mathjs/lib/cjs/function/matrix/transpose.js

"use strict";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.createTranspose = void 0;

var _object = require("../../utils/object.js");

var _string = require("../../utils/string.js");

var _factory = require("../../utils/factory.js");

var name = 'transpose';
var dependencies = ['typed', 'matrix'];
var createTranspose = /* #__PURE__ */(0, _factory.factory)(name, dependencies, function (_ref) {
  var typed = _ref.typed,
      matrix = _ref.matrix;

  /**
   * Transpose a matrix. All values of the matrix are reflected over its
   * main diagonal. Only applicable to two dimensional matrices containing
   * a vector (i.e. having size `[1,n]` or `[n,1]`). One dimensional
   * vectors and scalars return the input unchanged.
   *
   * Syntax:
   *
   *     math.transpose(x)
   *
   * Examples:
   *
   *     const 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
   */
  return typed('transpose', {
    Array: function Array(x) {
      // use dense matrix implementation
      return this(matrix(x)).valueOf();
    },
    Matrix: function Matrix(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: ' + (0, _string.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: ' + (0, _string.format)(this._size) + ')');
      }

      return c;
    },
    // scalars
    any: function any(x) {
      return (0, _object.clone)(x);
    }
  });

  function _denseTranspose(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] = (0, _object.clone)(data[i][j]);
      }
    } // return matrix


    return m.createDenseMatrix({
      data: transposed,
      size: [columns, rows],
      datatype: m._datatype
    });
  }

  function _sparseTranspose(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] = (0, _object.clone)(values[k]);
        }
      }
    } // return matrix


    return m.createSparseMatrix({
      values: cvalues,
      index: cindex,
      ptr: cptr,
      size: [columns, rows],
      datatype: m._datatype
    });
  }
});
exports.createTranspose = createTranspose;