return (1+a1*a2)*(1+a1*a2) Q = KernelMatrix(X, pk) # the linear term for the QP Y = concatenate([y,-y]) svr_epsilon = 4; b = Y + svr_epsilon * ones(Y.size) H = Q f = b Aeq = concatenate([ones(l), -ones(l)]).reshape(1,N) Beq = array([0]) lb = zeros(N) ub = zeros(N) + 0.5 alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub) #alpha = 2*alpha sv1 = SupportVectors(alpha[:l]) sv2 = SupportVectors(alpha[l:]) R = RegressionFunction(x, y, alpha, svr_epsilon, pk) xx = arange(min(x),max(x),0.1) pylab.plot(xx,R(xx)) pylab.plot(xx,R(xx)+svr_epsilon, 'k--') pylab.plot(xx,R(xx)-svr_epsilon, 'k--') pylab.plot(x[sv1],y[sv1],'ro') pylab.plot(x[sv2],y[sv2],'go') print alpha
if __name__ == '__main__':
npt = 20
from test_circle import xy
npt1 = xy.shape[0]
if npt is not npt1:
xy = getpoints((x0, y0, R0), npt)
else:
pass
Q = dot(xy, transpose(xy))
f = -diag(Q) + 10
H = Q * 2
A = ones((1, npt))
b = ones(1)
x = qld.quadprog2(H, f, None, None, A, b, zeros(npt), ones(npt))
center = dot(x, xy)
print("center: %s" % center)
# find support vectors (find numpy way please)
sv = []
for i, v in enumerate(x):
if v > 0.001: sv.append(i)
sv0 = sv[0]
print(sv)
R = linalg.norm(xy[sv0, :] - center)
plot(xy, sv, x0, y0, R0, center, R)
if __name__ == '__main__':
npt = 20
from test_circle import xy
npt1 = xy.shape[0]
if npt is not npt1:
xy = getpoints((x0,y0,R0),npt)
else:
pass
Q = dot(xy, transpose(xy))
f = -diag(Q)+10
H = Q*2
A = ones((1,npt))
b = ones(1)
x = qld.quadprog2(H, f, None, None, A, b, zeros(npt), ones(npt))
center = dot(x,xy)
print "center: " , center
# find support vectors (find numpy way please)
sv = []
for i,v in enumerate(x):
if v > 0.001: sv.append(i)
sv0 = sv[0]
print sv
R = linalg.norm(xy[sv0,:]-center)
plot(xy, sv, x0, y0, R0, center, R)
