scripts/builtin/pcaInverse.dml

#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------

# Principal Component Analysis (PCA) for reconstruction of approximation of the original data.
# This methods allows to reconstruct an approximation of the original matrix, and is useful for
# calculating how much information is lost in the PCA.
#
# INPUT:
# --------------------------------------------------------------------------------------
# Y            Input features that have PCA applied to them
# Clusters     The previous PCA components computed
# Centering    The column means of the PCA model, subtracted to construct the PCA
# ScaleFactor  The scaling of each dimension in the PCA model
# --------------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------------
# X     Output feature matrix reconstructing and approximation of the original matrix
# ------------------------------------------------------------------------------------

m_pcaInverse = function(Matrix[Double] Y, Matrix[Double] Clusters, 
  Matrix[Double] Centering = matrix(0, rows= 0, cols=0), 
  Matrix[Double] ScaleFactor = matrix(0, rows= 0, cols=0))
  return (Matrix[Double] X) 
{
  X = Y %*% t(Clusters)

  if(nrow(ScaleFactor) > 0 & ncol(ScaleFactor) > 0){
    X = X * ScaleFactor
  }

  if(nrow(Centering) > 0 & ncol(Centering) > 0){
    X = X + Centering
  }

}