Python gyroWalkingData示例

示例#1

	def __init__(self, training_subjects=['a1','a2','c1','c2'], test_subjects=['b'], db=None, sliding_window_size=3):
		if not db:
			db = gyroWalkingData()
		self.db = db

		self.trainingSet = self.datasetFromSubjects(training_subjects, sliding_window_size=sliding_window_size)
		self.testingSet = self.datasetFromSubjects(test_subjects, sliding_window_size=sliding_window_size)

示例#2

def classificationDataSet(subjects=['a2','b','c1','c2'], segClass=0, db=None, seg_width=10, usePCA=True, n_components=5, isTrainingData=False):
	if not db:
		db = gyroWalkingData()

	if usePCA:
		DS = ClassificationDataSet(n_components*3, nb_classes=2)
	else:
		DS = ClassificationDataSet(21*3, nb_classes=2)
	
	for subject in subjects:
		# Initialise data
		if usePCA:
			raw = db.pca_dict(n_components=n_components, whiten=False)[subject]
		else:
			raw = db.data[subject][:,2:]
		gradients, standardDeviations = summaryStatistics(raw, std_window=seg_width)

		# Initialise segments
		if 0 <= segClass < 4:
			segs = [s for s,c in db.manual_gait_segments[subject] if c == segClass]
		else:
			segs = db.segments[subject]

		# Add data
		for i in range(0,len(raw)):

			"""
			# Look for segments in window, including those of other classes
			hasSeg = 0
			hasOtherSeg = False
			for j in range(seg_width):
				if i+j in segs:
					hasSeg = 1
				else:
					if i+j in zip(*db.manual_gait_segments[subject])[0]:
						hasOtherSeg = True
				if hasOtherSeg:
					hasSeg = 0

			# Add segments to classifier, duplicating rare classes if it is training data
			for j in range(seg_width):
				if i+j < len(raw):
					DS.appendLinked( np.concatenate( [raw[i+j],gradients[i+j],standardDeviations[i+j]] ), [hasSeg] )
					if isTrainingData and (hasSeg or hasOtherSeg):
						for i in range(0):
							DS.appendLinked( np.concatenate( [raw[i+j],gradients[i+j],standardDeviations[i+j]] ), [hasSeg] )
			"""

			hasSeg = 0
			if i in segs:
				hasSeg = 1
			DS.appendLinked( np.concatenate( [raw[i],gradients[i],standardDeviations[i]] ), [hasSeg] )
	
	DS._convertToOneOfMany()
	if isTrainingData:
		DS = balanceClassRatios(DS)
	return DS

示例#3

def demo(subject='a1', segClass=0, plot_stages=True, saveGraph=False, n_most_likely=50, windowSize=5, clusterThresh=20):
	db = gyroWalkingData()
	print "Building neural network"
	net,trainer=networkAndTrainer(db, segClass=segClass, subjects=list( set(db.segments) - set([subject])), windowSize=windowSize)
	print "Training network..."
	train(trainer, epochs=25)
	print "Finding segments"
	testingData = classificationDataSet(subjects=[subject], segClass=segClass, seg_width=windowSize, db=db)
	top_segments, likelihoods = getSegments(net, db, testingData, subject=subject, n_segments=n_most_likely, plot=plot_stages)
	print "Clustering segments"
	clusters = getClusters(top_segments, db, subject=subject, thresh=clusterThresh, plot=plot_stages)
	print "Showing most likely segment from each cluster"
	segments = mostLikelySegsInClusters(top_segments, clusters, likelihoods)
	db.plotSegments(db.data[subject], segments, name=("%s - segClass %i"%(subject, segClass)), save=saveGraph)

示例#4

def demo(subject='a1', plot_stages=True):
	db = gyroWalkingData()
	print "Building neural network"
	net,trainer=networkAndTrainer(db, holdouts=[subject])
	print "Training network..."
	train(trainer, epochs=25)
	print "Finding segments"
	testingData = classificationDataSet(subject=subject, db=db)
	top_segments, likelihoods = getSegments(net, db, testingData, subject=subject, n_segments=140, plot=plot_stages)
	print "Clustering segments"
	clusters = getClusters(top_segments, db, subject=subject, plot=plot_stages)
	print "Showing most likely segment from each cluster"
	segments = mostLikelySegsInClusters(top_segments, clusters, likelihoods)
	db.plotSegments(db.data[subject], segments)

示例#5

def classificationDataSet(subject='a1', db=None):
	if not db:
		db = gyroWalkingData()

	raw = db.data[subject][:,2:]
	segs = db.segments[subject]
	DS = ClassificationDataSet(21, nb_classes=2)

	for i in range(0,len(raw),5):
		hasSeg = 0
		for j in range(5):
			if i+j in segs:
				hasSeg = 1
		for j in range(5):
			if i+j < len(raw):
				DS.appendLinked(raw[i+j],[hasSeg])
	DS._convertToOneOfMany()
	return DS

示例#6

def sequenceClassificationDataSet(subject='a1', db=None):
	"""Don't know if this is set up right or how to use it"""
	if not db:
		db = gyroWalkingData()

	raw = db.data[subject][:,2:]
	segs = db.segments[subject]
	DS = SequenceClassificationDataSet(21, 1)

	for i in range(0,len(raw),10):
		DS.newSequence()
		isSeg = 0
		for j in range(10):
			if i+j in segs:
				isSeg = 1
			else:
				isSeg = 0
			DS.appendLinked(raw[i+j],[isSeg])
	DS._convertToOneOfMany()
	return DS

示例#7

def classificationTrainingSet(holdouts=['a1'], db=None):
	if not db:
		db = gyroWalkingData()

	DS = ClassificationDataSet(21, nb_classes=2)
	
	for subject in db.data:
		if subject not in holdouts:
			raw = db.data[subject][:,2:]
			segs = db.segments[subject]

			seg_width = 2
			for i in range(0,len(raw),seg_width):
				hasSeg = 0
				for j in range(seg_width):
					if i+j in segs:
						hasSeg = 1
				for j in range(seg_width):
					if i+j < len(raw):
						DS.appendLinked(raw[i+j],[hasSeg])
	
	DS._convertToOneOfMany()
	return DS