XX = concatenate([c1,-c2]) nx = XX.shape[0] # quadratic and linear terms of QP Q = KernelMatrix(XX) b = -1 * ones(nx) H = Q f = b Aeq = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1,nx) Beq = array([0]) lb = zeros(nx) ub = zeros(nx) + 100000 #alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub) alpha,xxx = quapro.quadprog(H, f, None, None, Aeq, Beq, lb, ub) #t1 = time.time() #for i in range(1000): # alpha,xxx = quapro.quadprog(H, f, None, None, Aeq, Beq, lb, ub) # #alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub) #t2 = time.time() #print "1000 calls to QP took %0.3f s" % (t2-t1) print alpha # the labels and the points X = concatenate([c1,c2]) y = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1,nx) wv = WeightVector(alpha, X, y) sv1, sv2 = SupportVectors(alpha,y, eps=1e-6) bias = Bias(alpha, X, y)
XX = concatenate([c1,-c2]) nx = XX.shape[0] # quadratic and linear terms of QP Q = KernelMatrix(XX) b = -1 * ones(nx) H = Q f = b Aeq = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1,nx) Beq = array([0]) lb = zeros(nx) ub = zeros(nx) + 99999 #alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub) alpha, xxx= quapro.quadprog(H, f, None, None, Aeq=Aeq, beq=Beq, LB=lb, UB=ub) print alpha #alpha = array([fil(x) for x in alpha]) print "cons:", inner(Aeq,alpha) print "obj min: 0.5 * x'Hx + <x,f>", 0.5*inner(alpha,inner(H,alpha))+inner(f,alpha) # the labels and the points X = concatenate([c1,c2]) y = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1,nx) wv = WeightVector(alpha, X, y) sv1, sv2 = SupportVectors(alpha,y,eps=1e-6) bias = Bias(alpha, X, y) ym = (y.flatten()<0).nonzero()[0]
XX = concatenate([c1, -c2])
nx = XX.shape[0]
# quadratic and linear terms of QP
Q = KernelMatrix(XX)
b = -1 * ones(nx)
H = Q
f = b
Aeq = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1, nx)
Beq = array([0])
lb = zeros(nx)
ub = zeros(nx) + 100000
#alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub)
alpha, xxx = quapro.quadprog(H, f, None, None, Aeq, Beq, lb, ub)
#t1 = time.time()
#for i in range(1000):
# alpha,xxx = quapro.quadprog(H, f, None, None, Aeq, Beq, lb, ub)
# #alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub)
#t2 = time.time()
#print("1000 calls to QP took %0.3f s" % (t2-t1))
print(alpha)
# the labels and the points
X = concatenate([c1, c2])
y = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1, nx)
wv = WeightVector(alpha, X, y)
sv1, sv2 = SupportVectors(alpha, y, eps=1e-6)
bias = Bias(alpha, X, y)
XX = concatenate([c1,-c2])
nx = XX.shape[0]
# quadratic and linear terms of QP
Q = KernelMatrix(XX)
b = -1 * ones(nx)
H = Q
f = b
Aeq = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1,nx)
Beq = array([0])
lb = zeros(nx)
ub = zeros(nx) + 99999
#alpha = qld.quadprog2(H, f, None, None, Aeq, Beq, lb, ub)
alpha, xxx= quapro.quadprog(H, f, None, None, Aeq=Aeq, beq=Beq, LB=lb, UB=ub)
print(alpha)
#alpha = array([fil(x) for x in alpha])
print("cons:%s" % inner(Aeq,alpha))
print("obj min: 0.5 * x'Hx + <x,f> %s" % 0.5*inner(alpha,inner(H,alpha))+inner(f,alpha))
# the labels and the points
X = concatenate([c1,c2])
y = concatenate([ones(c1.shape[0]), -ones(c2.shape[0])]).reshape(1,nx)
wv = WeightVector(alpha, X, y)
sv1, sv2 = SupportVectors(alpha,y,eps=1e-6)
bias = Bias(alpha, X, y)
ym = (y.flatten()<0).nonzero()[0]
