diff options
Diffstat (limited to 'gensvm/cython_wrapper/wrapper.pyx')
| -rw-r--r-- | gensvm/cython_wrapper/wrapper.pyx | 25 |
1 files changed, 10 insertions, 15 deletions
diff --git a/gensvm/cython_wrapper/wrapper.pyx b/gensvm/cython_wrapper/wrapper.pyx index 009e70b..3a85e92 100644 --- a/gensvm/cython_wrapper/wrapper.pyx +++ b/gensvm/cython_wrapper/wrapper.pyx @@ -88,8 +88,7 @@ def train_wrap( raise ValueError(error_repl) # Do the actual training - with nogil: - gensvm_train(model, data, seed_model) + gensvm_train(model, data, seed_model) # update the number of variables (this may have changed due to kernel) n_var = get_m(model) @@ -134,12 +133,11 @@ def predict_wrap( # output vector cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions - predictions = np.empty((n_test_obs, ), dtype=np.int) + predictions = np.empty((n_test_obs, ), dtype=np.int_) # do the prediction - with nogil: - gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, - predictions.data) + gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, + predictions.data) return predictions @@ -174,10 +172,9 @@ def predict_kernels_wrap( cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions predictions = np.empty((n_obs_test, ), dtype=np.int) - with nogil: - gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, - V_cols, n_obs_train, n_obs_test, n_var, n_class, kernel_idx, - gamma, coef, degree, kernel_eigen_cutoff, predictions.data) + gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, V_cols, + n_obs_train, n_obs_test, n_var, n_class, kernel_idx, gamma, coef, + degree, kernel_eigen_cutoff, predictions.data) return predictions @@ -243,9 +240,7 @@ def grid_wrap( set_queue(queue, n_tasks, tasks) - with nogil: - gensvm_train_q_helper(queue, cv_idx.data, store_predictions, - verbosity) + gensvm_train_q_helper(queue, cv_idx.data, store_predictions, verbosity) cdef np.ndarray[np.int_t, ndim=1, mode='c'] pred cdef np.ndarray[np.double_t, ndim=1, mode='c'] dur @@ -264,12 +259,12 @@ def grid_wrap( results['params'].append(candidate_params[ID]) results['scores'].append(get_task_performance(tasks[ID])) if store_predictions: - pred = np.zeros((n_obs, ), dtype=np.int) + pred = np.zeros((n_obs, ), dtype=np.int_) copy_task_predictions(tasks[ID], pred.data, n_obs) results['predictions'].append(pred.copy()) dur = np.zeros((n_folds, ), dtype=np.double) copy_task_durations(tasks[ID], dur.data, n_folds) - results['durations'].append(dur.copy()) + results['durations'].append(dur) gensvm_free_queue(queue) free_data(data) |