Reupdate README

Author: theogf

README.md

@@ -6,7 +6,7 @@
 
 AugmentedGaussianProcesses! (previously OMGP) is a Julia package in development for **Data Augmented Sparse Gaussian Processes**. It contains a collection of models for different **gaussian and non-gaussian likelihoods**, which are transformed via data augmentation into **conditionally conjugate likelihood** allowing for **extremely fast inference** via block coordinate updates. There are also more options to use more traditional **variational inference** via quadrature or Monte Carlo integration.
 
-**Note that you can use the package in Python via [PyJulia](https://github.com/JuliaPy/pyjulia)**
+### You can also use the package in Python via [PyJulia](https://github.com/JuliaPy/pyjulia)!
 
 # Packages models :
 

docs/src/assets/classifiers.jl

@@ -36,7 +36,7 @@ svmpred = (proba_y(svmmodel,X_test).-0.5).*2
 # y1 = AugmentedGaussianProcesses.svmlikelihood(svmfstar)
 # y2 = AugmentedGaussianProcesses.svmlikelihood(-svmfstar)
 # plot(X_test,y1+y2)
-
+##
 default(legendfontsize=14.0,xtickfontsize=10.0,ytickfontsize=10.0)
 p = plot(X,y,t=:scatter,lab="Training Points")
 plot!(X_test,logitpred,lab="Logistic Prediction",lw=7.0)