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#!/usr/bin/env Rscript
#
# Copyright 2014 Google Inc. All rights reserved.
#
# Licensed 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.
library(RUnit)
library(Matrix) # for sparse matrices
source('tests/gen_counts.R')
TestGenerateCounts <- function() {
report_params <- list(k = 4, m = 2) # 2 cohorts, 4 bits each
map <- Matrix(0, nrow = 8, ncol = 3, sparse = TRUE) # 3 possible values
map[1,] <- c(1, 0, 0)
map[2,] <- c(0, 1, 0)
map[3,] <- c(0, 0, 1)
map[4,] <- c(1, 1, 1) # 4th bit of the first cohort gets signal from all
map[5,] <- c(0, 0, 1) # 1st bit of the second cohort gets signal from v3
colnames(map) <- c('v1', 'v2', 'v3')
partition <- c(3, 2, 1) * 10000
v <- 100 # reports per client
noise0 <- list(p = 0, q = 1, f = 0) # no noise at all
counts0 <- GenerateCounts(c(report_params, noise0), map, partition, v)
checkEqualsNumeric(sum(counts0[1,2:4]), counts0[1,1])
checkEqualsNumeric(counts0[1,5], counts0[1,1])
checkEqualsNumeric(partition[3] * v, counts0[1,4] + counts0[2,2])
checkEqualsNumeric(sum(partition) * v, counts0[1,1] + counts0[2,1])
pvalues <- chisq.test(counts0[,1] / v, p = c(.5, .5))$p.value
for(i in 2:4)
pvalues <- c(pvalues,
chisq.test(
c(counts0[1,i] / v, partition[i - 1] - counts0[1,i] / v),
p = c(.5, .5))$p.value)
noise1 <- list(p = .5, q = .5, f = 0) # truly random IRRs
counts1 <- GenerateCounts(c(report_params, noise1), map, partition, v)
for(i in 2:5)
for(j in 1:2)
pvalues <- c(pvalues,
chisq.test(c(counts1[j,1] - counts1[j,i], counts1[j,i]),
p = c(.5, .5))$p.value)
noise2 <- list(p = 0, q = 1, f = 1.0) # truly random PRRs
counts2 <- GenerateCounts(c(report_params, noise2), map, partition, v)
checkEqualsNumeric(0, max(counts2 %% v)) # all entries must be divisible by v
counts2 <- counts2 / v
for(i in 2:5)
for(j in 1:2)
pvalues <- c(pvalues,
chisq.test(c(counts2[j,1] - counts2[j,i], counts2[j,i]),
p = c(.5, .5))$p.value)
checkTrue(min(pvalues) > 1E-9, "Chi-squared test failed")
}
TestRandomPartition <- function() {
p1 <- RandomPartition(total = 100, dgeom(0:999, prob = .1))
p2 <- RandomPartition(total = 1000, dnorm(1:1000, mean = 500, sd = 1000 / 6))
p3 <- RandomPartition(total = 10000, dunif(1:1000))
# Totals must check out.
checkEqualsNumeric(100, sum(p1))
checkEqualsNumeric(1000, sum(p2))
checkEqualsNumeric(10000, sum(p3))
# Initialize the weights vector to 1 0 1 0 1 0 ...
weights <- rep(c(1, 0), 100)
p4 <- RandomPartition(total = 10000, weights)
# Check that all mass is allocated to non-zero weights.
checkEqualsNumeric(10000, sum(p4[weights == 1]))
checkTrue(all(p4[weights == 0] == 0))
p5 <- RandomPartition(total = 1000000, c(1, 2, 3, 4))
p.value <- chisq.test(p5, p = c(.1, .2, .3, .4))$p.value
# Apply the chi squared test and fail if p.value is too high or too low.
# Probability of failure is 2 * 1E-9, which should never happen.
checkTrue(p.value > 1E-9)
}
TestAll <- function(){
TestRandomPartition()
TestGenerateCounts()
}
TestAll()
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