def errorCurvePotinFiveByAnnotation(talkSeries):
ma = MimiAnalyser('data/test_data_extremelyPure.csv')
errorV1 = pd.Series()
countV1 = pd.Series()
errorA1 = pd.Series()
countA1 = pd.Series()
for talk, sentencesInTalk in talkSeries.iteritems():
dfAudioV = ma.get_V_FrameByTalk(talk, 'Audio')
dfAudioA = ma.get_A_FrameByTalk(talk, 'Audio')
aveAudioV = dfAudioV.apply(np.mean, axis=0)
aveAudioA = dfAudioA.apply(np.mean, axis=0)
dfTextV = ma.get_V_FrameByTalk(talk, 'text')
dfTextA = ma.get_A_FrameByTalk(talk, 'text')
aveTextV = dfTextV.apply(np.mean, axis=0)
aveTextA = dfTextA.apply(np.mean, axis=0)
for i in sentencesInTalk:
d = pointFive(aveAudioV[i])
eV = errorV1
cV = countV1
if eV.get(d) == None:
cV.at[d] = 1
eV.at[d] = np.abs(d - pointFive(aveTextV[i]))
else:
cV.at[d] += 1
eV.at[d] += np.abs(d - pointFive(aveTextV[i]))
for i in sentencesInTalk:
d = pointFive(aveAudioA[i])
eA = errorA1
cA = countA1
if eA.get(d) == None:
cA.at[d] = 1
eA.at[d] = np.abs(d - pointFive(aveTextA[i]))
else:
cA.at[d] += 1
eA.at[d] += np.abs(d - pointFive(aveTextA[i]))
eV = eV.div(cV).sort_index()
cV = cV.sort_index()
eA = eA.div(cA).sort_index()
cA = cA.sort_index()
errorArray = [eV, eA]
countArray = [cV, cA]
for index, countSeries in enumerate(countArray):
for label, count in countSeries.iteritems():
if count < 1:
countArray[index] = countArray[index].drop(label)
errorArray[index] = errorArray[index].drop(label)
return [errorArray[0], errorArray[1], countArray[0], countArray[1]]
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mimicry_analyser import MimiAnalyser
ma = MimiAnalyser('data/test_data_filtered.csv')
THE_TALK = 13
dfAudioV = ma.get_V_FrameByTalk(THE_TALK, 'Audio')
dfAudioA = ma.get_A_FrameByTalk(THE_TALK, 'Audio')
aveAudioV = dfAudioV.apply(np.mean, axis=0)
aveAudioA = dfAudioA.apply(np.mean, axis=0)
dfTextV = ma.get_V_FrameByTalk(THE_TALK, 'text')
dfTextA = ma.get_A_FrameByTalk(THE_TALK, 'text')
aveTextV = dfTextV.apply(np.mean, axis=0)
aveTextA = dfTextA.apply(np.mean, axis=0)
dfFumojiV = ma.get_V_FrameByTalk(THE_TALK, 'Fumoji')
dfFumojiA = ma.get_A_FrameByTalk(THE_TALK, 'Fumoji')
aveFumojiV = dfFumojiV.apply(np.mean, axis=0)
aveFumojiA = dfFumojiA.apply(np.mean, axis=0)
data = {
"aveAudioV": aveAudioV.values,
"aveFumojiV": aveFumojiV.values,
"aveTextV": aveTextV.values
}
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mimicry_analyser import MimiAnalyser
ma = MimiAnalyser('data/test_data_pure.csv')
THE_TALK = 12
dfAudioV = ma.get_V_FrameByTalk(THE_TALK, 'audio')
dfAudioA = ma.get_A_FrameByTalk(THE_TALK, 'audio')
averageV = dfAudioV.apply(np.mean, axis=0)
averageA = dfAudioA.apply(np.mean, axis=0)
quadrantV = ma.getQuadrant(dfAudioV)
quadrantA = ma.getQuadrant(dfAudioA)
averageQV = quadrantV.apply(np.mean, axis=0)
averageQA = quadrantA.apply(np.mean, axis=0)
correctionQV = []
correctionQA = []
for index, r in dfAudioV.iterrows():
correctionQV.append(ma.quadrantCorrection(averageQV, r))
for index, r in dfAudioA.iterrows():
correctionQA.append(ma.quadrantCorrection(averageQA, r))
quadrantCorrectionV = pd.Series(correctionQV,
index=ma.user_id[THE_TALK - 2]).sort_values()
quadrantCorrectionA = pd.Series(correctionQA,
def errorCurvePotinFive():
ma = MimiAnalyser('data/test_data_extremelyPure.csv')
errorV1 = pd.Series()
countV1 = pd.Series()
errorV2 = pd.Series()
countV2 = pd.Series()
errorA1 = pd.Series()
countA1 = pd.Series()
errorA2 = pd.Series()
countA2 = pd.Series()
for talk in ma.talk_id:
dfAudioV = ma.get_V_FrameByTalk(talk, 'Audio')
dfAudioA = ma.get_A_FrameByTalk(talk, 'Audio')
aveAudioV = dfAudioV.apply(np.mean, axis=0)
aveAudioA = dfAudioA.apply(np.mean, axis=0)
dfTextV = ma.get_V_FrameByTalk(talk, 'text')
dfTextA = ma.get_A_FrameByTalk(talk, 'text')
aveTextV = dfTextV.apply(np.mean, axis=0)
aveTextA = dfTextA.apply(np.mean, axis=0)
for i in range(aveAudioV.size):
d = pointFive(aveAudioV[i])
if aveAudioA[i] < 0:
eV = errorV1
cV = countV1
elif aveAudioA[i] > 0:
eV = errorV2
cV = countV2
if eV.get(d) == None:
cV.at[d] = 1
eV.at[d] = np.abs(d - pointFive(aveTextV[i]))
else:
cV.at[d] += 1
eV.at[d] += np.abs(d - pointFive(aveTextV[i]))
for i in range(aveAudioA.size):
d = pointFive(aveAudioA[i])
if aveAudioV[i] < 0:
eA = errorA1
cA = countA1
elif aveAudioV[i] > 0:
eA = errorA2
cA = countA2
if eA.get(d) == None:
cA.at[d] = 1
eA.at[d] = np.abs(d - pointFive(aveTextA[i]))
else:
cA.at[d] += 1
eA.at[d] += np.abs(d - pointFive(aveTextA[i]))
errorV1 = errorV1.div(countV1).sort_index()
countV1 = countV1.sort_index()
errorA1 = errorA1.div(countA1).sort_index()
countA1 = countA1.sort_index()
errorV2 = errorV2.div(countV2).sort_index()
countV2 = countV2.sort_index()
errorA2 = errorA2.div(countA2).sort_index()
countA2 = countA2.sort_index()
errorArray = [errorV1, errorV2, errorA1, errorA2]
countArray = [countV1, countV2, countA1, countA2]
for index, countSeries in enumerate(countArray):
for label, count in countSeries.iteritems():
if count < 3:
countArray[index] = countArray[index].drop(label)
errorArray[index] = errorArray[index].drop(label)
data = {
'when Arousal < 0': errorArray[0],
'when Arousal > 0': errorArray[1]
}
ma.plotDict(data, 'error of Valence')
data = {
'when Valence < 0': errorArray[2],
'when Valence > 0': errorArray[3]
}
ma.plotDict(data, 'error of Arousal')