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| author | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2019-12-04 12:30:19 +0000 |
|---|---|---|
| committer | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2019-12-04 12:30:19 +0000 |
| commit | d6e9e06edc555fdf73316b7bd222067fc8399828 (patch) | |
| tree | 1d29789aeccb640a23ddc52eef2f487032d50031 | |
| parent | Update submodule (diff) | |
| download | pygensvm-d6e9e06edc555fdf73316b7bd222067fc8399828.tar.gz pygensvm-d6e9e06edc555fdf73316b7bd222067fc8399828.zip | |
Remove 'with nogil' everywhere
Don't think it's needed
| -rw-r--r-- | gensvm/cython_wrapper/wrapper.pyx | 19 |
1 files changed, 7 insertions, 12 deletions
diff --git a/gensvm/cython_wrapper/wrapper.pyx b/gensvm/cython_wrapper/wrapper.pyx index 009e70b..3d7b87d 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) @@ -137,9 +136,8 @@ def predict_wrap( 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 |