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@ -3,6 +3,7 @@
This software package contains a Barnes-Hut implementation of the t-SNE algorithm. The implementation is described in [this paper](http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf).
**Note:** This repository is a version of t-SNE modified to support ongoing research. It may be slightly slower than the original. If you're just trying to run t-SNE, check the original repository that we forked from.
# Installation #

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@ -64,7 +64,7 @@ INITIAL_DIMENSIONS = 50
DEFAULT_PERPLEXITY = 50
DEFAULT_THETA = 0.5
EMPTY_SEED = -1
DEFAULT_USE_PCA = False
DEFAULT_USE_PCA = True
DEFAULT_MAX_ITERATIONS = 1000
###
@ -102,7 +102,8 @@ def _is_filelike_object(f):
return isinstance(f, io.IOBase)
def init_bh_tsne(samples, workdir, no_dims, initial_dims, perplexity, theta, randseed, verbose, use_pca, max_iter):
def init_bh_tsne(samples, workdir, no_dims=DEFAULT_NO_DIMS, initial_dims=INITIAL_DIMENSIONS, perplexity=DEFAULT_PERPLEXITY,
theta=DEFAULT_THETA, randseed=EMPTY_SEED, verbose=False, use_pca=DEFAULT_USE_PCA, max_iter=DEFAULT_MAX_ITERATIONS):
if use_pca:
samples = samples - np.mean(samples, axis=0)
@ -172,7 +173,9 @@ def bh_tsne(workdir, verbose=False):
# The last piece of data is the cost for each sample, we ignore it
#read_unpack('{}d'.format(sample_count), output_file)
def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1, verbose=False, initial_dims=50, use_pca=True, max_iter=1000):
def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1,
verbose=False, initial_dims=50, use_pca=True, max_iter=1000,
return_betas=False, return_cost_per_point=False, return_cost_per_iter=False):
'''
Run TSNE based on the Barnes-HT algorithm
@ -200,8 +203,8 @@ def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1, verbose=
if _is_filelike_object(data):
data = load_data(data)
init_bh_tsne(data, tmp_dir_path, no_dims, perplexity, theta, randseed, verbose, initial_dims, use_pca, max_iter)
sys.exit(0)
init_bh_tsne(data, tmp_dir_path, no_dims=no_dims, perplexity=perplexity, theta=theta, randseed=randseed,verbose=verbose, initial_dims=initial_dims, use_pca=use_pca, max_iter=max_iter)
os._exit(0)
else:
try:
os.waitpid(child_pid, 0)
@ -215,8 +218,23 @@ def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1, verbose=
for r in result:
sample_res.append(r)
res.append(sample_res)
ret = np.asarray(res, dtype='float64')
if return_betas:
betas = np.loadtxt(path_join(tmp_dir_path, 'betas.txt'))
ret = (ret, betas)
if return_cost_per_point:
cpp = np.loadtxt(path_join(tmp_dir_path, 'cost_per_point.txt'))
ret = (*ret, cpp)
if return_cost_per_iter:
cpi = np.loadtxt(path_join(tmp_dir_path, 'cost_per_iter.txt'))
ret = (*ret, cpi)
rmtree(tmp_dir_path)
return np.asarray(res, dtype='float64')
return ret
def main(args):

@ -0,0 +1 @@
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@ -0,0 +1 @@
{"duration": 8.928506851196289, "input_args": {"listofParamsPlusData": "[(array([[ 5, 1, 1, ..., 3, 1, 1],\n [ 5, 4, 4, ..., 3, 2, 1],\n [ 3, 1, 1, ..., 3, 1, 1],\n ...,\n [ 5, 10, 10, ..., 8, 10, 2],\n [ 4, 8, 6, ..., 10, 6, 1],\n [ 4, 8, 8, ..., 10, 4, 1]]), 2, 30, 10, 1137, False, 9, True, 500, True, True, True)]"}}

@ -1,5 +1,5 @@
# first line: 198
# first line: 217
def wrapGetResults(listofParamsPlusData):
pool = Pool()
return pool.map(multi_run_wrapper, listofParamsPlusData)
return zip(*pool.map(multi_run_wrapper, listofParamsPlusData))

@ -227,7 +227,7 @@ circle.swatch{
/* Legend of the Overview t-SNE canvas */
#legend4 {
height: 7vw;
width: 5vw;
width: 7vw;
margin-left: -20px;
margin-top: -10px;
text-align: left;
@ -512,7 +512,7 @@ ul {
height: 140px;
-webkit-animation: spin 2s linear infinite; /* Safari */
animation: spin 2s linear infinite;
margin-left: 540px;
margin-left: 450px;
margin-top: 500px;
margin-bottom: 500px;
}

@ -104,12 +104,9 @@
</div>
<div class="param">
<label for="param-learningrate" data-toggle="tooltip" data-placement="right" title="Tip: if the learning rate is too high, the data may look like a ‘ball’ with any point approximately equidistant from its nearest neighbours. If the learning rate is too low, most points may look compressed in a dense cloud with few outliers. If the cost function gets stuck in a bad local minimum increasing the learning rate may help. (Source: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)">Learning rate</label>
<input id="param-learningrate" type="range" min="1" max="150" value="10", step="1">
<output for="param-learningrate" id="param-learningrate-value">10</output>
</div>
<input id="param-learningrate" type="range" min="1" max="150" value="1", step="1">
<output for="param-learningrate" id="param-learningrate-value">1</output>
</div>
<div class="param">
<div class="row">
<div class="col-md-3">
@ -129,36 +126,23 @@
</div>
</div>
<div class="param">
<label for="param-distance" data-toggle="tooltip" data-placement="right" title="Tip: distance different measurements. Usually Euclidean distance is used.">Distance metric</label>
<select id="param-distance" name="param-distance">
<option value="euclideanDist" selected>Euclidean distance</option>
<option value="jaccardDist">Jaccard dissimilarity</option>
</select>
<div class="param" style="margin-top: -10px">
<input id="file-input" type="file" name="name" style="display: none;" />
<button type="button" class="button" onclick='loadAnalysis();' style="padding:0 12px 0 12px" data-toggle="tooltip" data-placement="right" title="Tip: load previously executed file in .txt format.">Load execut.</button>
</div>
<div class="param">
<label for="param-transform" data-toggle="tooltip" data-placement="right" title="Tip: distance transformation measurements. Usually either no transformation or Log10/Asinh (logarithmic/hyperbolic) distance are used.">Data transform</label>
<select id="param-transform" name="param-transform">
<option value="noTrans" selected>No transform</option>
<option value="logTrans">Log10</option>
<option value="asinhTrans">Asinh</option>
<option value="binTrans">Binarize</option>
</select>
<button type="button" class="button" onclick="SaveAnalysis()" style="padding:0 10px 0 10px" data-toggle="tooltip" data-placement="right" title="Tip: save/store previously executed file in .txt format.">Store execut.</button>
</div>
<div class="row">
<div class="col-md-8">
<p><div id="run-button"><button id="ExecuteBut" class="btn btn-primary btn-block" onclick="getData();" value="Execute new t-SNE analysis" style="margin-top: 4px"><i class="fas fa-running fa-lg" style="margin-right: 10px"></i>Execute new t-SNE analysis</button></div></p>
</div>
<div class="col-md-4" style="margin-top: 10px">
<button type="button" class="button" onclick='loadAnalysis();' style="padding:15px 12px 15px 12px; margin-left:15px" data-toggle="tooltip" data-placement="right" title="Tip: load previously executed file in .txt format.">Load execution</button>
<button type="button" class="button" onclick="SaveAnalysis()" style="padding:15px 12px 15px 12px; margin-left:20px" data-toggle="tooltip" data-placement="right" title="Tip: save/store previously executed file in .txt format.">Store execution</button>
<div class="col-md-" style="margin-top: 14px; margin-left:15px">
<label data-toggle="tooltip" data-placement="right" title="Tip: if you store distances the file size will be larger but on a loading of this execution it will be processed much quicker than without this option enabled.">
<input id="downloadDists" checked type="checkbox" >
Distances cached on "Store execution"
</label>
</div>
</div>
<div class="row">
<div class="col-md-12" style="margin-top:10px">
<p><div id="run-button"><button id="ExecuteBut" class="btn btn-primary btn-block" onclick="getData();" value="Execute new t-SNE analysis" style="margin-top: 4px"><i class="fas fa-running fa-lg" style="margin-right: 10px"></i>Execute new t-SNE analysis</button></div></p>
</div>
</div>
</div>
</div>
</div>
@ -291,7 +275,7 @@
<svg id="legend1" style = "margin-left: -25px"></svg>
</div>
<div class="col-md-2">
<svg id="legend4" style = "margin-left: -45px"></svg>
<svg id="legend4" style = "margin-left: -50px"></svg>
</div>
</div>
<div class="annotationAllClass" style="margin-top: 20px">
@ -318,7 +302,7 @@
</div>
<div class="row">
<div class="col-md-3" style="margin-top:-4px">
<div class="panel panel-default" style="padding-bottom : 60px">
<div class="panel panel-default" style="padding-bottom : 70px">
<div class="panel-heading">
<h2 class="panel-title" data-toggle="tooltip" data-placement="right" style="display:inline-block" title="Tip: a view related to the overall quality of the projection.">Shepard Heatmap</h2>
<div style="display:inline-block; float:right">
@ -423,6 +407,7 @@
$("#ExecuteBut").click(function(){
var mode = document.getElementById('param-EX-view').value
mode = parseInt(mode)
if (mode == 1) {
$("#myModal").modal('show');
$('.modal-backdrop').removeClass("modal-backdrop");
@ -434,7 +419,7 @@
$('#myModal').modal('hide');
}
$("#cost").html('(Unknown Iteration and Cost Values)');
$("#cost").html('(Unknown Overall Cost)');
$("#datasetDetails").html('(Unknown Number of Dimensions and Instances)');
$("#CategoryName").html('No Classification');
$("#knnBarChartDetails").html('(Number of Selected Points: 0/0)');

@ -19,7 +19,7 @@ var dim = document.getElementById('overviewRect').offsetWidth-2; var dimensions
// Category = the name of the category if it exists. The user has to add an asterisk ("*") mark in order to let the program identify this feature as a label/category name.
// ColorsCategorical = the categorical colors (maximum value = 10).
var Category; var ColorsCategorical; var valCategExists = 0;
var Category; var ColorsCategorical; var valCategExists = 0; var insideCall = false;
// This is for the removal of the distances cache.
var returnVal = false;
@ -34,7 +34,7 @@ var sliderTrigger = false; var sliderInsideTrigger = false; var parameters; var
var inside = 0; var kValuesLegend = []; var findNearestTable = []; var howManyPoints;
var maxKNN = 0
var mode = 1; var colors = ['#a6cee3','#fb9a99','#b2df8a','#33a02c','#1f78b4','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']; var projections = []; var metricsSorting = []; var dataReceivedFromServer = []; var dataReceivedFromServerOptimized = []; var metrics = []; var FocusedIDs = [];
var mode = 1; var colors = ['#a6cee3','#fb9a99','#b2df8a','#33a02c','#1f78b4','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']; var projections = []; var betas = []; var cost_per_point = []; var cost_overall; var metricsSorting = []; var dataReceivedFromServer = []; var dataReceivedFromServerOptimized = []; var metrics = []; var FocusedIDs = [];
var Category; var target_names = []
@ -140,6 +140,9 @@ function ReSort(flagInitialize) {
projections = dataReceivedFromServer['projections']
parameters = dataReceivedFromServer['parameters']
metricsSorting = dataReceivedFromServer['metrics']
betas = dataReceivedFromServer['betas']
cost_per_point = dataReceivedFromServer['cpp']
cost_overall = dataReceivedFromServer['cpi']
if (FocusedIDs.length != 0) {
if (globalFlagCheck) {
@ -147,11 +150,12 @@ function ReSort(flagInitialize) {
metricsCopy = metrics
}
globalFlagCheck = false
document.getElementById("textToChange").innerHTML = "[Sorting Projections According to Metric for the Current Points Selection:";
metricsSorting = dataReceivedFromServerOptimized['metrics']
metrics = dataReceivedFromServerOptimized['metricsEntire']
if (FocusedIDs.length == points.length) {
document.getElementById("textToChange").innerHTML = "[Sorting Metric:";
document.getElementById("textToChange").innerHTML = "[Sorting Projections According to Metric:";
} else {
document.getElementById("textToChange").innerHTML = "[Sorting Projections According to Metric for Current Selection:";
}
} else {
if (!globalFlagCheck) {
@ -669,6 +673,7 @@ if(k >= 6) {
Plotly.relayout(graphDiv, update)
sliderInsideTrigger = true
insideCall = true;
activeProjectionNumberProv = order[SelProjIDSProv[0]]
getData()
@ -676,6 +681,7 @@ if(k >= 6) {
if (flagInitialize) {
closeModalFun()
insideCall = true;
getData()
}
@ -804,19 +810,68 @@ function parseData(url) {
// results.data variable is all the columns except strings, undefined values, or "Version" plus beta and cost values."
// results.meta.fields variable is all the features (columns) plus beta and cost strings.
if (mode == 2) {
init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
if (insideCall) {
init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
} else {
$.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
});
var parametersLocal = []
parametersLocal.push(document.getElementById("param-perplexity-value").value)
parametersLocal.push(document.getElementById("param-learningrate-value").value)
parametersLocal.push(document.getElementById("param-maxiter-value").value)
parametersLocal.push(results_all)
$.post("http://127.0.0.1:5000/receiverSingle", JSON.stringify(parametersLocal), function(){
$.get("http://127.0.0.1:5000/senderSingle", function( data ) {
dataReceivedFromServer = data
projections = dataReceivedFromServer['projections']
betas = dataReceivedFromServer['betas']
cost_per_point = dataReceivedFromServer['cpp']
cost_overall = dataReceivedFromServer['cpi']
activeProjectionNumber = 0
activeProjectionNumberProv = 0
init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
});
});
}
} else {
// ajax the JSON to the server
$.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
});
$.post("http://127.0.0.1:5000/receiver", JSON.stringify(results_all), function(){
$.get("http://127.0.0.1:5000/sender", function( data ) {
dataReceivedFromServer = data
ReSortOver()
});
});
if (flagAnalysis) {
$('#myModal').modal('hide');
$.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
});
var parametersLocal = []
parametersLocal.push(document.getElementById("param-perplexity-value").value)
parametersLocal.push(document.getElementById("param-learningrate-value").value)
parametersLocal.push(document.getElementById("param-maxiter-value").value)
parametersLocal.push(results_all)
$.post("http://127.0.0.1:5000/receiverSingle", JSON.stringify(parametersLocal), function(){
$.get("http://127.0.0.1:5000/senderSingle", function( data ) {
dataReceivedFromServer = data
projections = dataReceivedFromServer['projections']
betas = dataReceivedFromServer['betas']
cost_per_point = dataReceivedFromServer['cpp']
cost_overall = dataReceivedFromServer['cpi']
activeProjectionNumber = 0
init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
});
});
} else {
// ajax the JSON to the server
$.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
});
$.post("http://127.0.0.1:5000/receiver", JSON.stringify(results_all), function(){
$.get("http://127.0.0.1:5000/sender", function( data ) {
dataReceivedFromServer = data
ReSortOver()
});
});
}
}
}
@ -834,6 +889,9 @@ function ReSortOver() {
parameters = dataReceivedFromServer['parameters']
metricsSorting = dataReceivedFromServer['metrics']
metrics = dataReceivedFromServer['metricsEntire']
betas = dataReceivedFromServer['betas']
cost_per_point = dataReceivedFromServer['cpp']
cost_overall = dataReceivedFromServer['cpi']
var traces = []
var target_names = []
@ -2629,10 +2687,10 @@ function init(data, results_all, fields) {
} else{
tsne = new tsnejs.tSNE(opt); // Set new t-SNE with specific perplexity.
dists = [];
dists = computeDistances(data, document.getElementById("param-distance").value, document.getElementById("param-transform").value); // Compute the distances in the high-dimensional space.
dists = computeDistances(data, 'euclideanDist', 'noTrans'); // Compute the distances in the high-dimensional space.
InitialFormDists.push(dists);
tsne.initDataDist(dists); // Init t-SNE with dists.
for(var i = 0; i < final_dataset.length; i++) {final_dataset[i].beta = tsne.beta[i]; beta_all[i] = tsne.beta[i];} // Calculate beta and bring it back from the t-SNE algorithm.
//tsne.initDataDist(dists); // Init t-SNE with dists.
//for(var i = 0; i < final_dataset.length; i++) {final_dataset[i].beta = tsne.beta[i]; beta_all[i] = tsne.beta[i];} // Calculate beta and bring it back from the t-SNE algorithm.
}
var object;
all_labels = [];
@ -2748,28 +2806,6 @@ function euclideanDist(data) {
}
// Calculate jaccard dist
function jaccardDist(data) {
dist = initDist(data);
for(var i = 0; i < data.length; i++) {
for(var j = i + 1; j < data.length; j++) {
for(var d in data[0]) {
if(d != Category) {
x = data[i][d];
y = data[j][d];
if(x == y) {
dist[i][j] += 1;
}
}
}
dist[j][i] = dist[i][j];
}
}
return dist;
}
// Normalize distances to prevent numerical issues.
function normDist(data, dist) {
@ -2793,51 +2829,6 @@ function noTrans(data) {
return data;
}
// Log tranformation
function logTrans(data) {
for(var i = 0; i < data.length; i++) {
for(var d in data[0]) {
if(d != Category) {
X = data[i][d];
data[i][d] = Math.log10(X + 1);
}
}
}
return data;
}
// asinh tranformation
function asinhTrans(data) {
for(var i = 0; i < data.length; i++) {
for(var d in data[0]) {
if(d != Category) {
X = data[i][d];
data[i][d] = Math.log(X + Math.sqrt(X * X + 1));
}
}
}
return data;
}
// Binarize tranformation
function binTrans(data) {
for(var i = 0; i < data.length; i++) {
for(var d in data[0]) {
if(d != Category) {
X = data[i][d];
if(X > 0) data[i][d] = 1;
if(X < 0) data[i][d] = 0;
}
}
}
return data;
}
// Compute the distances by applying the chosen distance functions and transformation functions.
function computeDistances(data, distFunc, transFunc) {
dist = eval(distFunc)(eval(transFunc)(data));
@ -2884,7 +2875,7 @@ function OverallCostLineChart(){
margin: {
l: 40,
r: 15,
b: 26,
b: 30,
t: 5
},
};
@ -2899,7 +2890,15 @@ function updateEmbedding(AnalysisResults) {
points = [];
points2d = [];
if (AnalysisResults == ""){ // Check if the embedding does not need to load because we had a previous analysis uploaded.
var Y = tsne.getSolution(); // We receive the solution from the t-SNE
if (sliderTrigger) {
if (sliderInsideTrigger) {
var Y = projections[activeProjectionNumberProv]
} else {
var Y = projections[activeProjectionNumber]
}
} else {
var Y = projections[activeProjectionNumber]
}
var xExt = d3.extent(Y, d => d[0]);
var yExt = d3.extent(Y, d => d[1]);
var maxExt = [Math.min(xExt[0], yExt[0]), Math.max(xExt[1], yExt[1])];
@ -2911,10 +2910,11 @@ function updateEmbedding(AnalysisResults) {
var y = d3.scaleLinear() // Scale the y points into the canvas width/height
.domain(maxExt)
.range([10, +dimensionsY-10]);
for(var i = 0; i < final_dataset.length; i++) {
for(var i = 0; i < betas[activeProjectionNumber].length; i++) {
x_position[i] = x(Y[i][0]); // x points position
y_position[i] = y(Y[i][1]); // y points position
points[i] = {id: i, x: x_position[i], y: y_position[i], beta: final_dataset[i].beta, cost: final_dataset[i].cost, selected: true, schemaInv: false, DimON: null, pcp: false}; // Create the points and points2D (2 dimensions)
points[i] = {id: i, x: x_position[i], y: y_position[i], beta: betas[activeProjectionNumber][i], cost: cost_per_point[activeProjectionNumber][i], selected: true, schemaInv: false, DimON: null, pcp: false}; // Create the points and points2D (2 dimensions)
points2d[i] = {x: x_position[i], y: y_position[i]}; // and add everything that we know about the points (e.g., selected = true, pcp = false in the beginning and so on)
points[i] = extend(points[i], ArrayContainsDataFeaturesCleared[i]);
points[i] = extend(points[i], dataFeatures[i]);
@ -2934,12 +2934,10 @@ function updateEmbedding(AnalysisResults) {
ArrayWithCostsList = AnalysisResults.slice(2*dataFeatures.length+length+10, 2*dataFeatures.length+length+11);
Iterations = IterationsList[0];
ArrayWithCosts = ArrayWithCostsList[0];
$("#cost").html("(Number. of Iter.: " + ParametersSet[3] + ", Ov. Cost: " + overallCost + ")");
$("#cost").html("(Overall Cost: " + overallCost + ")");
$('#param-perplexity-value').text(ParametersSet[1]);
$('#param-learningrate-value').text(ParametersSet[2]);
$('#param-maxiter-value').text(ParametersSet[3]);
document.getElementById("param-distance").value = ParametersSet[4];
document.getElementById("param-transform").value = ParametersSet[5];
} else{
var length = (AnalysisResults.length - 9) / 2;
points = AnalysisResults.slice(0, length); // Load the points from the previous analysis
@ -2950,12 +2948,10 @@ function updateEmbedding(AnalysisResults) {
ArrayWithCostsList = AnalysisResults.slice(2*length+8, 2*length+9);
Iterations = IterationsList[0];
ArrayWithCosts = ArrayWithCostsList[0];
$("#cost").html("(Number of Iter.: " + ParametersSet[3] + ", Ov. Cost: " + overallCost + ")");
$("#cost").html("(Overall Cost: " + overallCost + ")");
$('#param-perplexity-value').text(ParametersSet[1]);
$('#param-learningrate-value').text(ParametersSet[2]);
$('#param-maxiter-value').text(ParametersSet[3]);
document.getElementById("param-distance").value = ParametersSet[4];
document.getElementById("param-transform").value = ParametersSet[5];
}
$("#data").html(ParametersSet[0]); // Print on the screen the classification label.
$("#param-dataset").html('-');
@ -3014,7 +3010,7 @@ function ShepardHeatMap () {
dist_list2d = []; // Distances lists empty
dist_list = [];
// Calculate the 2D distances.
dists2d = computeDistances(points2d, document.getElementById("param-distance").value, document.getElementById("param-transform").value);
dists2d = computeDistances(points2d, 'euclideanDist', 'noTrans');
InitialFormDists2D.push(dists2d);
for (var j=0; j<dists2d.length; j++){ // Fill them with the distances 2D and high-dimensional, respectively.
dists2d[j] = dists2d[j].slice(0,j);
@ -3237,7 +3233,7 @@ function ShepardHeatMap () {
dist_list2d = []; // Distances lists empty
dist_list = [];
// Calculate the 2D distances.
dists2d = computeDistances(points2d, document.getElementById("param-distance").value, document.getElementById("param-transform").value);
dists2d = computeDistances(points2d, 'euclideanDist', 'noTrans');
InitialFormDists2D.push(dists2d);
}
@ -3381,12 +3377,43 @@ function ShepardHeatMap () {
function step() {
step_counter++;
if(step_counter <= max_counter) {
cost = tsne.step();
cost_each = cost[1];
for(var i = 0; i < final_dataset.length; i++) final_dataset[i].cost = cost_each[i];
$("#cost").html("(Number of Iter.: " + tsne.iter + ", Ov. Cost: " + cost[0].toFixed(3) + ")");
ArrayWithCosts.push(cost[0].toFixed(3));
Iterations.push(step_counter);
//cost = tsne.step();
//cost_each = cost[1];
//for(var i = 0; i < final_dataset.length; i++) final_dataset[i].cost = cost_each[i];
if (sliderTrigger) {
if (sliderInsideTrigger) {
if (cost_overall[activeProjectionNumberProv][step_counter-1].toFixed(3) < 0) {
$("#cost").html("(Overall Cost: 0.000)");
ArrayWithCosts.push(0);
Iterations.push(step_counter);
} else {
$("#cost").html("(Overall Cost: " + cost_overall[activeProjectionNumberProv][step_counter-1].toFixed(3) + ")");
ArrayWithCosts.push(cost_overall[activeProjectionNumberProv][step_counter-1].toFixed(3));
Iterations.push(step_counter);
}
} else {
if (cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) < 0) {
$("#cost").html("(Overall Cost: 0.000)");
ArrayWithCosts.push(0);
Iterations.push(step_counter);
} else {
$("#cost").html("(Overall Cost: " + cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) + ")");
ArrayWithCosts.push(cost_overall[activeProjectionNumber][step_counter-1].toFixed(3));
Iterations.push(step_counter);
}
}
} else {
if (cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) < 0) {
$("#cost").html("(Overall Cost: 0.000)");
ArrayWithCosts.push(0);
Iterations.push(step_counter);
} else {
$("#cost").html("(Overall Cost: " + cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) + ")");
ArrayWithCosts.push(cost_overall[activeProjectionNumber][step_counter-1].toFixed(3));
Iterations.push(step_counter);
}
}
}
else {
clearInterval(runner);
@ -3586,7 +3613,11 @@ function CostHistogram(points){
var max = (d3.max(points,function(d){ return d.cost; }));
var min = (d3.min(points,function(d){ return d.cost; }));
for (var i=0; i<points.length; i++){
frequency.push((points[i].cost-min)/(max-min));
if ((points[i].cost-min)/(max-min) < 0) {
frequency.push(0)
} else {
frequency.push((points[i].cost-min)/(max-min));
}
}
var trace2 = {
x: frequency,
@ -5179,10 +5210,10 @@ if (points.length) { // If points exist (at least 1 point)
var minSize1 = points[temp].cost;
for (var i=temp+1; i<points.length; i++){
if (minSize1 > points[i].cost){
minSize1 = points[i].cost;
minSize1 = points[i].cost;
}
}
var rscale1 = d3.scaleLinear()
.domain([minSize1, maxSize1])
.range([5,parseInt(12-(1-document.getElementById("param-costlim").value)*7)]);
@ -5233,7 +5264,12 @@ if (points.length) { // If points exist (at least 1 point)
.attr("transform", "translate(10,20)");
var SizeRange1 = [];
SizeRange1.push((minSize1).toFixed(4));
if (minSize1 < 0) {
SizeRange1.push(0.0000)
} else {
SizeRange1.push((minSize1).toFixed(4));
}
SizeRange1.push(((maxSize1-minSize1)/2).toFixed(4));
SizeRange1.push((maxSize1).toFixed(4));
@ -5267,7 +5303,11 @@ if (points.length) { // If points exist (at least 1 point)
var min = points[temp].cost;
for (var i=temp+1; i<points.length; i++){
if (min > points[i].cost){
min = points[i].cost;
if (points[i].cost < 0) {
min = 0;
} else {
min = points[i].cost;
}
}
}
@ -5294,7 +5334,7 @@ if (points.length) { // If points exist (at least 1 point)
labels_cost = d3.range(min, max+calcStep, calcStep);
for (var i=0; i<9; i++){
labels_cost[i] = labels_cost[i].toFixed(5);
abbr_labels_cost[i] = abbreviateNumber(labels_cost[i]);
abbr_labels_cost[i] = Math.abs(abbreviateNumber(labels_cost[i]));
}
var svg = d3.select("#legend1"); // Add the legend for the beta/cost
@ -5575,8 +5615,8 @@ if (points.length) { // If points exist (at least 1 point)
var abbr_labels_cost = [];
labels_cost = d3.range(min, max+calcStep, calcStep);
for (var i=0; i<7; i++){
labels_cost[i] = labels_cost[i].toFixed(5);
abbr_labels_cost[i] = abbreviateNumber(labels_cost[i]);
labels_cost[i] = labels_cost[i].toFixed(4);
abbr_labels_cost[i] = Math.abs(abbreviateNumber(labels_cost[i]));
}
var svg = d3.select("#legend1"); // Add the legend for the beta/cost
@ -5588,7 +5628,7 @@ if (points.length) { // If points exist (at least 1 point)
.labelFormat(d3.format(",.5f"))
.cells(7)
.labels([abbr_labels_cost[0],abbr_labels_cost[1],abbr_labels_cost[2],abbr_labels_cost[3],abbr_labels_cost[4],abbr_labels_cost[5],abbr_labels_cost[6]])
.title("Remaining Cost")
.title("Rem. Cost")
.scale(colorScale);
svg.select(".legendLinear")
@ -6027,8 +6067,8 @@ function SaveAnalysis(){ // Save the analysis into a .txt file
let perplexity = document.getElementById("param-perplexity-value").value;
let learningRate = document.getElementById("param-learningrate-value").value;
let IterValue = document.getElementById("param-maxiter-value").value;
let parDist = document.getElementById("param-distance").value;
let parTrans = document.getElementById("param-transform").value;
let parDist = 'euclideanDist';
let parTrans = 'noTrans';
let Parameters = [];
if (dataset == "empty"){
Parameters.push(new_file.name);

@ -43,9 +43,23 @@
using namespace std;
static double sign(double x) { return (x == .0 ? .0 : (x < .0 ? -1.0 : 1.0)); }
static void zeroMean(double* X, int N, int D);
static void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity);
static void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K);
static double randn();
static void computeExactGradient(double* P, double* Y, int N, int D, double* dC);
static void computeGradient(unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta);
static double evaluateError(double* P, double* Y, int N, int D);
static double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta, bool save_cost_per_point = false);
static void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD);
static void symmetrizeMatrix(unsigned int** row_P, unsigned int** col_P, double** val_P, int N);
static void save_1D_array(double* array, int n, const char* filename);
// Perform t-SNE
void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexity, double theta, int rand_seed,
bool skip_random_init, int max_iter, int stop_lying_iter, int mom_switch_iter) {
bool skip_random_init, int max_iter, int stop_lying_iter, int mom_switch_iter, int cost_iter_step) {
// Set random seed
if (skip_random_init != true) {
@ -133,20 +147,22 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
else { for(int i = 0; i < row_P[N]; i++) val_P[i] *= 12.0; }
// Initialize solution (randomly)
if (skip_random_init != true) {
for(int i = 0; i < N * no_dims; i++) Y[i] = randn() * .0001;
}
if (skip_random_init != true) {
for(int i = 0; i < N * no_dims; i++) Y[i] = randn() * .0001;
}
// Perform main training loop
if(exact) printf("Input similarities computed in %4.2f seconds!\nLearning embedding...\n", (float) (end - start) / CLOCKS_PER_SEC);
else printf("Input similarities computed in %4.2f seconds (sparsity = %f)!\nLearning embedding...\n", (float) (end - start) / CLOCKS_PER_SEC, (double) row_P[N] / ((double) N * (double) N));
start = clock();
double* cost_per_iter = new double[max_iter];
for(int iter = 0; iter < max_iter; iter++) {
// Compute (approximate) gradient
if(exact) computeExactGradient(P, Y, N, no_dims, dY);
else computeGradient(P, row_P, col_P, val_P, Y, N, no_dims, dY, theta);
else computeGradient(row_P, col_P, val_P, Y, N, no_dims, dY, theta);
// Update gains
for(int i = 0; i < N * no_dims; i++) gains[i] = (sign(dY[i]) != sign(uY[i])) ? (gains[i] + .2) : (gains[i] * .8);
@ -167,22 +183,29 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
if(iter == mom_switch_iter) momentum = final_momentum;
// Print out progress
if (iter > 0 && (iter % 50 == 0 || iter == max_iter - 1)) {
if (iter % cost_iter_step == 0 || iter == max_iter - 1) {
end = clock();
double C = .0;
if(exact) C = evaluateError(P, Y, N, no_dims);
else C = evaluateError(row_P, col_P, val_P, Y, N, no_dims, theta); // doing approximate computation here!
if(iter == 0)
printf("Iteration %d: error is %f\n", iter + 1, C);
printf("Iteration %d: error is %f\n", iter, C);
else {
total_time += (float) (end - start) / CLOCKS_PER_SEC;
printf("Iteration %d: error is %f (50 iterations in %4.2f seconds)\n", iter, C, (float) (end - start) / CLOCKS_PER_SEC);
printf("Iteration %d: error is %f (%d iterations in %4.2f seconds)\n", iter, C, cost_iter_step, (float) (end - start) / CLOCKS_PER_SEC);
}
cost_per_iter[iter] = C;
start = clock();
}
}
end = clock(); total_time += (float) (end - start) / CLOCKS_PER_SEC;
// Save the cost per iteration to file
save_1D_array(cost_per_iter, max_iter, "cost_per_iter.txt");
// This time it's for saving the final cost per point
evaluateError(row_P, col_P, val_P, Y, N, no_dims, theta, true);
// Clean up memory
free(dY);
free(uY);
@ -193,12 +216,13 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
free(col_P); col_P = NULL;
free(val_P); val_P = NULL;
}
delete[] cost_per_iter;
printf("Fitting performed in %4.2f seconds.\n", total_time);
}
// Compute gradient of the t-SNE cost function (using Barnes-Hut algorithm)
void TSNE::computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta)
static void computeGradient(unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta)
{
// Construct space-partitioning tree on current map
@ -222,7 +246,7 @@ void TSNE::computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp
}
// Compute gradient of the t-SNE cost function (exact)
void TSNE::computeExactGradient(double* P, double* Y, int N, int D, double* dC) {
static void computeExactGradient(double* P, double* Y, int N, int D, double* dC) {
// Make sure the current gradient contains zeros
for(int i = 0; i < N * D; i++) dC[i] = 0.0;
@ -272,7 +296,7 @@ void TSNE::computeExactGradient(double* P, double* Y, int N, int D, double* dC)
// Evaluate t-SNE cost function (exactly)
double TSNE::evaluateError(double* P, double* Y, int N, int D) {
static double evaluateError(double* P, double* Y, int N, int D) {
// Compute the squared Euclidean distance matrix
double* DD = (double*) malloc(N * N * sizeof(double));
@ -308,7 +332,7 @@ double TSNE::evaluateError(double* P, double* Y, int N, int D) {
}
// Evaluate t-SNE cost function (approximately)
double TSNE::evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta)
static double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta, bool save_cost_per_point)
{
// Get estimate of normalization term
@ -316,11 +340,13 @@ double TSNE::evaluateError(unsigned int* row_P, unsigned int* col_P, double* val
double* buff = (double*) calloc(D, sizeof(double));