Require all `findStationaryDistribution` options (#453)

I'm planning to make a `pagerank.js` module that is a clean entry point
for all the graph-pagerank-related code, so it will be cleaner to expose all
the default options there.

Test plan: travis

Paired with @wchargin

src/v3/core/attribution/markovChain.js

@@ -92,18 +92,12 @@ export function sparseMarkovChainAction(
 
 export function findStationaryDistribution(
   chain: SparseMarkovChain,
-  options?: {|
-    +verbose?: boolean,
-    +convergenceThreshold?: number,
-    +maxIterations?: number,
+  options: {|
+    +verbose: boolean,
+    +convergenceThreshold: number,
+    +maxIterations: number,
   |}
 ): Distribution {
-  const fullOptions = {
-    verbose: false,
-    convergenceThreshold: 1e-7,
-    maxIterations: 255,
-    ...(options || {}),
-  };
   let r0 = uniformDistribution(chain.length);
   function computeDelta(pi0, pi1) {
     let maxDelta = -Infinity;
@@ -117,8 +111,8 @@ export function findStationaryDistribution(
   }
   let iteration = 0;
   while (true) {
-    if (iteration >= fullOptions.maxIterations) {
-      if (fullOptions.verbose) {
+    if (iteration >= options.maxIterations) {
+      if (options.verbose) {
         console.log(`[${iteration}] FAILED to converge`);
       }
       return r0;
@@ -127,11 +121,11 @@ export function findStationaryDistribution(
     const r1 = sparseMarkovChainAction(chain, r0);
     const delta = computeDelta(r0, r1);
     r0 = r1;
-    if (fullOptions.verbose) {
+    if (options.verbose) {
       console.log(`[${iteration}] delta = ${delta}`);
     }
-    if (delta < fullOptions.convergenceThreshold) {
-      if (fullOptions.verbose) {
+    if (delta < options.convergenceThreshold) {
+      if (options.verbose) {
         console.log(`[${iteration}] CONVERGED`);
       }
       return r0;

src/v3/core/attribution/markovChain.test.js

@@ -151,7 +151,11 @@ describe("core/attribution/markovChain", () => {
         [0.25, 0, 0.75],
         [0.25, 0.75, 0],
       ]);
-      const pi = findStationaryDistribution(chain);
+      const pi = findStationaryDistribution(chain, {
+        maxIterations: 255,
+        convergenceThreshold: 1e-7,
+        verbose: false,
+      });
       expectStationary(chain, pi);
       const expected = new Float64Array([1, 0, 0]);
       expectAllClose(pi, expected);
@@ -169,7 +173,11 @@ describe("core/attribution/markovChain", () => {
         [0.5, 0, 0.25, 0, 0.25],
         [0.5, 0.25, 0, 0.25, 0],
       ]);
-      const pi = findStationaryDistribution(chain);
+      const pi = findStationaryDistribution(chain, {
+        maxIterations: 255,
+        convergenceThreshold: 1e-7,
+        verbose: false,
+      });
       expectStationary(chain, pi);
       const expected = new Float64Array([1 / 3, 1 / 6, 1 / 6, 1 / 6, 1 / 6]);
       expectAllClose(pi, expected);
@@ -177,7 +185,11 @@ describe("core/attribution/markovChain", () => {
 
     it("finds the stationary distribution of a periodic chain", () => {
       const chain = sparseMarkovChainFromTransitionMatrix([[0, 1], [1, 0]]);
-      const pi = findStationaryDistribution(chain);
+      const pi = findStationaryDistribution(chain, {
+        maxIterations: 255,
+        convergenceThreshold: 1e-7,
+        verbose: false,
+      });
       expectStationary(chain, pi);
       const expected = new Float64Array([0.5, 0.5]);
       expectAllClose(pi, expected);
@@ -185,7 +197,11 @@ describe("core/attribution/markovChain", () => {
 
     it("returns initial distribution if maxIterations===0", () => {
       const chain = sparseMarkovChainFromTransitionMatrix([[0, 1], [0, 1]]);
-      const pi = findStationaryDistribution(chain, {maxIterations: 0});
+      const pi = findStationaryDistribution(chain, {
+        verbose: false,
+        convergenceThreshold: 1e-7,
+        maxIterations: 0,
+      });
       const expected = new Float64Array([0.5, 0.5]);
       expect(pi).toEqual(expected);
     });