# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/classification/linear_svm.py
# def simple_init(interface, params):
# def map_fit(interface, state, label, inp):
# def reduce_fit(interface, state, label, inp):
# def map_predict(interface, state, label, inp):
# def fit(dataset, nu=0.1, save_results=True, show=False):
# def predict(dataset, fitmodel_url, save_results=True, show=False):


from discomll import dataset
from discomll.classification import linear_svm

train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/sonar/train/xaaaaa.gz", "http://ropot.ijs.si/data/sonar/train/xaaabj.gz"],
    data_type="gzip",
    generate_urls=True,
    X_indices=range(1, 61),

# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/classification/linear_svm.py
# def simple_init(interface, params):
# def map_fit(interface, state, label, inp):
# def reduce_fit(interface, state, label, inp):
# def map_predict(interface, state, label, inp):
# def fit(dataset, nu=0.1, save_results=True, show=False):
# def predict(dataset, fitmodel_url, save_results=True, show=False):


# Path: discomll/examples/big_data/svm_sonar.py
from discomll import dataset
from discomll.classification import linear_svm


train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/sonar/train/xaaaaa.gz", "http://ropot.ijs.si/data/sonar/train/xaaabj.gz"],
    data_type="gzip",
    generate_urls=True,
    X_indices=range(1, 61),

    id_index=0,