Update README.md

README.md

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 # GPnet
 Gaussian Process Regression and Classification on Graphs
 
-Code still in alpha concept proofing and is based on the code from Chapter 18 of Machine Learning: An Algorithmic Perspective (2nd Edition) by Stephen Marsland, implements the algorithms in Chapters 2 and 3 of Gaussian Processes for Machine Learning by C.E. Rasmussen.
+The code is loosely inspired on the code from Chapter 18 of Machine Learning: An Algorithmic Perspective (2nd Edition) by Stephen Marsland and implements the algorithms in Chapters 2 and 3 of Gaussian Processes for Machine Learning by C.E. Rasmussen.
 
 `GPnet` at the moment exposes two classes: `GPnetRegressor` and `GPnetClassifier`
 * **GPnetRegressor** provides basic regression functionality for functions defined on graph nodes
 * **GPnetClassfier** provides classification of test nodes, given a set of -1/+1 labels for the training nodes
 
-The only kernel available at the moment is a _squared exponential_ kernel, more kernels and custom kernel composition will be added in the future.
+The only kernel available at the moment is a _squared exponential_ kernel, more kernels and custom kernel composition will be added in the future
+
+- update -
+kernel composition is already possible via custom function definitions: "soft" kernel composition is yet to be implemented.
 
 Some basic parameter optimization is provided by `scipy.optimize`.
 
 ## Dependencies
 GPnet demos require `networkx`, `pandas` , `numpy`, `matplotlib`, `scipy.optimize`, and `random` to work: nothing too exotic.
 
 ## What's the future of this repo? 
-There's still much work to do, and that includes
+Possible future work directions include:
 * including support for multiple and custom-defined kernels
 * fixing numerical issues with covariance matrix estimation and parameter optimization