def findConcentrationAux(tagClassification):
featList=vars(tagClassification['a1'][0])
del featList['tE']
del featList['tag']
del featList['bBox']
del featList['center']
feats2Rec=copy.deepcopy(featList)
del feats2Rec['Coord']
del feats2Rec['Style']
del feats2Rec['coG']
l1=[symbol for symbol in tagClassification if symbol[-1]=='1']
l2=[symbol for symbol in tagClassification if symbol[-1]=='2']
l3=[symbol for symbol in tagClassification if symbol[-1]=='3']
standevs={}
for fe in feats2Rec:
for llet in tagClassification:
for i in range(len(tagClassification[llet])):
new=True
for j in range(len(vars(tagClassification[llet][i])[fe])):
if math.isnan(vars(tagClassification[llet][i])[fe][j]) and new:
tagClassification[llet][i].computeFeatures()
new=False
for symbol in tagClassification:
for i in range(len(tagClassification[symbol])):
if tagClassification[symbol][i].Style=='diagonal':
tagClassification[symbol][i].Style=[[1,1]]
elif tagClassification[symbol][i].Style=='horizontal':
tagClassification[symbol][i].Style=[[1,2]]
elif tagClassification[symbol][i].Style=='vertical':
tagClassification[symbol][i].Style=[[2,1]]
elif tagClassification[symbol][i].Style=='closed':
tagClassification[symbol][i].Style=[[2,2]]
for feature in featList:
print feature
if feature=='Coord' or feature=='Style' or feature=='coG':
standevs[feature]=[math.sqrt((((float(float(np.nansum([statistics.pstdev([vars(mostra)[feature][i][0] for mostra in tagClassification[symbol]])/np.mean([vars(mostra)[feature][i][0] for mostra in tagClassification[symbol]]) for i in range(len(vars(tagClassification[symbol][0])[feature]))]))/len(vars(tagClassification[symbol][0])[feature])))/(float((np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j][0] for mostra in tagClassification[altSymbol]] for altSymbol in l1]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j][0] for mostra in tagClassification[altSymbol]] for altSymbol in l1])) for j in range(len(vars(tagClassification['a1'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature]))+(np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j][0] for mostra in tagClassification[altSymbol]] for altSymbol in l2]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j][0] for mostra in tagClassification[altSymbol]] for altSymbol in l2])) for j in range(len(vars(tagClassification['+2'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature]))+(np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j][0] for mostra in tagClassification[altSymbol]] for altSymbol in l3]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j][0] for mostra in tagClassification[altSymbol]] for altSymbol in l3])) for j in range(len(vars(tagClassification['A3'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature])))/3))**2)+(((float(float(np.nansum([statistics.pstdev([vars(mostra)[feature][i][1] for mostra in tagClassification[symbol]])/np.mean([vars(mostra)[feature][i][1] for mostra in tagClassification[symbol]]) for i in range(len(vars(tagClassification[symbol][0])[feature]))]))/len(vars(tagClassification[symbol][0])[feature])))/(float((np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j][1] for mostra in tagClassification[altSymbol]] for altSymbol in l1]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j][1] for mostra in tagClassification[altSymbol]] for altSymbol in l1])) for j in range(len(vars(tagClassification['a1'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature]))+(np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j][1] for mostra in tagClassification[altSymbol]] for altSymbol in l2]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j][1] for mostra in tagClassification[altSymbol]] for altSymbol in l2])) for j in range(len(vars(tagClassification['+2'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature]))+(np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j][1] for mostra in tagClassification[altSymbol]] for altSymbol in l3]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j][1] for mostra in tagClassification[altSymbol]] for altSymbol in l3])) for j in range(len(vars(tagClassification['A3'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature])))/3))**2)) for symbol in tagClassification]
else:
standevs[feature]=[((float(float(np.nansum([statistics.pstdev([vars(mostra)[feature][i] for mostra in tagClassification[symbol]])/np.mean([vars(mostra)[feature][i] for mostra in tagClassification[symbol]]) for i in range(len(vars(tagClassification[symbol][0])[feature]))]))/len(vars(tagClassification[symbol][0])[feature])))/(float((np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j] for mostra in tagClassification[altSymbol]] for altSymbol in l1]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j] for mostra in tagClassification[altSymbol]] for altSymbol in l1])) for j in range(len(vars(tagClassification['a1'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature]))+(np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j] for mostra in tagClassification[altSymbol]] for altSymbol in l2]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j] for mostra in tagClassification[altSymbol]] for altSymbol in l2])) for j in range(len(vars(tagClassification['+2'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature]))+(np.nansum([statistics.pstdev(reduce(operator.add,[[vars(mostra)[feature][j] for mostra in tagClassification[altSymbol]] for altSymbol in l3]))/np.mean(reduce(operator.add,[[vars(mostra)[feature][j] for mostra in tagClassification[altSymbol]] for altSymbol in l3])) for j in range(len(vars(tagClassification['A3'][0])[feature]))])/len(vars(tagClassification[symbol][0])[feature])))/3)) for symbol in tagClassification]
print standevs
if os.path.isfile('varStandarDevs.txt'):
os.remove('varStandarDevs.txt')
f = open('varStandarDevs.txt','wb')
pickle.dump(standevs,f)
f.close()
#PonderateByConcentration: Retorna la ponderacio de les features segons les desviacions estandar que carrega del sistema
评论列表
文章目录
