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test_extract_replace_combine.R
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test_extract_replace_combine.R
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context("extract/replace/combine")
test_that("extract works like R", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- randn(10)
b <- randn(10, 1)
c <- randn(10, 5)
d <- randn(2, 2, 2)
# Can extract with a vector, regardless of dimension
check_expr(a[1:6], "a")
check_expr(b[1:6], "b")
check_expr(c[1:6], "c")
check_expr(d[1:6], "d")
# can extract first column, regardless of dimension
check_expr(b[1:6, ], "b")
check_expr(b[1:6, 1], "b")
check_expr(c[1:6, ], "c")
check_expr(c[1:6, 1], "c")
check_expr(d[1:2, , ], "d")
check_expr(d[1:2, 1, , drop = FALSE], "d")
# can extract with negative dimensions
check_expr(a[3:1], "a")
check_expr(d[2:1, , 1:2], "d")
# can extract with logicals
check_expr(a[c(TRUE, FALSE, TRUE)], "a")
# can extract with a mix of numerics and logicals
check_expr(d[2:1, , c(TRUE, FALSE), drop = FALSE], "d")
# can extract with missing entries in various places
check_expr(d[, , 2:1], "d")
check_expr(d[, 2:1, ], "d")
check_expr(d[2:1, , ], "d")
# can extract single elements without dropping dimensions
check_expr(d[, , 1, drop = FALSE], "d")
check_expr(d[, 1, , drop = FALSE], "d")
check_expr(d[1, , , drop = FALSE], "d")
# can do empty extracts
check_expr(a[], "a")
check_expr(b[], "b")
check_expr(c[], "c")
check_expr(d[], "d")
# can do negative extracts
check_expr(a[-1], "a")
check_expr(b[-1:-3, ], "b")
check_expr(c[-1:-4, ], "c")
check_expr(d[-1:-2, -1, , drop = FALSE], "d")
})
test_that("replace works like R", {
skip_if_not(check_tf_version())
source("helpers.R")
# check using expressions, and comparing the whole object to which replacement
# was applied
# Can replace with a vector, regardless of dimension
x <- randn(10)
check_expr({
x[1:6] <- seq_len(6)
x
})
x <- randn(10, 1)
check_expr({
x[1:6] <- seq_len(6)
x
})
x <- randn(10, 5)
check_expr({
x[1:6] <- seq_len(6)
x
})
x <- randn(2, 2, 2)
check_expr({
x[1:6] <- seq_len(6)
x
})
# can replace first column, regardless of dimension
x <- randn(10, 1)
check_expr({
x[1:6, 1] <- seq_len(6 * 1)
x
})
x <- randn(10, 5)
check_expr({
x[1:6, 1] <- seq_len(6 * 1)
x
})
x <- randn(2, 2, 2)
check_expr({
x[1:2, 1, ] <- seq_len(2 * 1 * 2)
x
})
# can replace a chunk, regardless of dimension
x <- randn(10, 1)
check_expr({
x[1:6, ] <- seq_len(6)
x
})
x <- randn(10, 5)
check_expr({
x[1:6, ] <- seq_len(6 * 5)
x
})
x <- randn(10, 2, 2)
check_expr({
x[1:2, , ] <- seq_len(2 * 2 * 2)
x
})
# can replace with negative dimensions
x <- randn(10)
check_expr({
x[3:1] <- seq_len(3)
x
})
x <- randn(10, 2, 2)
check_expr({
x[2:1, , 1:2] <- seq_len(2 * 2 * 2)
x
})
# can replace with logicals (the logical vector is repeated 3 1/3 times)
x <- randn(10)
check_expr({
x[c(TRUE, FALSE, TRUE)] <- seq_len(7)
x
})
# can extract with a mix of numerics and logicals
x <- randn(10, 2, 2)
check_expr({
x[2:1, , c(TRUE, FALSE)] <- seq_len(4)
x
})
# can assign with missing entries in various places
x <- randn(10, 2, 2)
check_expr({
x[, , 1:2] <- seq_len(10 * 2 * 2)
x
})
x <- randn(10, 2, 2)
check_expr({
x[, 1:2, ] <- seq_len(10 * 2 * 2)
x
})
x <- randn(10, 2, 2)
check_expr({
x[1:2, , ] <- seq_len(2 * 2 * 2)
x
})
# can assign single elements without dropping dimensions
x <- randn(10, 2, 2)
check_expr({
x[1, , ] <- seq_len(1 * 2 * 2)
x
})
x <- randn(10, 2, 2)
check_expr({
x[, 1, ] <- seq_len(10 * 1 * 2)
x
})
x <- randn(10, 2, 2)
check_expr({
x[, , 1] <- seq_len(10 * 2 * 1)
x
})
# can do full replacements
x <- randn(10)
check_expr({
x[] <- seq_len(10)
x
})
x <- randn(10, 1)
check_expr({
x[] <- seq_len(10)
x
})
x <- randn(10, 5)
check_expr({
x[] <- seq_len(10 * 5)
x
})
x <- randn(2, 2, 2)
check_expr({
x[] <- seq_len(2 * 2 * 2)
x
})
# can do negative replacements
x <- randn(10)
check_expr({
x[-1] <- seq_len(9)
x
})
x <- randn(10, 1)
check_expr({
x[-1:-3, ] <- seq_len(7 * 1)
x
})
x <- randn(10, 5)
check_expr({
x[-1:-4, ] <- seq_len(6 * 5)
x
})
x <- randn(2, 2, 2)
check_expr({
x[-1, -1, ] <- seq_len(1 * 1 * 2)
x
})
# can replace multiple entries with one;
x <- randn(10)
check_expr({
x[1:3] <- 1
x
})
x <- randn(10, 1)
check_expr({
x[1:3, 1] <- 1
x
})
x <- randn(10, 5)
check_expr({
x[1:3, ] <- 1
x
})
x <- randn(2, 2, 2)
check_expr({
x[1:2, , ] <- 1
x
})
# can replace multiple entries with a vector (with factorizing length)
x <- randn(10)
check_expr({
x[1:4] <- seq_len(2)
x
})
x <- randn(10, 1)
check_expr({
x[1:4, 1] <- seq_len(2)
x
})
x <- randn(10, 5)
check_expr({
x[1:9, ] <- seq_len(3)
x
})
x <- randn(2, 2, 2)
check_expr({
x[1:2, , 1] <- seq_len(2)
x
})
# replace with a greta array (e.g. with subset of self)
# check_expr seems to balls this one up, so do it longhand
x <- randn(10, 1)
ga_x <- as_data(x)
x[1:6, ] <- x[6:1, ]
ga_x[1:6, ] <- ga_x[6:1, ]
greta_out <- as.vector(grab(ga_x))
compare_op(x, greta_out)
})
test_that("rep works like R", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- randn(10)
b <- randn(10, 1)
c <- randn(10, 5)
d <- randn(10, 2, 2)
rep_times <- function(x)
rep(x, times = 3)
check_op(rep_times, a)
check_op(rep_times, b)
check_op(rep_times, c)
check_op(rep_times, d)
rep_length <- function(x)
rep(x, length.out = 3)
check_op(rep_length, a)
check_op(rep_length, b)
check_op(rep_length, c)
check_op(rep_length, d)
rep_times_each <- function(x)
rep(x, times = 3, each = 3)
check_op(rep_times_each, a)
check_op(rep_times_each, b)
check_op(rep_times_each, c)
check_op(rep_times_each, d)
rep_length_each <- function(x)
rep(x, length = 30, each = 3)
check_op(rep_length_each, a)
check_op(rep_length_each, b)
check_op(rep_length_each, c)
check_op(rep_length_each, d)
})
test_that("rbind, cbind and c work like R", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- randn(5, 1)
b <- randn(1, 5)
d <- randn(5, 5)
check_op(rbind, a, a)
check_op(rbind, b, d)
check_op(rbind, d, b)
check_op(cbind, b, b)
check_op(cbind, a, d)
check_op(cbind, d, d)
# flatten and concatenate arrays
check_op(c, a, d)
check_op(c, b, a)
check_op(c, d, b)
# unary c flattens arrays
check_op(c, a)
check_op(c, b)
check_op(c, d)
})
test_that("abind works like R", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- randn(5, 1, 3)
b <- randn(1, 1, 3)
c <- randn(5, 1, 1)
check_op(abind, a, b, other_args = list(along = 1))
check_op(abind, a, c)
check_op(abind, a, a, other_args = list(along = 0))
check_op(abind, a, a, other_args = list(along = 4))
})
test_that("abind errors informatively", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- ones(5, 1, 3)
b <- ones(1, 1, 3)
c <- ones(5, 1, 1)
expect_error(abind(a, b),
"dimension 1 had varying sizes")
expect_error(abind(a, c, along = 5),
"along must be between 0 and 4")
})
test_that("rbind and cbind can prepend R arrays to greta arrays", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- randn(5, 1)
b <- ones(5, 1)
z <- rbind(a, b)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(10L, 1L))
z <- rbind(b, a)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(10L, 1L))
z <- cbind(a, b)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(5L, 2L))
z <- cbind(b, a)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(5L, 2L))
})
test_that("assign errors on variable greta arrays", {
skip_if_not(check_tf_version())
source("helpers.R")
z <- normal(0, 1, dim = 5)
expect_error(z[1] <- 3,
"cannot replace values in a variable greta array")
})
test_that("rbind and cbind give informative error messages", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- as_data(randn(5, 1))
b <- as_data(randn(1, 5))
expect_error(rbind(a, b),
"all greta arrays must be have the same number of columns")
expect_error(cbind(a, b),
"all greta arrays must be have the same number of rows")
})
test_that("replacement gives informative error messages", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- ones(2, 2, 2)
expect_error(x[1:2, , 1] <- seq_len(3),
"not a multiple of replacement length")
expect_error(x[1, 1, 3] <- 1,
"subscript out of bounds")
x <- ones(2)
expect_error(x[3] <- 1,
"subscript out of bounds")
})
test_that("extraction gives informative error messages", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- ones(2, 2, 2)
expect_error(x[1, 1, 3],
"subscript out of bounds")
x <- ones(2)
expect_error(x[3],
"subscript out of bounds")
})
test_that("stochastic and operation greta arrays can be extracted", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- normal(0, 1, dim = c(3, 4))
a_sub <- a[1:2, 2:3]
expect_identical(dim(a_sub), c(2L, 2L))
b <- a * 2
b_sub <- b[1:2, ]
expect_identical(dim(b_sub), c(2L, 4L))
})
test_that("extract, replace, combine work in models", {
skip_if_not(check_tf_version())
source("helpers.R")
# extract
a <- normal(0, 1, dim = c(3, 4))
a_sub <- a[1:2, 2:3]
m_a <- model(a_sub)
expect_ok(draws_a <- mcmc(m_a, warmup = 3, n_samples = 3, verbose = FALSE))
# replace
b <- ones(4, 3)
x <- normal(0, 1, dim = 4)
b[, 2] <- x
m_b <- model(b)
expect_ok(draws_b <- mcmc(m_b, warmup = 3, n_samples = 3, verbose = FALSE))
# combine
d <- c(normal(0, 1, dim = 2),
lognormal(0, 1, dim = 3))
m_d <- model(d)
expect_ok(draws_d <- mcmc(m_d, warmup = 3, n_samples = 3, verbose = FALSE))
})
test_that("head and tail work", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- randn(10, 1)
b <- randn(10, 4)
c <- randn(10, 3, 3)
check_op(head, a)
check_op(tail, a)
check_op(head, b)
check_op(tail, b)
check_op(head, c)
check_op(tail, c)
})
test_that("length and dim work", {
skip_if_not(check_tf_version())
source("helpers.R")
ga_data <- as_data(matrix(1:9, nrow = 3))
ga_stochastic <- normal(0, 1, dim = c(3, 3))
ga_operation <- ga_data * ga_stochastic
# length
expect_identical(length(ga_data), 9L)
expect_identical(length(ga_stochastic), 9L)
expect_identical(length(ga_operation), 9L)
# dim
expect_identical(dim(ga_data), c(3L, 3L))
expect_identical(dim(ga_stochastic), c(3L, 3L))
expect_identical(dim(ga_operation), c(3L, 3L))
})
test_that("dim<- works", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- randn(3, 4, 2)
new_dim <- c(2, 2, 6)
check_op(`dim<-`, x, other_args = list(value = new_dim))
new_dim <- c(12, 2)
check_op(`dim<-`, x, other_args = list(value = new_dim))
new_dim <- NULL
check_op(`dim<-`, x, other_args = list(value = new_dim))
})
test_that("greta_array() reshapes array-like greta arrays like array", {
skip_if_not(check_tf_version())
source("helpers.R")
x_ <- randu(3, 4, 2)
x <- as_data(x_)
new_dim <- c(12, 2)
# for data greta arrays
y_ <- as_data(array(x_, dim = new_dim))
y <- greta_array(x, dim = new_dim)
compare_op(calculate(y)[[1]], calculate(y_)[[1]])
# for operation greta arrays
x <- abs(x)
y_ <- as_data(array(x_, dim = new_dim))
y <- greta_array(x, dim = new_dim)
compare_op(calculate(y)[[1]], calculate(y_)[[1]])
# for variable greta arrays
x <- variable(dim = dim(x))
y_ <- as_data(array(x_, dim = new_dim))
y <- greta_array(x, dim = new_dim)
compare_op(calculate(y, values = list(x = x_))[[1]], calculate(y_)[[1]])
})
test_that("greta_array() reshapes scalar greta arrays like array", {
skip_if_not(check_tf_version())
source("helpers.R")
x_ <- randu(1)
x <- as_data(x_)
new_dim <- c(12, 2)
# for data greta arrays
y_ <- as_data(array(x_, dim = new_dim))
y <- greta_array(x, dim = new_dim)
compare_op(calculate(y)[[1]], calculate(y_)[[1]])
# for operation greta arrays
x <- abs(x)
y_ <- as_data(array(x_, dim = new_dim))
y <- greta_array(x, dim = new_dim)
compare_op(calculate(y)[[1]], calculate(y_)[[1]])
# for variable greta arrays
x <- variable(dim = dim(x))
y_ <- as_data(array(x_, dim = new_dim))
y <- greta_array(x, dim = new_dim)
compare_op(calculate(y, values = list(x = x_))[[1]], calculate(y_)[[1]])
})
test_that("dim<- errors as expected", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- zeros(3, 4)
expect_error(dim(x) <- pi[0],
"length-0 dimension vector is invalid")
expect_error(dim(x) <- c(1, NA),
"the dims contain missing values")
expect_error(dim(x) <- c(1, -1),
"the dims contain negative values")
expect_error(dim(x) <- 13,
paste("dims \\[product 13\\] do not match the length of",
"object \\[12\\]"))
})
test_that("dim<- works in a model", {
skip_if_not(check_tf_version())
source("helpers.R")
y <- rnorm(5)
x1 <- greta_array(1:12, c(3, 4))
dim(x1) <- NULL
x2 <- greta_array(1:12, c(3, 4))
dim(x2) <- 12
x3 <- greta_array(1:12, c(3, 4))
dim(x3) <- c(6, 2)
z <- x1[6, ] * x2[7, ] * x3[5, 2]
distribution(y) <- normal(z, lognormal(0, 1))
expect_ok(m <- model(z))
expect_ok(mcmc(m, warmup = 0, n_samples = 2))
})
test_that("c handles NULLs and lists", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- normal(0, 1)
y <- as_data(3:1)
# NULLs should be dropped when concatenating
z <- c(x, NULL, y)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(4L, 1L))
# NULLs at the end should dissappear
z <- c(x, NULL)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(1L, 1L))
# greta arrays combined with things coercible to greta arrays should return a
# greta array
z <- c(x, 0, FALSE)
expect_s3_class(z, "greta_array")
expect_identical(dim(z), c(3L, 1L))
# greta arrays combined with other things should return a list
z <- c(x, mean)
expect_true(is.list(z))
expect_s3_class(z[[1]], "greta_array")
expect_identical(dim(z[[1]]), c(1L, 1L))
# even with a NULL in there
z <- c(x, NULL, mean)
expect_true(is.list(z))
expect_s3_class(z[[1]], "greta_array")
expect_identical(dim(z[[1]]), c(1L, 1L))
})