def cross_validation(prob, param, fold):
if param.gamma == 0:
param.gamma = 1.0/prob.maxlen
dblarr = svmc.new_double(prob.size)
svmc.svm_cross_validation(prob.prob, param.param, fold, dblarr)
ret = _double_array_to_list(dblarr, prob.size)
svmc.delete_double(dblarr)
return ret
def predict_values_raw(self,x):
#convert x into svm_node, allocate a double array for return
n = self.nr_class*(self.nr_class-1)/2
data = _convert_to_svm_node_array(x)
dblarr = svmc.new_double(n)
svmc.svm_predict_values(self.model, data, dblarr)
ret = _double_array_to_list(dblarr, n)
svmc.delete_double(dblarr)
svmc.svm_node_array_destroy(data)
return ret
def predict_probability(self,x):
#c code will do nothing on wrong type, so we have to check ourself
if self.svm_type == NU_SVR or self.svm_type == EPSILON_SVR:
raise TypeError, "call get_svr_probability or get_svr_pdf for probability output of regression"
elif self.svm_type == ONE_CLASS:
raise TypeError, "probability not supported yet for one-class problem"
#only C_SVC,NU_SVC goes in
if not self.probability:
raise TypeError, "model does not support probabiliy estimates"
#convert x into svm_node, alloc a double array to receive probabilities
data = _convert_to_svm_node_array(x)
dblarr = svmc.new_double(self.nr_class)
pred = svmc.svm_predict_probability(self.model, data, dblarr)
pv = _double_array_to_list(dblarr, self.nr_class)
svmc.delete_double(dblarr)
svmc.svm_node_array_destroy(data)
p = {}
for i in range(len(self.labels)):
p[self.labels[i]] = pv[i]
return pred, p
def _free_double_array(x):
if x != 'NULL' and x != None:
svmc.delete_double(x)
def __del__(self):
svmc.delete_svm_problem(self.prob)
svmc.delete_double(self.y_array)
for i in range(self.size):
svmc.svm_node_array_destroy(self.data[i])
svmc.svm_node_matrix_destroy(self.x_matrix)