```
context("evaluate_fitness")
# test data ----------------
Y <- as.matrix(mtcars$mpg)
X <- as.matrix(mtcars[2:ncol(mtcars)])
# get input data
C <- dim(X)[2] # number genes
P <- 2 * C # number of chromosomes
# generate chromosomes to test
geneSample <- sample(c(0, 1),
replace = TRUE,
size = ceiling(1.2 * C * P))
x <- seq_along(geneSample)
firstGen <- split(geneSample, ceiling(x / C))
generation_t0 <- matrix(unlist(unique(firstGen)[1:P]),
ncol = C, byrow = TRUE)
generation_t0 <- generation_t0[apply(generation_t0, 1,
function(x) !all(x == 0)), ]
# serial evaluation
test_that('serial fitness evaluation works',
{test <- evaluate_fitness(generation_t0, Y, X,
family = "gaussian",
nCores = 1, minimize = TRUE,
objective_function = stats::AIC,
rank_objective_function)
expect_is(test, "matrix")
expect_type(test, "double")
})
# parallel evaluation
test_that('parallel fitness evaluation works',
{test <- evaluate_fitness(generation_t0, Y, X,
family = "gaussian",
nCores = 2, minimize = TRUE,
objective_function = stats::AIC,
rank_objective_function)
expect_is(test, "matrix")
#expect_type(test, "double")
})
# test maximize evaluation and other objective functions
test_that('Other objective_functions work',
{test <- evaluate_fitness(generation_t0, Y, X,
family = "gaussian",
nCores = 1, minimize = FALSE,
objective_function = stats::logLik,
rank_objective_function)
expect_is(test, "matrix")
expect_type(test, "double")
})
```

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