| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| args <- commandArgs(TRUE) |
| |
| library("Matrix") |
| |
| D = as.matrix(readMM(paste(args[1], "X.mtx", sep=""))) |
| C = as.matrix(readMM(paste(args[1], "Y.mtx", sep=""))) |
| |
| # reading input args |
| numClasses = as.integer(args[2]); |
| laplace_correction = as.double(args[3]); |
| |
| numRows = nrow(D) |
| numFeatures = ncol(D) |
| |
| # Compute conditionals |
| |
| # Compute the feature counts for each class |
| classFeatureCounts = matrix(0, numClasses, numFeatures) |
| for (i in 1:numFeatures) { |
| Col = D[,i] |
| classFeatureCounts[,i] = aggregate(as.vector(Col), by=list(as.vector(C)), FUN=sum)[,2]; |
| } |
| |
| # Compute the total feature count for each class |
| # and add the number of features to this sum |
| # for subsequent regularization (Laplace's rule) |
| classSums = rowSums(classFeatureCounts) + numFeatures*laplace_correction |
| |
| # Compute class conditional probabilities |
| ones = matrix(1, 1, numFeatures) |
| repClassSums = classSums %*% ones; |
| class_conditionals = (classFeatureCounts + laplace_correction) / repClassSums; |
| |
| # Compute class priors |
| class_counts = aggregate(as.vector(C), by=list(as.vector(C)), FUN=length)[,2] |
| class_prior = class_counts / numRows; |
| |
| # Compute accuracy on training set |
| ones = matrix(1, numRows, 1) |
| D_w_ones = cbind(D, ones) |
| model = cbind(class_conditionals, class_prior) |
| log_probs = D_w_ones %*% t(log(model)) |
| pred = max.col(log_probs,ties.method="last"); |
| acc = sum(pred == C) / numRows * 100 |
| |
| print(paste("Training Accuracy (%): ", acc, sep="")) |
| |
| # write out the model |
| writeMM(as(class_prior, "CsparseMatrix"), paste(args[4], "prior", sep="")); |
| writeMM(as(class_conditionals, "CsparseMatrix"), paste(args[4], "conditionals", sep="")); |