test_that("Naive Bayes Model Achieves > 95 perecent accuracy on toy dataset", {
    inv_logit <- function (x) {
        exp(x)/(1+exp(x))
    }

    for (i in 1:10) {

        n <- 10^5
        d <- 1:n %% 5 == 0
        X <- cbind(
             as.integer(ifelse(d, runif(n)<.8, runif(n)<.2)),
             as.integer(ifelse(d, runif(n)<.9, runif(n)<.2)),
             as.integer(ifelse(d, runif(n)<.7, runif(n)<.2)),
             as.integer(ifelse(d, runif(n)<.6, runif(n)<.2)),
             as.integer(ifelse(d, runif(n)<.5, runif(n)<.2)),
             as.integer(ifelse(d, runif(n)<.1, runif(n)<.9)),
             as.integer(ifelse(d, runif(n)<.1, runif(n)<.9)),
             as.integer(ifelse(d, runif(n)<.8, runif(n)<.01))
             )

        x_sum <- rowSums(X)
        g <- inv_logit((x_sum - mean(x_sum))/sd(x_sum))
        out <- em_link(X, g,tol=.0001, max_iter = 100)

        confusion_vector <- c(prop.table(table(out>.5, d)))
        # Expect classifier gets better than  97 percent accurately reliably on toy example
        expect_true((confusion_vector[1] + confusion_vector[4]) > .97)
    }

})