Merge pull request #9 from Valeria1235/support-0_and_1_quantile

Support 0.0 and 1.0 quantile

Author: antonmyronyuk

quantile_estimator/__init__.py

@@ -81,6 +81,14 @@ def query(self, rank):
         if not current:
             return float("nan")
 
+        if rank == 0.0:
+            return current._value
+
+        if rank == 1.0:
+            while current._successor:
+                current = current._successor
+            return current._value
+
         mid_rank = math.floor(rank * self._observations)
         max_rank = mid_rank + math.floor(self._invariant(mid_rank, self._observations) / 2)
 
@@ -174,8 +182,8 @@ class _Quantile:
     def __init__(self, quantile, inaccuracy):
         self._quantile = quantile
         self._inaccuracy = inaccuracy
-        self._coefficient_i = (2.0 * inaccuracy) / (1.0 - quantile)
-        self._coefficient_ii = 2.0 * inaccuracy / quantile
+        self._coefficient_i = (2.0 * inaccuracy) / (1.0 - quantile) if quantile != 1. else float("nan")
+        self._coefficient_ii = 2.0 * inaccuracy / quantile if quantile != .0 else float("nan")
 
     """Computes the delta for the observation."""
     def _delta(self, rank, n):

tests/test_estimator.py

@@ -12,3 +12,14 @@ def test_random_observations(num_observations):
         estimator.observe(random.randint(1, 1000) / 100)
 
     assert 0 <= estimator.query(0.5) <= estimator.query(0.9) <= estimator.query(0.99) <= 10
+
+
+def test_border_invariants():
+    estimator = Estimator((0.0, 0.), (1.0, 0.))
+
+    values = [random.randint(1, 1000) for _ in range(1000)]
+    for x in values:
+        estimator.observe(x)
+
+    assert estimator.query(0) == min(values)
+    assert estimator.query(1) == max(values)