def CART(trainData):
if len(getClassesByData(trainData)) == 1:
leaf = {"type": "leaf", "class": trainData[0]["y"]}
return leaf
minE = len(trainData)
minT = 0.
minD = 0
dimN = len(trainData[0]["x"])
for i in xrange(dimN):
dimX = getDimN(trainData, i)
hyps = buildHyp(dimX)
for th in hyps:
da, db = splitData(trainData, i, th)
ip = imp(da) + imp(db)
#print i,th,ip
if ip < minE:
minE = ip
minT = th
minD = i
da, db = splitData(trainData, minD, minT)
#print minD,minT,minE,len(da),imp(da),len(db),imp(db)
node = {"type": "node", "dim": minD, "th": minT}
rt = CART(da)
rl = CART(db)
node["rtree"] = rt
node["ltree"] = rl
return node
def Ads(trainData,U):
dimN = len(trainData[0]["x"])
sign = [1,-1]
err = len(trainData)
en = len(trainData)
dim = 0
th = 0
sig = 1
meu = 0.
mcu = 0.
for i in range(dimN):
hyps = buildHyp(getDimN(trainData,i))
for s in sign:
for h in hyps:
eu,cu,n = getEviaDSu(trainData,U,s,i,h)
#print "\t",i,s,h
if eu < err:
err = eu
meu = eu
mcu = cu
en = n
dim = i
th = h
sig = s
#return Algo
err = meu/(meu+mcu)
#print en,sum(U),err,meu,mcu
#return {}
de = math.sqrt((1.0-err)/err)
algo = {"err":err,"sign":sig,"dim":dim,"th":th,"de":de,"alpha":math.log(de),"en":en}
return algo
def CART(trainData):
if len(getClassesByData(trainData)) == 1:
leaf = {"type":"leaf","class":trainData[0]["y"]}
return leaf
minE = len(trainData)
minT = 0.
minD = 0
dimN = len(trainData[0]["x"])
for i in xrange(dimN):
dimX = getDimN(trainData,i)
hyps = buildHyp(dimX)
for th in hyps:
da,db = splitData(trainData,i,th)
ip = imp(da) + imp(db)
#print i,th,ip
if ip < minE:
minE = ip
minT = th
minD = i
da,db = splitData(trainData,minD,minT)
#print minD,minT,minE,len(da),imp(da),len(db),imp(db)
node = {"type":"node","dim":minD,"th":minT}
rt = CART(da)
rl = CART(db)
node["rtree"] = rt
node["ltree"] = rl
return node
def Ads(trainData, U):
dimN = len(trainData[0]["x"])
sign = [1, -1]
err = len(trainData)
en = len(trainData)
dim = 0
th = 0
sig = 1
meu = 0.
mcu = 0.
for i in range(dimN):
hyps = buildHyp(getDimN(trainData, i))
for s in sign:
for h in hyps:
eu, cu, n = getEviaDSu(trainData, U, s, i, h)
#print "\t",i,s,h
if eu < err:
err = eu
meu = eu
mcu = cu
en = n
dim = i
th = h
sig = s
#return Algo
err = meu / (meu + mcu)
#print en,sum(U),err,meu,mcu
#return {}
de = math.sqrt((1.0 - err) / err)
algo = {
"err": err,
"sign": sig,
"dim": dim,
"th": th,
"de": de,
"alpha": math.log(de),
"en": en
}
return algo