test: use shouldBeApprox for floating-point comparisons in StatisticsSpec

CUDA backend produces sub-epsilon rounding in weighted-mean, varWeighted,
stdev, and corrCoef — switch those four tests to approximate equality.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: dmjio

test/ArrayFire/StatisticsSpec.hs

@@ -20,24 +20,22 @@ spec =
         `shouldBe`
            5.5
     it "Should find the weighted-mean" $ do
-      listToMaybe (toList (meanWeighted (vector @Double 10 [1..]) (vector @Double 10 [1..]) 0))
-        `shouldBe`
-           (Just 7.0)
+      case listToMaybe (toList (meanWeighted (vector @Double 10 [1..]) (vector @Double 10 [1..]) 0)) of
+        Nothing -> expectationFailure "expected Just 7.0, got Nothing"
+        Just v  -> v `shouldBeApprox` 7.0
     it "Should find the variance" $ do
       var (vector @Double 8 [1..8]) Population 0
         `shouldBe`
            5.25
     it "Should find the weighted variance (equal weights)" $ do
-      varWeighted (vector @Double 8 [1..]) (vector @Double 8 (repeat 1)) 0
-        `shouldBe`
-           5.25
+      head (toList (varWeighted (vector @Double 8 [1..]) (vector @Double 8 (repeat 1)) 0))
+        `shouldBeApprox` 5.25
     it "Should find the weighted variance (increasing weights)" $ do
       head (toList (varWeighted (vector @Double 10 [1..]) (vector @Double 10 [1..]) 0))
         `shouldBeApprox` (21/11 :: Double)
     it "Should find the standard deviation" $ do
-      stdev (vector @Double 10 (cycle [1,-1])) 0
-        `shouldBe`
-           1.0
+      head (toList (stdev (vector @Double 10 (cycle [1,-1])) 0))
+        `shouldBeApprox` 1.0
     it "Should find the covariance" $ do
       cov (vector @Double 10 (repeat 1)) (vector @Double 10 (repeat 1)) False
         `shouldBe`
@@ -66,7 +64,7 @@ spec =
         `shouldBe` 5.5
     it "Should find the correlation coefficient" $ do
       corrCoef (vector @Int 10 [1..]) (vector @Int 10 [10,9..])
-        `shouldBe` (-1.0)
+        `shouldBeApprox` (-1.0)
     it "Should find the top k elements" $ do
       let (vals,indexes) = topk ( vector @Double 10 [1..] ) 3 TopKDefault
       vals `shouldBe` vector @Double 3 [10,9,8]