class Prelude(object):
def __init__(self):
self.timer = None
self.isSpeaking = False
self.proactiveMode = False
self.brainLocation = "mind.mdu"
self.quantumRandomness = False
self.avoidLearnByRepeating = False
self.associater = MatchingAlgorithm.CosineTFIDF
self.saveTracking = time.time()
def initializeEngine(self):
self.mindInstance = Mind()
self.mindInstance.associater = self.associater
self.mindInstance.brainLocation = self.brainLocation
self.chatInitiated = time.strftime("%H:%M:%S")
self.mindInstance.analyzeShortTermMemory()
def chatWithPrelude(self, question):
if self.mindInstance is None:
return "Error: Mind not initialized"
if self.proactiveMode:
self.idleTime = 0
if self.timer is not None:
timer.Stop()
if self.quantumRandomness:
self.mindInstance.quantumRandomness = True
if self.avoidLearnByRepeating:
self.mindInstance.avoidLearnByRepeating = True
answer = ""
answer = self.mindInstance.listenToInput(question)
if self.isSpeaking:
self.speak(answer)
if self.proactiveMode:
self.setTimer()
self.autoSpeakInput = answer
self.timer.start()
elapsed_time = time.time() - self.saveTracking
if elapsed_time > 10:
print "soft save"
self.forceSaveMindFile()
self.saveTracking = time.time()
return answer
def setTimer(self):
pass
def monoLog(self):
pass
def speak(self, a):
pass
def stopEngine(self):
if self.mindInstance:
self.mindInstance.prepareCurrentMemoryForDisc()
if self.isContributable:
self.mindInstance.contributeClientMind()
def forceUpload(self):
pass
def forceSaveMindFile(self):
if self.mindInstance:
self.mindInstance.prepareCurrentMemoryForDisc()
def countMindMemory(self):
if self.mindInstance:
print "My memory contains " + str(len(
self.mindInstance.botsMemory)) + " neurons"
def setTimer(self):
self.timer = Timer(20 * 60, autoAnswer)
def autoAnswer(self):
try:
t = self.mindInstance.listenToInput(self.autoSpeakInput)
print "You (away):\t"
print "Prelude (bored):\t" + t
except:
print "Error: autoAnswer"
def setAssociationAlgorithm(self, assocType):
# if self.mindInstance:
# self.mindInstance.associater = assocType
self.associater = assocType
def setMindFileLocation(self, fileName):
self.brainLocation = fileName
def getVersionInfo(self):
return "Prelude@# Engine, version 1.2.7, 2004-2015(c) by Lennart Lopin "
def setBotClient(self):
pass
def setProactiveMode(self, flag):
pass
class Prelude(object):
def __init__(self):
self.timer = None
self.isSpeaking = False
self.proactiveMode = False
self.brainLocation = "mind.mdu"
self.quantumRandomness = False
self.avoidLearnByRepeating = False
self.associater = MatchingAlgorithm.CosineTFIDF
self.saveTracking = time.time()
def initializeEngine(self):
self.mindInstance = Mind()
self.mindInstance.associater = self.associater
self.mindInstance.brainLocation = self.brainLocation
self.chatInitiated = time.strftime("%H:%M:%S")
self.mindInstance.analyzeShortTermMemory()
def chatWithPrelude(self, question):
if self.mindInstance is None:
return "Error: Mind not initialized";
if self.proactiveMode:
self.idleTime = 0
if self.timer is not None:
timer.Stop()
if self.quantumRandomness:
self.mindInstance.quantumRandomness = True
if self.avoidLearnByRepeating:
self.mindInstance.avoidLearnByRepeating = True
answer = ""
answer = self.mindInstance.listenToInput(question)
if self.isSpeaking:
self.speak(answer)
if self.proactiveMode:
self.setTimer()
self.autoSpeakInput = answer
self.timer.start()
elapsed_time = time.time() - self.saveTracking
if elapsed_time > 10:
print "soft save"
self.forceSaveMindFile()
self.saveTracking = time.time()
return answer
def setTimer(self):
pass
def monoLog(self):
pass
def speak(self, a):
pass
def stopEngine(self):
if self.mindInstance:
self.mindInstance.prepareCurrentMemoryForDisc()
if self.isContributable:
self.mindInstance.contributeClientMind()
def forceUpload(self):
pass
def forceSaveMindFile(self):
if self.mindInstance:
self.mindInstance.prepareCurrentMemoryForDisc()
def countMindMemory(self):
if self.mindInstance:
print "My memory contains " + str(len(self.mindInstance.botsMemory)) + " neurons"
return str(len(self.mindInstance.botsMemory))
def setTimer(self):
self.timer = Timer(20 * 60, autoAnswer)
def autoAnswer(self):
try:
t = self.mindInstance.listenToInput(self.autoSpeakInput)
print "You (away):\t"
print "Prelude (bored):\t" + t
except:
print "Error: autoAnswer"
def setAssociationAlgorithm(self, assocType):
# if self.mindInstance:
# self.mindInstance.associater = assocType
self.associater = assocType
def setMindFileLocation(self, fileName):
self.brainLocation = fileName
def getVersionInfo(self):
return "Prelude@# Engine, version 1.2.7, 2004-2015(c) by Lennart Lopin ";
def setBotClient(self):
pass
def setProactiveMode(self, flag):
pass
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import numpy as np
import numpy.linalg as LA
from mind import Mind
m = Mind()
m.analyzeShortTermMemory()
#train_set = ["The sky is blue.", "The sun is bright."] #Documents
train_set = list(m.botsMemory.keys())
test_set = ["hello how are you"] #Query
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)
for vector in trainVectorizerArray:
#print vector
for testV in testVectorizerArray:
#print testV
cosine = cx(vector, testV)
