646 lines
17 KiB
JavaScript
646 lines
17 KiB
JavaScript
// Get the width and heigh of the SVG element.
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var width = +document.getElementById('svg').clientWidth,
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height = +document.getElementById('svg').clientHeight;
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var svg = d3.select("svg")
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.call(d3.zoom().scaleExtent([0.0001, 1000000]).on("zoom", function () {
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svg.attr("transform", d3.event.transform);
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}))
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.append("g");
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var div = d3.select("body").append("div")
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.attr("class", "tooltip")
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.style("opacity", 0);
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var brush = d3.brush()
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.extent([[-9999999, -9999999], [9999999, 9999999]])
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.on("end", brushEnded);
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svg.append("g")
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.attr("class", "brush")
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.call(brush);
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var intercom = Intercom.getInstance();
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intercom.on("select", unSelectNodes);
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var nodes, // as in Data points
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node, // as in SVG object that have all small circles on screen
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props,
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norm,
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p1 = 0,
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p2 = 0,
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size,
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distanceFunction,
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simulation,
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velocities = [],
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rendering = true, // Rendering during the execution.
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forceName = "forces",
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springForce = false,
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tooltipWidth = 0,
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fileName = "",
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selectedData,
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clickedIndex = -1,
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paused = false;
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// Default parameters
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var MULTIPLIER = 50,
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PERPLEXITY = 30,
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LEARNING_RATE = 10,
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NEIGHBOUR_SIZE = 6,
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SAMPLE_SIZE = 3,
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PIVOTS = false,
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NUM_PIVOTS = 3,
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ITERATIONS = 300,
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FULL_ITERATIONS = 20,
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NODE_SIZE = 10,
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COLOR_ATTRIBUTE = "",
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SELECTED_DISTANCE = 10,
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FULL_NEIGHBOUR_SIZE = 6,
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FULL_SAMPLE_SIZE = 3,
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FULL_SELECTED_DISTANCE = 10;
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// Create a color scheme for a range of numbers.
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var color = d3.scaleOrdinal(d3.schemeCategory10);
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/**
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* Parse the data from the provided csv file using Papa Parse library
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* @param {file} evt - csv file.
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*/
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function parseFile(evt) {
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// Clear the previous nodes
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d3.selectAll(".nodes").remove();
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springForce = false;
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fileName = evt.target.files[0].name;
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Papa.parse(evt.target.files[0], {
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header: true,
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dynamicTyping: true,
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skipEmptyLines: true,
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complete: function (results) {
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processData(results.data, results.error);
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}
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});
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}
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/**
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* Process the data and pass it into D3 force simulation.
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* @param {array} data
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* @param {object} error
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*/
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function processData(data, error) {
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if (error) throw error.message;
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nodes = data;
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size = nodes.length;
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simulation = d3.forceSimulation();
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// Calculate normalization arguments and get the list of
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// properties of the nodes.
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norm = calculateNormalization(nodes); // Used with distance fn
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props = Object.keys(nodes[0]); // Properties to consider by distance fn
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COLOR_ATTRIBUTE = props[props.length-1];
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var opts = document.getElementById('color_attr').options;
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props.forEach(function (d) {
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opts.add(new Option(d, d, (d === COLOR_ATTRIBUTE) ? true : false));
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});
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opts.selectedIndex = props.length-1;
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props.pop(); //Hide Iris index / last column from distance function
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//Put the nodes in random starting positions
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nodes.forEach(function (d) {
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d.x = (Math.random()-0.5) * 100000;
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d.y = (Math.random()-0.5) * 100000;
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});
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// Add the nodes to DOM.
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node = svg.append("g")
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.attr("class", "nodes")
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.selectAll("circle")
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.data(nodes)
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.enter().append("circle")
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.attr("r", NODE_SIZE)
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.attr("transform", "translate(" + width / 2 + "," + height / 2 + ")")
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// Color code the data points by a property (for Poker Hands,
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// it is a CLASS property).
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.attr("fill", function (d) {
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return color(d[COLOR_ATTRIBUTE]);
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})
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.on("mouseover", function (d) {
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div.transition()
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.duration(200)
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.style("opacity", .9);
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div.html(formatTooltip(d))
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.style("left", (d3.event.pageX) + "px")
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.style("top", (d3.event.pageY - (15 * props.length)) + "px")
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.style("width", (6 * tooltipWidth) + "px")
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.style("height", (14 * props.length) + "px");
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highlightOnHover(d[COLOR_ATTRIBUTE]);
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})
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.on("mouseout", function (d) {
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div.transition()
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.duration(500)
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.style("opacity", 0);
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node.attr("opacity", 1);
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})
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.on("click", function (d) {
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if (clickedIndex !== d.index) {
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if (springForce) {
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highlightNeighbours(Array.from(simulation.force(forceName).nodeNeighbours(d.index).keys()));
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clickedIndex = d.index;
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}
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} else {
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node.attr("r", NODE_SIZE).attr("stroke-width", 0);
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clickedIndex = -1;
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}
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});
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// Pass the nodes to the D3 force simulation.
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simulation
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.nodes(nodes)
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.on("tick", ticked)
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.on("end", ended);
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};
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function ticked() {
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// If rendering is selected, then draw at every iteration.
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if (rendering === true) {
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node // Each sub-circle in the SVG, update cx and cy
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.attr("cx", function (d) {
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return d.x;
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})
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.attr("cy", function (d) {
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return d.y;
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});
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}
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// Emit the distribution data to allow the drawing of the bar graph
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if (springForce) {
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intercom.emit("passedData", simulation.force(forceName).distributionData());
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}
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}
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function ended() {
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if (rendering !== true) { // Never drawn anything before? Now it's time.
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node
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.attr("cx", function (d) {
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return d.x;
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})
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.attr("cy", function (d) {
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return d.y;
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});
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}
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if (p1 !== 0) {
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// Performance time measurement
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p2 = performance.now();
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console.log("Execution time: " + (p2 - p1));
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// Do not calculate stress for data sets bigger than 100 000.
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// if (nodes.length <= 100000) {
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// console.log("Stress: ", simulation.force(forceName).stress());
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// }
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// console.log(simulation.force(forceName).nodeNeighbours());
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p1 = 0;
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p2 = 0;
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}
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}
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function brushEnded() {
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var s = d3.event.selection,
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results = [];
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if (s) {
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var x0 = s[0][0] - width / 2,
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y0 = s[0][1] - height / 2,
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x1 = s[1][0] - width / 2,
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y1 = s[1][1] - height / 2;
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if (nodes) {
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var sel = node.filter(function (d) {
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if (d.x > x0 && d.x < x1 && d.y > y0 && d.y < y1) {
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return true;
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}
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return false;
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}).data();
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results = sel.map(function (a) { return a.index; });
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}
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intercom.emit("select", { name: fileName, indices: results });
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d3.select(".brush").call(brush.move, null);
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}
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}
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/**
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* Format the tooltip for the data
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* @param {*} node
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*/
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function formatTooltip(node) {
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var textString = "",
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temp = "";
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tooltipWidth = 0;
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props.forEach(function (element) {
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temp = element + ": " + node[element] + "<br/>";
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textString += temp;
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if (temp.length > tooltipWidth) {
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tooltipWidth = temp.length;
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}
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});
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return textString;
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}
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/**
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* Initialize the Chalmers' 1996 algorithm and start simulation.
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*/
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function startNeighbourSamplingSimulation() {
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springForce = true;
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simulation.stop();
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p1 = performance.now();
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simulation
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.alphaDecay(1 - Math.pow(0.001, 1 / ITERATIONS))
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.force(forceName, d3.forceNeighbourSamplingDistance()
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// Set the parameters for the algorithm (optional).
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.neighbourSize(NEIGHBOUR_SIZE)
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.sampleSize(SAMPLE_SIZE)
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// .freeness(0.5)
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.distanceRange(SELECTED_DISTANCE * MULTIPLIER)
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// The distance function that will be used to calculate distances
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// between nodes.
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.distance(function (s, t) {
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return distanceFunction(s, t, props, norm) * MULTIPLIER;
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})
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.stableVelocity(1.2 * MULTIPLIER)
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.stableVeloHandler( function(){simulation.stop(); ended();} )
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);
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// Restart the simulation.
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console.log(simulation.force(forceName).neighbourSize(), simulation.force(forceName).sampleSize());
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simulation.alpha(1).restart();
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}
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/**
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* Initialize the hybrid layout algorithm and start simulation.
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*/
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function startHybridSimulation() {
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springForce = false;
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d3.selectAll(".nodes").remove();
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simulation.stop();
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p1 = performance.now();
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configuration = {
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iteration: ITERATIONS,
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neighbourSize: NEIGHBOUR_SIZE,
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sampleSize: SAMPLE_SIZE,
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distanceRange: SELECTED_DISTANCE * MULTIPLIER,
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fullIterations: FULL_ITERATIONS,
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fullNeighbourSize: FULL_NEIGHBOUR_SIZE,
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fullSampleSize: FULL_SAMPLE_SIZE,
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fullDistanceRange: FULL_SELECTED_DISTANCE * MULTIPLIER,
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distanceFn: function (s, t) {return distanceFunction(s, t, props, norm) * MULTIPLIER;},
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pivots: PIVOTS,
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numPivots: NUM_PIVOTS
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};
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console.log(configuration);
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hybridSimulation = d3.hybridSimulation(nodes, configuration);
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let sample = hybridSimulation.sample();
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let remainder = hybridSimulation.remainder();
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// Add the nodes to DOM.
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node = svg.append("g")
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.attr("class", "nodes")
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.selectAll("circle")
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.data(sample)
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.enter().append("circle")
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.attr("r", NODE_SIZE)
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.attr("transform", "translate(" + width / 2 + "," + height / 2 + ")")
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// Color code the data points by a property (for Poker Hands,
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// it is a CLASS property).
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.attr("fill", function (d) {
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return color(d[COLOR_ATTRIBUTE])
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})
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.on("mouseover", function (d) {
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div.transition()
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.duration(200)
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.style("opacity", .9);
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div.html(formatTooltip(d))
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.style("left", (d3.event.pageX) + "px")
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.style("top", (d3.event.pageY - (15 * props.length)) + "px")
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.style("width", (6 * tooltipWidth) + "px")
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.style("height", (14 * props.length) + "px");
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highlightOnHover(d[COLOR_ATTRIBUTE]);
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})
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.on("mouseout", function (d) {
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div.transition()
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.duration(500)
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.style("opacity", 0);
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node.attr("opacity", 1);
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});
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if (selectedData) {
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unSelectNodes(selectedData);
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}
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hybridSimulation
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.on("sampleTick", ticked)
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.on("fullTick", ticked)
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.on("startFull", startedFull)
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.on("end", endedHybrid);
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function startedFull() {
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d3.selectAll(".nodes").remove();
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// Add the nodes to DOM.
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node = svg.append("g")
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.attr("class", "nodes")
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.selectAll("circle")
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.data(nodes)
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.enter().append("circle")
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.attr("r", NODE_SIZE)
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.attr("transform", "translate(" + width / 2 + "," + height / 2 + ")")
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// Color code the data points by a property (for Poker Hands,
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// it is a CLASS property).
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.attr("fill", function (d) {
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return color(d[COLOR_ATTRIBUTE])
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})
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.on("mouseover", function (d) {
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div.transition()
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.duration(200)
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.style("opacity", .9);
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div.html(formatTooltip(d))
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.style("left", (d3.event.pageX) + "px")
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.style("top", (d3.event.pageY - (15 * props.length)) + "px")
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.style("width", (6 * tooltipWidth) + "px")
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.style("height", (14 * props.length) + "px");
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highlightOnHover(d[COLOR_ATTRIBUTE]);
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})
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.on("mouseout", function (d) {
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div.transition()
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.duration(500)
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.style("opacity", 0);
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node.attr("opacity", 1);
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});
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if (selectedData) {
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unSelectNodes(selectedData);
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}
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}
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function endedHybrid() {
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if (rendering !== true) {
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node
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.attr("cx", function (d) {
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return d.x;
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})
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.attr("cy", function (d) {
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return d.y;
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});
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}
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// Performance time measurement
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p2 = performance.now();
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console.log("Execution time: " + (p2 - p1));
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// Do not calculate stress for data sets bigger than 100 000.
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// if (nodes.length <= 100000) {
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// console.log("Stress: ", hybridSimulation.stress());
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// }
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p1 = 0;
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p2 = 0;
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}
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}
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/**
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* Initialize the t-SNE algorithm and start simulation.
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*/
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function starttSNE() {
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springForce = false;
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simulation.stop();
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p1 = performance.now();
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simulation
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.alphaDecay(1 - Math.pow(0.001, 1 / ITERATIONS))
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.force(forceName, d3.tSNE()
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// Set the parameter for the algorithm (optional).
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.perplexity(PERPLEXITY)
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.learningRate(LEARNING_RATE)
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// The distance function that will be used to calculate distances
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// between nodes.
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.distance(function (s, t) {
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return distanceFunction(s, t, props, norm) * MULTIPLIER;
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}));
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// Restart the simulation.
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console.log(simulation.force(forceName).perplexity(), simulation.force(forceName).learningRate());
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simulation.alpha(1).restart();
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}
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/**
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* Initialize the Barnes-Hut algorithm and start simulation.
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*/
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function startBarnesHutSimulation() {
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springForce = false;
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simulation.stop();
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p1 = performance.now();
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simulation
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.alphaDecay(1 - Math.pow(0.001, 1 / ITERATIONS))
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.force(forceName, d3.forceBarnesHut()
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// The distance function that will be used to calculate distances
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// between nodes.
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.distance(function (s, t) {
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return distanceFunction(s, t, props, norm) * MULTIPLIER;
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}));
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// Restart the simulation.
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simulation.alpha(1).restart();
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}
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/**
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* Initialize the link force algorithm and start simulation.
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*/
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function startLinkSimulation() {
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springForce = false;
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simulation.stop();
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p1 = performance.now();
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let links = [];
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// Initialize link array.
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nodes = simulation.nodes();
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for (i = 0; i < nodes.length; i++) {
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for (j = 0; j < nodes.length; j++) {
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if (i !== j) {
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links.push({
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source: nodes[i],
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target: nodes[j],
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});
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}
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}
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}
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// Add the links to the simulation.
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simulation.force(forceName, d3.forceLink().links(links));
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simulation
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.alphaDecay(1 - Math.pow(0.001, 1 / ITERATIONS))
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.force(forceName)
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// The distance function that will be used to calculate distances
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// between nodes.
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.distance(function (n) {
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return distanceFunction(n.source, n.target, props, norm) * MULTIPLIER;
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})
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// Set the parameter for the algorithm (optional).
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.strength(1);
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// Restart the simulation.
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simulation.alpha(1).restart();
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}
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/**
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* Halt the execution.
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*/
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function stopSimulation() {
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simulation.stop();
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if (typeof hybridSimulation !== 'undefined') {
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hybridSimulation.stop();
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}
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}
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/**
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* Calculate the average values of the array.
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* @param {array} array
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* @return {number} the mean of the array.
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*/
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function getAverage(array) {
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var total = 0;
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for (var i = 0; i < array.length; i++) {
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total += array[i];
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}
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return total / array.length;
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}
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/**
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* Deselect the nodes to match the selection from other window.
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* @param {*} data
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*/
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function unSelectNodes(data) {
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selectedData = data;
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if (fileName === data.name && nodes) {
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node
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.classed("notSelected", function (d) {
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if (data.indices.indexOf(d.index) < 0) {
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return true;
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}
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return false;
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});
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}
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}
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/**
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* Highlight the neighbours for neighbour and sampling algorithm
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* @param {*} indices
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*/
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function highlightNeighbours(indices) {
|
|
node
|
|
.attr("r", function (d) {
|
|
if (indices.indexOf(d.index) >= 0) {
|
|
return NODE_SIZE * 2;
|
|
}
|
|
return NODE_SIZE;
|
|
})
|
|
.attr("stroke-width", function (d) {
|
|
if (indices.indexOf(d.index) >= 0) {
|
|
return NODE_SIZE * 0.2 + "px";
|
|
}
|
|
return "0px";
|
|
})
|
|
.attr("stroke", "white");
|
|
}
|
|
|
|
|
|
/**
|
|
* Highlight all the nodes with the same class on hover
|
|
* @param {*} highlighValue
|
|
*/
|
|
function highlightOnHover(highlighValue) {
|
|
node.attr("opacity", function (d) {
|
|
return (highlighValue === d[COLOR_ATTRIBUTE]) ? 1 : 0.3;
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Color the nodes according to given attribute.
|
|
*/
|
|
function colorToAttribute() {
|
|
node.attr("fill", function (d) {
|
|
return color(d[COLOR_ATTRIBUTE])
|
|
});
|
|
}
|
|
|
|
|
|
/**
|
|
* Update the distance range.
|
|
*/
|
|
function updateDistanceRange() {
|
|
if (springForce) {
|
|
simulation.force(forceName).distanceRange(SELECTED_DISTANCE);
|
|
}
|
|
}
|
|
|
|
|
|
/**
|
|
* Implemented pause/resume functionality
|
|
*/
|
|
function pauseUnPause() {
|
|
if (simulation) {
|
|
if (paused) {
|
|
simulation.force(forceName).distanceRange(SELECTED_DISTANCE);
|
|
simulation.restart();
|
|
d3.select("#pauseButton").text("Pause");
|
|
paused = false;
|
|
} else {
|
|
simulation.stop();
|
|
d3.select("#pauseButton").text("Resume");
|
|
paused = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
/**
|
|
* Average distances for each node.
|
|
* @param {*} dataNodes
|
|
* @param {*} properties
|
|
* @param {*} normalization
|
|
*/
|
|
function calculateAverageDistance(dataNodes, properties, normalization) {
|
|
var sum = 0,
|
|
n = nodes.length;
|
|
|
|
for (var i = 0; i < n; i++) {
|
|
var sumNode = 0;
|
|
for (var j = 0; j < n; j++) {
|
|
if (i !== j) {
|
|
sumNode += distanceFunction(nodes[i], nodes[j], properties, normalization);
|
|
// console.log(sumNode);
|
|
}
|
|
}
|
|
sum += sumNode / (n - 1);
|
|
}
|
|
|
|
return sum / n;
|
|
}
|