def generateTwoTestAngleData():
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the calculated angles are used as features.
'''
testSamples = []
index = 0
for filename in sub2:
#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 list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def generateTwoTestAngleData():
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the calculated angles are used as features.
'''
testSamples = []
index = 0
for filename in sub2:
#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 list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def generateOneTestPositionData(means, stdDevs):
'''
This creates a list of sample objects that can
be used for testing. This list is built from the third
participant's data and uses joint positions as features.
The poisitions are also normalized as they are added to
the list.
'''
testSamples = []
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = returnLine(line)
samp = sample(features, labels[index])
samp.normalizeValues(means, stdDevs)
testSamples.append(samp)
fIn.close()
index += 1
return testSamples
def generateOneTestPositionData(means, stdDevs):
'''
This creates a list of sample objects that can
be used for testing. This list is built from the third
participant's data and uses joint positions as features.
The poisitions are also normalized as they are added to
the list.
'''
testSamples = []
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = returnLine(line)
samp = sample(features, labels[index])
samp.normalizeValues(means, stdDevs)
testSamples.append(samp)
fIn.close()
index += 1
return testSamples
def generateDetectData(feat):
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the calculated angles are used as features.
'''
detectSamples = []
for line in feat:
features = np.array(line)
detectSamples.append(sample(features))
return detectSamples
def generateTwoTestPositionData(means, stdDevs):
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the joint poisitions are used as features.
'''
testSamples = []
index = 0
for filename in sub2:
#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 list.
for line in fIn:
features = returnLine(line)
samp = sample(features, labels[index])
samp.normalizeValues(means, stdDevs)
testSamples.append(samp)
fIn.close()
index += 1
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = returnLine(line)
samp = sample(features, labels[index])
samp.normalizeValues(means, stdDevs)
testSamples.append(samp)
fIn.close()
index += 1
return testSamples
def generateTwoTestPositionData(means, stdDevs):
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the joint poisitions are used as features.
'''
testSamples = []
index = 0
for filename in sub2:
#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 list.
for line in fIn:
features = returnLine(line)
samp = sample(features, labels[index])
samp.normalizeValues(means, stdDevs)
testSamples.append(samp)
fIn.close()
index += 1
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = returnLine(line)
samp = sample(features, labels[index])
samp.normalizeValues(means, stdDevs)
testSamples.append(samp)
fIn.close()
index += 1
return testSamples
def generateDetectData(filename):
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the calculated angles are used as features.
'''
detectSamples = []
# Open the file
fIn = open(filename, 'r')
for line in fIn:
features = np.array(json.loads(line)['feature'])
detectSamples.append(sample(features))
fIn.close()
return detectSamples
def generateOneTestAngleData():
'''
This creates a list of sample objects that can
be used for testing. This list is built from the third
participant's data and uses angles as features.
'''
testSamples = []
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def generateOneTestAngleData():
'''
This creates a list of sample objects that can
be used for testing. This list is built from the third
participant's data and uses angles as features.
'''
testSamples = []
index = 0
for filename in sub3:
#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 list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples