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183 lines
6.2 KiB
Python
Executable File
183 lines
6.2 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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The sample demonstrates how to train Random Trees classifier
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(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.
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We use the sample database letter-recognition.data
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from UCI Repository, here is the link:
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Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
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UCI Repository of machine learning databases
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[http://www.ics.uci.edu/~mlearn/MLRepository.html].
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Irvine, CA: University of California, Department of Information and Computer Science.
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The dataset consists of 20000 feature vectors along with the
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responses - capital latin letters A..Z.
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The first 10000 samples are used for training
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and the remaining 10000 - to test the classifier.
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======================================================
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USAGE:
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letter_recog.py [--model <model>]
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[--data <data fn>]
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[--load <model fn>] [--save <model fn>]
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Models: RTrees, KNearest, Boost, SVM, MLP
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'''
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import numpy as np
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import cv2
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def load_base(fn):
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a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
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samples, responses = a[:,1:], a[:,0]
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return samples, responses
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class LetterStatModel(object):
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class_n = 26
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train_ratio = 0.5
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def load(self, fn):
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self.model.load(fn)
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def save(self, fn):
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self.model.save(fn)
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def unroll_samples(self, samples):
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sample_n, var_n = samples.shape
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new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
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new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
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new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
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return new_samples
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def unroll_responses(self, responses):
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sample_n = len(responses)
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new_responses = np.zeros(sample_n*self.class_n, np.int32)
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resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
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new_responses[resp_idx] = 1
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return new_responses
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class RTrees(LetterStatModel):
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def __init__(self):
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self.model = cv2.RTrees()
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def train(self, samples, responses):
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sample_n, var_n = samples.shape
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var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
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#CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
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params = dict(max_depth=10 )
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self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
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def predict(self, samples):
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return np.float32( [self.model.predict(s) for s in samples] )
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class KNearest(LetterStatModel):
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def __init__(self):
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self.model = cv2.KNearest()
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def train(self, samples, responses):
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self.model.train(samples, responses)
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def predict(self, samples):
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retval, results, neigh_resp, dists = self.model.find_nearest(samples, k = 10)
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return results.ravel()
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class Boost(LetterStatModel):
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def __init__(self):
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self.model = cv2.Boost()
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def train(self, samples, responses):
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sample_n, var_n = samples.shape
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new_samples = self.unroll_samples(samples)
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new_responses = self.unroll_responses(responses)
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var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
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#CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
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params = dict(max_depth=5) #, use_surrogates=False)
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self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)
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def predict(self, samples):
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new_samples = self.unroll_samples(samples)
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pred = np.array( [self.model.predict(s, returnSum = True) for s in new_samples] )
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pred = pred.reshape(-1, self.class_n).argmax(1)
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return pred
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class SVM(LetterStatModel):
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def __init__(self):
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self.model = cv2.SVM()
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def train(self, samples, responses):
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params = dict( kernel_type = cv2.SVM_LINEAR,
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svm_type = cv2.SVM_C_SVC,
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C = 1 )
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self.model.train(samples, responses, params = params)
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def predict(self, samples):
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return self.model.predict_all(samples).ravel()
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class MLP(LetterStatModel):
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def __init__(self):
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self.model = cv2.ANN_MLP()
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def train(self, samples, responses):
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sample_n, var_n = samples.shape
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new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
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layer_sizes = np.int32([var_n, 100, 100, self.class_n])
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self.model.create(layer_sizes)
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# CvANN_MLP_TrainParams::BACKPROP,0.001
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params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
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train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
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bp_dw_scale = 0.001,
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bp_moment_scale = 0.0 )
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self.model.train(samples, np.float32(new_responses), None, params = params)
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def predict(self, samples):
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ret, resp = self.model.predict(samples)
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return resp.argmax(-1)
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if __name__ == '__main__':
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import getopt
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import sys
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print __doc__
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models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
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models = dict( [(cls.__name__.lower(), cls) for cls in models] )
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args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
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args = dict(args)
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args.setdefault('--model', 'rtrees')
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args.setdefault('--data', '../data/letter-recognition.data')
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print 'loading data %s ...' % args['--data']
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samples, responses = load_base(args['--data'])
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Model = models[args['--model']]
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model = Model()
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train_n = int(len(samples)*model.train_ratio)
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if '--load' in args:
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fn = args['--load']
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print 'loading model from %s ...' % fn
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model.load(fn)
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else:
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print 'training %s ...' % Model.__name__
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model.train(samples[:train_n], responses[:train_n])
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print 'testing...'
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train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
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test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:])
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print 'train rate: %f test rate: %f' % (train_rate*100, test_rate*100)
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if '--save' in args:
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fn = args['--save']
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print 'saving model to %s ...' % fn
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model.save(fn)
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cv2.destroyAllWindows()
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