def matchInputWithMemory(self, idea):
"""
Position of best match for input in memory
Cosine, Euclid, Hamman, Forbes, Kulczynski, Manhattan, Pearson, Simpson, Yule, Russell-Rao
"""
self.matchRate = 0;
self.matchedMemoryValues = {}
self.allThoughts = []
#experimental - google query addon
#string foundKnowledge = ParseForKnowledge(a);
#if (!String.IsNullOrEmpty(foundKnowledge))
# botsMemory.Add(a, foundKnowledge);
lower_case = idea.lower() # Convert to lower case
inputSentenceTokenized = lower_case.split() # Split into words
#matrix operations are handled separately
if self.associater == MatchingAlgorithm.CosineTFIDF:
self.calculateCosine(lower_case)
else:
#run through memory
cntr = 0
for key, value in self.botsMemory.iteritems():
lower_case = value.lower()
t = lower_case.split()
cntr += 1
if self.associater == MatchingAlgorithm.Levensthein:
self.matchRate = self.calculateMatchRateLS(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Dice:
self.matchRate = self.calculateMatchRateDice(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Jaccard:
self.matchRate = self.calculateMatchRateJ(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Tanimoto:
self.matchRate = self.calculateMatchRateTanimoto(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Hamman:
self.matchRate = self.calculateMatchRateHamman(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Simpson:
self.matchRate = self.calculateMatchRateSimpson(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Kulczynski:
self.matchRate = self.calculateMatchRateKulczynski(inputSentenceTokenized, t)
else:
self.matchRate = self.calculateMatchRate(inputSentenceTokenized, t)
tt = Thought();
tt.MatchingMemory = value
tt.MatchingRate = self.matchRate
tt.PotentialResponse = key
self.allThoughts.append(tt)
if key not in self.matchedMemoryValues:
if self.matchRate != 0:
self.matchedMemoryValues[key] = self.matchRate
print "[" + str(cntr) + "] @" + str(self.matchRate) + " Matching: " + key
def matchInputWithMemory(self, idea):
"""
Position of best match for input in memory
Cosine, Euclid, Hamman, Forbes, Kulczynski, Manhattan, Pearson, Simpson, Yule, Russell-Rao
"""
self.matchRate = 0;
self.matchedMemoryValues = {}
self.allThoughts = []
#experimental - google query addon
#string foundKnowledge = ParseForKnowledge(a);
#if (!String.IsNullOrEmpty(foundKnowledge))
# botsMemory.Add(a, foundKnowledge);
lower_case = idea.lower() # Convert to lower case
inputSentenceTokenized = lower_case.split() # Split into words
#matrix operations are handled separately
if self.associater == MatchingAlgorithm.CosineTFIDF:
self.calculateCosine(lower_case)
else:
#run through memory
cntr = 0
for key, value in self.botsMemory.iteritems():
lower_case = value.lower()
t = lower_case.split()
cntr += 1
if self.associater == MatchingAlgorithm.Levensthein:
self.matchRate = self.calculateMatchRateLS(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Dice:
self.matchRate = self.calculateMatchRateDice(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Jaccard:
self.matchRate = self.calculateMatchRateJ(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Tanimoto:
self.matchRate = self.calculateMatchRateTanimoto(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Hamman:
self.matchRate = self.calculateMatchRateHamman(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Simpson:
self.matchRate = self.calculateMatchRateSimpson(inputSentenceTokenized, t)
elif self.associater == MatchingAlgorithm.Kulczynski:
self.matchRate = self.calculateMatchRateKulczynski(inputSentenceTokenized, t)
else:
self.matchRate = self.calculateMatchRate(inputSentenceTokenized, t)
tt = Thought();
tt.MatchingMemory = value
tt.MatchingRate = self.matchRate
tt.PotentialResponse = key
self.allThoughts.append(tt)
if key not in self.matchedMemoryValues:
if self.matchRate != 0:
self.matchedMemoryValues[key] = self.matchRate
print "[" + str(cntr) + "] @" + str(self.matchRate) + " Matching: " + key
def calculateCosine(self, idea):
self.matchedMemoryValues = {}
self.allThoughts = []
train_set = list(self.botsMemory.keys())
#print len(train_set)
#print idea
test_set = []
test_set.append(idea) #Query
#print test_set
vectorizer = CountVectorizer(stop_words = None)
#print vectorizer
transformer = TfidfTransformer()
#print transformer
trainVectorizerArray = vectorizer.fit_transform(train_set).toarray()
testVectorizerArray = vectorizer.transform(test_set).toarray()
#print 'Fit Vectorizer to train set', trainVectorizerArray
#print 'Transform Vectorizer to test set', testVectorizerArray
cx = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 6)
ctr = 0
for vector in trainVectorizerArray:
ctr += 1
#print vector
for testV in testVectorizerArray:
#print testV
cosine = cx(vector, testV)
if cosine > 0.0:
#print str(ctr) + "" + str(cosine)
self.matchRate = cosine
key = train_set[ctr-1]
tt = Thought();
tt.MatchingMemory = key
tt.MatchingRate = cosine
tt.PotentialResponse = self.botsMemory[key]
self.allThoughts.append(tt)
if key not in self.matchedMemoryValues:
self.matchedMemoryValues[key] = self.matchRate
def calculateCosine(self, idea):
self.matchedMemoryValues = {}
self.allThoughts = []
train_set = list(self.botsMemory.keys())
#print len(train_set)
#print idea
test_set = []
test_set.append(idea) #Query
#print test_set
vectorizer = CountVectorizer(stop_words = None)
#print vectorizer
transformer = TfidfTransformer()
#print transformer
trainVectorizerArray = vectorizer.fit_transform(train_set).toarray()
testVectorizerArray = vectorizer.transform(test_set).toarray()
#print 'Fit Vectorizer to train set', trainVectorizerArray
#print 'Transform Vectorizer to test set', testVectorizerArray
cx = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 6)
ctr = 0
for vector in trainVectorizerArray:
ctr += 1
#print vector
for testV in testVectorizerArray:
#print testV
cosine = cx(vector, testV)
if cosine > 0.0:
#print str(ctr) + "" + str(cosine)
self.matchRate = cosine
key = train_set[ctr-1]
tt = Thought();
tt.MatchingMemory = key
tt.MatchingRate = cosine
tt.PotentialResponse = self.botsMemory[key]
self.allThoughts.append(tt)
if key not in self.matchedMemoryValues:
self.matchedMemoryValues[key] = self.matchRate
