From 0bf632990157133b6139c38f199e4f27e3df6279 Mon Sep 17 00:00:00 2001
From: c-bata <contact@c-bata.link>
Date: Sun, 11 Apr 2021 01:48:01 +0900
Subject: [PATCH] Fuzzing for sep-CMA-ES

---
 cmaes/_sepcma.py      | 13 +++++++++++--
 tests/test_fuzzing.py | 30 +++++++++++++++++++++++++++++-
 2 files changed, 40 insertions(+), 3 deletions(-)

diff --git a/cmaes/_sepcma.py b/cmaes/_sepcma.py
index df56536..118104d 100644
--- a/cmaes/_sepcma.py
+++ b/cmaes/_sepcma.py
@@ -9,6 +9,7 @@
 
 
 _EPS = 1e-8
+_MEAN_MAX = 1e32
 _SIGMA_MAX = 1e32
 
 
@@ -73,6 +74,10 @@ def __init__(
     ):
         assert sigma > 0, "sigma must be non-zero positive value"
 
+        assert np.all(
+            np.abs(mean) < _MEAN_MAX
+        ), f"Abs of all elements of mean vector must be less than {_MEAN_MAX}"
+
         n_dim = len(mean)
         assert n_dim > 1, "The dimension of mean must be larger than 1"
 
@@ -233,8 +238,12 @@ def _repair_infeasible_params(self, param: np.ndarray) -> np.ndarray:
 
     def tell(self, solutions: List[Tuple[np.ndarray, float]]) -> None:
         """Tell evaluation values"""
-        if len(solutions) != self._popsize:
-            raise ValueError("Must tell popsize-length solutions.")
+
+        assert len(solutions) == self._popsize, "Must tell popsize-length solutions."
+        for s in solutions:
+            assert np.all(
+                np.abs(s[0]) < _MEAN_MAX
+            ), f"Abs of all param values must be less than {_MEAN_MAX} to avoid overflow errors"
 
         self._g += 1
         solutions.sort(key=lambda s: s[1])
diff --git a/tests/test_fuzzing.py b/tests/test_fuzzing.py
index df11c91..a567607 100644
--- a/tests/test_fuzzing.py
+++ b/tests/test_fuzzing.py
@@ -2,7 +2,7 @@
 import unittest
 from hypothesis import given, strategies as st
 
-from cmaes import CMA
+from cmaes import CMA, SepCMA
 
 
 class TestFuzzing(unittest.TestCase):
@@ -33,3 +33,31 @@ def test_cma_tell(self, data):
             except AssertionError:
                 return
             optimizer.ask()
+
+    @given(
+        data=st.data(),
+    )
+    def test_sepcma_tell(self, data):
+        dim = data.draw(st.integers(min_value=2, max_value=100))
+        mean = data.draw(npst.arrays(dtype=float, shape=dim))
+        sigma = data.draw(st.floats(min_value=1e-16))
+        n_iterations = data.draw(st.integers(min_value=1))
+        try:
+            optimizer = SepCMA(mean, sigma)
+        except AssertionError:
+            return
+        popsize = optimizer.population_size
+        for _ in range(n_iterations):
+            tell_solutions = data.draw(
+                st.lists(
+                    st.tuples(npst.arrays(dtype=float, shape=dim), st.floats()),
+                    min_size=popsize,
+                    max_size=popsize,
+                )
+            )
+            optimizer.ask()
+            try:
+                optimizer.tell(tell_solutions)
+            except AssertionError:
+                return
+            optimizer.ask()