julia mnist random forest

using HDF5
using DecisionTree

fn = "/home/phil/Downloads/mnist-original.h5"
n = 70000
data = float64(h5read( fn, "data/data", (1:n,:) ))
labels = uint64(h5read( fn, "data/label", )[1:n])


nData = size(data,1)

nTraining = 0.7*nData
selection = shuffle!(int(linspace(1,nData,nData)))

trainingData   = data[selection[1:nTraining],:]
trainingLabels = labels[selection[1:nTraining]]

testData   = data[selection[nTraining+1:end],:]
testLabels = labels[selection[nTraining+1:end]]

model = build_forest(trainingLabels, trainingData, 20, 10, 1.0)
pred  = apply_forest(model, testData)

accuracy = sum( pred .== testLabels ) / size(testLabels,1)

@printf "Accuracy=%f" accuracy