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pass test(s)
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@jdfekete
jdfekete committed Aug 11, 2015
1 parent ac40b54 commit c063faf
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241 changes: 17 additions & 224 deletions reorder.v1.js
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Original file line number Diff line number Diff line change
Expand Up @@ -2524,229 +2524,15 @@ function sumcols(v) {
return sumcol;
}

function multiply_by_transpose(a,b) {
var m = a.length,
n = b.length,
p = b[0].length,
i = -1,
j,
k;
if (p !== a[0].length) throw {"error": "columns(a) != rows(b); " + a[0].length + " != " + p};
var ab = new Array(m);
while (++i < m) {
ab[i] = new Array(n);
j = -1; while(++j < n) {
var s = 0;
k = -1; while (++k < p) s += a[i][k] * b[j][k];
ab[i][j] = s;
}
}
return ab;
}

// Implementation according to:
// http://web.univ-pau.fr/RECHERCHE/SET/LAFFLY/docs_laffly/INTRODUCTION_AFC.pdf
function ca_no_svd(v, eps) {
var n = v.length,
o = v[0].length,
sumrow = sumrows(v),
sumcol = sumcols(v),
s = sum(sumcol),
i, j, row,
tmp = Array(n),
orig = v;

for (i = 0; i < n; i++) {
row = v[i].slice();
for (j = 0; j < o; j++) {
row[j] /= s;
}
tmp[i] = row;
sumrow[i] /= s;
}
for (j = 0; j < o; j++)
sumcol[j] /= s;

v = tmp; // get rid of original v
if (reorder.debug) {
console.log('normalized matrix:');
reorder.printmat(v);
console.log('sumrow:'+sumrow);
console.log('sumcol:'+sumcol);
}

var pi = Array(n), // pre inertia matrix
ep; // don't store the expected values

for (i = 0; i < n; i++) {
row = pi[i] = Array(o);
for (j = 0; j < o; j++) {
ep = sumrow[i]*sumcol[j];
row[j] = (v[i][j] - ep) / Math.sqrt(ep);
}
}
if (reorder.debug) {
console.log('Pre inertia:');
reorder.printmat(pi);
}

var wc = Array(n); // weighted coordinates

for (i = 0; i < n; i++) {
row = wc[i] = Array(o);
for (j = 0; j < o; j++) {
ep = sumrow[i]*sumcol[j];
row[j] = (v[i][j] - ep) / (sumcol[j]*Math.sqrt(sumrow[i]));
}
}
if (reorder.debug) {
console.log('weighted coordinates:');
reorder.printmat(wc);
}

var inertia = multiply_by_transpose(pi, pi);
if (reorder.debug) {
console.log('inertia:');
reorder.printmat(inertia);
}

var eigenvector = reorder.poweriteration_n(inertia,1, null, eps),
eigenvalue = eigenvector[1][0];
eigenvector = eigenvector[0][0];
if (reorder.debug)
console.log('Eigenvalue: '+eigenvalue);

var cols = Array(o);
for (j = 0; j < o; j++) {
cols[j] = wc[0][j]*eigenvalue;
}
if (reorder.debug) {
console.log('Col vector:'+cols);
}

var rows = Array(n);
for (i = 0; i < n; i++) {
s = 0;
for (j = 0; j < o; j++) {
s += v[i][j]*cols[j];
}
rows[i] = s/(sumrow[i]*Math.sqrt(eigenvalue));
}
if (reorder.debug) {
console.log('Row vector:'+rows);
}

return [rows, cols];
}

function rescale(vec, vmin, vmax) {
var min = vec[0],
max = vec[0],
i = vec.length,
v,
scale;
while (i-- > 1) {
v = vec[i];
if (v < min)
min = v;
else if (v > max)
max = v;
}
scale = (vmax-vmin) / (max-min);
for (i = 0; i < vec.length; i++)
vec[i] = (vec[i]-min)*scale+vmin;
return (max - min);
}

function round10(vec) {
var n = vec.length;
while (n--)
vec[n] = Math.round(vec[n]*10)*0.1;
}

// Hill, M. O. 1973.
// Reciprocal averaging: an eigenvector method of ordination.
// J. Ecol. 61:237-49
// http://www.britishecologicalsociety.org/100papers/100_Ecological_Papers/100_Influential_Papers_035.pdf
function ca_reciprocal_averaging(v, eps, init, max_iter) {
var n = v.length,
o = v[0].length,
sumrow = sumrows(v),
sumcol = sumcols(v),
i, j, row, s, iter,
rows = reorder.zeroes(n),
cols = reorder.zeroes(o),
prev_cols = reorder.zeroes(n),
tmp,
eigenvalue;

if (! max_iter || max_iter < 10)
max_iter = 100;

if (! eps)
eps = 1e-9;

if (reorder.debug) {
console.log('n:'+n);
console.log('o:'+o);
console.log('sumrow:'+sumrow);
console.log('sumcol:'+sumcol);
}

if (init) {
for (j = 0; j < o; j++) {
cols[j] = init[j];
}
}
else{
for (j = 0; j < o; j++) {
cols[j] = sumcol[j] / n;
}
}
rescale(cols, 0, 100);
for (iter = 0; iter < max_iter; iter++) {
if (reorder.debug)
reorder.printvec(cols, 1, null, 'cols at iter '+iter+'=');
for (i = 0; i < n; i++) {
s = 0;
row = v[i];
for (j = 0; j < o; j++) {
s += cols[j]*row[j];
}
rows[i] = s / sumrow[i];
}
round10(rows);
if (reorder.debug)
reorder.printvec(rows, 1, null, 'rows at iter '+iter+'=');
for (j = 0; j < o; j++) {
s = 0;
for (i = 0; i < n; i++) {
s += rows[i]*v[i][j];
}
cols[j] = s / sumcol[j];
}
if (reorder.debug)
reorder.printvec(cols, 1, null, 'cols at iter '+iter+'=');
eigenvalue = rescale(cols, 0, 100);
if (reorder.distance.euclidean(cols, prev_cols) < (reorder.length(cols)*eps))
break;
for (j = 0; j < o; j++) {
prev_cols[j] = cols[j];
}
}
eigenvalue /= 100;
if (reorder.debug)
console.log('eigenvalue: '+eigenvalue);
return [rows, cols, eigenvalue];
}

reorder.ca_reciprocal_averaging = ca_reciprocal_averaging;

reorder.ca_no_svd = ca_no_svd;

reorder.ca = ca_no_svd;

// Implementation of the decorana fortran code
// See Hill, M. O. 1979. DECORANA - A FORTRAN program for detrended
// correspondence analysis an reciprocal averaging. Cornell University,
// Ithaca, New York.
// And
// Hill, M. O. 1973. Reciprocal averaging: an eigenvector method of
// ordination. J. Ecol. 61:237-49
// The Fortan implementation is available in the "vegan" R package:
// https://cran.r-project.org/web/packages/vegan/index.html

function decorana(dat) {
var ZEROEIG = 1e-7, // consider as zero eigenvalue
Expand Down Expand Up @@ -2776,8 +2562,6 @@ function decorana(dat) {
return s1;
}

reorder.ca_decorana = decorana;

function trans(y, yy, x, aidot, mi, n, dat,prt) {
var i, j, a1;
if (prt) console.log('TRANS '+prt);
Expand Down Expand Up @@ -3071,6 +2855,15 @@ function solve_tridiag(tol, a11, a12, a22, a23, a33, a34, a44) {
return [ax1, ax2, ax3, ax4];
}

reorder.ca_decorana = decorana;
reorder.ca = decorana;

reorder.ca_order = function(dat) {
var res = reorder.ca(dat);
return { rows: reorder.sort_order(res.rows),
cols: reorder.sort_order(res.cols),
details: res };
};

/*jshint loopfunc:true */
reorder.cuthill_mckee = function(graph, comp) {
Expand Down
2 changes: 1 addition & 1 deletion reorder.v1.min.js
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