def validation(Point_P, Point_Q, gEpG,radius,a=0.7):
P = compactPoint(Point_P)
P_list = P.keys()
Q = compactPoint(Point_Q)
Q_list = Q.keys()
vr = validationReport()
PQ_EpG = cross_EpG(gEpG,P_list,Q_list)
#identificazione prove
IJK,IKL,I=observations(P_list, Q_list, PQ_EpG)
if len(IJK)+len(IKL)<4:
vr.merge=True
PE=[]
QE=[]
vr.setMerge(P,Q,PE,QE)
#osservazione
inlier_IJK,rkp,inlier_IKL,rip,inlier_I=pointTransferVectorized(P,Q,PQ_EpG,maxLevel=radius,alfa=a,IJK=IJK,IKL=IKL,I=I)
#calcolo punteggio==>parte da rifare
ISB=iterativeScoreBoard(P_list,Q_list,IJK,inlier_IJK,IKL,inlier_IKL,I,inlier_I)
iterations=0
while ISB.board.outliers_count > 0 and ISB.tick(ExcludeClassifier):
iterations+=1
#now the point is stable under worst point exclusion
merge_x=ISB.mergeInputModel()
vr.merge=MergeClassifier.predict(merge_x)
if vr.merge:
PE=ISB.Pe
QE=ISB.Qe
vr.setMerge(P,Q,PE,QE)
else:
vr.setSplit(P,Q,I)
return vr
def scoring(self,realization,blameRatio=0.2):
self.iSB=iterativeScoreBoard(
self.P,self.Q,
self.IJK,realization[0],
self.IKL,realization[1],
self.I,realization[2],Pe=[],Qe=[])
g=self.iSB.board.gain()
iterations=0
while self.iSB.board.outliers_count > 0 and self.iSB.tick()>g:
g=self.iSB.board.gain()
#print iterations , self.iSB.board.inliers_count ," vs ", self.iSB.board.outliers_count
iterations+=1
merge=self.iSB.board.gain()>0
if merge:
P_exclude,Q_exclude=self.clearCommons(
self.I,realization[2],
self.iSB.board.P_inliers,
self.iSB.board.P_outliers,
self.iSB.board.Q_inliers,
self.iSB.board.Q_outliers)
self.iSB=iterativeScoreBoard(
self.P,self.Q,
self.IJK,realization[0],
self.IKL,realization[1],
self.I,realization[2],
Pe=P_exclude,Qe=Q_exclude)
g=self.iSB.board.gain()
iterations=0
while self.iSB.board.outliers_count > 0 and self.iSB.tick()>g:
g=self.iSB.board.gain()
#print iterations , self.iSB.board.inliers_count ," vs ", self.iSB.board.outliers_count
iterations+=1
P_exclude=self.iSB.Pe
Q_exclude=self.iSB.Qe
out=[self.mergeView(i,P_exclude,Q_exclude) for i in range(len(self.Pv))]
out=''.join(out)
else:
P_exclude,Q_exclude=self.splitCommons(
self.I,realization[2],
self.iSB.board.P_inliers,
self.iSB.board.P_outliers,
self.iSB.board.Q_inliers,
self.iSB.board.Q_outliers)
p_out=[self.Pv[i] if i not in P_exclude else '?' for i in range(len(self.Pv))]
q_out=[self.Qv[i] if i not in Q_exclude else '?' for i in range(len(self.Qv))]
out=[''.join(p_out),''.join(q_out)]
return merge,P_exclude,Q_exclude,out
def scoring2exclude(self,realization):
self.iSB=iterativeScoreBoard(
self.P,self.Q,
self.IJK,realization[0],
self.IKL,realization[1],
self.I,realization[2],Pe=[],Qe=[])
iterations=0
return self.iSB
def scoring2merge(self,realization,ExcludeClassifier):
self.iSB=iterativeScoreBoard(
self.P,self.Q,
self.IJK,realization[0],
self.IKL,realization[1],
self.I,realization[2],Pe=[],Qe=[])
iterations=0
while self.iSB.board.outliers_count > 0 and self.iSB.tick(ExcludeClassifier):
iterations+=1
return self.iSB
def scoring2merge_old(self,realization,blameRatio=0.2):
self.iSB=iterativeScoreBoard(
self.P,self.Q,
self.IJK,realization[0],
self.IKL,realization[1],
self.I,realization[2],Pe=[],Qe=[])
g=self.iSB.board.gain()
iterations=0
while self.iSB.board.outliers_count > 0 and self.iSB.tick(acceptance=blameRatio)>g:
g=self.iSB.board.gain()
#print iterations , self.iSB.board.inliers_count ," vs ", self.iSB.board.outliers_count
iterations+=1
return self.iSB
