def __init__(self, **kwargs):
"""
As minimal year 1970 is assumed, as maximal year the current year.
"""
DistanceFunction.__init__(self, **kwargs)
self._min, self._now = 1970, date.today().year
self._max_diff = (self._now - self._min) or 1
def __init__(self, no_rating=0, min_rating=1, max_rating=5, **kwargs):
"""
:param no_rating: The rating that unrated songs will have, e.g. 0 stars.
:param min_rating: The minimal rating you will have e.g. 1 stars.
:param max_rating: The maximal rating you will have e.g. 5 stars.
"""
DistanceFunction.__init__(self, **kwargs)
self._min_rating = min_rating
self._max_rating = max_rating
self._no_rating = no_rating
def __init__(self, no_rating=0, min_rating=1, max_rating=5, **kwargs):
"""
:param no_rating: The rating that unrated songs will have, e.g. 0 stars.
:param min_rating: The minimal rating you will have e.g. 1 stars.
:param max_rating: The maximal rating you will have e.g. 5 stars.
"""
DistanceFunction.__init__(self, **kwargs)
self._min_rating = min_rating
self._max_rating = max_rating
self._no_rating = no_rating
def __init__(self, name, mask, config=DEFAULT_CONFIG):
"""Create a new session:
:param name: The name of the session. Used to load it again from disk.
:param mask: The mask. See :term:`Mask`
:param config: A dictionary with config values. See :class:`DefaultConfig` for available keys.
"""
self._config = config
self._name = name
# Publicly readable attribute.
self.mapping = {}
# Make access to the mask more efficient
self._mask = copy(mask)
self._attribute_list = sorted(mask)
self._listidx_to_key = {k: i for i, k in enumerate(self._attribute_list)}
# Lookup tables for those attributes (fast access is crucial here)
def make_index(idx, default_func):
index = {}
for key, descr in self._mask.items():
if descr[idx] is not None:
index[key] = descr[idx]
else:
index[key] = default_func(key)
return index
# Import this locally, since we might get circular import otherway:
from munin.distance import DistanceFunction
from munin.provider import Provider
# Build indices and set default values:
self._key_to_providers = make_index(0,
lambda key: Provider()
)
self._key_to_distfuncs = make_index(1,
lambda key: DistanceFunction(self._key_to_providers[key])
)
self._key_to_weighting = make_index(2,
lambda key: 1.0
)
# Sum of the individual weights, pre-calculated once.
self._weight_sum = sum((descr[2] for descr in mask.values()))
# Create the associated database.
self._database = Database(self)
# Filtering related:
self._filtering_enabled = config['recom_history_sieving']
self._recom_history = RecommendationHistory(
penalty_map=config['recom_history_penalty']
)
# Publicly readable attribute.
self.mapping = bidict()
