@ -24,6 +24,9 @@ var Category; var ColorsCategorical;
// This is for the removal of the distances cache.
var returnVal = false ;
// This variable is for the kNN Bar Chart in order to store the first execution.
var inside = 0 ;
// Schema Investigation
// svgClick = Click a left mouse click in order to add a point.
// prevRightClick = When right click is pressed prevent any other action. Lock the current schema.
@ -742,6 +745,7 @@ function computeDistances(data, distFunc, transFunc) {
// Function that updates embedding
function updateEmbedding ( AnalysisResults ) {
inside = 0 ;
points = [ ] ;
points2d = [ ] ;
if ( AnalysisResults == "" ) { // Check if the embedding does not need to load because we had a previous analysis uploaded.
@ -1305,10 +1309,10 @@ var zoomer = d3v3.behavior.zoom()
// Margin of the main barchart
var main _margin = { top : 8 , right : 10 , bottom : 30 , left : 100 } ,
main _width = 500 - main _margin . left - main _margin . right ,
main _height = 320 - main _margin . top - main _margin . bottom ;
main _height = 475 - main _margin . top - main _margin . bottom ;
// Margin of the mini barchart
var mini _margin = { top : 8 , right : 10 , bottom : 30 , left : 10 } ,
mini _height = 320 - mini _margin . top - mini _margin . bottom ;
mini _height = 475 - mini _margin . top - mini _margin . bottom ;
mini _width = 100 - mini _margin . left - mini _margin . right ;
// Create the svg correlation component
@ -1583,11 +1587,11 @@ function CalculateCorrel(){ // Calculate the correlation is a function which has
for ( var j = 0 ; j < correlationResults . length ; j ++ ) {
for ( var i = 0 ; i < SignStore . length ; i ++ ) {
if ( SignStore [ i ] [ 1 ] * ( - 1 ) == correlationResults [ j ] [ 1 ] ) {
correlationResults [ j ] [ 1 ] = parseInt ( correlationResults [ j ] [ 1 ] * 100 ) * ( - 1 ) ; // Give the negative sign if needed and multiply by 100
correlationResults [ j ] [ 1 ] = ( correlationResults [ j ] [ 1 ] ) . toFixed ( 2 ) * ( - 1 ) ; // Give the negative sign if needed and multiply by 100
correlationResults [ j ] . push ( j ) ;
}
if ( SignStore [ i ] [ 1 ] == correlationResults [ j ] [ 1 ] ) {
correlationResults [ j ] [ 1 ] = parseInt ( correlationResults [ j ] [ 1 ] * 100 ) ; // Give a positive sign and multiply by 100
correlationResults [ j ] [ 1 ] = ( correlationResults [ j ] [ 1 ] ) . toFixed ( 2 ) ; // Give a positive sign and multiply by 100
correlationResults [ j ] . push ( j ) ;
}
}
@ -1692,8 +1696,8 @@ function drawBarChart(){ // Draw the horizontal barchart with the correlations.
/////////////////////// Update scales ///////////////////////
/////////////////////////////////////////////////////////////
//Update the scales
main _xScale . domain ( [ - 100 , 100 ] ) ;
mini _xScale . domain ( [ - 100 , 100 ] ) ;
main _xScale . domain ( [ - 1 , 1 ] ) ;
mini _xScale . domain ( [ - 1 , 1 ] ) ;
main _yScale . domain ( correlationResultsFinal . map ( function ( d ) { return d [ 0 ] ; } ) ) ;
mini _yScale . domain ( correlationResultsFinal . map ( function ( d ) { return d [ 0 ] ; } ) ) ;
@ -2117,11 +2121,12 @@ var IDS = [];
RadarChart ( "#starPlot" , wrapData , colors , IDS , radarChartOptions ) ;
function BetatSNE ( points ) { // Run the main visualization
inside = inside + 1 ;
if ( points . length ) { // If points exist (at least 1 point)
selectedPoints = [ ] ;
var findNearestTable = [ ] ;
var findNearestTableCombination = [ ] ;
for ( let m = 0 ; m < points . length ; m ++ ) {
if ( points [ m ] . selected == true ) {
selectedPoints . push ( points [ m ] ) ; // Add the selected points in to a new variable
@ -2140,12 +2145,10 @@ if (points.length) { // If points exist (at least 1 point)
var vh = viewport [ 1 ] * 0.042 ;
var maxKNN = document . getElementById ( "param-perplexity-value" ) . value ; // Specify the amount of k neighborhoods that we are going to calculate. According to "perplexity."
selectedPoints . sort ( function ( a , b ) { // Sort the points according to ID.
return parseFloat ( a . id ) - parseFloat ( b . id ) ;
} ) ;
$ ( "#kNNDetails" ) . html ( "Purity of the cluster was checked for k values starting from " + ( 1 ) + " to " + maxKNN + "." ) ; // Print on the screen the number of k values of kNN which we present!
for ( k = maxKNN ; k > 0 ; k -- ) { // Start from the maximum k value and go to the minimum (k=2).
findNearest = 0 ;
@ -2223,46 +2226,64 @@ if (points.length) { // If points exist (at least 1 point)
if ( isNaN ( findNearest ) ) {
findNearest = 0 ; // If kNN is fully uncorrelated then we say that the value is 0.
}
findNearestTable . push ( findNearest * vh * 2 ) ; // These values are multiplied by the height of the viewport because we need to draw the bins of the barchart representation
findNearestTable . push ( findNearest . toFixed ( 2 ) ) ; // These values are multiplied by the height of the viewport because we need to draw the bins of the barchart representation
}
findNearestTable . reverse ( ) ;
var barPadding = 5 ; // Leave some space between the bars
d3v3 . select ( "#knnBarChart" ) . selectAll ( "rect" ) . remove ( ) ;
var svg2 = d3v3 . select ( '#knnBarChart' ) // Create the barchart for the kNN
. attr ( "class" , "bar-chart" ) ;
vw = dimensions ;
var barWidth = ( vw / findNearestTable . length ) ; // Bar width.
var knnBarChartSVG = svg2 . selectAll ( "rect" ) // Draw the barchart!
. data ( findNearestTable )
. enter ( )
. append ( "rect" )
. attr ( "y" , function ( d ) {
return Math . round ( vh * 2 - d )
} )
. attr ( "height" , function ( d ) {
return d ;
} )
. attr ( "width" , barWidth - barPadding )
. attr ( "transform" , function ( d , i ) {
var translate = [ barWidth * i , 0 ] ;
return "translate(" + translate + ")" ;
} ) ;
// Here we have the code for the starplot
d3 . select ( "#starPlot" ) . selectAll ( 'g' ) . remove ( ) ; // Remove the starplot if there was one before
var coun = 0 ;
for ( var i = 0 ; i < selectedPoints . length ; i ++ ) {
if ( selectedPoints [ i ] . starplot == true ) { // Count the selected points
coun = coun + 1 ;
}
var kValuesLegend = [ ] ;
for ( var i = 1 ; i <= maxKNN ; i ++ ) {
kValuesLegend . push ( i ) ;
}
if ( inside == 1 ) {
StoreInitialFindNearestTable = findNearestTable ;
}
var trace1 = {
x : kValuesLegend ,
y : StoreInitialFindNearestTable ,
name : 'Entire Projection' ,
type : 'bar' ,
marker : {
color : 'rgb(0,0,0)'
}
} ;
var trace2 = {
x : kValuesLegend ,
y : findNearestTable ,
name : 'Lasso Selected Cluster' ,
type : 'bar' ,
marker : {
color : 'rgb(0, 187, 187)'
}
} ;
var LimitXaxis = Number ( maxKNN ) + 1 ;
var data = [ trace1 , trace2 ] ;
var layout = {
barmode : 'group' , autosize : false ,
width : dimensions * 0.99 ,
height : vh * 2.1 ,
margin : {
l : 30 ,
r : 30 ,
b : 20 ,
t : 20 ,
pad : 4
} ,
xaxis : { range : [ 0 , LimitXaxis ] } ,
yaxis : { range : [ 0 , 1 ] } } ;
Plotly . newPlot ( 'knnBarChart' , data , layout , { displayModeBar : false } , { staticPlot : true } ) ;
// Here we have the code for the starplot
d3 . select ( "#starPlot" ) . selectAll ( 'g' ) . remove ( ) ; // Remove the starplot if there was one before
var coun = 0 ;
for ( var i = 0 ; i < selectedPoints . length ; i ++ ) {
if ( selectedPoints [ i ] . starplot == true ) { // Count the selected points
coun = coun + 1 ;
}
}
if ( selectedPoints . length <= 10 && coun > 0 ) { // If points > 10 then do not draw! If points = 0 then do not draw!
