Classic AdaBoost Classifier

This a classic AdaBoost implementation, in one single file with easy understandable code.

The function consist of two parts a simple weak classifier and a boosting part:
The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes -1 and 1
The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier"
.
Training mode:
  [estimateclass,model]=adaboost('train',datafeatures,dataclass,itt)
Apply mode:
 estimateclass=adaboost('apply',datafeatures,model)
 
inputs/outputs:
   datafeatures : An Array with size number_samples x number_features
   dataclass : An array with the class off all examples, the class
                 can be -1 or 1
   itt : The number of training iterations
   model : A struct with the cascade of weak-classifiers
   estimateclass : The by the adaboost model classified data
            
.
Please leave a comment, if you like the code, find a bug or have a suggestion.

Changed Screenshot and example figure

Solved division by zero, causing NaN

Changed bug : ndims(datafeatures)to size(datafeatures,2)

Speed improvement (Replaced loops by 1D indexing and bsxfun operations.)
The function now limits features of the test data to the outer-boundaries of training data.

Fixed boundary bug