How to use variance method in Best

Best JavaScript code snippet using best

variance_chart.js

Source:variance_chart.js

1/**2 * Makes a variance chart from a file of variance data for principal components of one frequency3 * @param  {string} variance_datafile_name The name of the variance datafile in the data folder4 * @param  {string} dataset The name of the principal component datafile in the data folder5 * @param  {string} freq_sing_val_file The name of the datafile in the data folder holding the frequency's singular value data6 * @param  {array} selected_pca The array that will store selected principal components as they are selected. Begins empty.7 * @param  {svg} varchart The D3 created SVG (on a div in the html) that will contain the variance chart.8 * @param  {svg} heatmapsvg The D3 created SVG (on a div in the html) that will contain the heatmap for this variance chart's frequency9 * @param  {svg} heatmapsvg2 The D3 created SVG (on a div in the html) that will contain the heatmap excluded selected pca low-rank approximation10 * @param  {bool} lowrank Whether the heatmap this chart is tied to represents a low rank approx of data (if True) or multiple principal components side by side (if False)11 * @param  {array} whole_pca The array that will store all pca elements12 * @return {None}      Does not return anything, just manipulates svgs passed as parameters13 */14async function make_variance_chart(variance_datafile_name, dataset, freq_sing_val_file, selected_pca, varchart, heatmapsvg, heatmapsvg2, lowrank, whole_pca){15    // put loading label while data are loading16    /*17      const loading_message = varchart.append('text')18      .attr('id', 'var_loading_message')19      .attr('x', 100)20      .attr('y', 200)21      .attr('fill', '#ccc')22      .attr('font-family', 'Helvetica Neue, Arial')23      .attr('font-weight', 500)24      .attr('font-size', 60)25      .text("Loading...");26      */27      // Y label28      varchart.append("text")29      .attr("class", "y label")30      .attr("text-anchor", "start")31      .attr("y", -50)32      .attr("x", -150)33      .attr("dy", ".75em")34      .attr("font-size", 15)35      .attr("transform", "rotate(-90)")36      .text("% Variance Explained");37      // X label38    varchart.append("text")39        .attr("class", "x label")40        .attr("text-anchor", "end")41        .attr("x", width)42        .attr("y", height + 40)43        .attr("font-size", 15)44        .text("Component Number");45    // title46    varchart.append("text")47      .attr("x", (width / 2))48      .attr("y", 0 - (margin.top / 2))49      .attr("text-anchor", "middle")50      .style("font-size", 15)51      .attr("font-weight",700)52      .text("% Explained Variance Per Component");53    // Adding the second plot: explained variance54    d3.csv("data/"+ variance_datafile_name)55    .then(function (data) {56      data.forEach(function(d) {57      d.num = parseInt(d.num);58      d.variance = parseFloat(d.variance)});59      // Set x and y axis60      var	var_x = d3.scaleLinear().domain([0, d3.max(data, function(d) { return d.num; })]).range([0, width]);61      var	var_y = d3.scaleLinear().domain([0, d3.max(data, function(d) { return d.variance; })]).range([height, 0]);62      varchart.append("g")63      .attr("transform", "translate(0," + height + ")")64      .call(d3.axisBottom(var_x));65      varchart.append("g")66      .call(d3.axisLeft(var_y));67      var total_variance_captured = 0;68     // Add dots69      varchart.append('g')70        .selectAll("dot")71        .data(data)72        .enter()73        .append("circle")74          .attr("cx", function (d) { return var_x(d.num); } )75          .attr("cy", function (d) { return var_y(d.variance); } )76          .attr("r", 3.5)77          .style("fill", "#69b3a2")78        .on("mouseover", function(d,i) {79                d3.select(this).transition()80               .duration('10')81               .attr("r", 5)82        })83        .on('mouseout', function() {84               d3.select(this).transition()85               .duration('100')86               .attr("r", 3.5)87        })88        .on("click", function(d, i)89        {90          // get component number (stored in 0 indexed matrix so subtract 1)91          component = i.num - 1;92          // get variance93          component_variance = i.variance.toFixed(4);94          // get component data95          //pc_data = get_pc_data(component, 120);96          curr_color = d3.select(this).style("fill");97          if (document.querySelectorAll(".selected").length == 0){98            total_variance_captured = 099            selected_pca = []100          }101          if (document.querySelectorAll(".selected").length == 62){102            total_variance_captured = 1103            const range = (start, end, length = end - start + 1) =>104            Array.from({ length }, (_, i) => start + i)105            selected_pca = range(0, 61);106          }107          if (curr_color == "rgb(105, 179, 162)"){ // if point is being highlighted108            d3.select(this).style("fill", "rgb(254, 211, 72)");109            d3.select(this).attr("class","selected");110            total_variance_captured = parseFloat(total_variance_captured) + parseFloat(component_variance);111            selected_pca.push(component);112          } else if (curr_color == "rgb(254, 211, 72)"){ // if point is being de-selected113            d3.select(this).style("fill", "rgb(105, 179, 162)");114            d3.select(this).attr("class","");115            total_variance_captured = parseFloat(total_variance_captured) - parseFloat(component_variance);116            var index = selected_pca.indexOf(component);117            selected_pca.splice(index, 1);118          }119          // to make sure -0 never happens120          total_variance_captured = Math.abs(total_variance_captured);121          //clear heatmap122          d3.select(heatmapsvg.node())123            .selectAll("text")124            .remove();125          d3.select(heatmapsvg.node())126            .selectAll("rect")127            .remove();128          d3.select(heatmapsvg2.node())129          .selectAll("text")130          .remove();131          d3.select(heatmapsvg2.node())132            .selectAll("rect")133            .remove();134          // change heatmap135          if (selected_pca.length > 0) {136            rest_pca = whole_pca.filter( function( el ) {137              return selected_pca.indexOf( el ) < 0;138            });139          } else {140            rest_pca = [];141          }142          heatmap(dataset, freq_sing_val_file, selected_pca, heatmapsvg, lowrank, true, total_variance_captured);143          heatmap(dataset, freq_sing_val_file, rest_pca, heatmapsvg2, lowrank, false, total_variance_captured);144          // change variance explained label145          if (total_variance_captured == 0){146            variance_explained_label.text("0%");147          } else {148          variance_explained_label.text((total_variance_captured*100).toFixed(2) + "%");149          }150        })151      // total variance explained label152      const variance_explained_label = varchart.append('text')153        .attr('class', 'year')154        .attr('x', 200)155        .attr('y', 100)156        .attr('fill', '#ccc')157        .attr('font-family', 'Helvetica Neue, Arial')158        .attr('font-weight', 500)159        .attr('font-size', 60)160        .text(total_variance_captured + "%");161        });...

matrixMoments.js

Source:matrixMoments.js

1function columns2D(a,columns) {2	const {thread:{x},constants:{size}}=this,column=columns[x];3	let sum=0,sumSquared=0,sumCubed=0;4	for(let i=0;i<size; i++) {5		const v=a[i][column],v2=v*v,v3=v2*v;6		sum+=v;7		sumSquared+=v2;8		sumCubed+=v3;9	}10	const average=sum/size,11		variance=(sumSquared/size)-average**2,12		stdDev=Math.sqrt(variance),13		skew=(sumCubed-3*average*variance-average**3)/(variance*stdDev);14	return [average,variance,skew];15}16function columns3D(a) {17	const {thread:{x},constants:{size}}=this;18	let sum=0,sumSquared=0,sumCubed=0;19	for(let i=0;i<size; i++) {20		const v=a[i][x],v2=v*v,v3=v2*v;21		sum+=v;22		sumSquared+=v2;23		sumCubed+=v3;24	}25	const average=sum/size,26		variance=(sumSquared/size)-average**2,27		stdDev=Math.sqrt(variance),28		skew=(sumCubed-3*average*variance-average**3)/(variance*stdDev);29	return [average,variance,skew];30}31function rows2D(a,rows) {32	const {thread:{x},constants:{size}}=this,row=rows[x];33	let sum=0,sumSquared=0,sumCubed=0;34	for(let i=0;i<size; i++) {35		const v=a[row][i],v2=v*v,v3=v2*v;36		sum+=v;37		sumSquared+=v2;38		sumCubed+=v3;39	}40	const average=sum/size,41		variance=(sumSquared/size)-average**2,42		stdDev=Math.sqrt(variance),43		skew=(sumCubed-3*average*variance-average**3)/(variance*stdDev);44	return [average,variance,skew];45}46function rows3D(a) {47	const {thread:{x},constants:{size}}=this;48	let sum=0,sumSquared=0,sumCubed=0;49	for(let i=0;i<size; i++) {50		const v=a[x][i],v2=v*v,v3=v2*v;51		sum+=v;52		sumSquared+=v2;53		sumCubed+=v3;54	}55	const average=sum/size,56		variance=(sumSquared/size)-average**2,57		stdDev=Math.sqrt(variance),58		skew=(sumCubed-3*average*variance-average**3)/(variance*stdDev);59	return [average,variance,skew];60}61module.exports={62		columns2D:columns2D,63		columns3D:columns3D,64		rows2D:rows2D,65		rows3D:rows3D
...

variance-test.js

Source:variance-test.js

...5suite.addBatch({6  "variance": {7    topic: load("arrays/variance").expression("d3.variance"),8    "returns the variance value for numbers": function(variance) {9      assert.isUndefined(variance([1]));10      assert.equal(variance([5, 1, 2, 3, 4]), 2.5);11      assert.equal(variance([20, 3]), 144.5);12      assert.equal(variance([3, 20]), 144.5);13    },14    "ignores null, undefined and NaN": function(variance) {15      assert.equal(variance([NaN, 1, 2, 3, 4, 5]), 2.5);16      assert.equal(variance([1, 2, 3, 4, 5, NaN]), 2.5);17      assert.equal(variance([10, null, 3, undefined, 5, NaN]), 13);18    },19    "can handle large numbers without overflowing": function(variance) {20      assert.equal(variance([Number.MAX_VALUE, Number.MAX_VALUE]), 0);21      assert.equal(variance([-Number.MAX_VALUE, -Number.MAX_VALUE]), 0);22    },23    "returns undefined for empty array": function(variance) {24      assert.isUndefined(variance([]));25      assert.isUndefined(variance([null]));26      assert.isUndefined(variance([undefined]));27      assert.isUndefined(variance([NaN]));28      assert.isUndefined(variance([NaN, NaN]));29    },30    "applies the optional accessor function": function(variance) {31      assert.equal(variance([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10]], mean), 4.5);32      assert.equal(variance([1, 2, 3, 4, 5], function(d, i) { return i; }), 2.5);33    }34  }35});36function mean(array) {37  return array.reduce(function(p, v) { return p + v; }) / array.length;38}...

Using AI Code Generation

1var x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9];2var y = [1, 3, 2, 5, 7, 8, 8, 9, 10, 12];3var n = 10;4var sumX = 0;5var sumY = 0;6var sumXY = 0;7var sumX2 = 0;8var sumY2 = 0;9var i = 0;10var meanX = 0;11var meanY = 0;12var slope = 0;13var intercept = 0;14var r = 0;15var r2 = 0;16for (i = 0; i < n; i++) {17    sumX += x[i];18    sumY += y[i];19    sumXY += x[i] * y[i];20    sumX2 += x[i] * x[i];21    sumY2 += y[i] * y[i];22}23meanX = sumX / n;24meanY = sumY / n;25slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);26intercept = meanY - slope * meanX;27r = (n * sumXY - sumX * sumY) / Math.sqrt((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY));28r2 = r * r;29console.log("slope: " + slope);30console.log("intercept: " + intercept);31console.log("r: " + r);32console.log("r2: " + r2);

Using AI Code Generation

1var BestFitLine = require('./test3.js');2var line = new BestFitLine();3line.addDataPoint(0,0);4line.addDataPoint(1,1);5line.addDataPoint(2,2);6line.addDataPoint(3,3);7line.addDataPoint(4,4);8line.addDataPoint(5,5);9line.addDataPoint(6,6);10line.addDataPoint(7,7);11line.addDataPoint(8,8);12line.addDataPoint(9,9);13console.log(line.slope);14console.log(line.intercept);15console.log(line.rSquared);16var BestFitLine = function() {17  this.n = 0;18  this.sumX = 0;19  this.sumY = 0;20  this.sumXX = 0;21  this.sumXY = 0;22  this.slope = 0;23  this.intercept = 0;24  this.rSquared = 0;25};26BestFitLine.prototype.addDataPoint = function(x, y) {27  this.n++;28  this.sumX += x;29  this.sumY += y;30  this.sumXX += x * x;31  this.sumXY += x * y;32  this.slope = (this.n * this.sumXY - this.sumX * this.sumY) /33               (this.n * this.sumXX - this.sumX * this.sumX);34  this.intercept = (this.sumY - this.slope * this.sumX) / this.n;35  this.rSquared = Math.pow((this.n * this.sumXY - this.sumX * this.sumY) /36                           Math.sqrt((this.n * this.sumXX - this.sumX * this.sumX) *37                                     (this.n * this.sumYY - this.sumY * this.sumY)), 2);38};39BestFitLine.prototype.toString = function() {40  return 'y = ' + this.slope.toFixed(2) + 'x + ' + this.intercept.toFixed(2);41};42module.exports = BestFitLine;43var BestFitLine = require('./test3.js');44var line = new BestFitLine();45line.addDataPoint(0,0);46line.addDataPoint(1,1);47line.addDataPoint(2,2);48line.addDataPoint(3,3);49line.addDataPoint(4,4);50line.addDataPoint(5,5);51line.addDataPoint(6,6

Using AI Code Generation

1var BestFitLine = require("./BestFitLine");2var bfl = new BestFitLine();3bfl.readData("test4.dat");4console.log("Data read from file: ");5bfl.printData();6bfl.calcVariance();7bfl.printResults();8console.log("Best fit line: ");9bfl.printBestFitLine();

Using AI Code Generation

1var BFLV = require('./BFLV.js');2var BFLC = require('./BFLC.js');3var BFL = require('./BFL.js');4var x = [1, 2, 3, 4, 5];5var y = [1, 2, 3, 4, 5];6var bflv = new BFLV(x, y);7console.log(bflv.toString());8var bflc = new BFLC(x, y);9console.log(bflc.toString());10var bfl = new BFL(x, y);11console.log(bfl.toString());

Using AI Code Generation

1function bestFitLine(dataPoints, returnedValues) {2  var sumX = 0;3  var sumY = 0;4  var sumXY = 0;5  var sumX2 = 0;6  var n = dataPoints.length;7  for (var i = 0; i < n; i++) {8    sumX += dataPoints[i][0];9    sumY += dataPoints[i][1];10    sumXY += dataPoints[i][0] * dataPoints[i][1];11    sumX2 += dataPoints[i][0] * dataPoints[i][0];12  }13  var slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);14  var yIntercept = (sumY - slope * sumX) / n;15  returnedValues[0] = slope;16  returnedValues[1] = yIntercept;17}18];19var returnedValues = [];20bestFitLine(dataPoints, returnedValues);21document.write("slope: ", returnedValues[0], "<br>");22document.write("y-intercept: ", returnedValues[1], "<br>");

Using AI Code Generation

1var data = readFile("data.txt");2var line = getBestFitLine(data);3var y = line.predict(10);4var actualY = 100;5var error = y - actualY;6print("Predicted y value: " + y);7print("Actual y value: " + actualY);8print("Error: " + error);9var data = readFile("data.txt");10var line = getBestFitLine(data, "leastSquares");11var y = line.predict(10);12var actualY = 100;13var error = y - actualY;

Using AI Code Generation

1var dataSort = [];2for (var i = 0; i < data2.length; i++) {3    dataSort.push(data2[i]);4}5for (var i = 0; i < data3.length; i++) {6    dataSort.push(data3[i]);7}8dataSort.sort(function (a, b) {9    return a.independent - b.independent;10});11var sumX = 0;12var sumY = 0;13var sumXSquared = 0;14var sumYSquared = 0;15var sumXY = 0;

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