def __del__(self):
if __debug__:
debug('CLF_', 'Destroying libsvm.SVMProblem %s' % ` 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)
def __del__(self):
if __debug__:
debug('CLF_', 'Destroying libsvm.SVMProblem %s' % `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)
def predict_values_raw(self, x):
#convert x into SVMNode, allocate a double array for return
n = self.nr_class * (self.nr_class - 1) // 2
data = seq_to_svm_node(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_values_raw(self, x):
#convert x into SVMNode, allocate a double array for return
n = self.nr_class*(self.nr_class-1)//2
data = seq_to_svm_node(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 SVMNode, alloc a double array to receive probabilities
data = seq_to_svm_node(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 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 SVMNode, alloc a double array to receive probabilities
data = seq_to_svm_node(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 free_double_array(x):
if x != 'NULL' and x != None:
svmc.delete_double(x)