mathjs/lib/function/matrix/transpose.js
2015-07-18 13:22:38 +02:00

177 lines
4.2 KiB
JavaScript

'use strict';
var clone = require('../../utils/object').clone;
var format = require('../../utils/string').format;
function factory (type, config, load, typed) {
var latex = require('../../utils/latex');
var matrix = load(require('../../type/matrix/function/matrix'));
var DenseMatrix = type.DenseMatrix,
SparseMatrix = type.SparseMatrix;
/**
* Transpose a matrix. All values of the matrix are reflected over its
* main diagonal. Only two dimensional matrices are supported.
*
* Syntax:
*
* math.transpose(x)
*
* Examples:
*
* var A = [[1, 2, 3], [4, 5, 6]];
* math.transpose(A); // returns [[1, 4], [2, 5], [3, 6]]
*
* See also:
*
* diag, inv, subset, squeeze
*
* @param {Array | Matrix} x Matrix to be transposed
* @return {Array | Matrix} The transposed matrix
*/
var transpose = typed('transpose', {
'Array': function (x) {
// use dense matrix implementation
return transpose(matrix(x)).valueOf();
},
'Matrix': function (x) {
// matrix size
var size = x.size();
// result
var c;
// process dimensions
switch (size.length) {
case 1:
// vector
c = x.clone();
break;
case 2:
// rows and columns
var rows = size[0];
var columns = size[1];
// check columns
if (columns === 0) {
// throw exception
throw new RangeError('Cannot transpose a 2D matrix with no columns (size: ' + format(size) + ')');
}
// process storage format
switch (x.storage()) {
case 'dense':
c = _denseTranspose(x, rows, columns);
break;
case 'sparse':
c = _sparseTranspose(x, rows, columns);
break;
}
break;
default:
// multi dimensional
throw new RangeError('Matrix must be a vector or two dimensional (size: ' + format(this._size) + ')');
}
return c;
},
// scalars
'any': function (x) {
return clone(x);
}
});
var _denseTranspose = function (m, rows, columns) {
// matrix array
var data = m._data;
// transposed matrix data
var transposed = [];
var transposedRow;
// loop columns
for (var j = 0; j < columns; j++) {
// initialize row
transposedRow = transposed[j] = [];
// loop rows
for (var i = 0; i < rows; i++) {
// set data
transposedRow[i] = clone(data[i][j]);
}
}
// return matrix
return new DenseMatrix({
data: transposed,
size: [columns, rows],
datatype: m._datatype
});
};
var _sparseTranspose = function (m, rows, columns) {
// matrix arrays
var values = m._values;
var index = m._index;
var ptr = m._ptr;
// result matrices
var cvalues = values ? [] : undefined;
var cindex = [];
var cptr = [];
// row counts
var w = [];
for (var x = 0; x < rows; x++)
w[x] = 0;
// vars
var p, l, j;
// loop values in matrix
for (p = 0, l = index.length; p < l; p++) {
// number of values in row
w[index[p]]++;
}
// cumulative sum
var sum = 0;
// initialize cptr with the cummulative sum of row counts
for (var i = 0; i < rows; i++) {
// update cptr
cptr.push(sum);
// update sum
sum += w[i];
// update w
w[i] = cptr[i];
}
// update cptr
cptr.push(sum);
// loop columns
for (j = 0; j < columns; j++) {
// values & index in column
for (var k0 = ptr[j], k1 = ptr[j + 1], k = k0; k < k1; k++) {
// C values & index
var q = w[index[k]]++;
// C[j, i] = A[i, j]
cindex[q] = j;
// check we need to process values (pattern matrix)
if (values)
cvalues[q] = clone(values[k]);
}
}
// return matrix
return new SparseMatrix({
values: cvalues,
index: cindex,
ptr: cptr,
size: [columns, rows],
datatype: m._datatype
});
};
transpose.toTex = '\\left(${args[0]}\\right)' + latex.operators['transpose'];
return transpose;
}
exports.name = 'transpose';
exports.factory = factory;