'''
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.

We use the sample database letter-recognition.data
from UCI Repository, here is the link:

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html].
Irvine, CA: University of California, Department of Information and Computer Science.

The dataset consists of 20000 feature vectors along with the
responses - capital latin letters A..Z.
The first 10000 samples are used for training
and the remaining 10000 - to test the classifier.
======================================================
USAGE: 
  letter_recog.py [--model <model>] 
                  [--data <data fn>] 
                  [--load <model fn>] [--save <model fn>]

  Models: RTrees, KNearest, Boost, SVM, MLP
'''

import numpy as np
import cv2

def load_base(fn):
    a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
    samples, responses = a[:,1:], a[:,0]
    return samples, responses

class LetterStatModel(object):
    class_n = 26
    train_ratio = 0.5

    def load(self, fn):
        self.model.load(fn)
    def save(self, fn):
        self.model.save(fn)
    
    def unroll_samples(self, samples):
        sample_n, var_n = samples.shape
        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
        new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
        return new_samples
    
    def unroll_responses(self, responses):
        sample_n = len(responses)
        new_responses = np.zeros(sample_n*self.class_n, np.int32)
        resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
        new_responses[resp_idx] = 1
        return new_responses

class RTrees(LetterStatModel):
    def __init__(self):
        self.model = cv2.RTrees()

    def train(self, samples, responses):
        sample_n, var_n = samples.shape
        var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
        #CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
        params = dict(max_depth=10 )
        self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)

    def predict(self, samples):
        return np.float32( [self.model.predict(s) for s in samples] )
        

class KNearest(LetterStatModel):
    def __init__(self):
        self.model = cv2.KNearest()

    def train(self, samples, responses):
        self.model.train(samples, responses)

    def predict(self, samples):
        retval, results, neigh_resp, dists = self.model.find_nearest(samples, k = 10)
        return results.ravel()


class Boost(LetterStatModel):
    def __init__(self):
        self.model = cv2.Boost()
    
    def train(self, samples, responses):
        sample_n, var_n = samples.shape
        new_samples = self.unroll_samples(samples)
        new_responses = self.unroll_responses(responses)
        var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
        #CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
        params = dict(max_depth=5) #, use_surrogates=False)
        self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)

    def predict(self, samples):
        new_samples = self.unroll_samples(samples)
        pred = np.array( [self.model.predict(s, returnSum = True) for s in new_samples] )
        pred = pred.reshape(-1, self.class_n).argmax(1)
        return pred


class SVM(LetterStatModel):
    def __init__(self):
        self.model = cv2.SVM()

    def train(self, samples, responses):
        params = dict( kernel_type = cv2.SVM_LINEAR, 
                       svm_type = cv2.SVM_C_SVC,
                       C = 1 )
        self.model.train(samples, responses, params = params)

    def predict(self, samples):
        return self.model.predict_all(samples).ravel()


class MLP(LetterStatModel):
    def __init__(self):
        self.model = cv2.ANN_MLP()

    def train(self, samples, responses):
        sample_n, var_n = samples.shape
        new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)

        layer_sizes = np.int32([var_n, 100, 100, self.class_n])
        self.model.create(layer_sizes)
        
        # CvANN_MLP_TrainParams::BACKPROP,0.001
        params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
                       train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP, 
                       bp_dw_scale = 0.001,
                       bp_moment_scale = 0.0 )
        self.model.train(samples, np.float32(new_responses), None, params = params)

    def predict(self, samples):
        ret, resp = self.model.predict(samples)
        return resp.argmax(-1)


if __name__ == '__main__':
    import getopt
    import sys

    print  __doc__

    models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
    models = dict( [(cls.__name__.lower(), cls) for cls in models] )

    
    args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
    args = dict(args)
    args.setdefault('--model', 'rtrees')
    args.setdefault('--data', '../cpp/letter-recognition.data')

    print 'loading data %s ...' % args['--data']
    samples, responses = load_base(args['--data'])
    Model = models[args['--model']]
    model = Model()

    train_n = int(len(samples)*model.train_ratio)
    if '--load' in args:
        fn = args['--load']
        print 'loading model from %s ...' % fn
        model.load(fn)
    else:
        print 'training %s ...' % Model.__name__
        model.train(samples[:train_n], responses[:train_n])

    print 'testing...'
    train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
    test_rate  = np.mean(model.predict(samples[train_n:]) == responses[train_n:])

    print 'train rate: %f  test rate: %f' % (train_rate*100, test_rate*100)

    if '--save' in args:
        fn = args['--save']
        print 'saving model to %s ...' % fn
        model.save(fn)
    cv2.destroyAllWindows() 			
