[python] Move python files to avoid pythonpath conflicts

1 files changed, 0 insertions, 78 deletions

diff --git a/python/cdec/sa/extractor.py b/python/cdec/sa/extractor.py
deleted file mode 100644
index bb912e16..00000000
--- a/python/cdec/sa/extractor.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from itertools import chain
-import os
-import cdec.configobj
-from cdec.sa.features import EgivenFCoherent, SampleCountF, CountEF,\
-        MaxLexEgivenF, MaxLexFgivenE, IsSingletonF, IsSingletonFE
-import cdec.sa
-
-# maximum span of a grammar rule in TEST DATA
-MAX_INITIAL_SIZE = 15
-
-class GrammarExtractor:
-    def __init__(self, config):
-        if isinstance(config, str) or isinstance(config, unicode):
-            if not os.path.exists(config):
-                raise IOError('cannot read configuration from {0}'.format(config))
-            config = cdec.configobj.ConfigObj(config, unrepr=True)
-        alignment = cdec.sa.Alignment(from_binary=config['a_file'])
-        self.factory = cdec.sa.HieroCachingRuleFactory(
-                # compiled alignment object (REQUIRED)
-                alignment,
-                # name of generic nonterminal used by Hiero
-                category="[X]",
-                # maximum number of contiguous chunks of terminal symbols in RHS of a rule
-                max_chunks=config['max_nt']+1,
-                # maximum span of a grammar rule in TEST DATA
-                max_initial_size=MAX_INITIAL_SIZE,
-                # maximum number of symbols (both T and NT) allowed in a rule
-                max_length=config['max_len'],
-                # maximum number of nonterminals allowed in a rule (set >2 at your own risk)
-                max_nonterminals=config['max_nt'],
-                # maximum number of contiguous chunks of terminal symbols
-                # in target-side RHS of a rule.
-                max_target_chunks=config['max_nt']+1,
-                # maximum number of target side symbols (both T and NT) allowed in a rule.
-                max_target_length=MAX_INITIAL_SIZE,
-                # minimum span of a nonterminal in the RHS of a rule in TEST DATA
-                min_gap_size=1,
-                # filename of file containing precomputed collocations
-                precompute_file=config['precompute_file'],
-                # maximum frequency rank of patterns used to compute triples (< 20)
-                precompute_secondary_rank=config['rank2'],
-                # maximum frequency rank of patterns used to compute collocations (< 300)
-                precompute_rank=config['rank1'],
-                # require extracted rules to have at least one aligned word
-                require_aligned_terminal=True,
-                # require each contiguous chunk of extracted rules
-                # to have at least one aligned word
-                require_aligned_chunks=False,
-                # maximum span of a grammar rule extracted from TRAINING DATA
-                train_max_initial_size=config['max_size'],
-                # minimum span of an RHS nonterminal in a rule extracted from TRAINING DATA
-                train_min_gap_size=config['min_gap'],
-                # True if phrases should be tight, False otherwise (better but slower)
-                tight_phrases=True,
-                )
-
-        # lexical weighting tables
-        tt = cdec.sa.BiLex(from_binary=config['lex_file'])
-
-        self.models = (EgivenFCoherent, SampleCountF, CountEF, 
-                MaxLexFgivenE(tt), MaxLexEgivenF(tt), IsSingletonF, IsSingletonFE)
-
-        fsarray = cdec.sa.SuffixArray(from_binary=config['f_sa_file'])
-        edarray = cdec.sa.DataArray(from_binary=config['e_file'])
-
-        # lower=faster, higher=better; improvements level off above 200-300 range,
-        # -1 = don't sample, use all data (VERY SLOW!)
-        sampler = cdec.sa.Sampler(300, fsarray)
-
-        self.factory.configure(fsarray, edarray, sampler)
-
-    def grammar(self, sentence):
-        if isinstance(sentence, unicode):
-            sentence = sentence.encode('utf8')
-        cnet = chain(('<s>',), sentence.split(), ('</s>',))
-        cnet = (cdec.sa.sym_fromstring(word, terminal=True) for word in cnet)
-        cnet = tuple(((word, None, 1), ) for word in cnet)
-        return self.factory.input(cnet, self.models)