#!/usr/bin/env python
'''
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', '../data/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()