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groupPredict <- function(train, test, groups, K=20, alpha=0.5, t=20, method=1){
# Predicts subtype of new patients from labeled training set of patients
# using label propigation or local and global consistency.
#
# Args:
# train: List affinity matrices for samples with known labels
# test: List affinity matrices for samples with unknown labels.
# Length of test must match length of train (and order?)
# groups: Labels specifying the groups in train
# K: SNF parameter for number of neighbours in KNN step
# alpha: SNF Hyperparameter
# t: SNF varaible - number of iterations
# method: 0/1 specifies method used (1) Label propagation or
# (0) Local & global consistency.
#
# Returns:
# Vector of new labels assigned to the test samples
Wi <- vector("list", length=length(train))
for (i in 1:length(train)){
view <- standardNormalization(rbind(train[[i]],test[[i]]))
Dist1 <- dist2(view, view)
Wi[[i]] <- affinityMatrix(Dist1, K, alpha)
}
W <- SNF(Wi,K,t)
Y0 <- matrix(0,nrow(view), max(groups))
for (i in 1:length(groups)){
Y0[i,groups[i]] <- 1
}
Y <- .csPrediction(W,Y0,method)
newgroups <- rep(0,nrow(view))
for (i in 1:nrow(Y)){
newgroups[i] <- which(Y[i,] == max(Y[i,]))
}
return (newgroups)
}
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