hetong007 commented on a change in pull request #10450: [MXNET-212] [R] Fix
Random Samplers from Uniform and Gaussian distribution in R bindings
URL: https://github.com/apache/incubator-mxnet/pull/10450#discussion_r179979144
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File path: R-package/tests/testthat/test_random.R
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@@ -0,0 +1,19 @@
+require(mxnet)
+
+context("random")
+
+test_that("mx.runif", {
+ X <- mx.runif(shape=50000, min=0, max=4, ctx=mx.ctx.default())
+ expect_equal(X>=0, mx.nd.ones(50000))
+ expect_equal(X<=4, mx.nd.ones(50000))
+ sample_mean = mean(as.array(X))
+ expect_equal(sample_mean, 2, tolerance=1e-2)
+})
+
+test_that("mx.rnorm", {
+ X <- mx.rnorm(shape=50000, mean=5, sd=1, ctx=mx.ctx.default())
+ sample_mean = mean(as.array(X))
+ sample_sd = sd(as.array(X))
+ expect_equal(sample_mean, 5, tolerance=1e-2)
Review comment:
From 50k samples, the variance of the mean is `1/sqrt(50000)`, roughly
0.0044, this means that the probability of failing the `1e-2` tolerance from a
correct implementation is around 2.5%. Considering the number of test being
run, this probability is too high and may annoy the others.
A simple fix will be setting `sd=0.1`.
The `runif` case has the same issue. The fix can be `max=1`, and expect
equal to `0.5`.
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