<filename>discomll/examples/big_data/nb_lymphography.py
from discomll import dataset
from discomll.classification import naivebayes

train = dataset.Data(data_tag=["http://ropot.ijs.si/data/lymphography/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/lymphography/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     X_indices=range(2, 20),
                     id_index=0,
                     y_index=1,
                     X_meta=["d", "d", "d", "d", "d", "d", "d", "d", "c", "c", "d", "d", "d", "d", "d", "d", "d", "c"],
                     delimiter=",")

test = dataset.Data(data_tag=["http://ropot.ijs.si/data/lymphography/test/xaaaaa.gz",
                              "http://ropot.ijs.si/data/lymphography/test/xaaabj.gz"],
                    data_type="gzip",
                    generate_urls=True,
                    X_indices=range(2, 20),
                    id_index=0,
                    y_index=1,
                    X_meta=["d", "d", "d", "d", "d", "d", "d", "d", "c", "c", "d", "d", "d", "d", "d", "d", "d", "c"],
                    delimiter=",")

fit_model = naivebayes.fit(train)
predictions = naivebayes.predict(test, fit_model)
print predictions

<filename>discomll/examples/big_data/nb_lymphography.py
from discomll import dataset
from discomll.classification import naivebayes

train = dataset.Data(data_tag=["http://ropot.ijs.si/data/lymphography/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/lymphography/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     X_indices=range(2, 20),
                     id_index=0,
                     y_index=1,
                     X_meta=["d", "d", "d", "d", "d", "d", "d", "d", "c", "c", "d", "d", "d", "d", "d", "d", "d", "c"],
                     delimiter=",")

test = dataset.Data(data_tag=["http://ropot.ijs.si/data/lymphography/test/xaaaaa.gz",
                              "http://ropot.ijs.si/data/lymphography/test/xaaabj.gz"],
                    data_type="gzip",
                    generate_urls=True,
                    X_indices=range(2, 20),
                    id_index=0,
                    y_index=1,
                    X_meta=["d", "d", "d", "d", "d", "d", "d", "d", "c", "c", "d", "d", "d", "d", "d", "d", "d", "c"],
                    delimiter=",")

fit_model = naivebayes.fit(train)
predictions = naivebayes.predict(test, fit_model)
print predictions
