def fit(self, corpus, window=10, max_map_size=1000, ignore_missing=False):
"""
Perform a pass through the corpus to construct
the cooccurrence matrix.
Parameters:
- iterable of lists of strings corpus
- int window: the length of the (symmetric)
context window used for cooccurrence.
- int max_map_size: the maximum size of map-based row storage.
When exceeded a row will be converted to
more efficient array storage. Setting this
to a higher value will increase speed at
the expense of higher memory usage.
- bool ignore_missing: whether to ignore words missing from
the dictionary (if it was supplied).
Context window distances will be preserved
even if out-of-vocabulary words are
ignored.
If False, a KeyError is raised.
"""
self.matrix = construct_cooccurrence_matrix(
corpus, self.dictionary, int(self.dictionary_supplied),
int(window), int(ignore_missing), max_map_size)
def fit(self, corpus, window=10, max_map_size=1000, ignore_missing=False):
"""
Perform a pass through the corpus to construct
the cooccurrence matrix.
Parameters:
- iterable of lists of strings corpus
- int window: the length of the (symmetric)
context window used for cooccurrence.
- int max_map_size: the maximum size of map-based row storage.
When exceeded a row will be converted to
more efficient array storage. Setting this
to a higher value will increase speed at
the expense of higher memory usage.
- bool ignore_missing: whether to ignore words missing from
the dictionary (if it was supplied).
Context window distances will be preserved
even if out-of-vocabulary words are
ignored.
If False, a KeyError is raised.
"""
self.matrix = construct_cooccurrence_matrix(corpus,
self.dictionary,
int(self.dictionary_supplied),
int(window),
int(ignore_missing),
max_map_size)
def fit(self, corpus, window=10):
"""
Perform a pass through the corpus to construct
the cooccurrence matrix.
You must call fit_dictionary first.
Parameters:
- iterable of lists of strings corpus
- int window: the length of the (symmetric)
context window used for cooccurrence.
"""
self.dictionary, self.matrix = construct_cooccurrence_matrix(
corpus, int(window))
def fit(self, corpus, window=10):
"""
Perform a pass through the corpus to construct
the cooccurrence matrix.
You must call fit_dictionary first.
Parameters:
- iterable of lists of strings corpus
- int window: the length of the (symmetric)
context window used for cooccurrence.
"""
self.dictionary, self.matrix = construct_cooccurrence_matrix(corpus,
int(window))
def fit_matrix(self, corpus, window=10):
"""
Perform a pass through the corpus to construct
the cooccurrence matrix.
You must call fit_dictionary first.
Parameters:
- iterable of lists of strings corpus
- int window: the length of the (symmetric)
context window used for cooccurrence.
"""
if self.dictionary is None:
raise Exception('You must fit the dictionary before transforming the corpus')
self.matrix = construct_cooccurrence_matrix(corpus,
self.dictionary, int(window))
def fit(self, corpus, window=10, ignore_missing=False):
"""
Perform a pass through the corpus to construct
the cooccurrence matrix.
Parameters:
- iterable of lists of strings corpus
- int window: the length of the (symmetric)
context window used for cooccurrence.
- bool ignore_missing: whether to ignore words missing from
the dictionary (if it was supplied).
Context window distances will be preserved
even if out-of-vocabulary words are
ignored.
If False, a KeyError is raised.
"""
self.matrix = construct_cooccurrence_matrix(
corpus, self.dictionary, int(self.dictionary_supplied),
int(window), int(ignore_missing))
