ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

[SPARK-35851][GRAPHX] Modify the wrong variable used in GraphGenerators.sampleLogNormal

Browse source 
### What changes were proposed in this pull request?
modify the wrong variable used in GraphGenerators.sampleLogNormal

### Why are the changes needed?
wrong variable used

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
UT

Closes #33010 from zengruios/SPARK-35851.

Authored-by: zengruios <578395184@qq.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
This commit is contained in:
zengruios 2021-06-23 18:20:33 -05:00 committed by Sean Owen
parent ad187227f1
commit 1cf18a2277
1 changed files with 4 additions and 9 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

13
graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
View file

@ -87,11 +87,10 @@ object GraphGenerators extends Logging {
}
/**
* Randomly samples from a log normal distribution whose corresponding normal distribution has
* the given mean and standard deviation. It uses the formula `X = exp(m+s*Z)` where `m`,
* `s` are the mean, standard deviation of the lognormal distribution and
* `Z ~ N(0, 1)`. In this function,
* `m = e^(mu+sigma^2/2)` and `s = sqrt[(e^(sigma^2) - 1)(e^(2*mu+sigma^2))]`.
* Randomly samples from the given mean and standard deviation of the normal distribution.
* It uses the formula `X = exp(mu+sigma*Z)` where `mu`,
* `sigma` are the mean, standard deviation of the normal distribution and
* `Z ~ N(0, 1)`.
*
* @param mu the mean of the normal distribution
* @param sigma the standard deviation of the normal distribution
@ -102,10 +101,6 @@ object GraphGenerators extends Logging {
mu: Double, sigma: Double, maxVal: Int, seed: Long = -1): Int = {
val rand = if (seed == -1) new Random() else new Random(seed)
val sigmaSq = sigma * sigma
val m = math.exp(mu + sigmaSq / 2.0)
// expm1 is exp(m)-1 with better accuracy for tiny m
val s = math.sqrt(math.expm1(sigmaSq) * math.exp(2*mu + sigmaSq))
// Z ~ N(0, 1)
var X: Double = maxVal