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value | code stringlengths 52 373k | code_tokens list | docstring stringlengths 4 11.8k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 86 226 |
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22,600 | repetere/modelscript | build/modelscript.umd.js | calculateDistanceMatrix | function calculateDistanceMatrix(data, distance) {
var distanceMatrix = new Array(data.length);
for (var i = 0; i < data.length; ++i) {
for (var j = i; j < data.length; ++j) {
if (!distanceMatrix[i]) {
distanceMatrix... | javascript | function calculateDistanceMatrix(data, distance) {
var distanceMatrix = new Array(data.length);
for (var i = 0; i < data.length; ++i) {
for (var j = i; j < data.length; ++j) {
if (!distanceMatrix[i]) {
distanceMatrix... | [
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22,601 | repetere/modelscript | build/modelscript.umd.js | updateClusterID | function updateClusterID(data, centers, clusterID, distance) {
for (var i = 0; i < data.length; i++) {
clusterID[i] = src$5(centers, data[i], {distanceFunction: distance});
}
return clusterID;
} | javascript | function updateClusterID(data, centers, clusterID, distance) {
for (var i = 0; i < data.length; i++) {
clusterID[i] = src$5(centers, data[i], {distanceFunction: distance});
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return clusterID;
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22,602 | repetere/modelscript | build/modelscript.umd.js | updateCenters | function updateCenters(data, clusterID, K) {
const nDim = data[0].length;
// creates empty centers with 0 size
var centers = new Array(K);
var centersLen = new Array(K);
for (var i = 0; i < K; i++) {
centers[i] = new Ar... | javascript | function updateCenters(data, clusterID, K) {
const nDim = data[0].length;
// creates empty centers with 0 size
var centers = new Array(K);
var centersLen = new Array(K);
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22,603 | repetere/modelscript | build/modelscript.umd.js | converged | function converged(centers, oldCenters, distanceFunction, tolerance) {
for (var i = 0; i < centers.length; i++) {
if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {
return false;
}
}
return true;
... | javascript | function converged(centers, oldCenters, distanceFunction, tolerance) {
for (var i = 0; i < centers.length; i++) {
if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {
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22,604 | repetere/modelscript | build/modelscript.umd.js | clone | function clone(arr) {
var newArr = [];
for (var i=0; i<arr.length; i++) {
newArr.push(arr[i]);
}
return newArr;
} | javascript | function clone(arr) {
var newArr = [];
for (var i=0; i<arr.length; i++) {
newArr.push(arr[i]);
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22,605 | repetere/modelscript | build/modelscript.umd.js | pick | function pick() {
if (this._remainingOptions.length === 0) {
this._remainingOptions = clone(this._originalOptions);
}
var index = Math.floor(Math.random() * this._remainingOptions.length);
return this._remainingOptions.splice(index... | javascript | function pick() {
if (this._remainingOptions.length === 0) {
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}
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22,606 | repetere/modelscript | build/modelscript.umd.js | random$1 | function random$1(data, K) {
const rand = new Picker$1(data);
var ans = new Array(K);
for (var i = 0; i < K; ++i) {
ans[i] = rand.pick();
}
return ans;
} | javascript | function random$1(data, K) {
const rand = new Picker$1(data);
var ans = new Array(K);
for (var i = 0; i < K; ++i) {
ans[i] = rand.pick();
}
return ans;
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@param {Array<Array<Number>>} data - Points in the format to cluster [x,y,z,...]
@param {Number} K - Number of clusters
@return {Array<Array<Number>>} - Initial random points | [
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22,607 | repetere/modelscript | build/modelscript.umd.js | KMeansResult | function KMeansResult(clusters, centroids, converged, iterations, distance) {
this.clusters = clusters;
this.centroids = centroids;
this.converged = converged;
this.iterations = iterations;
this[distanceSymbol] = distance;
} | javascript | function KMeansResult(clusters, centroids, converged, iterations, distance) {
this.clusters = clusters;
this.centroids = centroids;
this.converged = converged;
this.iterations = iterations;
this[distanceSymbol] = distance;
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@param {Array<Number>} clusters - the cluster identifier for each data dot
@param {Array<Array<Object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster
@param {Boolean} converged - Converge criteria satisfied
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22,608 | repetere/modelscript | build/modelscript.umd.js | errorCalculation | function errorCalculation(
data,
parameters,
parameterizedFunction
) {
var error = 0;
const func = parameterizedFunction(parameters);
for (var i = 0; i < data.x.length; i++) {
error += Math.abs(data.y[i] - f... | javascript | function errorCalculation(
data,
parameters,
parameterizedFunction
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var error = 0;
const func = parameterizedFunction(parameters);
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error += Math.abs(data.y[i] - f... | [
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@param {Array<number>} parameters - Array of current parameter values
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22,609 | repetere/modelscript | build/modelscript.umd.js | gradientFunction | function gradientFunction(
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params,
gradientDifference,
paramFunction
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const n = params.length;
const m = data.x.length;
var ans = new Array(n);
for (var par... | javascript | function gradientFunction(
data,
evaluatedData,
params,
gradientDifference,
paramFunction
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const n = params.length;
const m = data.x.length;
var ans = new Array(n);
for (var par... | [
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22,610 | repetere/modelscript | build/modelscript.umd.js | step$1 | function step$1(
data,
params,
damping,
gradientDifference,
parameterizedFunction
) {
var identity = require$$0$2.Matrix.eye(params.length).mul(
damping * gradientDifference * gradientDifference
... | javascript | function step$1(
data,
params,
damping,
gradientDifference,
parameterizedFunction
) {
var identity = require$$0$2.Matrix.eye(params.length).mul(
damping * gradientDifference * gradientDifference
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22,611 | repetere/modelscript | build/modelscript.umd.js | levenbergMarquardt | function levenbergMarquardt(
data,
parameterizedFunction,
options = {}
) {
let {
maxIterations = 100,
gradientDifference = 10e-2,
damping = 0,
errorTolerance = 10e-3,
initi... | javascript | function levenbergMarquardt(
data,
parameterizedFunction,
options = {}
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let {
maxIterations = 100,
gradientDifference = 10e-2,
damping = 0,
errorTolerance = 10e-3,
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22,612 | repetere/modelscript | build/modelscript.umd.js | createTree | function createTree (X, Y, from, to, minWindow, threshold) {
minWindow = minWindow || 0.16;
threshold = threshold || 0.01;
if ((to - from) < minWindow)
return undefined;
var sum = 0;
for (var i = 0; X[i] < to; i++) {
... | javascript | function createTree (X, Y, from, to, minWindow, threshold) {
minWindow = minWindow || 0.16;
threshold = threshold || 0.01;
if ((to - from) < minWindow)
return undefined;
var sum = 0;
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22,613 | repetere/modelscript | build/modelscript.umd.js | S | function S(a, b, alpha, beta, gamma) {
if (a === undefined || b === undefined) {
return 0;
}
else {
var C = (alpha*Math.min(a.sum, b.sum)/Math.max(a.sum, b.sum)+ (1-alpha)*Math.exp(-gamma*Math.abs(a.center - b.center)));
... | javascript | function S(a, b, alpha, beta, gamma) {
if (a === undefined || b === undefined) {
return 0;
}
else {
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@param {{sum: number, center: number, left: {json}, right: {json}}} a - tree A node
@param {{sum: number, center: number, left: {json}, right: {json}}} b - tree B node
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22,614 | repetere/modelscript | build/modelscript.umd.js | tree | function tree(A, B, from, to, options) {
options = options || {};
for (var o in defaultOptions$n)
if (!options.hasOwnProperty(o)) {
options[o] = defaultOptions$n[o];
}
var Atree, Btree;
if (A.... | javascript | function tree(A, B, from, to, options) {
options = options || {};
for (var o in defaultOptions$n)
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options[o] = defaultOptions$n[o];
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22,630 | repetere/modelscript | build/modelscript.umd.js | getEquallySpacedSlot | function getEquallySpacedSlot(x, y, from, to, numberOfPoints) {
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var step = (to - from) / (numberOfPoints - 1);
var halfStep = step / 2;
var lastStep = x[x.length - 1] - x[x.length - 2];
var start = from - halfStep;... | javascript | function getEquallySpacedSlot(x, y, from, to, numberOfPoints) {
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22,631 | repetere/modelscript | build/modelscript.umd.js | integral | function integral(x0, x1, slope, intercept) {
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22,632 | repetere/modelscript | build/modelscript.umd.js | baseRange$1 | function baseRange$1(start, end, step, fromRight) {
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22,633 | repetere/modelscript | build/modelscript.umd.js | coefficientOfCorrelation | function coefficientOfCorrelation(actuals = [], estimates = []) {
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const sumX = sum(actuals);
const sumY = sum(estimates);
const sumProdXY = actuals.reduce((resul... | javascript | function coefficientOfCorrelation(actuals = [], estimates = []) {
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22,634 | repetere/modelscript | build/modelscript.umd.js | pivotVector | function pivotVector(vectors=[]) {
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... | javascript | function pivotVector(vectors=[]) {
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returnArray.push(arrays[ i ][ index ]);
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22,636 | repetere/modelscript | build/modelscript.umd.js | MinMaxScalerTransforms | function MinMaxScalerTransforms(vector = [], nan_value = -1, return_nan=false) {
const average = avg$2(vector);
const standard_dev = sd(vector);
const maximum = max$1(vector);
const minimum = min$1(vector);
const scale = (z) => {
... | javascript | function MinMaxScalerTransforms(vector = [], nan_value = -1, return_nan=false) {
const average = avg$2(vector);
const standard_dev = sd(vector);
const maximum = max$1(vector);
const minimum = min$1(vector);
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22,637 | repetere/modelscript | build/modelscript.umd.js | approximateZPercentile | function approximateZPercentile(z, alpha=true) {
// If z is greater than 6.5 standard deviations from the mean
// the number of significant digits will be outside of a reasonable
// range.
if (z < -6.5)
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// If z is greater than 6.5 standard deviations from the mean
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if (z < -6.5)
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22,638 | repetere/modelscript | build/modelscript.umd.js | trackingSignal | function trackingSignal(actuals, estimates) {
const runningSumOfForecastErrors = sum(forecastErrors(actuals, estimates));
const MAD = meanAbsoluteDeviation(actuals, estimates);
return runningSumOfForecastErrors / MAD;
} | javascript | function trackingSignal(actuals, estimates) {
const runningSumOfForecastErrors = sum(forecastErrors(actuals, estimates));
const MAD = meanAbsoluteDeviation(actuals, estimates);
return runningSumOfForecastErrors / MAD;
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22,639 | repetere/modelscript | build/modelscript.umd.js | FPNode | function FPNode(item, parent) {
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if (parent === void 0) { parent = null; }
this.item = item;
this.parent = parent;
/**
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22,640 | repetere/modelscript | build/modelscript.umd.js | FPTree | function FPTree(supports, _support) {
this.supports = supports;
this._support = _support;
/**
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this._isInit = false;
/**
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this.supports = supports;
this._support = _support;
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22,641 | repetere/modelscript | build/modelscript.umd.js | function(len) {
if(len === undefined) len=16;
var entropy = fs.randomBytes(len);
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for(var i=0; i<len; i++) {
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var entropy = fs.randomBytes(len);
var result = 0;
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22,642 | repetere/modelscript | build/modelscript.umd.js | function(n, loc, scale) {
n = this._v(n, "n");
loc = this._v(loc, "r", 0);
scale = this._v(scale, "nn", 1);
var toReturn = [];
for(var i=0; i<n; i++) {
var core = this.sample([-1,1])[0] * ln(thi... | javascript | function(n, loc, scale) {
n = this._v(n, "n");
loc = this._v(loc, "r", 0);
scale = this._v(scale, "nn", 1);
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22,643 | repetere/modelscript | build/modelscript.umd.js | function(x, min, max) {
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... | javascript | function(x, min, max) {
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min = this._v(min, "r", 0);
max = this._v(max, "r", 1);
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22,680 | repetere/modelscript | build/modelscript.umd.js | function(anchor, v1, v2) {
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22,681 | repetere/modelscript | build/modelscript.umd.js | function(obj) {
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return (this.contains(P) ? P : null);
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22,690 | repetere/modelscript | build/modelscript.umd.js | function(obj) {
if (obj.normal) {
// obj is a plane
var V = this.line.intersectionWith(obj);
if (V === null) { return null; }
return this.pointClosestTo(V);
} else {
// obj is a line (segment) or po... | javascript | function(obj) {
if (obj.normal) {
// obj is a plane
var V = this.line.intersectionWith(obj);
if (V === null) { return null; }
return this.pointClosestTo(V);
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22,691 | repetere/modelscript | build/modelscript.umd.js | function(startPoint, endPoint) {
startPoint = vector.create(startPoint).to3D();
endPoint = vector.create(endPoint).to3D();
if (startPoint === null || endPoint === null) { return null; }
this.line = line.create(startPoint, endPoint.subtract(startPoint));
... | javascript | function(startPoint, endPoint) {
startPoint = vector.create(startPoint).to3D();
endPoint = vector.create(endPoint).to3D();
if (startPoint === null || endPoint === null) { return null; }
this.line = line.create(startPoint, endPoint.subtract(startPoint));
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22,692 | repetere/modelscript | build/modelscript.umd.js | createCentroids | function createCentroids(k) {
var Centroid = [];
var maxes = this.Observations.maxColumns();
//console.log(maxes);
for(var i = 1; i <= k; i++) {
var centroid = [];
for(var j = 1; j <= this.Observations.cols(); j++) ... | javascript | function createCentroids(k) {
var Centroid = [];
var maxes = this.Observations.maxColumns();
//console.log(maxes);
for(var i = 1; i <= k; i++) {
var centroid = [];
for(var j = 1; j <= this.Observations.cols(); j++) ... | [
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22,693 | repetere/modelscript | build/modelscript.umd.js | distanceFrom | function distanceFrom(Centroids) {
var distances = [];
for(var i = 1; i <= this.Observations.rows(); i++) {
var distance = [];
for(var j = 1; j <= Centroids.rows(); j++) {
distance.push(this.Observations.row(i).distanceFro... | javascript | function distanceFrom(Centroids) {
var distances = [];
for(var i = 1; i <= this.Observations.rows(); i++) {
var distance = [];
for(var j = 1; j <= Centroids.rows(); j++) {
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22,694 | repetere/modelscript | build/modelscript.umd.js | cluster | function cluster(k) {
var Centroids = this.createCentroids(k);
var LastDistances = Matrix$d.Zero(this.Observations.rows(), this.Observations.cols());
var Distances = this.distanceFrom(Centroids);
var Groups;
while(!(LastDistances.eql(Dista... | javascript | function cluster(k) {
var Centroids = this.createCentroids(k);
var LastDistances = Matrix$d.Zero(this.Observations.rows(), this.Observations.cols());
var Distances = this.distanceFrom(Centroids);
var Groups;
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22,695 | repetere/modelscript | build/modelscript.umd.js | function (str) {
var allPairs = [], pairs;
var words = str.split(/\s+/);
for (var i = 0; i < words.length; i++) {
pairs = letterPairs(words[i]);
allPairs.push.apply(allPairs, pairs);
}
return allPairs;
} | javascript | function (str) {
var allPairs = [], pairs;
var words = str.split(/\s+/);
for (var i = 0; i < words.length; i++) {
pairs = letterPairs(words[i]);
allPairs.push.apply(allPairs, pairs);
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return allPairs;
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22,696 | repetere/modelscript | build/modelscript.umd.js | function (str1, str2) {
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var sanitized_str2 = sanitize(str2);
var pairs1 = wordLetterPairs(sanitized_str1);
var pairs2 = wordLetterPairs(sanitized_str2);
var intersection = 0, union = pairs1.length + pairs2.length;... | javascript | function (str1, str2) {
var sanitized_str1 = sanitize(str1);
var sanitized_str2 = sanitize(str2);
var pairs1 = wordLetterPairs(sanitized_str1);
var pairs2 = wordLetterPairs(sanitized_str2);
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22,697 | repetere/modelscript | build/modelscript.umd.js | function(tokens) {
if(typeof tokens === "string") {
tokens = [tokens];
}
var results = [];
var rule_count = rules$3.length;
var num_tokens = tokens.length;
var i, token, r, rule;
... | javascript | function(tokens) {
if(typeof tokens === "string") {
tokens = [tokens];
}
var results = [];
var rule_count = rules$3.length;
var num_tokens = tokens.length;
var i, token, r, rule;
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22,698 | repetere/modelscript | build/modelscript.cjs.js | StandardScalerTransforms | function StandardScalerTransforms(vector = [], nan_value = -1, return_nan=false) {
const average = avg(vector);
const standard_dev = sd(vector);
const maximum = max(vector);
const minimum = min(vector);
const scale = (z) => {
const scaledValue = (z - average) / standard_dev;
if (isNaN(scaledVal... | javascript | function StandardScalerTransforms(vector = [], nan_value = -1, return_nan=false) {
const average = avg(vector);
const standard_dev = sd(vector);
const maximum = max(vector);
const minimum = min(vector);
const scale = (z) => {
const scaledValue = (z - average) / standard_dev;
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22,699 | repetere/modelscript | build/modelscript.cjs.js | assocationRuleLearning | function assocationRuleLearning(transactions =[], options) {
return new Promise((resolve, reject) => {
try {
const config = Object.assign({}, {
support: 0.4,
minLength: 2,
summary: true,
valuesMap: new Map(),
}, options);
const fpgrowth = new FPGrowth(con... | javascript | function assocationRuleLearning(transactions =[], options) {
return new Promise((resolve, reject) => {
try {
const config = Object.assign({}, {
support: 0.4,
minLength: 2,
summary: true,
valuesMap: new Map(),
}, options);
const fpgrowth = new FPGrowth(con... | [
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@param {Array} transactions - sparse matrix of transactions
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