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fixed PCA

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Former-commit-id: 379a966365
master
Angelos Chatzimparmpas 5 years ago
parent a827280d58
commit 4df08effc0
4 changed files with 29 additions and 25 deletions
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
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  1. 2
      data/breast-cancer-wisconsin.csv
  2. 2
      data/diabetes.csv
  3. 2
      index.html
  4. 48
      js/tsne_vis.js

2
data/breast-cancer-wisconsin.csv
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@ -1,4 +1,4 @@
id;clump_thic.;size_unif.;shape_unif.;marg._adh.;epith._size;bare_nuc.;bland_chr.;normal_nuc.;mitoses;class*
id;clump_thic;size_un;shape_un;marg_adh;epith_size;bare_nuc;bland_chr;nor_nuc;mitoses;class*
1000025;5;1;1;1;2;1;3;1;1;Benign
1002945;5;4;4;5;7;10;3;2;1;Benign
1015425;3;1;1;1;2;2;3;1;1;Benign

1 id clump_thic. clump_thic size_unif. size_un shape_unif. shape_un marg._adh. marg_adh epith._size epith_size bare_nuc. bare_nuc bland_chr. bland_chr normal_nuc. nor_nuc mitoses class*
2 1000025 5 1 1 1 2 1 3 1 1 Benign
3 1002945 5 4 4 5 7 10 3 2 1 Benign
4 1015425 3 1 1 1 2 2 3 1 1 Benign

2
data/diabetes.csv
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@ -1,4 +1,4 @@
Pregnancies,Glucose,BloodPress.,SkinThick.,Insulin,BMI,DPF,Age,Outcome*
Pregnan,Glucose,BloodPress,SkinThick,Insulin,BMI,DPF,Age,Outcome*
6,148,72,35,0,33.6,0.627,50,1
1,85,66,29,0,26.6,0.351,31,0
8,183,64,0,0,23.3,0.672,32,1

1 Pregnancies Pregnan Glucose BloodPress. BloodPress SkinThick. SkinThick Insulin BMI DPF Age Outcome*
2 6 148 72 35 0 33.6 0.627 50 1
3 1 85 66 29 0 26.6 0.351 31 0
4 8 183 64 0 0 23.3 0.672 32 1

2
index.html
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@ -26,7 +26,7 @@
<!-- Basic scripts that we execute. -->
<script src="./js/tsne.js"></script>
<script src="./js/data_form_handler.js"></script>
<script src="./modules/pca/pca.js"></script>
<script src="./modules/pca/pca.js"></script>
<!-- CSS - Styling -->
<link rel="stylesheet" href="./css/style.css"/>

48
js/tsne_vis.js
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@ -2539,18 +2539,23 @@ if (points.length) { // If points exist (at least 1 point)
FeaturesSelectedPoints.push(ArrayContainsDataFeaturesClearedwithoutNull[selectedPoints[i].id]);
}
console.log(FeaturesSelectedPoints);
var vectors = PCA.getEigenVectors(FeaturesSelectedPoints); // Run a local PCA!
var PCAResults = PCA.computeAdjustedData(FeaturesSelectedPoints,vectors[0]); // Get the results for individual dimension.
var PCASelVec = [];
var PCAResults = PCA.computeAdjustedData(FeaturesSelectedPoints,vectors[0]); // Get the results with the first most variation.
var PCASelVec = []; var PCASelVecAbs = [];
PCASelVec = PCAResults.selectedVectors[0];
PCASelVec.forEach(element => {
element = Math.abs(element);
PCASelVecAbs.push(element);
});
var len = PCASelVec.length;
var len = PCASelVecAbs.length;
var indices = new Array(len);
for (var i = 0; i < len; ++i) indices[i] = i;
indices = indices.sort(function (a, b) { return PCASelVec[a] < PCASelVec[b] ? -1 : PCASelVec[a] > PCASelVec[b] ? 1 : 0; }); // Get the most important features first! Clockwise ordering
if (len > 8){
indices = indices.sort(function (a, b) { return PCASelVecAbs[a] > PCASelVecAbs[b] ? -1 : PCASelVecAbs[a] < PCASelVecAbs[b] ? 1 : 0; }); // Get the most important features first!
if (len > 8){ // Get only the 8 best dimensions.
indices = indices.slice(0,8);
}
@ -2560,21 +2565,21 @@ if (points.length) { // If points exist (at least 1 point)
var wrapData2 = [];
for (var i=0; i<selectedPoints.length; i++){
var data = [];
for (var j=0; j< Object.keys(dataFeatures[selectedPoints[i].id]).length; j++){
for (m=0; m < len; m++){
if (j == Object.keys(dataFeatures[selectedPoints[i].id]).length -1){
for (var m=0; m <= len; m++){
for (var j=0; j< Object.keys(dataFeatures[selectedPoints[i].id]).length; j++){
if (m == len){
if(format[0] == "diabetes"){
if (Object.values(dataFeatures[selectedPoints[i].id])[j] == 1){
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[j]]:"Positive"}); // Push the values into the pcp
if (Object.values(dataFeatures[selectedPoints[i].id])[m] == 1){
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[m]]:"Positive"}); // Push the values into the pcp
} else{
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[j]]:"Negative"}); // Push the values into the pcp
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[m]]:"Negative"}); // Push the values into the pcp
}
} else{
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[j]]:(Object.values(dataFeatures[selectedPoints[i].id])[j])}); // Push the values into the pcp
}
} else{
if (indices[m] == j){
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[m]]:parseFloat(Object.values(dataFeatures[selectedPoints[i].id])[m]).toFixed(1)}); // Push the values into the pcp
Object.assign(data,{[Object.keys(dataFeatures[selectedPoints[i].id])[indices[m]]]:parseFloat(Object.values(dataFeatures[selectedPoints[i].id])[indices[m]]).toFixed(1)}); // Push the values into the pcp
}
}
}
@ -2582,7 +2587,6 @@ if (points.length) { // If points exist (at least 1 point)
wrapData2.push(data);
}
var CategoryReplaced = Category;
wrapData2.sort(function(a, b){
if(a[CategoryReplaced] < b[CategoryReplaced]) { return -1; }
if(a[CategoryReplaced] > b[CategoryReplaced]) { return 1; }
@ -2611,21 +2615,21 @@ if (points.length) { // If points exist (at least 1 point)
var AllPointsWrapData2 = [];
for (var i=0; i<points.length; i++){
var data = [];
for (var j=0; j< Object.keys(dataFeatures[points[i].id]).length; j++){
for (m=0; m < len; m++){
if (j == Object.keys(dataFeatures[points[i].id]).length -1){
for (var m=0; m <= len; m++){
for (var j=0; j< Object.keys(dataFeatures[points[i].id]).length; j++){
if (m == len){
if(format[0] == "diabetes"){
if (Object.values(dataFeatures[points[i].id])[j] == 1){
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[j]]:"Positive"}); // Push the values into the pcp
if (Object.values(dataFeatures[points[i].id])[m] == 1){
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[m]]:"Positive"}); // Push the values into the pcp
} else{
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[j]]:"Negative"}); // Push the values into the pcp
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[m]]:"Negative"}); // Push the values into the pcp
}
} else{
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[j]]:(Object.values(dataFeatures[points[i].id])[j])}); // Push the values into the pcp
}
} else{
if (indices[m] == j){
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[m]]:parseFloat(Object.values(dataFeatures[points[i].id])[m]).toFixed(1)}); // Push the values into the pcp
Object.assign(data,{[Object.keys(dataFeatures[points[i].id])[indices[m]]]:parseFloat(Object.values(dataFeatures[points[i].id])[indices[m]]).toFixed(1)}); // Push the values into the pcp
}
}
}

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