jiangchengfeiyi-xiaochengxu/node_modules/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");
const name = 'transpose';
const dependencies = ['typed', 'matrix'];
const createTranspose = exports.createTranspose = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
  let {
    typed,
    matrix
  } = _ref;
  /**
   * 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(name, {
    Array: x => transposeMatrix(matrix(x)).valueOf(),
    Matrix: transposeMatrix,
    any: _object.clone // scalars
  });
  function transposeMatrix(x) {
    // matrix size
    const size = x.size();

    // result
    let c;

    // process dimensions
    switch (size.length) {
      case 1:
        // vector
        c = x.clone();
        break;
      case 2:
        {
          // rows and columns
          const rows = size[0];
          const 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)(size) + ')');
    }
    return c;
  }
  function _denseTranspose(m, rows, columns) {
    // matrix array
    const data = m._data;
    // transposed matrix data
    const transposed = [];
    let transposedRow;
    // loop columns
    for (let j = 0; j < columns; j++) {
      // initialize row
      transposedRow = transposed[j] = [];
      // loop rows
      for (let 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
    const values = m._values;
    const index = m._index;
    const ptr = m._ptr;
    // result matrices
    const cvalues = values ? [] : undefined;
    const cindex = [];
    const cptr = [];
    // row counts
    const w = [];
    for (let x = 0; x < rows; x++) {
      w[x] = 0;
    }
    // vars
    let 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
    let sum = 0;
    // initialize cptr with the cummulative sum of row counts
    for (let 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 (let k0 = ptr[j], k1 = ptr[j + 1], k = k0; k < k1; k++) {
        // C values & index
        const 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
    });
  }
});