commented out tests are now explained

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

@@ -0,0 +1,49 @@
+import { errorValueToString } from "../../src/js/index";
+import { testRun, failDefault, expectErrorToBeBounded } from "./TestHelpers";
+import * as fc from "fast-check";
+
+describe("Mean of mixture is weighted average of means", () => {
+  test("mx(beta(a,b), lognormal(m,s), [x,y])", () => {
+    fc.assert(
+      fc.property(
+        fc.float({ min: 1e-1 }), // alpha
+        fc.float({ min: 1 }), // beta
+        fc.float(), // mu
+        fc.float({ min: 1e-1 }), // sigma
+        fc.float({ min: 1e-7 }),
+        fc.float({ min: 1e-7 }),
+        (a, b, m, s, x, y) => {
+          let squiggleString = `mean(mx(beta(${a},${b}), lognormal(${m},${s}), [${x}, ${y}]))`;
+          let res = testRun(squiggleString);
+          switch (res.tag) {
+            case "Error":
+              expect(errorValueToString(res.value)).toEqual(
+                "<I wonder if test cases will find this>"
+              );
+              break;
+            case "Ok":
+              let weightDenom = x + y;
+              let betaWeight = x / weightDenom;
+              let lognormalWeight = y / weightDenom;
+              let betaMean = 1 / (1 + b / a);
+              let lognormalMean = m + s ** 2 / 2;
+              if (res.value.tag == "number") {
+                expectErrorToBeBounded(
+                  res.value.value,
+                  betaWeight * betaMean + lognormalWeight * lognormalMean,
+                  1,
+                  -1
+                );
+              } else {
+                expect(res.value.value).toEqual("some error message");
+              }
+              break;
+            default:
+              failDefault();
+              break;
+          }
+        }
+      )
+    );
+  });
+});

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

@@ -1,4 +1,5 @@
 import { Distribution } from "../../src/js/index";
+import { expectErrorToBeBounded, failDefault } from "./TestHelpers";
 import * as fc from "fast-check";
 
 // Beware: float64Array makes it appear in an infinite loop.
@@ -126,6 +127,7 @@ describe("SampleSet: cdf", () => {
 //  });
 // });
 
+// This should be true, but I can't get it to work.
 // describe("SampleSet: mean is mean", () => {
 // test("mean(samples(xs)) sampling twice as widely as the input", () => {
 //   fc.assert(
@@ -138,10 +140,12 @@ describe("SampleSet: cdf", () => {
 //           { tag: "SampleSet", value: ys },
 //           { sampleCount: 2 * n, xyPointLength: 4 * n }
 //         );
-//
+//         let mean = dist.mean()
-//          expect(dist.mean().value).toBeCloseTo(
+//         if (typeof mean.value == "number") {
-//            ys.reduce((a, b) => a + b, 0.0) / n
+//           expectErrorToBeBounded(mean.value, ys.reduce((a, b) => a + b, 0.0) / n, 5e-1, 1)
-//          );
+//         } else {
+//           failDefault()
+//         }
 //       }
 //     )
 //   );
@@ -156,47 +160,13 @@ describe("SampleSet: cdf", () => {
 //         let n = ys.length;
 //         let dist = new Distribution(
 //           { tag: "SampleSet", value: ys },
-//            { sampleCount: Math.floor(5 / 2), xyPointLength: 4 * n }
+//           { sampleCount: Math.floor(n / 2), xyPointLength: 4 * n }
 //         );
-//
+//         let mean = dist.mean()
-//          expect(dist.mean().value).toBeCloseTo(
+//         if (typeof mean.value == "number") {
-//            ys.reduce((a, b) => a + b, 0.0) / n
+//           expectErrorToBeBounded(mean.value, ys.reduce((a, b) => a + b, 0.0) / n, 5e-1, 1)
-//          );
+//         } else {
-//        }
+//           failDefault()
-//      )
-//    );
-//  });
-// });
-
-// describe("Mean of mixture is weighted average of means", () => {
-//  test("mx(beta(a,b), lognormal(m,s), [x,y])", () => {
-//    fc.assert(
-//      fc.property(
-//        fc.float({ min: 1e-1 }),  // alpha
-//        fc.float({ min: 1 }),   // beta
-//        fc.float(),  // mu
-//        fc.float({ min: 1e-1 }),  // sigma
-//        fc.float({ min: 1e-7 }),
-//        fc.float({ min: 1e-7 }),
-//        (a, b, m, s, x, y) => {
-//          let squiggleString = `mean(mx(beta(${a},${b}), lognormal(${m},${s}), [${x}, ${y}]))`;
-//          let res = testRun(squiggleString);
-//          switch (res.tag) {
-//            case "Error":
-//              expect(errorValueToString(res.value)).toEqual(
-//                "<I wonder if test cases will find this>"
-//              );
-//            case "Ok":
-//              let betaWeight = x / (x + y);
-//              let lognormalWeight = y / (x + y);
-//              let betaMean = 1 / (1 + b / a);
-//              let lognormalMean = m + s ** 2 / 2;
-//              expect(res.value).toEqual({
-//                tag: "number",
-//                value: betaWeight * betaMean + lognormalWeight * lognormalMean,
-//              });
-//            default:
-//              expect("mean returned").toBe(`something other than a number`);
 //         }
 //       }
 //     )

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

@@ -1,10 +1,4 @@
-import {
+import { errorValueToString } from "../../src/js/index";
-  run,
-  squiggleExpression,
-  errorValueToString,
-  errorValue,
-  result,
-} from "../../src/js/index";
 import { testRun } from "./TestHelpers";
 import * as fc from "fast-check";
 

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

@@ -13,5 +13,24 @@ export function testRun(x: string): any {
 }
 
 export function failDefault() {
-  expect("be reached").toBe("codepath should never be");
+  expect("be reached").toBe("codepath should never");
+}
+
+/**
+ * This appears also in `TestHelpers.res`. According to https://www.math.net/percent-error, it computes
+ * absolute error when numerical stability concerns make me not want to compute relative error.
+ * */
+export function expectErrorToBeBounded(
+  received: number,
+  expected: number,
+  epsilon: number,
+  digits: number
+) {
+  let distance = Math.abs(received - expected);
+  let expectedAbs = Math.abs(expected);
+  let normalizingDenom = Math.max(expectedAbs, 1);
+  let error = distance / normalizingDenom;
+  expect(Math.round(10 ** digits * error) / 10 ** digits).toBeLessThanOrEqual(
+    epsilon
+  );
 }