| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| m_naivebayes = function(Matrix[Double] D, Matrix[Double] C, Double laplace = 1, Boolean verbose = TRUE) |
| return (Matrix[Double] prior, Matrix[Double] classConditionals) |
| { |
| laplaceCorrection = laplace; |
| numRows = nrow(D); |
| numFeatures = ncol(D); |
| numClasses = max(C); |
| |
| # Compute conditionals |
| # Compute the feature counts for each class |
| classFeatureCounts = aggregate(target=D, groups=C, fn="sum", ngroups=as.integer(numClasses)); |
| |
| # 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*laplaceCorrection; |
| |
| # Compute class conditional probabilities |
| classConditionals = (classFeatureCounts + laplaceCorrection) / classSums; |
| |
| # Compute class priors |
| classCounts = aggregate(target=C, groups=C, fn="count", ngroups=as.integer(numClasses)); |
| prior = classCounts / numRows; |
| |
| # Compute accuracy on training set |
| if( verbose ) { |
| logProbs = D %*% t(log(classConditionals)) + t(log(prior)); |
| acc = sum(rowIndexMax(logProbs) == C) / numRows * 100; |
| print("Training Accuracy (%): " + acc); |
| } |
| } |