def svm_primal_sgd(X, Y, lam, T):
w = CuckooVector({})
x = CuckooVector({})
m = len(Y)
for t in range(T):
step = 1 / (lam * (t+1))
i = np.random.randint(m)
y = Y[i]
x.reset(X[i])
w.scale(1 - step * lam)
if y * w.dot(x) < 1:
w.add_scale(x, step * y)
return w
def pegasos(lam):
w = CuckooVector({})
wx = 0
t = 0
# while True:
# example = yield (w,wx)
# if example == None: break
# x,y = example
for xy in (yield (w, wx)):
x, y = xy
print(x)
print(y)
step = 1 / (lam * (t + 1))
w.scale(1 - step * lam)
wx = w.dot(x)
if y * wx < 1:
w.add_scale(x, step * y)
t = t + 1
def weightedError(w, Xt, Yt, thresh = 0):
xv = CuckooVector({})
return (1 + np.mean([(xv.reset(x) == None and np.sign(xv.dot(w) - thresh)*y) for (x,y) in zip(Xt,Yt)]))/2
