* rewrite the implementation of the marginalized kernel.
* implement four computing methods of the generalized random walk kernel.
* in the path kernel up to length h, use trie to save all paths, saving tremendous memories; use the Deep-first search to get paths from graphs.
* in model_selection_for_precomputed_kernel method, complete the part to do cross validation when Gram matrices are read from file.
* in get_dataset_attributes methods, correct three sub-methods about getting node degrees, add sub-methods to get fill factors of graphs.
* change default chunksize of pool.imap_unordered parallel method to 100.
* remove try... except blocks in case they hidden bugs.

2. update pygraph.utils.graphdatasets.get_dataset_attributes function, so that if a dataset has missing attributes it can still read the dimension of attributes.

2. model_selection_precomputed can now save all results as human readable text.
3. modify pygraph.utils.utils.floydTransformation and pygraph.utils.graphdataset.get_dataset_attributes.