Nothing

```
library(testthat)
library(PeakSegDP)
context("trivial examples")
pseg3 <- function(...){
y <- c(...)
w <- rep(1, length(y))
## TODO: when we write a real solver it should replace cDPA, which
## does not pass all these tests.
fit <- cDPA(y, rep(1, length(y)), 3)
ends <- getPath(fit)
e <- ends[3,]
if(any(is.na(e))){
stop("no feasible model with 3 segments")
}
e
}
test_that("infeasible models", {
expect_error({
pseg3(3, 2, 1)
}, "no feasible model with 3 segments")
expect_error({
pseg3(1, 2, 3)
}, "no feasible model with 3 segments")
})
test_that("feasible models", {
ends <- pseg3(1, 3, 2)
expect_equal(ends, 1:3)
ends <- pseg3(1, 5, 2)
expect_equal(ends, 1:3)
ends <- pseg3(2, 3, 1)
expect_equal(ends, 1:3)
})
test_that("solver cost same as PoissonLoss", {
x <- c(1, 5, 2)
fit <- cDPA(x, maxSegments=3)
mean1.vec <- cumsum(x)/seq_along(x)
expect_equal(fit$mean[1,], mean1.vec)
loss1 <- PoissonLoss(x, mean1.vec[3])
expect_equal(fit$loss[1,3], loss1)
mean2.vec <- cumsum(x[-1])/1:2
expect_equal(fit$mean[2,2:3], mean2.vec)
loss2 <- PoissonLoss(x, c(1, 3.5, 3.5))
expect_equal(fit$loss[2,3], loss2)
expect_equal(fit$mean[3,3], 2)
loss3 <- PoissonLoss(x, x)
expect_equal(fit$loss[3,3], loss3)
})
test_that("no feasible model for paper data set, forward", {
expect_error({
pseg3(1, 10, 14, 13)
}, "no feasible model with 3 segments")
})
test_that("sub-optimal model for paper data set, reverse", {
count.vec <- c(13, 14, 10, 1)
fit <- cDPA(count.vec, maxSegments=3L)
cdpa.loss <- fit$loss[3,4]
mean.vec <- c(11, 12, 12, 1)
other.loss <- PoissonLoss(count.vec, mean.vec)
expect_gt(cdpa.loss, other.loss)
})
```

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