def CAM(XX, pns_type=None, pns_thres=None, adj_after_pns=None, pruning_type=None):
# XX is a numpy array
string = '''
asSparseMatrix <- function(d){
return(as(matrix(0, d, d), "sparseMatrix"))
}
whichMax <- function(input){
return(which.max(input))
}
'''
selfpack = SignatureTranslatedAnonymousPackage(string, "selfpack")
n, d = XX.shape
maxNumParents = min(d - 1, round(n / 20))
X = numpy2ri.py2rpy(XX)
if pns_type != None:
if pns_thres != None & pns_thres >= 0 & pns_thres <= 1:
selMat = pns_type(X, pns_thres=pns_thres, verbose=False)
else:
raise ValueError
else:
if adj_after_pns == None:
selMat = np.ones((d, d))
else:
selMat = adj_after_pns
computeScoreMatTmp = robjects.r.computeScoreMat(X, scoreName='SEMGAM',
numParents=1, numCores=1, output=False,
selMat=numpy2ri.py2rpy(selMat),
parsScore=ListVector({'numBasisFcts': 10}), intervMat=float('nan'),
intervData=False)
scoreVec = []
edgeList = []
pathMatrix = robjects.r.matrix(0, d, d)
# Adj = selfpack.asSparseMatrix(d)
Adj = robjects.r.matrix(0, d, d)
scoreNodes = computeScoreMatTmp.rx('scoreEmptyNodes')[0]
scoreMat = computeScoreMatTmp.rx('scoreMat')[0]
counterUpdate = 0
while (sum(scoreMat.ro != -float('inf')) > 0):
# print(sum(scoreMat.ro != -float('inf')))
ix_max = robjects.r.arrayInd(selfpack.whichMax(scoreMat),
robjects.r.dim(scoreMat))
ix_max_backward = robjects.r.matrix(IntVector([ix_max[1], ix_max[0]]), 1, 2)
Adj.rx[ix_max] = 1
scoreNodes.rx[ix_max[1]] = scoreNodes.rx(ix_max[1]).ro + scoreMat.rx(ix_max)
scoreMat.rx[ix_max] = -float('inf')
pathMatrix.rx[ix_max[0], ix_max[1]] = 1
DescOfNewChild = robjects.r.which(pathMatrix.rx(ix_max[1], True).ro == 1)
AncOfNewChild = robjects.r.which(pathMatrix.rx(True, ix_max[0]).ro == 1)
pathMatrix.rx[AncOfNewChild, DescOfNewChild] = 1
scoreMat.rx[robjects.r.t(pathMatrix).ro == 1] = -float('inf')
scoreMat.rx[ix_max[1], ix_max[0]] = -float('inf')
scoreVec.append(sum(scoreNodes))
edgeList.append(list(ix_max))
scoreMat = robjects.r.updateScoreMat(scoreMat, X, scoreName='SEMGAM', i=ix_max[0], j=ix_max[1],
scoreNodes=scoreNodes, Adj=Adj, numCores=1, output=False,
maxNumParents=maxNumParents, parsScore=ListVector({'numBasisFcts': 10}),
intervMat=float('nan'), intervData=False)
counterUpdate = counterUpdate + 1
if pruning_type != None:
# Adj is the out put
pass
return np.array(Adj)
