Merge branch 'master' of https://github.com/OFAI/BayesianNonparametri…

…cs.jl

README.md

@@ -1,25 +1,26 @@
 BayesianNonparametrics.jl
 ===========
-BayesianNonparametrics.jl is a Julia package implementing state-of-the-art Bayesian nonparametric models for medium-sized unsupervised problems. The software package brings Bayesian nonparametrics to non-specialists allowing the widespread use of Bayesian nonparametric models. Emphasis is put on consistency, performance and ease of use allowing easy access to Bayesian nonparametric models inside Julia.
+[![Build Status](https://travis-ci.org/OFAI/BayesianNonparametrics.jl.svg?branch=master)](https://travis-ci.org/OFAI/BayesianNonparametrics.jl)
+[![Coverage Status](https://coveralls.io/repos/github/OFAI/BayesianNonparametrics.jl/badge.svg?branch=master)](https://coveralls.io/github/OFAI/BayesianNonparametrics.jl?branch=master)
+
+*BayesianNonparametrics* is a Julia package implementing state-of-the-art Bayesian nonparametric models for medium-sized unsupervised problems. The software package brings Bayesian nonparametrics to non-specialists allowing the widespread use of Bayesian nonparametric models. Emphasis is put on consistency, performance and ease of use allowing easy access to Bayesian nonparametric models inside Julia.
 
-BayesianNonparametrics.jl allows you to
+*BayesianNonparametrics* allows you to
 
 - explain discrete or continous data using: Dirichlet Process Mixtures or Hierarchical Dirichlet Process Mixtures
 - analyse variable dependencies using: Variable Clustering Model
 - fit multivariate or univariate distributions for discrete or continous data with conjugate priors
 - compute point estimtates of Dirichlet Process Mixtures posterior samples
 
-Requirements
-------------
-* julia version 0.6
-* packages listed in REQUIREMENTS file
+#### News
+*BayesianNonparametrics* is Julia 0.7  / 1.0 compatible
 
 Installation
 ------------
-You can clone the package into your running julia 0.5 installation using
+You can install the package into your running Julia installation using Julia's package manager, i.e.
 
 ```julia
-Pkg.add("BayesianNonparametrics")
+pkg> add BayesianNonparametrics
 ```
 
 Documentation
@@ -29,7 +30,7 @@ Documentation is available in Markdown:
 
 Example
 -------
-The following example illustrates the use of BayesianNonparametrics.jl for clustering of continuous observations using a Dirichlet Process Mixture of Gaussians. 
+The following example illustrates the use of *BayesianNonparametrics* for clustering of continuous observations using a Dirichlet Process Mixture of Gaussians. 
 
 After loading the package:
 
@@ -46,7 +47,7 @@ we can generate a 2D synthetic dataset (or use a multivariate continuous dataset
 and construct the parameters of our base distribution:
 
 ```julia
-μ0 = vec(mean(X, 1))
+μ0 = vec(mean(X, dims = 1))
 κ0 = 5.0
 ν0 = 9.0
 Σ0 = cov(X)
@@ -65,7 +66,7 @@ which is in this case a Dirichlet Process Mixture. Each model has to be initiali
 modelBuffer = init(X, model, KMeansInitialisation(k = 10))
 ```
 
-The resulting buffer object can now be used to apply posterior inference on the model given $X$. In the following we apply Gibbs sampling for 500 iterations without burn in or thining:
+The resulting buffer object can now be used to apply posterior inference on the model given `X`. In the following we apply Gibbs sampling for 500 iterations without burn in or thining:
 
 ```julia
 models = train(modelBuffer, DPMHyperparam(), Gibbs(maxiter = 500))
@@ -74,19 +75,19 @@ models = train(modelBuffer, DPMHyperparam(), Gibbs(maxiter = 500))
 You shoud see the progress of the sampling process in the command line. After applying Gibbs sampling, it is possible explore the posterior based on their posterior densities,
 
 ```julia
-densities = Float64[m.energy for m in models]
+densities = map(m -> m.energy, models)
 ```
 
 number of active components
 
 ```julia
-activeComponents = Int[sum(m.weights .> 0) for m in models]
+activeComponents = map(m -> sum(m.weights .> 0), models)
 ```
 
 or the groupings of the observations:
 
 ```julia
-assignments = [m.assignments for m in models]
+assignments = map(m -> m.assignments, models)
 ```
 
 The following animation illustrates posterior samples obtained by a Dirichlet Process Mixture: 
@@ -111,12 +112,10 @@ end
 and find the optimal partition which minimizes the lower bound of the variation of information:
 
 ```julia
-mink = minimum([length(m.weights) for m in models])
-maxk = maximum([length(m.weights) for m in models])
+mink = minimum(length(m.weights) for m in models)
+maxk = maximum(length(m.weights) for m in models)
 (peassignments, _) = pointestimate(PSM, method = :average, mink = mink, maxk = maxk)
 ```
 
 The grouping wich minimizes the lower bound of the variation of information is illustrated in the following image:
 ![alt text](pointestimate.png "Point Estimate")
-
-[![Build Status](https://travis-ci.org/OFAI/BayesianNonparametrics.jl.svg?branch=master)](https://travis-ci.org/OFAI/BayesianNonparametrics.jl)