| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| # generates a two column matrix of categorical |
| # variables |
| # used to test systemml's chi-squared bivariate stat |
| # computation |
| |
| # $1 is number of samples to generate |
| # $2 is number of categories for 1st categorical variable |
| # $3 is number of categories for 2nd categorical variable |
| # $4 is the file to write out the chi-squared statistic to |
| # $5 is the file to write out the generated data to |
| |
| numSamples = $1 |
| numCategories1 = $2 |
| numCategories2 = $3 |
| |
| o = Rand(rows=numCategories1, cols=numCategories2, min=0.0, max=1.0, pdf="uniform", seed=0) |
| o = o / sum(o) |
| |
| probs1 = rowSums(o) |
| probs1 = probs1 / sum(probs1) |
| probs2 = colSums(o) |
| probs2 = probs2 / sum(probs2) |
| e = probs1 %*% probs2 |
| |
| chisquared = sum((o-e)^2/e) |
| write(chisquared, $4, format="binary") |
| |
| oCDF = Rand(rows=numCategories1, cols=numCategories2, min=0.0, max=0.0, pdf="uniform", seed=0) |
| for(i in 1:numCategories1){ |
| for(j in 1:numCategories2){ |
| if(i==1 & j==1){ |
| oCDF[i,j] = o[1,1] |
| } |
| if(i != 1 & j == 1){ |
| oCDF[i,j] = oCDF[i-1,numCategories2] + o[i,j] |
| } |
| if(j > 1){ |
| oCDF[i,j] = oCDF[i,j-1] + o[i,j] |
| } |
| } |
| } |
| |
| one = Rand(rows=1, cols=1, min=1.0, max=1.0, pdf="uniform", seed=0) |
| data = Rand(rows=numSamples, cols=2, min=0.0, max=0.0, pdf="uniform", seed=0) |
| parfor(s in 1:numSamples){ |
| r_mat = Rand(rows=1, cols=1, min=0.0, max=1.0, pdf="uniform", seed=0) |
| r = as.scalar(r_mat) |
| |
| cat1 = -1 |
| cat2 = -1 |
| continue = 1 |
| for(i in 1:numCategories1){ |
| for(j in 1:numCategories2){ |
| cdf = as.scalar(oCDF[i,j]) |
| if(continue == 1 & r <= cdf){ |
| cat1 = i |
| cat2 = j |
| continue = 0 |
| } |
| } |
| } |
| |
| data[s,1] = cat1*one |
| data[s,2] = cat2*one |
| } |
| write(data, $5, format="binary") |
