def __setstate__(self,state):
if libsvm is None:
raise RuntimeError('LibSVM Library not found. Cannot use this classifier.')
S,self.output_probability,self.names = state
N = NamedTemporaryFile()
N.write(S)
N.flush()
self.model = libsvm.svm_model(N.name)
def demo1():
"""
Set of exercises to better understand workings of SVM
"""
pdb.set_trace()
prob = svm.svm_problem([1, -1], [[1, 0, 1], [-1, 0, -1]])
param = svm.svm_parameter()
mod = svm.svm_model(prob, param)
def __setstate__(self, state):
if libsvm is None:
raise RuntimeError(
'LibSVM Library not found. Cannot use this classifier.')
S, self.output_probability, self.names = state
N = NamedTemporaryFile()
N.write(S)
N.flush()
self.model = libsvm.svm_model(N.name)
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 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)