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umap-js - npm Package Compare versions
Comparing version 1.0.3 to 1.0.4
@@ -13,2 +13,3 @@ export declare class SparseMatrix { | ||
| get(row: number, col: number, defaultValue?: number): number; | ||
| getDims(): number[]; | ||
| getRows(): number[]; | ||
@@ -23,5 +24,12 @@ getCols(): number[]; | ||
| export declare function identity(size: number[]): SparseMatrix; | ||
| export declare function dotMultiply(a: SparseMatrix, b: SparseMatrix): SparseMatrix; | ||
| export declare function pairwiseMultiply(a: SparseMatrix, b: SparseMatrix): SparseMatrix; | ||
| export declare function add(a: SparseMatrix, b: SparseMatrix): SparseMatrix; | ||
| export declare function subtract(a: SparseMatrix, b: SparseMatrix): SparseMatrix; | ||
| export declare function multiplyScalar(a: SparseMatrix, scalar: number): SparseMatrix; | ||
| export declare function eliminateZeros(m: SparseMatrix): SparseMatrix; | ||
| export declare function normalize(m: SparseMatrix, normType?: NormType): SparseMatrix; | ||
| export declare const enum NormType { | ||
| max = "max", | ||
| l1 = "l1", | ||
| l2 = "l2" | ||
| } |
| "use strict"; | ||
| var __read = (this && this.__read) || function (o, n) { | ||
| var m = typeof Symbol === "function" && o[Symbol.iterator]; | ||
| if (!m) return o; | ||
| var i = m.call(o), r, ar = [], e; | ||
| try { | ||
| while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value); | ||
| } | ||
| catch (error) { e = { error: error }; } | ||
| finally { | ||
| try { | ||
| if (r && !r.done && (m = i["return"])) m.call(i); | ||
| } | ||
| finally { if (e) throw e.error; } | ||
| } | ||
| return ar; | ||
| }; | ||
| var __spread = (this && this.__spread) || function () { | ||
| for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i])); | ||
| return ar; | ||
| }; | ||
| var __values = (this && this.__values) || function (o) { | ||
| var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0; | ||
| if (m) return m.call(o); | ||
| return { | ||
| next: function () { | ||
| if (o && i >= o.length) o = void 0; | ||
| return { value: o && o[i++], done: !o }; | ||
| } | ||
| }; | ||
| }; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
| var _a; | ||
| var utils = require("./utils"); | ||
| var SparseMatrix = (function () { | ||
@@ -8,5 +40,5 @@ function SparseMatrix(rows, cols, values, dims) { | ||
| this.nCols = 0; | ||
| this.rows = rows.slice(); | ||
| this.cols = cols.slice(); | ||
| this.values = values.slice(); | ||
| this.rows = __spread(rows); | ||
| this.cols = __spread(cols); | ||
| this.values = __spread(values); | ||
| for (var i = 0; i < values.length; i++) { | ||
@@ -54,10 +86,13 @@ var key = this.makeKey(this.rows[i], this.cols[i]); | ||
| }; | ||
| SparseMatrix.prototype.getDims = function () { | ||
| return [this.nRows, this.nCols]; | ||
| }; | ||
| SparseMatrix.prototype.getRows = function () { | ||
| return this.rows.slice(); | ||
| return __spread(this.rows); | ||
| }; | ||
| SparseMatrix.prototype.getCols = function () { | ||
| return this.cols.slice(); | ||
| return __spread(this.cols); | ||
| }; | ||
| SparseMatrix.prototype.getValues = function () { | ||
| return this.values.slice(); | ||
| return __spread(this.values); | ||
| }; | ||
@@ -79,6 +114,5 @@ SparseMatrix.prototype.forEach = function (fn) { | ||
| var _this = this; | ||
| var rows = new Array(this.nRows).slice(); | ||
| var rows = utils.empty(this.nRows); | ||
| var output = rows.map(function () { | ||
| var cols = new Array(_this.nCols).slice(); | ||
| return cols.map(function () { return 0; }); | ||
| return utils.zeros(_this.nCols); | ||
| }); | ||
@@ -107,3 +141,3 @@ for (var i = 0; i < this.values.length; i++) { | ||
| function identity(size) { | ||
| var rows = size[0]; | ||
| var _a = __read(size, 1), rows = _a[0]; | ||
| var matrix = new SparseMatrix([], [], [], size); | ||
@@ -116,6 +150,6 @@ for (var i = 0; i < rows; i++) { | ||
| exports.identity = identity; | ||
| function dotMultiply(a, b) { | ||
| function pairwiseMultiply(a, b) { | ||
| return elementWise(a, b, function (x, y) { return x * y; }); | ||
| } | ||
| exports.dotMultiply = dotMultiply; | ||
| exports.pairwiseMultiply = pairwiseMultiply; | ||
| function add(a, b) { | ||
@@ -135,2 +169,77 @@ return elementWise(a, b, function (x, y) { return x + y; }); | ||
| exports.multiplyScalar = multiplyScalar; | ||
| function eliminateZeros(m) { | ||
| var zeroIndices = new Set(); | ||
| var values = m.getValues(); | ||
| var rows = m.getRows(); | ||
| var cols = m.getCols(); | ||
| for (var i = 0; i < values.length; i++) { | ||
| if (values[i] === 0) { | ||
| zeroIndices.add(i); | ||
| } | ||
| } | ||
| var removeByZeroIndex = function (_, index) { return !zeroIndices.has(index); }; | ||
| var nextValues = values.filter(removeByZeroIndex); | ||
| var nextRows = rows.filter(removeByZeroIndex); | ||
| var nextCols = cols.filter(removeByZeroIndex); | ||
| return new SparseMatrix(nextRows, nextCols, nextValues, m.getDims()); | ||
| } | ||
| exports.eliminateZeros = eliminateZeros; | ||
| function normalize(m, normType) { | ||
| if (normType === void 0) { normType = "l2"; } | ||
| var e_1, _a; | ||
| var normFn = normFns[normType]; | ||
| var colsByRow = new Map(); | ||
| m.forEach(function (_, row, col) { | ||
| var cols = colsByRow.get(row) || []; | ||
| cols.push(col); | ||
| colsByRow.set(row, cols); | ||
| }); | ||
| var nextMatrix = new SparseMatrix([], [], [], m.getDims()); | ||
| var _loop_1 = function (row) { | ||
| var cols = colsByRow.get(row).sort(); | ||
| var vals = cols.map(function (col) { return m.get(row, col); }); | ||
| var norm = normFn(vals); | ||
| for (var i = 0; i < norm.length; i++) { | ||
| nextMatrix.set(row, cols[i], norm[i]); | ||
| } | ||
| }; | ||
| try { | ||
| for (var _b = __values(colsByRow.keys()), _c = _b.next(); !_c.done; _c = _b.next()) { | ||
| var row = _c.value; | ||
| _loop_1(row); | ||
| } | ||
| } | ||
| catch (e_1_1) { e_1 = { error: e_1_1 }; } | ||
| finally { | ||
| try { | ||
| if (_c && !_c.done && (_a = _b.return)) _a.call(_b); | ||
| } | ||
| finally { if (e_1) throw e_1.error; } | ||
| } | ||
| return nextMatrix; | ||
| } | ||
| exports.normalize = normalize; | ||
| var normFns = (_a = {}, | ||
| _a["max"] = function (xs) { | ||
| var max = -Infinity; | ||
| for (var i = 0; i < xs.length; i++) { | ||
| max = xs[i] > max ? xs[i] : max; | ||
| } | ||
| return xs.map(function (x) { return x / max; }); | ||
| }, | ||
| _a["l1"] = function (xs) { | ||
| var sum = 0; | ||
| for (var i = 0; i < xs.length; i++) { | ||
| sum += xs[i]; | ||
| } | ||
| return xs.map(function (x) { return x / sum; }); | ||
| }, | ||
| _a["l2"] = function (xs) { | ||
| var sum = 0; | ||
| for (var i = 0; i < xs.length; i++) { | ||
| sum += Math.pow(xs[i], 2); | ||
| } | ||
| return xs.map(function (x) { return Math.sqrt(Math.pow(x, 2) / sum); }); | ||
| }, | ||
| _a); | ||
| function elementWise(a, b, op) { | ||
@@ -137,0 +246,0 @@ var visited = new Set(); |
| "use strict"; | ||
| var __read = (this && this.__read) || function (o, n) { | ||
| var m = typeof Symbol === "function" && o[Symbol.iterator]; | ||
| if (!m) return o; | ||
| var i = m.call(o), r, ar = [], e; | ||
| try { | ||
| while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value); | ||
| } | ||
| catch (error) { e = { error: error }; } | ||
| finally { | ||
| try { | ||
| if (r && !r.done && (m = i["return"])) m.call(i); | ||
| } | ||
| finally { if (e) throw e.error; } | ||
| } | ||
| return ar; | ||
| }; | ||
| var __spread = (this && this.__spread) || function () { | ||
| for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i])); | ||
| return ar; | ||
| }; | ||
| var __values = (this && this.__values) || function (o) { | ||
| var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0; | ||
| if (m) return m.call(o); | ||
| return { | ||
| next: function () { | ||
| if (o && i >= o.length) o = void 0; | ||
| return { value: o && o[i++], done: !o }; | ||
| } | ||
| }; | ||
| }; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
@@ -124,3 +154,3 @@ var utils = require("./utils"); | ||
| children[nodeNum][0] = -leafNum; | ||
| (_a = indices[leafNum]).splice.apply(_a, [0, tree.indices.length].concat(tree.indices)); | ||
| (_a = indices[leafNum]).splice.apply(_a, __spread([0, tree.indices.length], tree.indices)); | ||
| leafNum += 1; | ||
@@ -159,8 +189,18 @@ return { nodeNum: nodeNum, leafNum: leafNum }; | ||
| function makeLeafArray(rpForest) { | ||
| var e_1, _a; | ||
| if (rpForest.length > 0) { | ||
| var output = []; | ||
| for (var _i = 0, rpForest_1 = rpForest; _i < rpForest_1.length; _i++) { | ||
| var tree = rpForest_1[_i]; | ||
| output.push.apply(output, tree.indices); | ||
| try { | ||
| for (var rpForest_1 = __values(rpForest), rpForest_1_1 = rpForest_1.next(); !rpForest_1_1.done; rpForest_1_1 = rpForest_1.next()) { | ||
| var tree = rpForest_1_1.value; | ||
| output.push.apply(output, __spread(tree.indices)); | ||
| } | ||
| } | ||
| catch (e_1_1) { e_1 = { error: e_1_1 }; } | ||
| finally { | ||
| try { | ||
| if (rpForest_1_1 && !rpForest_1_1.done && (_a = rpForest_1.return)) _a.call(rpForest_1); | ||
| } | ||
| finally { if (e_1) throw e_1.error; } | ||
| } | ||
| return output; | ||
@@ -167,0 +207,0 @@ } |
@@ -0,1 +1,2 @@ | ||
| import * as matrix from './matrix'; | ||
| export declare type DistanceFn = (x: Vector, y: Vector) => number; | ||
@@ -5,3 +6,9 @@ export declare type EpochCallback = (epoch: number) => boolean | void; | ||
| export declare type Vectors = Vector[]; | ||
| export declare const enum TargetMetric { | ||
| categorical = "categorical", | ||
| l1 = "l1", | ||
| l2 = "l2" | ||
| } | ||
| export interface UMAPParameters { | ||
| minDist?: number; | ||
| nComponents?: number; | ||
@@ -11,8 +18,19 @@ nEpochs?: number; | ||
| random?: () => number; | ||
| spread?: number; | ||
| } | ||
| export interface UMAPSupervisedParams { | ||
| targetMetric?: TargetMetric; | ||
| targetWeight?: number; | ||
| targetNNeighbors?: number; | ||
| } | ||
| export declare class UMAP { | ||
| private nNeighbors; | ||
| private minDist; | ||
| private nComponents; | ||
| private nEpochs; | ||
| private nNeighbors; | ||
| private random; | ||
| private spread; | ||
| private targetMetric; | ||
| private targetWeight; | ||
| private targetNNeighbors; | ||
| private distanceFn; | ||
@@ -22,4 +40,5 @@ private knnIndices?; | ||
| private graph; | ||
| private data; | ||
| private X; | ||
| private isInitialized; | ||
| private Y?; | ||
| private embedding; | ||
@@ -30,3 +49,6 @@ private optimizationState; | ||
| fitAsync(X: Vectors, callback?: (epochNumber: number) => void | boolean): Promise<number[][]>; | ||
| initializeFit(X: Vectors, knnIndices?: number[][], knnDistances?: number[][]): number; | ||
| setSupervisedProjection(Y: number[], params?: UMAPSupervisedParams): void; | ||
| setPrecomputedKNN(knnIndices: number[][], knnDistances: number[][]): void; | ||
| initializeFit(X: Vectors): number; | ||
| private processGraphForSupervisedProjection; | ||
| step(): number; | ||
@@ -36,2 +58,3 @@ getEmbedding(): number[][]; | ||
| private fuzzySimplicialSet; | ||
| private categoricalSimplicialSetIntersection; | ||
| private smoothKNNDistance; | ||
@@ -48,1 +71,7 @@ private computeMembershipStrengths; | ||
| export declare function cosine(x: Vector, y: Vector): number; | ||
| export declare function findABParams(spread: number, minDist: number): { | ||
| a: number; | ||
| b: number; | ||
| }; | ||
| export declare function fastIntersection(graph: matrix.SparseMatrix, target: number[], unknownDist?: number, farDist?: number): matrix.SparseMatrix; | ||
| export declare function resetLocalConnectivity(simplicialSet: matrix.SparseMatrix): matrix.SparseMatrix; |
140
dist/umap.js
@@ -37,2 +37,22 @@ "use strict"; | ||
| }; | ||
| var __read = (this && this.__read) || function (o, n) { | ||
| var m = typeof Symbol === "function" && o[Symbol.iterator]; | ||
| if (!m) return o; | ||
| var i = m.call(o), r, ar = [], e; | ||
| try { | ||
| while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value); | ||
| } | ||
| catch (error) { e = { error: error }; } | ||
| finally { | ||
| try { | ||
| if (r && !r.done && (m = i["return"])) m.call(i); | ||
| } | ||
| finally { if (e) throw e.error; } | ||
| } | ||
| return ar; | ||
| }; | ||
| var __spread = (this && this.__spread) || function () { | ||
| for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i])); | ||
| return ar; | ||
| }; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
@@ -43,2 +63,3 @@ var matrix = require("./matrix"); | ||
| var utils = require("./utils"); | ||
| var LM = require("ml-levenberg-marquardt"); | ||
| var SMOOTH_K_TOLERANCE = 1e-5; | ||
@@ -49,6 +70,11 @@ var MIN_K_DIST_SCALE = 1e-3; | ||
| if (params === void 0) { params = {}; } | ||
| this.nNeighbors = 15; | ||
| this.minDist = 0.1; | ||
| this.nComponents = 2; | ||
| this.nEpochs = 0; | ||
| this.nNeighbors = 15; | ||
| this.random = Math.random; | ||
| this.spread = 1.0; | ||
| this.targetMetric = "categorical"; | ||
| this.targetWeight = 0.5; | ||
| this.targetNNeighbors = this.nNeighbors; | ||
| this.distanceFn = euclidean; | ||
@@ -58,2 +84,3 @@ this.isInitialized = false; | ||
| this.optimizationState = new OptimizationState(); | ||
| this.minDist = params.minDist || this.minDist; | ||
| this.nComponents = params.nComponents || this.nComponents; | ||
@@ -63,2 +90,3 @@ this.nEpochs = params.nEpochs || this.nEpochs; | ||
| this.random = params.random || this.random; | ||
| this.spread = params.spread || this.spread; | ||
| } | ||
@@ -85,12 +113,19 @@ UMAP.prototype.fit = function (X) { | ||
| }; | ||
| UMAP.prototype.initializeFit = function (X, knnIndices, knnDistances) { | ||
| if (this.data === X && this.isInitialized) { | ||
| UMAP.prototype.setSupervisedProjection = function (Y, params) { | ||
| if (params === void 0) { params = {}; } | ||
| this.Y = Y; | ||
| this.targetMetric = params.targetMetric || this.targetMetric; | ||
| this.targetWeight = params.targetWeight || this.targetWeight; | ||
| this.targetNNeighbors = params.targetNNeighbors || this.targetNNeighbors; | ||
| }; | ||
| UMAP.prototype.setPrecomputedKNN = function (knnIndices, knnDistances) { | ||
| this.knnIndices = knnIndices; | ||
| this.knnDistances = knnDistances; | ||
| }; | ||
| UMAP.prototype.initializeFit = function (X) { | ||
| if (this.X === X && this.isInitialized) { | ||
| return this.getNEpochs(); | ||
| } | ||
| this.data = X; | ||
| if (knnIndices && knnDistances) { | ||
| this.knnIndices = knnIndices; | ||
| this.knnDistances = knnDistances; | ||
| } | ||
| else { | ||
| this.X = X; | ||
| if (!this.knnIndices && !this.knnDistances) { | ||
| var knnResults = this.nearestNeighbors(X); | ||
@@ -100,3 +135,4 @@ this.knnIndices = knnResults.knnIndices; | ||
| } | ||
| this.graph = this.fuzzySimplicialSet(X); | ||
| this.graph = this.fuzzySimplicialSet(X, this.nNeighbors); | ||
| this.processGraphForSupervisedProjection(); | ||
| var _a = this.initializeSimplicialSetEmbedding(), head = _a.head, tail = _a.tail, epochsPerSample = _a.epochsPerSample; | ||
@@ -109,2 +145,15 @@ this.optimizationState.head = head; | ||
| }; | ||
| UMAP.prototype.processGraphForSupervisedProjection = function () { | ||
| var _a = this, Y = _a.Y, X = _a.X; | ||
| if (Y) { | ||
| if (Y.length !== X.length) { | ||
| throw new Error('Length of X and y must be equal'); | ||
| } | ||
| if (this.targetMetric === "categorical") { | ||
| var lt = this.targetWeight < 1.0; | ||
| var farDist = lt ? 2.5 * (1.0 / (1.0 - this.targetWeight)) : 1.0e12; | ||
| this.graph = this.categoricalSimplicialSetIntersection(this.graph, Y, farDist); | ||
| } | ||
| } | ||
| }; | ||
| UMAP.prototype.step = function () { | ||
@@ -137,6 +186,6 @@ var _a = this.optimizationState, currentEpoch = _a.currentEpoch, isInitialized = _a.isInitialized; | ||
| }; | ||
| UMAP.prototype.fuzzySimplicialSet = function (X, localConnectivity, setOpMixRatio) { | ||
| UMAP.prototype.fuzzySimplicialSet = function (X, nNeighbors, localConnectivity, setOpMixRatio) { | ||
| if (localConnectivity === void 0) { localConnectivity = 1.0; } | ||
| if (setOpMixRatio === void 0) { setOpMixRatio = 1.0; } | ||
| var _a = this, nNeighbors = _a.nNeighbors, _b = _a.knnIndices, knnIndices = _b === void 0 ? [] : _b, _c = _a.knnDistances, knnDistances = _c === void 0 ? [] : _c; | ||
| var _a = this, _b = _a.knnIndices, knnIndices = _b === void 0 ? [] : _b, _c = _a.knnDistances, knnDistances = _c === void 0 ? [] : _c; | ||
| var _d = this.smoothKNNDistance(knnDistances, nNeighbors, localConnectivity), sigmas = _d.sigmas, rhos = _d.rhos; | ||
@@ -147,3 +196,3 @@ var _e = this.computeMembershipStrengths(knnIndices, knnDistances, sigmas, rhos), rows = _e.rows, cols = _e.cols, vals = _e.vals; | ||
| var transpose = matrix.transpose(sparseMatrix); | ||
| var prodMatrix = matrix.dotMultiply(sparseMatrix, transpose); | ||
| var prodMatrix = matrix.pairwiseMultiply(sparseMatrix, transpose); | ||
| var a = matrix.subtract(matrix.add(sparseMatrix, transpose), prodMatrix); | ||
@@ -155,2 +204,8 @@ var b = matrix.multiplyScalar(a, setOpMixRatio); | ||
| }; | ||
| UMAP.prototype.categoricalSimplicialSetIntersection = function (simplicialSet, target, farDist, unknownDist) { | ||
| if (unknownDist === void 0) { unknownDist = 1.0; } | ||
| var intersection = fastIntersection(simplicialSet, target, unknownDist, farDist); | ||
| intersection = matrix.eliminateZeros(intersection); | ||
| return resetLocalConnectivity(intersection); | ||
| }; | ||
| UMAP.prototype.smoothKNNDistance = function (distances, k, localConnectivity, nIter, bandwidth) { | ||
@@ -318,4 +373,3 @@ if (localConnectivity === void 0) { localConnectivity = 1.0; } | ||
| var nVertices = this.graph.nCols; | ||
| var a = 1.5769434603113077; | ||
| var b = 0.8950608779109733; | ||
| var _b = findABParams(this.spread, this.minDist), a = _b.a, b = _b.b; | ||
| var dim = headEmbedding[0].length; | ||
@@ -325,4 +379,4 @@ var moveOther = headEmbedding.length === tailEmbedding.length; | ||
| var epochsPerNegativeSample = epochsPerSample.map(function (e) { return e / negativeSampleRate; }); | ||
| var epochOfNextNegativeSample = epochsPerNegativeSample.slice(); | ||
| var epochOfNextSample = epochsPerSample.slice(); | ||
| var epochOfNextNegativeSample = __spread(epochsPerNegativeSample); | ||
| var epochOfNextSample = __spread(epochsPerSample); | ||
| Object.assign(this.optimizationState, { | ||
@@ -524,1 +578,53 @@ isInitialized: true, | ||
| } | ||
| function findABParams(spread, minDist) { | ||
| var curve = function (_a) { | ||
| var _b = __read(_a, 2), a = _b[0], b = _b[1]; | ||
| return function (x) { | ||
| return 1.0 / (1.0 + a * Math.pow(x, (2 * b))); | ||
| }; | ||
| }; | ||
| var xv = utils | ||
| .linear(0, spread * 3, 300) | ||
| .map(function (val) { return (val < minDist ? 1.0 : val); }); | ||
| var yv = utils.zeros(xv.length).map(function (val, index) { | ||
| var gte = xv[index] >= minDist; | ||
| return gte ? Math.exp(-(xv[index] - minDist) / spread) : val; | ||
| }); | ||
| var initialValues = [0.5, 0.5]; | ||
| var data = { x: xv, y: yv }; | ||
| var options = { | ||
| damping: 1.5, | ||
| initialValues: initialValues, | ||
| gradientDifference: 10e-2, | ||
| maxIterations: 100, | ||
| errorTolerance: 10e-3, | ||
| }; | ||
| var parameterValues = LM(data, curve, options).parameterValues; | ||
| var _a = __read(parameterValues, 2), a = _a[0], b = _a[1]; | ||
| return { a: a, b: b }; | ||
| } | ||
| exports.findABParams = findABParams; | ||
| function fastIntersection(graph, target, unknownDist, farDist) { | ||
| if (unknownDist === void 0) { unknownDist = 1.0; } | ||
| if (farDist === void 0) { farDist = 5.0; } | ||
| return graph.map(function (value, row, col) { | ||
| if (target[row] === -1 || target[col] === -1) { | ||
| return value * Math.exp(-unknownDist); | ||
| } | ||
| else if (target[row] !== target[col]) { | ||
| return value * Math.exp(-farDist); | ||
| } | ||
| else { | ||
| return value; | ||
| } | ||
| }); | ||
| } | ||
| exports.fastIntersection = fastIntersection; | ||
| function resetLocalConnectivity(simplicialSet) { | ||
| simplicialSet = matrix.normalize(simplicialSet, "max"); | ||
| var transpose = matrix.transpose(simplicialSet); | ||
| var prodMatrix = matrix.pairwiseMultiply(transpose, simplicialSet); | ||
| simplicialSet = matrix.add(simplicialSet, matrix.subtract(transpose, prodMatrix)); | ||
| return matrix.eliminateZeros(simplicialSet); | ||
| } | ||
| exports.resetLocalConnectivity = resetLocalConnectivity; |
@@ -10,2 +10,3 @@ export declare function randomNormal2d(mean?: number, stdev?: number, size?: number[], rng?: () => number): number[][]; | ||
| export declare function ones(n: number): number[]; | ||
| export declare function linear(a: number, b: number, len: number): number[]; | ||
| export declare function sum(input: number[]): number; | ||
@@ -12,0 +13,0 @@ export declare function mean(input: number[]): number; |
| "use strict"; | ||
| var __values = (this && this.__values) || function (o) { | ||
| var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0; | ||
| if (m) return m.call(o); | ||
| return { | ||
| next: function () { | ||
| if (o && i >= o.length) o = void 0; | ||
| return { value: o && o[i++], done: !o }; | ||
| } | ||
| }; | ||
| }; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
@@ -35,7 +45,17 @@ function generateGaussian(mean, std, rng) { | ||
| function norm(vec) { | ||
| var e_1, _a; | ||
| var result = 0; | ||
| for (var _i = 0, vec_1 = vec; _i < vec_1.length; _i++) { | ||
| var item = vec_1[_i]; | ||
| result += Math.pow(item, 2); | ||
| try { | ||
| for (var vec_1 = __values(vec), vec_1_1 = vec_1.next(); !vec_1_1.done; vec_1_1 = vec_1.next()) { | ||
| var item = vec_1_1.value; | ||
| result += Math.pow(item, 2); | ||
| } | ||
| } | ||
| catch (e_1_1) { e_1 = { error: e_1_1 }; } | ||
| finally { | ||
| try { | ||
| if (vec_1_1 && !vec_1_1.done && (_a = vec_1.return)) _a.call(vec_1); | ||
| } | ||
| finally { if (e_1) throw e_1.error; } | ||
| } | ||
| return Math.sqrt(result); | ||
@@ -68,2 +88,8 @@ } | ||
| exports.ones = ones; | ||
| function linear(a, b, len) { | ||
| return empty(len).map(function (_, i) { | ||
| return a + i * ((b - a) / (len - 1)); | ||
| }); | ||
| } | ||
| exports.linear = linear; | ||
| function sum(input) { | ||
@@ -70,0 +96,0 @@ return input.reduce(function (sum, val) { return sum + val; }); |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -20,2 +20,1 @@ Licensed under the Apache License, Version 2.0 (the "License"); | ||
| }; | ||
| { | ||
| "name": "umap-js", | ||
| "version": "1.0.3", | ||
| "version": "1.0.4", | ||
| "description": "JavaScript implementation of UMAP", | ||
@@ -21,3 +21,2 @@ "author": { | ||
| "@types/node": "^10.12.21", | ||
| "dts-bundle-webpack": "^1.0.2", | ||
| "jest": "23", | ||
@@ -33,3 +32,5 @@ "prando": "^5.1.0", | ||
| }, | ||
| "dependencies": {}, | ||
| "dependencies": { | ||
| "ml-levenberg-marquardt": "^1.0.3" | ||
| }, | ||
| "publishConfig": { | ||
@@ -36,0 +37,0 @@ "registry": "https://wombat-dressing-room.appspot.com" |
@@ -0,1 +1,3 @@ | ||
| [](https://travis-ci.org/PAIR-code/umap-js) | ||
| # UMAP-JS | ||
@@ -53,2 +55,12 @@ | ||
| #### Supervised projection using labels | ||
| ```typescript | ||
| import { UMAP } from 'umap-js'; | ||
| const umap = new UMAP(); | ||
| umap.setSupervisedProjection(labels); | ||
| const embedding = umap.fit(data); | ||
| ``` | ||
| #### Parameters | ||
@@ -58,8 +70,10 @@ | ||
| | Parameter | Description | default | | ||
| | ------------- | --------------------------------------------------------------------- | ------------------------ | | ||
| | `nComponents` | The number of components (dimensions) to project the data to | 2 | | ||
| | `nEpochs` | The number of epochs to optimize embeddings via SGD | (computed automatically) | | ||
| | `nNeigbors` | The number of nearest neigbors to construct the fuzzy manifold | 15 | | ||
| | `random` | A pseudo-random-number generator for controlling stochastic processes | `Math.random` | | ||
| | Parameter | Description | default | | ||
| | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------ | | ||
| | `nComponents` | The number of components (dimensions) to project the data to | 2 | | ||
| | `nEpochs` | The number of epochs to optimize embeddings via SGD | (computed automatically) | | ||
| | `nNeigbors` | The number of nearest neigbors to construct the fuzzy manifold | 15 | | ||
| | `minDist` | The effective minimum distance between embedded points, used with `spread` to control the clumped/dispersed nature of the embedding | 0.1 | | ||
| | `spread` | The effective scale of embedded points, used with `minDist` to control the clumped/dispersed nature of the embedding | 1.0 | | ||
| | `random` | A pseudo-random-number generator for controlling stochastic processes | `Math.random` | | ||
@@ -66,0 +80,0 @@ ```typescript |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -3,0 +3,0 @@ Licensed under the Apache License, Version 2.0 (the "License"); |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
@@ -3,0 +3,0 @@ you may not use this file except in compliance with the License. |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
@@ -3,0 +3,0 @@ you may not use this file except in compliance with the License. |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -16,2 +16,4 @@ Licensed under the Apache License, Version 2.0 (the "License"); | ||
| import * as utils from './utils'; | ||
| /** | ||
@@ -87,2 +89,6 @@ * Internal 2-dimensional sparse matrix class | ||
| getDims(): number[] { | ||
| return [this.nRows, this.nCols]; | ||
| } | ||
| getRows(): number[] { | ||
@@ -116,6 +122,5 @@ return [...this.rows]; | ||
| toArray() { | ||
| const rows: number[][] = [...new Array(this.nRows)]; | ||
| const rows: undefined[] = utils.empty(this.nRows); | ||
| const output = rows.map(() => { | ||
| const cols = [...new Array(this.nCols)]; | ||
| return cols.map(() => 0); | ||
| return utils.zeros(this.nCols); | ||
| }); | ||
@@ -162,3 +167,6 @@ for (let i = 0; i < this.values.length; i++) { | ||
| */ | ||
| export function dotMultiply(a: SparseMatrix, b: SparseMatrix): SparseMatrix { | ||
| export function pairwiseMultiply( | ||
| a: SparseMatrix, | ||
| b: SparseMatrix | ||
| ): SparseMatrix { | ||
| return elementWise(a, b, (x, y) => x * y); | ||
@@ -191,2 +199,85 @@ } | ||
| /** | ||
| * Returns a new matrix with zero entries removed. | ||
| */ | ||
| export function eliminateZeros(m: SparseMatrix) { | ||
| const zeroIndices = new Set(); | ||
| const values = m.getValues(); | ||
| const rows = m.getRows(); | ||
| const cols = m.getCols(); | ||
| for (let i = 0; i < values.length; i++) { | ||
| if (values[i] === 0) { | ||
| zeroIndices.add(i); | ||
| } | ||
| } | ||
| const removeByZeroIndex = (_, index: number) => !zeroIndices.has(index); | ||
| const nextValues = values.filter(removeByZeroIndex); | ||
| const nextRows = rows.filter(removeByZeroIndex); | ||
| const nextCols = cols.filter(removeByZeroIndex); | ||
| return new SparseMatrix(nextRows, nextCols, nextValues, m.getDims()); | ||
| } | ||
| /** | ||
| * Normalization of a sparse matrix. | ||
| */ | ||
| export function normalize(m: SparseMatrix, normType = NormType.l2) { | ||
| const normFn = normFns[normType]; | ||
| const colsByRow = new Map<number, number[]>(); | ||
| m.forEach((_, row, col) => { | ||
| const cols = colsByRow.get(row) || []; | ||
| cols.push(col); | ||
| colsByRow.set(row, cols); | ||
| }); | ||
| const nextMatrix = new SparseMatrix([], [], [], m.getDims()); | ||
| for (let row of colsByRow.keys()) { | ||
| const cols = colsByRow.get(row)!.sort(); | ||
| const vals = cols.map(col => m.get(row, col)); | ||
| const norm = normFn(vals); | ||
| for (let i = 0; i < norm.length; i++) { | ||
| nextMatrix.set(row, cols[i], norm[i]); | ||
| } | ||
| } | ||
| return nextMatrix; | ||
| } | ||
| /** | ||
| * Vector normalization functions | ||
| */ | ||
| type NormFns = { [key in NormType]: (v: number[]) => number[] }; | ||
| const normFns: NormFns = { | ||
| [NormType.max]: (xs: number[]) => { | ||
| let max = -Infinity; | ||
| for (let i = 0; i < xs.length; i++) { | ||
| max = xs[i] > max ? xs[i] : max; | ||
| } | ||
| return xs.map(x => x / max); | ||
| }, | ||
| [NormType.l1]: (xs: number[]) => { | ||
| let sum = 0; | ||
| for (let i = 0; i < xs.length; i++) { | ||
| sum += xs[i]; | ||
| } | ||
| return xs.map(x => x / sum); | ||
| }, | ||
| [NormType.l2]: (xs: number[]) => { | ||
| let sum = 0; | ||
| for (let i = 0; i < xs.length; i++) { | ||
| sum += xs[i] ** 2; | ||
| } | ||
| return xs.map(x => Math.sqrt(x ** 2 / sum)); | ||
| }, | ||
| }; | ||
| export const enum NormType { | ||
| max = 'max', | ||
| l1 = 'l1', | ||
| l2 = 'l2', | ||
| } | ||
| /** | ||
| * Helper function for element-wise operations. | ||
@@ -193,0 +284,0 @@ */ |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -3,0 +3,0 @@ Licensed under the Apache License, Version 2.0 (the "License"); |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -3,0 +3,0 @@ Licensed under the Apache License, Version 2.0 (the "License"); |
259
src/umap.ts
@@ -1,9 +0,6 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
@@ -60,2 +57,3 @@ distributed under the License is distributed on an "AS IS" BASIS, | ||
| import * as utils from './utils'; | ||
| import * as LM from 'ml-levenberg-marquardt'; | ||
@@ -66,2 +64,7 @@ export type DistanceFn = (x: Vector, y: Vector) => number; | ||
| export type Vectors = Vector[]; | ||
| export const enum TargetMetric { | ||
| categorical = 'categorical', | ||
| l1 = 'l1', | ||
| l2 = 'l2', | ||
| } | ||
@@ -72,8 +75,71 @@ const SMOOTH_K_TOLERANCE = 1e-5; | ||
| export interface UMAPParameters { | ||
| /** | ||
| * The effective minimum distance between embedded points. Smaller values | ||
| * will result in a more clustered/clumped embedding where nearby points | ||
| * on the manifold are drawn closer together, while larger values will | ||
| * result on a more even dispersal of points. The value should be set | ||
| * relative to the ``spread`` value, which determines the scale at which | ||
| * embedded points will be spread out. | ||
| */ | ||
| minDist?: number; | ||
| /** | ||
| * The dimension of the space to embed into. This defaults to 2 to | ||
| * provide easy visualization, but can reasonably be set to any | ||
| * integer value in the range 2 to 100. | ||
| */ | ||
| nComponents?: number; | ||
| /** | ||
| * The number of training epochs to be used in optimizing the | ||
| * low dimensional embedding. Larger values result in more accurate | ||
| * embeddings. If None is specified a value will be selected based on | ||
| * the size of the input dataset (200 for large datasets, 500 for small). | ||
| */ | ||
| nEpochs?: number; | ||
| /** | ||
| * The size of local neighborhood (in terms of number of neighboring | ||
| * sample points) used for manifold approximation. Larger values | ||
| * result in more global views of the manifold, while smaller | ||
| * values result in more local data being preserved. In general | ||
| * values should be in the range 2 to 100. | ||
| */ | ||
| nNeighbors?: number; | ||
| /** | ||
| * The pseudo-random number generator used by the stochastic parts of the | ||
| * algorithm. | ||
| */ | ||
| random?: () => number; | ||
| /** | ||
| * The effective scale of embedded points. In combination with ``min_dist`` | ||
| * this determines how clustered/clumped the embedded points are. | ||
| */ | ||
| spread?: number; | ||
| } | ||
| export interface UMAPSupervisedParams { | ||
| /** | ||
| * The metric used to measure distance for a target array is using supervised | ||
| * dimension reduction. By default this is 'categorical' which will measure | ||
| * distance in terms of whether categories match or are different. Furthermore, | ||
| * if semi-supervised is required target values of -1 will be treated as | ||
| * unlabelled under the 'categorical' metric. If the target array takes | ||
| * continuous values (e.g. for a regression problem) then metric of 'l1' | ||
| * or 'l2' is probably more appropriate. | ||
| */ | ||
| targetMetric?: TargetMetric; | ||
| /** | ||
| * Weighting factor between data topology and target topology. A value of | ||
| * 0.0 weights entirely on data, a value of 1.0 weights entirely on target. | ||
| * The default of 0.5 balances the weighting equally between data and target. | ||
| */ | ||
| targetWeight?: number; | ||
| /** | ||
| * The number of nearest neighbors to use to construct the target simplcial | ||
| * set. Defaults to the `nearestNeighbors` parameter. | ||
| */ | ||
| targetNNeighbors?: number; | ||
| } | ||
| /** | ||
@@ -102,7 +168,14 @@ * UMAP projection system, based on the python implementation from McInnes, L, | ||
| export class UMAP { | ||
| private nNeighbors = 15; | ||
| private minDist = 0.1; | ||
| private nComponents = 2; | ||
| private nEpochs = 0; | ||
| private nNeighbors = 15; | ||
| private random = Math.random; | ||
| private spread = 1.0; | ||
| // Supervised projection params | ||
| private targetMetric = TargetMetric.categorical; | ||
| private targetWeight = 0.5; | ||
| private targetNNeighbors = this.nNeighbors; | ||
| private distanceFn: DistanceFn = euclidean; | ||
@@ -116,5 +189,8 @@ | ||
| private graph!: matrix.SparseMatrix; | ||
| private data!: Vectors; | ||
| private X!: Vectors; | ||
| private isInitialized = false; | ||
| // Supervised projection labels / targets | ||
| private Y?: number[]; | ||
| // Projected embedding | ||
@@ -125,2 +201,3 @@ private embedding: number[][] = []; | ||
| constructor(params: UMAPParameters = {}) { | ||
| this.minDist = params.minDist || this.minDist; | ||
| this.nComponents = params.nComponents || this.nComponents; | ||
@@ -130,2 +207,3 @@ this.nEpochs = params.nEpochs || this.nEpochs; | ||
| this.random = params.random || this.random; | ||
| this.spread = params.spread || this.spread; | ||
| } | ||
@@ -158,2 +236,20 @@ | ||
| /** | ||
| * Initializes parameters needed for supervised projection. | ||
| */ | ||
| setSupervisedProjection(Y: number[], params: UMAPSupervisedParams = {}) { | ||
| this.Y = Y; | ||
| this.targetMetric = params.targetMetric || this.targetMetric; | ||
| this.targetWeight = params.targetWeight || this.targetWeight; | ||
| this.targetNNeighbors = params.targetNNeighbors || this.targetNNeighbors; | ||
| } | ||
| /** | ||
| * Initializes umap with precomputed KNN indices and distances. | ||
| */ | ||
| setPrecomputedKNN(knnIndices: number[][], knnDistances: number[][]) { | ||
| this.knnIndices = knnIndices; | ||
| this.knnDistances = knnDistances; | ||
| } | ||
| /** | ||
| * Initializes fit by computing KNN and a fuzzy simplicial set, as well as | ||
@@ -164,18 +260,11 @@ * initializing the projected embeddings. Sets the optimization state ahead | ||
| */ | ||
| initializeFit( | ||
| X: Vectors, | ||
| knnIndices?: number[][], | ||
| knnDistances?: number[][] | ||
| ): number { | ||
| initializeFit(X: Vectors): number { | ||
| // We don't need to reinitialize if we've already initialized for this data. | ||
| if (this.data === X && this.isInitialized) { | ||
| if (this.X === X && this.isInitialized) { | ||
| return this.getNEpochs(); | ||
| } | ||
| this.data = X; | ||
| this.X = X; | ||
| if (knnIndices && knnDistances) { | ||
| this.knnIndices = knnIndices; | ||
| this.knnDistances = knnDistances; | ||
| } else { | ||
| if (!this.knnIndices && !this.knnDistances) { | ||
| const knnResults = this.nearestNeighbors(X); | ||
@@ -186,4 +275,7 @@ this.knnIndices = knnResults.knnIndices; | ||
| this.graph = this.fuzzySimplicialSet(X); | ||
| this.graph = this.fuzzySimplicialSet(X, this.nNeighbors); | ||
| // Check if supervised projection, then adjust the graph. | ||
| this.processGraphForSupervisedProjection(); | ||
| const { | ||
@@ -205,2 +297,26 @@ head, | ||
| /** | ||
| * Checks if we're using supervised projection, then process the graph | ||
| * accordingly. | ||
| */ | ||
| private processGraphForSupervisedProjection() { | ||
| const { Y, X } = this; | ||
| if (Y) { | ||
| if (Y.length !== X.length) { | ||
| throw new Error('Length of X and y must be equal'); | ||
| } | ||
| if (this.targetMetric === TargetMetric.categorical) { | ||
| const lt = this.targetWeight < 1.0; | ||
| const farDist = lt ? 2.5 * (1.0 / (1.0 - this.targetWeight)) : 1.0e12; | ||
| this.graph = this.categoricalSimplicialSetIntersection( | ||
| this.graph, | ||
| Y, | ||
| farDist | ||
| ); | ||
| } | ||
| // TODO (andycoenen@): add non-categorical supervised embeddings. | ||
| } | ||
| } | ||
| /** | ||
| * Manually step through the optimization process one epoch at a time. | ||
@@ -267,6 +383,7 @@ */ | ||
| X: Vectors, | ||
| nNeighbors: number, | ||
| localConnectivity = 1.0, | ||
| setOpMixRatio = 1.0 | ||
| ) { | ||
| const { nNeighbors, knnIndices = [], knnDistances = [] } = this; | ||
| const { knnIndices = [], knnDistances = [] } = this; | ||
@@ -290,3 +407,3 @@ const { sigmas, rhos } = this.smoothKNNDistance( | ||
| const transpose = matrix.transpose(sparseMatrix); | ||
| const prodMatrix = matrix.dotMultiply(sparseMatrix, transpose); | ||
| const prodMatrix = matrix.pairwiseMultiply(sparseMatrix, transpose); | ||
@@ -302,2 +419,24 @@ const a = matrix.subtract(matrix.add(sparseMatrix, transpose), prodMatrix); | ||
| /** | ||
| * Combine a fuzzy simplicial set with another fuzzy simplicial set | ||
| * generated from categorical data using categorical distances. The target | ||
| * data is assumed to be categorical label data (a vector of labels), | ||
| * and this will update the fuzzy simplicial set to respect that label data. | ||
| */ | ||
| private categoricalSimplicialSetIntersection( | ||
| simplicialSet: matrix.SparseMatrix, | ||
| target: number[], | ||
| farDist: number, | ||
| unknownDist = 1.0 | ||
| ) { | ||
| let intersection = fastIntersection( | ||
| simplicialSet, | ||
| target, | ||
| unknownDist, | ||
| farDist | ||
| ); | ||
| intersection = matrix.eliminateZeros(intersection); | ||
| return resetLocalConnectivity(intersection); | ||
| } | ||
| /** | ||
| * Compute a continuous version of the distance to the kth nearest | ||
@@ -522,6 +661,3 @@ * neighbor. That is, this is similar to knn-distance but allows continuous | ||
| // TODO -> Compute these values which are computed via a curve-fitting | ||
| // routine in the python implementation. | ||
| const a = 1.5769434603113077; | ||
| const b = 0.8950608779109733; | ||
| const { a, b } = findABParams(this.spread, this.minDist); | ||
@@ -789,1 +925,76 @@ const dim = headEmbedding[0].length; | ||
| } | ||
| /** | ||
| * Fit a, b params for the differentiable curve used in lower | ||
| * dimensional fuzzy simplicial complex construction. We want the | ||
| * smooth curve (from a pre-defined family with simple gradient) that | ||
| * best matches an offset exponential decay. | ||
| */ | ||
| export function findABParams(spread: number, minDist: number) { | ||
| const curve = ([a, b]) => (x: number) => { | ||
| return 1.0 / (1.0 + a * x ** (2 * b)); | ||
| }; | ||
| const xv = utils | ||
| .linear(0, spread * 3, 300) | ||
| .map(val => (val < minDist ? 1.0 : val)); | ||
| const yv = utils.zeros(xv.length).map((val, index) => { | ||
| const gte = xv[index] >= minDist; | ||
| return gte ? Math.exp(-(xv[index] - minDist) / spread) : val; | ||
| }); | ||
| const initialValues = [0.5, 0.5]; | ||
| const data = { x: xv, y: yv }; | ||
| // Default options for the algorithm (from github example) | ||
| const options = { | ||
| damping: 1.5, | ||
| initialValues, | ||
| gradientDifference: 10e-2, | ||
| maxIterations: 100, | ||
| errorTolerance: 10e-3, | ||
| }; | ||
| const { parameterValues } = LM(data, curve, options); | ||
| const [a, b] = parameterValues as number[]; | ||
| return { a, b }; | ||
| } | ||
| /** | ||
| * Under the assumption of categorical distance for the intersecting | ||
| * simplicial set perform a fast intersection. | ||
| */ | ||
| export function fastIntersection( | ||
| graph: matrix.SparseMatrix, | ||
| target: number[], | ||
| unknownDist = 1.0, | ||
| farDist = 5.0 | ||
| ) { | ||
| return graph.map((value, row, col) => { | ||
| if (target[row] === -1 || target[col] === -1) { | ||
| return value * Math.exp(-unknownDist); | ||
| } else if (target[row] !== target[col]) { | ||
| return value * Math.exp(-farDist); | ||
| } else { | ||
| return value; | ||
| } | ||
| }); | ||
| } | ||
| /** | ||
| * Reset the local connectivity requirement -- each data sample should | ||
| * have complete confidence in at least one 1-simplex in the simplicial set. | ||
| * We can enforce this by locally rescaling confidences, and then remerging the | ||
| * different local simplicial sets together. | ||
| */ | ||
| export function resetLocalConnectivity(simplicialSet: matrix.SparseMatrix) { | ||
| simplicialSet = matrix.normalize(simplicialSet, matrix.NormType.max); | ||
| const transpose = matrix.transpose(simplicialSet); | ||
| const prodMatrix = matrix.pairwiseMultiply(transpose, simplicialSet); | ||
| simplicialSet = matrix.add( | ||
| simplicialSet, | ||
| matrix.subtract(transpose, prodMatrix) | ||
| ); | ||
| return matrix.eliminateZeros(simplicialSet); | ||
| } |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -108,2 +108,11 @@ Licensed under the Apache License, Version 2.0 (the "License"); | ||
| /** | ||
| * Creates an array from a to b, of length len, inclusive | ||
| */ | ||
| export function linear(a: number, b: number, len: number): number[] { | ||
| return empty(len).map((_, i) => { | ||
| return a + i * ((b - a) / (len - 1)); | ||
| }); | ||
| } | ||
| /** | ||
| * Returns the sum of an array | ||
@@ -110,0 +119,0 @@ */ |
@@ -1,9 +0,6 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
@@ -20,6 +17,9 @@ distributed under the License is distributed on an "AS IS" BASIS, | ||
| identity, | ||
| dotMultiply, | ||
| pairwiseMultiply, | ||
| add, | ||
| subtract, | ||
| multiplyScalar, | ||
| eliminateZeros, | ||
| normalize, | ||
| NormType, | ||
| } from '../src/matrix'; | ||
@@ -44,3 +44,3 @@ | ||
| const cols = [0, 1, 0, 1]; | ||
| const vals = [1, 2]; | ||
| const vals = [1, 2, 3, 4]; | ||
| const dims = [2, 2]; | ||
@@ -84,3 +84,3 @@ const matrix = new SparseMatrix(rows, cols, vals, dims); | ||
| const entries = []; | ||
| const entries: number[][] = []; | ||
| matrix.forEach((value, row, col) => { | ||
@@ -117,4 +117,4 @@ entries.push([value, row, col]); | ||
| test('dot multiply method', () => { | ||
| const X = dotMultiply(A, B); | ||
| test('pairwise multiply method', () => { | ||
| const X = pairwiseMultiply(A, B); | ||
| expect(X.toArray()).toEqual([[1, 4], [9, 16]]); | ||
@@ -137,3 +137,62 @@ }); | ||
| }); | ||
| test('eliminateZeros method', () => { | ||
| const defaultValue = 11; | ||
| const rows = [0, 1, 1]; | ||
| const cols = [0, 0, 1]; | ||
| const vals = [0, 1, 3]; | ||
| const dims = [2, 2]; | ||
| const matrix = new SparseMatrix(rows, cols, vals, dims); | ||
| expect(matrix.get(0, 0, defaultValue)).toEqual(0); | ||
| const eliminated = eliminateZeros(matrix); | ||
| expect(eliminated.getValues()).toEqual([1, 3]); | ||
| expect(eliminated.getRows()).toEqual([1, 1]); | ||
| expect(eliminated.getCols()).toEqual([0, 1]); | ||
| expect(eliminated.get(0, 0, defaultValue)).toEqual(defaultValue); | ||
| }); | ||
| }); | ||
| describe('normalize method', () => { | ||
| let A: SparseMatrix; | ||
| beforeEach(() => { | ||
| const rows = [0, 0, 0, 1, 1, 1, 2, 2, 2]; | ||
| const cols = [0, 1, 2, 0, 1, 2, 0, 1, 2]; | ||
| const vals = [1, 2, 3, 4, 5, 6, 7, 8, 9]; | ||
| const dims = [3, 3]; | ||
| A = new SparseMatrix(rows, cols, vals, dims); | ||
| }); | ||
| test('max normalization method', () => { | ||
| const expected = [ | ||
| [0.3333333333333333, 0.6666666666666666, 1.0], | ||
| [0.6666666666666666, 0.8333333333333334, 1.0], | ||
| [0.7777777777777778, 0.8888888888888888, 1.0], | ||
| ]; | ||
| const n = normalize(A, NormType.max); | ||
| expect(n.toArray()).toEqual(expected); | ||
| }); | ||
| test('l1 normalization method', () => { | ||
| const expected = [ | ||
| [0.16666666666666666, 0.3333333333333333, 0.5], | ||
| [0.26666666666666666, 0.3333333333333333, 0.4], | ||
| [0.2916666666666667, 0.3333333333333333, 0.375], | ||
| ]; | ||
| const n = normalize(A, NormType.l1); | ||
| expect(n.toArray()).toEqual(expected); | ||
| }); | ||
| test('l2 normalization method (default)', () => { | ||
| const expected = [ | ||
| [0.2672612419124244, 0.5345224838248488, 0.8017837257372732], | ||
| [0.4558423058385518, 0.5698028822981898, 0.6837634587578277], | ||
| [0.5025707110324167, 0.5743665268941904, 0.6461623427559643], | ||
| ]; | ||
| const n = normalize(A); | ||
| expect(n.toArray()).toEqual(expected); | ||
| }); | ||
| }); |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -3,0 +3,0 @@ Licensed under the Apache License, Version 2.0 (the "License"); |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -17,6813 +17,348 @@ Licensed under the Apache License, Version 2.0 (the "License"); | ||
| export const testData: number[][] = [ | ||
| [ | ||
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| 13, | ||
| 16, | ||
| 6, | ||
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| 12, | ||
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| 11, | ||
| 0, | ||
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| ], | ||
| [ | ||
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| 15, | ||
| 1, | ||
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| 14, | ||
| 13, | ||
| 1, | ||
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| 10, | ||
| 15, | ||
| 3, | ||
| 15, | ||
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| [0,0,0,9,9,0,0,0,0,0,3,16,9,0,0,0,0,3,14,10,0,2,0,0,0,10,16,5,7,15,1,0,0,2,11,15,16,13,1,0,0,0,0,7,16,3,0,0,0,0,0,6,15,0,0,0,0,0,0,4,16,5,0,0], | ||
| [0,0,6,12,13,6,0,0,0,6,16,9,12,16,2,0,0,7,16,9,15,13,0,0,0,0,11,15,16,4,0,0,0,0,0,12,10,0,0,0,0,0,3,16,4,0,0,0,0,0,1,16,2,0,0,0,0,0,6,11,0,0,0,0], | ||
| [0,0,0,0,14,7,0,0,0,0,0,13,16,9,0,0,0,0,10,16,16,7,0,0,0,7,16,8,16,2,0,0,0,1,5,6,16,6,0,0,0,0,0,4,16,6,0,0,0,0,0,2,16,6,0,0,0,0,0,0,12,11,0,0], | ||
| [0,1,13,15,12,12,5,0,0,4,16,8,8,6,0,0,0,7,13,0,0,0,0,0,0,8,15,13,15,7,0,0,0,1,6,5,8,12,0,0,0,0,0,0,12,11,0,0,0,0,2,13,14,1,0,0,0,3,14,10,1,0,0,0], | ||
| [0,0,1,13,10,0,0,0,0,7,16,16,16,7,0,0,0,8,16,13,10,15,0,0,0,8,16,2,2,15,3,0,0,5,15,2,0,12,7,0,0,1,15,6,2,16,3,0,0,0,11,15,13,16,0,0,0,0,1,15,14,8,0,0], | ||
| [0,1,12,13,4,0,0,0,0,4,16,16,16,3,0,0,0,4,16,16,16,10,0,0,0,0,6,16,14,16,0,0,0,0,0,0,0,16,4,0,0,0,0,0,0,13,7,0,0,1,2,3,7,14,10,0,0,2,12,16,14,12,3,0], | ||
| [0,0,13,13,8,2,0,0,0,5,16,16,16,12,0,0,0,1,15,12,0,0,0,0,0,0,12,13,7,1,0,0,0,0,8,16,16,12,0,0,0,0,0,4,9,16,3,0,0,0,1,5,14,15,1,0,0,0,10,16,16,6,0,0], | ||
| [0,0,0,0,9,13,0,0,0,0,0,2,16,16,1,0,0,0,0,5,9,15,0,0,0,0,0,0,5,14,0,0,0,0,0,3,15,7,0,0,0,7,16,16,11,0,0,0,0,0,11,14,16,7,3,0,0,0,0,0,9,15,9,0], | ||
| [0,3,5,14,13,6,0,0,0,9,16,12,10,12,0,0,0,6,16,3,12,11,0,0,0,1,13,10,16,6,0,0,0,0,10,16,10,0,0,0,0,1,15,16,10,0,0,0,0,0,16,12,16,0,0,0,0,0,3,15,16,5,0,0], | ||
| [0,0,0,0,11,15,4,0,0,0,0,3,16,16,12,0,0,0,0,8,14,16,12,0,0,0,0,5,10,16,6,0,0,1,7,11,16,13,0,0,0,9,16,16,14,1,0,0,0,3,8,14,16,9,0,0,0,0,0,1,11,16,12,0], | ||
| [0,0,10,12,10,0,0,0,0,3,16,16,16,4,0,0,0,7,15,3,8,13,0,0,0,8,12,0,0,14,1,0,0,8,12,0,0,7,8,0,0,5,13,0,0,4,8,0,0,0,14,8,0,10,8,0,0,0,7,12,13,12,4,0], | ||
| [0,0,4,14,11,0,0,0,0,3,15,15,16,9,0,0,0,8,13,0,3,15,1,0,0,8,12,0,0,8,6,0,0,8,12,0,0,8,8,0,0,5,13,1,0,8,8,0,0,2,15,14,12,15,6,0,0,0,5,16,15,8,0,0], | ||
| [0,0,0,1,14,13,1,0,0,0,0,1,16,16,3,0,0,5,11,15,16,16,0,0,0,4,15,16,16,15,0,0,0,0,0,8,16,7,0,0,0,0,0,10,16,3,0,0,0,0,0,8,16,6,0,0,0,0,0,2,13,15,2,0], | ||
| [0,0,3,14,16,14,0,0,0,0,13,13,13,16,2,0,0,0,1,0,9,15,0,0,0,0,9,12,15,16,10,0,0,4,16,16,16,11,3,0,0,0,4,9,14,2,0,0,0,0,2,15,9,0,0,0,0,0,4,13,1,0,0,0], | ||
| [0,0,0,10,15,3,0,0,0,0,7,16,11,0,0,0,0,0,13,15,1,0,0,0,0,0,15,11,0,0,0,0,0,0,16,13,8,1,0,0,0,0,15,16,16,15,6,0,0,0,10,16,14,16,14,2,0,0,1,9,15,16,11,0], | ||
| [0,2,13,15,10,4,0,0,0,0,5,4,13,15,2,0,0,0,0,0,11,16,4,0,0,0,0,0,16,12,0,0,0,0,0,0,13,11,0,0,0,0,0,0,8,13,0,0,0,1,6,8,14,12,0,0,0,2,12,14,11,1,0,0], | ||
| [0,1,13,15,2,0,0,0,0,6,15,15,9,0,0,0,0,9,8,10,13,0,0,0,0,5,3,12,12,0,0,0,0,0,3,16,6,0,0,0,0,5,15,15,1,0,0,0,0,6,16,15,12,12,11,0,0,1,11,13,16,16,12,0], | ||
| [0,0,0,1,16,5,0,0,0,0,0,5,16,11,0,0,0,0,0,12,16,11,0,0,0,7,12,16,16,7,0,0,0,4,8,12,16,4,0,0,0,0,0,9,16,2,0,0,0,0,0,10,16,2,0,0,0,0,0,3,13,5,0,0], | ||
| [0,0,2,7,15,13,1,0,0,0,14,12,9,14,8,0,0,0,2,0,0,12,8,0,0,0,0,0,0,13,6,0,0,5,16,16,16,16,5,0,0,2,5,7,13,14,2,0,0,0,0,1,15,5,0,0,0,0,0,11,9,0,0,0], | ||
| [0,0,0,9,16,4,0,0,0,1,9,16,13,2,0,0,0,14,16,14,8,0,0,0,1,15,15,5,16,9,0,0,0,5,16,16,16,8,0,0,0,0,2,13,16,1,0,0,0,0,0,11,13,0,0,0,0,0,0,11,13,0,0,0], | ||
| [0,0,0,10,11,0,0,0,0,0,3,16,10,0,0,0,0,0,8,16,0,0,0,0,0,0,12,14,0,0,0,0,0,0,14,16,15,6,0,0,0,0,12,16,12,15,6,0,0,0,7,16,10,13,14,0,0,0,0,9,13,11,6,0], | ||
| [0,0,13,16,15,4,0,0,0,0,9,8,13,16,3,0,0,0,0,0,13,16,7,0,0,0,0,1,16,12,0,0,0,0,0,0,15,10,0,0,0,0,0,0,8,15,0,0,0,0,3,6,15,16,7,0,0,0,15,16,16,11,1,0], | ||
| [0,0,0,1,12,8,1,0,0,0,0,4,16,16,1,0,0,0,1,13,16,11,0,0,0,1,11,16,16,12,0,0,0,2,12,8,16,10,0,0,0,0,0,0,15,8,0,0,0,0,0,4,16,4,0,0,0,0,0,3,13,4,0,0], | ||
| [0,4,14,16,16,12,1,0,0,2,12,7,14,16,6,0,0,0,0,5,16,10,0,0,0,0,0,4,16,7,0,0,0,0,0,4,16,6,0,0,0,0,0,1,15,11,0,0,0,1,8,10,16,10,0,0,0,5,16,16,15,1,0,0], | ||
| [0,0,9,13,14,5,0,0,0,4,16,10,13,16,0,0,0,0,13,15,14,16,1,0,0,0,0,3,7,16,3,0,0,0,0,0,4,16,0,0,0,0,0,0,1,16,3,0,0,1,15,5,8,16,2,0,0,0,7,15,16,9,0,0], | ||
| [0,0,0,11,16,5,0,0,0,0,0,10,16,5,0,0,0,0,4,16,16,5,0,0,0,11,16,16,16,3,0,0,0,5,8,14,16,2,0,0,0,0,0,14,16,2,0,0,0,0,0,11,16,2,0,0,0,0,0,8,16,8,0,0], | ||
| [0,0,3,12,16,10,0,0,0,2,14,12,12,12,0,0,0,5,10,0,10,11,0,0,0,0,0,1,14,9,2,0,0,0,8,16,16,16,10,0,0,0,6,16,13,7,0,0,0,0,0,16,5,0,0,0,0,0,5,13,0,0,0,0], | ||
| [0,0,0,11,16,8,0,0,0,0,6,16,13,3,0,0,0,0,8,16,8,0,0,0,0,0,13,16,2,0,0,0,0,0,15,16,5,0,0,0,0,2,16,16,16,5,0,0,0,1,10,16,16,14,0,0,0,0,0,12,16,15,0,0], | ||
| [0,1,9,16,15,10,0,0,0,6,16,8,7,16,3,0,0,0,11,14,16,11,1,0,0,1,13,16,6,0,0,0,0,8,15,16,3,0,0,0,0,5,14,10,11,0,0,0,0,0,15,7,16,3,0,0,0,0,11,16,8,0,0,0], | ||
| [0,0,0,3,14,1,0,0,0,0,0,13,12,1,0,0,0,0,7,16,5,3,0,0,0,3,15,11,5,16,2,0,0,5,16,11,11,16,6,0,0,0,6,12,16,13,3,0,0,0,0,1,15,7,0,0,0,0,0,2,16,7,0,0], | ||
| [0,2,15,16,16,13,2,0,0,1,10,8,14,16,8,0,0,0,0,0,16,15,1,0,0,0,0,0,16,8,0,0,0,0,0,0,14,14,0,0,0,0,0,0,11,16,1,0,0,2,14,13,16,16,3,0,0,2,15,16,14,5,0,0], | ||
| [0,0,1,15,13,0,0,0,0,0,1,16,16,5,0,0,0,0,7,16,16,0,0,0,0,0,13,16,13,0,0,0,0,7,16,16,13,0,0,0,0,1,11,16,13,0,0,0,0,0,2,16,16,0,0,0,0,0,1,14,16,3,0,0] | ||
| ] | ||
| export const testLabels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 8, 4, 1, 7, 7, 3, 5, 1, 0, 0, 2, 2, 7, 8, 2, 0, 1, 2, 6, 3, 3, 7, 3, 3, 4, 6, 6, 6, 4, 9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 1, 7, 6, 3, 2, 1, 7, 4, 6, 3, 1, 3, 9, 1, 7, 6, 8, 4, 3, 1]; | ||
| export const testResults2D = [ | ||
| [-5.143780305349707, -2.5671849569488616], | ||
| [4.995176133810635, 2.5965849735960616], | ||
| [4.6222573528541195, 0.7484388202104252], | ||
| [-0.23893280866794067, -2.3894217277547574], | ||
| [3.8216947866212427, 3.604325608202465], | ||
| [-0.8709911223696308, -2.1282149782202637], | ||
| [2.81844275442799, 4.261801561329653], | ||
| [3.985242946889707, -0.69987158462082], | ||
| [2.5284916251895444, -1.1717656494894668], | ||
| [-1.1298218377874478, -2.0309819209879776], | ||
| [-4.948353903108204, -2.2610065007394597], | ||
| [5.699199110791917, 2.877521090385841], | ||
| [1.3286051310764506, -1.5204832935580381], | ||
| [-0.19671964605119277, -1.596286230364697], | ||
| [3.9570111329958686, 3.2758570149544686], | ||
| [1.480375921466857, -0.21889058755079255], | ||
| [2.13395255568376, 4.067673257375693], | ||
| [4.350679484478442, -1.0921026099289], | ||
| [2.557272070095345, -0.6584862616536931], | ||
| [-0.6280886867504241, -2.6005372521893624], | ||
| [-4.951734534828008, -2.4317574249282763], | ||
| [5.852195575975039, 2.683552415493007], | ||
| [1.2673544881756855, -1.7652377046307073], | ||
| [0.27834352770569465, -1.2263441812138562], | ||
| [4.203201251314217, 3.558689446601454], | ||
| [1.839093964057868, -0.32070267402461533], | ||
| [2.7402909986130384, 3.748514695536701], | ||
| [4.775870513157077, -1.2882767672080533], | ||
| [2.6680274630025087, -1.060172254152725], | ||
| [-0.8890871150861309, -2.5406054355985], | ||
| [-5.199219365246346, -2.5439548747978544], | ||
| [-0.7742912077362485, -2.4924772238682538], | ||
| [1.2101317364796087, -0.35983641761170776], | ||
| [1.3903614428637778, -0.5810630253592894], | ||
| [2.3312708440231273, 3.895422135976703], | ||
| [1.314610523244508, -0.4483167485489965], | ||
| [-4.809219291666345, -2.67224397563046], | ||
| [-0.9330458817876642, -2.6887418499828972], | ||
| [2.7222830858039107, -0.24300134035081575], | ||
| [-0.537006977044404, -2.131989085370945], | ||
| [2.5840966544219466, -0.8742501821039962], | ||
| [3.9067723616717593, 3.2304268903335442], | ||
| [5.807179557496094, 2.81976387725541], | ||
| [4.713582746786369, -1.0630236450960113], | ||
| [3.9641348734902975, -0.8967473157539272], | ||
| [-0.01530404877843643, -2.038490150958936], | ||
| [1.722873318704653, -0.6231473558362151], | ||
| [5.525874058784288, 2.579474068671955], | ||
| [-5.122152010646219, -1.9559304813772806], | ||
| [-4.909827414454853, -2.0381460953648247], | ||
| [4.435235743327855, 0.8087691590587817], | ||
| [4.776283370939164, 0.5623242625671616], | ||
| [4.509813103087742, -1.2722181689692529], | ||
| [3.0463351139730444, -0.8555512042899034], | ||
| [4.725418433330528, 0.5816264030007843], | ||
| [-4.649522276017468, -2.4558056605042777], | ||
| [5.735211401229367, 2.572494889130049], | ||
| [4.737476680160634, 0.8090144113193056], | ||
| [2.6457554352277217, 3.8441381986893037], | ||
| [0.044581381624577684, -1.6680091087552553], | ||
| [0.19114681572811032, -1.9416261786294828], | ||
| [4.240988491390998, -1.2283750038224575], | ||
| [0.033047744155285615, -1.6942531784944643], | ||
| [0.210357401540182, -1.5529939111833393], | ||
| [3.7843164767414215, 3.3752214765548647], | ||
| [2.3144307157068273, 3.720756280006736], | ||
| [2.548752989928164, 4.217062982760938], | ||
| [2.5197488164345256, 3.8180966717310905], | ||
| [4.312121231767462, 3.073116273541051], | ||
| [3.185703087156189, -0.43873712206302057], | ||
| [5.385753546184886, 3.0132761685647913], | ||
| [1.3110498934462607, -0.17522840170490206], | ||
| [-4.764009567265803, -2.316651916353176], | ||
| [-0.9188046781255212, -2.371412876499229], | ||
| [1.5926491058153243, -0.4728096731292803], | ||
| [4.947034446292605, 0.6912887747637944], | ||
| [2.982800179052141, -0.19053947432288632], | ||
| [4.906581467579906, 0.6288331829907543], | ||
| [-5.26150429961407, -2.081657583184234], | ||
| [-5.219440756729904, -2.2310942986208833], | ||
| [5.600877498649305, 2.4307033874744346], | ||
| [4.258399881791161, -1.1351423590515684], | ||
| [2.5188641178920497, 4.107750982721579], | ||
| [0.3264943601798401, -1.4395225384895831], | ||
| [1.6075393800777409, -1.7664958459209985], | ||
| [5.287144028790786, 2.740148154739708], | ||
| [4.482899087116274, -1.3737458694813844], | ||
| [4.455288440369896, 2.880675270281383], | ||
| [2.3066608854113504, 4.087990208296346], | ||
| [0.002310248832477981, -1.4675603226569485], | ||
| [5.595577005423542, 2.7708126642925532], | ||
| [0.4656167622568051, -1.2306090656722155], | ||
| [-0.4643213501515245, -1.9444537727637536], | ||
| [5.0352286799455, 2.7955713231541415], | ||
| [4.178213509614446, -1.2438448661365353], | ||
| [2.842812133288208, 3.9554256472054883], | ||
| [2.717878167304611, -0.3231556631453297], | ||
| [4.078991558237693, 3.3734852571453176], | ||
| [0.3851783401659291, -1.3515285398938426], | ||
| [4.669846645209024, 2.73779208827989], | ||
| [-2.904975618700953, 3.683494083841041], | ||
| [-0.879124321765863, -0.4426951405143409], | ||
| [-5.681244379231256, -0.7285120769550153], | ||
| [-10.97656385005008, -1.7526666493055492], | ||
| [-0.31624613607402396, 1.1790435717043253], | ||
| [-10.335486943961724, -1.7263038658200893], | ||
| [1.0183234049896575, 1.5616783163531822], | ||
| [-6.670890452694134, -2.599411074380109], | ||
| [-7.427928924098714, -1.05418551144876], | ||
| [-10.056474336257683, -1.7483236829216935], | ||
| [-2.84058073813134, 3.4605708652841742], | ||
| [-1.5561486242848588, -0.7689145308285977], | ||
| [-9.618253615211268, -0.9363390901794612], | ||
| [-10.96225008756357, -2.7757046759437696], | ||
| [-0.5471130812484319, 1.0412069799695824], | ||
| [-8.468671293896426, -1.9946633211861478], | ||
| [1.7476633852483103, 1.7135835405359496], | ||
| [-6.060552685379354, -2.5918267819546004], | ||
| [-7.612742599715047, -1.5731281884494046], | ||
| [-10.686213384810973, -1.2269058958867671], | ||
| [-2.8003412175539504, 3.516631590760736], | ||
| [-1.7105731474897927, -0.4229854943104262], | ||
| [-9.687787910507133, -0.8332650324863461], | ||
| [-11.562098613314996, -2.8768460480875233], | ||
| [-0.6169436126132575, 0.9271669091731843], | ||
| [-8.263457345038251, -1.7749721223830195], | ||
| [1.2702831264161745, 1.1345515631395546], | ||
| [-5.625939784888585, -2.81705722551124], | ||
| [-7.321567272072983, -1.1714267827612699], | ||
| [-10.498673585109545, -1.4172021055405306], | ||
| [-2.8361027291899963, 3.7566550423427216], | ||
| [-10.742499099483476, -1.3312462253382054], | ||
| [-8.742331565355999, -1.962339327316867], | ||
| [-8.683249673946568, -1.7431070207306547], | ||
| [1.519913836537469, 1.5812210418530623], | ||
| [-8.639230381438411, -1.83626003754962], | ||
| [-3.0714622950362083, 3.360214813815334], | ||
| [-10.321920349887177, -1.2166199317699589], | ||
| [-7.479960982731633, -2.0085158275034654], | ||
| [-10.64339464722167, -1.98466938098528], | ||
| [-7.444943872419686, -1.3202152645583067], | ||
| [-0.6659047931509448, 1.0689298563354555], | ||
| [-1.337476996462183, -0.7062062107669966], | ||
| [-5.722954727686442, -2.614991713970329], | ||
| [-6.535367593759711, -2.8240481849859242], | ||
| [-11.07458350701865, -2.1857932277021], | ||
| [-8.465490267069768, -1.5262082312357834], | ||
| [-1.4028562692810291, -0.3234834011238395], | ||
| [-2.303657152463291, 3.4811020923228124], | ||
| [-2.7185070579122725, 3.1727811960040686], | ||
| [-5.769483048641313, -0.9860570623329065], | ||
| [-5.615815263813969, -0.6062651823744999], | ||
| [-5.912314417301917, -2.8274353398822054], | ||
| [-6.979235936333255, -1.3486153549845195], | ||
| [-5.580947395294925, -0.8487153054662616], | ||
| [-2.519127036098348, 3.235839368692096], | ||
| [-1.5666266264773967, -0.4084044324487192], | ||
| [-5.482920294320904, -0.7132829553293972], | ||
| [1.291443869275501, 1.2088921598961009], | ||
| [-10.996594305162729, -2.7052817764388934], | ||
| [-11.230034148536227, -2.3267787723370223], | ||
| [-6.172936702531247, -2.8092873015313207], | ||
| [-11.31268843509639, -2.4664461470306835], | ||
| [-11.383742431873435, -2.526000902907926], | ||
| [-0.5135436868771529, 1.219682963441809], | ||
| [1.6600210033832736, 1.2986546898831781], | ||
| [1.3440665949247865, 1.6471365427370084], | ||
| [1.4289155033476695, 1.3398242847863313], | ||
| [-0.749955073590225, 0.5597391612835082], | ||
| [-7.196844128522498, -2.2154060964753044], | ||
| [-1.287126546227924, -0.1161850904218144], | ||
| [-8.736469581347375, -2.133470874953044], | ||
| [-2.6244524948342693, 3.2661482243617606], | ||
| [-10.488890956441548, -1.518682221199595], | ||
| [-9.080187424004665, -1.7921945653042823], | ||
| [-5.409549676705336, -0.7562583268806944], | ||
| [-7.131907767719736, -1.7819102538183373], | ||
| [-5.3945164159653105, -0.6311831012797511], | ||
| [-2.4235633967787837, 3.5752787074610253], | ||
| [-2.5646735303118913, 3.6545463352449956], | ||
| [-1.6293939125209715, -0.3261124147226215], | ||
| [-6.174169159339828, -2.816398862346779], | ||
| [1.5011647046090915, 1.4604002557978188], | ||
| [-11.415289227608609, -2.784266265081746], | ||
| [-9.372108978232719, -0.6382375099773951], | ||
| [-1.1395765495328711, -0.4341112548365201], | ||
| [-5.90712646029175, -2.693143905929763], | ||
| [-0.8163580669991641, 0.27205961797264744], | ||
| [1.6757381906829827, 1.6959697431903369], | ||
| [-11.377447482142799, -2.463933981648078], | ||
| [-1.4565032357599115, -0.5951550644235234], | ||
| [-11.521681218113521, -2.939480142411405], | ||
| [-10.698652055676758, -2.14080724624675], | ||
| [-0.8906495642468407, -0.3084980108495706], | ||
| [-6.20543225894981, -2.712538184223933], | ||
| [1.074802277722911, 1.3366879290550757], | ||
| [-7.369542577049855, -1.7665150872563606], | ||
| [-0.6479104154512673, 0.8639090216770717], | ||
| [-11.63001115748303, -2.7650660117667964], | ||
| [-0.6177124196423712, -0.1336295015576095], | ||
| ]; | ||
| export const testResults3D = [ | ||
| [-4.999350449966068, 1.646552093740238, 6.3209980182331975], | ||
| [2.8403788682574507, -2.5690234752364147, -0.4063832276708564], | ||
| [1.1833348034999547, -0.165914769440034, 1.4483763129412357], | ||
| [-3.1000569670380917, -3.388348490548774, 2.2939590758551605], | ||
| [1.590391725722708, -3.159107792780017, -0.9666315282361507], | ||
| [-2.3951803756711763, -2.875537534248424, 2.4888844450685776], | ||
| [1.3134087868452626, -4.839205855767238, -1.4684113194648682], | ||
| [-0.05862699213124383, -0.3230787022271161, 0.3909466032385804], | ||
| [-0.9818650602760947, -1.89052036738796, 0.7831797526779974], | ||
| [-2.2526555755620996, -2.6802831924164634, 2.546448994000601], | ||
| [-4.82304267864745, 1.6782749542805708, 6.351005545201137], | ||
| [3.525376540737558, -2.1831045439643115, -0.1957704511804789], | ||
| [-1.3146982433476153, -3.0779917025822088, 1.4477889446578915], | ||
| [-3.490624389399593, -3.2725536497663685, 2.4076973946962608], | ||
| [1.6046015330295582, -2.9611434864309123, -0.8593780060905368], | ||
| [-1.699544447645836, -1.642749673631885, 2.015345569855638], | ||
| [1.7342846706650423, -5.127050467625386, -1.2229636945836426], | ||
| [0.30235720908299474, -0.6590498012692595, -0.21010853583241784], | ||
| [-0.7547891213532484, -1.381181459704983, 1.2857859039621202], | ||
| [-2.405143781147724, -3.294790972121255, 2.2339921983105673], | ||
| [-4.6888145239183325, 1.5957495338043912, 6.066524279171597], | ||
| [3.3907864120350832, -1.9697227304551028, -0.35126345189817587], | ||
| [-1.1889833308558972, -2.986842318867589, 1.3290839509807597], | ||
| [-4.1965163088295805, -3.009606414989964, 2.280004284648866], | ||
| [1.8699507344843387, -2.8901613072466716, -0.830600419412514], | ||
| [-1.2517787389701411, -2.099937874882709, 1.7839399829560294], | ||
| [1.0708917156374802, -5.040659939484485, -1.451089571553497], | ||
| [0.6813397108314285, -0.2074722715948495, -0.11836522421473296], | ||
| [-0.9766999659990082, -1.599977553076435, 0.824970498549993], | ||
| [-2.4945221961883095, -3.1658696739622645, 2.3823771637216384], | ||
| [-4.899229005965406, 1.7685435183795715, 6.293268077232492], | ||
| [-2.7051503141017026, -3.4719168522662587, 2.365737648189881], | ||
| [-1.9250994663697636, -1.7104462379875267, 1.9011547899945314], | ||
| [-1.4894481361535463, -1.732734586496755, 1.8509188748432408], | ||
| [1.6165372485356557, -5.25131988631751, -1.3897749464436084], | ||
| [-1.5603087626214696, -1.815181035578557, 2.017380726207842], | ||
| [-4.71864837981744, 1.7869497011017945, 6.061729388856058], | ||
| [-2.2739760859343447, -3.0147636299303313, 2.262607608591685], | ||
| [-0.37109251169327256, -1.1043002671291442, 1.0253167282728668], | ||
| [-2.820969623808371, -3.0998329038438954, 2.396036632479787], | ||
| [-0.7005607382673469, -1.7002270390752574, 1.0510004240728807], | ||
| [1.4857504726622819, -2.833454022851123, -0.7149842931614216], | ||
| [3.1282727732040945, -2.240629557759072, -0.0194703003215026], | ||
| [0.49722410525448624, -0.1790547436210475, -0.1421387277822053], | ||
| [0.09781766189320759, -0.28309361000508254, 0.11780357683695647], | ||
| [-3.4873494863453067, -3.19663433127773, 2.607053817631071], | ||
| [-1.394546863463544, -1.8089301809170706, 1.7570392550055467], | ||
| [2.9492357183007285, -1.9585154862269956, -0.20808421980550004], | ||
| [-5.22357074906259, 1.6412010189222608, 6.044869008419269], | ||
| [-4.936611792437089, 1.829959701430522, 5.923757132103001], | ||
| [0.9856428272100313, -0.31122638985417883, 1.541888220378777], | ||
| [1.209163223244167, -0.4747701233033356, 1.6411155325893954], | ||
| [0.5298261787649933, -0.18663156089989155, 0.008270161212818027], | ||
| [-0.6662589815711999, -1.5607703637790205, 0.6435066358899714], | ||
| [1.418352281412417, -0.10764807283588383, 1.575872378114067], | ||
| [-4.555240623023682, 1.8072087498418707, 5.997825982675267], | ||
| [3.240143630100305, -1.9405200267144476, -0.31965641342931356], | ||
| [1.1499922939227833, -0.13459454836021864, 1.4272000080679261], | ||
| [1.6125476149045925, -5.1173136191753255, -1.7881270717654334], | ||
| [-3.6921959180484194, -3.3362767681393577, 2.179679240287428], | ||
| [-3.639459381261774, -3.117905607643891, 2.6472262960333692], | ||
| [0.2672042131204365, -0.5473997771077008, -0.05256561727345535], | ||
| [-3.7867686621215686, -2.8810304365179067, 2.582320769649838], | ||
| [-3.681651559523735, -3.088412486403305, 2.1989283128863546], | ||
| [1.477354480495836, -3.260757038729244, -1.0437334227819175], | ||
| [1.6462343893664715, -5.435555225918037, -1.6470710736463636], | ||
| [1.2719000848697173, -5.126682698632477, -1.7308887248203841], | ||
| [1.4942032644533132, -5.3640282721966805, -1.7989180398291422], | ||
| [1.913146785959154, -2.670915961959593, -0.6102127487772651], | ||
| [-0.2666484245094862, -1.0509976847223497, 0.8887493609418966], | ||
| [2.761473665451152, -2.2322803100331896, -0.08863916205797434], | ||
| [-1.7477489634205088, -1.5034624344789653, 1.8925715460796828], | ||
| [-4.731658917279828, 1.619745871364001, 5.874739099513872], | ||
| [-2.6555095221862364, -3.152804976526936, 2.4672114874195223], | ||
| [-1.709548442921802, -2.079181153742936, 2.0356373609865734], | ||
| [1.3596836250512536, -0.40332367575202277, 1.75177442940566], | ||
| [-0.20994833636849541, -1.5145952417600825, 1.0002114686085513], | ||
| [1.3207286114722674, -0.37340290400290377, 1.6787482056261671], | ||
| [-5.016050260138233, 1.4175111257931283, 5.899202327823393], | ||
| [-5.086724718741249, 1.5716093961869941, 6.03921201073803], | ||
| [3.263628017045966, -2.1117059598812458, -0.2055704803785597], | ||
| [0.24954732478375738, -0.2744252184542143, -0.014025851345480516], | ||
| [1.3359119235590569, -5.059645167131039, -1.561291300506062], | ||
| [-4.142837978742523, -2.847555281383964, 2.365603896961519], | ||
| [-0.9876231037669517, -3.062436809528216, 1.1199266838170152], | ||
| [2.906269122259462, -2.1934723519520247, -0.2935571114446365], | ||
| [0.4710945591432627, -0.4299725417899055, -0.223741599405139], | ||
| [2.164485907769525, -2.6518022877928185, -0.3949457076901189], | ||
| [1.5259368040909012, -5.11023176969685, -1.4155841804050042], | ||
| [-3.981046073547551, -3.100456373177016, 2.537924405568087], | ||
| [3.284582468988006, -2.0483724751519388, -0.05740936342908974], | ||
| [-3.9841636152475797, -2.901866851447435, 2.121223171846165], | ||
| [-3.0684236712372885, -3.0875360469144475, 2.4519377771603685], | ||
| [2.7093458942682282, -2.43935208804615, -0.3692812783851062], | ||
| [0.1480354518578467, -0.6060598102883284, 0.26434812544520253], | ||
| [1.172039183700656, -4.945614883308257, -1.1645387626634054], | ||
| [-0.3753833108922356, -1.4087919745438744, 1.125599123595257], | ||
| [1.7756200715237602, -2.9946438767029977, -0.8977646263946848], | ||
| [-3.9816581390155883, -2.9834721053610984, 2.28207294450481], | ||
| [2.556782981012287, -2.8317270564242976, -0.4581508277984368], | ||
| [7.689216888038721, 5.889500760682107, -0.9372910704861791], | ||
| [1.6289201733408833, 0.09644154452992573, -0.6515193779268037], | ||
| [1.8064203152613236, 1.0408971830461913, -3.759296358413371], | ||
| [3.719252646835446, 6.2652682378361995, -5.453359288044545], | ||
| [2.208579439712507, 1.459095083827724, 0.12000156409675383], | ||
| [3.2249615957947833, 5.960024670787041, -4.8481645679297305], | ||
| [1.72709168751186, 2.663674819440392, 2.0862843204012536], | ||
| [1.4390793446753154, 3.110175910025147, -2.64329402984594], | ||
| [2.322369780334226, 3.854174748115889, -4.1940933254107895], | ||
| [3.172586988457068, 5.988043163212074, -4.489868026985644], | ||
| [7.774232233860294, 5.728604682804332, -0.7492809764488474], | ||
| [1.216760464557976, 0.12651789052380594, -1.1160899217216294], | ||
| [2.9761272550252342, 5.127354763494007, -3.789929920251627], | ||
| [3.623435292999969, 6.467545035827176, -5.742880703018387], | ||
| [2.191615075396111, 1.3462025439777128, -0.06012668392608021], | ||
| [1.9792286512470967, 5.048455808227712, -4.503864167574659], | ||
| [2.289319043749516, 2.479364102096565, 2.06509502307054], | ||
| [1.7522008547912962, 3.028100683362274, -1.9757439514197939], | ||
| [1.9133011076476316, 3.976012857253692, -3.654786899504835], | ||
| [3.126237934842212, 6.121493120485744, -4.9163292854727025], | ||
| [7.59812385730979, 5.945122300518597, -0.5466031795420292], | ||
| [1.0642820336951215, -0.10032610638546975, -1.272495749311577], | ||
| [2.9120539005610295, 4.9615417834931055, -3.6557913237121338], | ||
| [3.6667322868678744, 6.432934502429571, -6.475883464450793], | ||
| [1.985579003136723, 1.1529793819422138, -0.14330347191913095], | ||
| [2.536689758242814, 4.94651699436971, -4.188789564228635], | ||
| [1.7588493803085914, 2.820477258013995, 2.356610282996252], | ||
| [1.3544004702547257, 2.5228876142069545, -2.0848215214143417], | ||
| [2.18648006037277, 3.6254018053870727, -4.0692699701558235], | ||
| [3.3080002706216582, 6.170491494769027, -4.909354375701165], | ||
| [7.838430931939201, 5.962279999222033, -0.8501265755322832], | ||
| [3.146812853890534, 6.420570628535107, -5.128511955440203], | ||
| [2.1655492350855243, 5.133684793191488, -4.794337818504037], | ||
| [2.156182788918373, 4.779020022109626, -4.458168130007138], | ||
| [2.1868295164680163, 2.651935395462085, 1.9402408691766964], | ||
| [2.1122898435485626, 5.101805045252335, -4.376461605992093], | ||
| [7.759497394541325, 5.99608504530489, -0.5721002846060569], | ||
| [3.2698139531816732, 5.945075221007509, -4.716167049727797], | ||
| [1.9537425223452107, 3.7990956170037817, -3.1702963336135563], | ||
| [3.5638849988193715, 6.286648383473683, -5.057728780776639], | ||
| [2.2260271578133892, 3.9062531861533376, -3.7926834207621583], | ||
| [2.3487636315682385, 1.2733078093794485, -0.20521957587061831], | ||
| [1.4763516191715822, -0.07339630024515807, -1.2568913530002406], | ||
| [1.4457541418594435, 2.6678280144185633, -1.968673498837688], | ||
| [1.3665146037972555, 3.1104612289267797, -2.3362646187488014], | ||
| [3.8775155788281355, 6.65251928161629, -5.665014425209378], | ||
| [2.243781465166822, 4.810096665722539, -4.325776535824173], | ||
| [1.3113157069794292, -0.3907556024946346, -0.9674678142514966], | ||
| [7.4707557181542885, 6.068425618027565, -0.9016754062087259], | ||
| [7.719682010298653, 6.293792082295646, -0.6943491194702454], | ||
| [1.889160988739313, 1.2825076320476096, -3.8797729494317945], | ||
| [1.528848553082611, 1.2588606673105436, -3.8859071748383442], | ||
| [1.328663194953698, 2.6696166169691624, -2.2155627880306157], | ||
| [2.1544579217204247, 3.4833076529627025, -3.93081895871594], | ||
| [1.678647726543417, 1.350749170768083, -3.6472298500923848], | ||
| [7.779378974045683, 5.941065599050965, -0.3768179153485322], | ||
| [1.090599105664963, -0.21897555239951227, -1.1245858483752145], | ||
| [1.9004151266296854, 1.0016306087525904, -3.8240643093652547], | ||
| [1.79519610139777, 3.072747351366555, 1.9924133395724823], | ||
| [3.385643758056163, 6.481555639718136, -5.946525871558117], | ||
| [3.739617985672955, 6.7734869473964165, -5.791002799859846], | ||
| [1.68329653285918, 2.9352559918931376, -2.1240958308135625], | ||
| [3.8689936660063227, 6.450100300090818, -5.863677747356829], | ||
| [3.5227324662391934, 6.304799948350529, -6.015458718806657], | ||
| [2.347687118964318, 1.422139375121024, 0.22278213875612188], | ||
| [2.2180670708690338, 2.9587217481110355, 1.9577285130476965], | ||
| [1.8383459478962132, 2.9516924800936106, 1.951773536386326], | ||
| [1.9915163516683876, 3.125657869624825, 1.9360001985910298], | ||
| [2.0586128716897076, 0.9319526850785679, -0.363347710318456], | ||
| [1.972391691031123, 3.8113757348470148, -3.013137897370146], | ||
| [1.379201705857466, -0.31634219940793756, -0.8396233794610332], | ||
| [1.9982809296461372, 4.979452278680934, -4.6935290145722695], | ||
| [7.572434557728682, 6.103563807021123, -0.45238952042062147], | ||
| [3.361781071733503, 6.347807072766981, -4.965261048582085], | ||
| [2.4457518923571953, 5.208909777707682, -4.488138746033763], | ||
| [1.515371062452698, 1.2451955950131388, -4.004308683680948], | ||
| [2.327020894876154, 3.945346529979229, -3.214052790232576], | ||
| [1.6340689753522157, 1.1646400128291376, -3.9523159112572697], | ||
| [7.3434417124707885, 6.210680610725227, -0.7232208833510627], | ||
| [7.487203581595136, 6.091453370170096, -0.814759660914381], | ||
| [1.2523055479826777, -0.03355046908458365, -1.1445314775885431], | ||
| [1.4031260098000506, 2.9095043917838788, -2.2536281371792706], | ||
| [1.920464298567037, 2.8073745017850773, 2.2462294375046903], | ||
| [3.8813522730544237, 6.681172127785995, -6.326452335509549], | ||
| [2.9558363810192625, 4.788647132234979, -3.4553572807116355], | ||
| [1.670714138250657, 0.026486419185176892, -0.991392665968696], | ||
| [1.581545066906788, 2.7844989846664046, -1.984244575230313], | ||
| [1.875907008651662, 0.6952868053471871, -0.5229128767158864], | ||
| [2.084307557879972, 2.646840109884875, 2.056998572415541], | ||
| [3.5806516860631024, 6.743248753046012, -6.105297199686208], | ||
| [1.462558096710222, -0.10092139207798385, -1.1898581884339972], | ||
| [3.654706027150071, 6.390585202444062, -6.357892180833042], | ||
| [3.6027095941866247, 6.383704215055882, -5.318204043566554], | ||
| [1.7245257657018247, 0.192478655399741, -0.7436440052075087], | ||
| [1.683165920875281, 3.243957316304348, -2.3259907808966545], | ||
| [1.745611159759802, 2.472672662103672, 1.9155422431593778], | ||
| [2.177305599456365, 3.9231388710705675, -3.3039650230819726], | ||
| [2.068771729441073, 1.222227121468545, -0.02797486874066797], | ||
| [3.773289766268219, 6.534098686594626, -6.250642359343324], | ||
| [1.7729320319143027, 0.3201914207876183, -0.40659921859631903], | ||
| ]; | ||
| export const treeData = { | ||
| "hyperplanes": [[0,0,0,-3,-1,11,2,0,0,0,7,3,-6,14,4,0,0,0,3,-14,-4,13,1,0,0,0,-6,-15,11,4,0,0,0,0,-6,-5,-7,-13,-5,0,0,8,1,-3,-9,-9,-14,0,0,5,11,2,2,0,-16,-2,0,0,0,-1,-3,-3,-8,0],[0,-1,-6,12,16,16,5,0,0,-1,-2,-6,-8,2,6,0,0,-6,-16,-11,-1,8,0,0,0,-2,-8,0,10,16,11,0,0,8,5,2,1,10,3,0,0,0,2,11,-9,-7,0,0,0,0,2,0,-15,-4,0,0,0,0,-1,-4,-4,0,0,0],[0,0,3,7,2,4,4,0,0,0,3,-4,-1,2,7,0,0,0,-6,-2,-1,1,9,0,0,5,11,7,-4,12,3,0,0,9,15,5,7,16,9,0,0,-7,-12,-4,16,6,0,0,0,-5,-14,-5,3,-2,0,0,0,0,4,10,-7,-13,-3,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,10,11,-1,-9,0,0,0,3,16,9,0,-9,0,0,0,7,11,-11,-8,4,0,0,0,-2,-4,-11,-16,6,1,0,0,8,12,-3,-16,1,8,0,0,5,13,-3,-16,-4,8,0,0,0,14,3,-16,0,8,0,0,0,7,10,-1,6,4,0],[0,0,10,-6,-7,2,0,0,0,3,6,0,-5,-8,-1,0,0,-1,-2,-9,-10,-16,-4,0,0,0,-2,5,0,-14,-6,0,0,-3,-1,10,9,-12,-7,0,0,-1,-15,-11,12,-11,-6,0,0,0,-7,-8,0,-11,0,0,0,0,9,3,-4,-4,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,-2,-13,-15,-5,-7,-11,0,0,-5,-16,-3,11,9,-1,0,0,-6,-12,7,13,9,0,0,0,1,0,-5,0,2,0,0,0,-3,-8,-1,5,-9,0,0,0,0,0,2,4,-7,0,0,0,0,-4,-8,0,4,0,0,0,-2,-16,-13,6,6,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,2,-1,-3,0,0,0,0,-7,0,3,3,0,0,0,-2,-9,0,13,0,0,0,0,-3,-3,4,12,0,0,0,0,2,0,2,8,0,0,0,0,-2,-5,0,-3,-16,-6,0,0,-1,-12,0,0,-16,-12,0,0,0,-2,2,1,-11,-7,0],[0,0,0,9,1,-6,0,0,0,0,4,3,-4,-9,0,0,0,0,5,-2,-14,4,3,0,0,-3,0,1,-12,14,10,0,0,8,11,5,-3,10,2,0,0,0,9,12,0,8,0,0,0,0,0,6,0,0,0,0,0,0,0,9,4,-9,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,-1,0,-1,0,0,0,0,-5,-2,2,9,14,0,0,0,-4,-3,8,1,10,1,0,0,-2,-3,0,0,1,0,0,0,0,-1,0,-2,3,6,0,0,0,0,0,4,3,3,0,0,0,0,-4,7,7,-10,0,0,0,-4,3,1,-6,-13,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], | ||
| "hyperplanes": [ | ||
| [0,0,0,-3,-1,11,2,0,0,0,7,3,-6,14,4,0,0,0,3,-14,-4,13,1,0,0,0,-6,-15,11,4,0,0,0,0,-6,-5,-7,-13,-5,0,0,8,1,-3,-9,-9,-14,0,0,5,11,2,2,0,-16,-2,0,0,0,-1,-3,-3,-8,0], | ||
| [0,-1,-6,12,16,16,5,0,0,-1,-2,-6,-8,2,6,0,0,-6,-16,-11,-1,8,0,0,0,-2,-8,0,10,16,11,0,0,8,5,2,1,10,3,0,0,0,2,11,-9,-7,0,0,0,0,2,0,-15,-4,0,0,0,0,-1,-4,-4,0,0,0], | ||
| [0,0,3,7,2,4,4,0,0,0,3,-4,-1,2,7,0,0,0,-6,-2,-1,1,9,0,0,5,11,7,-4,12,3,0,0,9,15,5,7,16,9,0,0,-7,-12,-4,16,6,0,0,0,-5,-14,-5,3,-2,0,0,0,0,4,10,-7,-13,-3,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,10,11,-1,-9,0,0,0,3,16,9,0,-9,0,0,0,7,11,-11,-8,4,0,0,0,-2,-4,-11,-16,6,1,0,0,8,12,-3,-16,1,8,0,0,5,13,-3,-16,-4,8,0,0,0,14,3,-16,0,8,0,0,0,7,10,-1,6,4,0], | ||
| [0,0,10,-6,-7,2,0,0,0,3,6,0,-5,-8,-1,0,0,-1,-2,-9,-10,-16,-4,0,0,0,-2,5,0,-14,-6,0,0,-3,-1,10,9,-12,-7,0,0,-1,-15,-11,12,-11,-6,0,0,0,-7,-8,0,-11,0,0,0,0,9,3,-4,-4,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,-2,-13,-15,-5,-7,-11,0,0,-5,-16,-3,11,9,-1,0,0,-6,-12,7,13,9,0,0,0,1,0,-5,0,2,0,0,0,-3,-8,-1,5,-9,0,0,0,0,0,2,4,-7,0,0,0,0,-4,-8,0,4,0,0,0,-2,-16,-13,6,6,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,0,2,-1,-3,0,0,0,0,-7,0,3,3,0,0,0,-2,-9,0,13,0,0,0,0,-3,-3,4,12,0,0,0,0,2,0,2,8,0,0,0,0,-2,-5,0,-3,-16,-6,0,0,-1,-12,0,0,-16,-12,0,0,0,-2,2,1,-11,-7,0], | ||
| [0,0,0,9,1,-6,0,0,0,0,4,3,-4,-9,0,0,0,0,5,-2,-14,4,3,0,0,-3,0,1,-12,14,10,0,0,8,11,5,-3,10,2,0,0,0,9,12,0,8,0,0,0,0,0,6,0,0,0,0,0,0,0,9,4,-9,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,-1,0,-1,0,0,0,0,-5,-2,2,9,14,0,0,0,-4,-3,8,1,10,1,0,0,-2,-3,0,0,1,0,0,0,0,-1,0,-2,3,6,0,0,0,0,0,4,3,3,0,0,0,0,-4,7,7,-10,0,0,0,-4,3,1,-6,-13,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], | ||
| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | ||
| ], | ||
| "offsets": [529.5,-244,-709,0,0,-164.5,896.5,0,0,640.5,0,0,381.5,-556,0,0,-263,0,0], | ||
| "children": [[1,12],[2,5],[3,4],[-0,-1],[-1,-1],[6,9],[7,8],[-2,-1],[-3,-1],[10,11],[-4,-1],[-5,-1],[13,16],[14,15],[-6,-1],[-7,-1],[17,18],[-8,-1],[-9,-1]], | ||
| "indices": [[7,17,23,27,43,44,52,61,81,83,86,89,94,98,-1],[2,13,38,50,51,53,54,57,60,75,77,-1,-1,-1,-1],[22,25,76,84,96,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1],[0,8,9,10,20,30,36,39,48,49,55,72,78,79,92],[12,18,28,40,47,56,59,62,63,69,80,-1,-1,-1,-1],[15,32,33,35,46,71,91,-1,-1,-1,-1,-1,-1,-1,-1],[41,68,87,97,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1],[1,3,11,19,21,31,42,45,70,85,90,93,99,-1,-1],[4,5,14,24,64,73,74,95,-1,-1,-1,-1,-1,-1,-1],[6,16,26,29,34,37,58,65,66,67,82,88,-1,-1,-1]]}; | ||
| "indices": [ | ||
| [7,17,23,27,43,44,52,61,81,83,86,89,94,98,-1], | ||
| [2,13,38,50,51,53,54,57,60,75,77,-1,-1,-1,-1], | ||
| [22,25,76,84,96,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1], | ||
| [0,8,9,10,20,30,36,39,48,49,55,72,78,79,92], | ||
| [12,18,28,40,47,56,59,62,63,69,80,-1,-1,-1,-1], | ||
| [15,32,33,35,46,71,91,-1,-1,-1,-1,-1,-1,-1,-1], | ||
| [41,68,87,97,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1], | ||
| [1,3,11,19,21,31,42,45,70,85,90,93,99,-1,-1], | ||
| [4,5,14,24,64,73,74,95,-1,-1,-1,-1,-1,-1,-1], | ||
| [6,16,26,29,34,37,58,65,66,67,82,88,-1,-1,-1], | ||
| ] | ||
| }; |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -3,0 +3,0 @@ Licensed under the Apache License, Version 2.0 (the "License"); |
@@ -1,9 +0,6 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
@@ -16,4 +13,10 @@ distributed under the License is distributed on an "AS IS" BASIS, | ||
| import { UMAP } from '../src/umap'; | ||
| import { testData, testResults2D, testResults3D } from './test_data'; | ||
| import { UMAP, findABParams, euclidean, TargetMetric } from '../src/umap'; | ||
| import * as utils from '../src/utils'; | ||
| import { | ||
| testData, | ||
| testLabels, | ||
| testResults2D, | ||
| testResults3D, | ||
| } from './test_data'; | ||
| import Prando from 'prando'; | ||
@@ -23,2 +26,8 @@ | ||
| let random: () => number; | ||
| // Expected "clustering" ratios, representing inter-cluster distance vs mean | ||
| // distance to other points. | ||
| const UNSUPERVISED_CLUSTER_RATIO = 0.15; | ||
| const SUPERVISED_CLUSTER_RATIO = 0.04; | ||
| beforeEach(() => { | ||
@@ -33,2 +42,3 @@ const prng = new Prando(42); | ||
| expect(embedding).toEqual(testResults2D); | ||
| checkClusters(embedding, testLabels, UNSUPERVISED_CLUSTER_RATIO); | ||
| }); | ||
@@ -40,2 +50,3 @@ | ||
| expect(embedding).toEqual(testResults3D); | ||
| checkClusters(embedding, testLabels, UNSUPERVISED_CLUSTER_RATIO); | ||
| }); | ||
@@ -70,3 +81,3 @@ | ||
| let nEpochsComputed = 0; | ||
| const embedding = await umap.fitAsync(testData, () => { | ||
| await umap.fitAsync(testData, () => { | ||
| nEpochsComputed += 1; | ||
@@ -93,4 +104,4 @@ }); | ||
| const umap = new UMAP({ random }); | ||
| umap.setPrecomputedKNN(knnIndices, knnDistances); | ||
| spyOn<any>(umap, 'nearestNeighbors'); | ||
| umap.initializeFit(testData, knnIndices, knnDistances); | ||
| umap.fit(testData); | ||
@@ -100,2 +111,84 @@ | ||
| }); | ||
| test('supervised projection', () => { | ||
| const umap = new UMAP({ random, nComponents: 2 }); | ||
| umap.setSupervisedProjection(testLabels); | ||
| const embedding = umap.fit(testData); | ||
| expect(embedding.length).toEqual(testResults2D.length); | ||
| checkClusters(embedding, testLabels, SUPERVISED_CLUSTER_RATIO); | ||
| }); | ||
| test('non-categorical supervised projection is not implemented', () => { | ||
| const umap = new UMAP({ random, nComponents: 2 }); | ||
| // Unimplemented target metric. | ||
| const targetMetric = TargetMetric.l1; | ||
| umap.setSupervisedProjection(testLabels, { targetMetric }); | ||
| const embedding = umap.fit(testData); | ||
| // Supervision with unimplemented target metric is a noop. | ||
| expect(embedding).toEqual(testResults2D); | ||
| }); | ||
| test('finds AB params using levenberg-marquardt', () => { | ||
| // The default parameters from the python implementation | ||
| const minDist = 0.1; | ||
| const spread = 1.0; | ||
| // The default results from the python sklearn curve-fitting algorithm | ||
| const a = 1.5769434603113077; | ||
| const b = 0.8950608779109733; | ||
| const epsilon = 0.01; | ||
| const params = findABParams(spread, minDist); | ||
| const diff = (x: number, y: number) => Math.abs(x - y); | ||
| expect(diff(params.a, a)).toBeLessThanOrEqual(epsilon); | ||
| expect(diff(params.b, b)).toBeLessThanOrEqual(epsilon); | ||
| }); | ||
| const computeMeanDistances = (vectors: number[][]) => { | ||
| return vectors.map(vector => { | ||
| return utils.mean( | ||
| vectors.map(other => { | ||
| return euclidean(vector, other); | ||
| }) | ||
| ); | ||
| }); | ||
| }; | ||
| /** | ||
| * Check the ratio between distances within a cluster and for all points to | ||
| * indicate "clustering" | ||
| */ | ||
| const checkClusters = ( | ||
| embeddings: number[][], | ||
| labels: number[], | ||
| expectedClusterRatio: number | ||
| ) => { | ||
| const distances = computeMeanDistances(embeddings); | ||
| const overallMeanDistance = utils.mean(distances); | ||
| const embeddingsByLabel = new Map<number, number[][]>(); | ||
| for (let i = 0; i < labels.length; i++) { | ||
| const label = labels[i]; | ||
| const embedding = embeddings[i]; | ||
| const group = embeddingsByLabel.get(label) || []; | ||
| group.push(embedding); | ||
| embeddingsByLabel.set(label, group); | ||
| } | ||
| let totalIntraclusterDistance = 0; | ||
| for (let label of embeddingsByLabel.keys()) { | ||
| const group = embeddingsByLabel.get(label)!; | ||
| const distances = computeMeanDistances(group); | ||
| const meanDistance = utils.mean(distances); | ||
| totalIntraclusterDistance += meanDistance * group.length; | ||
| } | ||
| const meanInterclusterDistance = | ||
| totalIntraclusterDistance / embeddings.length; | ||
| const clusterRatio = meanInterclusterDistance / overallMeanDistance; | ||
| expect(clusterRatio).toBeLessThan(expectedClusterRatio); | ||
| }; | ||
| }); |
@@ -1,2 +0,2 @@ | ||
| /* Copyright 2019 Google Inc. All Rights Reserved. | ||
| /* Copyright 2019 Google LLC. All Rights Reserved. | ||
@@ -66,2 +66,7 @@ Licensed under the Apache License, Version 2.0 (the "License"); | ||
| test('linear function', () => { | ||
| const results = utils.linear(0, 5, 5); | ||
| expect(results).toEqual([0, 1.25, 2.5, 3.75, 5]); | ||
| }); | ||
| test('sum function', () => { | ||
@@ -87,2 +92,1 @@ const results = utils.sum([1, 2, 3]); | ||
| }); | ||
@@ -17,2 +17,3 @@ { | ||
| "allowUnreachableCode": true, | ||
| "downlevelIteration": true, | ||
| "lib": ["dom", "es2015", "es2016", "es2017"] | ||
@@ -19,0 +20,0 @@ }, |
| import * as path from 'path'; | ||
| import * as DtsBundleWebpack from 'dts-bundle-webpack'; | ||
@@ -9,5 +8,5 @@ export default { | ||
| { | ||
| test: /\.ts$/, | ||
| test: /(\.ts$|\.js$)/, | ||
| exclude: /node_modules/, | ||
| use: 'ts-loader', | ||
| loader: 'ts-loader', | ||
| }, | ||
@@ -17,6 +16,6 @@ ], | ||
| resolve: { | ||
| extensions: ['.ts'], | ||
| extensions: ['.ts', '.js'], | ||
| }, | ||
| entry: { | ||
| lib: './src/lib.ts', | ||
| lib: path.resolve(__dirname, '../src/lib.ts'), | ||
| }, | ||
@@ -28,9 +27,2 @@ output: { | ||
| optimization: { minimize: false }, | ||
| plugins: [ | ||
| new DtsBundleWebpack({ | ||
| name: 'umap-js', | ||
| main: 'dist/umap.d.ts', | ||
| out: '../lib/umap-js.d.ts', | ||
| }), | ||
| ], | ||
| }; |
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New alerts
Major refactor
Supply chain riskPackage has recently undergone a major refactor. It may be unstable or indicate significant internal changes. Use caution when updating to versions that include significant changes.
Found 1 instance in 1 package
Improved metrics
- Total package byte prevSize
- increased by2.81%
606953
- Dev dependency count
- decreased by-8.33%
11
- Number of lines in readme file
- increased by17.28%
95
Worsened metrics
- Dependency count
- increased byInfinity%
1
- Lines of code
- decreased by-10.38%
10324
- Number of medium supply chain risk alerts
- increased byInfinity%
1
Dependency changes
+ Addedis-any-array@2.0.1(transitive)
+ Addedml-array-max@1.2.4(transitive)
+ Addedml-array-min@1.2.3(transitive)
+ Addedml-array-rescale@1.3.7(transitive)
+ Addedml-levenberg-marquardt@1.0.4(transitive)
+ Addedml-matrix@5.3.0(transitive)