def feature_vector_communicate(data, iscommunicate=False, test=False):
trimmed_data = trim_or_pad_data(data, TRIM_DATA_SIZE_COMMUNICATE)
featureVector = feature_vector_communicate_ind(trimmed_data, 'rightWrist_x', iscommunicate, test=True)
featureVector = np.append(featureVector, feature_vector_communicate_ind(trimmed_data, 'rightWrist_y', iscommunicate, test=True))
featureVector = np.append(featureVector, feature_vector_communicate_ind(trimmed_data, 'leftWrist_y', iscommunicate, test=True))
featureVector = np.append(featureVector, feature_vector_communicate_ind(trimmed_data, 'leftWrist_y', iscommunicate, test))
return featureVector
def feature_vector_buy(data, isBuy=False, test=False):
trimmed_data = trim_or_pad_data(data, TRIM_DATA_SIZE_BUY)
featureVector = feature_vector_buy_ind(trimmed_data,
'rightWrist_x',
isBuy,
test=True)
featureVector = np.append(
featureVector,
feature_vector_buy_ind(trimmed_data, 'rightWrist_y', isBuy, test))
return featureVector
def feature_vector_mother(data, isMother=False, test=False):
trimmed_data = trim_or_pad_data(data, TRIM_DATA_SIZE_MOTHER)
rY = trimmed_data['rightWrist_y']
normRawColumn = universal_normalization(rY, trimmed_data, x_norm=False)
normRawColumn = general_normalization(normRawColumn)
diffNormRawData = np.diff(normRawColumn)
zeroCrossingArray = np.array([])
maxDiffArray = np.array([])
if diffNormRawData[0] > 0:
initSign = 1
else:
initSign = 0
windowSize = 5
for x in range(1, len(diffNormRawData)):
if diffNormRawData[x] > 0:
newSign = 1
else:
newSign = 0
if initSign != newSign:
zeroCrossingArray = np.append(zeroCrossingArray, x)
initSign = newSign
maxIndex = np.minimum(len(diffNormRawData), x + windowSize)
minIndex = np.maximum(0, x - windowSize)
maxVal = np.amax(diffNormRawData[minIndex:maxIndex])
minVal = np.amin(diffNormRawData[minIndex:maxIndex])
maxDiffArray = np.append(maxDiffArray, (maxVal - minVal))
index = np.argsort(-maxDiffArray)
featureVector = np.array([])
featureVector = np.append(featureVector, zeroCrossingArray[index[0:5]])
featureVector = np.append(featureVector, maxDiffArray[index[0:5]])
if TRIM_DATA_SIZE_MOTHER - 1 > featureVector.shape[0]:
featureVector = np.pad(
featureVector,
(0, TRIM_DATA_SIZE_MOTHER - featureVector.shape[0] - 1),
'constant')
featureVector = featureVector[:TRIM_DATA_SIZE_MOTHER - 1]
if not test:
if isMother:
featureVector = np.append(featureVector, 1)
else:
featureVector = np.append(featureVector, 0)
return featureVector
def feature_vector_fun(data, isFun=False, test=False):
trimmed_data = trim_or_pad_data(data, TRIM_DATA_SIZE_FUN)
rX = trimmed_data['rightWrist_x']
normRawColumn = universal_normalization(rX, trimmed_data, x_norm=True)
normRawColumn = general_normalization(normRawColumn)
# Area under curve
auc = np.array([])
auc = np.append(auc, abs(integrate.simps(normRawColumn, dx=5)))
# Absolute Sum of Consecutive Differences
scd = fc.absolute_sum_of_changes(normRawColumn)
# Entropy
entropy = fc.approximate_entropy(normRawColumn, 2, 3)
# AutoCorrelation
ac = fc.autocorrelation(normRawColumn, lag=5)
# Count Above Mean
cam = fc.count_above_mean(normRawColumn)
# Count Below Mean
cbm = fc.count_below_mean(normRawColumn)
featureVector = np.array([])
featureVector = np.append(featureVector, auc)
featureVector = np.append(featureVector, scd)
featureVector = np.append(featureVector, entropy)
featureVector = np.append(featureVector, ac)
featureVector = np.append(featureVector, cam)
featureVector = np.append(featureVector, cbm)
if TRIM_DATA_SIZE_FUN - 1 > featureVector.shape[0]:
featureVector = np.pad(
featureVector,
(0, TRIM_DATA_SIZE_FUN - featureVector.shape[0] - 1), 'constant')
featureVector = featureVector[:TRIM_DATA_SIZE_FUN - 1]
if not test:
if isFun:
featureVector = np.append(featureVector, 1)
else:
featureVector = np.append(featureVector, 0)
return featureVector
def feature_vector_hope(data, isHope=False, test=False):
trimmed_data = trim_or_pad_data(data, TRIM_DATA_SIZE_HOPE)
rY = trimmed_data['rightWrist_y']
lY = trimmed_data['leftWrist_y']
normRawColumn = universal_normalization(rY, trimmed_data, x_norm=False)
normRawColumn = general_normalization(normRawColumn)
diffNormRawData = np.diff(normRawColumn)
zeroCrossingArray = np.array([])
maxDiffArray = np.array([])
#Fast Fourier Transform
fftArray = np.array([])
fftVal = []
fft_coefficients = fft(diffNormRawData, n=6)[1:]
fft_coefficients_real = [value.real for value in fft_coefficients]
fftVal += fft_coefficients_real
fftArray = np.append(fftArray, fftVal)
#Area under curve
auc = np.array([])
auc = np.append(auc, abs(integrate.simps(diffNormRawData, dx=5)))
#Kurtosis
kur = np.array([])
kur = np.append(kur, kurtosis(diffNormRawData))
if diffNormRawData[0] > 0:
initSign = 1
else:
initSign = 0
windowSize = 5
for x in range(1, len(diffNormRawData)):
if diffNormRawData[x] > 0:
newSign = 1
else:
newSign = 0
if initSign != newSign:
zeroCrossingArray = np.append(zeroCrossingArray, x)
initSign = newSign
maxIndex = np.minimum(len(diffNormRawData), x + windowSize)
minIndex = np.maximum(0, x - windowSize)
maxVal = np.amax(diffNormRawData[minIndex:maxIndex])
minVal = np.amin(diffNormRawData[minIndex:maxIndex])
maxDiffArray = np.append(maxDiffArray, (maxVal - minVal))
index = np.argsort(-maxDiffArray)
featureVector = np.array([])
featureVector = np.append(featureVector, fftArray)
featureVector = np.append(featureVector, auc)
featureVector = np.append(featureVector, kur)
featureVector = np.append(featureVector, maxDiffArray[index[0:5]])
if TRIM_DATA_SIZE_HOPE - 1 > featureVector.shape[0]:
featureVector = np.pad(
featureVector,
(0, TRIM_DATA_SIZE_HOPE - featureVector.shape[0] - 1), 'constant')
featureVector = featureVector[:TRIM_DATA_SIZE_HOPE - 1]
if not test:
if isHope:
featureVector = np.append(featureVector, 1)
else:
featureVector = np.append(featureVector, 0)
return featureVector