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class OnlineLearner(object):
def __init__(self, **kwargs):
self.last_misses = 0.
self.iratio = 0.
self.it = 1.
self.l = kwargs["l"]
self.max_ratio = -np.inf
self.threshold = 500.
def hinge_loss(self, vector, cls, weight):
p = self.predict(vector)
hl = max(0, weight-cls*p)
if hl >= weight:
self.last_misses += 1.
ir = hl/weight
self.iratio += ir
if self.max_ratio < ir: self.max_ratio = ir
self.it += 1
if self.it % self.threshold == 0:
print str(type(self).__name__),
print "l", self.last_misses/self.threshold, "r", self.iratio/self.threshold,
print "m", self.max_ratio
self.max_ratio = self.last_misses = self.iratio = 0
return hl
class Pegasos(OnlineLearner):
def __init__(self, **kwargs):
super(Pegasos, self).__init__(**kwargs)
self.w = Z(kwargs["dim"], dtype=np.float32)
self.learn = True
def update(self, vector, cls, weight):
eta_t = 1./(self.l*self.it)
loss = self.hinge_loss(vector, cls, weight)
if not self.learn: return
if loss > 0:
self.w = (1-eta_t*self.l)*self.w + eta_t*cls*vector
else:
self.w = (1-eta_t*self.l)*self.w
def predict(self, vector):
return np.dot(vector, self.w)
class KernelPegasos(OnlineLearner):
def __init__(self, **kw):
self.ws = []
self.y = []
super(KernelPegasos,self).__init__(**kw)
self.k = kw["kernel"]
self.learn = True
def update(self, vector, cls, weight):
loss = self.hinge_loss(vector, cls, weight)
if not self.learn: pass
if loss > 0:
self.ws.append(vector)
self.y.append(cls)
def predict(self, v):
return (1./(self.l*self.it))*sum(self.k(self.ws[i],v)*self.y[i]
for i in xrange(len(self.ws)))
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