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6820abd67f
The load() function returns a new object, and as such does not use the one it is called on. This commit updates the uses of model.load in this program so it will work as intended and not throw an error.
188 lines
6.1 KiB
Python
Executable File
188 lines
6.1 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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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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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 = 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 = cv.ml.RTrees_create()
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def train(self, samples, responses):
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self.model.setMaxDepth(20)
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self.model.train(samples, cv.ml.ROW_SAMPLE, responses.astype(int))
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def predict(self, samples):
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_ret, resp = self.model.predict(samples)
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return resp.ravel()
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class KNearest(LetterStatModel):
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def __init__(self):
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self.model = cv.ml.KNearest_create()
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def train(self, samples, responses):
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self.model.train(samples, cv.ml.ROW_SAMPLE, responses)
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def predict(self, samples):
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_retval, results, _neigh_resp, _dists = self.model.findNearest(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 = cv.ml.Boost_create()
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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([cv.ml.VAR_NUMERICAL] * var_n + [cv.ml.VAR_CATEGORICAL, cv.ml.VAR_CATEGORICAL], np.uint8)
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self.model.setWeakCount(15)
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self.model.setMaxDepth(10)
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self.model.train(cv.ml.TrainData_create(new_samples, cv.ml.ROW_SAMPLE, new_responses.astype(int), varType = var_types))
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def predict(self, samples):
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new_samples = self.unroll_samples(samples)
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_ret, resp = self.model.predict(new_samples)
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return resp.ravel().reshape(-1, self.class_n).argmax(1)
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class SVM(LetterStatModel):
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def __init__(self):
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self.model = cv.ml.SVM_create()
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def train(self, samples, responses):
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self.model.setType(cv.ml.SVM_C_SVC)
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self.model.setC(1)
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self.model.setKernel(cv.ml.SVM_RBF)
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self.model.setGamma(.1)
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self.model.train(samples, cv.ml.ROW_SAMPLE, responses.astype(int))
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def predict(self, samples):
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_ret, resp = self.model.predict(samples)
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return resp.ravel()
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class MLP(LetterStatModel):
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def __init__(self):
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self.model = cv.ml.ANN_MLP_create()
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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.setLayerSizes(layer_sizes)
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self.model.setTrainMethod(cv.ml.ANN_MLP_BACKPROP)
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self.model.setBackpropMomentumScale(0.0)
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self.model.setBackpropWeightScale(0.001)
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self.model.setTermCriteria((cv.TERM_CRITERIA_COUNT, 20, 0.01))
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self.model.setActivationFunction(cv.ml.ANN_MLP_SIGMOID_SYM, 2, 1)
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self.model.train(samples, cv.ml.ROW_SAMPLE, np.float32(new_responses))
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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', 'svm')
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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].astype(int))
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test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:].astype(int))
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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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cv.destroyAllWindows()
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