# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/clustering/kmeans.py
# def simple_init(interface, params):
# def map_init(interface, params):
# def random_init_map(interface, state, label, inp):
# def estimate_map(interface, state, label, inp):
# def estimate_reduce(interface, state, label, inp):
# def predict_map(interface, state, label, inp):
# def fit(dataset, n_clusters=5, max_iterations=10, random_state=None, save_results=True, show=False):
# def predict(dataset, fitmodel_url, save_results=True, show=False):


from discomll import dataset
from discomll.clustering import kmeans

train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/linear/train/xaaaaa.gz", "http://ropot.ijs.si/data/linear/train/xaaabj.gz"],
    data_type="gzip",
    generate_urls=True,

# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/clustering/kmeans.py
# def simple_init(interface, params):
# def map_init(interface, params):
# def random_init_map(interface, state, label, inp):
# def estimate_map(interface, state, label, inp):
# def estimate_reduce(interface, state, label, inp):
# def predict_map(interface, state, label, inp):
# def fit(dataset, n_clusters=5, max_iterations=10, random_state=None, save_results=True, show=False):
# def predict(dataset, fitmodel_url, save_results=True, show=False):


# Path: discomll/examples/big_data/kmeans_linear.py
from discomll import dataset
from discomll.clustering import kmeans


train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/linear/train/xaaaaa.gz", "http://ropot.ijs.si/data/linear/train/xaaabj.gz"],
    data_type="gzip",
    generate_urls=True,

    X_indices=range(1, 22),