def _computeSimilarityMatrix(self, symbols):
if symbols is None:
raise TypeError("Symbols cannot be None")
for symbol in symbols:
if not isinstance(symbol, Symbol):
raise TypeError(
"At least one specified symbol is not a valid symbol")
# Execute the Clustering part in C
debug = False
wrapper = WrapperArgsFactory(
"_libScoreComputation.computeSimilarityMatrix")
wrapper.typeList[wrapper.function](symbols)
self._logger.debug("wrapper = {0}".format(wrapper))
(listScores) = _libScoreComputation.computeSimilarityMatrix(
self.internalSlick, self._cb_executionStatus, self._isFinish,
debug, wrapper)
# Retrieve the scores for each association of symbols
scores = OrderedDict()
for (iuid, juid, score) in listScores:
if iuid not in list(scores.keys()):
scores[iuid] = OrderedDict()
if juid not in list(scores.keys()):
scores[juid] = OrderedDict()
scores[iuid][juid] = score
if iuid not in list(scores[juid].keys()):
scores[juid][iuid] = score
return scores
def _computeSimilarityMatrix(self, symbols):
if symbols is None:
raise TypeError("Symbols cannot be None")
for symbol in symbols:
if not isinstance(symbol, Symbol):
raise TypeError(
"At least one specified symbol is not a valid symbol")
# Execute the Clustering part in C
debug = False
wrapper = WrapperArgsFactory(
"_libScoreComputation.computeSimilarityMatrix")
wrapper.typeList[wrapper.function](symbols)
self._logger.debug("wrapper = {0}".format(wrapper))
(listScores) = _libScoreComputation.computeSimilarityMatrix(
self.internalSlick, self._cb_executionStatus, self._isFinish,
debug, wrapper)
# Retrieve the scores for each association of symbols
scores = OrderedDict()
for (iuid, juid, score) in listScores:
if iuid not in list(scores.keys()):
scores[iuid] = OrderedDict()
if juid not in list(scores.keys()):
scores[juid] = OrderedDict()
scores[iuid][juid] = score
if iuid not in list(scores[juid].keys()):
scores[juid][iuid] = score
return scores
def processUPGMA(self):
"""Computes the matrix of equivalences (in C) and reduce it
iteratively."""
self.log.debug("Computing the associated matrix")
# Execute the Clustering part in C
debug = False
wrapper = WrapperArgsFactory("_libScoreComputation.computeSimilarityMatrix")
wrapper.typeList[wrapper.function](self.symbols)
(listScores) = _libScoreComputation.computeSimilarityMatrix(self.doInternalSlick, self.cb_executionStatus, self.isFinish, debug, wrapper)
# Retrieve the scores for each association of symbols
self.scores = {}
for (iuid, juid, score) in listScores:
if self.isFinish():
return (None, None, None)
if iuid not in self.scores.keys():
self.scores[iuid] = {}
if juid not in self.scores.keys():
self.scores[juid] = {}
self.scores[iuid][juid] = score
if iuid not in self.scores[juid].keys():
self.scores[juid][iuid] = score
# Reduce the UPGMA matrix (merge symbols by similarity)
self.computePhylogenicTree()