├── .idea ├── MachineLearningNote.iml ├── inspectionProfiles │ └── Project_Default.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── DecisionTree ├── calc_entropy.py ├── classifierStorage.txt ├── decision_tree.py ├── lense_decision.py ├── lenses.txt └── plot_decision.py ├── EM(GMM) ├── Fremont.csv ├── GMM.ipynb ├── gmm_fremont.py └── gmm_newdata.py ├── EnsembleLearning ├── baggingexample.py ├── stackingexample.py ├── stackingexampleplus1.py ├── stackingexampleplus2.py ├── stackingexampleplus3.py └── votingexample.py ├── IsolationForest ├── iForest_example1.py └── iForest_example2.py ├── K-Means&DBSCAN ├── DBSCAN.py ├── K-Means.py ├── MiniBatch_K-Means.py ├── Partical_clustering_algorithm.py └── data.txt ├── KNN ├── Dating_Site_Pairing_KNN.py ├── KNN2.py ├── datingTestSet.txt ├── datingTestSet2.txt ├── sklearn-knn.py ├── sklearn_KNN_digits.py ├── testDigits │ ├── 0_0.txt │ ├── 0_1.txt │ ├── 0_10.txt │ ├── 0_11.txt │ ├── 0_12.txt │ ├── 0_13.txt │ ├── 0_14.txt │ ├── 0_15.txt │ ├── 0_16.txt │ ├── 0_17.txt │ ├── 0_18.txt │ ├── 0_19.txt │ ├── 0_2.txt │ ├── 0_20.txt │ ├── 0_21.txt │ ├── 0_22.txt │ ├── 0_23.txt │ ├── 0_24.txt │ ├── 0_25.txt │ ├── 0_26.txt │ ├── 0_27.txt │ ├── 0_28.txt │ ├── 0_29.txt │ ├── 0_3.txt │ ├── 0_30.txt │ ├── 0_31.txt │ ├── 0_32.txt │ ├── 0_33.txt │ ├── 0_34.txt │ ├── 0_35.txt │ ├── 0_36.txt │ ├── 0_37.txt │ ├── 0_38.txt │ ├── 0_39.txt │ ├── 0_4.txt │ ├── 0_40.txt │ ├── 0_41.txt │ ├── 0_42.txt │ ├── 0_43.txt │ ├── 0_44.txt │ ├── 0_45.txt │ ├── 0_46.txt │ ├── 0_47.txt │ ├── 0_48.txt │ ├── 0_49.txt │ ├── 0_5.txt │ ├── 0_50.txt │ ├── 0_51.txt │ ├── 0_52.txt │ ├── 0_53.txt │ ├── 0_54.txt │ ├── 0_55.txt │ ├── 0_56.txt │ ├── 0_57.txt │ ├── 0_58.txt │ ├── 0_59.txt │ ├── 0_6.txt │ ├── 0_60.txt │ ├── 0_61.txt │ ├── 0_62.txt │ ├── 0_63.txt │ ├── 0_64.txt │ ├── 0_65.txt │ ├── 0_66.txt │ ├── 0_67.txt │ ├── 0_68.txt │ ├── 0_69.txt │ ├── 0_7.txt │ ├── 0_70.txt │ ├── 0_71.txt │ ├── 0_72.txt │ ├── 0_73.txt │ ├── 0_74.txt │ ├── 0_75.txt │ ├── 0_76.txt │ ├── 0_77.txt │ ├── 0_78.txt │ ├── 0_79.txt │ ├── 0_8.txt │ ├── 0_80.txt │ ├── 0_81.txt │ ├── 0_82.txt │ ├── 0_83.txt │ ├── 0_84.txt │ ├── 0_85.txt │ ├── 0_86.txt │ ├── 0_9.txt │ ├── 1_0.txt │ ├── 1_1.txt │ ├── 1_10.txt │ ├── 1_11.txt │ ├── 1_12.txt │ ├── 1_13.txt │ ├── 1_14.txt │ ├── 1_15.txt │ ├── 1_16.txt │ ├── 1_17.txt │ ├── 1_18.txt │ ├── 1_19.txt │ ├── 1_2.txt │ ├── 1_20.txt │ ├── 1_21.txt │ ├── 1_22.txt │ ├── 1_23.txt │ ├── 1_24.txt │ ├── 1_25.txt │ ├── 1_26.txt │ ├── 1_27.txt │ ├── 1_28.txt │ ├── 1_29.txt │ ├── 1_3.txt │ ├── 1_30.txt │ ├── 1_31.txt │ ├── 1_32.txt │ ├── 1_33.txt │ ├── 1_34.txt │ ├── 1_35.txt │ ├── 1_36.txt │ ├── 1_37.txt │ ├── 1_38.txt │ ├── 1_39.txt │ ├── 1_4.txt │ ├── 1_40.txt │ ├── 1_41.txt │ ├── 1_42.txt │ ├── 1_43.txt │ ├── 1_44.txt │ ├── 1_45.txt │ ├── 1_46.txt │ ├── 1_47.txt │ ├── 1_48.txt │ ├── 1_49.txt │ ├── 1_5.txt │ ├── 1_50.txt │ ├── 1_51.txt │ ├── 1_52.txt │ ├── 1_53.txt │ ├── 1_54.txt │ ├── 1_55.txt │ ├── 1_56.txt │ ├── 1_57.txt │ ├── 1_58.txt │ ├── 1_59.txt │ ├── 1_6.txt │ ├── 1_60.txt │ ├── 1_61.txt │ ├── 1_62.txt │ ├── 1_63.txt │ ├── 1_64.txt │ ├── 1_65.txt │ ├── 1_66.txt │ ├── 1_67.txt │ ├── 1_68.txt │ ├── 1_69.txt │ ├── 1_7.txt │ ├── 1_70.txt │ ├── 1_71.txt │ ├── 1_72.txt │ ├── 1_73.txt │ ├── 1_74.txt │ ├── 1_75.txt │ ├── 1_76.txt │ ├── 1_77.txt │ ├── 1_78.txt │ ├── 1_79.txt │ ├── 1_8.txt │ ├── 1_80.txt │ ├── 1_81.txt │ ├── 1_82.txt │ ├── 1_83.txt │ ├── 1_84.txt │ ├── 1_85.txt │ ├── 1_86.txt │ ├── 1_87.txt │ ├── 1_88.txt │ ├── 1_89.txt │ ├── 1_9.txt │ ├── 1_90.txt │ ├── 1_91.txt │ ├── 1_92.txt │ ├── 1_93.txt │ ├── 1_94.txt │ ├── 1_95.txt │ ├── 1_96.txt │ ├── 2_0.txt │ ├── 2_1.txt │ ├── 2_10.txt │ ├── 2_11.txt │ ├── 2_12.txt │ ├── 2_13.txt │ ├── 2_14.txt │ ├── 2_15.txt │ ├── 2_16.txt │ ├── 2_17.txt │ ├── 2_18.txt │ ├── 2_19.txt │ ├── 2_2.txt │ ├── 2_20.txt │ ├── 2_21.txt │ ├── 2_22.txt │ ├── 2_23.txt │ ├── 2_24.txt │ ├── 2_25.txt │ ├── 2_26.txt │ ├── 2_27.txt │ ├── 2_28.txt │ ├── 2_29.txt │ ├── 2_3.txt │ ├── 2_30.txt │ ├── 2_31.txt │ ├── 2_32.txt │ ├── 2_33.txt │ ├── 2_34.txt │ ├── 2_35.txt │ ├── 2_36.txt │ ├── 2_37.txt │ ├── 2_38.txt │ ├── 2_39.txt │ ├── 2_4.txt │ ├── 2_40.txt │ ├── 2_41.txt │ ├── 2_42.txt │ ├── 2_43.txt │ ├── 2_44.txt │ ├── 2_45.txt │ ├── 2_46.txt │ ├── 2_47.txt │ ├── 2_48.txt │ ├── 2_49.txt │ ├── 2_5.txt │ ├── 2_50.txt │ ├── 2_51.txt │ ├── 2_52.txt │ ├── 2_53.txt │ ├── 2_54.txt │ ├── 2_55.txt │ ├── 2_56.txt │ ├── 2_57.txt │ ├── 2_58.txt │ ├── 2_59.txt │ ├── 2_6.txt │ ├── 2_60.txt │ ├── 2_61.txt │ ├── 2_62.txt │ ├── 2_63.txt │ ├── 2_64.txt │ ├── 2_65.txt │ ├── 2_66.txt │ ├── 2_67.txt │ ├── 2_68.txt │ ├── 2_69.txt │ ├── 2_7.txt │ ├── 2_70.txt │ ├── 2_71.txt │ ├── 2_72.txt │ ├── 2_73.txt │ ├── 2_74.txt │ ├── 2_75.txt │ ├── 2_76.txt │ ├── 2_77.txt │ ├── 2_78.txt │ ├── 2_79.txt │ ├── 2_8.txt │ ├── 2_80.txt │ ├── 2_81.txt │ ├── 2_82.txt │ ├── 2_83.txt │ ├── 2_84.txt │ ├── 2_85.txt │ ├── 2_86.txt │ ├── 2_87.txt │ ├── 2_88.txt │ ├── 2_89.txt │ ├── 2_9.txt │ ├── 2_90.txt │ ├── 2_91.txt │ ├── 3_0.txt │ ├── 3_1.txt │ ├── 3_10.txt │ ├── 3_11.txt │ ├── 3_12.txt │ ├── 3_13.txt │ ├── 3_14.txt │ ├── 3_15.txt │ ├── 3_16.txt │ ├── 3_17.txt │ ├── 3_18.txt │ ├── 3_19.txt │ ├── 3_2.txt │ ├── 3_20.txt │ ├── 3_21.txt │ ├── 3_22.txt │ ├── 3_23.txt │ ├── 3_24.txt │ ├── 3_25.txt │ ├── 3_26.txt │ ├── 3_27.txt │ ├── 3_28.txt │ ├── 3_29.txt │ ├── 3_3.txt │ ├── 3_30.txt │ ├── 3_31.txt │ ├── 3_32.txt │ ├── 3_33.txt │ ├── 3_34.txt │ ├── 3_35.txt │ ├── 3_36.txt │ ├── 3_37.txt │ ├── 3_38.txt │ ├── 3_39.txt │ ├── 3_4.txt │ ├── 3_40.txt │ ├── 3_41.txt │ ├── 3_42.txt │ ├── 3_43.txt │ ├── 3_44.txt │ ├── 3_45.txt │ ├── 3_46.txt │ ├── 3_47.txt │ ├── 3_48.txt │ ├── 3_49.txt │ ├── 3_5.txt │ ├── 3_50.txt │ ├── 3_51.txt │ ├── 3_52.txt │ ├── 3_53.txt │ ├── 3_54.txt │ ├── 3_55.txt │ ├── 3_56.txt │ ├── 3_57.txt │ ├── 3_58.txt │ ├── 3_59.txt │ ├── 3_6.txt │ ├── 3_60.txt │ ├── 3_61.txt │ ├── 3_62.txt │ ├── 3_63.txt │ ├── 3_64.txt │ ├── 3_65.txt │ ├── 3_66.txt │ ├── 3_67.txt │ ├── 3_68.txt │ ├── 3_69.txt │ ├── 3_7.txt │ ├── 3_70.txt │ ├── 3_71.txt │ ├── 3_72.txt │ ├── 3_73.txt │ ├── 3_74.txt │ ├── 3_75.txt │ ├── 3_76.txt │ ├── 3_77.txt │ ├── 3_78.txt │ ├── 3_79.txt │ ├── 3_8.txt │ ├── 3_80.txt │ ├── 3_81.txt │ ├── 3_82.txt │ ├── 3_83.txt │ ├── 3_84.txt │ ├── 3_9.txt │ ├── 4_0.txt │ ├── 4_1.txt │ ├── 4_10.txt │ ├── 4_100.txt │ ├── 4_101.txt │ ├── 4_102.txt │ ├── 4_103.txt │ ├── 4_104.txt │ ├── 4_105.txt │ ├── 4_106.txt │ ├── 4_107.txt │ ├── 4_108.txt │ ├── 4_109.txt │ ├── 4_11.txt │ ├── 4_110.txt │ ├── 4_111.txt │ ├── 4_112.txt │ ├── 4_113.txt │ ├── 4_12.txt │ ├── 4_13.txt │ ├── 4_14.txt │ ├── 4_15.txt │ ├── 4_16.txt │ ├── 4_17.txt │ ├── 4_18.txt │ ├── 4_19.txt │ ├── 4_2.txt │ ├── 4_20.txt │ ├── 4_21.txt │ ├── 4_22.txt │ ├── 4_23.txt │ ├── 4_24.txt │ ├── 4_25.txt │ ├── 4_26.txt │ ├── 4_27.txt │ ├── 4_28.txt │ ├── 4_29.txt │ ├── 4_3.txt │ ├── 4_30.txt │ ├── 4_31.txt │ ├── 4_32.txt │ ├── 4_33.txt │ ├── 4_34.txt │ ├── 4_35.txt │ ├── 4_36.txt │ ├── 4_37.txt │ ├── 4_38.txt │ ├── 4_39.txt │ ├── 4_4.txt │ ├── 4_40.txt │ ├── 4_41.txt │ ├── 4_42.txt │ ├── 4_43.txt │ ├── 4_44.txt │ ├── 4_45.txt │ ├── 4_46.txt │ ├── 4_47.txt │ ├── 4_48.txt │ ├── 4_49.txt │ ├── 4_5.txt │ ├── 4_50.txt │ ├── 4_51.txt │ ├── 4_52.txt │ ├── 4_53.txt │ ├── 4_54.txt │ ├── 4_55.txt │ ├── 4_56.txt │ ├── 4_57.txt │ ├── 4_58.txt │ ├── 4_59.txt │ ├── 4_6.txt │ ├── 4_60.txt │ ├── 4_61.txt │ ├── 4_62.txt │ ├── 4_63.txt │ ├── 4_64.txt │ ├── 4_65.txt │ ├── 4_66.txt │ ├── 4_67.txt │ ├── 4_68.txt │ ├── 4_69.txt │ ├── 4_7.txt │ ├── 4_70.txt │ ├── 4_71.txt │ ├── 4_72.txt │ ├── 4_73.txt │ ├── 4_74.txt │ ├── 4_75.txt │ ├── 4_76.txt │ ├── 4_77.txt │ ├── 4_78.txt │ ├── 4_79.txt │ ├── 4_8.txt │ ├── 4_80.txt │ ├── 4_81.txt │ ├── 4_82.txt │ ├── 4_83.txt │ ├── 4_84.txt │ ├── 4_85.txt │ ├── 4_86.txt │ ├── 4_87.txt │ ├── 4_88.txt │ ├── 4_89.txt │ ├── 4_9.txt │ ├── 4_90.txt │ ├── 4_91.txt │ ├── 4_92.txt │ ├── 4_93.txt │ ├── 4_94.txt │ ├── 4_95.txt │ ├── 4_96.txt │ ├── 4_97.txt │ ├── 4_98.txt │ ├── 4_99.txt │ ├── 5_0.txt │ ├── 5_1.txt │ ├── 5_10.txt │ ├── 5_100.txt │ ├── 5_101.txt │ ├── 5_102.txt │ ├── 5_103.txt │ ├── 5_104.txt │ ├── 5_105.txt │ ├── 5_106.txt │ ├── 5_107.txt │ ├── 5_11.txt │ ├── 5_12.txt │ ├── 5_13.txt │ ├── 5_14.txt │ ├── 5_15.txt │ ├── 5_16.txt │ ├── 5_17.txt │ ├── 5_18.txt │ ├── 5_19.txt │ ├── 5_2.txt │ ├── 5_20.txt │ ├── 5_21.txt │ ├── 5_22.txt │ ├── 5_23.txt │ ├── 5_24.txt │ ├── 5_25.txt │ ├── 5_26.txt │ ├── 5_27.txt │ ├── 5_28.txt │ ├── 5_29.txt │ ├── 5_3.txt │ ├── 5_30.txt │ ├── 5_31.txt │ ├── 5_32.txt │ ├── 5_33.txt │ ├── 5_34.txt │ ├── 5_35.txt │ ├── 5_36.txt │ ├── 5_37.txt │ ├── 5_38.txt │ ├── 5_39.txt │ ├── 5_4.txt │ ├── 5_40.txt │ ├── 5_41.txt │ ├── 5_42.txt │ ├── 5_43.txt │ ├── 5_44.txt │ ├── 5_45.txt │ ├── 5_46.txt │ ├── 5_47.txt │ ├── 5_48.txt │ ├── 5_49.txt │ ├── 5_5.txt │ ├── 5_50.txt │ ├── 5_51.txt │ ├── 5_52.txt │ ├── 5_53.txt │ ├── 5_54.txt │ ├── 5_55.txt │ ├── 5_56.txt │ ├── 5_57.txt │ ├── 5_58.txt │ ├── 5_59.txt │ ├── 5_6.txt │ ├── 5_60.txt │ ├── 5_61.txt │ ├── 5_62.txt │ ├── 5_63.txt │ ├── 5_64.txt │ ├── 5_65.txt │ ├── 5_66.txt │ ├── 5_67.txt │ ├── 5_68.txt │ ├── 5_69.txt │ ├── 5_7.txt │ ├── 5_70.txt │ ├── 5_71.txt │ ├── 5_72.txt │ ├── 5_73.txt │ ├── 5_74.txt │ ├── 5_75.txt │ ├── 5_76.txt │ ├── 5_77.txt │ ├── 5_78.txt │ ├── 5_79.txt │ ├── 5_8.txt │ ├── 5_80.txt │ ├── 5_81.txt │ ├── 5_82.txt │ ├── 5_83.txt │ ├── 5_84.txt │ ├── 5_85.txt │ ├── 5_86.txt │ ├── 5_87.txt │ ├── 5_88.txt │ ├── 5_89.txt │ ├── 5_9.txt │ ├── 5_90.txt │ ├── 5_91.txt │ ├── 5_92.txt │ ├── 5_93.txt │ ├── 5_94.txt │ ├── 5_95.txt │ ├── 5_96.txt │ ├── 5_97.txt │ ├── 5_98.txt │ ├── 5_99.txt │ ├── 6_0.txt │ ├── 6_1.txt │ ├── 6_10.txt │ ├── 6_11.txt │ ├── 6_12.txt │ ├── 6_13.txt │ ├── 6_14.txt │ ├── 6_15.txt │ ├── 6_16.txt │ ├── 6_17.txt │ ├── 6_18.txt │ ├── 6_19.txt │ ├── 6_2.txt │ ├── 6_20.txt │ ├── 6_21.txt │ ├── 6_22.txt │ ├── 6_23.txt │ ├── 6_24.txt │ ├── 6_25.txt │ ├── 6_26.txt │ ├── 6_27.txt │ ├── 6_28.txt │ ├── 6_29.txt │ ├── 6_3.txt │ ├── 6_30.txt │ ├── 6_31.txt │ ├── 6_32.txt │ ├── 6_33.txt │ ├── 6_34.txt │ ├── 6_35.txt │ ├── 6_36.txt │ ├── 6_37.txt │ ├── 6_38.txt │ ├── 6_39.txt │ ├── 6_4.txt │ ├── 6_40.txt │ ├── 6_41.txt │ ├── 6_42.txt │ ├── 6_43.txt │ ├── 6_44.txt │ ├── 6_45.txt │ ├── 6_46.txt │ ├── 6_47.txt │ ├── 6_48.txt │ ├── 6_49.txt │ ├── 6_5.txt │ ├── 6_50.txt │ ├── 6_51.txt │ ├── 6_52.txt │ ├── 6_53.txt │ ├── 6_54.txt │ ├── 6_55.txt │ ├── 6_56.txt │ ├── 6_57.txt │ ├── 6_58.txt │ ├── 6_59.txt │ ├── 6_6.txt │ ├── 6_60.txt │ ├── 6_61.txt │ ├── 6_62.txt │ ├── 6_63.txt │ ├── 6_64.txt │ ├── 6_65.txt │ ├── 6_66.txt │ ├── 6_67.txt │ ├── 6_68.txt │ ├── 6_69.txt │ ├── 6_7.txt │ ├── 6_70.txt │ ├── 6_71.txt │ ├── 6_72.txt │ ├── 6_73.txt │ ├── 6_74.txt │ ├── 6_75.txt │ ├── 6_76.txt │ ├── 6_77.txt │ ├── 6_78.txt │ ├── 6_79.txt │ ├── 6_8.txt │ ├── 6_80.txt │ ├── 6_81.txt │ ├── 6_82.txt │ ├── 6_83.txt │ ├── 6_84.txt │ ├── 6_85.txt │ ├── 6_86.txt │ ├── 6_9.txt │ ├── 7_0.txt │ ├── 7_1.txt │ ├── 7_10.txt │ ├── 7_11.txt │ ├── 7_12.txt │ ├── 7_13.txt │ ├── 7_14.txt │ ├── 7_15.txt │ ├── 7_16.txt │ ├── 7_17.txt │ ├── 7_18.txt │ ├── 7_19.txt │ ├── 7_2.txt │ ├── 7_20.txt │ ├── 7_21.txt │ ├── 7_22.txt │ ├── 7_23.txt │ ├── 7_24.txt │ ├── 7_25.txt │ ├── 7_26.txt │ ├── 7_27.txt │ ├── 7_28.txt │ ├── 7_29.txt │ ├── 7_3.txt │ ├── 7_30.txt │ ├── 7_31.txt │ ├── 7_32.txt │ ├── 7_33.txt │ ├── 7_34.txt │ ├── 7_35.txt │ ├── 7_36.txt │ ├── 7_37.txt │ ├── 7_38.txt │ ├── 7_39.txt │ ├── 7_4.txt │ ├── 7_40.txt │ ├── 7_41.txt │ ├── 7_42.txt │ ├── 7_43.txt │ ├── 7_44.txt │ ├── 7_45.txt │ ├── 7_46.txt │ ├── 7_47.txt │ ├── 7_48.txt │ ├── 7_49.txt │ ├── 7_5.txt │ ├── 7_50.txt │ ├── 7_51.txt │ ├── 7_52.txt │ ├── 7_53.txt │ ├── 7_54.txt │ ├── 7_55.txt │ ├── 7_56.txt │ ├── 7_57.txt │ ├── 7_58.txt │ ├── 7_59.txt │ ├── 7_6.txt │ ├── 7_60.txt │ ├── 7_61.txt │ ├── 7_62.txt │ ├── 7_63.txt │ ├── 7_64.txt │ ├── 7_65.txt │ ├── 7_66.txt │ ├── 7_67.txt │ ├── 7_68.txt │ ├── 7_69.txt │ ├── 7_7.txt │ ├── 7_70.txt │ ├── 7_71.txt │ ├── 7_72.txt │ ├── 7_73.txt │ ├── 7_74.txt │ ├── 7_75.txt │ ├── 7_76.txt │ ├── 7_77.txt │ ├── 7_78.txt │ ├── 7_79.txt │ ├── 7_8.txt │ ├── 7_80.txt │ ├── 7_81.txt │ ├── 7_82.txt │ ├── 7_83.txt │ ├── 7_84.txt │ ├── 7_85.txt │ ├── 7_86.txt │ ├── 7_87.txt │ ├── 7_88.txt │ ├── 7_89.txt │ ├── 7_9.txt │ ├── 7_90.txt │ ├── 7_91.txt │ ├── 7_92.txt │ ├── 7_93.txt │ ├── 7_94.txt │ ├── 7_95.txt │ ├── 8_0.txt │ ├── 8_1.txt │ ├── 8_10.txt │ ├── 8_11.txt │ ├── 8_12.txt │ ├── 8_13.txt │ ├── 8_14.txt │ ├── 8_15.txt │ ├── 8_16.txt │ ├── 8_17.txt │ ├── 8_18.txt │ ├── 8_19.txt │ ├── 8_2.txt │ ├── 8_20.txt │ ├── 8_21.txt │ ├── 8_22.txt │ ├── 8_23.txt │ ├── 8_24.txt │ ├── 8_25.txt │ ├── 8_26.txt │ ├── 8_27.txt │ ├── 8_28.txt │ ├── 8_29.txt │ ├── 8_3.txt │ ├── 8_30.txt │ ├── 8_31.txt │ ├── 8_32.txt │ ├── 8_33.txt │ ├── 8_34.txt │ ├── 8_35.txt │ ├── 8_36.txt │ ├── 8_37.txt │ ├── 8_38.txt │ ├── 8_39.txt │ ├── 8_4.txt │ ├── 8_40.txt │ ├── 8_41.txt │ ├── 8_42.txt │ ├── 8_43.txt │ ├── 8_44.txt │ ├── 8_45.txt │ ├── 8_46.txt │ ├── 8_47.txt │ ├── 8_48.txt │ ├── 8_49.txt │ ├── 8_5.txt │ ├── 8_50.txt │ ├── 8_51.txt │ ├── 8_52.txt │ ├── 8_53.txt │ ├── 8_54.txt │ ├── 8_55.txt │ ├── 8_56.txt │ ├── 8_57.txt │ ├── 8_58.txt │ ├── 8_59.txt │ ├── 8_6.txt │ ├── 8_60.txt │ ├── 8_61.txt │ ├── 8_62.txt │ ├── 8_63.txt │ ├── 8_64.txt │ ├── 8_65.txt │ ├── 8_66.txt │ ├── 8_67.txt │ ├── 8_68.txt │ ├── 8_69.txt │ ├── 8_7.txt │ ├── 8_70.txt │ ├── 8_71.txt │ ├── 8_72.txt │ ├── 8_73.txt │ ├── 8_74.txt │ ├── 8_75.txt │ ├── 8_76.txt │ ├── 8_77.txt │ ├── 8_78.txt │ ├── 8_79.txt │ ├── 8_8.txt │ ├── 8_80.txt │ ├── 8_81.txt │ ├── 8_82.txt │ ├── 8_83.txt │ ├── 8_84.txt │ ├── 8_85.txt │ ├── 8_86.txt │ ├── 8_87.txt │ ├── 8_88.txt │ ├── 8_89.txt │ ├── 8_9.txt │ ├── 8_90.txt │ ├── 9_0.txt │ ├── 9_1.txt │ ├── 9_10.txt │ ├── 9_11.txt │ ├── 9_12.txt │ ├── 9_13.txt │ ├── 9_14.txt │ ├── 9_15.txt │ ├── 9_16.txt │ ├── 9_17.txt │ ├── 9_18.txt │ ├── 9_19.txt │ ├── 9_2.txt │ ├── 9_20.txt │ ├── 9_21.txt │ ├── 9_22.txt │ ├── 9_23.txt │ ├── 9_24.txt │ ├── 9_25.txt │ ├── 9_26.txt │ ├── 9_27.txt │ ├── 9_28.txt │ ├── 9_29.txt │ ├── 9_3.txt │ ├── 9_30.txt │ ├── 9_31.txt │ ├── 9_32.txt │ ├── 9_33.txt │ ├── 9_34.txt │ ├── 9_35.txt │ ├── 9_36.txt │ ├── 9_37.txt │ ├── 9_38.txt │ ├── 9_39.txt │ ├── 9_4.txt │ ├── 9_40.txt │ ├── 9_41.txt │ ├── 9_42.txt │ ├── 9_43.txt │ ├── 9_44.txt │ ├── 9_45.txt │ ├── 9_46.txt │ ├── 9_47.txt │ ├── 9_48.txt │ ├── 9_49.txt │ ├── 9_5.txt │ ├── 9_50.txt │ ├── 9_51.txt │ ├── 9_52.txt │ ├── 9_53.txt │ ├── 9_54.txt │ ├── 9_55.txt │ ├── 9_56.txt │ ├── 9_57.txt │ ├── 9_58.txt │ ├── 9_59.txt │ ├── 9_6.txt │ ├── 9_60.txt │ ├── 9_61.txt │ ├── 9_62.txt │ ├── 9_63.txt │ ├── 9_64.txt │ ├── 9_65.txt │ ├── 9_66.txt │ ├── 9_67.txt │ ├── 9_68.txt │ ├── 9_69.txt │ ├── 9_7.txt │ ├── 9_70.txt │ ├── 9_71.txt │ ├── 9_72.txt │ ├── 9_73.txt │ ├── 9_74.txt │ ├── 9_75.txt │ ├── 9_76.txt │ ├── 9_77.txt │ ├── 9_78.txt │ ├── 9_79.txt │ ├── 9_8.txt │ ├── 9_80.txt │ ├── 9_81.txt │ ├── 9_82.txt │ ├── 9_83.txt │ ├── 9_84.txt │ ├── 9_85.txt │ ├── 9_86.txt │ ├── 9_87.txt │ ├── 9_88.txt │ └── 9_9.txt └── trainingDigits │ ├── 0_0.txt │ ├── 0_1.txt │ ├── 0_10.txt │ ├── 0_100.txt │ ├── 0_101.txt │ ├── 0_102.txt │ ├── 0_103.txt │ ├── 0_104.txt │ ├── 0_105.txt │ ├── 0_106.txt │ ├── 0_107.txt │ ├── 0_108.txt │ ├── 0_109.txt │ ├── 0_11.txt │ ├── 0_110.txt │ ├── 0_111.txt │ ├── 0_112.txt │ ├── 0_113.txt │ ├── 0_114.txt │ ├── 0_115.txt │ ├── 0_116.txt │ ├── 0_117.txt │ ├── 0_118.txt │ ├── 0_119.txt │ ├── 0_12.txt │ ├── 0_120.txt │ ├── 0_121.txt │ ├── 0_122.txt │ ├── 0_123.txt │ ├── 0_124.txt │ ├── 0_125.txt │ ├── 0_126.txt │ ├── 0_127.txt │ ├── 0_128.txt │ ├── 0_129.txt │ ├── 0_13.txt │ ├── 0_130.txt │ ├── 0_131.txt │ ├── 0_132.txt │ ├── 0_133.txt │ ├── 0_134.txt │ ├── 0_135.txt │ ├── 0_136.txt │ ├── 0_137.txt │ ├── 0_138.txt │ ├── 0_139.txt │ ├── 0_14.txt │ ├── 0_140.txt │ ├── 0_141.txt │ ├── 0_142.txt │ ├── 0_143.txt │ ├── 0_144.txt │ ├── 0_145.txt │ ├── 0_146.txt │ ├── 0_147.txt │ ├── 0_148.txt │ ├── 0_149.txt │ ├── 0_15.txt │ ├── 0_150.txt │ ├── 0_151.txt │ ├── 0_152.txt │ ├── 0_153.txt │ ├── 0_154.txt │ ├── 0_155.txt │ ├── 0_156.txt │ ├── 0_157.txt │ ├── 0_158.txt │ ├── 0_159.txt │ ├── 0_16.txt │ ├── 0_160.txt │ ├── 0_161.txt │ ├── 0_162.txt │ ├── 0_163.txt │ ├── 0_164.txt │ ├── 0_165.txt │ ├── 0_166.txt │ ├── 0_167.txt │ ├── 0_168.txt │ ├── 0_169.txt │ ├── 0_17.txt │ ├── 0_170.txt │ ├── 0_171.txt │ ├── 0_172.txt │ ├── 0_173.txt │ ├── 0_174.txt │ ├── 0_175.txt │ ├── 0_176.txt │ ├── 0_177.txt │ ├── 0_178.txt │ ├── 0_179.txt │ ├── 0_18.txt │ ├── 0_180.txt │ ├── 0_181.txt │ ├── 0_182.txt │ ├── 0_183.txt │ ├── 0_184.txt │ ├── 0_185.txt │ ├── 0_186.txt │ ├── 0_187.txt │ ├── 0_188.txt │ ├── 0_19.txt │ ├── 0_2.txt │ ├── 0_20.txt │ ├── 0_21.txt │ ├── 0_22.txt │ ├── 0_23.txt │ ├── 0_24.txt │ ├── 0_25.txt │ ├── 0_26.txt │ ├── 0_27.txt │ ├── 0_28.txt │ ├── 0_29.txt │ ├── 0_3.txt │ ├── 0_30.txt │ ├── 0_31.txt │ ├── 0_32.txt │ ├── 0_33.txt │ ├── 0_34.txt │ ├── 0_35.txt │ ├── 0_36.txt │ ├── 0_37.txt │ ├── 0_38.txt │ ├── 0_39.txt │ ├── 0_4.txt │ ├── 0_40.txt │ ├── 0_41.txt │ ├── 0_42.txt │ ├── 0_43.txt │ ├── 0_44.txt │ ├── 0_45.txt │ ├── 0_46.txt │ ├── 0_47.txt │ ├── 0_48.txt │ ├── 0_49.txt │ ├── 0_5.txt │ ├── 0_50.txt │ ├── 0_51.txt │ ├── 0_52.txt │ ├── 0_53.txt │ ├── 0_54.txt │ ├── 0_55.txt │ ├── 0_56.txt │ ├── 0_57.txt │ ├── 0_58.txt │ ├── 0_59.txt │ ├── 0_6.txt │ ├── 0_60.txt │ ├── 0_61.txt │ ├── 0_62.txt │ ├── 0_63.txt │ ├── 0_64.txt │ ├── 0_65.txt │ ├── 0_66.txt │ ├── 0_67.txt │ ├── 0_68.txt │ ├── 0_69.txt │ ├── 0_7.txt │ ├── 0_70.txt │ ├── 0_71.txt │ ├── 0_72.txt │ ├── 0_73.txt │ ├── 0_74.txt │ ├── 0_75.txt │ ├── 0_76.txt │ ├── 0_77.txt │ ├── 0_78.txt │ ├── 0_79.txt │ ├── 0_8.txt │ ├── 0_80.txt │ ├── 0_81.txt │ ├── 0_82.txt │ ├── 0_83.txt │ ├── 0_84.txt │ ├── 0_85.txt │ ├── 0_86.txt │ ├── 0_87.txt │ ├── 0_88.txt │ ├── 0_89.txt │ ├── 0_9.txt │ ├── 0_90.txt │ ├── 0_91.txt │ ├── 0_92.txt │ ├── 0_93.txt │ ├── 0_94.txt │ ├── 0_95.txt │ ├── 0_96.txt │ ├── 0_97.txt │ ├── 0_98.txt │ ├── 0_99.txt │ ├── 1_0.txt │ ├── 1_1.txt │ ├── 1_10.txt │ ├── 1_100.txt │ ├── 1_101.txt │ ├── 1_102.txt │ ├── 1_103.txt │ ├── 1_104.txt │ ├── 1_105.txt │ ├── 1_106.txt │ ├── 1_107.txt │ ├── 1_108.txt │ ├── 1_109.txt │ ├── 1_11.txt │ ├── 1_110.txt │ ├── 1_111.txt │ ├── 1_112.txt │ ├── 1_113.txt │ ├── 1_114.txt │ ├── 1_115.txt │ ├── 1_116.txt │ ├── 1_117.txt │ ├── 1_118.txt │ ├── 1_119.txt │ ├── 1_12.txt │ ├── 1_120.txt │ ├── 1_121.txt │ ├── 1_122.txt │ ├── 1_123.txt │ ├── 1_124.txt │ ├── 1_125.txt │ ├── 1_126.txt │ ├── 1_127.txt │ ├── 1_128.txt │ ├── 1_129.txt │ ├── 1_13.txt │ ├── 1_130.txt │ ├── 1_131.txt │ ├── 1_132.txt │ ├── 1_133.txt │ ├── 1_134.txt │ ├── 1_135.txt │ ├── 1_136.txt │ ├── 1_137.txt │ ├── 1_138.txt │ ├── 1_139.txt │ ├── 1_14.txt │ ├── 1_140.txt │ ├── 1_141.txt │ ├── 1_142.txt │ ├── 1_143.txt │ ├── 1_144.txt │ ├── 1_145.txt │ ├── 1_146.txt │ ├── 1_147.txt │ ├── 1_148.txt │ ├── 1_149.txt │ ├── 1_15.txt │ ├── 1_150.txt │ ├── 1_151.txt │ ├── 1_152.txt │ ├── 1_153.txt │ ├── 1_154.txt │ ├── 1_155.txt │ ├── 1_156.txt │ ├── 1_157.txt │ ├── 1_158.txt │ ├── 1_159.txt │ ├── 1_16.txt │ ├── 1_160.txt │ ├── 1_161.txt │ ├── 1_162.txt │ ├── 1_163.txt │ ├── 1_164.txt │ ├── 1_165.txt │ ├── 1_166.txt │ ├── 1_167.txt │ ├── 1_168.txt │ ├── 1_169.txt │ ├── 1_17.txt │ ├── 1_170.txt │ ├── 1_171.txt │ ├── 1_172.txt │ ├── 1_173.txt │ ├── 1_174.txt │ ├── 1_175.txt │ ├── 1_176.txt │ ├── 1_177.txt │ ├── 1_178.txt │ ├── 1_179.txt │ ├── 1_18.txt │ ├── 1_180.txt │ ├── 1_181.txt │ ├── 1_182.txt │ ├── 1_183.txt │ ├── 1_184.txt │ ├── 1_185.txt │ ├── 1_186.txt │ ├── 1_187.txt │ ├── 1_188.txt │ ├── 1_189.txt │ ├── 1_19.txt │ ├── 1_190.txt │ ├── 1_191.txt │ ├── 1_192.txt │ ├── 1_193.txt │ ├── 1_194.txt │ ├── 1_195.txt │ ├── 1_196.txt │ ├── 1_197.txt │ ├── 1_2.txt │ ├── 1_20.txt │ ├── 1_21.txt │ ├── 1_22.txt │ ├── 1_23.txt │ ├── 1_24.txt │ ├── 1_25.txt │ ├── 1_26.txt │ ├── 1_27.txt │ ├── 1_28.txt │ ├── 1_29.txt │ ├── 1_3.txt │ ├── 1_30.txt │ ├── 1_31.txt │ ├── 1_32.txt │ ├── 1_33.txt │ ├── 1_34.txt │ ├── 1_35.txt │ ├── 1_36.txt │ ├── 1_37.txt │ ├── 1_38.txt │ ├── 1_39.txt │ ├── 1_4.txt │ ├── 1_40.txt │ ├── 1_41.txt │ ├── 1_42.txt │ ├── 1_43.txt │ ├── 1_44.txt │ ├── 1_45.txt │ ├── 1_46.txt │ ├── 1_47.txt │ ├── 1_48.txt │ ├── 1_49.txt │ ├── 1_5.txt │ ├── 1_50.txt │ ├── 1_51.txt │ ├── 1_52.txt │ ├── 1_53.txt │ ├── 1_54.txt │ ├── 1_55.txt │ ├── 1_56.txt │ ├── 1_57.txt │ ├── 1_58.txt │ ├── 1_59.txt │ ├── 1_6.txt │ ├── 1_60.txt │ ├── 1_61.txt │ ├── 1_62.txt │ ├── 1_63.txt │ ├── 1_64.txt │ ├── 1_65.txt │ ├── 1_66.txt │ ├── 1_67.txt │ ├── 1_68.txt │ ├── 1_69.txt │ ├── 1_7.txt │ ├── 1_70.txt │ ├── 1_71.txt │ ├── 1_72.txt │ ├── 1_73.txt │ ├── 1_74.txt │ ├── 1_75.txt │ ├── 1_76.txt │ ├── 1_77.txt │ ├── 1_78.txt │ ├── 1_79.txt │ ├── 1_8.txt │ ├── 1_80.txt │ ├── 1_81.txt │ ├── 1_82.txt │ ├── 1_83.txt │ ├── 1_84.txt │ ├── 1_85.txt │ ├── 1_86.txt │ ├── 1_87.txt │ ├── 1_88.txt │ ├── 1_89.txt │ ├── 1_9.txt │ ├── 1_90.txt │ ├── 1_91.txt │ ├── 1_92.txt │ ├── 1_93.txt │ ├── 1_94.txt │ ├── 1_95.txt │ ├── 1_96.txt │ ├── 1_97.txt │ ├── 1_98.txt │ ├── 1_99.txt │ ├── 2_0.txt │ ├── 2_1.txt │ ├── 2_10.txt │ ├── 2_100.txt │ ├── 2_101.txt │ ├── 2_102.txt │ ├── 2_103.txt │ ├── 2_104.txt │ ├── 2_105.txt │ ├── 2_106.txt │ ├── 2_107.txt │ ├── 2_108.txt │ ├── 2_109.txt │ ├── 2_11.txt │ ├── 2_110.txt │ ├── 2_111.txt │ ├── 2_112.txt │ ├── 2_113.txt │ ├── 2_114.txt │ ├── 2_115.txt │ ├── 2_116.txt │ ├── 2_117.txt │ ├── 2_118.txt │ ├── 2_119.txt │ ├── 2_12.txt │ ├── 2_120.txt │ ├── 2_121.txt │ ├── 2_122.txt │ ├── 2_123.txt │ ├── 2_124.txt │ ├── 2_125.txt │ ├── 2_126.txt │ ├── 2_127.txt │ ├── 2_128.txt │ ├── 2_129.txt │ ├── 2_13.txt │ ├── 2_130.txt │ ├── 2_131.txt │ ├── 2_132.txt │ ├── 2_133.txt │ ├── 2_134.txt │ ├── 2_135.txt │ ├── 2_136.txt │ ├── 2_137.txt │ ├── 2_138.txt │ ├── 2_139.txt │ ├── 2_14.txt │ ├── 2_140.txt │ ├── 2_141.txt │ ├── 2_142.txt │ ├── 2_143.txt │ ├── 2_144.txt │ ├── 2_145.txt │ ├── 2_146.txt │ ├── 2_147.txt │ ├── 2_148.txt │ ├── 2_149.txt │ ├── 2_15.txt │ ├── 2_150.txt │ ├── 2_151.txt │ ├── 2_152.txt │ ├── 2_153.txt │ ├── 2_154.txt │ ├── 2_155.txt │ ├── 2_156.txt │ ├── 2_157.txt │ ├── 2_158.txt │ ├── 2_159.txt │ ├── 2_16.txt │ ├── 2_160.txt │ ├── 2_161.txt │ ├── 2_162.txt │ ├── 2_163.txt │ ├── 2_164.txt │ ├── 2_165.txt │ ├── 2_166.txt │ ├── 2_167.txt │ ├── 2_168.txt │ ├── 2_169.txt │ ├── 2_17.txt │ ├── 2_170.txt │ ├── 2_171.txt │ ├── 2_172.txt │ ├── 2_173.txt │ ├── 2_174.txt │ ├── 2_175.txt │ ├── 2_176.txt │ ├── 2_177.txt │ ├── 2_178.txt │ ├── 2_179.txt │ ├── 2_18.txt │ ├── 2_180.txt │ ├── 2_181.txt │ ├── 2_182.txt │ ├── 2_183.txt │ ├── 2_184.txt │ ├── 2_185.txt │ ├── 2_186.txt │ ├── 2_187.txt │ ├── 2_188.txt │ ├── 2_189.txt │ ├── 2_19.txt │ ├── 2_190.txt │ ├── 2_191.txt │ ├── 2_192.txt │ ├── 2_193.txt │ ├── 2_194.txt │ ├── 2_2.txt │ ├── 2_20.txt │ ├── 2_21.txt │ ├── 2_22.txt │ ├── 2_23.txt │ ├── 2_24.txt │ ├── 2_25.txt │ ├── 2_26.txt │ ├── 2_27.txt │ ├── 2_28.txt │ ├── 2_29.txt │ ├── 2_3.txt │ ├── 2_30.txt │ ├── 2_31.txt │ ├── 2_32.txt │ ├── 2_33.txt │ ├── 2_34.txt │ ├── 2_35.txt │ ├── 2_36.txt │ ├── 2_37.txt │ ├── 2_38.txt │ ├── 2_39.txt │ ├── 2_4.txt │ ├── 2_40.txt │ ├── 2_41.txt │ ├── 2_42.txt │ ├── 2_43.txt │ ├── 2_44.txt │ ├── 2_45.txt │ ├── 2_46.txt │ ├── 2_47.txt │ ├── 2_48.txt │ ├── 2_49.txt │ ├── 2_5.txt │ ├── 2_50.txt │ ├── 2_51.txt │ ├── 2_52.txt │ ├── 2_53.txt │ ├── 2_54.txt │ ├── 2_55.txt │ ├── 2_56.txt │ ├── 2_57.txt │ ├── 2_58.txt │ ├── 2_59.txt │ ├── 2_6.txt │ ├── 2_60.txt │ ├── 2_61.txt │ ├── 2_62.txt │ ├── 2_63.txt │ ├── 2_64.txt │ ├── 2_65.txt │ ├── 2_66.txt │ ├── 2_67.txt │ ├── 2_68.txt │ ├── 2_69.txt │ ├── 2_7.txt │ ├── 2_70.txt │ ├── 2_71.txt │ ├── 2_72.txt │ ├── 2_73.txt │ ├── 2_74.txt │ ├── 2_75.txt │ ├── 2_76.txt │ ├── 2_77.txt │ ├── 2_78.txt │ ├── 2_79.txt │ ├── 2_8.txt │ ├── 2_80.txt │ ├── 2_81.txt │ ├── 2_82.txt │ ├── 2_83.txt │ ├── 2_84.txt │ ├── 2_85.txt │ ├── 2_86.txt │ ├── 2_87.txt │ ├── 2_88.txt │ ├── 2_89.txt │ ├── 2_9.txt │ ├── 2_90.txt │ ├── 2_91.txt │ ├── 2_92.txt │ ├── 2_93.txt │ ├── 2_94.txt │ ├── 2_95.txt │ ├── 2_96.txt │ ├── 2_97.txt │ ├── 2_98.txt │ ├── 2_99.txt │ ├── 3_0.txt │ ├── 3_1.txt │ ├── 3_10.txt │ ├── 3_100.txt │ ├── 3_101.txt │ ├── 3_102.txt │ ├── 3_103.txt │ ├── 3_104.txt │ ├── 3_105.txt │ ├── 3_106.txt │ ├── 3_107.txt │ ├── 3_108.txt │ ├── 3_109.txt │ ├── 3_11.txt │ ├── 3_110.txt │ ├── 3_111.txt │ ├── 3_112.txt │ ├── 3_113.txt │ ├── 3_114.txt │ ├── 3_115.txt │ ├── 3_116.txt │ ├── 3_117.txt │ ├── 3_118.txt │ ├── 3_119.txt │ ├── 3_12.txt │ ├── 3_120.txt │ ├── 3_121.txt │ ├── 3_122.txt │ ├── 3_123.txt │ ├── 3_124.txt │ ├── 3_125.txt │ ├── 3_126.txt │ ├── 3_127.txt │ ├── 3_128.txt │ ├── 3_129.txt │ ├── 3_13.txt │ ├── 3_130.txt │ ├── 3_131.txt │ ├── 3_132.txt │ ├── 3_133.txt │ ├── 3_134.txt │ ├── 3_135.txt │ ├── 3_136.txt │ ├── 3_137.txt │ ├── 3_138.txt │ ├── 3_139.txt │ ├── 3_14.txt │ ├── 3_140.txt │ ├── 3_141.txt │ ├── 3_142.txt │ ├── 3_143.txt │ ├── 3_144.txt │ ├── 3_145.txt │ ├── 3_146.txt │ ├── 3_147.txt │ ├── 3_148.txt │ ├── 3_149.txt │ ├── 3_15.txt │ ├── 3_150.txt │ ├── 3_151.txt │ ├── 3_152.txt │ ├── 3_153.txt │ ├── 3_154.txt │ ├── 3_155.txt │ ├── 3_156.txt │ ├── 3_157.txt │ ├── 3_158.txt │ ├── 3_159.txt │ ├── 3_16.txt │ ├── 3_160.txt │ ├── 3_161.txt │ ├── 3_162.txt │ ├── 3_163.txt │ ├── 3_164.txt │ ├── 3_165.txt │ ├── 3_166.txt │ ├── 3_167.txt │ ├── 3_168.txt │ ├── 3_169.txt │ ├── 3_17.txt │ ├── 3_170.txt │ ├── 3_171.txt │ ├── 3_172.txt │ ├── 3_173.txt │ ├── 3_174.txt │ ├── 3_175.txt │ ├── 3_176.txt │ ├── 3_177.txt │ ├── 3_178.txt │ ├── 3_179.txt │ ├── 3_18.txt │ ├── 3_180.txt │ ├── 3_181.txt │ ├── 3_182.txt │ ├── 3_183.txt │ ├── 3_184.txt │ ├── 3_185.txt │ ├── 3_186.txt │ ├── 3_187.txt │ ├── 3_188.txt │ ├── 3_189.txt │ ├── 3_19.txt │ ├── 3_190.txt │ ├── 3_191.txt │ ├── 3_192.txt │ ├── 3_193.txt │ ├── 3_194.txt │ ├── 3_195.txt │ ├── 3_196.txt │ ├── 3_197.txt │ ├── 3_198.txt │ ├── 3_2.txt │ ├── 3_20.txt │ ├── 3_21.txt │ ├── 3_22.txt │ ├── 3_23.txt │ ├── 3_24.txt │ ├── 3_25.txt │ ├── 3_26.txt │ ├── 3_27.txt │ ├── 3_28.txt │ ├── 3_29.txt │ ├── 3_3.txt │ ├── 3_30.txt │ ├── 3_31.txt │ ├── 3_32.txt │ ├── 3_33.txt │ ├── 3_34.txt │ ├── 3_35.txt │ ├── 3_36.txt │ ├── 3_37.txt │ ├── 3_38.txt │ ├── 3_39.txt │ ├── 3_4.txt │ ├── 3_40.txt │ ├── 3_41.txt │ ├── 3_42.txt │ ├── 3_43.txt │ ├── 3_44.txt │ ├── 3_45.txt │ ├── 3_46.txt │ ├── 3_47.txt │ ├── 3_48.txt │ ├── 3_49.txt │ ├── 3_5.txt │ ├── 3_50.txt │ ├── 3_51.txt │ ├── 3_52.txt │ ├── 3_53.txt │ ├── 3_54.txt │ ├── 3_55.txt │ ├── 3_56.txt │ ├── 3_57.txt │ ├── 3_58.txt │ ├── 3_59.txt │ ├── 3_6.txt │ ├── 3_60.txt │ ├── 3_61.txt │ ├── 3_62.txt │ ├── 3_63.txt │ ├── 3_64.txt │ ├── 3_65.txt │ ├── 3_66.txt │ ├── 3_67.txt │ ├── 3_68.txt │ ├── 3_69.txt │ ├── 3_7.txt │ ├── 3_70.txt │ ├── 3_71.txt │ ├── 3_72.txt │ ├── 3_73.txt │ ├── 3_74.txt │ ├── 3_75.txt │ ├── 3_76.txt │ ├── 3_77.txt │ ├── 3_78.txt │ ├── 3_79.txt │ ├── 3_8.txt │ ├── 3_80.txt │ ├── 3_81.txt │ ├── 3_82.txt │ ├── 3_83.txt │ ├── 3_84.txt │ ├── 3_85.txt │ ├── 3_86.txt │ ├── 3_87.txt │ ├── 3_88.txt │ ├── 3_89.txt │ ├── 3_9.txt │ ├── 3_90.txt │ ├── 3_91.txt │ ├── 3_92.txt │ ├── 3_93.txt │ ├── 3_94.txt │ ├── 3_95.txt │ ├── 3_96.txt │ ├── 3_97.txt │ ├── 3_98.txt │ ├── 3_99.txt │ ├── 4_0.txt │ ├── 4_1.txt │ ├── 4_10.txt │ ├── 4_100.txt │ ├── 4_101.txt │ ├── 4_102.txt │ ├── 4_103.txt │ ├── 4_104.txt │ ├── 4_105.txt │ ├── 4_106.txt │ ├── 4_107.txt │ ├── 4_108.txt │ ├── 4_109.txt │ ├── 4_11.txt │ ├── 4_110.txt │ ├── 4_111.txt │ ├── 4_112.txt │ ├── 4_113.txt │ ├── 4_114.txt │ ├── 4_115.txt │ ├── 4_116.txt │ ├── 4_117.txt │ ├── 4_118.txt │ ├── 4_119.txt │ ├── 4_12.txt │ ├── 4_120.txt │ ├── 4_121.txt │ ├── 4_122.txt │ ├── 4_123.txt │ ├── 4_124.txt │ ├── 4_125.txt │ ├── 4_126.txt │ ├── 4_127.txt │ ├── 4_128.txt │ ├── 4_129.txt │ ├── 4_13.txt │ ├── 4_130.txt │ ├── 4_131.txt │ ├── 4_132.txt │ ├── 4_133.txt │ ├── 4_134.txt │ ├── 4_135.txt │ ├── 4_136.txt │ ├── 4_137.txt │ ├── 4_138.txt │ ├── 4_139.txt │ ├── 4_14.txt │ ├── 4_140.txt │ ├── 4_141.txt │ ├── 4_142.txt │ ├── 4_143.txt │ ├── 4_144.txt │ ├── 4_145.txt │ ├── 4_146.txt │ ├── 4_147.txt │ ├── 4_148.txt │ ├── 4_149.txt │ ├── 4_15.txt │ ├── 4_150.txt │ ├── 4_151.txt │ ├── 4_152.txt │ ├── 4_153.txt │ ├── 4_154.txt │ ├── 4_155.txt │ ├── 4_156.txt │ ├── 4_157.txt │ ├── 4_158.txt │ ├── 4_159.txt │ ├── 4_16.txt │ ├── 4_160.txt │ ├── 4_161.txt │ ├── 4_162.txt │ ├── 4_163.txt │ ├── 4_164.txt │ ├── 4_165.txt │ ├── 4_166.txt │ ├── 4_167.txt │ ├── 4_168.txt │ ├── 4_169.txt │ ├── 4_17.txt │ ├── 4_170.txt │ ├── 4_171.txt │ ├── 4_172.txt │ ├── 4_173.txt │ ├── 4_174.txt │ ├── 4_175.txt │ ├── 4_176.txt │ ├── 4_177.txt │ ├── 4_178.txt │ ├── 4_179.txt │ ├── 4_18.txt │ ├── 4_180.txt │ ├── 4_181.txt │ ├── 4_182.txt │ ├── 4_183.txt │ ├── 4_184.txt │ ├── 4_185.txt │ ├── 4_19.txt │ ├── 4_2.txt │ ├── 4_20.txt │ ├── 4_21.txt │ ├── 4_22.txt │ ├── 4_23.txt │ ├── 4_24.txt │ ├── 4_25.txt │ ├── 4_26.txt │ ├── 4_27.txt │ ├── 4_28.txt │ ├── 4_29.txt │ ├── 4_3.txt │ ├── 4_30.txt │ ├── 4_31.txt │ ├── 4_32.txt │ ├── 4_33.txt │ ├── 4_34.txt │ ├── 4_35.txt │ ├── 4_36.txt │ ├── 4_37.txt │ ├── 4_38.txt │ ├── 4_39.txt │ ├── 4_4.txt │ ├── 4_40.txt │ ├── 4_41.txt │ ├── 4_42.txt │ ├── 4_43.txt │ ├── 4_44.txt │ ├── 4_45.txt │ ├── 4_46.txt │ ├── 4_47.txt │ ├── 4_48.txt │ ├── 4_49.txt │ ├── 4_5.txt │ ├── 4_50.txt │ ├── 4_51.txt │ ├── 4_52.txt │ ├── 4_53.txt │ ├── 4_54.txt │ ├── 4_55.txt │ ├── 4_56.txt │ ├── 4_57.txt │ ├── 4_58.txt │ ├── 4_59.txt │ ├── 4_6.txt │ ├── 4_60.txt │ ├── 4_61.txt │ ├── 4_62.txt │ ├── 4_63.txt │ ├── 4_64.txt │ ├── 4_65.txt │ ├── 4_66.txt │ ├── 4_67.txt │ ├── 4_68.txt │ ├── 4_69.txt │ ├── 4_7.txt │ ├── 4_70.txt │ ├── 4_71.txt │ ├── 4_72.txt │ ├── 4_73.txt │ ├── 4_74.txt │ ├── 4_75.txt │ ├── 4_76.txt │ ├── 4_77.txt │ ├── 4_78.txt │ ├── 4_79.txt │ ├── 4_8.txt │ ├── 4_80.txt │ ├── 4_81.txt │ ├── 4_82.txt │ ├── 4_83.txt │ ├── 4_84.txt │ ├── 4_85.txt │ ├── 4_86.txt │ ├── 4_87.txt │ ├── 4_88.txt │ ├── 4_89.txt │ ├── 4_9.txt │ ├── 4_90.txt │ ├── 4_91.txt │ ├── 4_92.txt │ ├── 4_93.txt │ ├── 4_94.txt │ ├── 4_95.txt │ ├── 4_96.txt │ ├── 4_97.txt │ ├── 4_98.txt │ ├── 4_99.txt │ ├── 5_0.txt │ ├── 5_1.txt │ ├── 5_10.txt │ ├── 5_100.txt │ ├── 5_101.txt │ ├── 5_102.txt │ ├── 5_103.txt │ ├── 5_104.txt │ ├── 5_105.txt │ ├── 5_106.txt │ ├── 5_107.txt │ ├── 5_108.txt │ ├── 5_109.txt │ ├── 5_11.txt │ ├── 5_110.txt │ ├── 5_111.txt │ ├── 5_112.txt │ ├── 5_113.txt │ ├── 5_114.txt │ ├── 5_115.txt │ ├── 5_116.txt │ ├── 5_117.txt │ ├── 5_118.txt │ ├── 5_119.txt │ ├── 5_12.txt │ ├── 5_120.txt │ ├── 5_121.txt │ ├── 5_122.txt │ ├── 5_123.txt │ ├── 5_124.txt │ ├── 5_125.txt │ ├── 5_126.txt │ ├── 5_127.txt │ ├── 5_128.txt │ ├── 5_129.txt │ ├── 5_13.txt │ ├── 5_130.txt │ ├── 5_131.txt │ ├── 5_132.txt │ ├── 5_133.txt │ ├── 5_134.txt │ ├── 5_135.txt │ ├── 5_136.txt │ ├── 5_137.txt │ ├── 5_138.txt │ ├── 5_139.txt │ ├── 5_14.txt │ ├── 5_140.txt │ ├── 5_141.txt │ ├── 5_142.txt │ ├── 5_143.txt │ ├── 5_144.txt │ ├── 5_145.txt │ ├── 5_146.txt │ ├── 5_147.txt │ ├── 5_148.txt │ ├── 5_149.txt │ ├── 5_15.txt │ ├── 5_150.txt │ ├── 5_151.txt │ ├── 5_152.txt │ ├── 5_153.txt │ ├── 5_154.txt │ ├── 5_155.txt │ ├── 5_156.txt │ ├── 5_157.txt │ ├── 5_158.txt │ ├── 5_159.txt │ ├── 5_16.txt │ ├── 5_160.txt │ ├── 5_161.txt │ ├── 5_162.txt │ ├── 5_163.txt │ ├── 5_164.txt │ ├── 5_165.txt │ ├── 5_166.txt │ ├── 5_167.txt │ ├── 5_168.txt │ ├── 5_169.txt │ ├── 5_17.txt │ ├── 5_170.txt │ ├── 5_171.txt │ ├── 5_172.txt │ ├── 5_173.txt │ ├── 5_174.txt │ ├── 5_175.txt │ ├── 5_176.txt │ ├── 5_177.txt │ ├── 5_178.txt │ ├── 5_179.txt │ ├── 5_18.txt │ ├── 5_180.txt │ ├── 5_181.txt │ ├── 5_182.txt │ ├── 5_183.txt │ ├── 5_184.txt │ ├── 5_185.txt │ ├── 5_186.txt │ ├── 5_19.txt │ ├── 5_2.txt │ ├── 5_20.txt │ ├── 5_21.txt │ ├── 5_22.txt │ ├── 5_23.txt │ ├── 5_24.txt │ ├── 5_25.txt │ ├── 5_26.txt │ ├── 5_27.txt │ ├── 5_28.txt │ ├── 5_29.txt │ ├── 5_3.txt │ ├── 5_30.txt │ ├── 5_31.txt │ ├── 5_32.txt │ ├── 5_33.txt │ ├── 5_34.txt │ ├── 5_35.txt │ ├── 5_36.txt │ ├── 5_37.txt │ ├── 5_38.txt │ ├── 5_39.txt │ ├── 5_4.txt │ ├── 5_40.txt │ ├── 5_41.txt │ ├── 5_42.txt │ ├── 5_43.txt │ ├── 5_44.txt │ ├── 5_45.txt │ ├── 5_46.txt │ ├── 5_47.txt │ ├── 5_48.txt │ ├── 5_49.txt │ ├── 5_5.txt │ ├── 5_50.txt │ ├── 5_51.txt │ ├── 5_52.txt │ ├── 5_53.txt │ ├── 5_54.txt │ ├── 5_55.txt │ ├── 5_56.txt │ ├── 5_57.txt │ ├── 5_58.txt │ ├── 5_59.txt │ ├── 5_6.txt │ ├── 5_60.txt │ ├── 5_61.txt │ ├── 5_62.txt │ ├── 5_63.txt │ ├── 5_64.txt │ ├── 5_65.txt │ ├── 5_66.txt │ ├── 5_67.txt │ ├── 5_68.txt │ ├── 5_69.txt │ ├── 5_7.txt │ ├── 5_70.txt │ ├── 5_71.txt │ ├── 5_72.txt │ ├── 5_73.txt │ ├── 5_74.txt │ ├── 5_75.txt │ ├── 5_76.txt │ ├── 5_77.txt │ ├── 5_78.txt │ ├── 5_79.txt │ ├── 5_8.txt │ ├── 5_80.txt │ ├── 5_81.txt │ ├── 5_82.txt │ ├── 5_83.txt │ ├── 5_84.txt │ ├── 5_85.txt │ ├── 5_86.txt │ ├── 5_87.txt │ ├── 5_88.txt │ ├── 5_89.txt │ ├── 5_9.txt │ ├── 5_90.txt │ ├── 5_91.txt │ ├── 5_92.txt │ ├── 5_93.txt │ ├── 5_94.txt │ ├── 5_95.txt │ ├── 5_96.txt │ ├── 5_97.txt │ ├── 5_98.txt │ ├── 5_99.txt │ ├── 6_0.txt │ ├── 6_1.txt │ ├── 6_10.txt │ ├── 6_100.txt │ ├── 6_101.txt │ ├── 6_102.txt │ ├── 6_103.txt │ ├── 6_104.txt │ ├── 6_105.txt │ ├── 6_106.txt │ ├── 6_107.txt │ ├── 6_108.txt │ ├── 6_109.txt │ ├── 6_11.txt │ ├── 6_110.txt │ ├── 6_111.txt │ ├── 6_112.txt │ ├── 6_113.txt │ ├── 6_114.txt │ ├── 6_115.txt │ ├── 6_116.txt │ ├── 6_117.txt │ ├── 6_118.txt │ ├── 6_119.txt │ ├── 6_12.txt │ ├── 6_120.txt │ ├── 6_121.txt │ ├── 6_122.txt │ ├── 6_123.txt │ ├── 6_124.txt │ ├── 6_125.txt │ ├── 6_126.txt │ ├── 6_127.txt │ ├── 6_128.txt │ ├── 6_129.txt │ ├── 6_13.txt │ ├── 6_130.txt │ ├── 6_131.txt │ ├── 6_132.txt │ ├── 6_133.txt │ ├── 6_134.txt │ ├── 6_135.txt │ ├── 6_136.txt │ ├── 6_137.txt │ ├── 6_138.txt │ ├── 6_139.txt │ ├── 6_14.txt │ ├── 6_140.txt │ ├── 6_141.txt │ ├── 6_142.txt │ ├── 6_143.txt │ ├── 6_144.txt │ ├── 6_145.txt │ ├── 6_146.txt │ ├── 6_147.txt │ ├── 6_148.txt │ ├── 6_149.txt │ ├── 6_15.txt │ ├── 6_150.txt │ ├── 6_151.txt │ ├── 6_152.txt │ ├── 6_153.txt │ ├── 6_154.txt │ ├── 6_155.txt │ ├── 6_156.txt │ ├── 6_157.txt │ ├── 6_158.txt │ ├── 6_159.txt │ ├── 6_16.txt │ ├── 6_160.txt │ ├── 6_161.txt │ ├── 6_162.txt │ ├── 6_163.txt │ ├── 6_164.txt │ ├── 6_165.txt │ ├── 6_166.txt │ ├── 6_167.txt │ ├── 6_168.txt │ ├── 6_169.txt │ ├── 6_17.txt │ ├── 6_170.txt │ ├── 6_171.txt │ ├── 6_172.txt │ ├── 6_173.txt │ ├── 6_174.txt │ ├── 6_175.txt │ ├── 6_176.txt │ ├── 6_177.txt │ ├── 6_178.txt │ ├── 6_179.txt │ ├── 6_18.txt │ ├── 6_180.txt │ ├── 6_181.txt │ ├── 6_182.txt │ ├── 6_183.txt │ ├── 6_184.txt │ ├── 6_185.txt │ ├── 6_186.txt │ ├── 6_187.txt │ ├── 6_188.txt │ ├── 6_189.txt │ ├── 6_19.txt │ ├── 6_190.txt │ ├── 6_191.txt │ ├── 6_192.txt │ ├── 6_193.txt │ ├── 6_194.txt │ ├── 6_2.txt │ ├── 6_20.txt │ ├── 6_21.txt │ ├── 6_22.txt │ ├── 6_23.txt │ ├── 6_24.txt │ ├── 6_25.txt │ ├── 6_26.txt │ ├── 6_27.txt │ ├── 6_28.txt │ ├── 6_29.txt │ ├── 6_3.txt │ ├── 6_30.txt │ ├── 6_31.txt │ ├── 6_32.txt │ ├── 6_33.txt │ ├── 6_34.txt │ ├── 6_35.txt │ ├── 6_36.txt │ ├── 6_37.txt │ ├── 6_38.txt │ ├── 6_39.txt │ ├── 6_4.txt │ ├── 6_40.txt │ ├── 6_41.txt │ ├── 6_42.txt │ ├── 6_43.txt │ ├── 6_44.txt │ ├── 6_45.txt │ ├── 6_46.txt │ ├── 6_47.txt │ ├── 6_48.txt │ ├── 6_49.txt │ ├── 6_5.txt │ ├── 6_50.txt │ ├── 6_51.txt │ ├── 6_52.txt │ ├── 6_53.txt │ ├── 6_54.txt │ ├── 6_55.txt │ ├── 6_56.txt │ ├── 6_57.txt │ ├── 6_58.txt │ ├── 6_59.txt │ ├── 6_6.txt │ ├── 6_60.txt │ ├── 6_61.txt │ ├── 6_62.txt │ ├── 6_63.txt │ ├── 6_64.txt │ ├── 6_65.txt │ ├── 6_66.txt │ ├── 6_67.txt │ ├── 6_68.txt │ ├── 6_69.txt │ ├── 6_7.txt │ ├── 6_70.txt │ ├── 6_71.txt │ ├── 6_72.txt │ ├── 6_73.txt │ ├── 6_74.txt │ ├── 6_75.txt │ ├── 6_76.txt │ ├── 6_77.txt │ ├── 6_78.txt │ ├── 6_79.txt │ ├── 6_8.txt │ ├── 6_80.txt │ ├── 6_81.txt │ ├── 6_82.txt │ ├── 6_83.txt │ ├── 6_84.txt │ ├── 6_85.txt │ ├── 6_86.txt │ ├── 6_87.txt │ ├── 6_88.txt │ ├── 6_89.txt │ ├── 6_9.txt │ ├── 6_90.txt │ ├── 6_91.txt │ ├── 6_92.txt │ ├── 6_93.txt │ ├── 6_94.txt │ ├── 6_95.txt │ ├── 6_96.txt │ ├── 6_97.txt │ ├── 6_98.txt │ ├── 6_99.txt │ ├── 7_0.txt │ ├── 7_1.txt │ ├── 7_10.txt │ ├── 7_100.txt │ ├── 7_101.txt │ ├── 7_102.txt │ ├── 7_103.txt │ ├── 7_104.txt │ ├── 7_105.txt │ ├── 7_106.txt │ ├── 7_107.txt │ ├── 7_108.txt │ ├── 7_109.txt │ ├── 7_11.txt │ ├── 7_110.txt │ ├── 7_111.txt │ ├── 7_112.txt │ ├── 7_113.txt │ ├── 7_114.txt │ ├── 7_115.txt │ ├── 7_116.txt │ ├── 7_117.txt │ ├── 7_118.txt │ ├── 7_119.txt │ ├── 7_12.txt │ ├── 7_120.txt │ ├── 7_121.txt │ ├── 7_122.txt │ ├── 7_123.txt │ ├── 7_124.txt │ ├── 7_125.txt │ ├── 7_126.txt │ ├── 7_127.txt │ ├── 7_128.txt │ ├── 7_129.txt │ ├── 7_13.txt │ ├── 7_130.txt │ ├── 7_131.txt │ ├── 7_132.txt │ ├── 7_133.txt │ ├── 7_134.txt │ ├── 7_135.txt │ ├── 7_136.txt │ ├── 7_137.txt │ ├── 7_138.txt │ ├── 7_139.txt │ ├── 7_14.txt │ ├── 7_140.txt │ ├── 7_141.txt │ ├── 7_142.txt │ ├── 7_143.txt │ ├── 7_144.txt │ ├── 7_145.txt │ ├── 7_146.txt │ ├── 7_147.txt │ ├── 7_148.txt │ ├── 7_149.txt │ ├── 7_15.txt │ ├── 7_150.txt │ ├── 7_151.txt │ ├── 7_152.txt │ ├── 7_153.txt │ ├── 7_154.txt │ ├── 7_155.txt │ ├── 7_156.txt │ ├── 7_157.txt │ ├── 7_158.txt │ ├── 7_159.txt │ ├── 7_16.txt │ ├── 7_160.txt │ ├── 7_161.txt │ ├── 7_162.txt │ ├── 7_163.txt │ ├── 7_164.txt │ ├── 7_165.txt │ ├── 7_166.txt │ ├── 7_167.txt │ ├── 7_168.txt │ ├── 7_169.txt │ ├── 7_17.txt │ ├── 7_170.txt │ ├── 7_171.txt │ ├── 7_172.txt │ ├── 7_173.txt │ ├── 7_174.txt │ ├── 7_175.txt │ ├── 7_176.txt │ ├── 7_177.txt │ ├── 7_178.txt │ ├── 7_179.txt │ ├── 7_18.txt │ ├── 7_180.txt │ ├── 7_181.txt │ ├── 7_182.txt │ ├── 7_183.txt │ ├── 7_184.txt │ ├── 7_185.txt │ ├── 7_186.txt │ ├── 7_187.txt │ ├── 7_188.txt │ ├── 7_189.txt │ ├── 7_19.txt │ ├── 7_190.txt │ ├── 7_191.txt │ ├── 7_192.txt │ ├── 7_193.txt │ ├── 7_194.txt │ ├── 7_195.txt │ ├── 7_196.txt │ ├── 7_197.txt │ ├── 7_198.txt │ ├── 7_199.txt │ ├── 7_2.txt │ ├── 7_20.txt │ ├── 7_200.txt │ ├── 7_21.txt │ ├── 7_22.txt │ ├── 7_23.txt │ ├── 7_24.txt │ ├── 7_25.txt │ ├── 7_26.txt │ ├── 7_27.txt │ ├── 7_28.txt │ ├── 7_29.txt │ ├── 7_3.txt │ ├── 7_30.txt │ ├── 7_31.txt │ ├── 7_32.txt │ ├── 7_33.txt │ ├── 7_34.txt │ ├── 7_35.txt │ ├── 7_36.txt │ ├── 7_37.txt │ ├── 7_38.txt │ ├── 7_39.txt │ ├── 7_4.txt │ ├── 7_40.txt │ ├── 7_41.txt │ ├── 7_42.txt │ ├── 7_43.txt │ ├── 7_44.txt │ ├── 7_45.txt │ ├── 7_46.txt │ ├── 7_47.txt │ ├── 7_48.txt │ ├── 7_49.txt │ ├── 7_5.txt │ ├── 7_50.txt │ ├── 7_51.txt │ ├── 7_52.txt │ ├── 7_53.txt │ ├── 7_54.txt │ ├── 7_55.txt │ ├── 7_56.txt │ ├── 7_57.txt │ ├── 7_58.txt │ ├── 7_59.txt │ ├── 7_6.txt │ ├── 7_60.txt │ ├── 7_61.txt │ ├── 7_62.txt │ ├── 7_63.txt │ ├── 7_64.txt │ ├── 7_65.txt │ ├── 7_66.txt │ ├── 7_67.txt │ ├── 7_68.txt │ ├── 7_69.txt │ ├── 7_7.txt │ ├── 7_70.txt │ ├── 7_71.txt │ ├── 7_72.txt │ ├── 7_73.txt │ ├── 7_74.txt │ ├── 7_75.txt │ ├── 7_76.txt │ ├── 7_77.txt │ ├── 7_78.txt │ ├── 7_79.txt │ ├── 7_8.txt │ ├── 7_80.txt │ ├── 7_81.txt │ ├── 7_82.txt │ ├── 7_83.txt │ ├── 7_84.txt │ ├── 7_85.txt │ ├── 7_86.txt │ ├── 7_87.txt │ ├── 7_88.txt │ ├── 7_89.txt │ ├── 7_9.txt │ ├── 7_90.txt │ ├── 7_91.txt │ ├── 7_92.txt │ ├── 7_93.txt │ ├── 7_94.txt │ ├── 7_95.txt │ ├── 7_96.txt │ ├── 7_97.txt │ ├── 7_98.txt │ ├── 7_99.txt │ ├── 8_0.txt │ ├── 8_1.txt │ ├── 8_10.txt │ ├── 8_100.txt │ ├── 8_101.txt │ ├── 8_102.txt │ ├── 8_103.txt │ ├── 8_104.txt │ ├── 8_105.txt │ ├── 8_106.txt │ ├── 8_107.txt │ ├── 8_108.txt │ ├── 8_109.txt │ ├── 8_11.txt │ ├── 8_110.txt │ ├── 8_111.txt │ ├── 8_112.txt │ ├── 8_113.txt │ ├── 8_114.txt │ ├── 8_115.txt │ ├── 8_116.txt │ ├── 8_117.txt │ ├── 8_118.txt │ ├── 8_119.txt │ ├── 8_12.txt │ ├── 8_120.txt │ ├── 8_121.txt │ ├── 8_122.txt │ ├── 8_123.txt │ ├── 8_124.txt │ ├── 8_125.txt │ ├── 8_126.txt │ ├── 8_127.txt │ ├── 8_128.txt │ ├── 8_129.txt │ ├── 8_13.txt │ ├── 8_130.txt │ ├── 8_131.txt │ ├── 8_132.txt │ ├── 8_133.txt │ ├── 8_134.txt │ ├── 8_135.txt │ ├── 8_136.txt │ ├── 8_137.txt │ ├── 8_138.txt │ ├── 8_139.txt │ ├── 8_14.txt │ ├── 8_140.txt │ ├── 8_141.txt │ ├── 8_142.txt │ ├── 8_143.txt │ ├── 8_144.txt │ ├── 8_145.txt │ ├── 8_146.txt │ ├── 8_147.txt │ ├── 8_148.txt │ ├── 8_149.txt │ ├── 8_15.txt │ ├── 8_150.txt │ ├── 8_151.txt │ ├── 8_152.txt │ ├── 8_153.txt │ ├── 8_154.txt │ ├── 8_155.txt │ ├── 8_156.txt │ ├── 8_157.txt │ ├── 8_158.txt │ ├── 8_159.txt │ ├── 8_16.txt │ ├── 8_160.txt │ ├── 8_161.txt │ ├── 8_162.txt │ ├── 8_163.txt │ ├── 8_164.txt │ ├── 8_165.txt │ ├── 8_166.txt │ ├── 8_167.txt │ ├── 8_168.txt │ ├── 8_169.txt │ ├── 8_17.txt │ ├── 8_170.txt │ ├── 8_171.txt │ ├── 8_172.txt │ ├── 8_173.txt │ ├── 8_174.txt │ ├── 8_175.txt │ ├── 8_176.txt │ ├── 8_177.txt │ ├── 8_178.txt │ ├── 8_179.txt │ ├── 8_18.txt │ ├── 8_19.txt │ ├── 8_2.txt │ ├── 8_20.txt │ ├── 8_21.txt │ ├── 8_22.txt │ ├── 8_23.txt │ ├── 8_24.txt │ ├── 8_25.txt │ ├── 8_26.txt │ ├── 8_27.txt │ ├── 8_28.txt │ ├── 8_29.txt │ ├── 8_3.txt │ ├── 8_30.txt │ ├── 8_31.txt │ ├── 8_32.txt │ ├── 8_33.txt │ ├── 8_34.txt │ ├── 8_35.txt │ ├── 8_36.txt │ ├── 8_37.txt │ ├── 8_38.txt │ ├── 8_39.txt │ ├── 8_4.txt │ ├── 8_40.txt │ ├── 8_41.txt │ ├── 8_42.txt │ ├── 8_43.txt │ ├── 8_44.txt │ ├── 8_45.txt │ ├── 8_46.txt │ ├── 8_47.txt │ ├── 8_48.txt │ ├── 8_49.txt │ ├── 8_5.txt │ ├── 8_50.txt │ ├── 8_51.txt │ ├── 8_52.txt │ ├── 8_53.txt │ ├── 8_54.txt │ ├── 8_55.txt │ ├── 8_56.txt │ ├── 8_57.txt │ ├── 8_58.txt │ ├── 8_59.txt │ ├── 8_6.txt │ ├── 8_60.txt │ ├── 8_61.txt │ ├── 8_62.txt │ ├── 8_63.txt │ ├── 8_64.txt │ ├── 8_65.txt │ ├── 8_66.txt │ ├── 8_67.txt │ ├── 8_68.txt │ ├── 8_69.txt │ ├── 8_7.txt │ ├── 8_70.txt │ ├── 8_71.txt │ ├── 8_72.txt │ ├── 8_73.txt │ ├── 8_74.txt │ ├── 8_75.txt │ ├── 8_76.txt │ ├── 8_77.txt │ ├── 8_78.txt │ ├── 8_79.txt │ ├── 8_8.txt │ ├── 8_80.txt │ ├── 8_81.txt │ ├── 8_82.txt │ ├── 8_83.txt │ ├── 8_84.txt │ ├── 8_85.txt │ ├── 8_86.txt │ ├── 8_87.txt │ ├── 8_88.txt │ ├── 8_89.txt │ ├── 8_9.txt │ ├── 8_90.txt │ ├── 8_91.txt │ ├── 8_92.txt │ ├── 8_93.txt │ ├── 8_94.txt │ ├── 8_95.txt │ ├── 8_96.txt │ ├── 8_97.txt │ ├── 8_98.txt │ ├── 8_99.txt │ ├── 9_0.txt │ ├── 9_1.txt │ ├── 9_10.txt │ ├── 9_100.txt │ ├── 9_101.txt │ ├── 9_102.txt │ ├── 9_103.txt │ ├── 9_104.txt │ ├── 9_105.txt │ ├── 9_106.txt │ ├── 9_107.txt │ ├── 9_108.txt │ ├── 9_109.txt │ ├── 9_11.txt │ ├── 9_110.txt │ ├── 9_111.txt │ ├── 9_112.txt │ ├── 9_113.txt │ ├── 9_114.txt │ ├── 9_115.txt │ ├── 9_116.txt │ ├── 9_117.txt │ ├── 9_118.txt │ ├── 9_119.txt │ ├── 9_12.txt │ ├── 9_120.txt │ ├── 9_121.txt │ ├── 9_122.txt │ ├── 9_123.txt │ ├── 9_124.txt │ ├── 9_125.txt │ ├── 9_126.txt │ ├── 9_127.txt │ ├── 9_128.txt │ ├── 9_129.txt │ ├── 9_13.txt │ ├── 9_130.txt │ ├── 9_131.txt │ ├── 9_132.txt │ ├── 9_133.txt │ ├── 9_134.txt │ ├── 9_135.txt │ ├── 9_136.txt │ ├── 9_137.txt │ ├── 9_138.txt │ ├── 9_139.txt │ ├── 9_14.txt │ ├── 9_140.txt │ ├── 9_141.txt │ ├── 9_142.txt │ ├── 9_143.txt │ ├── 9_144.txt │ ├── 9_145.txt │ ├── 9_146.txt │ ├── 9_147.txt │ ├── 9_148.txt │ ├── 9_149.txt │ ├── 9_15.txt │ ├── 9_150.txt │ ├── 9_151.txt │ ├── 9_152.txt │ ├── 9_153.txt │ ├── 9_154.txt │ ├── 9_155.txt │ ├── 9_156.txt │ ├── 9_157.txt │ ├── 9_158.txt │ ├── 9_159.txt │ ├── 9_16.txt │ ├── 9_160.txt │ ├── 9_161.txt │ ├── 9_162.txt │ ├── 9_163.txt │ ├── 9_164.txt │ ├── 9_165.txt │ ├── 9_166.txt │ ├── 9_167.txt │ ├── 9_168.txt │ ├── 9_169.txt │ ├── 9_17.txt │ ├── 9_170.txt │ ├── 9_171.txt │ ├── 9_172.txt │ ├── 9_173.txt │ ├── 9_174.txt │ ├── 9_175.txt │ ├── 9_176.txt │ ├── 9_177.txt │ ├── 9_178.txt │ ├── 9_179.txt │ ├── 9_18.txt │ ├── 9_180.txt │ ├── 9_181.txt │ ├── 9_182.txt │ ├── 9_183.txt │ ├── 9_184.txt │ ├── 9_185.txt │ ├── 9_186.txt │ ├── 9_187.txt │ ├── 9_188.txt │ ├── 9_189.txt │ ├── 9_19.txt │ ├── 9_190.txt │ ├── 9_191.txt │ ├── 9_192.txt │ ├── 9_193.txt │ ├── 9_194.txt │ ├── 9_195.txt │ ├── 9_196.txt │ ├── 9_197.txt │ ├── 9_198.txt │ ├── 9_199.txt │ ├── 9_2.txt │ ├── 9_20.txt │ ├── 9_200.txt │ ├── 9_201.txt │ ├── 9_202.txt │ ├── 9_203.txt │ ├── 9_21.txt │ ├── 9_22.txt │ ├── 9_23.txt │ ├── 9_24.txt │ ├── 9_25.txt │ ├── 9_26.txt │ ├── 9_27.txt │ ├── 9_28.txt │ ├── 9_29.txt │ ├── 9_3.txt │ ├── 9_30.txt │ ├── 9_31.txt │ ├── 9_32.txt │ ├── 9_33.txt │ ├── 9_34.txt │ ├── 9_35.txt │ ├── 9_36.txt │ ├── 9_37.txt │ ├── 9_38.txt │ ├── 9_39.txt │ ├── 9_4.txt │ ├── 9_40.txt │ ├── 9_41.txt │ ├── 9_42.txt │ ├── 9_43.txt │ ├── 9_44.txt │ ├── 9_45.txt │ ├── 9_46.txt │ ├── 9_47.txt │ ├── 9_48.txt │ ├── 9_49.txt │ ├── 9_5.txt │ ├── 9_50.txt │ ├── 9_51.txt │ ├── 9_52.txt │ ├── 9_53.txt │ ├── 9_54.txt │ ├── 9_55.txt │ ├── 9_56.txt │ ├── 9_57.txt │ ├── 9_58.txt │ ├── 9_59.txt │ ├── 9_6.txt │ ├── 9_60.txt │ ├── 9_61.txt │ ├── 9_62.txt │ ├── 9_63.txt │ ├── 9_64.txt │ ├── 9_65.txt │ ├── 9_66.txt │ ├── 9_67.txt │ ├── 9_68.txt │ ├── 9_69.txt │ ├── 9_7.txt │ ├── 9_70.txt │ ├── 9_71.txt │ ├── 9_72.txt │ ├── 9_73.txt │ ├── 9_74.txt │ ├── 9_75.txt │ ├── 9_76.txt │ ├── 9_77.txt │ ├── 9_78.txt │ ├── 9_79.txt │ ├── 9_8.txt │ ├── 9_80.txt │ ├── 9_81.txt │ ├── 9_82.txt │ ├── 9_83.txt │ ├── 9_84.txt │ ├── 9_85.txt │ ├── 9_86.txt │ ├── 9_87.txt │ ├── 9_88.txt │ ├── 9_89.txt │ ├── 9_9.txt │ ├── 9_90.txt │ ├── 9_91.txt │ ├── 9_92.txt │ ├── 9_93.txt │ ├── 9_94.txt │ ├── 9_95.txt │ ├── 9_96.txt │ ├── 9_97.txt │ ├── 9_98.txt │ └── 9_99.txt ├── LDA ├── idrs_lda.py ├── iris.csv ├── lda_example1.py └── sklearn_LDA.py ├── LocalOutlierFactor(LOF) ├── LOFNoveltyDetection.py ├── LOFOutlierDetection.py └── lof_practice1.py ├── LogisticRegression ├── LogiReg_data.txt ├── horseColicTest.txt ├── horseColicTraining.txt ├── horse_SklearnlogRegress.py ├── horse_logRegress.py ├── iris_logRegress.py ├── logRegres.py ├── logisticregressionexample.py ├── testSet.txt └── 梯度下降求解逻辑回归.ipynb ├── NaiveBayes ├── Gaussian_Bayes.py ├── Multinomial_Bayes.py ├── Multinomial_Bayes_1.py ├── bayes_test.py ├── datingTestSet.txt └── train.zip ├── OneClassSVM ├── OneClassSVM_Mydata.py ├── OneClassSVM_RBF.py └── OneClassSVM_SVDD.py ├── PCA ├── PCA-iris.py ├── matplotlib-learn.py ├── mat——learn.py ├── pca_calc.py ├── pca_calc1.py ├── pca_testSet.py ├── python_pca.py ├── scikit-learn-PCA1.py ├── sklearn-PCA.py ├── testSet.txt └── testSet1.txt ├── README.md ├── RandomForest ├── ExtractFeaturebyRF.py ├── RandomForest_AdjustParm2.py ├── RandomForest_AdjustParm3.py ├── RandomForest_AdjustParm4.py ├── RandomForest_AdjustParm5.py ├── RandomForset_AdjustParm1.py ├── RandomForset_Example1.py ├── trainbySVR.py ├── wine.data ├── wine.txt ├── 进行预测的几种常用算法.py ├── 随机森林分类（包含其他分类算法的对比）算法.py ├── 随机森林和其他机器学习算法对比.py └── 随机森林回归（包含其他回归算法的对比）算法.py ├── SVD └── mysvdcode.py ├── SVM ├── svm_example1_1.py ├── svm_example1_2.py ├── svm_example2.py ├── svm_example3_1.py ├── svm_example3_2.py ├── svm_example3_3.py ├── svm_example3_4.py └── svm_example3_5.py └── Xgboost ├── xgb_example1（原生XGB）.py ├── xgb_example2（sklearn）.py ├── xgb_example3（python实例）.py ├── xgb_example4（根据特征重要性进行特征选择） - 複製.py ├── xgb_example5（网格搜索1）.py ├── xgb_example5（网格搜索2）.py ├── xgb使用sklearn 分类（模型参数）.py ├── 基于Xgboost的原生接口回归.py ├── 基于Xgboost的原生接口的分类.py ├── 基于sckit-learn接口的分类.py └── 基于sklearn接口的回归.py /.idea/MachineLearningNote.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 31 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /DecisionTree/calc_entropy.py: -------------------------------------------------------------------------------- 1 | from math import log 2 | 3 | # 初始熵值 4 | Entropy_base = -(9 / 14) * log(9 / 14, 2) - (5 / 14) * log(5 / 14, 2) 5 | # outlook=sunny 6 | Entropy_Outlook_sunny = -(2 / 5) * log(2 / 5, 2) - (3 / 5) * log(3 / 5, 2) 7 | # outlook=overcost 8 | Entropy_Outlook_overcost = -(4 / 4) * log(4 / 4, 2) 9 | # outlook=rainy 10 | Entropy_Outlook_rainy = -(3 / 5) * log(3 / 5, 2) - (2 / 5) * log(2 / 5, 2) 11 | print(Entropy_Outlook_sunny, Entropy_Outlook_overcost, Entropy_Outlook_rainy) 12 | # 0.9709505944546686 -0.0 0.9709505944546686 13 | Entropy_Outlook = (5 / 14) * Entropy_Outlook_sunny + (4 / 14) * Entropy_Outlook_overcost + ( 14 | 5 / 14) * Entropy_Outlook_rainy 15 | print('Entropy_Outlook:', Entropy_Outlook) 16 | Entropy_test = -(1 / 2) * log(1 / 2, 2) - (1 / 2) * log(1 / 2, 2) 17 | Entropy_test1 = -(1 / 3) * log(1 / 3, 2) - (1 / 3) * log(1 / 3, 2) - (1 / 3) * log(1 / 3, 2) 18 | Entropy_test2 = -(1 / 4) * log(1 / 4, 2) - (1 / 4) * log(1 / 4, 2) - (1 / 4) * log(1 / 4, 2) - (1 / 4) * log(1 / 4, 2) 19 | print(Entropy_test, Entropy_test1, Entropy_test2) 20 | # 1.0 1.584962500721156 2.0 21 | 22 | 23 | 24 | print((1/3)*log(Entropy_test1, 2)) 25 | print((1/4)*log(Entropy_test2, 2)) 26 | -------------------------------------------------------------------------------- /DecisionTree/classifierStorage.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LeBron-Jian/MachineLearningNote/b5d979e2906fa59e69506d4160b1ebe1c8aed1fa/DecisionTree/classifierStorage.txt -------------------------------------------------------------------------------- /DecisionTree/lenses.txt: -------------------------------------------------------------------------------- 1 | young myope no reduced no lenses 2 | young myope no normal soft 3 | young myope yes reduced no lenses 4 | young myope yes normal hard 5 | young hyper no reduced no lenses 6 | young hyper no normal soft 7 | young hyper yes reduced no lenses 8 | young hyper yes normal hard 9 | pre myope no reduced no lenses 10 | pre myope no normal soft 11 | pre myope yes reduced no lenses 12 | pre myope yes normal hard 13 | pre hyper no reduced no lenses 14 | pre hyper no normal soft 15 | pre hyper yes reduced no lenses 16 | pre hyper yes normal no lenses 17 | presbyopic myope no reduced no lenses 18 | presbyopic myope no normal no lenses 19 | presbyopic myope yes reduced no lenses 20 | presbyopic myope yes normal hard 21 | presbyopic hyper no reduced no lenses 22 | presbyopic hyper no normal soft 23 | presbyopic hyper yes reduced no lenses 24 | presbyopic hyper yes normal no lenses -------------------------------------------------------------------------------- /EM(GMM)/gmm_newdata.py: -------------------------------------------------------------------------------- 1 | from sklearn.datasets.samples_generator import make_blobs 2 | from matplotlib import pyplot as plt 3 | from sklearn.cluster import KMeans 4 | from sklearn.mixture import GaussianMixture 5 | import numpy as np 6 | 7 | # 生成新的数据 8 | X, y_true = make_blobs(n_samples=800, centers=4, random_state=11) 9 | # plt.scatter(X[:, 0], X[:, 1]) 10 | 11 | rng = np.random.RandomState(13) 12 | X_stretched = np.dot(X, rng.randn(2, 2)) 13 | 14 | X = X_stretched 15 | # plt.scatter(X[:, 0], X[:, 1]) 16 | 17 | kmeans = KMeans(n_clusters=4) 18 | kmeans.fit(X) 19 | y_kmeans = kmeans.predict(X) 20 | 21 | # plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis') 22 | # centers = kmeans.cluster_centers_ 23 | # print(centers) 24 | 25 | gmm = GaussianMixture(n_components=4).fit(X) 26 | labels = gmm.predict(X) 27 | plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis') 28 | -------------------------------------------------------------------------------- /EnsembleLearning/baggingexample.py: -------------------------------------------------------------------------------- 1 | from sklearn.ensemble import BaggingClassifier 2 | from sklearn.tree import DecisionTreeClassifier 3 | from sklearn import datasets 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.svm import SVC 6 | 7 | iris = datasets.load_iris() 8 | X, y = iris.data[:, 1:3], iris.target 9 | X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42) 10 | 11 | clf = DecisionTreeClassifier() 12 | clfb = BaggingClassifier(base_estimator=DecisionTreeClassifier(), 13 | max_samples=0.5, max_features=0.5) 14 | 15 | clfb1 = BaggingClassifier(base_estimator=SVC(), 16 | max_samples=0.5, max_features=0.5) 17 | model1 = clf.fit(X, y) 18 | model2 = clfb.fit(X, y) 19 | model3 = clfb1.fit(X, y) 20 | predict = model1.score(X_test, y_test) 21 | predictb = model2.score(X_test, y_test) 22 | predictb1 = model3.score(X_test, y_test) 23 | 24 | print(predict) # 1.0 25 | print(predictb) # 0.9473684210526315 26 | print(predictb1) # 0.9210526315789473 -------------------------------------------------------------------------------- /EnsembleLearning/stackingexample.py: -------------------------------------------------------------------------------- 1 | from mlxtend.classifier import StackingClassifier 2 | from sklearn.datasets import load_iris 3 | from sklearn.ensemble import RandomForestClassifier 4 | from sklearn.svm import LinearSVC 5 | from sklearn.linear_model import LogisticRegression 6 | from sklearn.preprocessing import StandardScaler 7 | from sklearn.pipeline import make_pipeline 8 | from sklearn.model_selection import train_test_split 9 | 10 | X, y = load_iris(return_X_y=True) 11 | estimators = [ 12 | RandomForestClassifier(n_estimators=10, random_state=42), 13 | make_pipeline(StandardScaler(), 14 | LinearSVC(random_state=42))] 15 | clf = StackingClassifier( 16 | classifiers=estimators, 17 | meta_classifier=LogisticRegression() 18 | ) 19 | X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42) 20 | model = clf.fit(X_train, y_train) 21 | score = model.score(X_test, y_test) 22 | print(score) # 0.868421052631579 -------------------------------------------------------------------------------- /EnsembleLearning/stackingexampleplus3.py: -------------------------------------------------------------------------------- 1 | from sklearn.datasets import load_iris 2 | from mlxtend.classifier import StackingClassifier 3 | from mlxtend.feature_selection import ColumnSelector 4 | from sklearn.pipeline import make_pipeline 5 | from sklearn.linear_model import LogisticRegression 6 | from xgboost.sklearn import XGBClassifier 7 | from sklearn.ensemble import RandomForestClassifier 8 | 9 | iris = load_iris() 10 | X = iris.data 11 | y = iris.target 12 | #基分类器1：xgboost 13 | pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)), 14 | XGBClassifier()) 15 | #基分类器2：RandomForest 16 | pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)), 17 | RandomForestClassifier()) 18 | 19 | sclf = StackingClassifier(classifiers=[pipe1, pipe2], 20 | meta_classifier=LogisticRegression()) 21 | 22 | sclf.fit(X, y) -------------------------------------------------------------------------------- /K-Means&DBSCAN/DBSCAN.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | from sklearn.datasets import make_blobs, make_circles 4 | from sklearn.cluster import KMeans, DBSCAN 5 | 6 | # 下面我们生成三组数据 7 | X1, y1 = make_circles(n_samples=5000, factor=0.6, noise=0.05) 8 | X2, y2 = make_blobs(n_samples=1000, n_features=2, centers=[[1.2, 1.2]], 9 | cluster_std=[[0.1]], random_state=9) 10 | X = np.concatenate((X1, X2)) 11 | clf = KMeans(n_clusters=3, random_state=9) 12 | clf1 = DBSCAN(eps=0.1, min_samples=10) 13 | y_pred = clf1.fit_predict(X) 14 | plt.scatter(X[:, 0], X[:, 1], c=y_pred, marker='o') 15 | plt.show() 16 | -------------------------------------------------------------------------------- /K-Means&DBSCAN/K-Means.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | from sklearn.datasets.samples_generator import make_blobs 4 | from sklearn.cluster import KMeans 5 | from sklearn import metrics 6 | 7 | 8 | # sklearn 中的make_blobs方法常被用来生成聚类算法的测试数据 9 | # X为样本特征，Y为样本簇类别，共1000个样本，每个样本2个特征，共4个簇 10 | # 簇中心在[-1,-1], [0,0],[1,1], [2,2]， 簇方差分别为[0.4, 0.2, 0.2] 11 | # n_samples表示产生多少个数据 n_features表示数据是几维的 12 | # centers表示数据点中心，可以输入整数，代表有几个中心，也可以输入几个坐标 13 | # cluster_std表示分布的标准差 14 | X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1, -1], [0, 0], [1, 1], [2, 2]], 15 | cluster_std=[0.4, 0.2, 0.2, 0.2], 16 | random_state=9) 17 | clf = KMeans(n_clusters=4, random_state=9) 18 | y_pred = clf.fit_predict(X) 19 | res = metrics.calinski_harabaz_score(X, y_pred) 20 | print(res) 21 | plt.scatter(X[:, 0], X[:, 1], c=y_pred, marker='o') 22 | plt.show() 23 | 24 | -------------------------------------------------------------------------------- /K-Means&DBSCAN/MiniBatch_K-Means.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | from sklearn.datasets.samples_generator import make_blobs 4 | from sklearn.cluster import KMeans, MiniBatchKMeans 5 | from sklearn import metrics 6 | 7 | X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1, -1], [0, 0], [1, 1], [2, 2]], 8 | cluster_std=[0.4, 0.2, 0.2, 0.2], 9 | random_state=9) 10 | 11 | for index, k in enumerate((2, 3, 4, 5)): 12 | plt.subplot(2, 2, index + 1) 13 | clf = MiniBatchKMeans(n_clusters=k, batch_size=200, random_state=9) 14 | y_pred = clf.fit_predict(X) 15 | score = metrics.calinski_harabaz_score(X, y_pred) 16 | plt.scatter(X[:, 0], X[:, 1], c=y_pred) 17 | plt.text(.99, .01, ('k=%d, score: %.2f' % (k, score)), 18 | transform=plt.gca().transAxes, size=10, 19 | horizontalalignment='right') 20 | plt.show() 21 | -------------------------------------------------------------------------------- /K-Means&DBSCAN/data.txt: -------------------------------------------------------------------------------- 1 | name calories sodium alcohol cost 2 | Budweiser 144 15 4.7 0.43 3 | Schlitz 151 19 4.9 0.43 4 | Lowenbrau 157 15 0.9 0.48 5 | Kronenbourg 170 7 5.2 0.73 6 | Heineken 152 11 5.0 0.77 7 | Old_Milwaukee 145 23 4.6 0.28 8 | Augsberger 175 24 5.5 0.40 9 | Srohs_Bohemian_Style 149 27 4.7 0.42 10 | Miller_Lite 99 10 4.3 0.43 11 | Budweiser_Light 113 8 3.7 0.40 12 | Coors 140 18 4.6 0.44 13 | Coors_Light 102 15 4.1 0.46 14 | Michelob_Light 135 11 4.2 0.50 15 | Becks 150 19 4.7 0.76 16 | Kirin 149 6 5.0 0.79 17 | Pabst_Extra_Light 68 15 2.3 0.38 18 | Hamms 139 19 4.4 0.43 19 | Heilemans_Old_Style 144 24 4.9 0.43 20 | Olympia_Goled_Light 72 6 2.9 0.46 21 | Schlitz_Light 97 7 4.2 0.47 -------------------------------------------------------------------------------- /KNN/testDigits/0_0.txt: -------------------------------------------------------------------------------- 1 | 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00000000111111111111000000000000 6 | 00000000011111111111110000000000 7 | 00000000111111111111110000000000 8 | 00000000111111111111111000000000 9 | 00000000111111111111111000000000 10 | 00000001111111111111111000000000 11 | 00000000111111111111111110000000 12 | 00000001111111111001111110000000 13 | 00000001111100000000111110000000 14 | 00000001111100000000011110000000 15 | 00000001111100000000001111000000 16 | 00000001111100000000001111000000 17 | 00000001111000000000001111000000 18 | 00000001111000000000011111000000 19 | 00000001111000000000011111000000 20 | 00000000111100000000011111100000 21 | 00000001111100000000111111000000 22 | 00000001111100000001111111000000 23 | 00000001111000000011111110000000 24 | 00000000111100000011111111000000 25 | 00000000111100000011111111000000 26 | 00000000111111111111111110000000 27 | 00000000111111111111111100000000 28 | 00000000011111111111111100000000 29 | 00000000001111111111111100000000 30 | 00000000001111111111110000000000 31 | 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00000000000111111111110000000000 4 | 00000000001111111111111000000000 5 | 00000000001111111111111100000000 6 | 00000000111111111111111110000000 7 | 00000000111111111111111111000000 8 | 00000001111111111001111111000000 9 | 00000001111111110000111111000000 10 | 00000001111111000000011111000000 11 | 00000011111111000000011111000000 12 | 00000011111110000000011111000000 13 | 00000011111100000000011111000000 14 | 00000011111100000000001111000000 15 | 00000011111100000000001111000000 16 | 00000011111100000000001111000000 17 | 00000011111100000000001111000000 18 | 00000011111100000000001111000000 19 | 00000011111100000000001111000000 20 | 00000001111100000000011111000000 21 | 00000011111100000000011110000000 22 | 00000001111100000000111110000000 23 | 00000001111100000001111100000000 24 | 00000001111100000011111100000000 25 | 00000001111111100111111000000000 26 | 00000001111111111111111000000000 27 | 00000000011111111111110000000000 28 | 00000000011111111111100000000000 29 | 00000000001111111111100000000000 30 | 00000000000111111110000000000000 31 | 00000000000111111100000000000000 32 | 00000000000001100000000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_35.txt: -------------------------------------------------------------------------------- 1 | 00000000000000100000000000000000 2 | 00000000000111110000000000000000 3 | 00000000000111111000000000000000 4 | 00000000001111111110000000000000 5 | 00000000001111111110000000000000 6 | 00000000011111111111000000000000 7 | 00000000111111111111000000000000 8 | 00000011111111111111100000000000 9 | 00000011111111101111100000000000 10 | 00000011111110000111110000000000 11 | 00000111111110000011110000000000 12 | 00000011111100000001111100000000 13 | 00000111111100000001111100000000 14 | 00000111111100000000111100000000 15 | 00000111111100000000111100000000 16 | 00000011111110000000001110000000 17 | 00000011111110000000001110000000 18 | 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00000000111111111110000000000000 30 | 00000000011111111100000000000000 31 | 00000000001111110000000000000000 32 | 00000000000111000000000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_41.txt: -------------------------------------------------------------------------------- 1 | 00000000000111111111000000000000 2 | 00000000001111111111100000000000 3 | 00000000011111111111110000000000 4 | 00000000011111111111110000000000 5 | 00000000111111111111111000000000 6 | 00000000111111000111111100000000 7 | 00000001111110000001111100000000 8 | 00000001111100000001111110000000 9 | 00000011111100000000111110000000 10 | 00000011111000000000111110000000 11 | 00000001111100000000011111000000 12 | 00000001111100000000001111100000 13 | 00000001111100000000001111100000 14 | 00000001111100000000001111100000 15 | 00000001111100000000001111100000 16 | 00000000111100000000001111100000 17 | 00000000111110000000001111100000 18 | 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00000000000111111111111000000000 30 | 00000000000111111111111000000000 31 | 00000000000001111111111000000000 32 | 00000000000000000111000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_60.txt: -------------------------------------------------------------------------------- 1 | 00000000000001100000000000000000 2 | 00000000000111110000000000000000 3 | 00000000001111111100000000000000 4 | 00000000011111111111100000000000 5 | 00000000011111111111100000000000 6 | 00000000011111111111110000000000 7 | 00000000111111111111110000000000 8 | 00000000111111111001111100000000 9 | 00000001111111011000111100000000 10 | 00000001111100000000111100000000 11 | 00000001111100000000111100000000 12 | 00000000111100000000011110000000 13 | 00000001111000000000011110000000 14 | 00000001111000000000011110000000 15 | 00000001111000000000011110000000 16 | 00000001111100000000001111000000 17 | 00000001111000000000011111000000 18 | 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00000001111110000000111000000000 8 | 00000000111110000000011100000000 9 | 00000011111110000000001100000000 10 | 00000011111110000000011100000000 11 | 00000011111110000000011100000000 12 | 00000011111111000000000110000000 13 | 00000011111100000000001110000000 14 | 00000011111100000000001110000000 15 | 00000011111000000000001110000000 16 | 00000001111100000000000111000000 17 | 00000001111000000000000111000000 18 | 00000011111000000000001111000000 19 | 00000001111100000000000111100000 20 | 00000001111100000000000111000000 21 | 00000001111000000000001111000000 22 | 00000001111000000000011110000000 23 | 00000000111100000000011111000000 24 | 00000000111100000001111110000000 25 | 00000000011100000111111110000000 26 | 00000000011111011111111100000000 27 | 00000000001111111111111110000000 28 | 00000000001111111111111110000000 29 | 00000000001111111111111000000000 30 | 00000000000011111111100000000000 31 | 00000000000001111110000000000000 32 | 00000000000000000000000000000000 33 | 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00000000011111000111111111000000 26 | 00000000011111001111111100000000 27 | 00000000111111111111111000000000 28 | 00000000011111111111111100000000 29 | 00000000011111111111111000000000 30 | 00000000001111111111110000000000 31 | 00000000000111111111110000000000 32 | 00000000000011111111000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_65.txt: -------------------------------------------------------------------------------- 1 | 00000000000001111000000000000000 2 | 00000000001111111110000000000000 3 | 00000000011111111110000000000000 4 | 00000000011111111111100000000000 5 | 00000000011111111111110000000000 6 | 00000000011111111111110000000000 7 | 00000000111111111111111000000000 8 | 00000001111111000011111000000000 9 | 00000000111110000000011100000000 10 | 00000001111100000000001110000000 11 | 00000001111110000000001110000000 12 | 00000001111110000000001110000000 13 | 00000001111000000000001110000000 14 | 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00000000001111111111111000000000 30 | 00000000000111111111110000000000 31 | 00000000000011111111110000000000 32 | 00000000000001111100000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_67.txt: -------------------------------------------------------------------------------- 1 | 00000000000000011000000000000000 2 | 00000000000011111110000000000000 3 | 00000000001111111111000000000000 4 | 00000000000111111111110000000000 5 | 00000000011111111111110000000000 6 | 00000000011111111111111000000000 7 | 00000000111111111111111000000000 8 | 00000000111111110001111100000000 9 | 00000000111111110000011110000000 10 | 00000000111111110000011110000000 11 | 00000001111111000000011110000000 12 | 00000001111111000000011110000000 13 | 00000011111100000000011110000000 14 | 00000011111100000000011110000000 15 | 00000001111100000000001111000000 16 | 00000001111000000000001111000000 17 | 00000001111000000000001111000000 18 | 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00000000001111111111110000000000 4 | 00000000111111111111111100000000 5 | 00000000111111111111111100000000 6 | 00000000111111111111111100000000 7 | 00000000111111111111111111000000 8 | 00000000111110000011111111000000 9 | 00000000111110000011111111000000 10 | 00000001111100000000111111000000 11 | 00000011111000000000111110000000 12 | 00000011111000000000111110000000 13 | 00000011111000000000011110000000 14 | 00000011111000000000011110000000 15 | 00000011111000000000011110000000 16 | 00000001111000000000011111000000 17 | 00000001111100000000001111000000 18 | 00000001111100000000011111000000 19 | 00000001111100000000011111000000 20 | 00000001111110000000011111000000 21 | 00000000111110000000011111000000 22 | 00000000111110000000011111000000 23 | 00000000111111100000011111000000 24 | 00000001111111111111111100000000 25 | 00000001111111111111111100000000 26 | 00000000111111111111111000000000 27 | 00000000111111111111110000000000 28 | 00000000111111111111110000000000 29 | 00000000011111111111110000000000 30 | 00000000001111111111000000000000 31 | 00000000001111111111000000000000 32 | 00000000000001111111000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_8.txt: -------------------------------------------------------------------------------- 1 | 00000000000001110000000000000000 2 | 00000000000111111000000000000000 3 | 00000000001111111000000000000000 4 | 00000000001111111000000000000000 5 | 00000000011111111100000000000000 6 | 00000000011111111111100000000000 7 | 00000000011111111111111000000000 8 | 00000000011111111111111100000000 9 | 00000000111111111111111100000000 10 | 00000000111111111111111110000000 11 | 00000000111111111111111110000000 12 | 00000001111111110000111110000000 13 | 00000001111110000000011110000000 14 | 00000000111100000000000111000000 15 | 00000001111000000000000111000000 16 | 00000001111000000000000111000000 17 | 00000001110000000000000111000000 18 | 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-------------------------------------------------------------------------------- /KNN/testDigits/0_81.txt: -------------------------------------------------------------------------------- 1 | 00000000000001111100000000000000 2 | 00000000001111111110000000000000 3 | 00000000001111111111100000000000 4 | 00000000011111111111110000000000 5 | 00000000111111111111111000000000 6 | 00000000111111000011111000000000 7 | 00000000111110000011111000000000 8 | 00000000111110000001111100000000 9 | 00000001111100000001111100000000 10 | 00000001111000000000111100000000 11 | 00000000111000000000011110000000 12 | 00000001111000000000001111000000 13 | 00000001111000000000000111000000 14 | 00000011111000000000000111000000 15 | 00000011111111000000000111000000 16 | 00000011111111000000001111000000 17 | 00000011111111000000001111000000 18 | 00000011111111000000001111000000 19 | 00000011111111000000011111000000 20 | 00000011111111000000011111000000 21 | 00000011111111000000011110000000 22 | 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00000000111111111111111000000000 26 | 00000000011111111111110000000000 27 | 00000000011111111111100000000000 28 | 00000000011111111111100000000000 29 | 00000000011111111111100000000000 30 | 00000000001111111111000000000000 31 | 00000000000111111100000000000000 32 | 00000000000001111000000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_84.txt: -------------------------------------------------------------------------------- 1 | 00000000000000011100000000000000 2 | 00000000000001111110000000000000 3 | 00000000000011111111000000000000 4 | 00000000000011111111000000000000 5 | 00000000000111111111100000000000 6 | 00000000000111111111110000000000 7 | 00000000000111111111111000000000 8 | 00000000000111111111111000000000 9 | 00000000000111111111111000000000 10 | 00000000000111110001111000000000 11 | 00000000000011110001111100000000 12 | 00000000000011110000111100000000 13 | 00000000000011110000011100000000 14 | 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00000000000011111111111000000000 30 | 00000000000011111111110000000000 31 | 00000000000011111111000000000000 32 | 00000000000000111100000000000000 33 | -------------------------------------------------------------------------------- /KNN/testDigits/0_86.txt: -------------------------------------------------------------------------------- 1 | 00000000000001110000000000000000 2 | 00000000000111111100000000000000 3 | 00000000001111111110000000000000 4 | 00000000001111111110000000000000 5 | 00000000011111011110000000000000 6 | 00000000011111001111000000000000 7 | 00000000011111000111110000000000 8 | 00000000111110000011111000000000 9 | 00000000111110000011111000000000 10 | 00000001111100000011111100000000 11 | 00000000111100000001111110000000 12 | 00000000111100000001111110000000 13 | 00000000111100000001111110000000 14 | 00000001111100000001111110000000 15 | 00000000111110000000111111000000 16 | 00000000111100000000111111000000 17 | 00000001111100000000111111000000 18 | 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-------------------------------------------------------------------------------- /KNN/testDigits/1_0.txt: -------------------------------------------------------------------------------- 1 | 00000000000001000000000000000000 2 | 00000000000111111000000000000000 3 | 00000000000111111111100000000000 4 | 00000000000111111111100000000000 5 | 00000000000011111111110000000000 6 | 00000000000011111111110000000000 7 | 00000000011111111111100000000000 8 | 00000000001111111111110000000000 9 | 00000000011111111111000000000000 10 | 00000000011111111111000000000000 11 | 00000000001111111111000000000000 12 | 00000000001111111111000000000000 13 | 00000000011111111111100000000000 14 | 00000000111111111110000000000000 15 | 00000000111111111110000000000000 16 | 00000001111111111111000000000000 17 | 00000011111111111110000000000000 18 | 00000011111111111110000000000000 19 | 00000001111111111111000000000000 20 | 00000001111111111111000000000000 21 | 00000000111111111100000000000000 22 | 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-------------------------------------------------------------------------------- 1 | 00000000000011111111100000000000 2 | 00000000000011111111100000000000 3 | 00000000001111111111111000000000 4 | 00000000011111111111110000000000 5 | 00000001111111111111110000000000 6 | 00000000011111111111110000000000 7 | 00000000000111111111110000000000 8 | 00000000000111111111110000000000 9 | 00000000000111111111111000000000 10 | 00000000000111111111111000000000 11 | 00000000000111111111111000000000 12 | 00000000000111111111111000000000 13 | 00000000001111111111110000000000 14 | 00000000001111111111110000000000 15 | 00000000001111111111110000000000 16 | 00000000001111111111110000000000 17 | 00000000001111111111110000000000 18 | 00000000000111111111110000000000 19 | 00000000000111111111111100000000 20 | 00000000000111111111111100000000 21 | 00000000000111111111111100000000 22 | 00000000001111111111111000000000 23 | 00000000001111111111111000000000 24 | 00000000001111111111111000000000 25 | 00000000001111111111111110000000 26 | 00000000001111111111111110000000 27 | 00000000001111111111111110000000 28 | 00000000001111111111111110000000 29 | 00000000001111111111111110000000 30 | 00000000001111111111111110000000 31 | 00000000011111111111110000000000 32 | 00000000000000011110000000000000 33 | -------------------------------------------------------------------------------- /LDA/lda_example1.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | from mpl_toolkits.mplot3d import Axes3D 4 | from sklearn.datasets.samples_generator import make_classification 5 | 6 | X, y = make_classification(n_samples=1000, n_features=3, n_redundant=0, 7 | n_classes=3, n_informative=2, n_clusters_per_class=1, 8 | class_sep=0.5, random_state=10) 9 | 10 | # fig = plt.figure() 11 | # ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=20, azim=20) 12 | # ax.scatter(X[:, 0], X[:, 1], X[:, 2], marker='o', c=y) 13 | 14 | # pca没有使用类别信息，对此数据降维后，样本特征和类别的信息关联几乎丢失 15 | pca = PCA(n_components=2) 16 | pca.fit(X) 17 | print(pca.explained_variance_ratio_) 18 | print(pca.explained_variance_) 19 | # [0.43377069 0.3716351 ] 20 | # [1.21083449 1.0373882 ] 21 | X_new = pca.transform(X) 22 | plt.scatter(X_new[:, 0], X_new[:, 1], marker='o', c=y) 23 | plt.show() 24 | 25 | # LDA是有监督学习，将为后样本特征信息之间的关系得以保留 26 | lda = LinearDiscriminantAnalysis() 27 | lda.fit(X, y) 28 | X_new = lda.transform(X) 29 | plt.scatter(X_new[:, 0], X_new[:, 1], marker='o', c=y) 30 | plt.show() -------------------------------------------------------------------------------- /LDA/sklearn_LDA.py: -------------------------------------------------------------------------------- 1 | # _*_coding:utf-8_*_ 2 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA 3 | from sklearn.linear_model import LogisticRegression 4 | 5 | lda = LDA(n_components=2) 6 | x_train_lda = lda.fit_transform(X_train_std,y_train) 7 | 8 | lr = LogisticRegression() 9 | lr = lr.fit(X_train_std,y_train) 10 | 11 | plot_decision_region(X_train_std,y_train,classmethod) -------------------------------------------------------------------------------- /LocalOutlierFactor(LOF)/lof_practice1.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.neighbors import LocalOutlierFactor as LOF 3 | 4 | X = [[-1.1], [0.2], [100.1], [0.3]] 5 | clf = LOF(n_neighbors=2) 6 | res = clf.fit_predict(X) 7 | print(res) 8 | print(clf.negative_outlier_factor_) 9 | 10 | ''' 11 | 如果 X = [[-1.1], [0.2], [100.1], [0.3]] 12 | [ 1 1 -1 1] 13 | [ -0.98214286 -1.03703704 -72.64219576 -0.98214286] 14 | 15 | 如果 X = [[-1.1], [0.2], [0.1], [0.3]] 16 | [-1 1 1 1] 17 | [-7.29166666 -1.33333333 -0.875 -0.875 ] 18 | 19 | 如果 X = [[0.15], [0.2], [0.1], [0.3]] 20 | [ 1 1 1 -1] 21 | [-1.33333333 -0.875 -0.875 -1.45833333] 22 | ''' 23 | -------------------------------------------------------------------------------- /LogisticRegression/iris_logRegress.py: -------------------------------------------------------------------------------- 1 | from sklearn import datasets 2 | from numpy import * 3 | from sklearn.linear_model import LogisticRegression 4 | from sklearn.model_selection import train_test_split 5 | 6 | 7 | def colicSklearn(): 8 | iris = datasets.load_iris() 9 | X = iris.data 10 | Y = iris.target 11 | trainingSet,testSet,trainingLabels,testLabels = train_test_split(X,Y,test_size=0.25,random_state=40) 12 | classifier = LogisticRegression(solver='sag', max_iter=5000).fit(trainingSet, trainingLabels) 13 | test_accurcy = classifier.score(testSet, testLabels) * 100 14 | print("正确率为%s%%" % test_accurcy) 15 | 16 | if __name__ == '__main__': 17 | colicSklearn() -------------------------------------------------------------------------------- /NaiveBayes/Gaussian_Bayes.py: -------------------------------------------------------------------------------- 1 | from sklearn import metrics 2 | from sklearn.naive_bayes import GaussianNB 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.preprocessing import MinMaxScaler 5 | 6 | # 读取数据 7 | X = [] 8 | Y = [] 9 | fr = open("datingTestSet.txt", encoding='utf-8') 10 | print(fr) 11 | index = 0 12 | for line in fr.readlines(): 13 | # print(line) 14 | line = line.strip() 15 | line = line.split('\t') 16 | X.append(line[:3]) 17 | Y.append(line[-1]) 18 | 19 | # 归一化 20 | scaler = MinMaxScaler() 21 | # print(X) 22 | X = scaler.fit_transform(X) 23 | # print(X) 24 | 25 | # 交叉分类 26 | train_X, test_X, train_y, test_y = train_test_split(X, Y, test_size=0.2) 27 | 28 | #高斯贝叶斯模型 29 | model = GaussianNB() 30 | model.fit(train_X, train_y) 31 | 32 | # 预测测试集数据 33 | predicted = model.predict(test_X) 34 | # 输出分类信息 35 | res = metrics.classification_report(test_y, predicted) 36 | # print(res) 37 | # 去重复，得到标签类别 38 | label = list(set(Y)) 39 | # print(label) 40 | # 输出混淆矩阵信息 41 | matrix_info = metrics.confusion_matrix(test_y, predicted, labels=label) 42 | # print(matrix_info) -------------------------------------------------------------------------------- /NaiveBayes/train.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LeBron-Jian/MachineLearningNote/b5d979e2906fa59e69506d4160b1ebe1c8aed1fa/NaiveBayes/train.zip -------------------------------------------------------------------------------- /OneClassSVM/OneClassSVM_SVDD.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | plt.style.use('fivethirtyeight') 3 | from numpy import genfromtxt 4 | 5 | def read_dataset(filePath, delimiter=','): 6 | return genfromtxt(filePath, delimiter=delimiter) 7 | 8 | # use the same dataset 9 | tr_data = read_dataset('tr_data.csv') 10 | 11 | clf = svm.OneClassSVM(nu=0.05, kernel='rbf', gamma=0.1) 12 | ''' 13 | OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma=0.1, kernel='rbf', 14 | max_iter=-1, nu=0.05, random_state=None, shrinking=True, tol=0.001, 15 | verbose=False) 16 | ''' 17 | clf.fit(tr_data) 18 | pred = clf.predict(tr_data) 19 | 20 | # inliers are labeled 1 , outliers are labeled -1 21 | normal = tr_data[pred == 1] 22 | abnormal = tr_data[pred == -1] 23 | 24 | plt.plot(normal[:, 0], normal[:, 1], 'bx) 25 | plt.plot(abnormal[:, 0], abnormal[:, 1], 'ro') -------------------------------------------------------------------------------- /PCA/PCA-iris.py: -------------------------------------------------------------------------------- 1 | #_*_coding:utf-8_*_ 2 | import matplotlib.pyplot as plt 3 | from sklearn import datasets 4 | from sklearn.decomposition import PCA 5 | from mpl_toolkits.mplot3d import Axes3D 6 | import numpy as np 7 | 8 | 9 | np.random.seed(5) 10 | 11 | centers = [[1, 1], [-1, -1], [1, -1]] 12 | iris = datasets.load_iris() 13 | X = iris.data 14 | y = iris.target 15 | 16 | fig = plt.figure(1, figsize=(4, 3)) 17 | plt.clf() 18 | ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134) 19 | 20 | plt.cla() 21 | pca = PCA(n_components=3) 22 | pca.fit(X) 23 | X = pca.transform(X) 24 | for name,label in [('Setosa', 0), ('Versico',1), ('Viriginica', 2)]: 25 | ax.text3D(X[y == label, 0].mean(), 26 | X[y == label, 1].mean() + 1.5, 27 | X[y == label, 2].mean(), name, 28 | horizontalalignment = 'center', 29 | bbox = dict(alpha=0.5, edgecolor ='w',facecolor='w')) 30 | y = np.choose(y, [1, 2, 0]).astype(np.float) 31 | y1 = plt.scatter(X[:,0], X[:, 1], X[:, 2], c=z, cmap=plt.cm.nipy_spectral, 32 | edgecolor='r') 33 | 34 | ax.w_xaxis.set_ticklabels([]) 35 | ax.w_yaxis.set_ticklabels([]) 36 | ax.w_zaxis.set_ticklabels([]) 37 | 38 | plt.show() -------------------------------------------------------------------------------- /PCA/matplotlib-learn.py: -------------------------------------------------------------------------------- 1 | from mpl_toolkits.mplot3d import axes3d 2 | import matplotlib.pyplot as plt 3 | from matplotlib import style 4 | import numpy as np 5 | 6 | style.use('fivethirtyeight') 7 | 8 | fig = plt.figure() 9 | ax1 = fig.add_subplot(111, projection ='3d') 10 | 11 | # Make data 12 | 13 | u = np.linspace(0, 2 * np.pi, 100) 14 | 15 | v = np.linspace(0, np.pi, 100) 16 | 17 | x = 10 * np.outer(np.cos(u), np.sin(v)) 18 | 19 | y = 10 * np.outer(np.sin(u), np.sin(v)) 20 | 21 | z = 10 * np.outer(np.ones(np.size(u)), np.cos(v)) 22 | 23 | # Plot the surface 24 | 25 | ax1.plot_surface(x, y, z, color='b') 26 | 27 | plt.show() -------------------------------------------------------------------------------- /PCA/pca_calc.py: -------------------------------------------------------------------------------- 1 | x = [2.5, 0.5, 2.2, 1.9, 3.1, 2.3, 2, 1, 1.5, 1.1] 2 | y = [2.4, 0.7, 2.9, 2.2, 3.0, 2.7, 1.6, 1.1, 1.6, 0.9] 3 | import numpy as np 4 | 5 | mean_x = np.mean(x) 6 | mean_y = np.mean(y) 7 | 8 | result_x = [round(x[i] - mean_x, 2) for i in range(len(x))] 9 | result_y = [round(y[i] - mean_y, 2) for i in range(len(y))] 10 | # print(result_x) 11 | # print(result_y) 12 | z = np.vstack((result_x, result_y)) 13 | print(z) 14 | cov = np.cov(z) 15 | print(cov) 16 | 17 | -------------------------------------------------------------------------------- /PCA/pca_testSet.py: -------------------------------------------------------------------------------- 1 | import matplotlib 2 | import matplotlib.pyplot as plt 3 | 4 | dataMat = loadDataSet('testSet.txt') 5 | lowDMat, reconMat = pca(dataMat,1) 6 | print "shape(lowDMat): ",shape(lowDMat) 7 | 8 | fig = plt.figure() 9 | ax = fig.add_subplot(111) 10 | ax.scatter(dataMat[:,0].flatten().A[0],dataMat[:,1].flatten().A[0],marker='^',s=90) 11 | ax.scatter(reconMat[:,0].flatten().A[0],reconMat[:,1].flatten().A[0],marker='o',s=50,c='red') 12 | plt.show() -------------------------------------------------------------------------------- /RandomForest/RandomForest_AdjustParm2.py: -------------------------------------------------------------------------------- 1 | def random_forest_parameter_tuning2(feature_data, label_data, test_feature): 2 | from sklearn.ensemble import RandomForestRegressor 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.metrics import mean_squared_error 5 | from sklearn.model_selection import GridSearchCV 6 | 7 | X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23) 8 | param_test2 = { 9 | 'max_depth': range(3, 14, 2), 10 | 'min_samples_split': range(50, 201, 20) 11 | } 12 | model = GridSearchCV(estimator=RandomForestRegressor( 13 | n_estimators=70, min_samples_leaf=20, max_features='sqrt', oob_score=True, 14 | random_state=10), param_grid=param_test2, cv=5 15 | ) 16 | model.fit(X_train, y_train) 17 | # 对测试集进行预测 18 | y_pred = model.predict(X_test) 19 | # 计算准确率 20 | MSE = mean_squared_error(y_test, y_pred) 21 | RMSE = np.sqrt(MSE) 22 | print(RMSE) 23 | return model.best_score_, model.best_params_ -------------------------------------------------------------------------------- /RandomForest/RandomForest_AdjustParm3.py: -------------------------------------------------------------------------------- 1 | def random_forest_parameter_tuning3(feature_data, label_data, test_feature): 2 | from sklearn.ensemble import RandomForestRegressor 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.metrics import mean_squared_error 5 | from sklearn.model_selection import GridSearchCV 6 | 7 | X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23) 8 | param_test3 = { 9 | 'min_samples_split': range(10, 90, 20), 10 | 'min_samples_leaf': range(10, 60, 10), 11 | } 12 | model = GridSearchCV(estimator=RandomForestRegressor( 13 | n_estimators=70, max_depth=13, max_features='sqrt', oob_score=True, 14 | random_state=10), param_grid=param_test3, cv=5 15 | ) 16 | model.fit(X_train, y_train) 17 | # 对测试集进行预测 18 | y_pred = model.predict(X_test) 19 | # 计算准确率 20 | MSE = mean_squared_error(y_test, y_pred) 21 | RMSE = np.sqrt(MSE) 22 | print(RMSE) 23 | return model.best_score_, model.best_params_ -------------------------------------------------------------------------------- /RandomForest/RandomForest_AdjustParm4.py: -------------------------------------------------------------------------------- 1 | def random_forest_parameter_tuning4(feature_data, label_data, test_feature): 2 | from sklearn.ensemble import RandomForestRegressor 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.metrics import mean_squared_error 5 | from sklearn.model_selection import GridSearchCV 6 | 7 | X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23) 8 | param_test3 = { 9 | 'max_features': range(3, 9, 2), 10 | } 11 | model = GridSearchCV(estimator=RandomForestRegressor( 12 | n_estimators=70, max_depth=13, min_samples_split=10, min_samples_leaf=10, oob_score=True, 13 | random_state=10), param_grid=param_test3, cv=5 14 | ) 15 | model.fit(X_train, y_train) 16 | # 对测试集进行预测 17 | y_pred = model.predict(X_test) 18 | # 计算准确率 19 | MSE = mean_squared_error(y_test, y_pred) 20 | RMSE = np.sqrt(MSE) 21 | print(RMSE) 22 | return model.best_score_, model.best_params_ -------------------------------------------------------------------------------- /RandomForest/RandomForest_AdjustParm5.py: -------------------------------------------------------------------------------- 1 | def random_forest_train(feature_data, label_data, test_feature, submitfile): 2 | from sklearn.ensemble import RandomForestRegressor 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.metrics import mean_squared_error 5 | 6 | X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23) 7 | params = { 8 | 'n_estimators': 70, 9 | 'max_depth': 13, 10 | 'min_samples_split': 10, 11 | 'min_samples_leaf': 10, 12 | 'max_features': 7 13 | } 14 | model = RandomForestRegressor(**params) 15 | model.fit(X_train, y_train) 16 | # 对测试集进行预测 17 | y_pred = model.predict(X_test) 18 | # 计算准确率 19 | MSE = mean_squared_error(y_test, y_pred) 20 | RMSE = np.sqrt(MSE) 21 | print(RMSE) 22 | 23 | submit = pd.read_csv(submitfile) 24 | submit['y'] = model.predict(test_feature) 25 | submit.to_csv('my_random_forest_prediction1.csv', index=False) -------------------------------------------------------------------------------- /RandomForest/RandomForset_AdjustParm1.py: -------------------------------------------------------------------------------- 1 | def random_forest_parameter_tuning1(feature_data, label_data, test_feature): 2 | from sklearn.ensemble import RandomForestRegressor 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.metrics import mean_squared_error 5 | from sklearn.model_selection import GridSearchCV 6 | 7 | X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23) 8 | param_test1 = { 9 | 'n_estimators': range(10, 71, 10) 10 | } 11 | model = GridSearchCV(estimator=RandomForestRegressor( 12 | min_samples_split=100, min_samples_leaf=20, max_depth=8, max_features='sqrt', 13 | random_state=10), param_grid=param_test1, cv=5 14 | ) 15 | model.fit(X_train, y_train) 16 | # 对测试集进行预测 17 | y_pred = model.predict(X_test) 18 | # 计算准确率 19 | MSE = mean_squared_error(y_test, y_pred) 20 | RMSE = np.sqrt(MSE) 21 | print(RMSE) 22 | return model.best_score_, model.best_params_ -------------------------------------------------------------------------------- /RandomForest/RandomForset_Example1.py: -------------------------------------------------------------------------------- 1 | >>> from sklearn.ensemble import RandomForestClassifier 2 | >>> from sklearn.datasets import make_classification 3 | >>> 4 | >>> X, y = make_classification(n_samples=1000, n_features=4, 5 | ... n_informative=2, n_redundant=0, 6 | ... random_state=0, shuffle=False) 7 | >>> clf = RandomForestClassifier(max_depth=2, random_state=0) 8 | >>> clf.fit(X, y) 9 | RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', 10 | max_depth=2, max_features='auto', max_leaf_nodes=None, 11 | min_impurity_decrease=0.0, min_impurity_split=None, 12 | min_samples_leaf=1, min_samples_split=2, 13 | min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, 14 | oob_score=False, random_state=0, verbose=0, warm_start=False) 15 | >>> print(clf.feature_importances_) 16 | [ 0.17287856 0.80608704 0.01884792 0.00218648] 17 | >>> print(clf.predict([[0, 0, 0, 0]])) 18 | [1] -------------------------------------------------------------------------------- /RandomForest/进行预测的几种常用算法.py: -------------------------------------------------------------------------------- 1 | >>> from sklearn.cross_validation import cross_val_score 2 | >>> from sklearn.datasets import make_blobs 3 | >>> from sklearn.ensemble import RandomForestClassifier 4 | >>> from sklearn.ensemble import ExtraTreesClassifier 5 | >>> from sklearn.tree import DecisionTreeClassifier 6 | 7 | >>> X, y = make_blobs(n_samples=10000, n_features=10, centers=100, 8 | ... random_state=0) 9 | 10 | >>> clf = DecisionTreeClassifier(max_depth=None, min_samples_split=1, 11 | ... random_state=0) 12 | >>> scores = cross_val_score(clf, X, y) 13 | >>> scores.mean() 14 | 0.97... 15 | 16 | >>> clf = RandomForestClassifier(n_estimators=10, max_depth=None, 17 | ... min_samples_split=1, random_state=0) 18 | >>> scores = cross_val_score(clf, X, y) 19 | >>> scores.mean() 20 | 0.999... 21 | 22 | >>> clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, 23 | ... min_samples_split=1, random_state=0) 24 | >>> scores = cross_val_score(clf, X, y) 25 | >>> scores.mean() > 0.999 26 | True -------------------------------------------------------------------------------- /SVD/mysvdcode.py: -------------------------------------------------------------------------------- 1 | #_*_ coding:utf-8_*_ 2 | import numpy as np 3 | import cv2 4 | 5 | img = cv2.imread('harden.jpg') 6 | print('origin image shape is ', img.shape) 7 | # 表示 RGB 中各有一个矩阵，都为300*532 8 | # origin image shape is (300, 532, 3) 9 | 10 | 11 | def svd_compression(img, k): 12 | res_image = np.zeros_like(img) 13 | for i in range(img.shape[2]): 14 | # 进行奇异值分解, 从svd函数中得到的奇异值sigma 是从大到小排列的 15 | U, Sigma, VT = np.linalg.svd(img[:,:,i]) 16 | res_image[:, :, i] = U[:,:k].dot(np.diag(Sigma[:k])).dot(VT[:k,:]) 17 | 18 | return res_image 19 | 20 | 21 | # 保留前 k 个奇异值 22 | res1 = svd_compression(img, k=300) 23 | res2 = svd_compression(img, k=200) 24 | res3 = svd_compression(img, k=100) 25 | res4 = svd_compression(img, k=50) 26 | 27 | row11 = np.hstack((res1, res2)) 28 | row22 = np.hstack((res3, res4)) 29 | res = np.vstack((row11, row22)) 30 | 31 | cv2.imshow('img', res) 32 | cv2.waitKey(0) 33 | cv2.destroyAllWindows() 34 | -------------------------------------------------------------------------------- /SVM/svm_example3_1.py: -------------------------------------------------------------------------------- 1 | # _*_coding:utf-8_*_ 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | 5 | 6 | X1D = np.linspace(-4, 4, 9).reshape(-1, 1) 7 | # np.c_是按行连接两个矩阵，就是把两矩阵左右相加，要求行数相等。 8 | # x2把 x1和 x1的平方项组合在一起 9 | X2D = np.c_[X1D, X1D ** 2] 10 | y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0]) 11 | 12 | plt.figure(figsize=(11, 4)) 13 | 14 | plt.subplot(121) 15 | plt.grid(True, which='both') 16 | plt.axhline(y=0, color='k') 17 | plt.plot(X1D[:, 0][y == 0], np.zeros(4), 'bs') 18 | plt.plot(X1D[:, 0][y == 1], np.zeros(5), 'g*') 19 | plt.gca().get_yaxis().set_ticks([]) 20 | plt.xlabel(r'$x_1$', fontsize=20) 21 | plt.axis([-4.5, 4.5, -0.2, 0.2]) 22 | 23 | plt.subplot(122) 24 | plt.grid(True, which='both') 25 | plt.axhline(y=0, color='k') 26 | plt.axvline(x=0, color='k') 27 | plt.plot(X2D[:, 0][y == 0], X2D[:, 1][y == 0], 'bs') 28 | plt.plot(X2D[:, 0][y == 1], X2D[:, 1][y == 1], 'g*') 29 | plt.xlabel(r'$x_1$', fontsize=20) 30 | plt.ylabel(r'$x_2$', fontsize=20, rotation=0) 31 | plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16]) 32 | plt.plot([-4.5, 4.5], [6.5, 6.5], 'r--', linewidth=3) 33 | plt.axis([-4.5, 4.5, -1, 17]) 34 | 35 | # plt.subplots_adjust(right=1) 36 | plt.show() 37 | -------------------------------------------------------------------------------- /Xgboost/xgb_example5（网格搜索1）.py: -------------------------------------------------------------------------------- 1 | from sklearn.model_selection import GridSearchCV 2 | tuned_parameters= [{'n_estimators':[100,200,500], 3 | 'max_depth':[3,5,7], ##range(3,10,2) 4 | 'learning_rate':[0.5, 1.0], 5 | 'subsample':[0.75,0.8,0.85,0.9] 6 | }] 7 | tuned_parameters= [{'n_estimators':[100,200,500,1000] 8 | }] 9 | clf = GridSearchCV(XGBClassifier(silent=0,nthread=4,learning_rate= 0.5,min_child_weight=1, max_depth=3,gamma=0,subsample=1,colsample_bytree=1,reg_lambda=1,seed=1000), param_grid=tuned_parameters,scoring='roc_auc',n_jobs=4,iid=False,cv=5) 10 | clf.fit(X_train, y_train) 11 | ##clf.grid_scores_, clf.best_params_, clf.best_score_ 12 | print(clf.best_params_) 13 | y_true, y_pred = y_test, clf.predict(X_test) 14 | print"Accuracy : %.4g" % metrics.accuracy_score(y_true, y_pred) 15 | y_proba=clf.predict_proba(X_test)[:,1] 16 | print "AUC Score (Train): %f" % metrics.roc_auc_score(y_true, y_proba) -------------------------------------------------------------------------------- /Xgboost/xgb_example5（网格搜索2）.py: -------------------------------------------------------------------------------- 1 | from sklearn.model_selection import GridSearchCV 2 | parameters= [{'learning_rate':[0.01,0.1,0.3],'n_estimators':[1000,1200,1500,2000,2500]}] 3 | clf = GridSearchCV(XGBClassifier( 4 | max_depth=3, 5 | min_child_weight=1, 6 | gamma=0.5, 7 | subsample=0.6, 8 | colsample_bytree=0.6, 9 | objective= 'binary:logistic', #逻辑回归损失函数 10 | scale_pos_weight=1, 11 | reg_alpha=0, 12 | reg_lambda=1, 13 | seed=27 14 | ), 15 | param_grid=parameters,scoring='roc_auc') 16 | clf.fit(X_train, y_train) 17 | print(clf.best_params_) 18 | y_pre= clf.predict(X_test) 19 | y_pro= clf.predict_proba(X_test)[:,1] 20 | print "AUC Score : %f" % metrics.roc_auc_score(y_test, y_pro) 21 | print"Accuracy : %.4g" % metrics.accuracy_score(y_test, y_pre) -------------------------------------------------------------------------------- /Xgboost/xgb使用sklearn 分类（模型参数）.py: -------------------------------------------------------------------------------- 1 | from xgboost.sklearn import XGBClassifier 2 | 3 | clf = XGBClassifier( 4 | silent=0, # 设置成1则没有运行信息输出，最好是设置为0，是否在运行升级时打印消息 5 | # nthread = 4 # CPU 线程数 默认最大 6 | learning_rate=0.3 , # 如同学习率 7 | min_child_weight = 1, 8 | # 这个参数默认为1，是每个叶子里面h的和至少是多少，对正负样本不均衡时的0-1分类而言 9 | # 假设h在0.01附近，min_child_weight为1 意味着叶子节点中最少需要包含100个样本 10 | # 这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易过拟合 11 | max_depth=6, # 构建树的深度，越大越容易过拟合 12 | gamma = 0,# 树的叶子节点上做进一步分区所需的最小损失减少，越大越保守，一般0.1 0.2这样子 13 | subsample=1, # 随机采样训练样本，训练实例的子采样比 14 | max_delta_step=0, # 最大增量步长，我们允许每个树的权重估计 15 | colsample_bytree=1, # 生成树时进行的列采样 16 | reg_lambda=1, #控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合 17 | # reg_alpha=0, # L1正则项参数 18 | # scale_pos_weight =1 # 如果取值大于0的话，在类别样本不平衡的情况下有助于快速收敛，平衡正负权重 19 | # objective = 'multi:softmax', # 多分类问题，指定学习任务和响应的学习目标 20 | # num_class = 10, # 类别数，多分类与multisoftmax并用 21 | n_estimators=100, # 树的个数 22 | seed = 1000, # 随机种子 23 | # eval_metric ='auc' 24 | ) 25 | 26 | -------------------------------------------------------------------------------- /Xgboost/基于Xgboost的原生接口回归.py: -------------------------------------------------------------------------------- 1 | import xgboost as xgb 2 | from xgboost import plot_importance 3 | from matplotlib import pyplot as plt 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.datasets import load_boston 6 | from sklearn.metrics import mean_squared_error 7 | 8 | # 加载数据集,此数据集时做回归的 9 | boston = load_boston() 10 | X,y = boston.data,boston.target 11 | 12 | # Xgboost训练过程 13 | X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0) 14 | 15 | # 算法参数 16 | params = { 17 | 'booster':'gbtree', 18 | 'objective':'reg:gamma', 19 | 'gamma':0.1, 20 | 'max_depth':5, 21 | 'lambda':3, 22 | 'subsample':0.7, 23 | 'colsample_bytree':0.7, 24 | 'min_child_weight':3, 25 | 'slient':1, 26 | 'eta':0.1, 27 | 'seed':1000, 28 | 'nthread':4, 29 | } 30 | 31 | dtrain = xgb.DMatrix(X_train,y_train) 32 | num_rounds = 300 33 | plst = params.items() 34 | model = xgb.train(plst,dtrain,num_rounds) 35 | 36 | # 对测试集进行预测 37 | dtest = xgb.DMatrix(X_test) 38 | ans = model.predict(dtest) 39 | 40 | # 显示重要特征 41 | plot_importance(model) 42 | plt.show() -------------------------------------------------------------------------------- /Xgboost/基于sckit-learn接口的分类.py: -------------------------------------------------------------------------------- 1 | from sklearn.datasets import load_iris 2 | import xgboost as xgb 3 | from xgboost import plot_importance 4 | from matplotlib import pyplot as plt 5 | from sklearn.model_selection import train_test_split 6 | from sklearn.metrics import accuracy_score 7 | 8 | # 加载样本数据集 9 | iris = load_iris() 10 | X,y = iris.data,iris.target 11 | X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=12343) 12 | 13 | # 训练模型 14 | model = xgb.XGBClassifier(max_depth=5,learning_rate=0.1,n_estimators=160,silent=True,objective='multi:softmax') 15 | model.fit(X_train,y_train) 16 | 17 | # 对测试集进行预测 18 | y_pred = model.predict(X_test) 19 | 20 | #计算准确率 21 | accuracy = accuracy_score(y_test,y_pred) 22 | print('accuracy:%2.f%%'%(accuracy*100)) 23 | 24 | # 显示重要特征 25 | plot_importance(model) 26 | plt.show() -------------------------------------------------------------------------------- /Xgboost/基于sklearn接口的回归.py: -------------------------------------------------------------------------------- 1 | import xgboost as xgb 2 | from xgboost import plot_importance 3 | from matplotlib import pyplot as plt 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.datasets import load_boston 6 | 7 | # 导入数据集 8 | boston = load_boston() 9 | X ,y = boston.data,boston.target 10 | 11 | # Xgboost训练过程 12 | X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0) 13 | 14 | model = xgb.XGBRegressor(max_depth=5,learning_rate=0.1,n_estimators=160,silent=True,objective='reg:gamma') 15 | model.fit(X_train,y_train) 16 | 17 | # 对测试集进行预测 18 | ans = model.predict(X_test) 19 | 20 | # 显示重要特征 21 | plot_importance(model) 22 | plt.show() --------------------------------------------------------------------------------