BEP-Visualization/pca.html at master · HumanGenomeCenter/BEP-Visualization · GitHub

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<!DOCTYPE html>
<meta charset="utf-8">
<title>Streamgraph</title>
<style>

body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  margin: auto;
  position: relative;
  width: 960px;
}

button {
  position: absolute;
  right: 10px;
  top: 10px;
}

</style>

<script src="./js/numeric-1.2.6.min.js"></script>
<script>
function pca(X) {
	console.log(X);
    /*
        Return matrix of all principle components as column vectors
    */
//	console.log(X[0], X);
	console.time("pca");
    var m = X.length;
    var sigma = numeric.div(numeric.dot(numeric.transpose(X), X), m);
	console.timeEnd("pca");
    return numeric.svd(sigma).U;
}

function pcaReduce(U, k) {
    /*
        Return matrix of k first principle components as column vectors
    */
    return U.map(function(row) {
        return row.slice(0, k)
    });
}

function pcaProject(X, Ureduce) {
    /*
        Project matrix X onto reduced principle components matrix
    */
    return numeric.dot(X, Ureduce);
}

function pcaRecover(Z, Ureduce) {
    /*
        Recover matrix from projection onto reduced principle components
    */
    return numeric.dot(Z, numeric.transpose(Ureduce));
}

window.onload = function() {
    var x, y,z, X = [];

    var noise = function() {return Math.random() * 0.2 - 0.1};

    // Create random dataset with slope of 0.357 and noise
    for (var i = 0; i < 1000; i++) {
        x = Math.random() * 2 - 1;
        y = x * 0.357;
		z = Math.random() * 2 - 1;
		a = Math.random() * 2 - 1;
        X.push([x + noise(), y + noise(), z, a]);
    }

    // Get principle components
    var U = pca(X);
    console.log('U', U);
    // Print slope of first principle component
    document.write(Math.abs(U[0][1] / U[0][0]).toFixed(3));
};


</script>