cv402_14425.txt
│ │ ├── cv403_6621.txt
│ │ ├── cv404_20315.txt
│ │ ├── cv405_20399.txt
│ │ ├── cv406_21020.txt
│ │ ├── cv407_22637.txt
│ │ ├── cv408_5297.txt
│ │ ├── cv409_29786.txt
│ │ ├── cv410_24266.txt
│ │ ├── cv411_15007.txt
│ │ ├── cv412_24095.txt
│ │ ├── cv413_7398.txt
│ │ ├── cv414_10518.txt
│ │ ├── cv415_22517.txt
│ │ ├── cv416_11136.txt
│ │ ├── cv417_13115.txt
│ │ ├── cv418_14774.txt
│ │ ├── cv419_13394.txt
│ │ ├── cv420_28795.txt
│ │ ├── cv421_9709.txt
│ │ ├── cv422_9381.txt
│ │ ├── cv423_11155.txt
│ │ ├── cv424_8831.txt
│ │ ├── cv425_8250.txt
│ │ ├── cv426_10421.txt
│ │ ├── cv427_10825.txt
│ │ ├── cv428_11347.txt
│ │ ├── cv429_7439.txt
│ │ ├── cv430_17351.txt
│ │ ├── cv431_7085.txt
│ │ ├── cv432_14224.txt
│ │ ├── cv433_10144.txt
│ │ ├── cv434_5793.txt
│ │ ├── cv435_23110.txt
│ │ ├── cv436_19179.txt
│ │ ├── cv437_22849.txt
│ │ ├── cv438_8043.txt
│ │ ├── cv439_15970.txt
│ │ ├── cv440_15243.txt
│ │ ├── cv441_13711.txt
│ │ ├── cv442_13846.txt
│ │ ├── cv443_21118.txt
│ │ ├── cv444_9974.txt
│ │ ├── cv445_25882.txt
│ │ ├── cv446_11353.txt
│ │ ├── cv447_27332.txt
│ │ ├── cv448_14695.txt
│ │ ├── cv449_8785.txt
│ │ ├── cv450_7890.txt
│ │ ├── cv451_10690.txt
│ │ ├── cv452_5088.txt
│ │ ├── cv453_10379.txt
│ │ ├── cv454_2053.txt
│ │ ├── cv455_29000.txt
│ │ ├── cv456_18985.txt
│ │ ├── cv457_18453.txt
│ │ ├── cv458_8604.txt
│ │ ├── cv459_20319.txt
│ │ ├── cv460_10842.txt
│ │ ├── cv461_19600.txt
│ │ ├── cv462_19350.txt
│ │ ├── cv463_10343.txt
│ │ ├── cv464_15650.txt
│ │ ├── cv465_22431.txt
│ │ ├── cv466_18722.txt
│ │ ├── cv467_25773.txt
│ │ ├── cv468_15228.txt
│ │ ├── cv469_20630.txt
│ │ ├── cv470_15952.txt
│ │ ├── cv471_16858.txt
│ │ ├── cv472_29280.txt
│ │ ├── cv473_7367.txt
│ │ ├── cv474_10209.txt
│ │ ├── cv475_21692.txt
│ │ ├── cv476_16856.txt
│ │ ├── cv477_22479.txt
│ │ ├── cv478_14309.txt
│ │ ├── cv479_5649.txt
│ │ ├── cv480_19817.txt
│ │ ├── cv481_7436.txt
│ │ ├── cv482_10580.txt
│ │ ├── cv483_16378.txt
│ │ ├── cv484_25054.txt
│ │ ├── cv485_26649.txt
│ │ ├── cv486_9799.txt
│ │ ├── cv487_10446.txt
│ │ ├── cv488_19856.txt
│ │ ├── cv489_17906.txt
│ │ ├── cv490_17872.txt
│ │ ├── cv491_12145.txt
│ │ ├── cv492_18271.txt
│ │ ├── cv493_12839.txt
│ │ ├── cv494_17389.txt
│ │ ├── cv495_14518.txt
│ │ ├── cv496_10530.txt
│ │ ├── cv497_26980.txt
│ │ ├── cv498_8832.txt
│ │ ├── cv499_10658.txt
│ │ ├── cv500_10251.txt
│ │ ├── cv501_11657.txt
│ │ ├── cv502_10406.txt
│ │ ├── cv503_10558.txt
│ │ ├── cv504_29243.txt
│ │ ├── cv505_12090.txt
│ │ ├── cv506_15956.txt
│ │ ├── cv507_9220.txt
│ │ ├── cv508_16006.txt
│ │ ├── cv509_15888.txt
│ │ ├── cv510_23360.txt
│ │ ├── cv511_10132.txt
│ │ ├── cv512_15965.txt
│ │ ├── cv513_6923.txt
│ │ ├── cv514_11187.txt
│ │ ├── cv515_17069.txt
│ │ ├── cv516_11172.txt
│ │ ├── cv517_19219.txt
│ │ ├── cv518_13331.txt
│ │ ├── cv519_14661.txt
│ │ ├── cv520_12295.txt
│ │ ├── cv521_15828.txt
│ │ ├── cv522_5583.txt
│ │ ├── cv523_16615.txt
│ │ ├── cv524_23627.txt
│ │ ├── cv525_16122.txt
│ │ ├── cv526_12083.txt
│ │ ├── cv527_10123.txt
│ │ ├── cv528_10822.txt
│ │ ├── cv529_10420.txt
│ │ ├── cv530_16212.txt
│ │ ├── cv531_26486.txt
│ │ ├── cv532_6522.txt
│ │ ├── cv533_9821.txt
│ │ ├── cv534_14083.txt
│ │ ├── cv535_19728.txt
│ │ ├── cv536_27134.txt
│ │ ├── cv537_12370.txt
│ │ ├── cv538_28667.txt
│ │ ├── cv539_20347.txt
│ │ ├── cv540_3421.txt
│ │ ├── cv541_28835.txt
│ │ ├── cv542_18980.txt
│ │ ├── cv543_5045.txt
│ │ ├── cv544_5108.txt
│ │ ├── cv545_12014.txt
│ │ ├── cv546_11767.txt
│ │ ├── cv547_16324.txt
│ │ ├── cv548_17731.txt
│ │ ├── cv549_21443.txt
│ │ ├── cv550_22211.txt
│ │ ├── cv551_10565.txt
│ │ ├── cv552_10016.txt
│ │ ├── cv553_26915.txt
│ │ ├── cv554_13151.txt
│ │ ├── cv555_23922.txt
│ │ ├── cv556_14808.txt
│ │ ├── cv557_11449.txt
│ │ ├── cv558_29507.txt
│ │ ├── cv559_0050.txt
│ │ ├── cv560_17175.txt
│ │ ├── cv561_9201.txt
│ │ ├── cv562_10359.txt
│ │ ├── cv563_17257.txt
│ │ ├── cv564_11110.txt
│ │ ├── cv565_29572.txt
│ │ ├── cv566_8581.txt
│ │ ├── cv567_29611.txt
│ │ ├── cv568_15638.txt
│ │ ├── cv569_26381.txt
│ │ ├── cv570_29082.txt
│ │ ├── cv571_29366.txt
│ │ ├── cv572_18657.txt
│ │ ├── cv573_29525.txt
│ │ ├── cv574_22156.txt
│ │ ├── cv575_21150.txt
│ │ ├── cv576_14094.txt
│ │ ├── cv577_28549.txt
│ │ ├── cv578_15094.txt
│ │ ├── cv579_11605.txt
│ │ ├── cv580_14064.txt
│ │ ├── cv581_19381.txt
│ │ ├── cv582_6559.txt
│ │ ├── cv583_29692.txt
│ │ ├── cv584_29722.txt
│ │ ├── cv585_22496.txt
│ │ ├── cv586_7543.txt
│ │ ├── cv587_19162.txt
│ │ ├── cv588_13008.txt
│ │ ├── cv589_12064.txt
│ │ ├── cv590_19290.txt
│ │ ├── cv591_23640.txt
│ │ ├── cv592_22315.txt
│ │ ├── cv593_10987.txt
│ │ ├── cv594_11039.txt
│ │ ├── cv595_25335.txt
│ │ ├── cv596_28311.txt
│ │ ├── cv597_26360.txt
│ │ ├── cv598_16452.txt
│ │ ├── cv599_20988.txt
│ │ ├── cv600_23878.txt
│ │ ├── cv601_23453.txt
│ │ ├── cv602_8300.txt
│ │ ├── cv603_17694.txt
│ │ ├── cv604_2230.txt
│ │ ├── cv605_11800.txt
│ │ ├── cv606_15985.txt
│ │ ├── cv607_7717.txt
│ │ ├── cv608_23231.txt
│ │ ├── cv609_23877.txt
│ │ ├── cv610_2287.txt
│ │ ├── cv611_21120.txt
│ │ ├── cv612_5461.txt
│ │ ├── cv613_21796.txt
│ │ ├── cv614_10626.txt
│ │ ├── cv615_14182.txt
│ │ ├── cv616_29319.txt
│ │ ├── cv617_9322.txt
│ │ ├── cv618_8974.txt
│ │ ├── cv619_12462.txt
│ │ ├── cv620_24265.txt
│ │ ├── cv621_14368.txt
│ │ ├── cv622_8147.txt
│ │ ├── cv623_15356.txt
│ │ ├── cv624_10744.txt
│ │ ├── cv625_12440.txt
│ │ ├── cv626_7410.txt
│ │ ├── cv627_11620.txt
│ │ ├── cv628_19325.txt
│ │ ├── cv629_14909.txt
│ │ ├── cv630_10057.txt
│ │ ├── cv631_4967.txt
│ │ ├── cv632_9610.txt
│ │ ├── cv633_29837.txt
│ │ ├── cv634_11101.txt
│ │ ├── cv635_10022.txt
│ │ ├── cv636_15279.txt
│ │ ├── cv637_1250.txt
│ │ ├── cv638_2953.txt
│ │ ├── cv639_10308.txt
│ │ ├── cv640_5378.txt
│ │ ├── cv641_12349.txt
│ │ ├── cv642_29867.txt
│ │ ├── cv643_29349.txt
│ │ ├── cv644_17154.txt
│ │ ├── cv645_15668.txt
│ │ ├── cv646_15065.txt
│ │ ├── cv647_13691.txt
│ │ ├── cv648_15792.txt
│ │ ├── cv649_12735.txt
│ │ ├── cv650_14340.txt
│ │ ├── cv651_10492.txt
│ │ ├── cv652_13972.txt
│ │ ├── cv653_19583.txt
│ │ ├── cv654_18246.txt
│ │ ├── cv655_11154.txt
│ │ ├── cv656_24201.txt
│ │ ├── cv657_24513.txt
│ │ ├── cv658_10532.txt
│ │ ├── cv659_19944.txt
│ │ ├── cv660_21893.txt
│ │ ├── cv661_2450.txt
│ │ ├── cv662_13320.txt
│ │ ├── cv663_13019.txt
│ │ ├── cv664_4389.txt
│ │ ├── cv665_29538.txt
│ │ ├── cv666_18963.txt
│ │ ├── cv667_18467.txt
│ │ ├── cv668_17604.txt
│ │ ├── cv669_22995.txt
│ │ ├── cv670_25826.txt
│ │ ├── cv671_5054.txt
│ │ ├── cv672_28083.txt
│ │ ├── cv673_24714.txt
│ │ ├── cv674_10732.txt
│ │ ├── cv675_21588.txt
│ │ ├── cv676_21090.txt
│ │ ├── cv677_17715.txt
│ │ ├── cv678_13419.txt
│ │ ├── cv679_28559.txt
│ │ ├── cv680_10160.txt
│ │ ├── cv681_9692.txt
│ │ ├── cv682_16139.txt
│ │ ├── cv683_12167.txt
│ │ ├── cv684_11798.txt
│ │ ├── cv685_5947.txt
│ │ ├── cv686_13900.txt
│ │ ├── cv687_21100.txt
│ │ ├── cv688_7368.txt
│ │ ├── cv689_12587.txt
│ │ ├── cv690_5619.txt
│ │ ├── cv691_5043.txt
│ │ ├── cv692_15451.txt
│ │ ├── cv693_18063.txt
│ │ ├── cv694_4876.txt
│ │ ├── cv695_21108.txt
│ │ ├── cv696_29740.txt
│ │ ├── cv697_11162.txt
│ │ ├── cv698_15253.txt
│ │ ├── cv699_7223.txt
│ │ ├── cv700_21947.txt
│ │ ├── cv701_14252.txt
│ │ ├── cv702_11500.txt
│ │ ├── cv703_16143.txt
│ │ ├── cv704_15969.txt
│ │ ├── cv705_11059.txt
│ │ ├── cv706_24716.txt
│ │ ├── cv707_10678.txt
│ │ ├── cv708_28729.txt
│ │ ├── cv709_10529.txt
│ │ ├── cv710_22577.txt
│ │ ├── cv711_11665.txt
│ │ ├── cv712_22920.txt
│ │ ├── cv713_29155.txt
│ │ ├── cv714_18502.txt
│ │ ├── cv715_18179.txt
│ │ ├── cv716_10514.txt
│ │ ├── cv717_15953.txt
│ │ ├── cv718_11434.txt
│ │ ├── cv719_5713.txt
│ │ ├── cv720_5389.txt
│ │ ├── cv721_29121.txt
│ │ ├── cv722_7110.txt
│ │ ├── cv723_8648.txt
│ │ ├── cv724_13681.txt
│ │ ├── cv725_10103.txt
│ │ ├── cv726_4719.txt
│ │ ├── cv727_4978.txt
│ │ ├── cv728_16133.txt
│ │ ├── cv729_10154.txt
│ │ ├── cv730_10279.txt
│ │ ├── cv731_4136.txt
│ │ ├── cv732_12245.txt
│ │ ├── cv733_9839.txt
│ │ ├── cv734_21568.txt
│ │ ├── cv735_18801.txt
│ │ ├── cv736_23670.txt
│ │ ├── cv737_28907.txt
│ │ ├── cv738_10116.txt
│ │ ├── cv739_11209.txt
│ │ ├── cv740_12445.txt
│ │ ├── cv741_11890.txt
│ │ ├── cv742_7751.txt
│ │ ├── cv743_15449.txt
│ │ ├── cv744_10038.txt
│ │ ├── cv745_12773.txt
│ │ ├── cv746_10147.txt
│ │ ├── cv747_16556.txt
│ │ ├── cv748_12786.txt
│ │ ├── cv749_17765.txt
│ │ ├── cv750_10180.txt
│ │ ├── cv751_15719.txt
│ │ ├── cv752_24155.txt
│ │ ├── cv753_10875.txt
│ │ ├── cv754_7216.txt
│ │ ├── cv755_23616.txt
│ │ ├── cv756_22540.txt
│ │ ├── cv757_10189.txt
│ │ ├── cv758_9671.txt
│ │ ├── cv759_13522.txt
│ │ ├── cv760_8597.txt
│ │ ├── cv761_12620.txt
│ │ ├── cv762_13927.txt
│ │ ├── cv763_14729.txt
│ │ ├── cv764_11739.txt
│ │ ├── cv765_19037.txt
│ │ ├── cv766_7540.txt
│ │ ├── cv767_14062.txt
│ │ ├── cv768_11751.txt
│ │ ├── cv769_8123.txt
│ │ ├── cv770_10451.txt
│ │ ├── cv771_28665.txt
│ │ ├── cv772_12119.txt
│ │ ├── cv773_18817.txt
│ │ ├── cv774_13845.txt
│ │ ├── cv775_16237.txt
│ │ ├── cv776_20529.txt
│ │ ├── cv777_10094.txt
│ │ ├── cv778_17330.txt
│ │ ├── cv779_17881.txt
│ │ ├── cv780_7984.txt
│ │ ├── cv781_5262.txt
│ │ ├── cv782_19526.txt
│ │ ├── cv783_13227.txt
│ │ ├── cv784_14394.txt
│ │ ├── cv785_22600.txt
│ │ ├── cv786_22497.txt
│ │ ├── cv787_13743.txt
│ │ ├── cv788_25272.txt
│ │ ├── cv789_12136.txt
│ │ ├── cv790_14600.txt
│ │ ├── cv791_16302.txt
│ │ ├── cv792_3832.txt
│ │ ├── cv793_13650.txt
│ │ ├── cv794_15868.txt
│ │ ├── cv795_10122.txt
│ │ ├── cv796_15782.txt
│ │ ├── cv797_6957.txt
│ │ ├── cv798_23531.txt
│ │ ├── cv799_18543.txt
│ │ ├── cv800_12368.txt
│ │ ├── cv801_25228.txt
│ │ ├── cv802_28664.txt
│ │ ├── cv803_8207.txt
│ │ ├── cv804_10862.txt
│ │ ├── cv805_19601.txt
│ │ ├── cv806_8842.txt
│ │ ├── cv807_21740.txt
│ │ ├── cv808_12635.txt
│ │ ├── cv809_5009.txt
│ │ ├── cv810_12458.txt
│ │ ├── cv811_21386.txt
│ │ ├── cv812_17924.txt
│ │ ├── cv813_6534.txt
│ │ ├── cv814_18975.txt
│ │ ├── cv815_22456.txt
│ │ ├── cv816_13655.txt
│ │ ├── cv817_4041.txt
│ │ ├── cv818_10211.txt
│ │ ├── cv819_9364.txt
│ │ ├── cv820_22892.txt
│ │ ├── cv821_29364.txt
│ │ ├── cv822_20049.txt
│ │ ├── cv823_15569.txt
│ │ ├── cv824_8838.txt
│ │ ├── cv825_5063.txt
│ │ ├── cv826_11834.txt
│ │ ├── cv827_18331.txt
│ │ ├── cv828_19831.txt
│ │ ├── cv829_20289.txt
│ │ ├── cv830_6014.txt
│ │ ├── cv831_14689.txt
│ │ ├── cv832_23275.txt
│ │ ├── cv833_11053.txt
│ │ ├── cv834_22195.txt
│ │ ├── cv835_19159.txt
│ │ ├── cv836_12968.txt
│ │ ├── cv837_27325.txt
│ │ ├── cv838_24728.txt
│ │ ├── cv839_21467.txt
│ │ ├── cv840_16321.txt
│ │ ├── cv841_3967.txt
│ │ ├── cv842_5866.txt
│ │ ├── cv843_15544.txt
│ │ ├── cv844_12690.txt
│ │ ├── cv845_14290.txt
│ │ ├── cv846_29497.txt
│ │ ├── cv847_1941.txt
│ │ ├── cv848_10036.txt
│ │ ├── cv849_15729.txt
│ │ ├── cv850_16466.txt
│ │ ├── cv851_20469.txt
│ │ ├── cv852_27523.txt
│ │ ├── cv853_29233.txt
│ │ ├── cv854_17740.txt
│ │ ├── cv855_20661.txt
│ │ ├── cv856_29013.txt
│ │ ├── cv857_15958.txt
│ │ ├── cv858_18819.txt
│ │ ├── cv859_14107.txt
│ │ ├── cv860_13853.txt
│ │ ├── cv861_1198.txt
│ │ ├── cv862_14324.txt
│ │ ├── cv863_7424.txt
│ │ ├── cv864_3416.txt
│ │ ├── cv865_2895.txt
│ │ ├── cv866_29691.txt
│ │ ├── cv867_16661.txt
│ │ ├── cv868_11948.txt
│ │ ├── cv869_23611.txt
│ │ ├── cv870_16348.txt
│ │ ├── cv871_24888.txt
│ │ ├── cv872_12591.txt
│ │ ├── cv873_18636.txt
│ │ ├── cv874_11236.txt
│ │ ├── cv875_5754.txt
│ │ ├── cv876_9390.txt
│ │ ├── cv877_29274.txt
│ │ ├── cv878_15694.txt
│ │ ├── cv879_14903.txt
│ │ ├── cv880_29800.txt
│ │ ├── cv881_13254.txt
│ │ ├── cv882_10026.txt
│ │ ├── cv883_27751.txt
│ │ ├── cv884_13632.txt
│ │ ├── cv885_12318.txt
│ │ ├── cv886_18177.txt
│ │ ├── cv887_5126.txt
│ │ ├── cv888_24435.txt
│ │ ├── cv889_21430.txt
│ │ ├── cv890_3977.txt
│ │ ├── cv891_6385.txt
│ │ ├── cv892_17576.txt
│ │ ├── cv893_26269.txt
│ │ ├── cv894_2068.txt
│ │ ├── cv895_21022.txt
│ │ ├── cv896_16071.txt
│ │ ├── cv897_10837.txt
│ │ ├── cv898_14187.txt
│ │ ├── cv899_16014.txt
│ │ ├── cv900_10331.txt
│ │ ├── cv901_11017.txt
│ │ ├── cv902_12256.txt
│ │ ├── cv903_17822.txt
│ │ ├── cv904_24353.txt
│ │ ├── cv905_29114.txt
│ │ ├── cv906_11491.txt
│ │ ├── cv907_3541.txt
│ │ ├── cv908_16009.txt
│ │ ├── cv909_9960.txt
│ │ ├── cv910_20488.txt
│ │ ├── cv911_20260.txt
│ │ ├── cv912_5674.txt
│ │ ├── cv913_29252.txt
│ │ ├── cv914_28742.txt
│ │ ├── cv915_8841.txt
│ │ ├── cv916_15467.txt
│ │ ├── cv917_29715.txt
│ │ ├── cv918_2693.txt
│ │ ├── cv919_16380.txt
│ │ ├── cv920_29622.txt
│ │ ├── cv921_12747.txt
│ │ ├── cv922_10073.txt
│ │ ├── cv923_11051.txt
│ │ ├── cv924_29540.txt
│ │ ├── cv925_8969.txt
│ │ ├── cv926_17059.txt
│ │ ├── cv927_10681.txt
│ │ ├── cv928_9168.txt
│ │ ├── cv929_16908.txt
│ │ ├── cv930_13475.txt
│ │ ├── cv931_17563.txt
│ │ ├── cv932_13401.txt
│ │ ├── cv933_23776.txt
│ │ ├── cv934_19027.txt
│ │ ├── cv935_23841.txt
│ │ ├── cv936_15954.txt
│ │ ├── cv937_9811.txt
│ │ ├── cv938_10220.txt
│ │ ├── cv939_10583.txt
│ │ ├── cv940_17705.txt
│ │ ├── cv941_10246.txt
│ │ ├── cv942_17082.txt
│ │ ├── cv943_22488.txt
│ │ ├── cv944_13521.txt
│ │ ├── cv945_12160.txt
│ │ ├── cv946_18658.txt
│ │ ├── cv947_10601.txt
│ │ ├── cv948_24606.txt
│ │ ├── cv949_20112.txt
│ │ ├── cv950_12350.txt
│ │ ├── cv951_10926.txt
│ │ ├── cv952_25240.txt
│ │ ├── cv953_6836.txt
│ │ ├── cv954_18628.txt
│ │ ├── cv955_25001.txt
│ │ ├── cv956_11609.txt
│ │ ├── cv957_8737.txt
│ │ ├── cv958_12162.txt
│ │ ├── cv959_14611.txt
│ │ ├── cv960_29007.txt
│ │ ├── cv961_5682.txt
│ │ ├── cv962_9803.txt
│ │ ├── cv963_6895.txt
│ │ ├── cv964_6021.txt
│ │ ├── cv965_26071.txt
│ │ ├── cv966_28832.txt
│ │ ├── cv967_5788.txt
│ │ ├── cv968_24218.txt
│ │ ├── cv969_13250.txt
│ │ ├── cv970_18450.txt
│ │ ├── cv971_10874.txt
│ │ ├── cv972_26417.txt
│ │ ├── cv973_10066.txt
│ │ ├── cv974_22941.txt
│ │ ├── cv975_10981.txt
│ │ ├── cv976_10267.txt
│ │ ├── cv977_4938.txt
│ │ ├── cv978_20929.txt
│ │ ├── cv979_18921.txt
│ │ ├── cv980_10953.txt
│ │ ├── cv981_14989.txt
│ │ ├── cv982_21103.txt
│ │ ├── cv983_22928.txt
│ │ ├── cv984_12767.txt
│ │ ├── cv985_6359.txt
│ │ ├── cv986_13527.txt
│ │ ├── cv987_6965.txt
│ │ ├── cv988_18740.txt
│ │ ├── cv989_15824.txt
│ │ ├── cv990_11591.txt
│ │ ├── cv991_18645.txt
│ │ ├── cv992_11962.txt
│ │ ├── cv993_29737.txt
│ │ ├── cv994_12270.txt
│ │ ├── cv995_21821.txt
│ │ ├── cv996_11592.txt
│ │ ├── cv997_5046.txt
│ │ ├── cv998_14111.txt
│ │ └── cv999_13106.txt
│ ├── vocab.txt
│ └── word_embeddings_for_text.ipynb
├── README.md
├── Statistics/
│ ├── .ipynb_checkpoints/
│ │ ├── A_Gentle_Intro_to_Calculating_Normal_Summary_Stats-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Chi_Squared_Test_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Data_Visualization_Methods_in_Python-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Estimation_Stats_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Nonparametric_Stats-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Normality_Tests_in_Python-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Data_Distributions-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Hypothesis_Tests-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Sampling_and_Resampling-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_Statistical_Tolerance_Intervals_in_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_k_fold_cross_validation-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_the_Bootstrap_Method-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_the_Central_Limit_Theorem_for_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Intro_to_the_Law_of_Large_Numbers_in_ML-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Effect_Size_Measures_in_Python-checkpoint.ipynb
│ │ ├── A_Gentle_Introduction_to_Statistical_Power_and_Power_Analysis_in_Python-checkpoint.ipynb
│ │ ├── Confidence_Intervals_for_ML-checkpoint.ipynb
│ │ ├── Critical_Values_for_Statistical_Hypothesis_Testing_in_Python-checkpoint.ipynb
│ │ ├── Estimate_the_Number_of_Experiment_Repeats_for_Stochastic_Machine_Learning_Algorithms-checkpoint.ipynb
│ │ ├── How_To_Generate_Random_Numbers_in_Python-checkpoint.ipynb
│ │ ├── How_to_Calculate_Bootstrap_Confidence_Interval_for_ML_Results_in_Python-checkpoint.ipynb
│ │ ├── How_to_Calculate_Nonparametric_Rank_Correlation_in_Python-checkpoint.ipynb
│ │ ├── How_to_Calculate_the_5_Number_Summary_for_your_data-checkpoint.ipynb
│ │ ├── How_to_Report_Classifier_Performance_with_Confidence_Intervals-checkpoint.ipynb
│ │ ├── How_to_Transform_Data_to_Better_Fit_the_Normal_Distribution-checkpoint.ipynb
│ │ ├── How_to_Use_Correlation_to_Understand_the_Relationship_Between_Variables-checkpoint.ipynb
│ │ ├── How_to_Use_Parametric_Statistical_Significance_Tests_in_Python-checkpoint.ipynb
│ │ ├── How_to_Use_Statistical_Significance_Tests_to_Interpret_ML_Results-checkpoint.ipynb
│ │ ├── How_to_Use_Stats_to_Identify_Outliers_in_Data-checkpoint.ipynb
│ │ ├── Intro_to_Nonparametric_Statistical_Significance_Tests_in_Python-checkpoint.ipynb
│ │ ├── Intro_to_Random_Number_Generators_for_ML_in_Python-checkpoint.ipynb
│ │ ├── Prediction_Intervals_for_ML-checkpoint.ipynb
│ │ └── how_to_code_t_test_from_scratch-checkpoint.ipynb
│ ├── A_Gentle_Intro_to_Calculating_Normal_Summary_Stats.ipynb
│ ├── A_Gentle_Intro_to_Chi_Squared_Test_for_ML.ipynb
│ ├── A_Gentle_Intro_to_Data_Visualization_Methods_in_Python.ipynb
│ ├── A_Gentle_Intro_to_Estimation_Stats_for_ML.ipynb
│ ├── A_Gentle_Intro_to_Nonparametric_Stats.ipynb
│ ├── A_Gentle_Intro_to_Normality_Tests_in_Python.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Data_Distributions.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Hypothesis_Tests.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Sampling_and_Resampling.ipynb
│ ├── A_Gentle_Intro_to_Statistical_Tolerance_Intervals_in_ML.ipynb
│ ├── A_Gentle_Intro_to_k_fold_cross_validation.ipynb
│ ├── A_Gentle_Intro_to_the_Bootstrap_Method.ipynb
│ ├── A_Gentle_Intro_to_the_Central_Limit_Theorem_for_ML.ipynb
│ ├── A_Gentle_Intro_to_the_Law_of_Large_Numbers_in_ML.ipynb
│ ├── A_Gentle_Introduction_to_Effect_Size_Measures_in_Python.ipynb
│ ├── A_Gentle_Introduction_to_Statistical_Power_and_Power_Analysis_in_Python.ipynb
│ ├── Confidence_Intervals_for_ML.ipynb
│ ├── Critical_Values_for_Statistical_Hypothesis_Testing_in_Python.ipynb
│ ├── Estimate_the_Number_of_Experiment_Repeats_for_Stochastic_Machine_Learning_Algorithms.ipynb
│ ├── How_To_Generate_Random_Numbers_in_Python.ipynb
│ ├── How_to_Calculate_Bootstrap_Confidence_Interval_for_ML_Results_in_Python.ipynb
│ ├── How_to_Calculate_Nonparametric_Rank_Correlation_in_Python.ipynb
│ ├── How_to_Calculate_the_5_Number_Summary_for_your_data.ipynb
│ ├── How_to_Report_Classifier_Performance_with_Confidence_Intervals.ipynb
│ ├── How_to_Transform_Data_to_Better_Fit_the_Normal_Distribution.ipynb
│ ├── How_to_Use_Correlation_to_Understand_the_Relationship_Between_Variables.ipynb
│ ├── How_to_Use_Parametric_Statistical_Significance_Tests_in_Python.ipynb
│ ├── How_to_Use_Statistical_Significance_Tests_to_Interpret_ML_Results.ipynb
│ ├── How_to_Use_Stats_to_Identify_Outliers_in_Data.ipynb
│ ├── Intro_to_Nonparametric_Statistical_Significance_Tests_in_Python.ipynb
│ ├── Intro_to_Random_Number_Generators_for_ML_in_Python.ipynb
│ ├── Prediction_Intervals_for_ML.ipynb
│ ├── README.md
│ ├── how_to_code_t_test_from_scratch.ipynb
│ ├── pima-indians-diabetes.csv
│ ├── results.csv
│ ├── results1.csv
│ └── results2.csv
├── Time-Series-Forecasting/
│ ├── .ipynb_checkpoints/
│ │ ├── 7_time_series_dataset_for_machine_learning-checkpoint.ipynb
│ │ ├── backtest_machine_learning_models_for_time_series_forecasting-checkpoint.ipynb
│ │ ├── basic_feature_engineering_with_time_series_data_in_python-checkpoint.ipynb
│ │ ├── how_to_Grid_Search_ARIMA_model_hyperparameters_with_Python-checkpoint.ipynb
│ │ ├── how_to_check_if_time_series_data_is_stationary_with_Python-checkpoint.ipynb
│ │ ├── how_to_create_an_ARIMA_model_for_Time_Series_Forecasting_with_Python-checkpoint.ipynb
│ │ ├── how_to_make_baseline_predictions_for_time_series_forecasting_with_python-checkpoint.ipynb
│ │ ├── how_to_work_through_a_time_series_forecast_project-checkpoint.ipynb
│ │ └── load_and_explore_time_series_data_in_python-checkpoint.ipynb
│ ├── 7_time_series_dataset_for_machine_learning.ipynb
│ ├── README.md
│ ├── backtest_machine_learning_models_for_time_series_forecasting.ipynb
│ ├── basic_feature_engineering_with_time_series_data_in_python.ipynb
│ ├── daily-minimum-temperatures-in-me.csv
│ ├── daily-total-female-births.csv
│ ├── how_to_Grid_Search_ARIMA_model_hyperparameters_with_Python.ipynb
│ ├── how_to_check_if_time_series_data_is_stationary_with_Python.ipynb
│ ├── how_to_create_an_ARIMA_model_for_Time_Series_Forecasting_with_Python.ipynb
│ ├── how_to_make_baseline_predictions_for_time_series_forecasting_with_python.ipynb
│ ├── how_to_work_through_a_time_series_forecast_project.ipynb
│ ├── international-airline-passengers.csv
│ ├── load_and_explore_time_series_data_in_python.ipynb
│ ├── shampoo-sales.csv
│ └── sunspots.csv
└── XGBoost/
├── .ipynb_checkpoints/
│ ├── avoid_overfitting_by_early_stopping_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── data_preparation_for_gradient_boosting_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── feature_importance_and_feature_selection_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── how_to_best_tune_multithreading_support_for_XGBoost_in_Python-checkpoint.ipynb
│ ├── how_to_configure_the_gradient_boosting_algorithm-checkpoint.ipynb
│ ├── how_to_evaluate_gradient_boosting_models_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── how_to_tune_the_number_and_size_of_decision_trees_with_XGBoost_in_Python-checkpoint.ipynb
│ ├── stochastic_gradient_boosting_with_XGBoost_and_Scikitlearn_in_Python-checkpoint.ipynb
│ └── tune_learning_rate_for_gradient_boosting_with_XGBoost_in_Python-checkpoint.ipynb
├── README.md
├── avoid_overfitting_by_early_stopping_with_XGBoost_in_Python.ipynb
├── data_preparation_for_gradient_boosting_with_XGBoost_in_Python.ipynb
├── feature_importance_and_feature_selection_with_XGBoost_in_Python.ipynb
├── horse-colic.csv
├── how_to_best_tune_multithreading_support_for_XGBoost_in_Python.ipynb
├── how_to_configure_the_gradient_boosting_algorithm.ipynb
├── how_to_evaluate_gradient_boosting_models_with_XGBoost_in_Python.ipynb
├── how_to_tune_the_number_and_size_of_decision_trees_with_XGBoost_in_Python.ipynb
├── iris.csv
├── pima-indians-diabetes.csv
├── stochastic_gradient_boosting_with_XGBoost_and_Scikitlearn_in_Python.ipynb
├── train.csv
└── tune_learning_rate_for_gradient_boosting_with_XGBoost_in_Python.ipynb
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[
{
"path": "Algorithms-From-Scratch/algorithm-test-harness.py",
"chars": 4702,
"preview": "# A test harness provides a consistent way to evaluate machine learning algorithms on a dataset.\n\n# It involves 3 elemen..."
},
{
"path": "Algorithms-From-Scratch/backpropagation.py",
"chars": 6009,
"preview": "# Backprop on the Seeds Dataset\nfrom random import seed\nfrom random import randrange\nfrom random import random\nfrom csv..."
},
{
"path": "Algorithms-From-Scratch/bagging.py",
"chars": 6181,
"preview": "# Bagging Algorithm on the Sonar dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\n\n#..."
},
{
"path": "Algorithms-From-Scratch/data_banknote_authentication.csv",
"chars": 46400,
"preview": "3.6216,8.6661,-2.8073,-0.44699,0\r\n4.5459,8.1674,-2.4586,-1.4621,0\r\n3.866,-2.6383,1.9242,0.10645,0\r\n3.4566,9.5228,-4.0112..."
},
{
"path": "Algorithms-From-Scratch/decision-tree.py",
"chars": 5028,
"preview": "# CART on the Bank Note dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\n\n# Load a CS..."
},
{
"path": "Algorithms-From-Scratch/ionosphere.csv",
"chars": 76467,
"preview": "1,0,0.99539,-0.05889,0.85243,0.02306,0.83398,-0.37708,1,0.03760,0.85243,-0.17755,0.59755,-0.44945,0.60536,-0.38223,0.843..."
},
{
"path": "Algorithms-From-Scratch/iris.data.csv",
"chars": 3858,
"preview": "sepal_length,sepal_width,petal_length,petal_width,species\n5.1,3.5,1.4,0.2,setosa\n4.9,3.0,1.4,0.2,setosa\n4.7,3.2,1.3,0.2,..."
},
{
"path": "Algorithms-From-Scratch/knn.py",
"chars": 2151,
"preview": "# Example of kNN implemented from Scratch in Python\n\nimport csv\nimport random\nimport math\nimport operator\n\ndef loadDatas..."
},
{
"path": "Algorithms-From-Scratch/learning-vector-quantization.py",
"chars": 4085,
"preview": "# LVQ for the Ionosphere Dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\nfrom math i..."
},
{
"path": "Algorithms-From-Scratch/linear-reg-SGD.py",
"chars": 3835,
"preview": "# Linear Regression With Stochastic Gradient Descent for Wine Quality\nfrom random import seed\nfrom random import randran..."
},
{
"path": "Algorithms-From-Scratch/logistic-reg-SGD.py",
"chars": 3660,
"preview": "# Logistic Regression on Diabetes Dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\nfr..."
},
{
"path": "Algorithms-From-Scratch/naive-bayes.py",
"chars": 3004,
"preview": "# Example of Naive Bayes implemented from Scratch in Python\nimport csv\nimport random\nimport math\n\ndef loadCsv(filename):..."
},
{
"path": "Algorithms-From-Scratch/perceptron.py",
"chars": 3375,
"preview": "# Perceptron Algorithm on the Sonar Dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader..."
},
{
"path": "Algorithms-From-Scratch/performance-metrics.py",
"chars": 2821,
"preview": "# You must estimate the quality of a set of predictions when training a machine learning model. Performance metrics like..."
},
{
"path": "Algorithms-From-Scratch/pima-indians-diabetes.data.csv",
"chars": 23278,
"preview": "6,148,72,35,0,33.6,0.627,50,1\n1,85,66,29,0,26.6,0.351,31,0\n8,183,64,0,0,23.3,0.672,32,1\n1,89,66,23,94,28.1,0.167,21,0\n0,..."
},
{
"path": "Algorithms-From-Scratch/random-forest.py",
"chars": 6446,
"preview": "# Random Forest Algorithm on Sonar Dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\nf..."
},
{
"path": "Algorithms-From-Scratch/resampling.py",
"chars": 1587,
"preview": "# The goal of resampling methods is to make the best use of your training data in order to accurately estimate the perfo..."
},
{
"path": "Algorithms-From-Scratch/simple-linear-regression.py",
"chars": 3964,
"preview": "# Simple Linear Regression\n# Linear regression assumes a linear or straight line relationship between the input variable..."
},
{
"path": "Algorithms-From-Scratch/sonar.all-data.csv",
"chars": 87776,
"preview": "0.0200,0.0371,0.0428,0.0207,0.0954,0.0986,0.1539,0.1601,0.3109,0.2111,0.1609,0.1582,0.2238,0.0645,0.0660,0.2273,0.3100,0..."
},
{
"path": "Algorithms-From-Scratch/stack-generalization.py",
"chars": 5767,
"preview": "# Test stacking on the sonar dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\nfrom ma..."
},
{
"path": "Algorithms-From-Scratch/wheat-seeds.csv",
"chars": 9300,
"preview": "15.26,14.84,0.871,5.763,3.312,2.221,5.22,1\n14.88,14.57,0.8811,5.554,3.333,1.018,4.956,1\n14.29,14.09,0.905,5.291,3.337,2...."
},
{
"path": "Algorithms-From-Scratch/winequality-white.csv",
"chars": 264256,
"preview": "7;0.27;0.36;20.7;0.045;45;170;1.001;3;0.45;8.8;6\n6.3;0.3;0.34;1.6;0.049;14;132;0.994;3.3;0.49;9.5;6\n8.1;0.28;0.4;6.9;0.0..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/5_step_life_cycle_neural_network_models_in_keras-checkpoint.ipynb",
"chars": 28662,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 5 Step Life-Cycle for Neural Netw..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/crash_course_in_convolutional_neural_networks_for_machine_learning-checkpoint.ipynb",
"chars": 14131,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Crash Course in Convolutional Neu..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/crash_course_on_multilayer_perceptron_neural_networks-checkpoint.ipynb",
"chars": 14047,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Crash Course On Multi-Layer Perce..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/crash_course_recurrent_neural_networks_for_deep_learning-checkpoint.ipynb",
"chars": 11735,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Crash Course in Recurrent Neural..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/display_deep_learning_model_training_history_keras-checkpoint.ipynb",
"chars": 85898,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Display Deep Learning Model Train..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/dropout_regularization_in_deep_learning_models_with_keras-checkpoint.ipynb",
"chars": 17240,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Dropout Regularization in Deep Le..."
},
{
"path": "Deep-Learning/.ipynb_checkpoints/grid_search_hyperparameters_for_deep_learning_models_in_python_with_keras-checkpoint.ipynb",
"chars": 43348,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to Grid Search Hyperparameter..."
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// ... and 2140 more files (download for full content)
About this extraction
This page contains the full source code of the khanhnamle1994/applied-machine-learning GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2346 files (37.3 MB), approximately 6.8M tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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