def generateTwoPositionTrainingData():
'''
This function creates training data using the first
two participants' data and has joint positions as features
for the samples.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
#For each line of the files, create a feature vector
# of the raw joint positions. Add that to the trainingData.
for line in fIn:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
index += 1
return trainingData
def generateTwoAngleTrainingData():
'''
This function creates training data using the first
two participants' data and has joint angles as features
for the samples.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
index += 1
return trainingData
def generateOneTrainPositionData():
'''
This creates a data set to be used to train a classifier.
The data is from participant 1 and uses the joint positions
as features.
'''
trainingData = TrainingData("Position Data")
index = 0
for filename in sub1:
#Open the file
fIn = open(filename, 'r')
#For each line of the files, create a feature vector
# of the raw joint positions. Add that to the trainingData.
for line in fIn:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
index += 1
return trainingData
def generateOneTrainPositionData():
'''
This creates a data set to be used to train a classifier.
The data is from participant 1 and uses the joint positions
as features.
'''
trainingData = TrainingData("Position Data")
index = 0
for filename in sub1:
#Open the file
fIn = open(filename, 'r')
#For each line of the files, create a feature vector
# of the raw joint positions. Add that to the trainingData.
for line in fIn:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
index += 1
return trainingData
def generateTwoPositionTrainingData():
'''
This function creates training data using the first
two participants' data and has joint positions as features
for the samples.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
#For each line of the files, create a feature vector
# of the raw joint positions. Add that to the trainingData.
for line in fIn:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
index += 1
return trainingData
def generateTwoAngleTrainingData():
'''
This function creates training data using the first
two participants' data and has joint angles as features
for the samples.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
index += 1
return trainingData
def generateTrainData():
'''
This creates a data set to be used to train a classifier.
The data is from participant 1 and uses the calculated
joint angles as features.
'''
trainingData = TrainingData("train data")
index = 0
for filename in sub1:
# Open the file
fIn = open(filename, 'r')
# For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = np.array(json.loads(line)['feature'])
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
index += 1
return trainingData
def crossValidationPositions():
'''
Performs 10 fold cross validation on the total
joint position dataset
'''
theData = generateAllPositionTrainingData()
means, stdDevs = theData.normalizeData()
k = 10
#Partition the data into 10 subsets
dataSets = theData.getKSegments(k)
#For each of the 10 subsets leave one out, train on the
# other 9, test on the one left out, print the accuracy.
results = confusionMatrix(labels)
for i in xrange(k):
print i
#testing set
testSet = dataSets[i]
#Build the training set
trainingSet = TrainingData("CrossVal")
trainingList = copy.deepcopy(dataSets)
trainingList.pop(i)
for elem in trainingList:
trainingSet.combineWithNewData(elem)
#train the classifier on the trainingSet
testForest = RandomForest(trainingSet)
testForest.train()
#Evaluate the classifer on the test set
for samp in testSet.getData():
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def crossValidationPositions():
'''
Performs 10 fold cross validation on the total
joint position dataset
'''
theData = generateAllPositionTrainingData()
means, stdDevs = theData.normalizeData()
k = 10
#Partition the data into 10 subsets
dataSets = theData.getKSegments(k)
#For each of the 10 subsets leave one out, train on the
# other 9, test on the one left out, print the accuracy.
results = confusionMatrix(labels)
for i in xrange(k):
print i
#testing set
testSet = dataSets[i]
#Build the training set
trainingSet = TrainingData("CrossVal")
trainingList = copy.deepcopy(dataSets)
trainingList.pop(i)
for elem in trainingList:
trainingSet.combineWithNewData(elem)
#train the classifier on the trainingSet
testForest = RandomForest(trainingSet)
testForest.train()
#Evaluate the classifer on the test set
for samp in testSet.getData():
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def generateOneTrainAngleData():
'''
This creates a data set to be used to train a classifier.
The data is from participant 1 and uses the calculated
joint angles as features.
'''
trainingData = TrainingData("Angle Data")
index = 0
for filename in sub1:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
index += 1
return trainingData
def generateAllAngleTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
calculated angles as the features for each data sample.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
for line in f3In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f3In.close()
index += 1
#Return the data object
return trainingData
def generateAllPositionTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
regular position data as the features for each data sample.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files, create a feature vector
# of the raw joint positions. Add that to the trainingData.
for line in fIn:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
for line in f3In:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
f3In.close()
index += 1
#Return the data
return trainingData
def generateAllAngleTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
calculated angles as the features for each data sample.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
for line in f3In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f3In.close()
index += 1
#Return the data object
return trainingData
def generateAllPositionTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
regular position data as the features for each data sample.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files, create a feature vector
# of the raw joint positions. Add that to the trainingData.
for line in fIn:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
for line in f3In:
features = returnLine(line)
trainingData.addSampleFromFeatures(features, labels[index])
f3In.close()
index += 1
#Return the data
return trainingData
