diff options
Diffstat (limited to 'core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala')
| -rw-r--r-- | core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala index 1314217023..3c61528ab5 100644 --- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala +++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala @@ -54,7 +54,7 @@ object RandomSampler { /** * Default maximum gap-sampling fraction. * For sampling fractions <= this value, the gap sampling optimization will be applied. - * Above this value, it is assumed that "tradtional" Bernoulli sampling is faster. The + * Above this value, it is assumed that "traditional" Bernoulli sampling is faster. The * optimal value for this will depend on the RNG. More expensive RNGs will tend to make * the optimal value higher. The most reliable way to determine this value for a new RNG * is to experiment. When tuning for a new RNG, I would expect a value of 0.5 to be close @@ -319,7 +319,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( /** * Skip elements with replication factor zero (i.e. elements that won't be sampled). * Samples 'k' from geometric distribution P(k) = (1-q)(q)^k, where q = e^(-f), that is - * q is the probabililty of Poisson(0; f) + * q is the probability of Poisson(0; f) */ private def advance(): Unit = { val u = math.max(rng.nextDouble(), epsilon) |