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# Related to [SYSTEMDS-2662] dependency function for cleaning pipelines

# impute the data by mean value and if the feature is categorical then by mode value

# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# X               Double    ---        Data Matrix (Recoded Matrix for categorical features)
# mask           Double    ---        A 0/1 row vector for identifying numeric (0) and categorical features (1)
# ---------------------------------------------------------------------------------------------
 

#Output(s)
# ---------------------------------------------------------------------------------------------
# NAME                  TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# X               Double   ---        imputed dataset




m_imputeByMean = function(Matrix[Double] X, Matrix[Double] mask)
return(Matrix[Double] X)
{
  # print("mean in \n"+toString(X))
  nX = removeEmpty(target=X, margin="cols", select=(mask==0))
  cX = removeEmpty(target=X, margin="cols", select=mask)
  Mask_n = is.na(nX);  
  nX = replace(target=nX, pattern=NaN, replacement=0);
  #  mean imputation
  X_n = nX+(Mask_n*colMeans(nX))
  # mode imputation
  X_c = imputeByMode(cX)
  p = table(seq(1, ncol(nX)), removeEmpty(target=seq(1, ncol(mask)), margin="rows", 
    select=t(mask==0)), ncol(nX), ncol(X))
  q = table(seq(1, ncol(cX)), removeEmpty(target=seq(1, ncol(mask)), margin="rows", 
    select=t(mask)), ncol(cX), ncol(X))
  X = (X_n %*% p) + (X_c %*% q)
  print("imputeByMean: no of NaNs "+sum(is.na(X)))
}
