def __init__(self,y,x): assert len(y) == len(x) self.prob = prob = svmc.new_svm_problem() self.size = size = len(y) self.y_array = y_array = svmc.new_double(size) for i in range(size): svmc.double_setitem(y_array,i,y[i]) self.x_matrix = x_matrix = svmc.svm_node_matrix(size) self.data = [] self.maxlen = 0; for i in range(size): data = _convert_to_svm_node_array(x[i]) self.data.append(data); svmc.svm_node_matrix_set(x_matrix,i,data) if type(x[i]) == dict: if (len(x[i]) > 0): self.maxlen = max(self.maxlen,max(x[i].keys())) else: self.maxlen = max(self.maxlen,len(x[i])) svmc.svm_problem_l_set(prob,size) svmc.svm_problem_y_set(prob,y_array) svmc.svm_problem_x_set(prob,x_matrix)
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 _double_array(seq): size = len(seq) array = svmc.new_double(size) i = 0 for item in seq: svmc.double_setitem(array,i,item) i = i + 1 return array
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
