| #------------------------------------------------------------- |
| # |
| # 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 random data for F-test |
| # |
| # $1 is number of groups (some of |
| # which may share a gaussian) |
| # $2 is number of actual groups |
| # $3 is number of points |
| # $4 is mean of the gaussian means |
| # $5 is mean of the gaussian std. deviations |
| # $6 is file to store computed f-statistic |
| # $7 is file to store generated data |
| |
| numGroups = $1 |
| numActualGroups = $2 |
| numSamples = $3 |
| meanOfMeans = $4 |
| meanOfStddevs = $5 |
| |
| cntProbs = Rand(rows=numGroups, cols=1, min=0.0, max=1.0, pdf="uniform", seed=0) |
| cntProbs = cntProbs/sum(cntProbs) |
| cntArr = round(cntProbs * numSamples) |
| last_cnt = cntArr[numGroups,1] |
| cntArr[numGroups,1] = numSamples - (sum(cntArr) - last_cnt) |
| |
| permut = Rand(rows=numActualGroups, cols=numGroups, min=0.0, max=0.0, pdf="uniform") |
| ones = Rand(rows=numActualGroups, cols=1, min=1.0, max=1.0, pdf="uniform") |
| permut[,1:numActualGroups] = diag(ones) |
| |
| one = Rand(rows=1, cols=1, min=1.0, max=1.0, pdf="uniform") |
| copy_start_index = numActualGroups+1 |
| parfor(i in copy_start_index:numGroups){ |
| r = Rand(rows=1, cols=1, min=1.0, max=numActualGroups, pdf="uniform", seed=0) |
| j = as.scalar(round(r)) |
| permut[j,i] = one |
| } |
| |
| means_std = Rand(rows=numActualGroups, cols=1, pdf="normal", seed=0) |
| abs_means = means_std + meanOfMeans |
| means = t(t(abs_means) %*% permut) |
| |
| stddevs_std = Rand(rows=numActualGroups, cols=1, pdf="normal", seed=0) |
| abs_stddevs = stddevs_std + meanOfStddevs |
| stddevs = t(t(abs_stddevs) %*% permut) |
| |
| overall_mean = sum(means*cntArr)/numSamples |
| |
| explained_variance = sum(cntArr * (means - overall_mean)^2) / (numGroups-1.0) |
| unexplained_variance = sum(cntArr * stddevs^2) / (numSamples - numGroups) |
| f = explained_variance / unexplained_variance |
| write(f, $6, format="binary") |
| |
| cntCDFs = cntProbs |
| for(i in 2:numGroups){ |
| cntCDFs[i,1] = cntCDFs[i-1,1] + cntProbs[i,1] |
| } |
| |
| data = Rand(rows=numSamples, cols=1, min=0.0, max=0.0, pdf="uniform") |
| parfor(i in 1:numSamples){ |
| r_mat = Rand(rows=1, cols=1, min=0.0, max=1.0, pdf="uniform", seed=0) |
| r1 = as.scalar(r_mat) |
| |
| g = -1 |
| continue = 1 |
| for(k in 1:numGroups){ |
| cdf = as.scalar(cntCDFs[k,1]) |
| if(continue==1 & r1<=cdf){ |
| g = k |
| continue=0 |
| } |
| } |
| |
| point = Rand(rows=1, cols=1, pdf="normal", seed=0) |
| data[i,1] = point*stddevs[g,1] + means[g,1] |
| } |
| write(data, $7, format="binary") |
