R/KLFDA.R

In DA: Discriminant Analysis for Evolutionary Inference

### This function provides the unified solution for mutiple Kernel choice. the kernel choice here will allow users to choose any types of kernel they want, not only constrains on gaussian kernel

KLFDA=function (x, y,kernel=kernlab::polydot(degree = 1, scale = 1, offset = 1), r=20, tol,prior, CV=FALSE,usekernel = TRUE, fL = 0.5,metric = c('weighted', 'orthonormalized', 'plain'),
                  knn = 6, reg = 0.001,...) {## reg regularization parameter (default: 0.001)
  
  #
  
  tol=tol
  obj.trainData = x
  obj.trainClass = as.factor(y)
  obj.classes = sort(unique(obj.trainClass));
  obj.nClasses = length(obj.classes)
 # require("kernlab")
  k=kernlab::kernelMatrix(kernel, obj.trainData,obj.trainData)
  #k=multinomial_kernel(obj.trainData,obj.trainData,order=2)
  
  obj.nObservations=dim(obj.trainData)[1]
  obj.nFeatures = dim(obj.trainData)[2]
  #k=k/obj.nObservations
  
  cl <- match.call()
  metric <- match.arg(metric) # the type of the transforming matrix (metric)
  x=as.matrix(k)
  p <- ncol(k)
  n <- nrow(k) # number of samples
  
  
  if(n != length(y))
    stop("nrow(x) and length(y) are different")
  g <- as.factor(y)
  lev <- lev1 <- levels(g)
  counts <- as.vector(table(g))
  
  if(is.null(prior)==FALSE) {
    if(any(prior < 0) || round(sum(prior), 5) != 1) stop("invalid 'prior'")
    if(length(prior) != nlevels(g)) stop("'prior' is of incorrect length")
    prior <- prior[counts > 0L]
    
  }
  
  
  if(any(counts == 0L)) {
    empty <- lev[counts == 0L]
    warning(sprintf(ngettext(length(empty),
                             "group %s is empty",
                             "groups %s are empty"),
                    paste(empty, collapse = " ")), domain = NA)
    lev1 <- lev[counts > 0L]
    g <- factor(g, levels = lev1)
    counts <- as.vector(table(g))
  }
  
  proportions <- counts/n
  ng <- length(proportions)
  
  
  group.means <- tapply(c(x), list(rep(g, p), col(x)), mean)
  
  ### new section  from local da
  
  y <- t(as.matrix(y)) # transpose of original class labels
  d=nrow(k)
  if(is.null(r)) r <- n # if no dimension reduction requested, set r to n
  
  tSb <- mat.or.vec(n, n) # initialize between-class scatter matrix (to be maximized)
  tSw <- mat.or.vec(n, n) # initialize within-class scatter matrix (to be minimized)
  requireNamespace("lfda")
  # compute the optimal scatter matrices in a classwise manner
  for (i in unique(as.vector(t(y)))) {
    
    Kcc <- k[y == i, y == i] # data for this class
    Kc <- k[, y == i]
    nc <- nrow(Kcc)
    
    # Define classwise affinity matrix
    Kccdiag <- diag(Kcc) # diagonals of the class-specific data
    distance2 <- repmat(Kccdiag, 1, nc) + repmat(t(Kccdiag), nc, 1) - 2 * Kcc
    
    # Get affinity matrix
    A <- getAffinityMatrix(distance2, knn, nc)
    
    Kc1 <- as.matrix(rowSums(Kc))
    Z <- Kc %*% (repmat(as.matrix(colSums(A)), 1, n) * t(Kc)) - Kc %*% A %*% t(Kc)
    tSb <- tSb + (Z/n) + Kc %*% t(Kc) * (1 - nc/n) + Kc1 %*% (t(Kc1)/n)
    tSw <- tSw + Z/nc
  }
  
  K1 <- as.matrix(rowSums(k))
  tSb <- tSb - K1 %*% t(K1)/n - tSw
  
  tSb <- (tSb + t(tSb))/2 # final between-class cluster matrix
  tSw <- (tSw + t(tSw))/2 # final within-class cluster matrix
  
  # find generalized eigenvalues and normalized eigenvectors of the problem
  eigTmp <- suppressWarnings(rARPACK::eigs(A = solve(tSw + reg * diag(1, nrow(tSw), ncol(tSw)),tol=tol,bounds = list(a=c(x> 0))) %*% tSb,
                                           k = r,which ='LM')) # r largest magnitude eigenvalues
  eigVec <- Re(eigTmp$vectors) # the raw transforming matrix
  eigVal <- as.matrix(Re(eigTmp$values))
  
  # options to require a particular type of returned transform matrix
  # transforming matrix (do not change the "=" in the switch statement)
  Tr <- getMetricOfType(metric, eigVec, eigVal, n)
  
  
  Z <- t(t(Tr) %*% k) # transformed data
  
  
  
  
  classVecsTrain = matrix(nrow=obj.nObservations, ncol=obj.nClasses);
  obj.nObsPerClas = matrix(nrow=1, ncol=obj.nClasses);
  for (i in 1:obj.nClasses) {
    clas = obj.classes[i]
    classVecsTrain[, i] = match(obj.trainClass, clas,nomatch = 0)
    
    obj.nObsPerClas[i] = sum(classVecsTrain[,i])
  }
  
  
  
  redZ=list()
  
  obj.means1 = list()
  obj.covariances1=list()
  for (i in 1 : obj.nClasses){
    
    redZ[[i]]=list()
    #redProjData[[i]] = projData[as.logical(classVecsTrain[,i]), (1 : (obj.nClasses - 1))]### here obj,nClasses -1 can be subsetuted by d, the reduced number of dim
    redZ[[i]] = Z[as.logical(classVecsTrain[,i]), (1 : r)]
    
    obj.means1[[i]]= colMeans(redZ[[i]])
    obj.covariances1[[i]]=list()
    obj.covariances1[[i]] = cov(redZ[[i]])
  }
  ## Generate priors and likelihood functions
  # Priors
  
  if (is.null(prior)==TRUE){
    obj.priors = matrix(data=0,nrow=1, ncol=obj.nClasses);
    for (i in  1 : obj.nClasses){
      obj.priors[,i] = obj.nObsPerClas[,i] / obj.nObservations;
    }
  }
  if(is.null(prior)==FALSE) {
    priorsTmp = prior;
    obj.priors = matrix(data=0,nrow=1, ncol=obj.nClasses)
    for (i in 1 : obj.nClasses){
      clas = obj.classes[i]
      obj.priors[,i]= priorsTmp[i]
    }
  }
  prior=obj.priors
  names(prior) <- names(counts) <- lev1
  # Likelihood
  obj.likelihoodFuns = list()
  # warning('error', 'nearlySingularMatrix')
  
  
  ### the methods of Z
  obj.covInv1=list()
  obj.covDet1=list()
  factor1=list()
  #obj.covInv=solve(obj.covariances[[i]])
  for (i in 1 : obj.nClasses){
    
    obj.covInv1[[i]]=list()
    obj.covDet1[[i]]=list()
    obj.covInv1[[i]] = solve(obj.covariances1[[i]],tol=tol,bounds = list(a=c(obj.covariances1[[i]]> 0)));# ginv(obj.co)
    obj.covDet1[[i]] = det(obj.covariances1[[i]]);
    
    #pvaCov = vpa(obj.covariances[[i]]);
    #obj.covInv[[i]] = double((pvaCov));
    #obj.covDet.(clas) = double(det(pvaCov));
    factor1[[i]]=list()
    factor1[[i]] = (2 * pi) ^ (-(obj.nClasses - 1) / 2) * (obj.covDet1[[i]] ^ -0.5)
  }
  
  
  likelihoods1 = matrix(data=0,nrow=obj.nObservations, ncol=obj.nClasses);
  
  posteriors1 = matrix(data=0,nrow=obj.nObservations, ncol=obj.nClasses)
  
  dist1=matrix(data=0,nrow=obj.nObservations, ncol=obj.nClasses)
  ## here from the  scripts  of Perr, each row of new data has nclass of 
  # (rpdTest[j,] - obj.means[[i]]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means[[i]])
  for (j in 1 : obj.nObservations){
    
    for (i in 1 : obj.nClasses){
      # clas = obj.classes[i]
      
      
      #mean((rpdTest[j,] - obj.means0[[i]]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means0[[i]]))
      
      dist1[j,i]=mean((Z[j,] - obj.means1[[i]]) %*% obj.covInv1[[i]] * t(Z[j,] -obj.means1[[i]]))
      # likelihoods[[j]][[i]] =factor[[i]] * exp(-0.5 * (rpdTest[j,] - obj.means[i]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means[i]))
      #likelihoods1 =factor[[i]] * exp(-0.5 * (rpdTest[j,] - obj.means0[[i]]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means0[[i]]))
      
      likelihoods1[j,i] =factor1[[i]] * exp(-0.5 * dist1[j,i])
      
      
    }
    
    posteriors1[j,] = likelihoods1[j,] * obj.priors / sum(likelihoods1[j,] * obj.priors)
  }
  #}
  ### posteriors.class is the normal prodiction using projData posteriors.classZ using Z
  
  ## Predicting the class of each data point
  
  
  posteriors.classZ=factor(obj.classes[max.col(posteriors1)], levels = obj.classes)
  
  
  
  
  # if(CV) {
  #   x <- t(t(Tr) %*% k) # transformed data Z
  #   dm <- group.means %*% Tr
  ### K is deternmined from n.pc, ng is number of gropus
  #   K = ng 
  #   dist <- matrix(0, n, ng) # n row and ng col
  
  ## dev matrix
  #   for(i in 1L:ng) {
  #     dev <- x - matrix(dm[i,  ], n, r, byrow = TRUE)
  #      dist[, i] <- rowSums(dev^2)### dis or devation of each class
  #    }
  #   ind <- cbind(1L:n, g) ### give the order number (sequence) and group(class )
  #   nc <- counts[g] ## number of class
  #   cc <- nc/((nc-1)*(n-K)) ### proportation of eac
  #    dist2 <- dist
  #   for(i in 1L:ng) {
  #     dev <- x - matrix(dm[i,  ], n, r, byrow = TRUE)
  #     dev2 <- x - dm[g, ]
  #    tmp <- rowSums(dev*dev2) 
  #     dist[, i] <- (n-1L-K)/(n-K) * (dist2[, i] +  cc*tmp^2/(1 - cc*dist2[ind]))
  #  }
  ### dist should be discriminat function 
  #   dist[ind] <- dist2[ind] * (n-1L-K)/(n-K) * (nc/(nc-1))^2 /(1 - cc*dist2[ind])
  #    dist <- 0.5 * dist - matrix(log(prior), n, ng, byrow = TRUE) # proboloty of discrim density function 
  
  #   dist <- exp(-(dist - min(dist, na.rm = TRUE))) #### density function proporbility of pi this is distribution Normal distribution function
  #   cl <- factor(lev1[max.col(dist)], levels = lev)
  ## calculate distance is   
  #    deltaTrain[,i]=deltaTrain[,i]-0.5*t(Mu_k[,i])%*%sigmaMinus1%*%Mu_k[,i]+log(Pi_k[i])
  ##  convert to posterior probabilities
  
  #  posterior <- dist/drop(dist %*% rep(1, length(prior)))
  #    dimnames(posterior) <- list(rownames(x), lev1)
  #   return(list( eigTmp=eigTmp,eifVec=eigVec,eigVal=eigVal,bayes=bayes,bayes_judgement=bayes_judgement,bayes_assigment=bayes_assigment, post_class=cl,posterior = posterior,Z=x,T=Tr))
  #  }
  # xbar <- colSums(prior %*% group.means)
  #  fac <-1/(ng - 1)
  #  X <- sqrt((n * prior)*fac) * scale(group.means, center = xbar, scale = FALSE) %*% Tr
  # X.s <- svd(X, nu = 0L)
  # rank <- sum(X.s$d > tol * X.s$d[1L])
  # rank=ng
  #  T_lda <- Tr %*% X.s$v[, 1L:rank]
  #  if(is.null(dimnames(x)))
  #   dimnames(T_lda) <- list(NULL, paste("LD", 1L:rank, sep = ""))
  #  else {
  #   dimnames(T_lda) <- list(colnames(x), paste("LD", 1L:rank, sep = ""))
  #    dimnames(group.means)[[2L]] <- colnames(x)
  # }
  #  svd = X.s$d[1L:rank]
  #  PLD1=svd[1]^2/sum(svd^2)
  # PLD2=svd[2]^2/sum(svd^2)
  # PLD=as.data.frame(cbind(PLD1,PLD2))
  #require(WMDB) ## FOR dbayes function the  function
  bayes_judgement=Mabayes(Z,obj.trainClass,var.equal = FALSE,tol=tol)
#  require(klaR)
  bayes=klaR::NaiveBayes(as.data.frame(Z), obj.trainClass, prior=obj.priors, usekernel, fL = fL,...)
  bayes_assigment=predict.NaiveBayes(bayes,threshold = 0.001)
  # t_lda=lda(Z,obj.trainClass,...)
  cl <- match.call()
  cl[[1L]] <- as.name("klfda")
  structure(list(kernel=kernel, tol=tol,usekernel=usekernel,fL=fL,obj.classes=obj.classes,obj.nObservations=obj.nObservations,obj.means1=obj.means1,obj.covInv1=obj.covInv1,factor1=factor1, eigTmp=eigTmp,eigVec=eigVec,eigVal=eigVal, prior = prior, bayes=bayes,posteriors.classZ=posteriors.classZ,posteriors1=posteriors1, bayes_judgement=bayes_judgement,bayes_assigment=bayes_assigment,means=group.means,
                 obj.priors=obj.priors,T=Tr,obj.nClasses=obj.nClasses,obj.trainData=obj.trainData, obj.trainClass=obj.trainClass, Z=Z,lev = lev, N = n, call = cl),class = "klfda")
}

## other functions  0.023329207797073   


project_data = function(object,X, nDims,obj.trainData, newdata, obj.order){
  ## Project data on the discriminant axes
  # Arguments:
  # - X: same as in the constructor function
  # - nDims: integre, the number of dimensions on which the data
  # is going to be projected
  
  K = multinomial_kernel(obj.trainData, newdata, obj.order)
  #K = K / obj.nObservations;
  projData = K %*% object$eigVec[, 1 : nDims]
  Z=K%*%object$Tr
}


#' @export
predict.KLFDA=function(object,prior=NULL,testData,...){
  tol=object$tol
  nObsTest=dim(testData)[1]
  nFeaturesTest = dim(testData)[2]
  obj.nClasses=object$obj.nClasses
  obj.nFeatures=object$obj.nFeatures
  obj.nObservations=object$obj.nObservations
  ## requireNamespace("kernlab")
  kernel=object$kernel
  ktestData=kernlab::kernelMatrix(kernel, object$obj.trainData,testData)
  #ktestData=multinomial_kernel(object$obj.trainData,testData,order=2)
  #testData=as.matrix(testData)
  ktestData=ktestData/obj.nObservations
  Z=object$Z
  Y=object$obj.trainClass
  Trans=as.matrix(object$T)
  #Z2=as.matrix(ktestData) %*% Trans 
  Z2=t(as.matrix(ktestData)) %*% Trans 
  # if (is.null(prior)==TRUE){
  if (is.null(prior))
    prior=object$obj.priors
  else prior=prior  
  
  # }
  usekernel=object$usekernel
  fL=object$fL
  # t_lda=object$t_lda
  #requireNamespace("klaR") ## FOR Nativebaye function
  bayes_jud_pred=Mabayes(Z,Y,TstX = Z2,var.equal = FALSE,tol=tol)
  # bayes=NaiveBayes(Z, Y, prior, usekernel, fL,kernel,bw = "nrd0", adjust = 1,weights = NULL, window = kernel, give.Rkern = FALSE,...)
  Nbayes_assig_pred=predict.NaiveBayes(object$bayes,as.data.frame(Z2),threshold = 0.001,dkernel=kernel,...)
  #  p_lda=predict(t_lda,Z2,...)
  
  ## Retrieving the likelihood of each test point
  
  likelihoods1 = matrix(data=0,nrow=nObsTest, ncol=obj.nClasses);
  
  posteriors1 = matrix(data=0,nrow=nObsTest, ncol=obj.nClasses)
  
  dist1=matrix(data=0,nrow=nObsTest, ncol=obj.nClasses)
  ## here from the  scripts  of Perr, each row of new data has nclass of 
  # (rpdTest[j,] - obj.means[[i]]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means[[i]])
  
  obj.means1=object$obj.means1
  obj.covInv1=object$obj.covInv1
  
  factor1=object$factor1
  obj.classes=object$obj.classes
  obj.priors=object$obj.priors
  
  for (j in 1 : nObsTest){
    
    for (i in 1 : obj.nClasses){
      # clas = obj.classes[i]
      
      
      dist1[j,i]=mean((Z2[j,] - obj.means1[[i]]) %*% obj.covInv1[[i]] * t(Z2[j,] -obj.means1[[i]]))
      # likelihoods[[j]][[i]] =factor[[i]] * exp(-0.5 * (rpdTest[j,] - obj.means[i]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means[i]))
      #likelihoods1 =factor[[i]] * exp(-0.5 * (rpdTest[j,] - obj.means0[[i]]) %*% obj.covInv[[i]] * t(rpdTest[j,] -obj.means0[[i]]))
      
      likelihoods1[j,i] =factor1[[i]] * exp(-0.5 * dist1[j,i])
      
      
    }
    
    posteriors1[j,] = likelihoods1[j,] * obj.priors / sum(likelihoods1[j,] * obj.priors)## Z
  }
  #}
  
  
  ## Predicting the class of each data point
  
  
  posteriors.class1=factor(obj.classes[max.col(posteriors1)], levels = obj.classes)
  
  lev=object$lev
  ng=length(lev)
  
  means <- colSums(prior %*% object$means)
  scaling <- object$scaling
  x <-Z2
  dm <- scale(object$means, center = means, scale = FALSE) %*% Trans
  
  dimen <- length(object$svd) 
  N <- object$N
  
  dm <- dm[, 1L:dimen, drop = FALSE]
  dist <- matrix(0.5 * rowSums(dm^2) - log(prior), nrow(x),
                 length(prior), byrow = TRUE) - x[, 1L:dimen, drop=FALSE] %*% t(dm)
  dist <- exp( -(dist - apply(dist, 1L, min, na.rm=TRUE)))
  
  posterior <- dist / drop(dist %*% rep(1, length(prior)))
  nm <- names(object$prior)
  cl <- factor(nm[max.col(posterior)], levels = object$lev)
  dimnames(posterior) <- list(rownames(x), nm)
  list(class = cl, posterior = posterior,posteriors1=posteriors1, posteriors.class1=posteriors.class1,x = x[, 1L:dimen, drop = FALSE],bayes_jud_pred=bayes_jud_pred,bayes_assig_pred=Nbayes_assig_pred)
}