# 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/logistic_regression.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, alpha=1e-8, max_iterations=10, save_results=True, show=False):
# def predict(dataset, fitmodel_url, save_results=True, show=False):
#     H = [0 for i in range(len(state["X_indices"]) + 1)]
#     J = [0]  # J cost function values for every iteration


from discomll import dataset
from discomll.classification import logistic_regression

train = dataset.Data(data_tag=["http://ropot.ijs.si/data/ionosphere/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/ionosphere/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     id_index=0,
                     X_indices=range(1, 35),

# 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/logistic_regression.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, alpha=1e-8, max_iterations=10, save_results=True, show=False):
# def predict(dataset, fitmodel_url, save_results=True, show=False):
#     H = [0 for i in range(len(state["X_indices"]) + 1)]
#     J = [0]  # J cost function values for every iteration


# Path: discomll/examples/big_data/logreg_ionosphere.py
from discomll import dataset
from discomll.classification import logistic_regression


train = dataset.Data(data_tag=["http://ropot.ijs.si/data/ionosphere/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/ionosphere/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     id_index=0,
                     X_indices=range(1, 35),

                     X_meta=["c" for i in range(1, 35)],