def __init__(self, arg1, arg2=None):
if arg2 == None:
# create model from file
filename = arg1
self.model = svmc.svm_load_model(filename)
else:
# create model from problem and parameter
prob, param = arg1, arg2
self.prob = prob
if param.gamma == 0:
param.gamma = 1.0 / prob.maxlen
msg = svmc.svm_check_parameter(prob.prob, param.param)
if msg:
raise ValueError(msg)
self.model = svmc.svm_train(prob.prob, param.param)
#setup some classwide variables
self.nr_class = svmc.svm_get_nr_class(self.model)
self.svm_type = svmc.svm_get_svm_type(self.model)
#create labels(classes)
intarr = svmc.new_int(self.nr_class)
svmc.svm_get_labels(self.model, intarr)
self.labels = int_array_to_list(intarr, self.nr_class)
svmc.delete_int(intarr)
#check if valid probability model
self.probability = svmc.svm_check_probability_model(self.model)
def __init__(self, arg1, arg2=None):
if arg2 == None:
# create model from file
filename = arg1
self.model = svmc.svm_load_model(filename)
else:
# create model from problem and parameter
prob, param = arg1, arg2
self.prob = prob
if param.gamma == 0:
param.gamma = 1.0/prob.maxlen
msg = svmc.svm_check_parameter(prob.prob, param.param)
if msg:
raise ValueError, msg
self.model = svmc.svm_train(prob.prob, param.param)
#setup some classwide variables
self.nr_class = svmc.svm_get_nr_class(self.model)
self.svm_type = svmc.svm_get_svm_type(self.model)
#create labels(classes)
intarr = svmc.new_int(self.nr_class)
svmc.svm_get_labels(self.model, intarr)
self.labels = int_array_to_list(intarr, self.nr_class)
svmc.delete_int(intarr)
#check if valid probability model
self.probability = svmc.svm_check_probability_model(self.model)
def int_array(seq):
size = len(seq)
array = svmc.new_int(size)
for i, item in enumerate(seq):
svmc.int_setitem(array, i, int(item))
return array
def int_array(seq):
size = len(seq)
array = svmc.new_int(size)
for i, item in enumerate(seq):
svmc.int_setitem(array, i, int(item))
return array