Tried to fix tests of math issues

packages/squiggle-lang/__tests__/Distributions/Invariants/AlgebraicCombination_test.res

@@ -51,6 +51,7 @@ describe("(Algebraic) addition of distributions", () => {
       ->toBe(Some(2.5e1))
     })
 
+
     test("uniform(low=9, high=10) + beta(alpha=2, beta=5)", () => {
       // let uniformMean = (9.0 +. 10.0) /. 2.0
       // let betaMean = 1.0 /. (1.0 +. 5.0 /. 2.0)
@@ -65,7 +66,8 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=2.
-      | Some(x) => x->expect->toBeSoCloseTo(0.01927225696028752, ~digits=1) // (uniformMean +. betaMean)
+      // Answer found from WolframAlpha: ``mean(uniform(9,10)) + mean(betaDistribution(2,5))``
+      | Some(x) => x->expect->toBeSoCloseTo(9.786, ~digits=1) // (uniformMean +. betaMean)
       }
     })
     test("beta(alpha=2, beta=5) + uniform(low=9, high=10)", () => {
@@ -82,7 +84,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=2.
-      | Some(x) => x->expect->toBeSoCloseTo(0.019275414920485248, ~digits=1) // (uniformMean +. betaMean)
+      | Some(x) => x->expect->toBeSoCloseTo(9.786, ~digits=1) // (uniformMean +. betaMean)
       }
     })
   })
@@ -163,7 +165,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=4.
-      | Some(x) => x->expect->toBeSoCloseTo(0.001978994877226945, ~digits=3)
+      | Some(x) => x->expect->toBeSoCloseTo(1.025, ~digits=1)
       }
     })
     test("(beta(alpha=2, beta=5) + uniform(low=9, high=10)).pdf(10)", () => {
@@ -178,7 +180,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=4.
-      | Some(x) => x->expect->toBeSoCloseTo(0.001978994877226945, ~digits=3)
+      | Some(x) => x->expect->toBeSoCloseTo(0.98, ~digits=1)
       }
     })
   })
@@ -254,7 +256,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=4.
-      | Some(x) => x->expect->toBeSoCloseTo(0.0013961779932477507, ~digits=3)
+      | Some(x) => x->expect->toBeSoCloseTo(0.70, ~digits=1)
       }
     })
     test("(beta(alpha=2, beta=5) + uniform(low=9, high=10)).cdf(10)", () => {
@@ -269,7 +271,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=4.
-      | Some(x) => x->expect->toBeSoCloseTo(0.001388898111625753, ~digits=3)
+      | Some(x) => x->expect->toBeSoCloseTo(0.71, ~digits=1)
       }
     })
   })
@@ -346,7 +348,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=2.
-      | Some(x) => x->expect->toBeSoCloseTo(10.927078217530806, ~digits=0)
+      | Some(x) => x->expect->toBeSoCloseTo(9.174960348568693, ~digits=0)
       }
     })
     test("(beta(alpha=2, beta=5) + uniform(low=9, high=10)).inv(2e-2)", () => {
@@ -361,7 +363,7 @@ describe("(Algebraic) addition of distributions", () => {
       | None => "algebraicAdd has"->expect->toBe("failed")
       // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
       // sometimes it works with ~digits=2.
-      | Some(x) => x->expect->toBeSoCloseTo(10.915396627014363, ~digits=0)
+      | Some(x) => x->expect->toBeSoCloseTo(9.168291999681523, ~digits=0)
       }
     })
   })

packages/squiggle-lang/__tests__/TS/JS_test.ts

@@ -56,16 +56,16 @@ describe("Distribution", () => {
   );
 
   test("mean", () => {
-    expect(dist.mean().value).toBeCloseTo(3.737);
+    expect(dist.mean().value).toBeCloseTo(5.3913);
   });
   test("pdf", () => {
     expect(dist.pdf(5.0).value).toBeCloseTo(0.0431);
   });
   test("cdf", () => {
-    expect(dist.cdf(5.0).value).toBeCloseTo(0.155);
+    expect(dist.cdf(5.0).value).toBeCloseTo(0.224);
   });
   test("inv", () => {
-    expect(dist.inv(0.5).value).toBeCloseTo(9.458);
+    expect(dist.inv(0.5).value).toBeCloseTo(6.0);
   });
   test("toPointSet", () => {
     expect(
@@ -87,6 +87,6 @@ describe("Distribution", () => {
       resultMap(dist.pointwiseAdd(dist2), (r: Distribution) =>
         r.toSparkline(20)
       ).value
-    ).toEqual(Ok("▁▂▅██▅▅▅▆▇█▆▅▃▃▂▂▁▁▁"));
+    ).toEqual(Ok("▁▂▅█▇▅▅▆▇██▇▅▄▃▃▃▂▁▁"));
   });
 });

packages/squiggle-lang/__tests__/TS/SampleSet_test.ts

@@ -46,7 +46,7 @@ describe("cumulative density function", () => {
     );
   });
 
-  test("at the highest number in the sample is close to 1", () => {
+  test("at the highest number in the sample to be approximately 1", () => {
     fc.assert(
       fc.property(arrayGen(), (xs_) => {
         let xs = Array.from(xs_);
@@ -57,13 +57,7 @@ describe("cumulative density function", () => {
           { sampleCount: n, xyPointLength: 100 }
         );
         let cdfValue = dist.cdf(max).value;
-        let min = Math.min(...xs);
+        expect(cdfValue).toBeCloseTo(1.0, 2)
-        let epsilon = 5e-3;
-        if (max - min < epsilon) {
-          expect(cdfValue).toBeLessThan(1 - epsilon);
-        } else {
-          expect(dist.cdf(max).value).toBeGreaterThan(1 - epsilon);
-        }
       })
     );
   });