| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| #------------------------------------------------------------- |
| |
| # Related to [SYSTEMDS-2902] dependency function for cleaning pipelines |
| |
| # impute the data by mode value |
| |
| # INPUT PARAMETERS: |
| # --------------------------------------------------------------------------------------------- |
| # NAME TYPE DEFAULT MEANING |
| # --------------------------------------------------------------------------------------------- |
| # X Double --- Data Matrix (Recoded Matrix for categorical features) |
| # --------------------------------------------------------------------------------------------- |
| |
| |
| #Output(s) |
| # --------------------------------------------------------------------------------------------- |
| # NAME TYPE DEFAULT MEANING |
| # --------------------------------------------------------------------------------------------- |
| # X Double --- imputed dataset |
| |
| |
| m_imputeByMode = function(Matrix[Double] X) |
| return(Matrix[Double] X) |
| { |
| |
| Mask = is.na(X) |
| X = replace(target=X, pattern=NaN, replacement=0) |
| colMode = matrix(0, 1, ncol(X)) |
| for(i in 1: ncol(X)) { |
| X_c = removeEmpty(target=X[, i], margin = "rows", select=(X[, i] < 1)==0) |
| if(sum(X_c) == 0) |
| colMode[1, i] = 1 |
| else { |
| cat_counts = table(X_c, 1, nrow(X_c), 1); # counts for each category |
| colMode[1,i] = as.scalar(rowIndexMax(t(cat_counts))) # mode |
| } |
| } |
| Mask = Mask * colMode |
| X = X + Mask |
| } |
| |