├── .gitattributes ├── README.md ├── data ├── arrhythmia.mat ├── breastw.mat ├── cardio.mat ├── ionosphere.mat ├── letter.mat ├── literature │ ├── Ionosphere │ │ └── Ionosphere_withoutdupl_norm.arff │ ├── KDDCup99 │ │ └── KDDCup99_idf.arff │ ├── Lymphography │ │ └── Lymphography_withoutdupl_idf.arff │ ├── Shuttle │ │ └── Shuttle_withoutdupl_v01.arff │ ├── WBC │ │ └── WBC_withoutdupl_v01.arff │ ├── WDBC │ │ └── WDBC_withoutdupl_v01.arff │ ├── WPBC │ │ └── WPBC_withoutdupl_norm.arff │ └── Waveform │ │ └── Waveform_withoutdupl_v01.arff ├── lympho.mat ├── mammography.mat ├── optdigits.mat ├── pima.mat ├── satellite.mat ├── satimage-2.mat ├── semantic │ ├── Arrhythmia │ │ └── Arrhythmia_withoutdupl_norm_46.arff │ ├── Cardiotocography │ │ └── Cardiotocography_withoutdupl_norm_22.arff │ ├── HeartDisease │ │ └── HeartDisease_withoutdupl_norm_44.arff │ ├── Hepatitis │ │ └── Hepatitis_withoutdupl_norm_16.arff │ ├── InternetAds │ │ └── InternetAds_withoutdupl_norm_19.arff │ ├── Pima │ │ └── Pima_withoutdupl_norm_35.arff │ ├── SpamBase │ │ └── SpamBase_withoutdupl_norm_40.arff │ └── Stamps │ │ └── Stamps_withoutdupl_norm_09.arff ├── shuttle.mat ├── speech.mat ├── wbc.mat └── wine.mat ├── models ├── __init__.py └── cod.py └── results ├── benchmark_arff.py ├── benchmark_mat.py ├── benchmark_time.py ├── cod_arff.py ├── cod_example.py └── cod_mat.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # COPOD 2 | **Cop**ula-based **O**utlier **D**etection: a *fast*, *parameter-free*, and *highly interpretable* unsupervised outlier detection method. 3 | 4 | ----- 5 | 6 | 7 | Li, Z., Zhao, Y., Botta, N., Ionescu, C. and Hu, X. COPOD: Copula-Based Outlier Detection. *IEEE International Conference on Data Mining (ICDM)*, 2020. 8 | 9 | Please cite the paper as: 10 | 11 | @inproceedings{li2020copod, 12 | title={{COPOD:} Copula-Based Outlier Detection}, 13 | author={Li, Zheng and Zhao, Yue and Botta, Nicola and Ionescu, Cezar and Hu, Xiyang}, 14 | booktitle={IEEE International Conference on Data Mining (ICDM)}, 15 | year={2020}, 16 | organization={IEEE}, 17 | } 18 | 19 | 20 | [PDF for Personal Use (raw version)](https://www.andrew.cmu.edu/user/yuezhao2/papers/20-icdm-copod-preprint.pdf) | 21 | [Presentation Slides](https://github.com/winstonll/COPOD) | 22 | [API Documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#pyod-models-copod-module) | 23 | [Example with PyOD](https://github.com/yzhao062/pyod/blob/master/examples/copod_example.py) 24 | 25 | 26 | **News**: **COPOD is now officially integrated to [PyOD](https://github.com/yzhao062/pyod), the most popular anomaly detection library, as a featured algorithm**! 27 | The API is simply as below: 28 | 29 | ````python 30 | # train the COPOD detector 31 | from pyod.models.copod import COPOD 32 | clf = COPOD() 33 | clf.fit(X_train) 34 | 35 | # get the prediction label and outlier scores of the training data 36 | y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers) 37 | y_train_scores = clf.decision_scores_ # raw outlier scores 38 | 39 | # get the prediction on the test data 40 | y_test_pred = clf.predict(X_test) # outlier labels (0 or 1) 41 | y_test_scores = clf.decision_function(X_test) # outlier scores 42 | ```` 43 | 44 | Direct examples can be found [HERE](https://github.com/yzhao062/pyod/blob/master/examples/copod_example.py). 45 | 46 | ------------ 47 | 48 | ## Introduction 49 | Outlier detection refers to the identification of rare items that are deviant from the general data distribution. 50 | Existing unsupervised approaches suffer from high computational complexity, low predictive capability, and limited interpretability. 51 | As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by statistical methods for modeling multivariate data distribution. 52 | COPOD first constructs the empirical copula, and then uses the fitted model to predict tail probabilities of each given data point to determine its level of “extremeness”. 53 | Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, as well as computationally efficient. 54 | Moreover, COPOD is parameter-free and require no tuning, which reduces human subjectivity and bias. 55 | In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases, at the same time is also one of the fastest outlier detection algorithms, 56 | and 3) release an easy-to-use Python implementation for reproducibility. 57 | 58 | 59 | ## Dependency 60 | The experiment codes are writen in Python 3.6 and built on a number of Python packages: 61 | - numpy>=1.13 62 | - numba>=0.35 63 | - pyod 64 | - scipy>=0.19 65 | - scikit_learn>=0.19 66 | -statsmodel 67 | 68 | Batch installation is possible using the supplied "requirements.txt" with pip or conda. 69 | 70 | ````cmd 71 | pip install -r requirements.txt 72 | ```` 73 | 74 | ## Reproducibility & Production Level Code 75 | 76 | To reproduce the results included in the paper, run the python scripts in results folder. 77 | 78 | For production and benchmarking, directly use PyOD version. 79 | 80 | 81 | 82 | -------------------------------------------------------------------------------- /data/arrhythmia.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/arrhythmia.mat -------------------------------------------------------------------------------- /data/breastw.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/breastw.mat -------------------------------------------------------------------------------- /data/cardio.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/cardio.mat -------------------------------------------------------------------------------- /data/ionosphere.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/ionosphere.mat -------------------------------------------------------------------------------- /data/letter.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/letter.mat -------------------------------------------------------------------------------- /data/literature/Lymphography/Lymphography_withoutdupl_idf.arff: -------------------------------------------------------------------------------- 1 | @RELATION 'Lymphography, without duplicates, idf' 2 | 3 | @ATTRIBUTE 'id' real 4 | @ATTRIBUTE 'att2' real 5 | @ATTRIBUTE 'att3' real 6 | @ATTRIBUTE 'att4' real 7 | @ATTRIBUTE 'att5' real 8 | @ATTRIBUTE 'att6' real 9 | @ATTRIBUTE 'att7' real 10 | @ATTRIBUTE 'att8' real 11 | @ATTRIBUTE 'att9' real 12 | @ATTRIBUTE 'att10' real 13 | @ATTRIBUTE 'att11' real 14 | @ATTRIBUTE 'att12' real 15 | @ATTRIBUTE 'att13' real 16 | @ATTRIBUTE 'att14' real 17 | @ATTRIBUTE 'att15' real 18 | @ATTRIBUTE 'att16' real 19 | @ATTRIBUTE 'att17' real 20 | @ATTRIBUTE 'att18' real 21 | @ATTRIBUTE 'att19' real 22 | @ATTRIBUTE 'outlier' {'no','yes'} 23 | 24 | @DATA 25 | 1.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.77833644889591,1.1905497839938,0.653406851910431,1.08518926833597,0.235038338966359,3.0,'no' 26 | 2.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.77833644889591,1.1905497839938,1.66500776358891,1.08518926833597,0.235038338966359,2.0,'no' 27 | 3.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.77833644889591,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'no' 28 | 4.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,2.05277329459767,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 29 | 5.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,3.20545280453606,3.0513021247088,1.66500776358891,0.412244795093543,0.235038338966359,2.0,'no' 30 | 6.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.08518926833597,1.77833644889591,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,5.0,'no' 31 | 7.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.25954265548075,1.1905497839938,1.23601215807055,1.08518926833597,0.235038338966359,4.0,'no' 32 | 8.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.56322506927897,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 33 | 9.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,2.05277329459767,0.653406851910431,1.08518926833597,1.56322506927897,2.0,'no' 34 | 10.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 35 | 11.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 36 | 12.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.10539197565349,0.679724160227805,1.1905497839938,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 37 | 13.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.66500776358891,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 38 | 14.0,1.50070471229763,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,0.679724160227805,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,3.0,'no' 39 | 15.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.77833644889591,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 40 | 16.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.10539197565349,0.679724160227805,2.05277329459767,1.23601215807055,0.412244795093543,1.56322506927897,1.0,'no' 41 | 17.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.25954265548075,1.56322506927897,0.653406851910431,1.08518926833597,0.235038338966359,5.0,'no' 42 | 18.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.66500776358891,1.23601215807055,0.412244795093543,0.235038338966359,4.0,'no' 43 | 19.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.653406851910431,1.08518926833597,1.77833644889591,2.35815494414886,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 44 | 20.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,2.69462718077007,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,1.77833644889591,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,5.0,'no' 45 | 21.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,1.0,3.20545280453606,3.89859998509601,3.20545280453606,2.05277329459767,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 46 | 22.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,2.69462718077007,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.66500776358891,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 47 | 23.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.56322506927897,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 48 | 24.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,4.30406509320417,0.653406851910431,1.08518926833597,1.56322506927897,1.0,'no' 49 | 25.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,3.0,0.822825003868478,1.08518926833597,0.679724160227805,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 50 | 26.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,1.25954265548075,2.35815494414886,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 51 | 27.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,2.35815494414886,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 52 | 28.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.10539197565349,1.25954265548075,1.1905497839938,1.66500776358891,0.412244795093543,1.56322506927897,1.0,'no' 53 | 29.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,0.653406851910431,1.08518926833597,0.235038338966359,5.0,'no' 54 | 30.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,1.25954265548075,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 55 | 31.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.16857087727502,1.77833644889591,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 56 | 32.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 57 | 33.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,4.0,0.653406851910431,1.08518926833597,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 58 | 34.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 59 | 35.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,1.25954265548075,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 60 | 36.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 61 | 37.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.66500776358891,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 62 | 38.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,1.66500776358891,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 63 | 39.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.08518926833597,0.679724160227805,1.66500776358891,1.66500776358891,0.412244795093543,0.235038338966359,3.0,'no' 64 | 40.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,2.35815494414886,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 65 | 41.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,4.0,0.822825003868478,1.16857087727502,0.679724160227805,1.56322506927897,1.23601215807055,0.412244795093543,1.56322506927897,1.0,'no' 66 | 42.0,1.16857087727502,0.590493026499862,1.73911573574263,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,3.0,0.653406851910431,1.16857087727502,0.679724160227805,1.56322506927897,0.653406851910431,1.08518926833597,0.235038338966359,2.0,'no' 67 | 43.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,0.679724160227805,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 68 | 44.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,2.69462718077007,1.21302263984585,3.0,1.0,3.20545280453606,1.10539197565349,3.20545280453606,1.66500776358891,0.653406851910431,1.08518926833597,1.56322506927897,7.0,'yes' 69 | 45.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,2.69462718077007,1.21302263984585,3.0,1.0,3.20545280453606,1.08518926833597,1.25954265548075,1.66500776358891,0.653406851910431,1.08518926833597,0.235038338966359,4.0,'yes' 70 | 46.0,1.16857087727502,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,2.69462718077007,0.352821374622742,3.0,1.0,3.20545280453606,1.10539197565349,1.25954265548075,1.1905497839938,1.66500776358891,0.412244795093543,0.235038338966359,4.0,'yes' 71 | 47.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,1.77833644889591,1.56322506927897,0.653406851910431,1.08518926833597,0.235038338966359,6.0,'no' 72 | 48.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 73 | 49.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 74 | 50.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.25954265548075,4.30406509320417,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 75 | 51.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,4.0,'no' 76 | 52.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,2.05277329459767,0.653406851910431,1.08518926833597,0.235038338966359,1.0,'no' 77 | 53.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.08518926833597,1.25954265548075,1.56322506927897,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 78 | 54.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,1.08518926833597,1.56322506927897,1.0,'no' 79 | 55.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,2.35815494414886,1.66500776358891,0.412244795093543,0.235038338966359,3.0,'no' 80 | 56.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,2.05277329459767,0.653406851910431,1.08518926833597,0.235038338966359,1.0,'no' 81 | 57.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,4.30406509320417,0.653406851910431,1.08518926833597,0.235038338966359,4.0,'no' 82 | 58.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.10539197565349,0.679724160227805,2.05277329459767,1.23601215807055,1.08518926833597,1.56322506927897,1.0,'no' 83 | 59.0,1.16857087727502,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,1.08518926833597,0.235038338966359,1.0,'no' 84 | 60.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,1.23601215807055,0.412244795093543,1.56322506927897,1.0,'no' 85 | 61.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.77833644889591,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,5.0,'no' 86 | 62.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,4.0,0.653406851910431,1.10539197565349,1.77833644889591,3.0513021247088,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'no' 87 | 63.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.66500776358891,0.653406851910431,1.08518926833597,1.56322506927897,2.0,'no' 88 | 64.0,1.50070471229763,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,1.0,0.653406851910431,1.10539197565349,3.20545280453606,2.05277329459767,1.66500776358891,0.412244795093543,0.235038338966359,2.0,'no' 89 | 65.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,1.77833644889591,2.35815494414886,0.653406851910431,0.412244795093543,1.56322506927897,1.0,'no' 90 | 66.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,1.77833644889591,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 91 | 67.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.66500776358891,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 92 | 68.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,2.0,1.0,0.822825003868478,1.08518926833597,1.25954265548075,1.1905497839938,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 93 | 69.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.56322506927897,0.653406851910431,1.08518926833597,0.235038338966359,2.0,'no' 94 | 70.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.08518926833597,0.679724160227805,1.56322506927897,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 95 | 71.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,2.05277329459767,1.23601215807055,1.08518926833597,0.235038338966359,1.0,'no' 96 | 72.0,1.16857087727502,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,1.77833644889591,2.35815494414886,1.23601215807055,0.412244795093543,1.56322506927897,1.0,'no' 97 | 73.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,1.0,0.653406851910431,1.08518926833597,0.679724160227805,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 98 | 74.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,0.679724160227805,2.05277329459767,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 99 | 75.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,1.0,'no' 100 | 76.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.16857087727502,0.679724160227805,3.0513021247088,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 101 | 77.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.10539197565349,0.679724160227805,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 102 | 78.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 103 | 79.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,0.679724160227805,2.05277329459767,0.653406851910431,0.412244795093543,0.235038338966359,5.0,'no' 104 | 80.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,2.05277329459767,0.653406851910431,0.412244795093543,1.56322506927897,1.0,'no' 105 | 81.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,1.23601215807055,1.08518926833597,0.235038338966359,2.0,'no' 106 | 82.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,1.0,0.653406851910431,1.10539197565349,0.679724160227805,2.05277329459767,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 107 | 83.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,1.56322506927897,1.66500776358891,0.412244795093543,1.56322506927897,2.0,'no' 108 | 84.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.10539197565349,0.679724160227805,2.35815494414886,1.23601215807055,1.08518926833597,1.56322506927897,1.0,'no' 109 | 85.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.66500776358891,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 110 | 86.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,1.77833644889591,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,1.0,'no' 111 | 87.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,1.0,'no' 112 | 88.0,0.792519654373149,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,3.0,'no' 113 | 89.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,0.679724160227805,1.56322506927897,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 114 | 90.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,2.0,1.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,0.653406851910431,1.08518926833597,0.235038338966359,8.0,'no' 115 | 91.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.25954265548075,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 116 | 92.0,1.50070471229763,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,2.69462718077007,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.77833644889591,3.0513021247088,0.653406851910431,0.412244795093543,0.235038338966359,8.0,'no' 117 | 93.0,1.50070471229763,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.77833644889591,3.0513021247088,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 118 | 94.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,1.66500776358891,1.66500776358891,1.08518926833597,0.235038338966359,2.0,'no' 119 | 95.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,4.0,0.822825003868478,1.08518926833597,1.25954265548075,3.0513021247088,0.653406851910431,0.412244795093543,0.235038338966359,5.0,'no' 120 | 96.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 121 | 97.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.10539197565349,1.25954265548075,1.56322506927897,1.23601215807055,1.08518926833597,1.56322506927897,2.0,'no' 122 | 98.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,1.0,'no' 123 | 99.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 124 | 100.0,1.16857087727502,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,3.0,0.822825003868478,1.08518926833597,0.679724160227805,1.56322506927897,1.23601215807055,0.412244795093543,0.235038338966359,3.0,'no' 125 | 101.0,1.16857087727502,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,0.679724160227805,1.56322506927897,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 126 | 102.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,1.08518926833597,0.235038338966359,2.0,'no' 127 | 103.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.10539197565349,0.679724160227805,1.56322506927897,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 128 | 104.0,4.30406509320417,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,3.89859998509601,3.20545280453606,2.35815494414886,1.66500776358891,1.08518926833597,1.56322506927897,2.0,'yes' 129 | 105.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,4.0,'no' 130 | 106.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,1.41369333530801,0.706752832615724,2.69462718077007,0.352821374622742,1.0,3.0,0.653406851910431,1.16857087727502,1.77833644889591,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'no' 131 | 107.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,3.0,0.653406851910431,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 132 | 108.0,1.16857087727502,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,2.69462718077007,0.352821374622742,1.0,4.0,0.822825003868478,1.16857087727502,1.77833644889591,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'no' 133 | 109.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.10539197565349,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 134 | 110.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.08518926833597,1.77833644889591,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,1.0,'no' 135 | 111.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.1905497839938,1.23601215807055,0.412244795093543,0.235038338966359,1.0,'no' 136 | 112.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,2.35815494414886,1.23601215807055,0.412244795093543,1.56322506927897,1.0,'no' 137 | 113.0,1.16857087727502,0.590493026499862,1.73911573574263,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,1.23601215807055,1.08518926833597,0.235038338966359,2.0,'no' 138 | 114.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.08518926833597,0.679724160227805,1.56322506927897,1.66500776358891,0.412244795093543,0.235038338966359,2.0,'no' 139 | 115.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.25954265548075,2.05277329459767,1.23601215807055,1.08518926833597,1.56322506927897,1.0,'no' 140 | 116.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,1.41369333530801,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,0.653406851910431,1.08518926833597,1.56322506927897,1.0,'no' 141 | 117.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,2.0,0.653406851910431,1.10539197565349,0.679724160227805,1.66500776358891,1.23601215807055,1.08518926833597,0.235038338966359,2.0,'no' 142 | 118.0,1.50070471229763,0.590493026499862,1.73911573574263,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,0.679724160227805,2.05277329459767,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 143 | 119.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,5.0,'no' 144 | 120.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.653406851910431,1.10539197565349,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'no' 145 | 121.0,0.792519654373149,0.590493026499862,1.73911573574263,0.0484523833859467,1.41369333530801,0.679724160227805,2.69462718077007,0.352821374622742,1.0,4.0,0.653406851910431,1.10539197565349,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,6.0,'no' 146 | 122.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.77833644889591,2.05277329459767,1.23601215807055,0.412244795093543,1.56322506927897,1.0,'no' 147 | 123.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,1.77833644889591,1.1905497839938,1.66500776358891,0.412244795093543,0.235038338966359,2.0,'no' 148 | 124.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,1.77833644889591,1.66500776358891,0.653406851910431,0.412244795093543,0.235038338966359,4.0,'no' 149 | 125.0,1.16857087727502,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'no' 150 | 126.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,1.66500776358891,0.653406851910431,1.08518926833597,0.235038338966359,1.0,'no' 151 | 127.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,1.0,0.653406851910431,1.10539197565349,0.679724160227805,2.05277329459767,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 152 | 128.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,3.0,0.822825003868478,1.08518926833597,0.679724160227805,1.56322506927897,0.653406851910431,1.08518926833597,1.56322506927897,1.0,'no' 153 | 129.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,1.21302263984585,1.0,1.0,0.822825003868478,1.16857087727502,0.679724160227805,2.05277329459767,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 154 | 130.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.10539197565349,1.25954265548075,1.56322506927897,1.23601215807055,0.412244795093543,0.235038338966359,2.0,'no' 155 | 131.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.10539197565349,1.25954265548075,1.1905497839938,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'no' 156 | 132.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.10539197565349,0.679724160227805,1.1905497839938,1.23601215807055,1.08518926833597,1.56322506927897,1.0,'no' 157 | 133.0,4.30406509320417,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,1.0,3.20545280453606,3.89859998509601,3.20545280453606,4.30406509320417,1.66500776358891,1.08518926833597,1.56322506927897,1.0,'yes' 158 | 134.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,1.77833644889591,1.1905497839938,1.23601215807055,1.08518926833597,0.235038338966359,2.0,'no' 159 | 135.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 160 | 136.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 161 | 137.0,1.50070471229763,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,4.0,0.653406851910431,1.10539197565349,1.77833644889591,3.0513021247088,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 162 | 138.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.08518926833597,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,3.0,'no' 163 | 139.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.653406851910431,1.08518926833597,1.25954265548075,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,4.0,'no' 164 | 140.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.16857087727502,0.679724160227805,2.35815494414886,1.23601215807055,0.412244795093543,0.235038338966359,3.0,'no' 165 | 141.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,3.0,0.822825003868478,1.08518926833597,0.679724160227805,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 166 | 142.0,1.16857087727502,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,0.679724160227805,2.35815494414886,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 167 | 143.0,0.792519654373149,0.807557531737689,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,1.21302263984585,1.0,1.0,3.20545280453606,1.10539197565349,1.25954265548075,2.05277329459767,1.66500776358891,0.412244795093543,0.235038338966359,1.0,'no' 168 | 144.0,1.50070471229763,0.590493026499862,1.73911573574263,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.16857087727502,0.679724160227805,1.1905497839938,0.653406851910431,0.412244795093543,0.235038338966359,2.0,'no' 169 | 145.0,0.792519654373149,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.706752832615724,0.0699585886069105,0.352821374622742,1.0,2.0,0.653406851910431,1.10539197565349,0.679724160227805,1.1905497839938,0.653406851910431,1.08518926833597,0.235038338966359,1.0,'no' 170 | 146.0,1.16857087727502,0.590493026499862,0.193191229030859,0.0484523833859467,0.27871340246902,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.16857087727502,0.679724160227805,2.35815494414886,1.23601215807055,0.412244795093543,0.235038338966359,3.0,'no' 171 | 147.0,1.50070471229763,0.590493026499862,0.193191229030859,0.0484523833859467,1.41369333530801,0.679724160227805,0.0699585886069105,0.352821374622742,1.0,2.0,0.822825003868478,1.10539197565349,0.679724160227805,2.35815494414886,0.653406851910431,0.412244795093543,0.235038338966359,4.0,'no' 172 | 148.0,1.16857087727502,0.590493026499862,1.73911573574263,3.0513021247088,1.41369333530801,0.679724160227805,2.69462718077007,1.21302263984585,2.0,2.0,0.653406851910431,1.08518926833597,1.25954265548075,1.56322506927897,0.653406851910431,0.412244795093543,0.235038338966359,7.0,'yes' 173 | 174 | 175 | -------------------------------------------------------------------------------- /data/literature/WBC/WBC_withoutdupl_v01.arff: -------------------------------------------------------------------------------- 1 | @RELATION 'WBC, without duplicates, v01' 2 | 3 | @ATTRIBUTE 'att2' real 4 | @ATTRIBUTE 'att3' real 5 | @ATTRIBUTE 'att4' real 6 | @ATTRIBUTE 'att5' real 7 | @ATTRIBUTE 'att6' real 8 | @ATTRIBUTE 'att7' real 9 | @ATTRIBUTE 'att8' real 10 | @ATTRIBUTE 'att9' real 11 | @ATTRIBUTE 'att10' real 12 | @ATTRIBUTE 'id' real 13 | @ATTRIBUTE 'outlier' {'yes','no'} 14 | 15 | @DATA 16 | 9.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,1.0,426.0,'yes' 17 | 6.0,1.0,3.0,1.0,4.0,5.0,5.0,10.0,1.0,289.0,'yes' 18 | 1.0,5.0,8.0,6.0,5.0,8.0,7.0,10.0,1.0,187.0,'yes' 19 | 10.0,6.0,5.0,8.0,5.0,10.0,8.0,6.0,1.0,457.0,'yes' 20 | 9.0,5.0,5.0,4.0,4.0,5.0,4.0,3.0,3.0,143.0,'yes' 21 | 9.0,10.0,10.0,1.0,10.0,8.0,3.0,3.0,1.0,63.0,'yes' 22 | 5.0,6.0,6.0,2.0,4.0,10.0,3.0,6.0,1.0,256.0,'yes' 23 | 6.0,5.0,5.0,8.0,4.0,10.0,3.0,4.0,1.0,337.0,'yes' 24 | 4.0,10.0,4.0,7.0,3.0,10.0,9.0,10.0,1.0,516.0,'yes' 25 | 8.0,10.0,10.0,10.0,6.0,10.0,10.0,10.0,1.0,587.0,'yes' 26 | 5.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,1.0,'no' 27 | 5.0,4.0,4.0,5.0,7.0,10.0,3.0,2.0,1.0,2.0,'no' 28 | 3.0,1.0,1.0,1.0,2.0,2.0,3.0,1.0,1.0,3.0,'no' 29 | 6.0,8.0,8.0,1.0,3.0,4.0,3.0,7.0,1.0,4.0,'no' 30 | 4.0,1.0,1.0,3.0,2.0,1.0,3.0,1.0,1.0,5.0,'no' 31 | 1.0,1.0,1.0,1.0,2.0,10.0,3.0,1.0,1.0,7.0,'no' 32 | 2.0,1.0,2.0,1.0,2.0,1.0,3.0,1.0,1.0,8.0,'no' 33 | 2.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,5.0,9.0,'no' 34 | 4.0,2.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,10.0,'no' 35 | 1.0,1.0,1.0,1.0,1.0,1.0,3.0,1.0,1.0,11.0,'no' 36 | 2.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,12.0,'no' 37 | 1.0,1.0,1.0,1.0,2.0,3.0,3.0,1.0,1.0,14.0,'no' 38 | 4.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,17.0,'no' 39 | 4.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,18.0,'no' 40 | 6.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,20.0,'no' 41 | 3.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,23.0,'no' 42 | 1.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,25.0,'no' 43 | 3.0,2.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,27.0,'no' 44 | 5.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,28.0,'no' 45 | 1.0,1.0,3.0,1.0,2.0,1.0,1.0,1.0,1.0,30.0,'no' 46 | 3.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,31.0,'no' 47 | 2.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,32.0,'no' 48 | 2.0,1.0,1.0,2.0,2.0,1.0,3.0,1.0,1.0,34.0,'no' 49 | 3.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,35.0,'no' 50 | 6.0,2.0,1.0,1.0,1.0,1.0,7.0,1.0,1.0,38.0,'no' 51 | 1.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,2.0,46.0,'no' 52 | 1.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,48.0,'no' 53 | 1.0,1.0,1.0,1.0,2.0,2.0,2.0,1.0,1.0,62.0,'no' 54 | 1.0,1.0,1.0,1.0,2.0,1.0,3.0,2.0,1.0,70.0,'no' 55 | 5.0,1.0,3.0,1.0,2.0,1.0,2.0,1.0,1.0,71.0,'no' 56 | 1.0,3.0,3.0,2.0,2.0,1.0,7.0,2.0,1.0,73.0,'no' 57 | 1.0,1.0,2.0,1.0,2.0,2.0,4.0,2.0,1.0,76.0,'no' 58 | 1.0,1.0,4.0,1.0,2.0,1.0,2.0,1.0,1.0,77.0,'no' 59 | 5.0,3.0,1.0,2.0,2.0,1.0,2.0,1.0,1.0,78.0,'no' 60 | 3.0,1.0,1.0,1.0,2.0,3.0,3.0,1.0,1.0,79.0,'no' 61 | 2.0,1.0,1.0,1.0,3.0,1.0,2.0,1.0,1.0,80.0,'no' 62 | 2.0,2.0,2.0,1.0,1.0,1.0,7.0,1.0,1.0,81.0,'no' 63 | 4.0,1.0,1.0,2.0,2.0,1.0,2.0,1.0,1.0,82.0,'no' 64 | 5.0,2.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,83.0,'no' 65 | 3.0,1.0,1.0,1.0,2.0,2.0,7.0,1.0,1.0,84.0,'no' 66 | 2.0,1.0,1.0,2.0,3.0,1.0,2.0,1.0,1.0,90.0,'no' 67 | 3.0,1.0,1.0,2.0,2.0,1.0,1.0,1.0,1.0,92.0,'no' 68 | 2.0,1.0,1.0,2.0,2.0,1.0,1.0,1.0,1.0,97.0,'no' 69 | 4.0,1.0,2.0,1.0,2.0,1.0,3.0,1.0,1.0,103.0,'no' 70 | 1.0,1.0,1.0,1.0,2.0,1.0,2.0,3.0,1.0,109.0,'no' 71 | 1.0,3.0,1.0,2.0,2.0,2.0,5.0,3.0,2.0,111.0,'no' 72 | 3.0,3.0,2.0,1.0,2.0,3.0,3.0,1.0,1.0,115.0,'no' 73 | 1.0,1.0,1.0,1.0,2.0,5.0,1.0,1.0,1.0,116.0,'no' 74 | 8.0,3.0,3.0,1.0,2.0,2.0,3.0,2.0,1.0,117.0,'no' 75 | 1.0,1.0,1.0,1.0,4.0,3.0,1.0,1.0,1.0,119.0,'no' 76 | 3.0,2.0,1.0,1.0,2.0,2.0,3.0,1.0,1.0,120.0,'no' 77 | 1.0,1.0,2.0,2.0,2.0,1.0,3.0,1.0,1.0,121.0,'no' 78 | 4.0,2.0,1.0,1.0,2.0,2.0,3.0,1.0,1.0,122.0,'no' 79 | 3.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,128.0,'no' 80 | 1.0,1.0,1.0,1.0,10.0,1.0,1.0,1.0,1.0,130.0,'no' 81 | 3.0,1.0,1.0,1.0,2.0,1.0,2.0,2.0,1.0,134.0,'no' 82 | 3.0,1.0,1.0,1.0,3.0,1.0,2.0,1.0,1.0,135.0,'no' 83 | 5.0,1.0,1.0,1.0,2.0,2.0,3.0,3.0,1.0,136.0,'no' 84 | 3.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,138.0,'no' 85 | 4.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,139.0,'no' 86 | 2.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,142.0,'no' 87 | 1.0,1.0,1.0,1.0,3.0,2.0,2.0,1.0,1.0,148.0,'no' 88 | 3.0,1.0,1.0,3.0,8.0,1.0,5.0,8.0,1.0,149.0,'no' 89 | 4.0,1.0,1.0,1.0,2.0,3.0,1.0,1.0,1.0,154.0,'no' 90 | 1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,155.0,'no' 91 | 1.0,2.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,157.0,'no' 92 | 4.0,1.0,1.0,1.0,2.0,1.0,3.0,2.0,1.0,162.0,'no' 93 | 1.0,1.0,1.0,2.0,1.0,3.0,1.0,1.0,7.0,164.0,'no' 94 | 4.0,1.0,1.0,1.0,2.0,2.0,3.0,2.0,1.0,166.0,'no' 95 | 1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,170.0,'no' 96 | 2.0,1.0,1.0,1.0,1.0,1.0,3.0,1.0,1.0,186.0,'no' 97 | 1.0,2.0,3.0,1.0,2.0,1.0,3.0,1.0,1.0,190.0,'no' 98 | 8.0,4.0,4.0,5.0,4.0,7.0,7.0,8.0,2.0,197.0,'no' 99 | 5.0,1.0,1.0,4.0,2.0,1.0,3.0,1.0,1.0,198.0,'no' 100 | 5.0,1.0,1.0,1.0,1.0,1.0,3.0,1.0,1.0,210.0,'no' 101 | 6.0,1.0,3.0,1.0,2.0,1.0,3.0,1.0,1.0,220.0,'no' 102 | 1.0,1.0,1.0,2.0,2.0,1.0,3.0,1.0,1.0,221.0,'no' 103 | 8.0,4.0,6.0,3.0,3.0,1.0,4.0,3.0,1.0,233.0,'no' 104 | 3.0,3.0,2.0,1.0,3.0,1.0,3.0,6.0,1.0,235.0,'no' 105 | 5.0,1.0,3.0,3.0,2.0,2.0,2.0,3.0,1.0,241.0,'no' 106 | 3.0,1.0,1.0,3.0,1.0,1.0,3.0,1.0,1.0,242.0,'no' 107 | 1.0,1.0,1.0,1.0,2.0,5.0,5.0,1.0,1.0,244.0,'no' 108 | 5.0,1.0,1.0,2.0,2.0,2.0,3.0,1.0,1.0,246.0,'no' 109 | 4.0,1.0,1.0,1.0,2.0,1.0,3.0,6.0,1.0,249.0,'no' 110 | 1.0,2.0,2.0,1.0,2.0,1.0,1.0,1.0,1.0,251.0,'no' 111 | 6.0,3.0,3.0,5.0,3.0,10.0,3.0,5.0,3.0,253.0,'no' 112 | 5.0,7.0,7.0,1.0,5.0,8.0,3.0,4.0,1.0,260.0,'no' 113 | 5.0,1.0,4.0,1.0,2.0,1.0,3.0,2.0,1.0,266.0,'no' 114 | 3.0,1.0,1.0,1.0,2.0,1.0,3.0,2.0,1.0,275.0,'no' 115 | 5.0,3.0,4.0,3.0,4.0,5.0,4.0,7.0,1.0,297.0,'no' 116 | 8.0,2.0,1.0,1.0,5.0,1.0,1.0,1.0,1.0,299.0,'no' 117 | 3.0,1.0,1.0,1.0,2.0,5.0,5.0,1.0,1.0,310.0,'no' 118 | 1.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,315.0,'no' 119 | 1.0,1.0,1.0,1.0,5.0,1.0,3.0,1.0,1.0,319.0,'no' 120 | 4.0,4.0,4.0,4.0,6.0,5.0,7.0,3.0,1.0,320.0,'no' 121 | 3.0,2.0,2.0,1.0,2.0,1.0,2.0,3.0,1.0,326.0,'no' 122 | 5.0,1.0,1.0,1.0,2.0,1.0,3.0,1.0,2.0,332.0,'no' 123 | 5.0,2.0,2.0,2.0,2.0,1.0,2.0,2.0,1.0,333.0,'no' 124 | 5.0,2.0,2.0,2.0,3.0,1.0,1.0,3.0,1.0,347.0,'no' 125 | 1.0,1.0,1.0,1.0,1.0,1.0,1.0,3.0,1.0,348.0,'no' 126 | 5.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,1.0,351.0,'no' 127 | 3.0,4.0,5.0,3.0,7.0,3.0,4.0,6.0,1.0,353.0,'no' 128 | 4.0,1.0,1.0,1.0,3.0,1.0,2.0,2.0,1.0,356.0,'no' 129 | 3.0,2.0,2.0,1.0,4.0,3.0,2.0,1.0,1.0,363.0,'no' 130 | 4.0,4.0,4.0,2.0,2.0,3.0,2.0,1.0,1.0,364.0,'no' 131 | 1.0,1.0,3.0,1.0,1.0,1.0,2.0,1.0,1.0,370.0,'no' 132 | 4.0,3.0,2.0,1.0,3.0,1.0,2.0,1.0,1.0,371.0,'no' 133 | 5.0,1.0,1.0,2.0,2.0,1.0,2.0,1.0,1.0,374.0,'no' 134 | 3.0,1.0,1.0,4.0,3.0,1.0,2.0,2.0,1.0,379.0,'no' 135 | 5.0,3.0,4.0,1.0,4.0,1.0,3.0,1.0,1.0,380.0,'no' 136 | 3.0,2.0,2.0,2.0,2.0,1.0,3.0,2.0,1.0,383.0,'no' 137 | 3.0,3.0,2.0,2.0,3.0,1.0,1.0,2.0,3.0,386.0,'no' 138 | 5.0,3.0,3.0,2.0,3.0,1.0,3.0,1.0,1.0,388.0,'no' 139 | 2.0,1.0,1.0,1.0,2.0,1.0,2.0,2.0,1.0,389.0,'no' 140 | 5.0,1.0,1.0,1.0,3.0,2.0,2.0,2.0,1.0,390.0,'no' 141 | 1.0,1.0,1.0,2.0,2.0,1.0,2.0,1.0,1.0,391.0,'no' 142 | 1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,394.0,'no' 143 | 1.0,2.0,3.0,1.0,2.0,1.0,2.0,1.0,1.0,395.0,'no' 144 | 4.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,398.0,'no' 145 | 3.0,2.0,1.0,1.0,2.0,1.0,2.0,2.0,1.0,399.0,'no' 146 | 1.0,2.0,3.0,1.0,2.0,1.0,1.0,1.0,1.0,400.0,'no' 147 | 5.0,3.0,3.0,1.0,2.0,1.0,2.0,1.0,1.0,403.0,'no' 148 | 3.0,1.0,1.0,1.0,2.0,4.0,1.0,1.0,1.0,404.0,'no' 149 | 1.0,2.0,1.0,3.0,2.0,1.0,1.0,2.0,1.0,405.0,'no' 150 | 4.0,2.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,407.0,'no' 151 | 2.0,3.0,2.0,2.0,2.0,2.0,3.0,1.0,1.0,409.0,'no' 152 | 5.0,1.0,2.0,1.0,2.0,1.0,3.0,1.0,1.0,414.0,'no' 153 | 3.0,3.0,2.0,6.0,3.0,3.0,3.0,5.0,1.0,416.0,'no' 154 | 5.0,2.0,2.0,2.0,2.0,2.0,3.0,2.0,2.0,419.0,'no' 155 | 2.0,3.0,1.0,1.0,5.0,1.0,1.0,1.0,1.0,420.0,'no' 156 | 3.0,2.0,2.0,3.0,2.0,3.0,3.0,1.0,1.0,421.0,'no' 157 | 4.0,3.0,3.0,1.0,2.0,1.0,3.0,3.0,1.0,423.0,'no' 158 | 5.0,3.0,6.0,1.0,2.0,1.0,1.0,1.0,1.0,427.0,'no' 159 | 1.0,3.0,1.0,1.0,2.0,1.0,2.0,2.0,1.0,431.0,'no' 160 | 5.0,1.0,1.0,3.0,4.0,1.0,3.0,2.0,1.0,432.0,'no' 161 | 5.0,1.0,1.0,1.0,2.0,1.0,2.0,2.0,1.0,433.0,'no' 162 | 3.0,2.0,2.0,3.0,2.0,1.0,1.0,1.0,1.0,434.0,'no' 163 | 6.0,9.0,7.0,5.0,5.0,8.0,4.0,2.0,1.0,435.0,'no' 164 | 4.0,1.0,3.0,3.0,2.0,1.0,1.0,1.0,1.0,439.0,'no' 165 | 5.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,440.0,'no' 166 | 5.0,2.0,2.0,4.0,2.0,4.0,1.0,1.0,1.0,442.0,'no' 167 | 1.0,1.0,1.0,3.0,2.0,3.0,1.0,1.0,1.0,443.0,'no' 168 | 1.0,1.0,1.0,1.0,2.0,2.0,1.0,1.0,1.0,444.0,'no' 169 | 5.0,1.0,1.0,6.0,3.0,1.0,2.0,1.0,1.0,445.0,'no' 170 | 4.0,1.0,1.0,3.0,1.0,1.0,2.0,1.0,1.0,451.0,'no' 171 | 3.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,1.0,453.0,'no' 172 | 2.0,3.0,1.0,1.0,3.0,1.0,1.0,1.0,1.0,455.0,'no' 173 | 5.0,1.0,2.0,1.0,2.0,1.0,1.0,1.0,1.0,459.0,'no' 174 | 5.0,1.0,3.0,1.0,2.0,1.0,1.0,1.0,1.0,460.0,'no' 175 | 3.0,1.0,1.0,1.0,2.0,5.0,1.0,1.0,1.0,462.0,'no' 176 | 6.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,1.0,463.0,'no' 177 | 4.0,1.0,1.0,1.0,2.0,1.0,1.0,2.0,1.0,464.0,'no' 178 | 1.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,470.0,'no' 179 | 6.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,473.0,'no' 180 | 4.0,1.0,2.0,1.0,2.0,1.0,1.0,1.0,1.0,477.0,'no' 181 | 5.0,2.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,479.0,'no' 182 | 5.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,481.0,'no' 183 | 5.0,3.0,2.0,4.0,2.0,1.0,1.0,1.0,1.0,482.0,'no' 184 | 1.0,1.0,1.0,3.0,1.0,3.0,1.0,1.0,1.0,486.0,'no' 185 | 4.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,493.0,'no' 186 | 5.0,1.0,2.0,10.0,4.0,5.0,2.0,1.0,1.0,495.0,'no' 187 | 4.0,2.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,498.0,'no' 188 | 3.0,3.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,506.0,'no' 189 | 1.0,1.0,1.0,1.0,2.0,4.0,1.0,1.0,1.0,508.0,'no' 190 | 3.0,1.0,2.0,2.0,2.0,1.0,1.0,1.0,1.0,519.0,'no' 191 | 1.0,1.0,1.0,1.0,3.0,1.0,1.0,1.0,1.0,521.0,'no' 192 | 4.0,1.0,1.0,1.0,3.0,1.0,1.0,1.0,1.0,522.0,'no' 193 | 6.0,1.0,3.0,2.0,2.0,1.0,1.0,1.0,1.0,529.0,'no' 194 | 1.0,1.0,3.0,2.0,2.0,1.0,3.0,1.0,1.0,536.0,'no' 195 | 6.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,540.0,'no' 196 | 5.0,1.0,1.0,1.0,2.0,2.0,2.0,1.0,1.0,541.0,'no' 197 | 5.0,3.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,543.0,'no' 198 | 2.0,1.0,3.0,2.0,2.0,1.0,2.0,1.0,1.0,545.0,'no' 199 | 2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,548.0,'no' 200 | 3.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,549.0,'no' 201 | 3.0,2.0,2.0,2.0,2.0,1.0,4.0,2.0,1.0,553.0,'no' 202 | 4.0,4.0,2.0,1.0,2.0,5.0,2.0,1.0,2.0,554.0,'no' 203 | 4.0,3.0,1.0,1.0,2.0,1.0,4.0,8.0,1.0,556.0,'no' 204 | 5.0,2.0,2.0,2.0,1.0,1.0,2.0,1.0,1.0,557.0,'no' 205 | 3.0,1.0,2.0,1.0,2.0,1.0,3.0,1.0,1.0,567.0,'no' 206 | 4.0,1.0,1.0,1.0,2.0,3.0,2.0,1.0,1.0,568.0,'no' 207 | 5.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,581.0,'no' 208 | 5.0,1.0,1.0,6.0,3.0,1.0,1.0,1.0,1.0,585.0,'no' 209 | 5.0,1.0,3.0,1.0,2.0,1.0,3.0,1.0,1.0,598.0,'no' 210 | 5.0,2.0,4.0,1.0,1.0,1.0,1.0,1.0,1.0,600.0,'no' 211 | 4.0,1.0,1.0,2.0,2.0,1.0,1.0,1.0,1.0,607.0,'no' 212 | 2.0,3.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,614.0,'no' 213 | 2.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,615.0,'no' 214 | 4.0,1.0,3.0,1.0,2.0,1.0,2.0,1.0,1.0,616.0,'no' 215 | 6.0,3.0,3.0,3.0,3.0,2.0,6.0,1.0,1.0,622.0,'no' 216 | 7.0,1.0,2.0,3.0,2.0,1.0,2.0,1.0,1.0,623.0,'no' 217 | 5.0,1.0,1.0,2.0,1.0,1.0,2.0,1.0,1.0,625.0,'no' 218 | 3.0,1.0,3.0,1.0,3.0,4.0,1.0,1.0,1.0,626.0,'no' 219 | 2.0,1.0,1.0,1.0,2.0,5.0,1.0,1.0,1.0,628.0,'no' 220 | 6.0,2.0,3.0,1.0,2.0,1.0,1.0,1.0,1.0,631.0,'no' 221 | 3.0,1.0,4.0,1.0,2.0,1.0,1.0,1.0,1.0,636.0,'no' 222 | 4.0,2.0,4.0,3.0,2.0,2.0,2.0,1.0,1.0,638.0,'no' 223 | 4.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,1.0,641.0,'no' 224 | 1.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,1.0,648.0,'no' 225 | 3.0,1.0,1.0,2.0,3.0,4.0,1.0,1.0,1.0,651.0,'no' 226 | 1.0,2.0,1.0,3.0,2.0,1.0,2.0,1.0,1.0,652.0,'no' 227 | 5.0,4.0,5.0,1.0,8.0,1.0,3.0,6.0,1.0,658.0,'no' 228 | 1.0,1.0,3.0,1.0,2.0,1.0,2.0,1.0,1.0,663.0,'no' 229 | 3.0,1.0,1.0,3.0,2.0,1.0,2.0,1.0,1.0,665.0,'no' 230 | 5.0,2.0,2.0,2.0,2.0,1.0,1.0,1.0,2.0,667.0,'no' 231 | 3.0,2.0,1.0,2.0,2.0,1.0,3.0,1.0,1.0,672.0,'no' 232 | 5.0,3.0,2.0,1.0,3.0,1.0,1.0,1.0,1.0,674.0,'no' 233 | 4.0,1.0,4.0,1.0,2.0,1.0,1.0,1.0,1.0,676.0,'no' 234 | 5.0,1.0,1.0,1.0,2.0,1.0,3.0,2.0,1.0,683.0,'no' 235 | 3.0,1.0,1.0,1.0,2.0,1.0,2.0,3.0,1.0,688.0,'no' 236 | 1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,8.0,690.0,'no' 237 | 3.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,2.0,694.0,'no' 238 | 3.0,1.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,695.0,'no' 239 | 240 | 241 | -------------------------------------------------------------------------------- /data/lympho.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/lympho.mat -------------------------------------------------------------------------------- /data/mammography.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/mammography.mat -------------------------------------------------------------------------------- /data/optdigits.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/optdigits.mat -------------------------------------------------------------------------------- /data/pima.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/pima.mat -------------------------------------------------------------------------------- /data/satellite.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/satellite.mat -------------------------------------------------------------------------------- /data/satimage-2.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/satimage-2.mat -------------------------------------------------------------------------------- /data/semantic/HeartDisease/HeartDisease_withoutdupl_norm_44.arff: -------------------------------------------------------------------------------- 1 | @RELATION 'Heart Disease, without duplicates, with normalization, 44 pct of outliers' 2 | 3 | @ATTRIBUTE 'id' real 4 | @ATTRIBUTE 'Age' real 5 | @ATTRIBUTE 'Sex' real 6 | @ATTRIBUTE 'ChestPainType' real 7 | @ATTRIBUTE 'RestBloodPressure' real 8 | @ATTRIBUTE 'SerumCholestoral' real 9 | @ATTRIBUTE 'FastingBloodSugar' real 10 | @ATTRIBUTE 'ResElectrocardiographic' real 11 | @ATTRIBUTE 'MaxHeartRate' real 12 | @ATTRIBUTE 'ExerciseInduced' real 13 | @ATTRIBUTE 'Oldpeak' real 14 | @ATTRIBUTE 'Slope' real 15 | @ATTRIBUTE 'MajorVessels' real 16 | @ATTRIBUTE 'Thal' real 17 | @ATTRIBUTE 'outlier' {'yes','no'} 18 | 19 | @DATA 20 | 1.0,0.854166666666667,1.0,1.0,0.339622641509434,0.447488584474886,0.0,1.0,0.290076335877863,0.0,0.387096774193548,0.5,1.0,0.0,'yes' 21 | 2.0,0.791666666666667,0.0,0.666666666666667,0.19811320754717,1.0,0.0,1.0,0.679389312977099,0.0,0.258064516129032,0.5,0.0,1.0,'no' 22 | 3.0,0.583333333333333,1.0,0.333333333333333,0.283018867924528,0.308219178082192,0.0,0.0,0.534351145038168,0.0,0.0483870967741936,0.0,0.0,1.0,'yes' 23 | 4.0,0.729166666666667,1.0,1.0,0.320754716981132,0.312785388127854,0.0,0.0,0.259541984732824,1.0,0.032258064516129,0.5,0.333333333333333,1.0,'no' 24 | 5.0,0.9375,0.0,0.333333333333333,0.245283018867925,0.32648401826484,0.0,1.0,0.381679389312977,1.0,0.032258064516129,0.0,0.333333333333333,0.0,'no' 25 | 6.0,0.75,1.0,1.0,0.245283018867925,0.116438356164384,0.0,0.0,0.526717557251908,0.0,0.0645161290322581,0.0,0.0,1.0,'no' 26 | 7.0,0.5625,1.0,0.666666666666667,0.339622641509434,0.296803652968036,1.0,1.0,0.541984732824428,1.0,0.0967741935483871,0.5,0.333333333333333,0.75,'yes' 27 | 8.0,0.625,1.0,1.0,0.150943396226415,0.257990867579909,0.0,1.0,0.541984732824428,1.0,0.193548387096774,0.5,0.333333333333333,1.0,'yes' 28 | 9.0,0.645833333333333,1.0,1.0,0.433962264150943,0.381278538812785,0.0,1.0,0.755725190839695,0.0,0.193548387096774,0.5,0.666666666666667,1.0,'yes' 29 | 10.0,0.708333333333333,0.0,1.0,0.528301886792453,0.641552511415525,0.0,1.0,0.633587786259542,0.0,0.0645161290322581,0.5,1.0,1.0,'yes' 30 | 11.0,0.625,1.0,1.0,0.386792452830189,0.246575342465753,0.0,0.0,0.687022900763359,0.0,0.0806451612903226,0.5,0.0,1.0,'no' 31 | 12.0,0.5,1.0,1.0,0.452830188679245,0.228310502283105,0.0,1.0,0.305343511450382,1.0,0.0,0.0,0.0,1.0,'no' 32 | 13.0,0.3125,1.0,0.666666666666667,0.433962264150943,0.248858447488584,0.0,1.0,0.83206106870229,0.0,0.0,0.0,0.0,0.0,'no' 33 | 14.0,0.666666666666667,1.0,0.0,0.377358490566038,0.246575342465753,0.0,0.0,0.564885496183206,0.0,0.419354838709677,0.5,0.666666666666667,0.0,'yes' 34 | 15.0,0.583333333333333,0.0,1.0,0.320754716981132,0.404109589041096,0.0,1.0,0.67175572519084,0.0,0.0,0.0,0.333333333333333,0.0,'no' 35 | 16.0,0.875,0.0,1.0,0.169811320754717,0.0525114155251142,0.0,0.0,0.412213740458015,0.0,0.258064516129032,0.5,0.0,0.0,'no' 36 | 17.0,0.354166666666667,1.0,1.0,0.433962264150943,0.422374429223744,0.0,0.0,0.374045801526718,1.0,0.290322580645161,0.5,0.666666666666667,1.0,'yes' 37 | 18.0,0.5,1.0,1.0,0.433962264150943,0.175799086757991,1.0,1.0,0.641221374045801,1.0,0.5,1.0,0.0,1.0,'yes' 38 | 19.0,0.729166666666667,1.0,0.0,0.150943396226415,0.194063926940639,0.0,1.0,0.557251908396947,1.0,0.290322580645161,0.5,0.0,0.0,'no' 39 | 20.0,0.229166666666667,1.0,0.0,0.433962264150943,0.166666666666667,0.0,0.0,0.816793893129771,1.0,0.225806451612903,0.0,0.0,1.0,'no' 40 | 21.0,0.791666666666667,1.0,1.0,0.245283018867925,0.235159817351598,0.0,1.0,0.442748091603053,1.0,0.419354838709677,0.5,0.666666666666667,1.0,'yes' 41 | 22.0,0.395833333333333,1.0,0.333333333333333,0.339622641509434,0.271689497716895,0.0,1.0,0.83206106870229,0.0,0.032258064516129,0.5,0.0,0.0,'no' 42 | 23.0,0.291666666666667,1.0,1.0,0.19811320754717,0.404109589041096,0.0,0.0,0.83969465648855,0.0,0.193548387096774,0.5,0.0,0.0,'no' 43 | 24.0,0.375,1.0,1.0,0.169811320754717,0.178082191780822,0.0,0.0,0.549618320610687,0.0,0.0161290322580645,0.0,0.0,0.0,'no' 44 | 25.0,0.520833333333333,0.0,0.333333333333333,0.358490566037736,0.36986301369863,1.0,1.0,0.67175572519084,1.0,0.0,0.0,0.333333333333333,0.0,'no' 45 | 26.0,0.395833333333333,0.0,0.666666666666667,0.339622641509434,0.340182648401826,0.0,0.0,0.519083969465649,0.0,0.032258064516129,0.0,0.0,0.0,'no' 46 | 27.0,0.354166666666667,0.0,1.0,0.415094339622642,0.267123287671233,0.0,1.0,0.618320610687023,1.0,0.0,0.5,0.0,0.0,'no' 47 | 28.0,0.458333333333333,0.0,0.666666666666667,0.245283018867925,0.385844748858447,0.0,1.0,0.656488549618321,0.0,0.0967741935483871,0.0,0.0,0.0,'no' 48 | 29.0,0.604166666666667,1.0,0.666666666666667,0.169811320754717,0.237442922374429,0.0,1.0,0.717557251908397,0.0,0.403225806451613,0.5,0.333333333333333,1.0,'yes' 49 | 30.0,0.875,0.0,0.666666666666667,0.150943396226415,0.317351598173516,1.0,1.0,0.450381679389313,0.0,0.0,0.0,0.333333333333333,0.0,'no' 50 | 31.0,0.583333333333333,1.0,0.666666666666667,0.320754716981132,0.235159817351598,0.0,1.0,0.603053435114504,0.0,0.0645161290322581,0.5,0.333333333333333,1.0,'yes' 51 | 32.0,0.770833333333333,1.0,1.0,0.622641509433962,0.232876712328767,0.0,1.0,0.511450381679389,0.0,0.370967741935484,0.0,0.0,0.75,'no' 52 | 33.0,0.166666666666667,0.0,0.666666666666667,0.245283018867925,0.203196347031963,0.0,0.0,0.755725190839695,0.0,0.0,0.0,0.0,0.0,'no' 53 | 34.0,0.625,1.0,1.0,0.716981132075472,0.45662100456621,0.0,1.0,0.526717557251908,1.0,0.548387096774194,1.0,0.0,1.0,'yes' 54 | 35.0,0.4375,1.0,1.0,0.471698113207547,0.168949771689498,0.0,1.0,0.419847328244275,1.0,0.145161290322581,0.5,0.0,1.0,'yes' 55 | 36.0,0.395833333333333,1.0,1.0,0.339622641509434,0.296803652968036,1.0,1.0,0.603053435114504,1.0,0.0,0.0,0.666666666666667,1.0,'yes' 56 | 37.0,0.666666666666667,1.0,1.0,0.433962264150943,0.184931506849315,0.0,1.0,0.511450381679389,1.0,0.306451612903226,0.0,0.333333333333333,1.0,'yes' 57 | 38.0,0.625,1.0,0.0,0.622641509433962,0.335616438356164,0.0,1.0,0.412213740458015,0.0,0.0,0.0,0.0,0.0,'yes' 58 | 39.0,0.270833333333333,1.0,0.666666666666667,0.339622641509434,0.123287671232877,0.0,0.0,0.603053435114504,0.0,0.0,0.0,0.0,0.0,'no' 59 | 40.0,0.395833333333333,1.0,1.0,0.264150943396226,0.219178082191781,0.0,1.0,0.877862595419847,0.0,0.0,0.0,0.0,0.0,'no' 60 | 41.0,0.229166666666667,1.0,1.0,0.547169811320755,0.221461187214612,0.0,0.0,0.83969465648855,0.0,0.0,0.0,0.0,1.0,'yes' 61 | 42.0,0.6875,0.0,1.0,0.283018867924528,0.189497716894977,0.0,0.0,0.702290076335878,0.0,0.0,0.0,0.0,0.0,'no' 62 | 43.0,0.3125,1.0,0.666666666666667,0.339622641509434,0.244292237442922,0.0,0.0,0.824427480916031,1.0,0.0645161290322581,0.0,0.0,0.0,'no' 63 | 44.0,0.354166666666667,1.0,0.333333333333333,0.0660377358490566,0.162100456621005,1.0,0.0,0.648854961832061,0.0,0.0,0.0,0.0,1.0,'no' 64 | 45.0,0.625,1.0,0.666666666666667,0.30188679245283,0.210045662100457,1.0,0.0,0.480916030534351,0.0,0.354838709677419,0.5,0.333333333333333,0.75,'yes' 65 | 46.0,0.604166666666667,1.0,0.666666666666667,0.433962264150943,0.194063926940639,1.0,1.0,0.717557251908397,0.0,0.0,0.0,0.0,0.0,'no' 66 | 47.0,0.416666666666667,1.0,0.666666666666667,0.226415094339623,0.0525114155251142,0.0,1.0,0.419847328244275,0.0,0.129032258064516,0.0,1.0,0.0,'yes' 67 | 48.0,0.3125,1.0,1.0,0.150943396226415,0.162100456621005,0.0,1.0,0.809160305343512,0.0,0.0,0.0,0.333333333333333,0.0,'yes' 68 | 49.0,0.770833333333333,1.0,0.333333333333333,0.622641509433962,0.273972602739726,0.0,0.0,0.374045801526718,1.0,0.0,0.5,1.0,0.75,'yes' 69 | 50.0,0.75,0.0,1.0,0.528301886792453,0.226027397260274,0.0,1.0,0.32824427480916,0.0,0.0161290322580645,0.5,1.0,1.0,'yes' 70 | 51.0,0.270833333333333,1.0,1.0,0.39622641509434,0.431506849315069,0.0,0.0,0.412213740458015,1.0,0.290322580645161,0.5,0.0,0.75,'yes' 71 | 52.0,0.479166666666667,1.0,0.333333333333333,0.320754716981132,0.180365296803653,1.0,0.0,0.862595419847328,0.0,0.0,0.0,0.0,0.0,'no' 72 | 53.0,0.75,0.0,0.666666666666667,0.433962264150943,0.664383561643836,1.0,1.0,0.656488549618321,0.0,0.129032258064516,0.0,0.333333333333333,0.0,'no' 73 | 54.0,0.708333333333333,0.0,0.333333333333333,0.433962264150943,0.157534246575342,0.0,0.0,0.824427480916031,0.0,0.0,0.0,0.666666666666667,0.0,'no' 74 | 55.0,0.333333333333333,0.0,0.333333333333333,0.339622641509434,0.246575342465753,0.0,1.0,0.793893129770992,0.0,0.0967741935483871,0.5,0.0,0.0,'no' 75 | 56.0,0.25,0.0,0.333333333333333,0.10377358490566,0.164383561643836,0.0,0.0,0.740458015267176,0.0,0.0,0.0,0.333333333333333,0.0,'no' 76 | 57.0,0.666666666666667,1.0,1.0,0.415094339622642,0.091324200913242,0.0,1.0,0.412213740458015,1.0,0.580645161290323,0.5,0.333333333333333,0.0,'yes' 77 | 58.0,0.645833333333333,0.0,0.666666666666667,0.245283018867925,0.118721461187215,1.0,0.0,0.190839694656489,0.0,0.0,0.0,0.0,0.0,'no' 78 | 59.0,0.625,0.0,1.0,0.754716981132076,0.280821917808219,0.0,0.0,0.549618320610687,1.0,0.0,0.5,0.0,0.0,'yes' 79 | 60.0,0.6875,1.0,0.333333333333333,0.245283018867925,0.353881278538813,0.0,1.0,0.244274809160305,0.0,0.225806451612903,0.5,0.333333333333333,1.0,'yes' 80 | 61.0,0.583333333333333,1.0,0.666666666666667,0.528301886792453,0.0,1.0,0.0,0.778625954198473,0.0,0.032258064516129,0.0,0.333333333333333,1.0,'no' 81 | 62.0,0.458333333333333,0.0,1.0,0.339622641509434,0.408675799086758,0.0,0.0,0.541984732824428,1.0,0.193548387096774,0.5,0.0,1.0,'yes' 82 | 63.0,0.3125,1.0,0.666666666666667,0.245283018867925,0.228310502283105,0.0,0.0,0.748091603053435,0.0,0.0,0.0,0.0,0.0,'no' 83 | 64.0,0.645833333333333,0.0,0.0,0.528301886792453,0.26027397260274,0.0,0.0,0.763358778625954,0.0,0.145161290322581,0.0,0.0,0.0,'no' 84 | 65.0,0.708333333333333,1.0,0.0,0.481132075471698,0.244292237442922,1.0,1.0,0.603053435114504,0.0,0.370967741935484,1.0,0.0,0.75,'no' 85 | 66.0,0.583333333333333,1.0,1.0,0.528301886792453,0.342465753424657,0.0,1.0,0.312977099236641,1.0,0.0967741935483871,0.5,0.333333333333333,0.75,'yes' 86 | 67.0,0.458333333333333,1.0,1.0,0.433962264150943,0.308219178082192,0.0,1.0,0.877862595419847,1.0,0.0,0.0,0.0,0.0,'no' 87 | 68.0,0.604166666666667,0.0,0.333333333333333,0.39622641509434,0.440639269406393,1.0,1.0,0.618320610687023,0.0,0.0,0.0,0.666666666666667,0.0,'yes' 88 | 69.0,0.3125,0.0,0.666666666666667,0.226415094339623,0.264840182648402,0.0,0.0,0.595419847328244,0.0,0.0483870967741936,0.5,0.333333333333333,0.0,'no' 89 | 70.0,0.375,1.0,0.666666666666667,0.132075471698113,0.267123287671233,0.0,0.0,0.618320610687023,0.0,0.0,0.0,0.0,0.0,'yes' 90 | 71.0,0.666666666666667,1.0,1.0,0.245283018867925,0.305936073059361,0.0,0.0,0.526717557251908,1.0,0.580645161290323,0.5,0.333333333333333,1.0,'yes' 91 | 72.0,0.583333333333333,0.0,1.0,0.245283018867925,0.520547945205479,0.0,0.0,0.702290076335878,1.0,0.0967741935483871,0.0,0.0,0.0,'no' 92 | 73.0,0.854166666666667,1.0,0.333333333333333,0.584905660377358,0.271689497716895,0.0,1.0,0.549618320610687,0.0,0.0,0.0,0.0,0.0,'no' 93 | 74.0,0.979166666666667,0.0,0.666666666666667,0.433962264150943,0.162100456621005,0.0,0.5,0.343511450381679,0.0,0.17741935483871,0.5,0.0,0.0,'no' 94 | 75.0,0.791666666666667,0.0,1.0,0.113207547169811,0.221461187214612,0.0,0.0,0.541984732824428,0.0,0.0483870967741936,0.0,0.666666666666667,0.0,'no' 95 | 76.0,0.333333333333333,1.0,1.0,0.452830188679245,0.417808219178082,0.0,1.0,0.580152671755725,1.0,0.0,0.5,1.0,1.0,'yes' 96 | 77.0,0.333333333333333,1.0,1.0,0.0943396226415094,0.187214611872146,0.0,1.0,0.587786259541985,1.0,0.0483870967741936,0.5,0.0,0.0,'no' 97 | 78.0,0.208333333333333,0.0,0.666666666666667,0.0,0.166666666666667,0.0,0.0,0.824427480916031,0.0,0.0,0.0,0.0,0.0,'no' 98 | 79.0,0.270833333333333,0.0,0.666666666666667,0.245283018867925,0.189497716894977,0.0,0.0,0.778625954198473,0.0,0.0,0.5,0.0,0.0,'no' 99 | 80.0,0.5625,1.0,0.333333333333333,0.245283018867925,0.251141552511416,0.0,0.0,0.816793893129771,0.0,0.129032258064516,0.0,0.0,0.0,'no' 100 | 81.0,0.604166666666667,1.0,1.0,0.490566037735849,0.210045662100457,0.0,0.0,0.259541984732824,0.0,0.032258064516129,0.5,0.333333333333333,1.0,'yes' 101 | 82.0,0.125,1.0,1.0,0.245283018867925,0.164383561643836,0.0,0.0,0.450381679389313,1.0,0.258064516129032,0.5,0.0,1.0,'yes' 102 | 83.0,0.604166666666667,1.0,1.0,0.528301886792453,0.328767123287671,0.0,1.0,0.305343511450382,1.0,0.129032258064516,0.0,0.0,1.0,'yes' 103 | 84.0,0.25,1.0,0.666666666666667,0.339622641509434,0.200913242009132,0.0,1.0,0.740458015267176,0.0,0.032258064516129,0.5,0.0,0.0,'no' 104 | 85.0,0.583333333333333,1.0,1.0,0.150943396226415,0.171232876712329,0.0,0.0,0.419847328244275,1.0,0.241935483870968,0.5,0.0,0.75,'no' 105 | 86.0,0.270833333333333,1.0,0.0,0.509433962264151,0.269406392694064,0.0,1.0,0.816793893129771,0.0,0.129032258064516,0.0,0.666666666666667,0.0,'no' 106 | 87.0,0.6875,1.0,0.333333333333333,0.320754716981132,0.187214611872146,1.0,1.0,0.526717557251908,0.0,0.0,0.0,0.0,0.0,'no' 107 | 88.0,0.625,1.0,0.0,0.792452830188679,0.328767123287671,0.0,1.0,0.564885496183206,0.0,0.67741935483871,1.0,0.0,1.0,'no' 108 | 89.0,0.25,0.0,0.333333333333333,0.30188679245283,0.410958904109589,0.0,0.0,0.702290076335878,0.0,0.0,0.0,0.0,0.0,'no' 109 | 90.0,0.4375,1.0,1.0,0.528301886792453,0.267123287671233,0.0,1.0,0.435114503816794,0.0,0.419354838709677,0.5,0.0,1.0,'yes' 110 | 91.0,0.625,1.0,0.333333333333333,0.433962264150943,0.21689497716895,0.0,0.0,0.709923664122137,1.0,0.0,0.0,0.0,0.0,'no' 111 | 92.0,0.666666666666667,0.0,1.0,0.339622641509434,0.465753424657534,0.0,1.0,0.748091603053435,0.0,0.0,0.0,0.0,0.0,'yes' 112 | 93.0,0.520833333333333,1.0,1.0,0.283018867924528,0.319634703196347,0.0,1.0,0.290076335877863,1.0,0.354838709677419,0.5,0.333333333333333,1.0,'yes' 113 | 94.0,0.520833333333333,1.0,1.0,0.150943396226415,0.182648401826484,0.0,1.0,0.282442748091603,1.0,0.0,0.5,0.333333333333333,0.0,'yes' 114 | 95.0,0.479166666666667,1.0,1.0,0.292452830188679,0.19634703196347,0.0,0.0,0.740458015267176,0.0,0.0161290322580645,0.0,0.666666666666667,1.0,'yes' 115 | 96.0,0.375,1.0,1.0,0.150943396226415,0.340182648401826,0.0,1.0,0.358778625954198,1.0,0.0161290322580645,0.5,0.333333333333333,0.0,'yes' 116 | 97.0,0.770833333333333,1.0,1.0,0.245283018867925,0.401826484018265,0.0,1.0,0.610687022900763,0.0,0.0645161290322581,0.5,0.0,0.0,'no' 117 | 98.0,0.604166666666667,1.0,1.0,0.0566037735849057,0.246575342465753,0.0,0.0,0.648854961832061,0.0,0.0161290322580645,0.0,0.333333333333333,1.0,'yes' 118 | 99.0,0.729166666666667,0.0,0.666666666666667,0.433962264150943,0.426940639269406,0.0,0.0,0.473282442748092,0.0,0.032258064516129,0.0,0.0,1.0,'no' 119 | 100.0,0.4375,0.0,0.333333333333333,0.245283018867925,0.269406392694064,0.0,0.0,0.694656488549618,0.0,0.17741935483871,0.0,0.0,0.0,'no' 120 | 101.0,0.3125,0.0,0.666666666666667,0.132075471698113,0.0342465753424658,0.0,0.0,0.793893129770992,0.0,0.0967741935483871,0.5,0.0,0.0,'no' 121 | 102.0,0.791666666666667,1.0,1.0,0.245283018867925,0.253424657534247,0.0,0.0,0.0,0.0,0.0161290322580645,0.5,0.0,0.0,'yes' 122 | 103.0,0.416666666666667,0.0,1.0,0.339622641509434,0.32648401826484,0.0,0.0,0.702290076335878,0.0,0.0,0.0,0.0,0.0,'no' 123 | 104.0,0.583333333333333,1.0,1.0,0.669811320754717,0.372146118721461,1.0,1.0,0.404580152671756,0.0,0.0161290322580645,0.5,1.0,1.0,'yes' 124 | 105.0,0.708333333333333,1.0,1.0,0.339622641509434,0.292237442922374,0.0,1.0,0.580152671755725,0.0,0.225806451612903,0.5,0.333333333333333,1.0,'yes' 125 | 106.0,0.395833333333333,1.0,1.0,0.283018867924528,0.337899543378995,0.0,1.0,0.725190839694656,0.0,0.0806451612903226,0.5,0.0,1.0,'yes' 126 | 107.0,0.458333333333333,1.0,0.666666666666667,0.0566037735849057,0.219178082191781,0.0,0.0,0.549618320610687,1.0,0.193548387096774,0.5,0.0,0.0,'no' 127 | 108.0,0.645833333333333,0.0,1.0,0.528301886792453,0.301369863013699,0.0,1.0,0.656488549618321,0.0,0.419354838709677,0.5,0.666666666666667,1.0,'yes' 128 | 109.0,0.625,1.0,1.0,0.433962264150943,0.116438356164384,0.0,0.0,0.694656488549618,1.0,0.0,0.0,0.333333333333333,1.0,'yes' 129 | 110.0,0.333333333333333,0.0,0.333333333333333,0.169811320754717,0.0776255707762557,0.0,0.0,0.511450381679389,0.0,0.0,0.5,0.0,0.0,'no' 130 | 111.0,0.541666666666667,0.0,1.0,0.811320754716981,0.458904109589041,0.0,0.5,0.351145038167939,1.0,0.548387096774194,0.5,0.0,0.0,'yes' 131 | 112.0,0.25,1.0,0.333333333333333,0.150943396226415,0.248858447488584,0.0,0.0,0.625954198473283,0.0,0.0,0.0,0.0,0.0,'no' 132 | 113.0,0.645833333333333,0.0,1.0,0.60377358490566,0.408675799086758,0.0,1.0,0.687022900763359,0.0,0.0,0.0,0.0,0.0,'yes' 133 | 114.0,0.520833333333333,0.0,0.666666666666667,0.386792452830189,0.406392694063927,1.0,0.0,0.755725190839695,0.0,0.0,0.0,0.0,0.0,'no' 134 | 115.0,0.270833333333333,1.0,0.333333333333333,0.245283018867925,0.385844748858447,0.0,0.0,0.694656488549618,0.0,0.0,0.0,0.0,0.0,'no' 135 | 116.0,0.416666666666667,0.0,0.333333333333333,0.377358490566038,0.331050228310502,0.0,0.0,0.694656488549618,0.0,0.0,0.5,0.0,0.0,'no' 136 | 117.0,0.354166666666667,1.0,1.0,0.245283018867925,0.280821917808219,0.0,1.0,0.557251908396947,0.0,0.129032258064516,0.0,0.0,1.0,'yes' 137 | 118.0,0.5625,0.0,1.0,1.0,0.36986301369863,1.0,1.0,0.473282442748092,1.0,0.0645161290322581,1.0,0.666666666666667,1.0,'yes' 138 | 119.0,0.770833333333333,0.0,0.0,0.528301886792453,0.228310502283105,0.0,0.0,0.32824427480916,0.0,0.419354838709677,1.0,0.0,0.0,'no' 139 | 120.0,0.5625,1.0,1.0,0.339622641509434,0.358447488584475,1.0,1.0,0.244274809160305,1.0,0.258064516129032,1.0,0.0,1.0,'yes' 140 | 121.0,0.416666666666667,1.0,0.666666666666667,0.245283018867925,0.141552511415525,0.0,0.0,0.519083969465649,0.0,0.032258064516129,0.5,1.0,1.0,'yes' 141 | 122.0,0.520833333333333,1.0,1.0,0.264150943396226,0.365296803652968,0.0,1.0,0.343511450381679,1.0,0.516129032258065,0.5,0.666666666666667,0.0,'yes' 142 | 123.0,0.583333333333333,1.0,1.0,0.547169811320755,0.337899543378995,0.0,0.0,0.129770992366412,1.0,0.193548387096774,0.5,0.333333333333333,1.0,'yes' 143 | 124.0,0.75,0.0,0.666666666666667,0.622641509433962,0.534246575342466,0.0,1.0,0.610687022900763,0.0,0.129032258064516,0.0,0.0,0.0,'no' 144 | 125.0,0.520833333333333,1.0,0.666666666666667,0.292452830188679,0.335616438356164,0.0,1.0,0.618320610687023,0.0,0.0806451612903226,1.0,0.333333333333333,0.0,'no' 145 | 126.0,0.520833333333333,0.0,0.666666666666667,0.622641509433962,0.171232876712329,0.0,0.0,0.702290076335878,0.0,0.0,0.0,0.333333333333333,0.0,'no' 146 | 127.0,0.6875,1.0,1.0,0.245283018867925,0.321917808219178,0.0,0.0,0.213740458015267,1.0,0.290322580645161,0.5,0.666666666666667,1.0,'yes' 147 | 128.0,0.479166666666667,0.0,0.666666666666667,0.39622641509434,0.159817351598174,0.0,1.0,0.748091603053435,0.0,0.0161290322580645,0.5,0.0,0.0,'no' 148 | 129.0,0.479166666666667,1.0,0.333333333333333,0.377358490566038,0.171232876712329,0.0,0.0,0.66412213740458,0.0,0.129032258064516,0.0,0.333333333333333,0.0,'no' 149 | 130.0,0.645833333333333,1.0,1.0,0.216981132075472,0.237442922374429,1.0,0.0,0.679389312977099,1.0,0.225806451612903,0.0,0.666666666666667,1.0,'yes' 150 | 131.0,0.708333333333333,0.0,1.0,0.132075471698113,0.32648401826484,0.0,0.0,0.748091603053435,1.0,0.290322580645161,0.5,0.666666666666667,0.0,'yes' 151 | 132.0,0.770833333333333,1.0,1.0,0.169811320754717,0.19634703196347,0.0,1.0,0.465648854961832,1.0,0.0161290322580645,0.0,0.333333333333333,0.0,'yes' 152 | 133.0,0.270833333333333,1.0,1.0,0.433962264150943,0.228310502283105,0.0,0.0,0.816793893129771,0.0,0.0,0.0,0.0,0.0,'no' 153 | 134.0,0.729166666666667,1.0,1.0,0.245283018867925,0.273972602739726,0.0,1.0,0.190839694656489,1.0,0.354838709677419,1.0,0.333333333333333,0.0,'yes' 154 | 135.0,0.520833333333333,1.0,0.666666666666667,0.528301886792453,0.242009132420091,0.0,1.0,0.717557251908397,0.0,0.258064516129032,0.0,0.0,1.0,'no' 155 | 136.0,0.354166666666667,0.0,0.666666666666667,0.452830188679245,0.116438356164384,0.0,1.0,0.679389312977099,1.0,0.225806451612903,1.0,0.0,0.0,'no' 156 | 137.0,0.791666666666667,0.0,0.666666666666667,0.547169811320755,0.344748858447489,0.0,0.0,0.770992366412214,0.0,0.0,0.0,0.333333333333333,0.0,'no' 157 | 138.0,0.5625,1.0,1.0,0.292452830188679,0.280821917808219,1.0,1.0,0.557251908396947,1.0,0.193548387096774,0.5,0.333333333333333,0.0,'yes' 158 | 139.0,0.104166666666667,0.0,0.333333333333333,0.226415094339623,0.191780821917808,0.0,0.0,0.923664122137405,0.0,0.112903225806452,0.0,0.0,0.0,'no' 159 | 140.0,0.583333333333333,1.0,1.0,0.358490566037736,0.184931506849315,0.0,0.0,0.740458015267176,1.0,0.0,0.0,0.0,1.0,'no' 160 | 141.0,0.729166666666667,1.0,1.0,0.481132075471698,0.19634703196347,0.0,1.0,0.465648854961832,0.0,0.032258064516129,0.5,0.666666666666667,0.75,'yes' 161 | 142.0,0.625,1.0,1.0,0.415094339622642,0.331050228310502,0.0,1.0,0.847328244274809,0.0,0.0,0.0,0.0,0.0,'no' 162 | 143.0,0.4375,1.0,0.666666666666667,0.433962264150943,0.244292237442922,0.0,0.0,0.702290076335878,0.0,0.0967741935483871,0.5,0.333333333333333,1.0,'yes' 163 | 144.0,0.458333333333333,1.0,0.0,0.292452830188679,0.198630136986301,0.0,1.0,0.412213740458015,1.0,0.225806451612903,0.0,0.333333333333333,0.0,'no' 164 | 145.0,0.520833333333333,1.0,0.333333333333333,0.924528301886792,0.358447488584475,0.0,1.0,0.946564885496183,0.0,0.0,0.0,0.333333333333333,1.0,'yes' 165 | 146.0,0.5,1.0,1.0,0.273584905660377,0.356164383561644,0.0,0.0,0.183206106870229,1.0,0.032258064516129,0.5,0.666666666666667,1.0,'yes' 166 | 147.0,0.479166666666667,1.0,1.0,0.169811320754717,0.237442922374429,0.0,0.0,0.679389312977099,0.0,0.0,0.0,0.333333333333333,0.0,'yes' 167 | 148.0,0.229166666666667,1.0,1.0,0.150943396226415,0.0936073059360731,0.0,1.0,0.32824427480916,1.0,0.032258064516129,0.5,0.0,1.0,'yes' 168 | 149.0,0.604166666666667,1.0,0.666666666666667,0.358490566037736,0.223744292237443,0.0,1.0,0.778625954198473,0.0,0.516129032258065,0.0,0.666666666666667,1.0,'yes' 169 | 150.0,0.25,0.0,0.666666666666667,0.169811320754717,0.324200913242009,0.0,1.0,0.770992366412214,1.0,0.0,0.0,0.0,0.0,'no' 170 | 151.0,0.25,1.0,0.666666666666667,0.169811320754717,0.28310502283105,0.0,0.0,0.824427480916031,0.0,0.0,0.0,0.0,0.0,'no' 171 | 152.0,0.4375,0.0,0.666666666666667,0.245283018867925,0.212328767123288,0.0,0.0,0.66412213740458,0.0,0.258064516129032,0.5,0.0,0.0,'no' 172 | 153.0,0.520833333333333,0.0,0.666666666666667,0.132075471698113,0.321917808219178,0.0,1.0,0.732824427480916,0.0,0.0,0.0,0.0,0.0,'no' 173 | 154.0,0.729166666666667,0.0,1.0,0.339622641509434,0.404109589041096,0.0,0.0,0.389312977099237,0.0,0.032258064516129,0.5,0.666666666666667,0.0,'no' 174 | 155.0,0.458333333333333,0.0,0.666666666666667,0.339622641509434,0.296803652968036,0.0,1.0,0.595419847328244,0.0,0.0806451612903226,0.0,0.0,0.0,'no' 175 | 156.0,0.354166666666667,0.0,0.333333333333333,0.10377358490566,0.178082191780822,0.0,0.0,0.770992366412214,0.0,0.0,0.0,0.0,0.0,'no' 176 | 157.0,0.541666666666667,1.0,1.0,0.433962264150943,0.207762557077626,0.0,0.0,0.305343511450382,1.0,0.903225806451613,1.0,0.0,1.0,'yes' 177 | 158.0,0.333333333333333,1.0,0.333333333333333,0.320754716981132,0.415525114155251,0.0,1.0,0.755725190839695,0.0,0.0,0.0,0.0,0.0,'no' 178 | 159.0,0.5625,1.0,0.0,0.245283018867925,0.15296803652968,0.0,1.0,0.694656488549618,0.0,0.306451612903226,0.5,0.0,1.0,'no' 179 | 160.0,0.770833333333333,0.0,1.0,0.792452830188679,0.232876712328767,1.0,0.0,0.717557251908397,1.0,0.0161290322580645,0.5,0.666666666666667,1.0,'yes' 180 | 161.0,0.1875,1.0,0.0,0.245283018867925,0.23972602739726,0.0,0.0,0.847328244274809,1.0,0.612903225806452,0.5,0.0,1.0,'yes' 181 | 162.0,0.6875,0.0,1.0,0.528301886792453,0.269406392694064,0.0,0.0,0.633587786259542,1.0,0.225806451612903,0.5,0.0,0.0,'yes' 182 | 163.0,0.541666666666667,1.0,0.333333333333333,0.339622641509434,0.310502283105023,0.0,0.0,0.641221374045801,0.0,0.0,0.0,0.0,0.0,'no' 183 | 164.0,0.604166666666667,1.0,1.0,0.320754716981132,0.30365296803653,0.0,1.0,0.450381679389313,1.0,0.0483870967741936,0.5,0.666666666666667,1.0,'yes' 184 | 165.0,0.291666666666667,1.0,1.0,0.150943396226415,0.194063926940639,0.0,0.0,0.687022900763359,0.0,0.0,0.0,0.0,1.0,'no' 185 | 166.0,0.729166666666667,0.0,1.0,0.811320754716981,0.454337899543379,0.0,0.0,0.633587786259542,1.0,0.0,0.0,0.0,0.0,'no' 186 | 167.0,0.4375,0.0,1.0,0.150943396226415,0.292237442922374,0.0,1.0,0.67175572519084,0.0,0.0,0.0,0.0,0.0,'no' 187 | 168.0,0.5,1.0,0.666666666666667,0.339622641509434,0.162100456621005,1.0,1.0,0.618320610687023,0.0,0.193548387096774,1.0,0.0,0.0,'no' 188 | 169.0,0.333333333333333,0.0,1.0,0.415094339622642,0.251141552511416,0.0,1.0,0.618320610687023,1.0,0.032258064516129,0.5,0.0,0.0,'no' 189 | 170.0,0.75,1.0,0.0,0.415094339622642,0.356164383561644,1.0,1.0,0.786259541984733,0.0,0.225806451612903,0.5,0.333333333333333,0.0,'yes' 190 | 171.0,0.833333333333333,1.0,0.0,0.622641509433962,0.246575342465753,1.0,1.0,0.458015267175573,0.0,0.0161290322580645,0.5,0.333333333333333,0.0,'no' 191 | 172.0,0.833333333333333,1.0,0.666666666666667,0.433962264150943,0.292237442922374,0.0,1.0,0.572519083969466,0.0,0.032258064516129,0.5,1.0,1.0,'yes' 192 | 173.0,0.791666666666667,1.0,1.0,0.0566037735849057,0.394977168949772,0.0,1.0,0.412213740458015,1.0,0.145161290322581,0.5,0.666666666666667,0.0,'yes' 193 | 174.0,0.8125,0.0,0.666666666666667,0.245283018867925,0.194063926940639,0.0,1.0,0.33587786259542,0.0,0.241935483870968,0.5,0.0,0.0,'no' 194 | 175.0,0.104166666666667,1.0,0.0,0.226415094339623,0.127853881278539,0.0,1.0,0.786259541984733,0.0,0.0,0.0,0.0,0.0,'no' 195 | 176.0,0.6875,0.0,1.0,0.415094339622642,0.383561643835616,1.0,0.0,0.267175572519084,0.0,0.306451612903226,0.5,1.0,0.0,'yes' 196 | 177.0,0.458333333333333,1.0,1.0,0.433962264150943,0.392694063926941,0.0,0.0,0.389312977099237,1.0,0.67741935483871,0.5,1.0,1.0,'yes' 197 | 178.0,0.354166666666667,1.0,0.666666666666667,0.528301886792453,0.23972602739726,0.0,0.0,0.580152671755725,0.0,0.580645161290323,0.5,0.0,0.0,'yes' 198 | 179.0,0.791666666666667,1.0,1.0,0.292452830188679,0.292237442922374,1.0,0.0,0.702290076335878,0.0,0.032258064516129,0.5,0.666666666666667,1.0,'yes' 199 | 180.0,0.4375,1.0,0.666666666666667,0.330188679245283,0.159817351598174,0.0,0.0,0.702290076335878,0.0,0.0,0.0,0.0,0.0,'no' 200 | 181.0,0.270833333333333,1.0,0.666666666666667,0.245283018867925,0.26027397260274,1.0,0.0,0.938931297709924,0.0,0.129032258064516,1.0,0.0,1.0,'no' 201 | 182.0,0.5625,0.0,1.0,0.377358490566038,0.646118721461187,0.0,1.0,0.603053435114504,1.0,0.306451612903226,0.5,0.666666666666667,1.0,'yes' 202 | 183.0,0.25,1.0,1.0,0.150943396226415,0.105022831050228,0.0,1.0,0.66412213740458,0.0,0.0,0.0,0.0,1.0,'yes' 203 | 184.0,0.270833333333333,0.0,1.0,0.0754716981132075,0.317351598173516,0.0,1.0,0.389312977099237,0.0,0.0967741935483871,0.5,0.0,0.0,'no' 204 | 185.0,0.5,1.0,0.666666666666667,0.339622641509434,0.273972602739726,1.0,1.0,0.778625954198473,0.0,0.0,0.0,1.0,0.0,'no' 205 | 186.0,0.291666666666667,1.0,0.666666666666667,0.339622641509434,0.431506849315069,0.0,0.0,0.694656488549618,0.0,0.306451612903226,0.0,0.333333333333333,0.0,'no' 206 | 187.0,0.5625,1.0,1.0,0.358490566037736,0.132420091324201,0.0,1.0,0.259541984732824,1.0,0.338709677419355,0.5,0.333333333333333,0.75,'yes' 207 | 188.0,0.479166666666667,1.0,1.0,0.132075471698113,0.244292237442922,1.0,0.0,0.580152671755725,0.0,0.0161290322580645,0.0,1.0,1.0,'no' 208 | 189.0,0.6875,0.0,1.0,0.433962264150943,0.611872146118721,0.0,1.0,0.656488549618321,0.0,0.193548387096774,0.5,0.0,0.0,'no' 209 | 190.0,0.854166666666667,1.0,0.666666666666667,0.622641509433962,0.32648401826484,0.0,0.0,0.312977099236641,1.0,0.467741935483871,0.5,0.333333333333333,1.0,'yes' 210 | 191.0,0.520833333333333,1.0,1.0,0.433962264150943,0.257990867579909,0.0,0.0,0.679389312977099,0.0,0.193548387096774,0.0,0.0,0.0,'no' 211 | 192.0,0.854166666666667,1.0,1.0,0.481132075471698,0.10958904109589,0.0,0.0,0.412213740458015,1.0,0.419354838709677,1.0,0.0,1.0,'yes' 212 | 193.0,0.520833333333333,1.0,0.333333333333333,0.132075471698113,0.417808219178082,0.0,0.0,0.648854961832061,0.0,0.0,0.0,0.0,1.0,'no' 213 | 194.0,0.125,1.0,1.0,0.30188679245283,0.356164383561644,0.0,1.0,0.648854961832061,1.0,0.0,0.0,0.0,1.0,'yes' 214 | 195.0,0.395833333333333,1.0,0.666666666666667,0.283018867924528,0.294520547945205,1.0,0.0,0.793893129770992,0.0,0.0,0.0,0.666666666666667,0.0,'no' 215 | 196.0,0.541666666666667,0.0,0.333333333333333,0.386792452830189,0.28310502283105,0.0,1.0,0.687022900763359,0.0,0.225806451612903,0.5,0.0,0.0,'no' 216 | 197.0,0.604166666666667,0.0,1.0,0.0566037735849057,0.278538812785388,0.0,1.0,0.389312977099237,0.0,0.0161290322580645,0.5,0.0,0.0,'no' 217 | 198.0,0.520833333333333,0.0,0.666666666666667,0.150943396226415,0.200913242009132,0.0,0.0,0.66412213740458,0.0,0.258064516129032,0.5,0.0,0.0,'no' 218 | 199.0,0.833333333333333,0.0,0.0,0.433962264150943,0.257990867579909,0.0,0.0,0.610687022900763,0.0,0.290322580645161,0.0,0.666666666666667,0.0,'no' 219 | 200.0,1.0,1.0,1.0,0.292452830188679,0.406392694063927,0.0,1.0,0.694656488549618,1.0,0.0,0.0,1.0,0.0,'yes' 220 | 201.0,0.8125,1.0,0.666666666666667,0.226415094339623,0.344748858447489,0.0,0.0,0.610687022900763,0.0,0.0161290322580645,0.0,0.333333333333333,1.0,'no' 221 | 202.0,0.604166666666667,1.0,1.0,0.292452830188679,0.397260273972603,0.0,1.0,0.763358778625954,0.0,0.0,0.0,0.666666666666667,1.0,'yes' 222 | 203.0,0.645833333333333,1.0,1.0,0.292452830188679,0.301369863013699,0.0,1.0,0.534351145038168,1.0,0.451612903225806,0.5,0.333333333333333,1.0,'yes' 223 | 204.0,0.458333333333333,1.0,1.0,0.433962264150943,0.394977168949772,0.0,0.0,0.778625954198473,1.0,0.258064516129032,0.0,0.0,1.0,'yes' 224 | 205.0,0.541666666666667,1.0,1.0,0.622641509433962,0.372146118721461,0.0,1.0,0.564885496183206,1.0,0.129032258064516,0.5,0.333333333333333,1.0,'yes' 225 | 206.0,0.479166666666667,1.0,0.0,0.547169811320755,0.392694063926941,1.0,0.0,0.816793893129771,0.0,0.193548387096774,0.5,0.0,1.0,'no' 226 | 207.0,0.645833333333333,0.0,0.666666666666667,0.0754716981132075,0.438356164383562,0.0,0.0,0.679389312977099,0.0,0.0,0.0,0.333333333333333,0.0,'no' 227 | 208.0,0.604166666666667,1.0,0.666666666666667,0.10377358490566,0.26027397260274,0.0,1.0,0.633587786259542,1.0,0.0967741935483871,0.5,0.0,1.0,'no' 228 | 209.0,0.729166666666667,1.0,0.666666666666667,0.292452830188679,0.417808219178082,0.0,0.0,0.458015267175573,1.0,0.290322580645161,0.5,0.0,1.0,'yes' 229 | 210.0,0.166666666666667,1.0,0.666666666666667,0.339622641509434,0.28310502283105,0.0,0.0,0.885496183206107,0.0,0.564516129032258,1.0,0.0,0.0,'no' 230 | 211.0,0.625,1.0,0.0,0.716981132075472,0.36986301369863,0.0,1.0,0.67175572519084,0.0,0.032258064516129,0.5,0.0,1.0,'yes' 231 | 212.0,0.458333333333333,1.0,0.666666666666667,0.292452830188679,0.271689497716895,1.0,1.0,0.725190839694656,0.0,0.387096774193548,0.5,0.0,0.0,'no' 232 | 213.0,0.291666666666667,0.0,0.666666666666667,0.264150943396226,0.198630136986301,0.0,0.0,0.717557251908397,0.0,0.032258064516129,0.5,0.0,0.0,'no' 233 | 214.0,0.604166666666667,1.0,1.0,0.320754716981132,0.205479452054795,0.0,1.0,0.458015267175573,1.0,0.354838709677419,0.5,1.0,1.0,'yes' 234 | 215.0,0.0,1.0,0.333333333333333,0.339622641509434,0.178082191780822,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,'no' 235 | 216.0,0.25,0.0,0.333333333333333,0.339622641509434,0.178082191780822,0.0,1.0,0.770992366412214,0.0,0.225806451612903,0.0,0.0,0.0,'no' 236 | 217.0,0.708333333333333,0.0,0.666666666666667,0.386792452830189,0.287671232876712,0.0,1.0,0.770992366412214,0.0,0.0,0.0,0.0,0.0,'no' 237 | 218.0,0.458333333333333,1.0,0.666666666666667,0.0,0.230593607305936,0.0,0.0,0.633587786259542,1.0,0.0,0.0,0.333333333333333,1.0,'no' 238 | 219.0,0.520833333333333,1.0,0.666666666666667,0.245283018867925,0.301369863013699,0.0,1.0,0.580152671755725,0.0,0.0645161290322581,0.5,0.0,1.0,'no' 239 | 220.0,0.3125,1.0,0.333333333333333,0.245283018867925,0.214611872146119,0.0,0.0,0.755725190839695,0.0,0.0,0.0,0.0,0.0,'no' 240 | 221.0,0.520833333333333,1.0,1.0,0.150943396226415,0.257990867579909,0.0,0.0,0.419847328244275,1.0,0.451612903225806,0.5,0.333333333333333,1.0,'yes' 241 | 222.0,0.75,1.0,1.0,0.386792452830189,0.292237442922374,0.0,1.0,0.427480916030534,0.0,0.451612903225806,0.5,0.333333333333333,1.0,'yes' 242 | 223.0,0.583333333333333,1.0,0.666666666666667,0.528301886792453,0.0958904109589041,0.0,0.0,0.786259541984733,0.0,0.258064516129032,0.0,0.0,0.0,'no' 243 | 224.0,0.708333333333333,1.0,1.0,0.339622641509434,0.465753424657534,1.0,1.0,0.465648854961832,1.0,0.290322580645161,0.0,1.0,1.0,'yes' 244 | 225.0,0.125,0.0,1.0,0.415094339622642,0.13013698630137,0.0,0.0,0.847328244274809,0.0,0.225806451612903,0.0,0.0,0.0,'no' 245 | 226.0,0.25,1.0,0.333333333333333,0.386792452830189,0.175799086757991,0.0,0.0,0.465648854961832,0.0,0.0,0.5,0.0,0.75,'no' 246 | 227.0,0.6875,0.0,0.666666666666667,0.339622641509434,0.312785388127854,0.0,0.0,0.198473282442748,0.0,0.193548387096774,0.5,0.333333333333333,1.0,'yes' 247 | 228.0,0.291666666666667,0.0,1.0,0.358490566037736,0.490867579908676,1.0,1.0,0.49618320610687,1.0,0.0483870967741936,0.5,0.0,1.0,'yes' 248 | 229.0,0.604166666666667,0.0,0.0,0.528301886792453,0.358447488584475,1.0,1.0,0.694656488549618,0.0,0.0161290322580645,0.0,0.0,0.0,'no' 249 | 230.0,0.479166666666667,1.0,0.0,0.226415094339623,0.136986301369863,0.0,1.0,0.908396946564885,0.0,0.0,0.5,0.0,0.75,'no' 250 | 231.0,0.666666666666667,0.0,1.0,0.481132075471698,0.41324200913242,0.0,1.0,0.572519083969466,1.0,0.0161290322580645,0.5,0.0,1.0,'yes' 251 | 232.0,0.208333333333333,1.0,1.0,0.226415094339623,0.212328767123288,0.0,0.0,0.526717557251908,0.0,0.193548387096774,0.5,0.0,1.0,'yes' 252 | 233.0,0.333333333333333,1.0,1.0,0.19811320754717,0.305936073059361,0.0,1.0,0.870229007633588,0.0,0.0,0.0,0.0,0.0,'no' 253 | 234.0,0.479166666666667,1.0,1.0,0.320754716981132,0.294520547945205,0.0,0.0,0.687022900763359,1.0,0.0,0.0,0.333333333333333,1.0,'yes' 254 | 235.0,0.6875,1.0,0.666666666666667,0.339622641509434,0.23972602739726,0.0,0.0,0.572519083969466,0.0,0.290322580645161,0.5,1.0,1.0,'no' 255 | 236.0,0.6875,0.0,1.0,0.622641509433962,0.0867579908675799,0.0,1.0,0.564885496183206,0.0,1.0,1.0,1.0,1.0,'yes' 256 | 237.0,0.5,0.0,1.0,0.415094339622642,0.246575342465753,0.0,1.0,0.679389312977099,0.0,0.0,0.0,0.0,0.0,'no' 257 | 238.0,0.291666666666667,1.0,1.0,0.245283018867925,0.116438356164384,0.0,1.0,0.374045801526718,1.0,0.403225806451613,0.5,0.0,1.0,'yes' 258 | 239.0,0.375,1.0,0.666666666666667,0.415094339622642,0.299086757990868,0.0,1.0,0.648854961832061,0.0,0.0,0.0,0.0,0.0,'no' 259 | 240.0,0.479166666666667,1.0,0.333333333333333,0.245283018867925,0.454337899543379,0.0,0.0,0.770992366412214,0.0,0.032258064516129,0.0,0.0,0.0,'no' 260 | 241.0,0.8125,1.0,0.666666666666667,0.811320754716981,0.337899543378995,1.0,1.0,0.603053435114504,1.0,0.258064516129032,0.5,0.0,1.0,'yes' 261 | 242.0,0.208333333333333,1.0,0.666666666666667,0.433962264150943,0.445205479452055,0.0,1.0,0.847328244274809,0.0,0.0,0.0,0.0,0.0,'no' 262 | 243.0,0.5,0.0,1.0,0.339622641509434,0.315068493150685,0.0,1.0,0.549618320610687,0.0,0.0645161290322581,0.5,0.0,0.0,'no' 263 | 244.0,0.6875,0.0,1.0,0.433962264150943,0.324200913242009,0.0,1.0,0.679389312977099,0.0,0.580645161290323,1.0,0.666666666666667,0.0,'yes' 264 | 245.0,0.458333333333333,0.0,0.666666666666667,0.433962264150943,0.415525114155251,0.0,1.0,0.541984732824428,0.0,0.241935483870968,0.0,0.333333333333333,0.0,'no' 265 | 246.0,0.645833333333333,1.0,1.0,0.339622641509434,0.289954337899543,0.0,0.0,0.557251908396947,1.0,0.225806451612903,0.0,0.333333333333333,1.0,'yes' 266 | 247.0,0.75,1.0,1.0,0.150943396226415,0.278538812785388,0.0,1.0,0.66412213740458,0.0,0.0967741935483871,0.0,0.666666666666667,0.75,'yes' 267 | 248.0,0.75,0.0,0.666666666666667,0.575471698113208,0.32648401826484,0.0,0.0,0.587786259541985,0.0,0.129032258064516,0.0,0.0,0.0,'no' 268 | 249.0,0.645833333333333,1.0,0.666666666666667,0.433962264150943,0.134703196347032,0.0,1.0,0.641221374045801,0.0,0.0483870967741936,0.5,0.0,0.0,'yes' 269 | 250.0,0.645833333333333,1.0,1.0,0.481132075471698,0.356164383561644,0.0,1.0,0.541984732824428,1.0,0.451612903225806,0.5,0.666666666666667,1.0,'yes' 270 | 251.0,0.520833333333333,1.0,1.0,0.245283018867925,0.141552511415525,0.0,0.0,0.320610687022901,0.0,0.225806451612903,0.5,0.333333333333333,1.0,'yes' 271 | 252.0,0.3125,1.0,0.333333333333333,0.339622641509434,0.212328767123288,0.0,1.0,0.893129770992366,0.0,0.0,0.0,0.0,0.0,'no' 272 | 253.0,0.3125,1.0,1.0,0.169811320754717,0.374429223744292,0.0,1.0,0.625954198473283,0.0,0.0,0.0,0.333333333333333,0.0,'yes' 273 | 254.0,0.458333333333333,1.0,0.666666666666667,0.150943396226415,0.111872146118721,0.0,0.0,0.396946564885496,0.0,0.0967741935483871,0.0,0.0,0.0,'no' 274 | 255.0,0.625,1.0,0.666666666666667,0.528301886792453,0.19634703196347,1.0,0.0,0.656488549618321,0.0,0.258064516129032,0.0,0.0,0.0,'no' 275 | 256.0,0.875,0.0,0.333333333333333,0.622641509433962,0.401826484018265,0.0,0.0,0.694656488549618,0.0,0.0645161290322581,0.0,0.666666666666667,0.0,'no' 276 | 257.0,0.666666666666667,1.0,0.666666666666667,0.528301886792453,0.267123287671233,1.0,0.0,0.50381679389313,1.0,0.0161290322580645,0.5,0.0,0.0,'no' 277 | 258.0,0.541666666666667,1.0,1.0,0.358490566037736,0.518264840182648,0.0,0.0,0.465648854961832,1.0,0.193548387096774,0.5,0.333333333333333,1.0,'yes' 278 | 259.0,0.729166666666667,1.0,0.666666666666667,0.433962264150943,0.47716894977169,0.0,0.0,0.66412213740458,0.0,0.0,0.0,0.0,0.0,'yes' 279 | 260.0,0.291666666666667,1.0,1.0,0.528301886792453,0.276255707762557,0.0,0.0,0.763358778625954,0.0,0.241935483870968,0.0,0.0,0.0,'no' 280 | 261.0,0.604166666666667,0.0,0.666666666666667,0.245283018867925,0.488584474885845,0.0,0.0,0.770992366412214,0.0,0.0,0.0,0.0,0.0,'no' 281 | 262.0,0.645833333333333,1.0,1.0,0.339622641509434,0.182648401826484,0.0,1.0,0.465648854961832,1.0,0.387096774193548,0.5,0.666666666666667,1.0,'yes' 282 | 263.0,0.604166666666667,1.0,0.333333333333333,0.245283018867925,0.360730593607306,0.0,1.0,0.679389312977099,0.0,0.290322580645161,0.5,0.0,0.0,'yes' 283 | 264.0,0.416666666666667,1.0,0.333333333333333,0.339622641509434,0.319634703196347,0.0,0.0,0.763358778625954,0.0,0.0967741935483871,0.0,0.0,0.0,'no' 284 | 265.0,0.395833333333333,1.0,0.333333333333333,0.150943396226415,0.235159817351598,0.0,0.0,0.740458015267176,0.0,0.0161290322580645,1.0,0.0,1.0,'yes' 285 | 266.0,0.479166666666667,1.0,0.666666666666667,0.735849056603773,0.166666666666667,1.0,0.0,0.694656488549618,0.0,0.0806451612903226,0.0,0.0,1.0,'no' 286 | 267.0,0.3125,1.0,0.333333333333333,0.245283018867925,0.312785388127854,0.0,0.0,0.778625954198473,0.0,0.0,0.0,0.0,1.0,'no' 287 | 268.0,0.5625,0.0,0.333333333333333,0.433962264150943,0.383561643835616,0.0,1.0,0.625954198473283,0.0,0.209677419354839,0.5,0.0,0.0,'no' 288 | 269.0,0.583333333333333,1.0,1.0,0.433962264150943,0.150684931506849,0.0,0.0,0.587786259541985,0.0,0.0645161290322581,0.5,0.0,0.75,'no' 289 | 270.0,0.791666666666667,1.0,1.0,0.622641509433962,0.365296803652968,0.0,1.0,0.282442748091603,1.0,0.241935483870968,0.5,1.0,0.0,'yes' 290 | 291 | 292 | -------------------------------------------------------------------------------- /data/semantic/Hepatitis/Hepatitis_withoutdupl_norm_16.arff: -------------------------------------------------------------------------------- 1 | @RELATION 'Hepatitis, without duplicates, with normalization, 16 pct of outliers' 2 | 3 | @ATTRIBUTE 'Age' real 4 | @ATTRIBUTE 'Sex' real 5 | @ATTRIBUTE 'Steroid' real 6 | @ATTRIBUTE 'Antivirals' real 7 | @ATTRIBUTE 'Fatigue' real 8 | @ATTRIBUTE 'Malaise' real 9 | @ATTRIBUTE 'Anorexia' real 10 | @ATTRIBUTE 'LiverBig' real 11 | @ATTRIBUTE 'LiverFirm' real 12 | @ATTRIBUTE 'SpleenPalpable' real 13 | @ATTRIBUTE 'Spiders' real 14 | @ATTRIBUTE 'Ascites' real 15 | @ATTRIBUTE 'Varices' real 16 | @ATTRIBUTE 'Bilirubin' real 17 | @ATTRIBUTE 'AlkPhosphate' real 18 | @ATTRIBUTE 'Sgot' real 19 | @ATTRIBUTE 'AlbuMin' real 20 | @ATTRIBUTE 'ProTime' real 21 | @ATTRIBUTE 'Histology' real 22 | @ATTRIBUTE 'outlier' {'no','yes'} 23 | @ATTRIBUTE 'id' real 24 | 25 | @DATA 26 | 0.2692307692307692,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.13333333333333336,0.27165354330708663,0.034482758620689655,0.6551724137931034,0.75,0.0,'no',1.0 27 | 0.36538461538461536,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.2222222222222222,0.2047244094488189,0.03940886699507389,0.7931034482758622,0.85,0.0,'no',2.0 28 | 0.23076923076923078,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.15555555555555556,0.12992125984251968,0.5788177339901478,0.5517241379310345,0.54,0.0,'no',3.0 29 | 0.40384615384615385,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.13333333333333336,0.21653543307086615,0.11330049261083744,0.6206896551724137,0.52,0.0,'no',4.0 30 | 0.19230769230769232,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.4222222222222223,0.1220472440944882,0.32019704433497537,0.9655172413793105,0.78,0.0,'no',5.0 31 | 0.34615384615384615,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.37777777777777777,0.18110236220472442,0.18472906403940886,0.2758620689655172,0.46,0.0,'no',6.0 32 | 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0.4423076923076923,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.19999999999999998,0.29133858267716534,0.012315270935960592,0.3448275862068966,0.42,1.0,'yes',80.0 106 | 107 | 108 | -------------------------------------------------------------------------------- /data/semantic/Stamps/Stamps_withoutdupl_norm_09.arff: -------------------------------------------------------------------------------- 1 | @RELATION 'Stamps, without duplicates, with normalization, 09 pct of outliers' 2 | 3 | @ATTRIBUTE 'att1' real 4 | @ATTRIBUTE 'att2' real 5 | @ATTRIBUTE 'att3' real 6 | @ATTRIBUTE 'att4' real 7 | @ATTRIBUTE 'att5' real 8 | @ATTRIBUTE 'att6' real 9 | @ATTRIBUTE 'att7' real 10 | @ATTRIBUTE 'att8' real 11 | @ATTRIBUTE 'att9' real 12 | @ATTRIBUTE 'id' real 13 | @ATTRIBUTE 'outlier' {'yes','no'} 14 | 15 | @DATA 16 | 0.112011,0.49187222745964454,0.424364,0.681209,0.748623,0.066976,0.865725,0.020098184408972285,0.181869,1.0,'yes' 17 | 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0.297373,0.32178414237048947,0.177849,0.488406,0.639552,0.079686,0.82482,0.001940272818969244,0.108272,10.0,'yes' 26 | 0.248615,0.6441454936640043,0.050519,0.293778,0.618525,0.082937,0.842893,0.0016921100241758596,0.112497,11.0,'yes' 27 | 0.380704,0.7474358158715103,0.2108,0.669397,0.850165,0.069547,0.952015,0.0024005747770537472,0.144705,12.0,'yes' 28 | 0.303415,0.49187222745964454,0.063908,0.363761,0.738628,0.097983,0.832667,0.001348884868473719,0.08898,13.0,'yes' 29 | 0.309379,0.7367742233995177,0.22798,0.674576,0.850013,0.097417,0.911863,1.0106629949246702E-4,0.106952,14.0,'yes' 30 | 0.186166,0.6585410148003772,0.063531,0.353188,0.74806,0.11067,0.822576,9.105973518628219E-4,0.081082,15.0,'yes' 31 | 0.272903,0.7241207396139987,0.280876,0.797992,0.929297,0.098834,0.932678,0.002395571494900655,0.095687,16.0,'yes' 32 | 0.223316,0.49187222745964454,0.061532,0.34644,0.758095,0.070865,0.879004,0.001709121183496374,0.125967,17.0,'yes' 33 | 0.306888,0.7063660707572542,0.252349,0.734974,0.899233,0.060368,0.959314,0.0037544629276805583,0.174448,18.0,'yes' 34 | 0.205507,0.49187222745964454,0.315577,0.325885,0.362638,0.076228,0.306056,0.0452857074240702,0.164185,19.0,'yes' 35 | 0.259283,0.637116037501229,0.062308,0.343291,0.650035,0.090627,0.802793,0.0020263292720024335,0.092747,20.0,'yes' 36 | 0.301419,0.7277841076685502,0.216952,0.620856,0.750236,0.101493,0.88654,0.0,0.098141,21.0,'yes' 37 | 0.161471,0.49187222745964454,0.283301,0.327988,0.307959,0.116271,0.012706,0.038386181334955735,0.096512,22.0,'yes' 38 | 0.317443,0.13078698214585224,0.114872,0.530859,0.947785,0.083602,0.946035,0.015850397860996814,0.216357,23.0,'yes' 39 | 0.427038,0.09017530262982863,0.121529,0.539617,0.88048,0.10797,0.915387,0.0010907155093741493,0.138919,24.0,'yes' 40 | 0.367135,0.11082874014609516,0.134861,0.597404,0.930407,0.124082,0.913069,0.002179429705887062,0.105024,25.0,'yes' 41 | 0.378282,0.0951596578388789,0.104927,0.471542,0.828432,0.089701,0.924927,6.664371827919116E-4,0.136859,26.0,'yes' 42 | 0.287911,0.49187222745964454,0.120267,0.57602,0.941536,0.09401,0.850689,0.07280375926607854,0.313765,27.0,'yes' 43 | 0.194043,0.49187222745964454,0.14267,0.548291,0.816383,0.125414,0.811123,0.0027157815526985705,0.096941,28.0,'yes' 44 | 0.202888,0.49187222745964454,0.108036,0.477078,0.913456,0.098546,0.875793,0.004647048463792248,0.08308,29.0,'yes' 45 | 0.249951,0.49187222745964454,0.139281,0.609276,0.91506,0.079997,0.875734,0.2044531212475384,0.494937,30.0,'yes' 46 | 0.283375,0.49187222745964454,0.088702,0.377645,0.680387,0.076697,0.877858,0.0018912406538689381,0.134355,31.0,'yes' 47 | 0.055131,0.49187222745964454,0.031358,0.222233,0.507031,0.004841,0.998375,0.1233008853808098,0.984391,32.0,'no' 48 | 0.050512,0.49187222745964454,0.029074,0.192086,0.476147,0.010827,0.988046,0.015216982340415311,0.582047,33.0,'no' 49 | 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0.047562,0.49187222745964454,0.365856,0.512397,0.528434,0.013755,0.986727,0.013523871659808834,0.573255,42.0,'no' 58 | 0.033021,0.49187222745964454,0.280589,0.469827,0.47808,0.016311,0.962484,0.031008341472005634,0.544174,43.0,'no' 59 | 0.059034,0.14575161508608975,0.025432,0.211695,0.561254,0.010272,0.996058,0.07244652492034774,0.746971,44.0,'no' 60 | 0.12453,0.49187222745964454,0.03802,0.230967,0.460932,0.007942,0.994403,0.02797134920507853,0.706946,45.0,'no' 61 | 0.104566,0.49187222745964454,0.02708,0.224832,0.737294,0.025758,0.957048,0.02071659008309451,0.499365,46.0,'no' 62 | 0.099405,0.49187222745964454,0.037046,0.211547,0.41389,0.011855,0.966572,0.04476936870587105,0.647288,47.0,'no' 63 | 0.058272,0.5443751048287748,0.031347,0.229502,0.559074,0.031302,0.980386,0.007896179894010471,0.299214,48.0,'no' 64 | 0.072751,0.49187222745964454,0.442735,0.512665,0.546384,0.104959,0.655413,0.058474359179621835,0.164605,49.0,'no' 65 | 0.048841,0.8005008646566533,0.049443,0.282454,0.505609,0.02937,0.975125,0.010802086168526553,0.328321,50.0,'no' 66 | 0.04431,0.8147668318864554,0.059504,0.323276,0.61394,0.017501,0.990312,0.026488376374901933,0.541862,51.0,'no' 67 | 0.054903,0.5491003522246836,0.03814,0.253819,0.543233,0.031572,0.969934,0.010874133431531085,0.330363,52.0,'no' 68 | 0.089743,0.8147506376481339,0.02184,0.193346,0.553198,0.010459,0.996139,0.058393306008741735,0.749333,53.0,'no' 69 | 0.030756,1.0,0.020946,0.172374,0.507866,0.010472,0.989551,0.025357634608303044,0.659914,54.0,'no' 70 | 0.066358,0.49187222745964454,0.018897,0.138929,0.294125,0.009525,0.971951,0.052830656910933575,0.690438,55.0,'no' 71 | 0.062288,0.49187222745964454,0.037779,0.23907,0.501786,0.004906,0.997972,0.0911027634127988,0.94685,56.0,'no' 72 | 0.04489,0.49187222745964454,0.030361,0.178719,0.426959,0.003962,0.996537,0.02991662530620087,0.806229,57.0,'no' 73 | 0.02996,0.8147506376481339,0.014326,0.125734,0.340921,0.018756,0.968241,0.045720992971389235,0.593013,58.0,'no' 74 | 0.093915,0.5978137778266175,0.056452,0.294355,0.561544,0.010414,0.995977,0.057741878672409104,0.766115,59.0,'no' 75 | 0.039582,0.3095378280056217,0.047083,0.247633,0.451058,0.011927,0.984824,0.019835011767719624,0.59726,60.0,'no' 76 | 0.047634,0.8527770226892847,0.028863,0.180182,0.401283,0.01732,0.97238,0.02716682143486127,0.563715,61.0,'no' 77 | 0.041457,0.49187222745964454,0.033955,0.223837,0.504237,0.006324,0.998764,0.11545273699546903,0.866635,62.0,'no' 78 | 0.077711,0.8149704165967808,0.046734,0.357887,0.84736,0.046967,0.938991,0.025569773771594164,0.747377,63.0,'no' 79 | 0.036577,0.49187222745964454,0.206793,0.483369,0.76885,0.009641,0.950928,0.29477537264445475,0.997015,64.0,'no' 80 | 0.064867,0.49187222745964454,0.054918,0.26552,0.911031,0.05599,0.839905,0.04519664900174515,0.695242,65.0,'no' 81 | 0.059074,0.49187222745964454,0.127263,0.441554,0.589261,0.01076,0.996419,0.05579660257128677,0.695838,66.0,'no' 82 | 0.09254,0.18824760990393347,0.072403,0.311833,0.5068,0.01202,0.990647,0.020273299284330523,0.59701,67.0,'no' 83 | 0.07386,0.8717508863453654,0.067483,0.297928,0.879271,0.059965,0.86317,0.02818949230695336,0.557844,68.0,'no' 84 | 0.083278,0.49187222745964454,0.024111,0.192069,0.534414,0.004773,1.0,0.11613718599401207,0.890078,69.0,'no' 85 | 0.065172,0.9714981405544213,0.014653,0.141073,0.425887,0.007511,0.985839,0.028926976096319185,0.663737,70.0,'no' 86 | 0.048071,0.9572483675629406,0.027192,0.167734,0.353132,0.010653,0.957547,0.06665072287420548,0.687,71.0,'no' 87 | 0.055145,0.49187222745964454,0.197376,0.493836,0.576958,0.005728,0.998112,0.10757657023007092,0.907724,72.0,'no' 88 | 0.023686,0.49187222745964454,0.052102,0.236681,0.448037,0.010273,0.989621,0.020568492931362974,0.680633,73.0,'no' 89 | 0.060033,0.49187222745964454,0.168192,0.445277,0.494401,0.00999,0.995656,0.08802974751436941,0.806298,74.0,'no' 90 | 0.061554,0.49187222745964454,0.172066,0.330385,0.415563,0.0097,0.977571,0.051180574456843694,0.681829,75.0,'no' 91 | 0.044251,0.49187222745964454,0.244599,0.560466,0.586592,0.01503,0.992862,0.06793156310539714,0.750738,76.0,'no' 92 | 0.080077,0.4001041058177801,0.043745,0.287977,0.642381,0.033215,0.982017,0.011184336925022815,0.361241,77.0,'no' 93 | 0.115808,0.49187222745964454,0.218562,0.51866,0.53297,0.012255,0.994687,0.057842944971901566,0.762086,78.0,'no' 94 | 0.154412,0.94301594554109,0.079849,0.400407,0.698772,0.034825,0.978006,0.011849773451384108,0.361147,79.0,'no' 95 | 0.151021,0.49187222745964454,0.376156,0.664823,0.732194,0.03333,0.972343,0.020447413503258138,0.504531,80.0,'no' 96 | 0.064896,0.49187222745964454,0.036191,0.213656,0.55716,0.008597,0.996273,0.11892901743543764,0.916566,81.0,'no' 97 | 0.062699,0.49187222745964454,0.040332,0.26182,0.940076,0.024724,0.979312,0.050770305320290116,0.762861,82.0,'no' 98 | 0.102603,0.49187222745964454,0.296103,0.602466,0.654713,0.017786,0.988849,0.0644492787268448,0.788743,83.0,'no' 99 | 0.096998,0.49187222745964454,0.043207,0.327288,0.966743,0.087193,0.896169,0.06096199106613939,0.732745,84.0,'no' 100 | 0.122426,0.2880897160803003,0.127511,0.572804,0.908484,0.148173,0.82837,0.020911718087065115,0.535753,85.0,'no' 101 | 0.26834,0.3450529493756543,0.064527,0.337234,0.705536,0.11568,0.885949,0.006764437470980963,0.362147,86.0,'no' 102 | 0.12205,0.49187222745964454,0.0276,0.234651,0.656919,0.059591,0.931335,0.04629937238828672,0.721262,87.0,'no' 103 | 0.14608,0.49187222745964454,0.70612,0.689218,0.940069,0.20029,0.687614,0.01361993467714821,0.476306,88.0,'no' 104 | 0.117043,0.1634137454381409,0.051482,0.281735,0.634089,0.061569,0.901709,0.020436406282521333,0.425185,89.0,'no' 105 | 0.092009,0.32381536254851045,0.031048,0.221121,0.537697,0.006374,0.998364,0.0997964664820122,0.890111,90.0,'no' 106 | 0.075949,0.46623443473432774,0.028163,0.185496,0.445216,0.006894,0.993202,0.025761899806272914,0.735547,91.0,'no' 107 | 0.151306,0.5586248778202555,0.047796,0.321332,0.89322,0.102897,0.851782,0.024624153444659697,0.644242,92.0,'no' 108 | 0.136018,0.9715328424936813,0.087559,0.400621,0.696094,0.01642,0.992173,0.05196909172417106,0.694211,93.0,'no' 109 | 0.126959,0.40220820006824715,0.068338,0.313441,0.498189,0.023209,0.98711,0.022278614771289965,0.55176,94.0,'no' 110 | 0.138262,0.5090392768115858,0.064159,0.322455,0.627139,0.023426,0.985053,0.014192310155461981,0.517069,95.0,'no' 111 | 0.03214,0.49187222745964454,0.028896,0.228724,0.595763,0.005765,0.997493,0.10193186730495205,0.902074,96.0,'no' 112 | 0.036139,0.49187222745964454,0.029668,0.196553,0.475993,0.005541,0.995155,0.04500352231063578,0.829476,97.0,'no' 113 | 0.033009,0.49187222745964454,0.019256,0.160005,0.45483,0.008163,0.990437,0.02342436638434813,0.706534,98.0,'no' 114 | 0.06629,0.49187222745964454,0.016256,0.115572,0.290595,0.005136,0.990526,0.04453821707039818,0.775251,99.0,'no' 115 | 0.084571,0.5160560089299657,0.025988,0.202582,0.513121,0.007483,0.997933,0.09217646776285242,0.845228,100.0,'no' 116 | 0.067827,0.4946877114649424,0.020464,0.160216,0.472647,0.01011,0.985688,0.023384340127123395,0.638136,101.0,'no' 117 | 0.034795,0.35230449794969376,0.020581,0.243201,0.867983,0.184749,0.697722,0.01675799324356778,0.409741,102.0,'no' 118 | 0.037209,0.49187222745964454,0.003311,0.06361,0.352968,0.006696,0.997975,0.08642369394322676,0.837922,103.0,'no' 119 | 0.046382,0.49187222745964454,0.031777,0.204351,0.458431,0.018138,0.956952,0.028602763412798795,0.609049,104.0,'no' 120 | 0.04777,0.49187222745964454,0.019024,0.137141,0.314509,0.03732,0.8934,0.04377771818312814,0.553398,105.0,'no' 121 | 0.007818,0.1323057703541333,0.091931,0.266093,0.307743,0.02556,0.799217,0.06855597271810308,0.484818,106.0,'no' 122 | 0.006224,0.36400946206210494,0.105395,0.275807,0.303402,0.018867,0.795605,0.10182479706687587,0.596357,107.0,'no' 123 | 0.075235,0.43753824442889283,0.011647,0.143191,0.539735,0.008948,0.996231,0.0654829568196737,0.758482,108.0,'no' 124 | 0.092614,0.8432490268997866,0.016502,0.155384,0.462681,0.011257,0.988,0.025674842696809107,0.647555,109.0,'no' 125 | 0.045382,0.2882377776878098,0.022658,0.163913,0.415992,0.011587,0.979117,0.033901239212923685,0.660901,110.0,'no' 126 | 0.090627,0.5018316840272757,0.013339,0.150713,0.532602,0.009584,0.995769,0.053096831521478094,0.70603,111.0,'no' 127 | 0.057482,0.49187222745964454,0.007018,0.093871,0.41258,0.011457,0.987621,0.023238244288253093,0.60862,112.0,'no' 128 | 0.048704,0.8481767022747122,0.011796,0.119448,0.380133,0.013487,0.982188,0.031544693318817145,0.640688,113.0,'no' 129 | 0.045382,0.4163943528377511,0.013335,0.154497,0.527051,0.016886,0.992132,0.02485530508013257,0.505884,114.0,'no' 130 | 0.115322,0.4804703269501044,0.056105,0.327465,0.695621,0.028332,0.979622,0.030031700795721993,0.49965,115.0,'no' 131 | 0.041906,0.2999369581436776,0.0,0.0,0.0,0.026623,0.979204,0.010263733008853809,0.30995,116.0,'no' 132 | 0.113254,0.8432490268997866,0.026694,0.196173,0.496645,0.039633,0.950884,0.027274892329368066,0.388173,117.0,'no' 133 | 0.083849,0.7437435295342422,0.116686,0.411199,0.559661,0.015963,0.990629,0.01551517795673962,0.539087,118.0,'no' 134 | 0.193013,0.21685357516729228,0.042344,0.266126,0.669146,0.080821,0.893537,0.012979514561552379,0.469507,119.0,'no' 135 | 0.0617,0.5018189599828804,0.035669,0.247266,0.530017,0.01708,0.991894,0.031142429433708513,0.536928,120.0,'no' 136 | 0.044007,0.6370304393843876,0.053979,0.295698,0.60649,0.028769,0.97885,0.008977889495509053,0.323057,121.0,'no' 137 | 0.122802,0.6228003308251543,0.023489,0.212643,0.594075,0.045279,0.952813,0.0181178853327783,0.349616,122.0,'no' 138 | 0.100307,0.4686092041110231,0.002221,0.053423,0.367446,0.042835,0.967683,0.007360828703629581,0.302377,123.0,'no' 139 | 0.073746,0.49187222745964454,0.277966,0.610553,0.627403,0.024078,0.98121,0.016751989304984068,0.469774,124.0,'no' 140 | 0.149238,0.2988183989681957,0.31623,0.687459,0.839423,0.13784,0.804331,0.016767999807873968,0.449794,125.0,'no' 141 | 0.053159,0.49187222745964454,0.309062,0.667739,0.703692,0.019058,0.985705,0.021527121791895483,0.533314,126.0,'no' 142 | 0.121308,0.49187222745964454,0.197685,0.52081,0.615215,0.023191,0.978913,0.01606453833714917,0.533457,127.0,'no' 143 | 0.053831,0.49187222745964454,0.266359,0.5883,0.685979,0.024252,0.977209,0.011863782641412766,0.486887,128.0,'no' 144 | 0.046641,0.49187222745964454,0.291828,0.581883,0.691912,0.028752,0.971244,0.009141997150130485,0.421493,129.0,'no' 145 | 1.0,0.4441188888439049,0.214823,0.586667,0.837926,0.185832,0.791318,0.004439912582654221,0.259163,130.0,'no' 146 | 0.150825,0.94301594554109,0.13464,0.486149,0.668944,0.034336,0.978067,0.009935517699610945,0.341616,131.0,'no' 147 | 0.058081,0.49187222745964454,0.184701,0.564663,0.712295,0.032851,0.970632,0.010458861012824412,0.363408,132.0,'no' 148 | 0.09785,0.313041567139577,0.225058,0.634724,0.736303,0.048926,0.965966,0.005941897885012568,0.195017,133.0,'no' 149 | 0.109849,0.0745015933973777,0.241716,0.665673,0.831115,0.037235,0.972802,0.024896331993787924,0.525076,134.0,'no' 150 | 0.110169,0.11012776097304237,0.120535,0.496672,0.763635,0.032347,0.977017,0.021360012167982195,0.532209,135.0,'no' 151 | 0.093582,0.6975355839468829,0.479174,0.794936,0.866151,0.045009,0.964564,0.09142597543988856,0.668841,136.0,'no' 152 | 0.140607,0.8717508863453654,0.301528,0.716891,0.747064,0.038205,0.915081,0.04520665556605133,0.708028,137.0,'no' 153 | 0.149266,0.49187222745964454,0.386596,0.727173,0.824395,0.038673,0.967406,0.01463660161065659,0.472287,138.0,'no' 154 | 0.165257,0.49187222745964454,0.209861,0.563067,0.62787,0.027034,0.984144,0.013540882819129348,0.489041,139.0,'no' 155 | 0.176323,0.5374520679463971,0.30922,0.792885,0.98558,0.125442,0.874177,0.019193590995693174,0.504373,140.0,'no' 156 | 0.101637,0.779195030682298,0.254155,0.661205,0.859781,0.039893,0.982875,0.025268576185978,0.440511,141.0,'no' 157 | 0.103,0.49187222745964454,0.214759,0.57294,0.72484,0.028347,0.980116,0.027559078755663713,0.591484,142.0,'no' 158 | 0.079204,0.49187222745964454,0.366451,0.707869,0.72045,0.031806,0.975121,0.013687979314430266,0.360637,143.0,'no' 159 | 0.152878,0.49187222745964454,0.348141,0.627343,0.703948,0.024321,0.975656,0.014817720424598536,0.460824,144.0,'no' 160 | 0.15621,0.49187222745964454,0.39883,0.767971,0.82983,0.035574,0.971684,0.013167637970508652,0.344136,145.0,'no' 161 | 0.127185,0.49187222745964454,0.401057,0.733141,0.810347,0.043644,0.950438,0.01566627707776301,0.44805,146.0,'no' 162 | 0.068039,0.49187222745964454,0.171181,0.569031,0.908834,0.054969,0.934695,0.013733008853808099,0.398686,147.0,'no' 163 | 0.169555,0.49187222745964454,0.470916,0.614334,0.66626,0.021452,0.979777,0.014695640340063081,0.507125,148.0,'no' 164 | 0.314437,0.7010821221392589,0.295085,0.873825,0.94672,0.183761,0.823525,0.011408483965481355,0.354931,149.0,'no' 165 | 0.308757,0.09492252792060196,0.340689,0.90705,0.942319,1.0,0.174055,0.0032741478409836853,0.0,150.0,'no' 166 | 0.13755,0.49187222745964454,0.162428,0.555429,0.96875,0.054983,0.955887,0.031161441905890264,0.569394,151.0,'no' 167 | 0.096208,0.06379257494172966,0.222817,0.695556,0.969191,0.075519,0.933116,0.024636161321827117,0.550127,152.0,'no' 168 | 0.077611,0.8504438956397014,0.181332,0.597353,0.89978,0.051183,0.959566,0.023647512768376056,0.542722,153.0,'no' 169 | 0.094169,0.49187222745964454,0.377995,0.694576,0.805323,0.041664,0.948391,0.014599577322723706,0.402895,154.0,'no' 170 | 0.089968,0.4202867536914188,0.101423,0.451338,0.867534,0.055478,0.956508,0.02436298211626827,0.554596,155.0,'no' 171 | 0.108501,0.7082573264469263,0.164754,0.541329,0.785262,0.041587,0.972868,0.03399129829167935,0.604562,156.0,'no' 172 | 0.126684,0.15287013955963238,0.122066,0.458342,0.821012,0.035185,0.977662,0.03546326390111914,0.620673,157.0,'no' 173 | 0.142917,0.49187222745964454,0.309093,0.576329,0.674996,0.040531,0.950523,0.013471837525416672,0.426783,158.0,'no' 174 | 0.033267,0.4455254741152451,0.02081,0.139764,0.315416,0.035653,0.781853,0.056217878928577145,0.475837,159.0,'no' 175 | 0.017192,0.4874199686525816,0.062213,0.236994,0.314315,0.037284,0.738666,0.0603996221521318,0.472107,160.0,'no' 176 | 0.046901,0.38960792592292703,0.245636,0.535985,0.586592,0.02598,0.976772,0.01076406122416305,0.328088,161.0,'no' 177 | 0.045878,0.9073735837271041,0.379998,0.582822,0.596732,0.018124,0.981458,0.016032517331369376,0.415005,162.0,'no' 178 | 0.044898,0.013856484346533566,0.229328,0.544272,0.575201,0.021515,0.978316,0.014569557629805153,0.408122,163.0,'no' 179 | 0.052614,0.3332450361767717,0.039154,0.184156,0.271269,0.035251,0.813098,0.05639199314750477,0.500783,164.0,'no' 180 | 0.051599,0.6710741985297944,0.115478,0.287874,0.375765,0.041853,0.7574,0.053256936550377054,0.452034,165.0,'no' 181 | 0.051261,0.10149391848514468,0.205934,0.498289,0.572681,0.023055,0.976696,0.01165664676027474,0.368675,166.0,'no' 182 | 0.022394,0.2538747607012105,0.071613,0.243216,0.33272,0.026629,0.806597,0.07228541923501816,0.573636,167.0,'no' 183 | 0.042911,0.38982423467764793,0.151477,0.456878,0.574727,0.017898,0.983291,0.02139103251733137,0.465678,168.0,'no' 184 | 0.100485,0.00534641210866334,0.473731,0.857385,0.916337,0.195579,0.74353,0.012967506684384957,0.337751,169.0,'no' 185 | 0.156156,0.2777589487625867,0.069976,0.30364,0.684953,0.090517,0.739503,0.021948398149185866,0.464553,170.0,'no' 186 | 0.09698,0.05567463461749788,0.415736,0.85944,0.908423,0.241186,0.633662,0.017870723194415537,0.342818,171.0,'no' 187 | 0.05286,0.8041364711597967,0.269352,0.549681,0.570121,0.013052,0.9831,0.03279451320065964,0.603324,172.0,'no' 188 | 0.053275,0.49187222745964454,0.312458,0.551109,0.584299,0.013861,0.987044,0.030878256136025233,0.575415,173.0,'no' 189 | 0.060121,0.8219258419558014,0.264856,0.446525,0.64682,0.017117,0.968014,0.026831601530604078,0.548547,174.0,'no' 190 | 0.081866,0.49187222745964454,0.183419,0.373512,0.423477,0.005742,0.995923,0.05312484990153541,0.7513,175.0,'no' 191 | 0.09685,0.49187222745964454,0.507209,0.802914,0.914603,0.064307,0.895337,0.015446132663026944,0.391784,176.0,'no' 192 | 0.060007,0.49187222745964454,0.188991,0.49645,0.646217,0.006868,0.990124,0.08674190268816344,0.834634,177.0,'no' 193 | 0.118661,0.49187222745964454,0.143211,0.408623,0.516408,0.004598,0.994203,0.416205030500008,0.921877,178.0,'no' 194 | 0.107822,0.0789677329801447,0.386518,0.849321,0.948324,0.226154,0.703179,0.02089270561488336,0.449227,179.0,'no' 195 | 0.087416,0.49187222745964454,0.237147,0.47292,0.575358,0.003927,0.99879,0.10888442818488928,0.941056,180.0,'no' 196 | 0.081179,0.49187222745964454,0.084966,0.268107,0.485162,0.006206,0.995441,0.1351266430778591,0.922856,181.0,'no' 197 | 0.020281,0.49187222745964454,0.174454,0.467942,0.565414,0.003577,0.996128,0.4221349205078532,0.951096,182.0,'no' 198 | 0.058642,0.49187222745964454,0.280356,0.57183,0.561185,0.003928,0.995874,0.35089618789926197,0.942163,183.0,'no' 199 | 0.079636,0.49187222745964454,0.248648,0.56685,0.825361,0.021843,0.959413,0.07399854304423702,0.808275,184.0,'no' 200 | 0.062715,0.864641615722292,0.031109,0.19984,0.435833,0.023898,0.880091,0.049878720440609044,0.522444,185.0,'no' 201 | 0.026461,0.8717508863453654,0.027899,0.139904,0.219195,0.007789,0.755585,0.30430862645895707,0.880158,186.0,'no' 202 | 0.047825,0.25261739376868847,0.029623,0.188652,0.403922,0.017658,0.908388,0.048572863798651916,0.561248,187.0,'no' 203 | 0.021393,0.28111346955772376,0.05064,0.188121,0.230377,0.010909,0.665086,0.27422289021598173,0.862941,188.0,'no' 204 | 0.066205,0.8025517492669216,0.173617,0.546737,0.67657,0.021649,0.989094,0.01050589186506348,0.337459,189.0,'no' 205 | 0.044458,0.49187222745964454,0.026981,0.182576,0.410444,0.014952,0.93251,0.0418814742471061,0.52747,190.0,'no' 206 | 0.037873,0.8788751944754514,0.027401,0.142895,0.224948,0.010004,0.670624,0.30843633423525835,0.845132,191.0,'no' 207 | 0.052979,0.6441547475144737,0.044929,0.231778,0.413535,0.011704,0.948356,0.05205614883363487,0.62154,192.0,'no' 208 | 0.074762,0.7687809787103602,0.17957,0.535396,0.731813,0.019327,0.987322,0.009417177668550568,0.425337,193.0,'no' 209 | 0.019944,0.6441547475144737,0.051713,0.197939,0.257749,0.010924,0.730972,0.23684937318881188,0.803545,194.0,'no' 210 | 0.047298,0.07450275012868637,0.029614,0.187725,0.413301,0.01462,0.919268,0.05455178597159737,0.608766,195.0,'no' 211 | 0.08677,0.43958218865130916,0.144045,0.517408,0.674486,0.022883,0.986674,0.010294753358202981,0.383865,196.0,'no' 212 | 0.048069,0.0958779879815617,0.05127,0.183493,0.229316,0.015312,0.5474,0.28053102835460064,0.813165,197.0,'no' 213 | 0.323621,0.017503658162763662,0.183582,0.641709,0.843354,0.212078,0.832529,0.0027147808962679518,0.095907,198.0,'no' 214 | 0.338444,0.03744686265550807,0.394468,0.686291,0.83595,0.067539,0.910374,0.3357402456011143,0.757723,199.0,'no' 215 | 0.261651,0.33460766565838257,0.022004,0.256929,0.832453,0.038481,0.974189,0.006565306841287885,0.380015,200.0,'no' 216 | 0.053374,0.49187222745964454,0.029983,0.18999,0.382648,0.012762,0.935496,0.05319089322595623,0.596653,201.0,'no' 217 | 0.078614,0.9073735837271041,0.181474,0.537533,0.720022,0.020479,0.987519,0.008527594101730736,0.351066,202.0,'no' 218 | 0.040982,0.8362427053631848,0.033346,0.205843,0.429642,0.027719,0.850332,0.04359259674346372,0.509954,203.0,'no' 219 | 0.090521,0.9216256701812019,0.183305,0.573341,0.757629,0.028873,0.979983,0.00924406410605357,0.337943,204.0,'no' 220 | 0.105612,0.3523357296950278,0.146287,0.587209,0.963543,0.110179,0.896668,0.02262484189628396,0.515875,205.0,'no' 221 | 0.16288,0.6407100016772604,0.150227,0.545793,0.797873,0.059052,0.956283,0.008496573752381562,0.364652,206.0,'no' 222 | 0.049895,0.4991388135406967,0.166556,0.537764,0.765232,0.042641,0.944537,0.012358106918138299,0.375179,207.0,'no' 223 | 0.08376,0.15061798370165586,0.071412,0.308328,0.523944,0.045274,0.858099,0.031167445844473972,0.452436,208.0,'no' 224 | 0.130991,0.9358754431726827,0.101858,0.461144,0.874657,0.063541,0.942622,0.03255035303158872,0.581901,209.0,'no' 225 | 0.184739,0.3451929138640031,0.09854,0.416708,0.680933,0.027514,0.98247,0.008619654493347636,0.38448,210.0,'no' 226 | 0.055771,0.873577365081752,0.036652,0.227993,0.564014,0.032489,0.947387,0.017728629981267712,0.358195,211.0,'no' 227 | 0.064649,0.5962984598122626,0.088906,0.374447,0.609823,0.030172,0.981092,0.01647380681727213,0.522892,212.0,'no' 228 | 0.067347,0.4121179171896056,0.086368,0.40338,0.802738,0.019361,0.986195,0.012447165340463345,0.480897,213.0,'no' 229 | 0.072269,0.6191739781724802,0.321237,0.620273,0.670741,0.073507,0.934855,0.027941329512159975,0.108356,214.0,'no' 230 | 0.072921,0.38082833529013715,0.011777,0.139151,0.467981,0.018225,0.988604,0.014779695480235035,0.483045,215.0,'no' 231 | 0.081831,0.10831631974366834,0.243859,0.656557,0.990399,0.031552,0.976238,0.02068757104660657,0.550398,216.0,'no' 232 | 0.094808,0.2198460390628163,0.136547,0.505117,0.766577,0.072024,0.936137,0.029643446100642023,0.552964,217.0,'no' 233 | 0.100125,0.3295111075123915,0.0957,0.402699,0.615751,0.037968,0.972384,0.0072427512448166,0.365973,218.0,'no' 234 | 0.151489,0.8362045332299987,0.077064,0.356979,0.823202,0.042864,0.957982,0.007460894346691428,0.268036,219.0,'no' 235 | 0.103155,0.9358754431726827,0.077361,0.368419,0.737349,0.012378,0.992427,0.04810355593269185,0.744946,220.0,'no' 236 | 0.083812,0.49187222745964454,0.103436,0.364619,0.553699,0.016757,0.967703,0.036528962999727826,0.687462,221.0,'no' 237 | 0.059971,0.49187222745964454,0.022088,0.149196,0.322004,0.007968,0.966295,0.08163455226628669,0.837838,222.0,'no' 238 | 0.140675,0.6583813858797809,0.100523,0.415059,0.702265,0.023794,0.979959,0.04929633839798908,0.747585,223.0,'no' 239 | 0.117795,0.49187222745964454,0.114217,0.559997,1.0,0.034679,0.974097,0.04502553675210939,0.661616,224.0,'no' 240 | 0.121573,0.9216256701812019,0.078406,0.447893,0.990384,0.052294,0.946744,0.044701324068588996,0.634096,225.0,'no' 241 | 0.124721,0.15287129629094107,0.088803,0.42879,0.924888,0.029667,0.97666,0.040247402295906114,0.658111,226.0,'no' 242 | 0.163993,0.4805246933216118,0.071692,0.377933,0.719167,0.022228,0.983013,0.029797547190957268,0.64703,227.0,'no' 243 | 0.086868,0.9073735837271041,0.0872,0.430555,0.791898,0.029594,0.977417,0.044318072655662115,0.668539,228.0,'no' 244 | 0.088468,0.6371576798283412,0.072194,0.335715,0.609886,0.018675,0.981661,0.014839734866072143,0.549648,229.0,'no' 245 | 0.085038,0.11725091237181971,0.101378,0.509642,0.880624,0.087154,0.905279,0.0684218847564002,0.68936,230.0,'no' 246 | 0.047423,0.8220507689371376,0.055763,0.27156,0.554174,0.017484,0.977122,0.01872028050401063,0.541097,231.0,'no' 247 | 0.142126,0.49187222745964454,0.058164,0.333985,0.727274,0.027752,0.977759,0.0315096703437455,0.679125,232.0,'no' 248 | 0.140032,0.6584739243844744,0.068441,0.372233,0.713286,0.015733,0.99125,0.05085135849117021,0.734674,233.0,'no' 249 | 0.129396,0.9073735837271041,0.048219,0.277349,0.682945,0.019541,0.979524,0.015587225219744153,0.545193,234.0,'no' 250 | 0.123848,0.08875252312016704,0.077897,0.421937,0.875455,0.044588,0.962957,0.025775908996301573,0.616133,235.0,'no' 251 | 0.208037,0.49187222745964454,0.282468,0.682609,0.660073,0.061387,0.950946,0.04120803246929986,0.379354,236.0,'no' 252 | 0.142,0.49187222745964454,0.046282,0.282295,0.605628,0.012769,0.992913,0.05320990569813798,0.722184,237.0,'no' 253 | 0.193191,0.49187222745964454,0.544494,1.0,0.99439,0.056514,0.95322,0.08285735442450247,0.451467,238.0,'no' 254 | 0.135385,0.49187222745964454,0.061043,0.302341,0.584089,0.014887,0.991546,0.03714836933028067,0.690017,239.0,'no' 255 | 0.115131,0.5421888826553924,0.060231,0.294351,0.473058,0.029194,0.955646,0.01762456171248339,0.490554,240.0,'no' 256 | 0.05322,0.49187222745964454,0.023428,0.193358,0.515836,0.011083,0.986874,0.14782497318240764,0.888209,241.0,'no' 257 | 0.097997,0.49187222745964454,0.115199,0.40093,0.675544,0.016233,0.955248,0.03069413535279143,0.604091,242.0,'no' 258 | 0.036563,0.49187222745964454,0.051134,0.215645,0.428226,0.009015,0.962462,0.059548063529675475,0.700194,243.0,'no' 259 | 0.064327,0.49187222745964454,0.102286,0.347513,0.503817,0.004099,0.996537,0.16578075217342578,1.0,244.0,'no' 260 | 0.033077,0.49187222745964454,0.114453,0.391385,0.519458,0.003482,0.997896,0.14564654413295122,0.978237,245.0,'no' 261 | 0.066002,0.49187222745964454,0.193652,0.365023,0.39315,0.005461,0.976431,0.06419110936774523,0.815811,246.0,'no' 262 | 0.051248,0.49187222745964454,0.114416,0.28731,0.373575,0.006091,0.972762,0.05583262620278903,0.749346,247.0,'no' 263 | 0.047495,0.49187222745964454,0.11588,0.29058,0.372557,0.005652,0.941592,0.12744460366000096,0.873504,248.0,'no' 264 | 0.03228,0.49187222745964454,0.09598,0.239426,0.334519,0.004608,0.956116,0.1111529163131014,0.857124,249.0,'no' 265 | 0.108471,0.9714981405544213,0.095328,0.408378,0.644725,0.0155,0.989105,0.07462095134408171,0.758332,250.0,'no' 266 | 0.043852,0.49187222745964454,0.072306,0.303381,0.480977,0.010258,0.992106,0.02202544869434349,0.626636,251.0,'no' 267 | 0.088716,0.637116037501229,0.059566,0.24144,0.343676,0.019,0.950572,0.0424548503818505,0.651278,252.0,'no' 268 | 0.117748,0.49187222745964454,0.087231,0.366069,0.56777,0.011558,0.995964,0.05221125058038073,0.68929,253.0,'no' 269 | 0.087201,0.7614877878092087,0.089993,0.398032,0.681552,0.030297,0.975252,0.019333682895979764,0.508859,254.0,'no' 270 | 0.055668,0.8646254214839707,0.06793,0.29189,0.487796,0.025139,0.960284,0.02130997934645127,0.442398,255.0,'no' 271 | 0.060581,0.7010751817514069,0.09722,0.3886,0.553743,0.009708,0.996568,0.04463428008773756,0.63869,256.0,'no' 272 | 0.075078,0.3615143926293081,0.119517,0.419651,0.552158,0.015203,0.991334,0.0305030099745433,0.552645,257.0,'no' 273 | 0.053804,0.37157101462686737,0.077483,0.33161,0.620407,0.027399,0.964168,0.015595230471189098,0.394747,258.0,'no' 274 | 0.059204,0.49187222745964454,0.025508,0.196964,0.494983,0.004207,0.999958,0.10256128019981108,0.966847,259.0,'no' 275 | 0.027003,0.35945078397464447,0.022126,0.145518,0.338736,0.009365,0.980705,0.030332898381338156,0.667014,260.0,'no' 276 | 0.031638,0.49187222745964454,0.030308,0.156607,0.286949,0.00742,0.993504,0.04156226484573881,0.772851,261.0,'no' 277 | 0.057201,0.49187222745964454,0.049985,0.288546,0.579544,0.006444,0.997802,0.10908756144030483,0.942594,262.0,'no' 278 | 0.091887,0.49187222745964454,0.622962,0.371535,0.509877,0.058935,0.929772,0.15019953089226534,0.491129,263.0,'no' 279 | 0.040018,0.7723599053793789,0.051412,0.254635,0.451943,0.008769,0.995525,0.025999055380329494,0.735666,264.0,'no' 280 | 0.04448,0.49187222745964454,0.117984,0.419068,0.560038,0.007329,0.99746,0.06386189340207175,0.828613,265.0,'no' 281 | 0.0602,0.49187222745964454,0.098389,0.319168,0.418727,0.010517,0.987407,0.024371988024143837,0.682348,266.0,'no' 282 | 0.08403,0.49187222745964454,0.69443,0.5365,0.555386,0.041832,0.930668,0.11006019949086601,0.521049,267.0,'no' 283 | 0.061388,0.11725206910312838,0.047582,0.200637,0.342073,0.010484,0.977105,0.0573846443266783,0.72497,268.0,'no' 284 | 0.025053,0.49187222745964454,0.105618,0.388275,0.615893,0.005504,0.998066,0.09893990457740276,0.89957,269.0,'no' 285 | 0.066657,0.49187222745964454,0.084901,0.368771,0.665806,0.050264,0.942627,0.018350037624681792,0.611018,270.0,'no' 286 | 0.030401,0.49187222745964454,0.19905,0.555384,0.63883,0.565278,0.0,0.08688299524488065,0.16372,271.0,'no' 287 | 0.028491,0.8646254214839707,0.05366,0.242495,0.417129,0.01268,0.972785,0.04418298403752862,0.677727,272.0,'no' 288 | 0.057722,0.49187222745964454,0.652062,0.376804,0.448976,0.045007,0.931607,0.05498907283177765,0.328725,273.0,'no' 289 | 0.033303,0.49187222745964454,0.079536,0.319425,0.520828,0.005509,0.999014,0.08190973278470677,0.904966,274.0,'no' 290 | 0.029743,0.49187222745964454,0.104623,0.336695,0.448096,0.008325,0.99647,0.025926007460894344,0.747294,275.0,'no' 291 | 0.099616,0.49187222745964454,0.765384,0.374704,0.523005,0.036761,0.966141,0.09483521189900575,0.449025,276.0,'no' 292 | 0.035222,0.49187222745964454,0.052347,0.209449,0.333195,0.010062,0.992799,0.025265574216686147,0.724953,277.0,'no' 293 | 0.04507,0.49187222745964454,0.104053,0.377004,0.501937,0.008824,0.997047,0.05227028930978722,0.778053,278.0,'no' 294 | 0.110687,0.3950711678937658,0.105052,0.379747,0.517803,0.015424,0.989531,0.01743143502137402,0.573592,279.0,'no' 295 | 0.085006,0.7579990861822662,0.020554,0.132501,0.288205,0.00747,0.991285,0.03576946476888839,0.707319,280.0,'no' 296 | 0.05208,0.49187222745964454,0.087903,0.353153,0.694577,0.006955,0.997546,0.07222638050561167,0.871548,281.0,'no' 297 | 0.078225,0.49187222745964454,0.062323,0.269843,0.509562,0.012597,0.993667,0.018258977889495508,0.647684,282.0,'no' 298 | 0.081675,0.32383734044337514,0.048272,0.211275,0.405865,0.010034,0.993423,0.028015378088025747,0.686875,283.0,'no' 299 | 0.096356,0.49187222745964454,0.076055,0.340488,0.634767,0.008477,0.997403,0.08100113674570518,0.899411,284.0,'no' 300 | 0.094128,0.49187222745964454,0.097946,0.333981,0.46835,0.010935,0.995641,0.023534438591716163,0.739581,285.0,'no' 301 | 0.075958,0.49187222745964454,0.100899,0.409799,0.647745,0.008274,0.997712,0.11185337581453432,0.844912,286.0,'no' 302 | 0.09744,0.49187222745964454,0.08834,0.299899,0.452332,0.009817,0.990773,0.02287700731679982,0.695088,287.0,'no' 303 | 0.076293,0.49187222745964454,0.063263,0.262493,0.503501,0.018963,0.953568,0.0418054243583791,0.609499,288.0,'no' 304 | 0.030412,0.49187222745964454,0.125774,0.41871,0.521846,0.007775,0.997861,0.0912168382458893,0.836543,289.0,'no' 305 | 0.097022,0.49187222745964454,0.134835,0.423875,0.644019,0.009571,0.995649,0.09218647432715861,0.845689,290.0,'no' 306 | 0.039409,0.49187222745964454,0.029839,0.202076,0.473632,0.005443,0.999278,0.11724691397556797,0.912028,291.0,'no' 307 | 0.060346,0.49187222745964454,0.0407,0.245361,0.526793,0.008216,0.996135,0.0829944443554972,0.827348,292.0,'no' 308 | 0.094802,0.49187222745964454,0.075721,0.323801,0.622937,0.017585,0.972283,0.018809338926335677,0.560729,293.0,'no' 309 | 0.031017,0.49187222745964454,0.03629,0.201083,0.51235,0.007597,0.982905,0.04002125394258634,0.738281,294.0,'no' 310 | 0.031055,0.49187222745964454,0.018272,0.126085,0.283844,0.012882,0.930248,0.08192374197473543,0.724635,295.0,'no' 311 | 0.034761,0.49187222745964454,0.084682,0.334918,0.534824,0.005362,0.998852,0.10275740886021228,0.89983,296.0,'no' 312 | 0.057004,0.49187222745964454,0.449869,0.232534,0.436181,0.045797,0.935198,0.07507524936358251,0.39483,297.0,'no' 313 | 0.130281,0.11724975564051104,0.090493,0.303437,0.377719,0.013455,0.972813,0.0276231207672233,0.543495,298.0,'no' 314 | 0.129718,0.6940977784975217,0.080766,0.287302,0.463714,0.015221,0.961911,0.02857174306344962,0.536132,299.0,'no' 315 | 0.098782,0.8505514716514075,0.051682,0.241657,0.608943,0.009568,0.976827,0.0492102819449559,0.73806,300.0,'no' 316 | 0.091239,0.928749978311288,0.041444,0.202053,0.353337,0.007804,0.985287,0.035397220576698314,0.705413,301.0,'no' 317 | 0.052859,0.49187222745964454,0.106536,0.301462,0.451024,0.006449,0.9838,0.036204750316207436,0.578063,302.0,'no' 318 | 0.129594,0.49187222745964454,0.044474,0.211677,0.368069,0.00764,0.980338,0.0319319473574665,0.531691,303.0,'no' 319 | 0.101922,0.49187222745964454,0.103068,0.313422,0.658945,0.053593,0.856076,0.038555292271730254,0.655843,304.0,'no' 320 | 0.048811,0.49187222745964454,0.039806,0.206032,0.367581,0.005994,0.982871,0.03815402904305225,0.616173,305.0,'no' 321 | 0.107533,0.25967692494548905,0.107717,0.311843,0.401637,0.04307,0.864252,0.028046398437374918,0.249262,306.0,'no' 322 | 0.113331,0.3843586792441918,0.148866,0.304207,0.3229,0.013073,0.952269,0.04090783554011432,0.5463,307.0,'no' 323 | 0.030662,0.2668498157905391,0.162856,0.36478,0.723425,0.015135,0.948226,0.0389905778190493,0.559539,308.0,'no' 324 | 0.029279,0.2382253428262416,0.087191,0.267522,0.302129,0.006361,0.987512,0.036553979410493286,0.486006,309.0,'no' 325 | 0.077016,0.03887773928433034,0.348578,0.51798,0.827307,0.035666,0.886971,0.03988216269873037,0.606824,310.0,'no' 326 | 0.077874,0.07403658741129318,0.120516,0.302714,0.642057,0.018964,0.933829,0.038895515458140545,0.456826,311.0,'no' 327 | 0.07882,0.2196806264856768,0.142318,0.318338,0.311764,0.00684,0.984032,0.03847824172657263,0.494802,312.0,'no' 328 | 0.084312,0.0,0.080335,0.378333,0.786775,0.05959,0.821449,0.0359535855521222,0.54161,313.0,'no' 329 | 0.179304,0.044711135273942895,0.125196,0.480062,0.885421,0.124103,0.811298,0.027874285531308537,0.505971,314.0,'no' 330 | 0.121776,0.49187222745964454,0.075938,0.235151,0.320237,0.00858,0.969158,0.07236146912374516,0.784045,315.0,'no' 331 | 0.207576,0.03443126413381068,0.330368,0.759449,0.833859,0.186136,0.782098,0.0206455434765206,0.513521,316.0,'no' 332 | 0.061611,0.06017894633345094,0.134063,0.344769,0.469204,0.0054,0.992688,0.06915636657647416,0.68889,317.0,'no' 333 | 0.044399,0.13850469343728491,0.159312,0.474862,0.768858,0.051836,0.833458,0.06879412894859027,0.641192,318.0,'no' 334 | 0.055069,0.28276990879173636,0.120452,0.393786,0.70985,0.046304,0.866547,0.08219992314958613,0.706349,319.0,'no' 335 | 0.096308,0.051267488331472925,0.11925,0.50195,0.808318,0.05651,0.835994,0.05021394034486624,0.579407,320.0,'no' 336 | 0.064808,0.33647578672188133,0.050631,0.229004,0.389023,0.01795,0.953743,0.06190961270593509,0.644757,321.0,'no' 337 | 0.060111,0.8647977744489622,0.104112,0.266663,0.413468,0.002052,0.997076,0.04239180902672154,0.772744,322.0,'no' 338 | 0.040361,0.0770001330241005,0.073173,0.288461,0.459157,0.009169,0.983621,0.06831281320546277,0.658939,323.0,'no' 339 | 0.05139,0.3571523588643212,0.087157,0.297994,0.452452,0.016261,0.969348,0.031230487199602936,0.51165,324.0,'no' 340 | 0.063635,0.12910972174828372,0.056286,0.244692,0.354751,0.007342,0.991084,0.038492250916601294,0.514369,325.0,'no' 341 | 0.216444,0.49187222745964454,0.325284,0.436391,0.538461,0.037843,0.949193,0.05585964392641573,0.801654,326.0,'no' 342 | 0.041889,0.49187222745964454,0.301062,0.56323,0.875501,0.012027,0.955666,0.054596815510975207,0.750558,327.0,'no' 343 | 0.250511,0.49187222745964454,1.0,0.273556,0.664266,0.221397,0.576035,0.0635126643077859,0.293631,328.0,'no' 344 | 0.04564,0.08637197008692836,0.143468,0.444244,0.714342,0.050753,0.880337,0.03799392401415329,0.490779,329.0,'no' 345 | 0.034729,0.20274955032070374,0.168479,0.398753,0.452303,0.012184,0.970124,0.10222906226484574,0.710928,330.0,'no' 346 | 0.163026,0.046003204145725014,0.190265,0.494123,0.795661,0.041421,0.868441,0.038079980467186476,0.640409,331.0,'no' 347 | 0.133545,0.5993568573923806,0.266307,0.549261,0.835433,0.045208,0.888198,0.040102307113466434,0.651768,332.0,'no' 348 | 0.052732,0.4855796091404908,0.128313,0.356403,0.464446,0.007537,0.985075,0.10811892601546613,0.78841,333.0,'no' 349 | 0.175318,0.08635693257991568,0.519772,0.844296,0.915637,0.16594,0.79054,0.02342036375862566,0.560935,334.0,'no' 350 | 0.016278,0.49187222745964454,0.247472,0.487663,0.514827,0.021797,0.934628,0.12064414255751772,0.716651,335.0,'no' 351 | 0.109534,0.49187222745964454,0.18222,0.273354,0.299064,0.003863,0.98513,0.04709189228133656,0.697086,336.0,'no' 352 | 0.243312,0.16181629950087043,0.322854,0.672288,0.860632,0.222093,0.745162,0.018605205014489503,0.502425,337.0,'no' 353 | 0.023518,0.8576480182300855,0.183025,0.308972,0.309716,0.009696,0.957676,0.04232876767159257,0.627981,338.0,'no' 354 | 0.035955,0.49187222745964454,0.086736,0.303824,0.434079,0.016849,0.957541,0.15237395731599931,0.85781,339.0,'no' 355 | 0.068418,0.2643605300142856,0.192991,0.322632,0.33068,0.012361,0.958803,0.04266398757584976,0.664235,340.0,'no' 356 | 357 | 358 | -------------------------------------------------------------------------------- /data/shuttle.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/shuttle.mat -------------------------------------------------------------------------------- /data/speech.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/speech.mat -------------------------------------------------------------------------------- /data/wbc.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/wbc.mat -------------------------------------------------------------------------------- /data/wine.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/data/wine.mat -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/winstonll/COPOD/3ca17400016386546d2c199886e81bdcc859b36b/models/__init__.py -------------------------------------------------------------------------------- /models/cod.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import numpy as np 5 | import pandas as pd 6 | from pyod.models.base import BaseDetector 7 | from statsmodels.distributions.empirical_distribution import ECDF 8 | from scipy.stats import skew 9 | import matplotlib.pyplot as plt 10 | 11 | class COPOD(BaseDetector): 12 | def __init__(self, contamination=0.1): 13 | super(COPOD, self).__init__(contamination=contamination) 14 | 15 | def ecdf(self, X): 16 | ecdf = ECDF(X) 17 | return ecdf(X) 18 | 19 | def fit(self, X, y=None): 20 | self.X_train = X 21 | 22 | def decision_function(self, X): 23 | if hasattr(self, 'X_train'): 24 | original_size = X.shape[0] 25 | X = np.concatenate((self.X_train, X), axis=0) 26 | size = X.shape[0] 27 | dim = X.shape[1] 28 | self.U_l = pd.DataFrame(-1*np.log(np.apply_along_axis(self.ecdf, 0, X))) 29 | self.U_r = pd.DataFrame(-1*np.log(np.apply_along_axis(self.ecdf, 0, -X))) 30 | skewness = np.sign(np.apply_along_axis(skew, 0, X)) 31 | self.U_skew = self.U_l * -1*np.sign(skewness - 1) + self.U_r * np.sign(skewness + 1) 32 | self.O = np.maximum(self.U_skew, np.add(self.U_l, self.U_r)/2) 33 | if hasattr(self, 'X_train'): 34 | self.decision_scores_ = self.O.sum(axis=1).to_numpy()[-original_size:] 35 | else: 36 | self.decision_scores_ = self.O.sum(axis=1).to_numpy() 37 | self.threshold_ = np.percentile(self.decision_scores_, (1-self.contamination)*100) 38 | self.labels_ = np.zeros(len(self.decision_scores_)) 39 | for i in range(len(self.decision_scores_)): 40 | self.labels_[i] = 1 if self.decision_scores_[i] >= self.threshold_ else 0 41 | return self.decision_scores_ 42 | 43 | def explain_outlier(self, ind, cutoffs=None): 44 | cutoffs = [1-self.contamination, 0.99] if cutoffs is None else cutoffs 45 | plt.plot(range(1, self.O.shape[1] + 1), self.O.iloc[ind], label='Outlier Score') 46 | for i in cutoffs: 47 | plt.plot(range(1, self.O.shape[1] + 1), self.O.quantile(q=i, axis=0), '-', label=f'{i} Cutoff Band') 48 | plt.xlim([1, self.O.shape[1] + 1]) 49 | plt.ylim([0, int(self.O.max().max()) + 1]) 50 | plt.ylabel('Dimensional Outlier Score') 51 | plt.xlabel('Dimension') 52 | plt.xticks(range(1, self.O.shape[1] + 1)) 53 | plt.yticks(range(0, int(self.O.max().max()) + 1)) 54 | label = 'Outlier' if self.labels_[ind] == 1 else 'Inlier' 55 | plt.title(f'Outlier Score Breakdown for Data #{ind+1} ({label})') 56 | plt.legend() 57 | plt.show() 58 | return self.O.iloc[ind], self.O.quantile(q=cutoffs[0], axis=0), self.O.quantile(q=cutoffs[1], axis=0) -------------------------------------------------------------------------------- /results/benchmark_arff.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from sklearn.metrics import average_precision_score 3 | from sklearn.metrics import roc_auc_score 4 | from pyod.utils.utility import precision_n_scores 5 | from pyod.utils.utility import standardizer 6 | from pyod.models.sos import SOS 7 | from pyod.models.sod import SOD 8 | from pyod.models.pca import PCA 9 | from pyod.models.ocsvm import OCSVM 10 | from pyod.models.mcd import MCD 11 | from pyod.models.lof import LOF 12 | from pyod.models.loda import LODA 13 | from pyod.models.lmdd import LMDD 14 | from pyod.models.knn import KNN 15 | from pyod.models.iforest import IForest 16 | from pyod.models.hbos import HBOS 17 | from pyod.models.feature_bagging import FeatureBagging 18 | from pyod.models.cof import COF 19 | from pyod.models.cblof import CBLOF 20 | from pyod.models.abod import ABOD 21 | import arff 22 | from scipy.io import loadmat 23 | from sklearn.model_selection import train_test_split 24 | import pandas as pd 25 | import numpy as np 26 | 27 | import os 28 | import sys 29 | from time import time 30 | import pdb 31 | # supress warnings for clean output 32 | import warnings 33 | 34 | warnings.filterwarnings("ignore") 35 | 36 | sys.path.append( 37 | os.path.abspath(os.path.join(os.path.dirname("__file__"), '..'))) 38 | from models.cod import COD 39 | 40 | 41 | def read_arff(file_path, misplaced_list): 42 | misplaced = False 43 | for item in misplaced_list: 44 | if item in file_path: 45 | misplaced = True 46 | 47 | file = arff.load(open(file_path)) 48 | data_value = np.asarray(file['data']) 49 | attributes = file['attributes'] 50 | 51 | X = data_value[:, 0:-2] 52 | if not misplaced: 53 | y = data_value[:, -1] 54 | else: 55 | y = data_value[:, -2] 56 | y[y == 'no'] = 0 57 | y[y == 'yes'] = 1 58 | y = y.astype('float').astype('int').ravel() 59 | 60 | if y.sum() > len(y): 61 | print(attributes) 62 | raise ValueError('wrong sum') 63 | 64 | return X, y, attributes 65 | 66 | 67 | # Define data file and read X and y 68 | file_names = [ 69 | 'Arrhythmia', 70 | 'Cardiotocography', 71 | 'HeartDisease', 72 | 'Hepatitis', 73 | 'InternetAds', 74 | 'Ionosphere', 75 | 'KDDCup99', 76 | 'Lymphography', 77 | 'Pima', 78 | 'Shuttle', 79 | 'SpamBase', 80 | 'Stamps', 81 | 'Waveform', 82 | 'WBC', 83 | 'WDBC', 84 | 'WPBC', 85 | ] 86 | 87 | ############################################################################# 88 | misplaced_list = ['Arrhythmia', 'Cardiotocography', 'Hepatitis', 'ALOI', 89 | 'KDDCup99'] 90 | arff_list = [ 91 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 92 | 'semantic', 'Arrhythmia', 'Arrhythmia_withoutdupl_46.arff')), 93 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'semantic', 'Cardiotocography', 94 | 'Cardiotocography_withoutdupl_22.arff')), 95 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'semantic', 'HeartDisease', 96 | 'HeartDisease_withoutdupl_44.arff')), 97 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 98 | 'semantic', 'Hepatitis', 'Hepatitis_withoutdupl_16.arff')), 99 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'semantic', 'InternetAds', 100 | 'InternetAds_withoutdupl_norm_19.arff')), 101 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 102 | 'literature', 'Ionosphere', 'Ionosphere_withoutdupl_norm.arff')), 103 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 104 | 'data', 'literature', 'KDDCup99', 'KDDCup99_idf.arff')), 105 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 106 | 'literature', 'Lymphography', 'Lymphography_withoutdupl_idf.arff')), 107 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 108 | 'data', 'semantic', 'Pima', 'Pima_withoutdupl_35.arff')), 109 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 110 | 'literature', 'Shuttle', 'Shuttle_withoutdupl_v01.arff')), 111 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 112 | 'data', 'semantic', 'SpamBase', 'SpamBase_withoutdupl_40.arff')), 113 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 114 | 'data', 'semantic', 'Stamps', 'Stamps_withoutdupl_09.arff')), 115 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 116 | 'literature', 'Waveform', 'Waveform_withoutdupl_v01.arff')), 117 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 118 | 'data', 'literature', 'WBC', 'WBC_withoutdupl_v01.arff')), 119 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 120 | 'data', 'literature', 'WDBC', 'WDBC_withoutdupl_v01.arff')), 121 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 122 | 'data', 'literature', 'WPBC', 'WPBC_withoutdupl_norm.arff')) 123 | ] 124 | 125 | n_ite = 10 126 | n_classifiers = 10 127 | 128 | df_columns = ['Data', '# Samples', '# Dimensions', 'Outlier Perc', 129 | 'ABOD', 'CBLOF', 'HBOS', 'IForest', 'KNN', 'LODA', 130 | 'LOF', 'OCSVM', 'PCA', 'COD'] 131 | 132 | # initialize the container for saving the results 133 | roc_df = pd.DataFrame(columns=df_columns) 134 | prn_df = pd.DataFrame(columns=df_columns) 135 | ap_df = pd.DataFrame(columns=df_columns) 136 | time_df = pd.DataFrame(columns=df_columns) 137 | 138 | for j in range(len(arff_list)): 139 | mat_file = file_names[j] 140 | mat_file_path = arff_list[j] 141 | X, y, attributes = read_arff(mat_file_path, misplaced_list) 142 | 143 | outliers_fraction = np.count_nonzero(y) / len(y) 144 | outliers_percentage = round(outliers_fraction * 100, ndigits=4) 145 | 146 | # construct containers for saving results 147 | roc_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 148 | prn_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 149 | ap_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 150 | time_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 151 | 152 | roc_mat = np.zeros([n_ite, n_classifiers]) 153 | prn_mat = np.zeros([n_ite, n_classifiers]) 154 | ap_mat = np.zeros([n_ite, n_classifiers]) 155 | time_mat = np.zeros([n_ite, n_classifiers]) 156 | 157 | for i in range(n_ite): 158 | print("\n... Processing", mat_file, '...', 'Iteration', i + 1) 159 | random_state = np.random.RandomState(i) 160 | 161 | # 60% data for training and 40% for testing 162 | X_train, X_test, y_train, y_test = \ 163 | train_test_split(X, y, test_size=0.4, random_state=random_state) 164 | 165 | # standardizing data for processing 166 | X_train_norm, X_test_norm = standardizer(X_train, X_test) 167 | 168 | classifiers = {'Angle-based Outlier Detector (ABOD)': ABOD( 169 | contamination=outliers_fraction), 170 | 'Cluster-based Local Outlier Factor': CBLOF( 171 | n_clusters=10, 172 | contamination=outliers_fraction, 173 | check_estimator=False, 174 | random_state=random_state), 175 | 'Histogram-base Outlier Detection (HBOS)': HBOS( 176 | contamination=outliers_fraction), 177 | 'Isolation Forest': IForest(contamination=outliers_fraction, 178 | random_state=random_state), 179 | 'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction), 180 | 'Lightweight on-line detector of anomalies (LODA)': LODA(contamination=outliers_fraction), 181 | 'Local Outlier Factor (LOF)': LOF( 182 | contamination=outliers_fraction), 183 | 'One-class SVM (OCSVM)': OCSVM(contamination=outliers_fraction), 184 | 'Principal Component Analysis (PCA)': PCA( 185 | contamination=outliers_fraction, random_state=random_state), 186 | 'COD': COD(contamination=outliers_fraction) 187 | } 188 | classifiers_indices = { 189 | 'Angle-based Outlier Detector (ABOD)': 0, 190 | 'Cluster-based Local Outlier Factor': 1, 191 | 'Histogram-base Outlier Detection (HBOS)': 2, 192 | 'Isolation Forest': 3, 193 | 'K Nearest Neighbors (KNN)': 4, 194 | 'Lightweight on-line detector of anomalies (LODA)': 5, 195 | 'Local Outlier Factor (LOF)': 6, 196 | 'One-class SVM (OCSVM)': 7, 197 | 'Principal Component Analysis (PCA)': 8, 198 | 'COD': 9 199 | } 200 | 201 | for clf_name, clf in classifiers.items(): 202 | t0 = time() 203 | clf.fit(X_train_norm) 204 | test_scores = clf.decision_function(X_test_norm) 205 | t1 = time() 206 | duration = round(t1 - t0, ndigits=4) 207 | test_scores = np.nan_to_num(test_scores) 208 | 209 | roc = round(roc_auc_score(y_test, test_scores), ndigits=4) 210 | prn = round(precision_n_scores(y_test, test_scores), ndigits=4) 211 | ap = round(average_precision_score(y_test, test_scores), ndigits=4) 212 | 213 | print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, AP:{ap},' 214 | 'execution time: {duration}s'.format( 215 | clf_name=clf_name, roc=roc, prn=prn, ap=ap, duration=duration)) 216 | 217 | time_mat[i, classifiers_indices[clf_name]] = duration 218 | roc_mat[i, classifiers_indices[clf_name]] = roc 219 | prn_mat[i, classifiers_indices[clf_name]] = prn 220 | ap_mat[i, classifiers_indices[clf_name]] = ap 221 | 222 | time_list = time_list + np.mean(time_mat, axis=0).tolist() 223 | temp_df = pd.DataFrame(time_list).transpose() 224 | temp_df.columns = df_columns 225 | time_df = pd.concat([time_df, temp_df], axis=0) 226 | 227 | roc_list = roc_list + np.mean(roc_mat, axis=0).tolist() 228 | temp_df = pd.DataFrame(roc_list).transpose() 229 | temp_df.columns = df_columns 230 | roc_df = pd.concat([roc_df, temp_df], axis=0) 231 | 232 | prn_list = prn_list + np.mean(prn_mat, axis=0).tolist() 233 | temp_df = pd.DataFrame(prn_list).transpose() 234 | temp_df.columns = df_columns 235 | prn_df = pd.concat([prn_df, temp_df], axis=0) 236 | 237 | ap_list = ap_list + np.mean(ap_mat, axis=0).tolist() 238 | temp_df = pd.DataFrame(ap_list).transpose() 239 | temp_df.columns = df_columns 240 | ap_df = pd.concat([ap_df, temp_df], axis=0) 241 | 242 | # Save the results for each run 243 | time_df.to_csv('time.csv', index=False, float_format='%.3f') 244 | roc_df.to_csv('roc.csv', index=False, float_format='%.3f') 245 | prn_df.to_csv('prc.csv', index=False, float_format='%.3f') 246 | ap_df.to_csv('ap.csv', index=False, float_format='%.3f') 247 | -------------------------------------------------------------------------------- /results/benchmark_mat.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import os 5 | import sys 6 | from time import time 7 | import pdb 8 | # supress warnings for clean output 9 | import warnings 10 | 11 | warnings.filterwarnings("ignore") 12 | 13 | import numpy as np 14 | import pandas as pd 15 | from sklearn.model_selection import train_test_split 16 | from scipy.io import loadmat 17 | 18 | from pyod.models.abod import ABOD 19 | from pyod.models.cblof import CBLOF 20 | from pyod.models.cof import COF 21 | from pyod.models.feature_bagging import FeatureBagging 22 | from pyod.models.hbos import HBOS 23 | from pyod.models.iforest import IForest 24 | from pyod.models.knn import KNN 25 | from pyod.models.lmdd import LMDD 26 | from pyod.models.loda import LODA 27 | from pyod.models.lof import LOF 28 | from pyod.models.mcd import MCD 29 | from pyod.models.ocsvm import OCSVM 30 | from pyod.models.pca import PCA 31 | from pyod.models.sod import SOD 32 | from pyod.models.sos import SOS 33 | 34 | from pyod.utils.utility import standardizer 35 | from pyod.utils.utility import precision_n_scores 36 | from sklearn.metrics import roc_auc_score 37 | from sklearn.metrics import average_precision_score 38 | 39 | sys.path.append( 40 | os.path.abspath(os.path.join(os.path.dirname("__file__"), '..'))) 41 | from models.cod import COD 42 | 43 | # Define data file and read X and y 44 | mat_file_list = [ 45 | 'arrhythmia.mat', 46 | 'breastw.mat', 47 | 'cardio.mat', 48 | 'cover.mat', 49 | 'ionosphere.mat', 50 | 'lympho.mat', 51 | 'mammography.mat', 52 | 'optdigits.mat', 53 | 'pima.mat', 54 | 'satellite.mat', 55 | 'satimage-2.mat', 56 | 'speech.mat', 57 | 'wbc.mat', 58 | 'wine.mat'] 59 | 60 | # define the number of iterations 61 | n_ite = 10 62 | n_classifiers = 10 63 | 64 | df_columns = ['Data', '# Samples', '# Dimensions', 'Outlier Perc', 65 | 'ABOD', 'CBLOF', 'HBOS', 'IForest', 'KNN', 'LODA', 66 | 'LOF', 'OCSVM', 'PCA', 'COD'] 67 | 68 | 69 | # initialize the container for saving the results 70 | roc_df = pd.DataFrame(columns=df_columns) 71 | prn_df = pd.DataFrame(columns=df_columns) 72 | ap_df = pd.DataFrame(columns=df_columns) 73 | time_df = pd.DataFrame(columns=df_columns) 74 | 75 | for j in range(len(mat_file_list)): 76 | mat_file = mat_file_list[j] 77 | mat = loadmat(os.path.abspath(os.path.join(os.path.dirname(__file__), '..') + '/data/' + mat_file)) 78 | X = mat['X'] 79 | y = mat['y'].ravel() 80 | if X.shape[0] > 10000: 81 | index = np.random.choice(X.shape[0], 10000, replace=False) 82 | X = X[index] 83 | y = y[index] 84 | 85 | outliers_fraction = np.count_nonzero(y) / len(y) 86 | outliers_percentage = round(outliers_fraction * 100, ndigits=4) 87 | 88 | # construct containers for saving results 89 | roc_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 90 | prn_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 91 | ap_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 92 | time_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 93 | 94 | roc_mat = np.zeros([n_ite, n_classifiers]) 95 | prn_mat = np.zeros([n_ite, n_classifiers]) 96 | ap_mat = np.zeros([n_ite, n_classifiers]) 97 | time_mat = np.zeros([n_ite, n_classifiers]) 98 | 99 | for i in range(n_ite): 100 | print("\n... Processing", mat_file, '...', 'Iteration', i + 1) 101 | random_state = np.random.RandomState(i) 102 | 103 | # 60% data for training and 40% for testing 104 | X_train, X_test, y_train, y_test = \ 105 | train_test_split(X, y, test_size=0.4, random_state=random_state) 106 | 107 | # standardizing data for processing 108 | X_train_norm, X_test_norm = standardizer(X_train, X_test) 109 | 110 | classifiers = {'Angle-based Outlier Detector (ABOD)': ABOD( 111 | contamination=outliers_fraction), 112 | 'Cluster-based Local Outlier Factor': CBLOF( 113 | n_clusters=10, 114 | contamination=outliers_fraction, 115 | check_estimator=False, 116 | random_state=random_state), 117 | 'Histogram-base Outlier Detection (HBOS)': HBOS( 118 | contamination=outliers_fraction), 119 | 'Isolation Forest': IForest(contamination=outliers_fraction, 120 | random_state=random_state), 121 | 'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction), 122 | 'Lightweight on-line detector of anomalies (LODA)': LODA(contamination=outliers_fraction), 123 | 'Local Outlier Factor (LOF)': LOF( 124 | contamination=outliers_fraction), 125 | 'One-class SVM (OCSVM)': OCSVM(contamination=outliers_fraction), 126 | 'Principal Component Analysis (PCA)': PCA( 127 | contamination=outliers_fraction, random_state=random_state), 128 | 'COD': COD(contamination=outliers_fraction) 129 | } 130 | classifiers_indices = { 131 | 'Angle-based Outlier Detector (ABOD)': 0, 132 | 'Cluster-based Local Outlier Factor': 1, 133 | 'Histogram-base Outlier Detection (HBOS)': 2, 134 | 'Isolation Forest': 3, 135 | 'K Nearest Neighbors (KNN)': 4, 136 | 'Lightweight on-line detector of anomalies (LODA)': 5, 137 | 'Local Outlier Factor (LOF)': 6, 138 | 'One-class SVM (OCSVM)': 7, 139 | 'Principal Component Analysis (PCA)': 8, 140 | 'COD': 9 141 | } 142 | 143 | for clf_name, clf in classifiers.items(): 144 | t0 = time() 145 | clf.fit(X_train_norm) 146 | test_scores = clf.decision_function(X_test_norm) 147 | t1 = time() 148 | duration = round(t1 - t0, ndigits=4) 149 | test_scores = np.nan_to_num(test_scores) 150 | 151 | roc = round(roc_auc_score(y_test, test_scores), ndigits=4) 152 | prn = round(precision_n_scores(y_test, test_scores), ndigits=4) 153 | ap = round(average_precision_score(y_test, test_scores), ndigits=4) 154 | 155 | print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, AP:{ap}, \ 156 | execution time: {duration}s'.format( 157 | clf_name=clf_name, roc=roc, prn=prn, ap=ap, duration=duration)) 158 | 159 | time_mat[i, classifiers_indices[clf_name]] = duration 160 | roc_mat[i, classifiers_indices[clf_name]] = roc 161 | prn_mat[i, classifiers_indices[clf_name]] = prn 162 | ap_mat[i, classifiers_indices[clf_name]] = ap 163 | 164 | time_list = time_list + np.mean(time_mat, axis=0).tolist() 165 | temp_df = pd.DataFrame(time_list).transpose() 166 | temp_df.columns = df_columns 167 | time_df = pd.concat([time_df, temp_df], axis=0) 168 | 169 | roc_list = roc_list + np.mean(roc_mat, axis=0).tolist() 170 | temp_df = pd.DataFrame(roc_list).transpose() 171 | temp_df.columns = df_columns 172 | roc_df = pd.concat([roc_df, temp_df], axis=0) 173 | 174 | prn_list = prn_list + np.mean(prn_mat, axis=0).tolist() 175 | temp_df = pd.DataFrame(prn_list).transpose() 176 | temp_df.columns = df_columns 177 | prn_df = pd.concat([prn_df, temp_df], axis=0) 178 | 179 | ap_list = ap_list + np.mean(ap_mat, axis=0).tolist() 180 | temp_df = pd.DataFrame(ap_list).transpose() 181 | temp_df.columns = df_columns 182 | ap_df = pd.concat([ap_df, temp_df], axis=0) 183 | 184 | # Save the results for each run 185 | time_df.to_csv('time.csv', index=False, float_format='%.3f') 186 | roc_df.to_csv('roc.csv', index=False, float_format='%.3f') 187 | prn_df.to_csv('prc.csv', index=False, float_format='%.3f') 188 | ap_df.to_csv('ap.csv', index=False, float_format='%.3f') -------------------------------------------------------------------------------- /results/benchmark_time.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import numpy as np 4 | from time import time 5 | 6 | sys.path.append( 7 | os.path.abspath(os.path.join(os.path.dirname("__file__"), '..'))) 8 | from models.cod import COD 9 | 10 | for dim in [10, 100, 1000, 10000]: 11 | for row in [1000, 10000, 100000, 1000000]: 12 | X = np.random.rand(row, dim) 13 | clf = COD() 14 | t0 = time() 15 | test_scores = clf.decision_function(X) 16 | t1 = time() 17 | duration = round(t1 - t0, ndigits=4) 18 | print('Dim: ', dim, 'Row: ', row, duration) -------------------------------------------------------------------------------- /results/cod_arff.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from sklearn.metrics import average_precision_score 3 | from sklearn.metrics import roc_auc_score 4 | from pyod.utils.utility import precision_n_scores 5 | from pyod.utils.utility import standardizer 6 | from pyod.models.sos import SOS 7 | from pyod.models.sod import SOD 8 | from pyod.models.pca import PCA 9 | from pyod.models.ocsvm import OCSVM 10 | from pyod.models.mcd import MCD 11 | from pyod.models.lof import LOF 12 | from pyod.models.loda import LODA 13 | from pyod.models.lmdd import LMDD 14 | from pyod.models.knn import KNN 15 | from pyod.models.iforest import IForest 16 | from pyod.models.hbos import HBOS 17 | from pyod.models.feature_bagging import FeatureBagging 18 | from pyod.models.cof import COF 19 | from pyod.models.cblof import CBLOF 20 | from pyod.models.abod import ABOD 21 | import arff 22 | from scipy.io import loadmat 23 | from sklearn.model_selection import train_test_split 24 | import pandas as pd 25 | import numpy as np 26 | 27 | import os 28 | import sys 29 | from time import time 30 | import pdb 31 | # supress warnings for clean output 32 | import warnings 33 | 34 | warnings.filterwarnings("ignore") 35 | 36 | sys.path.append( 37 | os.path.abspath(os.path.join(os.path.dirname("__file__"), '..'))) 38 | from models.cod_exp import COD 39 | 40 | 41 | def read_arff(file_path, misplaced_list): 42 | misplaced = False 43 | for item in misplaced_list: 44 | if item in file_path: 45 | misplaced = True 46 | 47 | file = arff.load(open(file_path)) 48 | data_value = np.asarray(file['data']) 49 | attributes = file['attributes'] 50 | 51 | X = data_value[:, 0:-2] 52 | if not misplaced: 53 | y = data_value[:, -1] 54 | else: 55 | y = data_value[:, -2] 56 | y[y == 'no'] = 0 57 | y[y == 'yes'] = 1 58 | y = y.astype('float').astype('int').ravel() 59 | 60 | if y.sum() > len(y): 61 | print(attributes) 62 | raise ValueError('wrong sum') 63 | 64 | return X, y, attributes 65 | 66 | 67 | # Define data file and read X and y 68 | file_names = [ 69 | 'Arrhythmia', 70 | 'Cardiotocography', 71 | 'HeartDisease', 72 | 'Hepatitis', 73 | 'InternetAds', 74 | 'Ionosphere', 75 | 'KDDCup99', 76 | 'Lymphography', 77 | 'Pima', 78 | 'Shuttle', 79 | 'SpamBase', 80 | 'Stamps', 81 | 'Waveform', 82 | 'WBC', 83 | 'WDBC', 84 | 'WPBC', 85 | ] 86 | 87 | ############################################################################# 88 | misplaced_list = ['Arrhythmia', 'Cardiotocography', 'Hepatitis', 'ALOI', 89 | 'KDDCup99'] 90 | arff_list = [ 91 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 92 | 'semantic', 'Arrhythmia', 'Arrhythmia_withoutdupl_46.arff')), 93 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'semantic', 'Cardiotocography', 94 | 'Cardiotocography_withoutdupl_22.arff')), 95 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'semantic', 'HeartDisease', 96 | 'HeartDisease_withoutdupl_44.arff')), 97 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 98 | 'semantic', 'Hepatitis', 'Hepatitis_withoutdupl_16.arff')), 99 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'semantic', 'InternetAds', 100 | 'InternetAds_withoutdupl_norm_19.arff')), 101 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 102 | 'literature', 'Ionosphere', 'Ionosphere_withoutdupl_norm.arff')), 103 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 104 | 'data', 'literature', 'KDDCup99', 'KDDCup99_idf.arff')), 105 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 106 | 'literature', 'Lymphography', 'Lymphography_withoutdupl_idf.arff')), 107 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 108 | 'data', 'semantic', 'Pima', 'Pima_withoutdupl_35.arff')), 109 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 110 | 'literature', 'Shuttle', 'Shuttle_withoutdupl_v01.arff')), 111 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 112 | 'data', 'semantic', 'SpamBase', 'SpamBase_withoutdupl_40.arff')), 113 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 114 | 'data', 'semantic', 'Stamps', 'Stamps_withoutdupl_09.arff')), 115 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 116 | 'literature', 'Waveform', 'Waveform_withoutdupl_v01.arff')), 117 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 118 | 'data', 'literature', 'WBC', 'WBC_withoutdupl_v01.arff')), 119 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 120 | 'data', 'literature', 'WDBC', 'WDBC_withoutdupl_v01.arff')), 121 | os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 122 | 'data', 'literature', 'WPBC', 'WPBC_withoutdupl_norm.arff')) 123 | ] 124 | 125 | n_ite = 10 126 | n_classifiers = 6 127 | 128 | df_columns = ['Data', '# Samples', '# Dimensions', 'Outlier Perc', 129 | 'COD_L', 'COD_R', 'COD_B', 'COD_S', 'COD_M', 'COD'] 130 | 131 | # initialize the container for saving the results 132 | roc_df = pd.DataFrame(columns=df_columns) 133 | prn_df = pd.DataFrame(columns=df_columns) 134 | ap_df = pd.DataFrame(columns=df_columns) 135 | time_df = pd.DataFrame(columns=df_columns) 136 | 137 | for j in range(len(arff_list)): 138 | mat_file = file_names[j] 139 | mat_file_path = arff_list[j] 140 | X, y, attributes = read_arff(mat_file_path, misplaced_list) 141 | 142 | outliers_fraction = np.count_nonzero(y) / len(y) 143 | outliers_percentage = round(outliers_fraction * 100, ndigits=4) 144 | 145 | # construct containers for saving results 146 | roc_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 147 | prn_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 148 | ap_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 149 | time_list = [mat_file, X.shape[0], X.shape[1], outliers_percentage] 150 | 151 | roc_mat = np.zeros([n_ite, n_classifiers]) 152 | prn_mat = np.zeros([n_ite, n_classifiers]) 153 | ap_mat = np.zeros([n_ite, n_classifiers]) 154 | time_mat = np.zeros([n_ite, n_classifiers]) 155 | 156 | for i in range(n_ite): 157 | print("\n... Processing", mat_file, '...', 'Iteration', i + 1) 158 | random_state = np.random.RandomState(i) 159 | 160 | # 60% data for training and 40% for testing 161 | X_train, X_test, y_train, y_test = \ 162 | train_test_split(X, y, test_size=0.4, random_state=random_state) 163 | 164 | # standardizing data for processing 165 | X_train_norm, X_test_norm = standardizer(X_train, X_test) 166 | 167 | classifiers = {'COD_L': COD(contamination=outliers_fraction, tail='left'), 168 | 'COD_R': COD(contamination=outliers_fraction, tail='right'), 169 | 'COD_B': COD(contamination=outliers_fraction, tail='both'), 170 | 'COD_S': COD(contamination=outliers_fraction, tail='skew'), 171 | 'COD_M': COD(contamination=outliers_fraction, tail='max'), 172 | 'COD': COD(contamination=outliers_fraction) 173 | } 174 | classifiers_indices = { 175 | 'COD_L': 0, 176 | 'COD_R': 1, 177 | 'COD_B': 2, 178 | 'COD_S': 3, 179 | 'COD_M': 4, 180 | 'COD': 5 181 | } 182 | 183 | for clf_name, clf in classifiers.items(): 184 | t0 = time() 185 | clf.fit(X_train_norm) 186 | test_scores = clf.decision_function(X_test_norm) 187 | t1 = time() 188 | duration = round(t1 - t0, ndigits=4) 189 | test_scores = np.nan_to_num(test_scores) 190 | 191 | roc = round(roc_auc_score(y_test, test_scores), ndigits=4) 192 | prn = round(precision_n_scores(y_test, test_scores), ndigits=4) 193 | ap = round(average_precision_score(y_test, test_scores), ndigits=4) 194 | 195 | print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, AP:{ap},' 196 | 'execution time: {duration}s'.format( 197 | clf_name=clf_name, roc=roc, prn=prn, ap=ap, duration=duration)) 198 | 199 | time_mat[i, classifiers_indices[clf_name]] = duration 200 | roc_mat[i, classifiers_indices[clf_name]] = roc 201 | prn_mat[i, classifiers_indices[clf_name]] = prn 202 | ap_mat[i, classifiers_indices[clf_name]] = ap 203 | 204 | time_list = time_list + np.mean(time_mat, axis=0).tolist() 205 | temp_df = pd.DataFrame(time_list).transpose() 206 | temp_df.columns = df_columns 207 | time_df = pd.concat([time_df, temp_df], axis=0) 208 | 209 | roc_list = roc_list + np.mean(roc_mat, axis=0).tolist() 210 | temp_df = pd.DataFrame(roc_list).transpose() 211 | temp_df.columns = df_columns 212 | roc_df = pd.concat([roc_df, temp_df], axis=0) 213 | 214 | prn_list = prn_list + np.mean(prn_mat, axis=0).tolist() 215 | temp_df = pd.DataFrame(prn_list).transpose() 216 | temp_df.columns = df_columns 217 | prn_df = pd.concat([prn_df, temp_df], axis=0) 218 | 219 | ap_list = ap_list + np.mean(ap_mat, axis=0).tolist() 220 | temp_df = pd.DataFrame(ap_list).transpose() 221 | temp_df.columns = df_columns 222 | ap_df = pd.concat([ap_df, temp_df], axis=0) 223 | 224 | # Save the results for each run 225 | time_df.to_csv('time.csv', index=False, float_format='%.3f') 226 | roc_df.to_csv('roc.csv', index=False, float_format='%.3f') 227 | prn_df.to_csv('prc.csv', index=False, float_format='%.3f') 228 | ap_df.to_csv('ap.csv', index=False, float_format='%.3f') 229 | -------------------------------------------------------------------------------- /results/cod_example.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import os 5 | import sys 6 | import numpy as np 7 | 8 | from pyod.utils.data import generate_data 9 | from pyod.utils.data import evaluate_print 10 | from pyod.utils.example import visualize 11 | from sklearn.metrics import roc_auc_score 12 | from scipy.io import loadmat 13 | from sklearn.model_selection import train_test_split 14 | 15 | 16 | sys.path.append( 17 | os.path.abspath(os.path.join(os.path.dirname("__file__"), '..'))) 18 | from models.cod import COD 19 | 20 | if __name__ == "__main__": 21 | mat = loadmat(os.path.abspath(os.path.join(os.path.dirname(__file__), '..') + '/data/breastw.mat')) 22 | X = mat['X'] 23 | y = mat['y'].ravel() 24 | 25 | # Generate sample data 26 | X_train, X_test, y_train, y_test = \ 27 | train_test_split(X, y, test_size=0.4, random_state=42) 28 | 29 | clf = COD() 30 | clf.decision_function(X) 31 | a, b, c = clf.explain_outlier(69) 32 | print(a, b, c) 33 | a, b, c = clf.explain_outlier(97) 34 | print(a, b, c) -------------------------------------------------------------------------------- /results/cod_mat.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import os 5 | import sys 6 | from time import time 7 | import pdb 8 | # supress warnings for clean output 9 | import warnings 10 | 11 | warnings.filterwarnings("ignore") 12 | 13 | import numpy as np 14 | import pandas as pd 15 | from sklearn.model_selection import train_test_split 16 | from scipy.io import loadmat 17 | 18 | from pyod.models.abod import ABOD 19 | from pyod.models.cblof import CBLOF 20 | from pyod.models.cof import COF 21 | from pyod.models.feature_bagging import FeatureBagging 22 | from pyod.models.hbos import HBOS 23 | from pyod.models.iforest import IForest 24 | from pyod.models.knn import KNN 25 | from pyod.models.lmdd import LMDD 26 | from pyod.models.loda import LODA 27 | from pyod.models.lof import LOF 28 | from pyod.models.mcd import MCD 29 | from pyod.models.ocsvm import OCSVM 30 | from pyod.models.pca import PCA 31 | from pyod.models.sod import SOD 32 | from pyod.models.sos import SOS 33 | 34 | from pyod.utils.utility import standardizer 35 | from pyod.utils.utility import precision_n_scores 36 | from sklearn.metrics import roc_auc_score 37 | from sklearn.metrics import average_precision_score 38 | 39 | sys.path.append( 40 | os.path.abspath(os.path.join(os.path.dirname("__file__"), '..'))) 41 | from models.cod_exp import COD 42 | 43 | # Define data file and read X and y 44 | mat_file_list = [ 45 | 'arrhythmia.mat', 46 | 'breastw.mat', 47 | 'cardio.mat', 48 | 'cover.mat', 49 | 'ionosphere.mat', 50 | 'lympho.mat', 51 | 'mammography.mat', 52 | 'optdigits.mat', 53 | 'pima.mat', 54 | 'satellite.mat', 55 | 'satimage-2.mat', 56 | 'speech.mat', 57 | 'wbc.mat', 58 | 'wine.mat'] 59 | 60 | # define the number of iterations 61 | n_ite = 10 62 | n_classifiers = 6 63 | 64 | df_columns = ['Data', '# Samples', '# Dimensions', 'Outlier Perc', 65 | 'COD_L', 'COD_R', 'COD_B', 'COD_S', 'COD_M', 'COD'] 66 | 67 | # initialize the container for saving the results 68 | roc_df = pd.DataFrame(columns=df_columns) 69 | prn_df = pd.DataFrame(columns=df_columns) 70 | ap_df = pd.DataFrame(columns=df_columns) 71 | time_df = pd.DataFrame(columns=df_columns) 72 | 73 | for j in range(len(mat_file_list)): 74 | mat_file = mat_file_list[j] 75 | mat = loadmat(os.path.abspath(os.path.join(os.path.dirname(__file__), '..') + '/data/' + mat_file)) 76 | X = mat['X'] 77 | y = mat['y'].ravel() 78 | if X.shape[0] > 10000: 79 | index = np.random.choice(X.shape[0], 10000, replace=False) 80 | X = X[index] 81 | y = y[index] 82 | 83 | outliers_fraction = np.count_nonzero(y) / len(y) 84 | outliers_percentage = round(outliers_fraction * 100, ndigits=4) 85 | 86 | # construct containers for saving results 87 | roc_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 88 | prn_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 89 | ap_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 90 | time_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage] 91 | 92 | roc_mat = np.zeros([n_ite, n_classifiers]) 93 | prn_mat = np.zeros([n_ite, n_classifiers]) 94 | ap_mat = np.zeros([n_ite, n_classifiers]) 95 | time_mat = np.zeros([n_ite, n_classifiers]) 96 | 97 | for i in range(n_ite): 98 | print("\n... Processing", mat_file, '...', 'Iteration', i + 1) 99 | random_state = np.random.RandomState(i) 100 | 101 | # 60% data for training and 40% for testing 102 | X_train, X_test, y_train, y_test = \ 103 | train_test_split(X, y, test_size=0.4, random_state=random_state) 104 | 105 | # standardizing data for processing 106 | X_train_norm, X_test_norm = standardizer(X_train, X_test) 107 | 108 | classifiers = {'COD_L': COD(contamination=outliers_fraction, tail='left'), 109 | 'COD_R': COD(contamination=outliers_fraction, tail='right'), 110 | 'COD_B': COD(contamination=outliers_fraction, tail='both'), 111 | 'COD_S': COD(contamination=outliers_fraction, tail='skew'), 112 | 'COD_M': COD(contamination=outliers_fraction, tail='max'), 113 | 'COD': COD(contamination=outliers_fraction) 114 | } 115 | classifiers_indices = { 116 | 'COD_L': 0, 117 | 'COD_R': 1, 118 | 'COD_B': 2, 119 | 'COD_S': 3, 120 | 'COD_M': 4, 121 | 'COD': 5 122 | } 123 | 124 | for clf_name, clf in classifiers.items(): 125 | t0 = time() 126 | clf.fit(X_train_norm) 127 | test_scores = clf.decision_function(X_test_norm) 128 | t1 = time() 129 | duration = round(t1 - t0, ndigits=4) 130 | test_scores = np.nan_to_num(test_scores) 131 | 132 | roc = round(roc_auc_score(y_test, test_scores), ndigits=4) 133 | prn = round(precision_n_scores(y_test, test_scores), ndigits=4) 134 | ap = round(average_precision_score(y_test, test_scores), ndigits=4) 135 | 136 | print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, AP:{ap}, \ 137 | execution time: {duration}s'.format( 138 | clf_name=clf_name, roc=roc, prn=prn, ap=ap, duration=duration)) 139 | 140 | time_mat[i, classifiers_indices[clf_name]] = duration 141 | roc_mat[i, classifiers_indices[clf_name]] = roc 142 | prn_mat[i, classifiers_indices[clf_name]] = prn 143 | ap_mat[i, classifiers_indices[clf_name]] = ap 144 | 145 | time_list = time_list + np.mean(time_mat, axis=0).tolist() 146 | temp_df = pd.DataFrame(time_list).transpose() 147 | temp_df.columns = df_columns 148 | time_df = pd.concat([time_df, temp_df], axis=0) 149 | 150 | roc_list = roc_list + np.mean(roc_mat, axis=0).tolist() 151 | temp_df = pd.DataFrame(roc_list).transpose() 152 | temp_df.columns = df_columns 153 | roc_df = pd.concat([roc_df, temp_df], axis=0) 154 | 155 | prn_list = prn_list + np.mean(prn_mat, axis=0).tolist() 156 | temp_df = pd.DataFrame(prn_list).transpose() 157 | temp_df.columns = df_columns 158 | prn_df = pd.concat([prn_df, temp_df], axis=0) 159 | 160 | ap_list = ap_list + np.mean(ap_mat, axis=0).tolist() 161 | temp_df = pd.DataFrame(ap_list).transpose() 162 | temp_df.columns = df_columns 163 | ap_df = pd.concat([ap_df, temp_df], axis=0) 164 | 165 | # Save the results for each run 166 | time_df.to_csv('time.csv', index=False, float_format='%.3f') 167 | roc_df.to_csv('roc.csv', index=False, float_format='%.3f') 168 | prn_df.to_csv('prc.csv', index=False, float_format='%.3f') 169 | ap_df.to_csv('ap.csv', index=False, float_format='%.3f') --------------------------------------------------------------------------------