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Predictions with markov chains.html
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<!-- ############################################################################################################################## -->
<!-- # John Wiley & Sons, Inc. # -->
<!-- # # -->
<!-- # Book: Algorithms in Bioinformatics: Theory and Implementation # -->
<!-- # Author: Dr. Paul A. Gagniuc # -->
<!-- # # -->
<!-- # Institution: # -->
<!-- # University Politehnica of Bucharest # -->
<!-- # Faculty of Engineering in Foreign Languages # -->
<!-- # Department of Engineering in Foreign Languages # -->
<!-- # # -->
<!-- # Area: European Union # -->
<!-- # Date: 04/01/2021 # -->
<!-- # # -->
<!-- # Mode: Native JavaScript # -->
<!-- # # -->
<!-- # Cite this work as: # -->
<!-- # Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, 2021, ISBN: 9781119697961. # -->
<!-- # # -->
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<h4 class="w3-left">Predictions with Markov Chains on an unlimited number of states</h4>
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" class="w3-right w3-image" style='height:50px;'>
</div>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p>Height:</p>
</div>
<div class="w3-container w3-cell">
<p><input type="range" min="200" max="1000" value="300" class="slider" id="canvas_h"></p>
</div>
<div class="w3-container w3-cell">
<p><div class="slidecontainer" id="info_canvas_h">300 px</div></p>
</div>
<div class="w3-container w3-cell">
<p>Width:</p>
</div>
<div class="w3-container w3-cell">
<p><input type="range" min="200" max="2000" value="1100" class="slider" id="canvas_w"></p>
</div>
<div class="w3-container w3-cell">
<p><div class="slidecontainer" id="info_canvas_w">1100 px</div></p>
</div>
</div>
<canvas id="bio" height="300" width="1100" class="w3-animate-left"></canvas>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p>Steps:</p>
</div>
<div class="w3-container w3-cell">
<p><input type="range" min="2" max="100" value="50" class="slider" id="steps"></p>
</div>
<div class="w3-container w3-cell">
<p><div class="slidecontainer" id="info_steps">50</div></p>
</div>
</div>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p><label>Input sequence</label></p>
<p><div class="slidecontainer" id="info_bp"></div></p>
</div>
<div class="w3-container w3-cell" style="width:900px;">
<p><input class="w3-input w3-border w3-round-xxlarge w3-animate-right" type="text" id="sequence"></p>
</div>
</div>
<hr><label id="show_init_vect"> Initial probability vector:</label><br>
<br>
<div class="slidecontainer" id="initial"></div>
<hr>
Transition matrix from sequence:
<br>
<div class="slidecontainer" id="matrix"></div>
<hr>
<div class="slidecontainer" id="test"></div>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p>
<input class="w3-check" type="checkbox" id="erase" checked="checked">
<label>Show values</label>
</p>
</div>
</div>
<script>
//Input text
document.getElementById("sequence").oninput = function() {
var tmp_b = '(' + document.getElementById("sequence").value.length + " b)";
document.getElementById("info_bp").innerHTML = tmp_b;
tmp = DPD(this.value, '(p)');
document.getElementById("matrix").innerHTML = SMC(tmp[0], tmp[1], tmp[2], x, y);
make(tmp[3], 0);
predict(tmp[0],tmp[3]);
}
//Canvas Height
document.getElementById("canvas_h").oninput = function() {
document.getElementById('bio').height = this.value;
document.getElementById("info_canvas_h").innerHTML = this.value + " px";
predict(tmp[0],tmp[3]);
//y = this.value;
//tmp = DPD(document.getElementById("sequence").value, '(p)');
//document.getElementById("matrix").innerHTML = SMC(tmp[0], tmp[1], tmp[2], x, y);
}
//Canvas Width
document.getElementById("canvas_w").oninput = function() {
document.getElementById('bio').width = this.value;
document.getElementById("info_canvas_w").innerHTML = this.value + " px";
predict(tmp[0],tmp[3]);
//x = this.value;
//tmp = DPD(document.getElementById("sequence").value, '(p)');
//document.getElementById("matrix").innerHTML = SMC(tmp[0], tmp[1], tmp[2], x, y);
}
//Show value
document.getElementById("erase").oninput = function() {
tmp = DPD(document.getElementById("sequence").value, '(p)');
document.getElementById("matrix").innerHTML = SMC(tmp[0], tmp[1], tmp[2], x, y);
}
//Steps
document.getElementById("steps").oninput = function() {
document.getElementById("info_steps").innerHTML = this.value;
tmp = DPD(document.getElementById("sequence").value, '(p)');
document.getElementById("matrix").innerHTML = SMC(tmp[0], tmp[1], tmp[2], x, y);
predict(tmp[0],tmp[3]);
}
//Initial vector
document.getElementById("initial").oninput = function() {
predict(tmp[0],tmp[3]);
}
var z = "ABBAACCCCCGDHWTHFGHFGSHGFJHFGBNBVVBVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVCCACCCCCCCCCC";
//var z = "ABBAACCCCCCCCCCCCCCCCC";
var x=1000;
var y=400;
var tmp;
document.getElementById("info_bp").innerHTML = '(' + z.length + " b)";
document.getElementById("sequence").value = z;
tmp = DPD(z, '(p)');
document.getElementById("matrix").innerHTML = SMC(tmp[0], tmp[1], tmp[2], x, y);
make(tmp[3], 0);
predict(tmp[0],tmp[3]);
function make(a,t){
var r = '';
var l = '';
var b = '';
for(var i=0; i<a.length; i++){
var ch = '';
if(i==t){ch = 'checked';}
b += '<td>' + a[i] + '</td>';
r += '<td><input class="w3-check" type="radio" id="ini' + i + '" name="ini" '+ch+'></td>';
l += '<td><input class="w3-check" type="checkbox" id="line' + i + '" checked="checked"></td>';
}
r = '<table style="table-layout:fixed;text-align:center;vertical-align:middle;border-collapse: collapse;"><tr>' + b + '</tr><tr>' + r + '</tr>' + l + '</tr></table>';
document.getElementById("initial").innerHTML = r;
}
function predict(m, a){
var p;
var chain = document.getElementById("steps").value;
var v = [];
var s = [];
document.getElementById("show_init_vect").innerHTML = ' Initial probability vector: (';
for(var j=0; j<a.length; j++){
v[j]=[];
v[j][0] = 0;
v[j][1] = 0;
if(document.getElementById("ini" + j).checked){v[j][0] = 1;}
//if(document.getElementById("line" + j).checked) {s[j] = v[j][0] + ',';}
s[j] = v[j][0] + ',';
var ch = '';
if(j<a.length-1){ch = ', ';}
document.getElementById("show_init_vect").innerHTML += v[j][0] + ch;
}
document.getElementById("show_init_vect").innerHTML += ')';
for(var k=1; k<=chain; k++){
for(var i=1; i<m.length; i++){
for(var j=1; j<m[0].length; j++){
v[i-1][1] += v[j-1][0] * m[j][i];
}
}
var d = '';
if(k<chain){d = ',';}
for(var i=0; i<a.length; i++){
if(document.getElementById("line" + i).checked) {s[i] += v[i][1] + d;}
v[i][0] = v[i][1];
v[i][1] = 0;
}
}
var e = 'y';
var color = '#000000';
for(var i=0; i<a.length; i++){
if(document.getElementById("ini" + i).checked){color = '#ff0000';} else {color = '#000000';}
if(document.getElementById("line" + i).checked){Chart(s[i], color, e);}
if(document.getElementById("line" + i).checked && e == 'y'){e = 'n';}
}
}
function alpha(s){
//--------------[ Phase one ]--------------|
var a = [];
var k = s.length;
for(var i=0; i<=k; i++){
var q = 1;
for(var j=0; j<=a.length; j++){
if (s[i] === a[j]) {q = 0;}
}
if (q === 1) {a.push(s[i]);}
}
return a;
//--------------[ Phase one ]--------------|
}
// Discrete Probability Detector
function DPD(s, n){
//--------------[ Phase one ]--------------|
var a = alpha(s);
//--------------[ Phase two ]--------------|
var d = a.length-1;
var m = [];
var e = [];
var l = [];
for(var i=0; i<=d; i++){
m[i]=[];
e[i]=[];
for(var j=0; j<=d; j++){
m[i][j]=0;
if (j === 0) {
e[i][0]=a[i];
e[i][1]=0;
}
}
}
//-------------[ Phase three ]-------------|
l[0]="";
l[1]="";
for(var i=0; i<s.length-1; i++){
l[0] = s.substr(i, 1);
l[1] = s.substr(i + 1, 1);
for(var j=0; j<=d; j++){
if (l[0] === e[j][0]) {
e[j][1] = e[j][1] + 1;
r = j;
}
if (l[1] === e[j][0]) {c=j;}
}
m[r][c] = m[r][c] + 1;
}
var max=0;
var min=0;
//-------------[ Phase four ]--------------|
for(var i=0; i<=d; i++){
for(var j=0; j<=d; j++){
if (e[i][1] > 0) {
m[i][j]=(m[i][j]/e[i][1]);
if(m[i][j] > max){max = m[i][j];}
if(m[i][j] < min){min = m[i][j];}
}
}
}
//----------------[ END ]------------------|
var t = [];
for(var i=0; i<=d+1; i++){
t[i]=[];
for(var j=0; j<=d+1; j++){
if (j == 0 && i == 0) {t[i][j] = n;}
if (i>0 && j>0) {t[i][j] = m[i-1][j-1];}
if (i == 0 && j>0 && j<=d+1) {t[i][j]=a[j-1];}
if (j == 0 && i>0 && i<=d+1) {t[i][j]=a[i-1];}
}
}
return [t, max, min, a];
}
// SHOW MATRIX CONTENT
function SMC(v, MMax, MMin, sX, sY) {
var f = '';
var result = "<div class='w3-container w3-cell'><table style='table-layout:fixed;width: "+sX+"px;height: "+sY+"px; text-align:center;vertical-align:middle;border-collapse: collapse;'>";
for(var i=0; i<v.length; i++) {
result += "<tr>";
for(var j=0; j<v[i].length; j++){
var color = getColor(v[i][j], MMin, MMax);
if(document.getElementById("erase").checked) {f=Number(v[i][j]).toFixed(2);}
if (j == 0 && i > 0 || i == 0 && j > 0 || i == 0 && j == 0) {
result += "<td style='color: #fff; background-color: rgb(10,10,10);'>"+v[i][j]+"</td>";
} else {
result += "<td style='color: #fff; background-color: rgb(" + color.r + "," + color.g + "," + color.b + ");'>"+f+"</td>";
}
}
result += "</tr>";
}
result += "</table></div>";
return result;
}
function getColor(v, min, max) {
function getC(f, l, r) {
return {
r: Math.floor((1 - f) * l.r + f * r.r),
g: Math.floor((1 - f) * l.g + f * r.g),
b: Math.floor((1 - f) * l.b + f * r.b),
};
}
var left = { r: 40, g: 40, b: 40 },
middle = { r: 222, g: 222, b: 0 },
right = { r: 255, g: 0, b: 0 },
mid = (max - min) / 2;
return v < min + mid ?
getC((v - min) / mid, left, middle) :
getC((v - min - mid) / mid, middle, right);
}
function Chart(q,c,e) {
var s = q.split(",");
if(s.length<=2){return;}
var mx = 1;
var canvas = document.getElementById('bio');
var w = canvas.width;
var h = canvas.height;
if (canvas.getContext) {
var ctx = canvas.getContext('2d');
if(e=='y'){ctx.clearRect(0, 0, w, h);}
ctx.moveTo(0, 0);
ctx.beginPath();
var d = ((w-80)/s.length);
for (var i=0; i<=s.length-1; i++)
{
var y = h - 15 - (((h-15) / mx) * s[i]);
var x = d * i;
ctx.lineTo(x, y+1);
}
ctx.lineWidth = 2;
ctx.strokeStyle = c;
ctx.stroke();
if(s.length>=2 && s.length<=(w-80) && e=='y'){
ctx.closePath();
ctx.beginPath();
//horisontal divisions
ctx.strokeStyle = '#454545';
//distance between divisions must be visible
if((w/s.length)>=5){
for (var i=0; i<s.length; i++)
{
ctx.moveTo(Math.floor(d*i), h-10);
ctx.lineTo(Math.floor(d*i), h);
}
ctx.stroke();
}
//bottom line
ctx.moveTo(0, h-1);
ctx.lineTo(w-80, h-1);
ctx.stroke();
//left vertical line
ctx.moveTo((w-80), 0);
ctx.lineTo((w-80), h);
ctx.stroke();
//top axis
ctx.moveTo((w-80), 1);
ctx.lineTo((w-50), 1);
ctx.stroke();
//top left vertical line
ctx.moveTo((w-55), 0);
ctx.lineTo((w-55), 14);
ctx.stroke();
//bottom axis
ctx.moveTo((w-80), h-15);
ctx.lineTo((w-70), h-15);
ctx.stroke();
//vertical divisions
for (var i=0; i<10; i++)
{
ctx.moveTo(w-80, ((h-15)/10)*i);
ctx.lineTo(w-70, ((h-15)/10)*i);
}
ctx.stroke();
//MAX value on chart
text = '1';
dim = ctx.measureText(text).width
ctx.save();
ctx.translate((w-80)+18,18);
ctx.rotate(Math.PI / 2);
ctx.font = "18px Arial";
ctx.fillStyle = "#000000";
ctx.textAlign = "left";
ctx.fillText(text, 0, 0);
ctx.restore();
//MAX text
text = 'MAX';
dim = ctx.measureText(text).width
ctx.save();
ctx.translate((w-65)+20,14);
ctx.font = "18px Arial";
ctx.fillStyle = "#000000";
ctx.textAlign = "left";
ctx.fillText(text, 0, 0);
ctx.restore();
text = '0';
dim = ctx.measureText(text).width
ctx.save();
ctx.translate((w-80)+20,h-10);
ctx.font = "18px Arial";
ctx.fillStyle = "#000000";
ctx.textAlign = "left";
ctx.fillText(text, 0, 0);
ctx.restore();
}
}
}
</script>
<hr>
Paul A. Gagniuc. <i>Algorithms in Bioinformatics: Theory and Implementation</i>. John Wiley & Sons, USA, 2021, ISBN: 9781119697961.<br>
Paul A. Gagniuc. <i>Markov Chains: From Theory to Implementation and Experimentation</i>. Hoboken, NJ, John Wiley & Sons, USA, 2017, ISBN: 978-1-119-38755-8.
<hr>