[poincare] Fix HypergeometricDistribution::EvaluateAtAbscissa with nu…

…merical errors
numworks · Apr 16, 2024 · f86d9a9 · f86d9a9
1 parent deb447b
commit f86d9a9
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Showing 3 changed files with 17 additions and 3 deletions.
diff --git a/apps/distributions/test/distributions_results.cpp b/apps/distributions/test/distributions_results.cpp
@@ -350,6 +350,14 @@ QUIZ_CASE(distributions_results_hypergeometric) {
   assert_cumulative_distributive_function_direct_is(&distribution, 70.0, 0.0);
   assert_finite_integral_between_abscissas_is(&distribution, 95.0, 102.0,
                                               0.67319633087273711);
+
+  // H(88888888888888880, 68, 88888888888888880)
+  distribution.setParameterAtIndex(88888888888888880, 0);
+  distribution.setParameterAtIndex(68, 1);
+  distribution.setParameterAtIndex(88888888888888880, 2);
+  assert_roughly_equal<float>(distribution.evaluateAtAbscissa(68), 1);
+  assert_roughly_equal<float>(distribution.evaluateAtAbscissa(66), NAN, 0,
+                              true);
 }
 
 QUIZ_CASE(distributions_results_poisson) {

diff --git a/apps/shared/plot_view_plots.cpp b/apps/shared/plot_view_plots.cpp
@@ -457,6 +457,7 @@ void WithHistogram::HistogramDrawing::draw(const AbstractPlotView *plotView,
     //   Step 2.2: Bar height
     KDCoordinate top =
         plotView->floatToKDCoordinatePixel(AbstractPlotView::Axis::Vertical, y);
+    assert(top >= 0);
     KDCoordinate barHeight = plotViewHeight - top;
     assert(barHeight >= 0);
     KDCoordinate minBarHeight = 1 + borderWidth;

diff --git a/poincare/src/hypergeometric_distribution.cpp b/poincare/src/hypergeometric_distribution.cpp
@@ -26,9 +26,14 @@ T HypergeometricDistribution::EvaluateAtAbscissa(T k, T N, T K, T n) {
   if (k > K || (n - k) > (N - K)) {
     return 0;
   }
-  return BinomialCoefficientNode::compute(k, K) *
-         BinomialCoefficientNode::compute(n - k, N - K) /
-         BinomialCoefficientNode::compute(n, N);
+  T result = BinomialCoefficientNode::compute(k, K) *
+             BinomialCoefficientNode::compute(n - k, N - K) /
+             BinomialCoefficientNode::compute(n, N);
+  if (result < 0 || result > 1) {
+    // Precision errors
+    return NAN;
+  }
+  return result;
 }
 
 template <typename T>