cleanup: remove test duplication
In a previous commit (#1182) I inadvertently duplicated some tests. They have now been removed. Test plan: `yarn test` passes.
This commit is contained in:
Dandelion Mané
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414fb9f89f
commit
bcf805b9c8
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@ -2,7 +2,7 @@
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import sortBy from "lodash.sortby";
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import sortBy from "lodash.sortby";
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import {Graph, NodeAddress} from "../graph";
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import {Graph} from "../graph";
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import {
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import {
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createConnections,
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createConnections,
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createOrderedSparseMarkovChain,
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createOrderedSparseMarkovChain,
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@ -10,10 +10,6 @@ import {
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normalizeNeighbors,
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normalizeNeighbors,
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permute,
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permute,
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} from "./graphToMarkovChain";
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} from "./graphToMarkovChain";
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import {
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distributionToNodeDistribution,
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weightedDistribution,
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} from "./nodeDistribution";
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import * as MapUtil from "../../util/map";
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import * as MapUtil from "../../util/map";
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import {node, advancedGraph, edge} from "../graphTestUtil";
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import {node, advancedGraph, edge} from "../graphTestUtil";
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@ -269,81 +265,4 @@ describe("core/attribution/graphToMarkovChain", () => {
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expect(normalize(osmc)).toEqual(normalize(expected));
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expect(normalize(osmc)).toEqual(normalize(expected));
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});
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});
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});
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});
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describe("distributionToNodeDistribution", () => {
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it("works", () => {
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const pi = new Float64Array([0.25, 0.75]);
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expect(distributionToNodeDistribution([n1, n2], pi)).toEqual(
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new Map().set(n1, 0.25).set(n2, 0.75)
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);
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});
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});
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describe("weightedDistribution", () => {
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const a = NodeAddress.fromParts(["a"]);
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const b = NodeAddress.fromParts(["b"]);
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const c = NodeAddress.fromParts(["c"]);
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const d = NodeAddress.fromParts(["d"]);
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const order = () => [a, b, c, d];
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it("gives a uniform distribution for an empty map", () => {
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expect(weightedDistribution(order(), new Map())).toEqual(
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new Float64Array([0.25, 0.25, 0.25, 0.25])
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);
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});
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it("gives a uniform distribution for a map with 0 weight", () => {
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const map = new Map().set(a, 0);
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expect(weightedDistribution(order(), map)).toEqual(
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new Float64Array([0.25, 0.25, 0.25, 0.25])
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);
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});
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it("can put all weight on one node", () => {
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const map = new Map().set(b, 0.1);
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expect(weightedDistribution(order(), map)).toEqual(
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new Float64Array([0, 1, 0, 0])
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);
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});
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it("can split weight unequally", () => {
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const map = new Map().set(b, 1).set(c, 3);
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expect(weightedDistribution(order(), map)).toEqual(
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new Float64Array([0, 0.25, 0.75, 0])
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);
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});
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it("can create a uniform distribution if all weights are equal", () => {
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const map = new Map()
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.set(a, 1)
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.set(b, 1)
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.set(c, 1)
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.set(d, 1);
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expect(weightedDistribution(order(), map)).toEqual(
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new Float64Array([0.25, 0.25, 0.25, 0.25])
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);
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});
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describe("errors if", () => {
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it("has a weighted node that is not in the order", () => {
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const z = NodeAddress.fromParts(["z"]);
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const map = new Map().set(z, 1);
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expect(() => weightedDistribution(order(), map)).toThrowError(
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"weights included nodes not present in the nodeOrder"
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);
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});
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it("has a node with negative weight", () => {
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const map = new Map().set(a, -1);
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expect(() => weightedDistribution(order(), map)).toThrowError(
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"Invalid weight -1"
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);
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});
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it("has a node with NaN weight", () => {
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const map = new Map().set(a, NaN);
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expect(() => weightedDistribution(order(), map)).toThrowError(
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"Invalid weight NaN"
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);
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});
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it("has a node with infinite weight", () => {
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const map = new Map().set(a, Infinity);
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expect(() => weightedDistribution(order(), map)).toThrowError(
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"Invalid weight Infinity"
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);
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});
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});
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});
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});
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});
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@ -108,24 +108,6 @@ describe("core/attribution/markovChain", () => {
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});
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});
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});
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});
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describe("uniformDistribution", () => {
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it("computes the uniform distribution with domain of size 1", () => {
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const pi = uniformDistribution(1);
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expect(pi).toEqual(new Float64Array([1]));
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});
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it("computes the uniform distribution with domain of size 4", () => {
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const pi = uniformDistribution(4);
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expect(pi).toEqual(new Float64Array([0.25, 0.25, 0.25, 0.25]));
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});
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[0, -1, Infinity, NaN, 3.5, '"beluga"', null, undefined].forEach((bad) => {
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it(`fails when given domain ${String(bad)}`, () => {
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expect(() => uniformDistribution((bad: any))).toThrow(
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"positive integer"
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);
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});
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});
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});
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describe("sparseMarkovChainAction", () => {
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describe("sparseMarkovChainAction", () => {
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it("acts properly on a nontrivial chain", () => {
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it("acts properly on a nontrivial chain", () => {
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// Note: this test case uses only real numbers that are exactly
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// Note: this test case uses only real numbers that are exactly
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