Random Forest Prediction

It is light random forest for prediction. The implementation is exact as the Piotr Dollar's. In this directory include two files:

• forest.hpp: does random forest prediction (label and probabilities)
• convertPiotrRF2Cpp.m: convert Piotr Dollar's random forest model into *.txt so that the previous C++ file could read it.

This implementation requires much lower amoung memory for random forest model, does not like OpenCV version. This version requires x100 lesser than OpenCV

How to use

From Matlab:

X = [randn(100,2); randn(100,2)];
y = [ones(100,1); 2*ones(100,1)]; % just 2 labels (1 and 2)
model = forestTrain(X,y,'M', 20); % here, trains a random forest of 20 trees
convertPiotrRF3Cpp(model, 'model.txt'); % here, does conversion

From C++:

#include "forest.hpp"

...
Forest forest;
forest.read("model.txt"); // read random forest model

float X[2] = {0.1, 0.2};
float *probabilities = new float[2]; // just 2 labels
memset(probabilities, 0, sizeof(float) * 2);

int label = forest.predict(X, probabilities);
// probabilities[0] is probability of label 1
// probabilities[1] is probability of label 2

...