diff --git a/lang/zh/notebooks/else/run_ssp_symonly.py b/lang/zh/notebooks/else/run_ssp_symonly.py
new file mode 100644
index 0000000..081d596
--- /dev/null
+++ b/lang/zh/notebooks/else/run_ssp_symonly.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec 23 16:40:52 2018
+
+@author: ljia
+"""
+
+import functools
+from libs import *
+import multiprocessing
+
+from gklearn.kernels.ssp_sym import structuralspkernel
+from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
+
+dslist = [
+    {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},
+    # node nsymb
+    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},
+    # node symb/nsymb
+]
+estimator = structuralspkernel
+mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
+param_grid_precomputed = {'node_kernels': 
+    [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
+    'edge_kernels': 
+    [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]}
+param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},
+              {'alpha': np.logspace(-10, 10, num=41, base=10)}]
+
+for ds in dslist:
+    print()
+    print(ds['name'])
+    model_selection_for_precomputed_kernel(
+        ds['dataset'],
+        estimator,
+        param_grid_precomputed,
+        (param_grid[1] if ('task' in ds and ds['task']
+                           == 'regression') else param_grid[0]),
+        (ds['task'] if 'task' in ds else 'classification'),
+        NUM_TRIALS=30,
+        datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),
+        extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
+        ds_name=ds['name'],
+        n_jobs=multiprocessing.cpu_count(),
+        read_gm_from_file=False)
+    print()
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