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44 lines
1.5 KiB
Plaintext
Executable File
44 lines
1.5 KiB
Plaintext
Executable File
iris.csv
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vehicle.csv
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wine.csv
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glass.csv
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heart.csv # spaces
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breast # 683 vs 690 samples in dataset
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diabetes # from Kaggle
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fourclass # taken from libsvm samples TKH96a Tin Kam Ho and Eugene M. Kleinberg.
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#Building projectable classifiers of arbitrary complexity.
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#In Proceedings of the 13th International Conference on Pattern Recognition, pages 880-885, Vienna, Austria, August 1996.
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segment # sparse libsvm para cargar X, y = np.load("data/segment.npy", allow_pickle=True) para leer .scale sklearn.datasets.load_svmlight_file
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letter
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sat (has to be satimage)
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usps # kaggle import h5py
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with h5py.File(path, 'r') as hf:
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train = hf.get('train')
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X_tr = train.get('data')[:]
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y_tr = train.get('target')[:]
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test = hf.get('test')
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X_te = test.get('data')[:]
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y_te = test.get('target')[:]
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pendigits
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protein # https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#connect-4
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dna # openml
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connect 4 # https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#connect-4
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ijcnn1 # libsvm https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
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Datasets que hay que cargar X e y y son sparse matrices
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=======================================================
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(X, y= np.load("data/connect4.npy", allow_pickle=True))
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connect4
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fourclass
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ijcnn1
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protein
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segment
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vehicle
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from svmlight_loader import (load_svmlight_file, load_svmlight_files,
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dump_svmlight_file)
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para enlazar matrices sparse:
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from scipy.sparse import vstack
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X = vstack((Xs, Xt)) |