def train(self, features, labels):
labels,names = normaliselabels(labels)
if self.auto_weighting:
nlabels = labels.max() + 1
self.param.nr_weight = int(nlabels)
self.param.weight_label = range(nlabels)
self.param.weight = [(labels != i).mean() for i in xrange(nlabels)]
problem = libsvm.svm_problem(labels.astype(float), features)
model = libsvm.svm_model(problem, self.param)
return libsvmModel(model, names, self.output_probability)
def test_normaliselabels():
np.random.seed(22)
labels = np.zeros(120, np.uint8)
labels[40:] += 1
labels[65:] += 1
reorder = np.argsort(np.random.rand(len(labels)))
labels = labels[reorder]
labels2, names = normaliselabels(labels)
for new_n, old_n in enumerate(names):
assert np.all((labels == old_n) == (labels2 == new_n))
def train(self, features, labels):
labels, names = normaliselabels(labels)
if self.auto_weighting:
nlabels = labels.max() + 1
self.param.nr_weight = int(nlabels)
self.param.weight_label = list(range(nlabels))
self.param.weight = [(labels != i).mean() for i in range(nlabels)]
problem = libsvm.svm_problem(labels.astype(float), features)
model = libsvm.svm_model(problem, self.param)
return libsvmModel(model, names, self.output_probability)
def test_normaliselabels():
np.random.seed(22)
labels = np.zeros(120, np.uint8)
labels[40:] += 1
labels[65:] += 1
reorder = np.argsort(np.random.rand(len(labels)))
labels = labels[reorder]
labels2,names = normaliselabels(labels)
for new_n,old_n in enumerate(names):
assert np.all( (labels == old_n) == (labels2 == new_n) )
