caiman2: CAIMAN models

library(tidyverse)
.caiman.pred <-
function(CT, newx) {
  classes = CT[['classes']]
  Xtx = CT[['xtx']]
  m = CT[['centroid']]
  K = genhiddenSpace(classes, newx, Xtx, m)
  genmin(CT$classes, K)
}
.caiman.train <-
function(y, x) {
  classes = levels(y)
  df = x %>% as.data.frame()
  df$y = y 
  
  list(xtx=genXtX(df, classes), centroid=genCentroide(df, classes), classes=classes)
}
.comb <-
function(df, classes, a) {
  y = df$y
  K = .genEspace(df, classes) %>% as.data.frame()
  df = K
  df$y = y
  KK = .genEspace(df, classes)
  
  combK = a*K + (1-a)*KK
  
  return(combK)
}
.dcaiman.pred <-
function(CT, x, a = NULL) {
  classes = CT[['classes']]
  xtx = CT[['xtx']]
  xtk = CT[['xtk']]
  mx = CT[['cx']]
  mk = CT[['ck']]
  if(is.null(a)) {
    a = CT[['a']]
  }
  
  valx = genhiddenSpace(classes, x = x, xxt = xtx, m = mx)
  valy = genhiddenSpace(classes, x = valx, xxt=xtk, m = mk)
  
  k = genhiddenSpace(classes, x, xtx, m = mx)
  
  kk = genhiddenSpace(classes, k, xtk, m = mk)
  kf = a*k + (1-a)*kk
  genmin(classes, kf)
  
}
.dcaiman.train <-
function(y, xtrain, xvalidation, grid) {
  classes = levels(y)
  df = xtrain %>% as.data.frame()
  df$y = y
  a = grid
  
  
  xtx = genXtX(df, classes)
  centroidx = genCentroide(df, classes)
  
  k = genEspace(df, classes) %>% as.data.frame()
  k$y = y
  xtxk = genXtX(k, classes)
  centroidk = genCentroide(k, classes)
  
  valx = genhiddenSpace(levels(y), x = xvalidation, xxt = xtx, m = centroidx)
  valy = genhiddenSpace(levels(y), x = valx, xxt=xtk, m = centroidk)
  
  erro = numeric(length(a))
  
  
  for(i in seq_along(a)) {
    valfinal = a[i]*valx + (1-a[i])*valy
    erro[i] = mean(validacao$V58!=genmin(levels(y), valfinal))
    
  }
  
  
  afinal = a[which.min(erro)]
  
  return(list(xtx=xtx,xtk=xtk,cx=centroidx, ck=centroidk, a=afinal, classes=classes))
  
}
.genBanco <-
function(df, classes) {
  bancos = list()
  for(i in classes) {
    bancos[[i]] = df %>% filter(y==i) %>% select(-y) %>% as.matrix()
  }
  return(bancos)
}
.genBancoC <-
function(df, classes) {
  bancos = list()
  for(i in classes) {
    bancos[[i]] = df %>% filter(y==i) %>% select(-y) %>% scale(center=T, scale=F) %>% 
      as.matrix()
  }
  return(bancos)
}
.genCentroide <-
function(df, classes) {
  centroide = list()
  for(i in classes) {
    centroide[[i]] = df %>% filter(y==i) %>% select(-y) %>% apply(FUN=mean, MARGIN=2) %>%
      as.matrix()
  }
  return(centroide)
}
.genEspace <-
function(df, classes) {
  K = matrix(0, nrow=nrow(df), ncol=length(classes))
  colnames(K) = classes
  
  m = .genCentroide(df, classes)
  xtx = .genXtX(df, classes)
  xm = df %>% select(-y) %>% as.matrix()
  y = df$y
  
  for(i in 1:nrow(df)) {
    for(g in classes) {
      h = t(cbind(xm[i,])-m[[g]])%*%xtx[[g]]%*%(cbind(xm[i,])-m[[g]])
      if(y[i]==g) {
        K[i,g] = h/(1-h)
      }else{
        K[i,g] = h
      }
    }
  }
  return(K)
}
.genhiddenSpace <-
function(classes, x, xxt, m) {
  K = matrix(0, nrow=nrow(x), ncol=length(classes))
  colnames(K) = classes
  xm = x %>% as.matrix()
  
  
  for(i in 1:nrow(x)) {
    for(g in classes) {
      h = t(cbind(xm[i,])-m[[g]])%*%xxt[[g]]%*%(cbind(xm[i,])-m[[g]])
      K[i,g] = h
      
      
    }
  }
  return(K)
}
.genmin <-
function(classes, Space) {
  K = Space
  pred = apply(K, FUN=which.min, MARGIN=1) 
  pred = .getlabel(classes, pred)
  return(pred)
}
.genXtX <-
function(df, classes) {
  banco = .genBancoC(df, classes)
  XtX = list()
  for(i in classes) {
    XtX[[i]] = solve(t(banco[[i]]%>%as.matrix())%*%(banco[[i]]%>%as.matrix()))
  }
  return(XtX)
}
.getlabel <-
function(classe, pred) {
    sapply(pred, FUN=function(classe, x){classe[x]}, classe=classe)
  }
.Random.seed <-
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