distributions: support a zero max value in Zipf.
There is no documentation that says zero isn't okay, and the closed interval
[0, k] described by the documentation is perfectly well-defined even when k is
zero. As far as I can tell, there is no reason *not* to support zero: a random
variable that always returns the same value is still a random variable.
absl::Uniform will happily generate on the interval [0, 1) for the same
reason.
PiperOrigin-RevId: 694649518
Change-Id: Ib940406f762a30e27c19c846c45bd908ae8411c3
diff --git a/absl/random/distributions_test.cc b/absl/random/distributions_test.cc
index 850796e..4340aeb 100644
--- a/absl/random/distributions_test.cc
+++ b/absl/random/distributions_test.cc
@@ -470,6 +470,13 @@
EXPECT_NEAR(6.5944, moments.mean, 2000) << moments;
}
+TEST_F(RandomDistributionsTest, ZipfWithZeroMax) {
+ absl::InsecureBitGen gen;
+ for (int i = 0; i < 100; ++i) {
+ EXPECT_EQ(0, absl::Zipf(gen, 0));
+ }
+}
+
TEST_F(RandomDistributionsTest, Gaussian) {
std::vector<double> values(kSize);
diff --git a/absl/random/zipf_distribution.h b/absl/random/zipf_distribution.h
index 0600cfc..15f03ee 100644
--- a/absl/random/zipf_distribution.h
+++ b/absl/random/zipf_distribution.h
@@ -57,7 +57,7 @@
public:
using distribution_type = zipf_distribution;
- // Preconditions: k > 0, v > 0, q > 1
+ // Preconditions: k >= 0, v > 0, q > 1
// The precondidtions are validated when NDEBUG is not defined via
// a pair of assert() directives.
// If NDEBUG is defined and either or both of these parameters take invalid
@@ -152,7 +152,7 @@
: k_(k), q_(q), v_(v), one_minus_q_(1 - q) {
assert(q > 1);
assert(v > 0);
- assert(k > 0);
+ assert(k >= 0);
one_minus_q_inv_ = 1 / one_minus_q_;
// Setup for the ZRI algorithm (pg 17 of the paper).
