<gh_stars>100-1000
from discomll import dataset
from discomll.clustering import kmeans

train = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/segmentation/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     X_indices=range(2, 21),
                     id_index=0,
                     X_meta=["c" for i in range(2, 21)],
                     delimiter=",")

test = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/test/xaaaaa.gz",
                              "http://ropot.ijs.si/data/segmentation/test/xaaabj.gz"],
                    data_type="gzip",
                    generate_urls=True,
                    X_indices=range(2, 21),
                    id_index=0,
                    X_meta=["c" for i in range(2, 21)],
                    delimiter=",")

fit_model = kmeans.fit(train, n_clusters=7, max_iterations=10, random_state=0)
predictions = kmeans.predict(test, fit_model)
print predictions

from discomll import dataset
from discomll.clustering import kmeans

train = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/segmentation/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     X_indices=range(2, 21),
                     id_index=0,
                     X_meta=["c" for i in range(2, 21)],
                     delimiter=",")

test = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/test/xaaaaa.gz",
                              "http://ropot.ijs.si/data/segmentation/test/xaaabj.gz"],
                    data_type="gzip",
                    generate_urls=True,
                    X_indices=range(2, 21),
                    id_index=0,
                    X_meta=["c" for i in range(2, 21)],
                    delimiter=",")

fit_model = kmeans.fit(train, n_clusters=7, max_iterations=10, random_state=0)
predictions = kmeans.predict(test, fit_model)
print predictions