def getPybrainDataSet(source='Rachelle'):
first = False#True
qualities, combinations = cp.getCombinations()
moods = combinations.keys()
ds = None
l=0
for mood in moods:
if mood=='neutral':
continue
for typeNum in range(1,21):
for take in range(1,10):
fileName = 'recordings/'+source+'/'+mood+'/'+\
str(typeNum)+'_'+str(take)+'.skl'
try:
data, featuresNames = ge.getFeatureVec(fileName, first)
first = False
except IOError:
continue
if ds is None:#initialization
ds = ClassificationDataSet( len(data), len(qualities) )
output = np.zeros((len(qualities)))
for q in combinations[mood][typeNum]:
output[qualities.index(q)] = 1
ds.appendLinked(data , output)
l+=sum(output)
return ds, featuresNames
from matplotlib.mlab import PCA
import mlpy
#coupels = [['anger', 'happy']]
coupels = [['anger', 'fear'], ['anger', 'happy'], ['anger', 'sad'], ['happy', 'fear'], ['sad', 'fear'], ['sad', 'happy']]
#moods = ['anger', 'fear']#, 'happy', 'sad', 'neutral']
#data2 = [[],[]]
for couple in coupels:
i=0
data = []
for mood in couple:
for typeNum in range(1,21):
for take in range(1,10):
fileName = '../inputs/Rachelle/v2/recordingsByMood/'+mood+'/'+\
str(typeNum)+'_'+str(take)+'.skl'
try:
vec = ge.getFeatureVec(fileName, ['WristRight_X', 'WristLeft_X'])
except IOError:
continue
data.append(vec)
#data2[couple.index(mood)].append(vec)
i+=couple.index(mood)
a= np.array(data)
print a.shape
results = PCA(a)
print results.fracs
x = []
y = []
for item in results.Y[:-i]:
x.append(item[0])
y.append(item[1])
plt.figure()
