def addPattern(self, x, i) :
if type(x) == type(numpy.array(1)) or type(x) == type([]) :
values = arrayWrap.doubleVector(x)
else:
raise TypeError, "data vectors must be list or array"
self.container.addPattern(self, values)
def addPattern(self, x, i):
if type(x) == type(numpy.array(1)) or type(x) == type([]):
values = arrayWrap.doubleVector(x)
else:
raise TypeError, "data vectors must be list or array"
self.container.addPattern(self, values)
def runGradientDescent(classifier, data):
C = classifier.getClist(data)
alphaVec = arrayWrap.doubleVector()
cgist.runGradientDescent(data.castToBase(), C, alphaVec, int(classifier.cacheSize), 10000)
alpha = [alphaVec[i] for i in range(len(alphaVec))]
return alpha, 0.0
def runGradientDescent(classifier, data):
C = classifier.getClist(data)
alphaVec = arrayWrap.doubleVector()
cgist.runGradientDescent(data.castToBase(), C, alphaVec,
int(classifier.cacheSize), 10000)
alpha = [alphaVec[i] for i in range(len(alphaVec))]
return alpha, 0.0
def addPattern(self, x, i):
if type(x) == type({}):
keys, values = arrayWrap.dict2vectors(x)
elif type(x) == type(numpy.array(1)) or type(x) == type([]):
keys = arrayWrap.longVector([])
values = arrayWrap.doubleVector(x)
else:
raise TypeError, "data vectors must be dictionary, list or arrays"
self.container.addPattern(self, keys, values)
def test(self, data, **args) :
testStart = time.clock()
if data.testingFunc is not None :
data.test(self.trainingData, **args)
cdecisionFunc = arrayWrap.doubleVector([])
cY = self.ckmeans.test(data.castToBase(), cdecisionFunc)
res.log.testingTime = time.clock() - testStart
def addPattern(self, x, i) :
if type(x) == type({}) :
keys,values = arrayWrap.dict2vectors(x)
elif type(x) == type(numpy.array(1)) or type(x) == type([]) :
keys = arrayWrap.longVector([])
values = arrayWrap.doubleVector(x)
else:
raise TypeError,"data vectors must be dictionary, list or arrays"
self.container.addPattern(self, keys, values)
def test(self, data, **args):
testStart = time.clock()
if data.testingFunc is not None:
data.test(self.trainingData, **args)
cdecisionFunc = arrayWrap.doubleVector([])
cY = self.ckmeans.test(data.castToBase(), cdecisionFunc)
res.log.testingTime = time.clock() - testStart