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 validation_old(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(Point_P,P,Point_Q,Q)
PQ_EpG = cross_EpG(gEpG,P_list,Q_list)
#identificazione prove
IJK,IKL,I=observations(P_list, Q_list, PQ_EpG)
#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
inliers_count,outliers_count,P_inliers,Q_inliers,P_outliers,Q_outliers=score(P_list,Q_list,IJK,inlier_IJK,IKL,inlier_IKL,I,inlier_I)
vr.R_in = inliers_count
vr.R_out = outliers_count
vr.merge=vr.R_in>vr.R_out
if vr.merge :
clearCommons(I,inlier_I,vr,P_inliers,P_outliers,Q_inliers,Q_outliers)
qualityRatio=float(vr.R_in)/(vr.R_in+vr.R_out)
P_worst = max(P_list, key=lambda I: P_outliers[I]-P_inliers[I] if I not in vr.P_exclude else -10000)
Q_worst = max(Q_list, key=lambda I: Q_outliers[I]-Q_inliers[I] if I not in vr.Q_exclude else -10000)
P_gain = P_outliers[P_worst]-P_inliers[P_worst]
Q_gain = Q_outliers[Q_worst]-Q_inliers[Q_worst]
P_qualityRatio=P_inliers[P_worst]/float(P_inliers[P_worst]+P_outliers[P_worst]) if P_outliers[P_worst]>0 else 0.
Q_qualityRatio=Q_inliers[Q_worst]/float(Q_inliers[Q_worst]+Q_outliers[Q_worst]) if Q_outliers[Q_worst]>0 else 0.
if P_gain>0 and P_qualityRatio<qualityRatio and len(P_list)>2:
vr.P_exclude.add(P_worst)
if Q_gain>0 and Q_qualityRatio<qualityRatio and len(Q_list)>2:
vr.Q_exclude.add(Q_worst)
return vr
def validation(Point_P, Point_Q):
P = compactPoint(Point_P)
P_list = P.keys()
Q = compactPoint(Point_Q)
Q_list = Q.keys()
vr = validationReport()
vr.merge=True
PE=[]
QE=[]
vr.setMerge(P,Q,PE,QE)
return vr