fix bug load_from_file #148

python/thundersvm/thundersvm.py

@@ -465,19 +465,19 @@ def load_from_file(self, path):
  n_feature = (c_int * 1)()
  thundersvm.get_sv_max_index(c_void_p(self.model), n_feature)
  self.n_features = n_feature[0]
-
  csr_row = (c_int * (self.n_sv + 1))()
  csr_col = (c_int * (self.n_sv * self.n_features))()
  csr_data = (c_float * (self.n_sv * self.n_features))()
  data_size = (c_int * 1)()
- thundersvm.get_sv(csr_row, csr_col, csr_data, data_size, c_void_p(self.model))
+ sv_indices = (c_int * self.n_sv)()
+ thundersvm.get_sv(csr_row, csr_col, csr_data, data_size, sv_indices, c_void_p(self.model))
  self.row = np.array([csr_row[index] for index in range(0, self.n_sv + 1)])
  self.col = np.array([csr_col[index] for index in range(0, data_size[0])])
  self.data = np.array([csr_data[index] for index in range(0, data_size[0])])
  self.support_vectors_ = sp.csr_matrix((self.data, self.col, self.row))
  # if self._sparse == False:
  # self.support_vectors_ = self.support_vectors_.toarray(order = 'C')
-
+ self.support_ = np.array([sv_indices[index] for index in range(0, self.n_sv)]).astype(int)
  dual_coef = (c_float * ((self.n_classes - 1) * self.n_sv))()
  thundersvm.get_coef(dual_coef, self.n_classes, self.n_sv, c_void_p(self.model))
  self.dual_coef_ = np.array([dual_coef[index] for index in range(0, (self.n_classes - 1) * self.n_sv)]).astype(float)