# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/regression/locally_weighted_linear_regression.py
# def simple_init(interface, params):
# def map_fit(interface, state, label, inp):
# def map_predict(interface, state, label, inp):
# def reduce_fit(interface, state, label, inp):
# def _fit_predict(fit_data, samples, tau, save_results, show):
# def fit_predict(training_data, fitting_data, tau=1, samples_per_job=0, save_results=True, show=False):
#     A = [0 for i in range(len(state["X_indices"]) + 1)]
#             A = [0 for i in range(len(state["X_indices"]) + 1)]


from discomll import dataset
from discomll.regression import locally_weighted_linear_regression

train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/linear/train/xaaaaa.gz", "http://ropot.ijs.si/data/linear/train/xaaabj.gz"],
    generate_urls=True,
    data_type="gzip",
    X_indices=range(1, 21),
    id_index=0,
    y_index=21,
    delimiter=",")


# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/regression/locally_weighted_linear_regression.py
# def simple_init(interface, params):
# def map_fit(interface, state, label, inp):
# def map_predict(interface, state, label, inp):
# def reduce_fit(interface, state, label, inp):
# def _fit_predict(fit_data, samples, tau, save_results, show):
# def fit_predict(training_data, fitting_data, tau=1, samples_per_job=0, save_results=True, show=False):
#     A = [0 for i in range(len(state["X_indices"]) + 1)]
#             A = [0 for i in range(len(state["X_indices"]) + 1)]


# Path: discomll/examples/big_data/lwlr_linear.py
from discomll import dataset
from discomll.regression import locally_weighted_linear_regression


train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/linear/train/xaaaaa.gz", "http://ropot.ijs.si/data/linear/train/xaaabj.gz"],
    generate_urls=True,
    data_type="gzip",
    X_indices=range(1, 21),
    id_index=0,
    y_index=21,
    delimiter=",")


test = dataset.Data(data_tag=[["http://ropot.ijs.si/data/linear/test/xaaaaa.gz"]],