Usage.md

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-
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-- Install any packages you want to use. For our example, switch to the package manager with "\]" and run:
-
-   ```
-   add LowRankModels
-   add DecisionTree
-   ```
-- Congratulations! As an example, run:
-
-   ```
-   using ConML
-   using LowRankModels # some algorithms for Construction and Reconstruction, use what you want
-   using DecisionTree
-   
-   toy = open("Path\\To\\toy.csv", "r")
-   readline(toy) # skip header
-   
-   # empty arrays for the data
-   T = Vector{Int}()
-   Sigma = Vector{String}()
-   Z = Vector{String}()
-   data = Vector{Vector{Float64}}()
-   
-   # separate data from metadata columns
-   for rline in readlines(toy)
-       line = split(rline,',')
-       push!(T, parse(Int,line[353]))
-       push!(Sigma, string(line[354]))
-       push!(Z, string(line[355]))
-       push!(data, map(x -> parse(Float64,x),line[2:352]))
-   end
-   
-   # cast it into a proper format for the algorithm
-   block = ConML.VMS{Float64}(T, Sigma, Z, data)
-   
-   # set parameters
-   # we have to specify values for which no defaults exist
-   par = ConML.ParametersConML(LearnBlockMinimum = 1, maxCategories = 10, MinCategorySize = 20, maxFilterFeatures = 1000, maxFilterSamples = 2000)
-   # create empty knowledge base
-   kb = ConML.KnowledgeBase{Int}(ConML.VMS{Int}(),Vector{ConML.MachineModel}())
-   # create steps for the algorithm
-   myConstruction = ConML.Construct([LowRankModels.KMeans(k=2), LowRankModels.KMeans(k=3), LowRankModels.KMeans(k=4)])
-   myReconstruction = ConML.Reconstruct([DecisionTree.DecisionTreeClassifier(max_depth=2), DecisionTree.AdaBoostStumpClassifier(n_iterations=10)])
-   featureSelection = ConML.FeatureSelector()
-   
-   # make a pipeline
-   learn = ConML.LearnerConML(kb, par, [ConML.searchLearnBlocks, myConstruction, featureSelection, myReconstruction])
-   
-   # feed it!
-   learn(block)
-   
-   ```
-- To update the packages, navigate to the project folder and do a git pull. Afterwards, go to the julia package manager ("\]") and run "update".
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+## Package Installation
+Install any packages you want to use. For our example, switch to the package manager with `\]` and run:
+```julia
+add LowRankModels
+add DecisionTree
+```
+
+## Quickstart
+Congratulations! As an example, run:
+
+```julia
+using ConML
+using LowRankModels # some algorithms for Construction and Reconstruction, use what you want
+using DecisionTree
+
+toy = open("Path\\To\\toy.csv", "r")
+readline(toy) # skip header
+
+# empty arrays for the data
+T = Vector{Int}()
+Sigma = Vector{String}()
+Z = Vector{String}()
+data = Vector{Vector{Float64}}()
+
+# separate data from metadata columns
+for rline in readlines(toy)
+      line = split(rline,',')
+      push!(T, parse(Int,line[353]))
+      push!(Sigma, string(line[354]))
+      push!(Z, string(line[355]))
+      push!(data, map(x -> parse(Float64,x),line[2:352]))
+end
+
+# cast it into a proper format for the algorithm
+block = ConML.VMS{Float64}(T, Sigma, Z, data)
+
+# set parameters
+# we have to specify values for which no defaults exist
+par = ConML.ParametersConML(LearnBlockMinimum = 1, maxCategories = 10, MinCategorySize = 20, maxFilterFeatures = 1000, maxFilterSamples = 2000)
+# create empty knowledge base
+kb = ConML.KnowledgeBase{Int}(ConML.VMS{Int}(),Vector{ConML.MachineModel}())
+# create steps for the algorithm
+myConstruction = ConML.Construct([LowRankModels.KMeans(k=2), LowRankModels.KMeans(k=3), LowRankModels.KMeans(k=4)])
+myReconstruction = ConML.Reconstruct([DecisionTree.DecisionTreeClassifier(max_depth=2), DecisionTree.AdaBoostStumpClassifier(n_iterations=10)])
+featureSelection = ConML.FeatureSelector()
+
+# make a pipeline
+learn = ConML.LearnerConML(kb, par, [ConML.searchLearnBlocks, myConstruction, featureSelection, myReconstruction])
+
+# feed it!
+learn(block)
+
+```
+
+## Update
+Navigate to the project folder and run `git pull`. Afterwards, go to the julia package manager (`\]`) and run `update`.
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