def __train__(self, data, labels):
l = labels.reshape((-1,1))
self.__trainingData__ = data
self.__trainingLabels__ = l
N = len(l)
H = zeros((N,N))
for i in range(N):
for j in range(N):
H[i,j] = self.__trainingLabels__[i]*self.__trainingLabels__[j]*self.__kernelFunc__(self.__trainingData__[i],self.__trainingData__[j])
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
suppressOut = True
if suppressOut:
devnull = open('/dev/null', 'w')
oldstdout_fno = os.dup(sys.stdout.fileno())
os.dup2(devnull.fileno(), 1)
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
if suppressOut:
os.dup2(oldstdout_fno, 1)
lim = 1e-4
r.xf[where(abs(r.xf)<lim)] = 0
self.__lambdas__ = r.xf
nonzeroindexes = where(r.xf>lim)[0]
# l1 = nonzeroindexes[0]
# self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)
def train(self, data, labels):
l = labels.reshape((-1,1))
xy = data * l
H = dot(xy,transpose(xy))
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
r.xf[where(r.xf<1e-3)] = 0
self.w = dot(r.xf*labels,data)
nonzeroindexes = where(r.xf>1e-4)[0]
l1 = nonzeroindexes[0]
self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)
def __train__(self, data, labels):
l = labels.reshape((-1,1))
xy = data * l
H = dot(xy,transpose(xy))
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
devnull = open('/dev/null', 'w')
oldstdout_fno = os.dup(sys.stdout.fileno())
os.dup2(devnull.fileno(), 1)
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
os.dup2(oldstdout_fno, 1)
lim = 1e-4
r.xf[where(r.xf<lim)] = 0
self.w = dot(r.xf*labels,data)
nonzeroindexes = where(r.xf>lim)[0]
l1 = nonzeroindexes[0]
self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)