def __init__(self, model, dataset, kernel):
self.kernel = kernel
modelc = model.contents
self.svm_type = modelc.param.svm_type
if is_classification_problem(self.svm_type) \
and modelc.nSV[0] == 0:
raise ValueError, 'model contains no support vectors'
if is_classification_problem(self.svm_type):
self.nr_class = modelc.nr_class
self.labels = N.array(modelc.labels[:self.nr_class])
nrho = self.nr_class * (self.nr_class - 1) / 2
self.rho = modelc.rho[:nrho]
self.sv_coef = [modelc.sv_coef[i][:modelc.l]
for i in range(self.nr_class - 1)]
self.nSV = [modelc.nSV[i] for i in range(self.nr_class)]
start = N.zeros((self.nr_class,), N.intc)
for i in range(1, self.nr_class):
start[i] = start[i - 1] + modelc.nSV[i - 1]
self.start = start
else:
self.rho = modelc.rho[0]
self.sv_coef = modelc.sv_coef[0][:modelc.l]
if modelc.param.kernel_type != libsvm.PRECOMPUTED:
svptrs = [modelc.SV[i] for i in range(modelc.l)]
support_vectors = [dataset.iddatamap[addressof(svptr[0])]
for svptr in svptrs]
else:
ids = [int(modelc.SV[i][0].value) for i in range(modelc.l)]
support_vectors = [dataset[id] for id in ids]
self.support_vectors = support_vectors
self.is_compact = False
libsvm.svm_destroy_model(model)
def __init__(self, model, dataset, kernel):
self.kernel = kernel
modelc = model.contents
self.svm_type = modelc.param.svm_type
if is_classification_problem(self.svm_type) \
and modelc.nSV[0] == 0:
raise ValueError, 'model contains no support vectors'
if is_classification_problem(self.svm_type):
self.nr_class = modelc.nr_class
self.labels = N.array(modelc.labels[:self.nr_class])
nrho = self.nr_class * (self.nr_class - 1) / 2
self.rho = modelc.rho[:nrho]
self.sv_coef = [modelc.sv_coef[i][:modelc.l]
for i in range(self.nr_class - 1)]
self.nSV = [modelc.nSV[i] for i in range(self.nr_class)]
start = N.zeros((self.nr_class,), N.intc)
for i in range(1, self.nr_class):
start[i] = start[i - 1] + modelc.nSV[i - 1]
self.start = start
else:
self.rho = modelc.rho[0]
self.sv_coef = modelc.sv_coef[0][:modelc.l]
if modelc.param.kernel_type != libsvm.PRECOMPUTED:
svptrs = [modelc.SV[i] for i in range(modelc.l)]
support_vectors = [dataset.iddatamap[addressof(svptr[0])]
for svptr in svptrs]
else:
ids = [int(modelc.SV[i][0].value) for i in range(modelc.l)]
support_vectors = [dataset[id] for id in ids]
self.support_vectors = support_vectors
self.is_compact = False
libsvm.svm_destroy_model(model)
def __del__(self):
libsvm.svm_destroy_model(self.model)
def __del__(self):
libsvm.svm_destroy_model(self.model)