# Part of the "structmcmc" package, https://github.com/rjbgoudie/structmcmc
#
# This software is distributed under the GPL-3 license. It is free,
# open source, and has the attribution requirements (GPL Section 7) in
# https://github.com/rjbgoudie/structmcmc
#
# Note that it is required that attributions are retained with each function.
#
# Copyright 2008 Robert J. B. Goudie, University of Warwick
context("Cross-validation tests")
test_that("3-node Bayesian Network", {
x1 <- as.factor(c(1, 1, 0, 1, 0, 0, 1, 0, 1, 0))
x2 <- as.factor(c(0, 1, 0, 1, 0, 1, 1, 0, 1, 0))
x3 <- as.factor(c(0, 1, 1, 1, 0, 1, 1, 0, 1, 0))
train <- data.frame(x1 = x1, x2 = x2, x3 = x3)
x <- bn(integer(0), c(1,3), integer(0))
test <- data.frame(x1 = x1[c(2,3,4,2,3)],
x2 = x2[c(6,4,2,4,2)],
x3 = x3[c(2,3,4,3,4)])
bayes(x, train)
residualsMultDir(x, train, test)
})
test_that("3-node Bayesian Network", {
x1 <- as.factor(c(1, 1, 0, 1, 0, 0, 1, 0, 1, 0))
x2 <- as.factor(c(0, 1, 0, 1, 0, 1, 1, 0, 1, 0))
x3 <- as.factor(c(0, 1, 1, 1, 0, 1, 1, 0, 1, 0))
train <- data.frame(x1 = x1, x2 = x2, x3 = x3)
x <- bn(integer(0), c(3), integer(0))
y <- bn(integer(0), c(1), integer(0))
test <- data.frame(x1 = x1[c(2,3,4,2,3,8,7)],
x2 = x2[c(6,4,2,4,2,7,8)],
x3 = x3[c(2,3,4,3,4,7,7)])
predictModelAverageNode(2, list(x, y), train, test, weights = c(0.1, 0.9))
expect_that(residualsMultDir(bn.list(x, y), weights = c(0.1, 0.9), train, test), equals(c(4, 4, 0)))
})
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