{"id":"node_modules/mathjs/es/type/matrix/DenseMatrix.js","dependencies":[{"name":"/home/hernan/workspace/tests/test-simulador/package.json","includedInParent":true,"mtime":1597264365661},{"name":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/package.json","includedInParent":true,"mtime":1593627585935},{"name":"../../utils/is","loc":{"line":1,"column":84},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/utils/is.js"},{"name":"../../utils/array","loc":{"line":2,"column":97},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/utils/array.js"},{"name":"../../utils/string","loc":{"line":3,"column":23},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/utils/string.js"},{"name":"../../utils/number","loc":{"line":4,"column":26},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/utils/number.js"},{"name":"../../utils/object","loc":{"line":5,"column":39},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/utils/object.js"},{"name":"../../error/DimensionError","loc":{"line":6,"column":31},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/error/DimensionError.js"},{"name":"../../utils/factory","loc":{"line":7,"column":24},"parent":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/type/matrix/DenseMatrix.js","resolved":"/home/hernan/workspace/tests/test-simulador/node_modules/mathjs/es/utils/factory.js"}],"generated":{"js":"\"use strict\";\n\nObject.defineProperty(exports, \"__esModule\", {\n  value: true\n});\nexports.createDenseMatrixClass = void 0;\n\nvar _is = require(\"../../utils/is\");\n\nvar _array = require(\"../../utils/array\");\n\nvar _string = require(\"../../utils/string\");\n\nvar _number = require(\"../../utils/number\");\n\nvar _object = require(\"../../utils/object\");\n\nvar _DimensionError = require(\"../../error/DimensionError\");\n\nvar _factory = require(\"../../utils/factory\");\n\nvar name = 'DenseMatrix';\nvar dependencies = ['Matrix'];\nvar createDenseMatrixClass = /* #__PURE__ */(0, _factory.factory)(name, dependencies, function (_ref) {\n  var Matrix = _ref.Matrix;\n  /**\n   * Dense Matrix implementation. A regular, dense matrix, supporting multi-dimensional matrices. This is the default matrix type.\n   * @class DenseMatrix\n   */\n\n  function DenseMatrix(data, datatype) {\n    if (!(this instanceof DenseMatrix)) {\n      throw new SyntaxError('Constructor must be called with the new operator');\n    }\n\n    if (datatype && !(0, _is.isString)(datatype)) {\n      throw new Error('Invalid datatype: ' + datatype);\n    }\n\n    if ((0, _is.isMatrix)(data)) {\n      // check data is a DenseMatrix\n      if (data.type === 'DenseMatrix') {\n        // clone data & size\n        this._data = (0, _object.clone)(data._data);\n        this._size = (0, _object.clone)(data._size);\n        this._datatype = datatype || data._datatype;\n      } else {\n        // build data from existing matrix\n        this._data = data.toArray();\n        this._size = data.size();\n        this._datatype = datatype || data._datatype;\n      }\n    } else if (data && (0, _is.isArray)(data.data) && (0, _is.isArray)(data.size)) {\n      // initialize fields from JSON representation\n      this._data = data.data;\n      this._size = data.size; // verify the dimensions of the array\n\n      (0, _array.validate)(this._data, this._size);\n      this._datatype = datatype || data.datatype;\n    } else if ((0, _is.isArray)(data)) {\n      // replace nested Matrices with Arrays\n      this._data = preprocess(data); // get the dimensions of the array\n\n      this._size = (0, _array.arraySize)(this._data); // verify the dimensions of the array, TODO: compute size while processing array\n\n      (0, _array.validate)(this._data, this._size); // data type unknown\n\n      this._datatype = datatype;\n    } else if (data) {\n      // unsupported type\n      throw new TypeError('Unsupported type of data (' + (0, _is.typeOf)(data) + ')');\n    } else {\n      // nothing provided\n      this._data = [];\n      this._size = [0];\n      this._datatype = datatype;\n    }\n  }\n\n  DenseMatrix.prototype = new Matrix();\n  /**\n   * Create a new DenseMatrix\n   */\n\n  DenseMatrix.prototype.createDenseMatrix = function (data, datatype) {\n    return new DenseMatrix(data, datatype);\n  };\n  /**\n   * Attach type information\n   */\n\n\n  DenseMatrix.prototype.type = 'DenseMatrix';\n  DenseMatrix.prototype.isDenseMatrix = true;\n  /**\n   * Get the matrix type\n   *\n   * Usage:\n   *    const matrixType = matrix.getDataType()  // retrieves the matrix type\n   *\n   * @memberOf DenseMatrix\n   * @return {string}   type information; if multiple types are found from the Matrix, it will return \"mixed\"\n   */\n\n  DenseMatrix.prototype.getDataType = function () {\n    return (0, _array.getArrayDataType)(this._data, _is.typeOf);\n  };\n  /**\n   * Get the storage format used by the matrix.\n   *\n   * Usage:\n   *     const format = matrix.storage()  // retrieve storage format\n   *\n   * @memberof DenseMatrix\n   * @return {string}           The storage format.\n   */\n\n\n  DenseMatrix.prototype.storage = function () {\n    return 'dense';\n  };\n  /**\n   * Get the datatype of the data stored in the matrix.\n   *\n   * Usage:\n   *     const format = matrix.datatype()   // retrieve matrix datatype\n   *\n   * @memberof DenseMatrix\n   * @return {string}           The datatype.\n   */\n\n\n  DenseMatrix.prototype.datatype = function () {\n    return this._datatype;\n  };\n  /**\n   * Create a new DenseMatrix\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @param {string} [datatype]\n   */\n\n\n  DenseMatrix.prototype.create = function (data, datatype) {\n    return new DenseMatrix(data, datatype);\n  };\n  /**\n   * Get a subset of the matrix, or replace a subset of the matrix.\n   *\n   * Usage:\n   *     const subset = matrix.subset(index)               // retrieve subset\n   *     const value = matrix.subset(index, replacement)   // replace subset\n   *\n   * @memberof DenseMatrix\n   * @param {Index} index\n   * @param {Array | Matrix | *} [replacement]\n   * @param {*} [defaultValue=0]      Default value, filled in on new entries when\n   *                                  the matrix is resized. If not provided,\n   *                                  new matrix elements will be filled with zeros.\n   */\n\n\n  DenseMatrix.prototype.subset = function (index, replacement, defaultValue) {\n    switch (arguments.length) {\n      case 1:\n        return _get(this, index);\n      // intentional fall through\n\n      case 2:\n      case 3:\n        return _set(this, index, replacement, defaultValue);\n\n      default:\n        throw new SyntaxError('Wrong number of arguments');\n    }\n  };\n  /**\n   * Get a single element from the matrix.\n   * @memberof DenseMatrix\n   * @param {number[]} index   Zero-based index\n   * @return {*} value\n   */\n\n\n  DenseMatrix.prototype.get = function (index) {\n    if (!(0, _is.isArray)(index)) {\n      throw new TypeError('Array expected');\n    }\n\n    if (index.length !== this._size.length) {\n      throw new _DimensionError.DimensionError(index.length, this._size.length);\n    } // check index\n\n\n    for (var x = 0; x < index.length; x++) {\n      (0, _array.validateIndex)(index[x], this._size[x]);\n    }\n\n    var data = this._data;\n\n    for (var i = 0, ii = index.length; i < ii; i++) {\n      var indexI = index[i];\n      (0, _array.validateIndex)(indexI, data.length);\n      data = data[indexI];\n    }\n\n    return data;\n  };\n  /**\n   * Replace a single element in the matrix.\n   * @memberof DenseMatrix\n   * @param {number[]} index   Zero-based index\n   * @param {*} value\n   * @param {*} [defaultValue]        Default value, filled in on new entries when\n   *                                  the matrix is resized. If not provided,\n   *                                  new matrix elements will be left undefined.\n   * @return {DenseMatrix} self\n   */\n\n\n  DenseMatrix.prototype.set = function (index, value, defaultValue) {\n    if (!(0, _is.isArray)(index)) {\n      throw new TypeError('Array expected');\n    }\n\n    if (index.length < this._size.length) {\n      throw new _DimensionError.DimensionError(index.length, this._size.length, '<');\n    }\n\n    var i, ii, indexI; // enlarge matrix when needed\n\n    var size = index.map(function (i) {\n      return i + 1;\n    });\n\n    _fit(this, size, defaultValue); // traverse over the dimensions\n\n\n    var data = this._data;\n\n    for (i = 0, ii = index.length - 1; i < ii; i++) {\n      indexI = index[i];\n      (0, _array.validateIndex)(indexI, data.length);\n      data = data[indexI];\n    } // set new value\n\n\n    indexI = index[index.length - 1];\n    (0, _array.validateIndex)(indexI, data.length);\n    data[indexI] = value;\n    return this;\n  };\n  /**\n   * Get a submatrix of this matrix\n   * @memberof DenseMatrix\n   * @param {DenseMatrix} matrix\n   * @param {Index} index   Zero-based index\n   * @private\n   */\n\n\n  function _get(matrix, index) {\n    if (!(0, _is.isIndex)(index)) {\n      throw new TypeError('Invalid index');\n    }\n\n    var isScalar = index.isScalar();\n\n    if (isScalar) {\n      // return a scalar\n      return matrix.get(index.min());\n    } else {\n      // validate dimensions\n      var size = index.size();\n\n      if (size.length !== matrix._size.length) {\n        throw new _DimensionError.DimensionError(size.length, matrix._size.length);\n      } // validate if any of the ranges in the index is out of range\n\n\n      var min = index.min();\n      var max = index.max();\n\n      for (var i = 0, ii = matrix._size.length; i < ii; i++) {\n        (0, _array.validateIndex)(min[i], matrix._size[i]);\n        (0, _array.validateIndex)(max[i], matrix._size[i]);\n      } // retrieve submatrix\n      // TODO: more efficient when creating an empty matrix and setting _data and _size manually\n\n\n      return new DenseMatrix(_getSubmatrix(matrix._data, index, size.length, 0), matrix._datatype);\n    }\n  }\n  /**\n   * Recursively get a submatrix of a multi dimensional matrix.\n   * Index is not checked for correct number or length of dimensions.\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @param {Index} index\n   * @param {number} dims   Total number of dimensions\n   * @param {number} dim    Current dimension\n   * @return {Array} submatrix\n   * @private\n   */\n\n\n  function _getSubmatrix(data, index, dims, dim) {\n    var last = dim === dims - 1;\n    var range = index.dimension(dim);\n\n    if (last) {\n      return range.map(function (i) {\n        (0, _array.validateIndex)(i, data.length);\n        return data[i];\n      }).valueOf();\n    } else {\n      return range.map(function (i) {\n        (0, _array.validateIndex)(i, data.length);\n        var child = data[i];\n        return _getSubmatrix(child, index, dims, dim + 1);\n      }).valueOf();\n    }\n  }\n  /**\n   * Replace a submatrix in this matrix\n   * Indexes are zero-based.\n   * @memberof DenseMatrix\n   * @param {DenseMatrix} matrix\n   * @param {Index} index\n   * @param {DenseMatrix | Array | *} submatrix\n   * @param {*} defaultValue          Default value, filled in on new entries when\n   *                                  the matrix is resized.\n   * @return {DenseMatrix} matrix\n   * @private\n   */\n\n\n  function _set(matrix, index, submatrix, defaultValue) {\n    if (!index || index.isIndex !== true) {\n      throw new TypeError('Invalid index');\n    } // get index size and check whether the index contains a single value\n\n\n    var iSize = index.size();\n    var isScalar = index.isScalar(); // calculate the size of the submatrix, and convert it into an Array if needed\n\n    var sSize;\n\n    if ((0, _is.isMatrix)(submatrix)) {\n      sSize = submatrix.size();\n      submatrix = submatrix.valueOf();\n    } else {\n      sSize = (0, _array.arraySize)(submatrix);\n    }\n\n    if (isScalar) {\n      // set a scalar\n      // check whether submatrix is a scalar\n      if (sSize.length !== 0) {\n        throw new TypeError('Scalar expected');\n      }\n\n      matrix.set(index.min(), submatrix, defaultValue);\n    } else {\n      // set a submatrix\n      // validate dimensions\n      if (iSize.length < matrix._size.length) {\n        throw new _DimensionError.DimensionError(iSize.length, matrix._size.length, '<');\n      }\n\n      if (sSize.length < iSize.length) {\n        // calculate number of missing outer dimensions\n        var i = 0;\n        var outer = 0;\n\n        while (iSize[i] === 1 && sSize[i] === 1) {\n          i++;\n        }\n\n        while (iSize[i] === 1) {\n          outer++;\n          i++;\n        } // unsqueeze both outer and inner dimensions\n\n\n        submatrix = (0, _array.unsqueeze)(submatrix, iSize.length, outer, sSize);\n      } // check whether the size of the submatrix matches the index size\n\n\n      if (!(0, _object.deepStrictEqual)(iSize, sSize)) {\n        throw new _DimensionError.DimensionError(iSize, sSize, '>');\n      } // enlarge matrix when needed\n\n\n      var size = index.max().map(function (i) {\n        return i + 1;\n      });\n\n      _fit(matrix, size, defaultValue); // insert the sub matrix\n\n\n      var dims = iSize.length;\n      var dim = 0;\n\n      _setSubmatrix(matrix._data, index, submatrix, dims, dim);\n    }\n\n    return matrix;\n  }\n  /**\n   * Replace a submatrix of a multi dimensional matrix.\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @param {Index} index\n   * @param {Array} submatrix\n   * @param {number} dims   Total number of dimensions\n   * @param {number} dim\n   * @private\n   */\n\n\n  function _setSubmatrix(data, index, submatrix, dims, dim) {\n    var last = dim === dims - 1;\n    var range = index.dimension(dim);\n\n    if (last) {\n      range.forEach(function (dataIndex, subIndex) {\n        (0, _array.validateIndex)(dataIndex);\n        data[dataIndex] = submatrix[subIndex[0]];\n      });\n    } else {\n      range.forEach(function (dataIndex, subIndex) {\n        (0, _array.validateIndex)(dataIndex);\n\n        _setSubmatrix(data[dataIndex], index, submatrix[subIndex[0]], dims, dim + 1);\n      });\n    }\n  }\n  /**\n   * Resize the matrix to the given size. Returns a copy of the matrix when\n   * `copy=true`, otherwise return the matrix itself (resize in place).\n   *\n   * @memberof DenseMatrix\n   * @param {number[]} size           The new size the matrix should have.\n   * @param {*} [defaultValue=0]      Default value, filled in on new entries.\n   *                                  If not provided, the matrix elements will\n   *                                  be filled with zeros.\n   * @param {boolean} [copy]          Return a resized copy of the matrix\n   *\n   * @return {Matrix}                 The resized matrix\n   */\n\n\n  DenseMatrix.prototype.resize = function (size, defaultValue, copy) {\n    // validate arguments\n    if (!(0, _is.isArray)(size)) {\n      throw new TypeError('Array expected');\n    } // matrix to resize\n\n\n    var m = copy ? this.clone() : this; // resize matrix\n\n    return _resize(m, size, defaultValue);\n  };\n\n  function _resize(matrix, size, defaultValue) {\n    // check size\n    if (size.length === 0) {\n      // first value in matrix\n      var v = matrix._data; // go deep\n\n      while ((0, _is.isArray)(v)) {\n        v = v[0];\n      }\n\n      return v;\n    } // resize matrix\n\n\n    matrix._size = size.slice(0); // copy the array\n\n    matrix._data = (0, _array.resize)(matrix._data, matrix._size, defaultValue); // return matrix\n\n    return matrix;\n  }\n  /**\n   * Reshape the matrix to the given size. Returns a copy of the matrix when\n   * `copy=true`, otherwise return the matrix itself (reshape in place).\n   *\n   * NOTE: This might be better suited to copy by default, instead of modifying\n   *       in place. For now, it operates in place to remain consistent with\n   *       resize().\n   *\n   * @memberof DenseMatrix\n   * @param {number[]} size           The new size the matrix should have.\n   * @param {boolean} [copy]          Return a reshaped copy of the matrix\n   *\n   * @return {Matrix}                 The reshaped matrix\n   */\n\n\n  DenseMatrix.prototype.reshape = function (size, copy) {\n    var m = copy ? this.clone() : this;\n    m._data = (0, _array.reshape)(m._data, size);\n    m._size = size.slice(0);\n    return m;\n  };\n  /**\n   * Enlarge the matrix when it is smaller than given size.\n   * If the matrix is larger or equal sized, nothing is done.\n   * @memberof DenseMatrix\n   * @param {DenseMatrix} matrix           The matrix to be resized\n   * @param {number[]} size\n   * @param {*} defaultValue          Default value, filled in on new entries.\n   * @private\n   */\n\n\n  function _fit(matrix, size, defaultValue) {\n    var // copy the array\n    newSize = matrix._size.slice(0);\n\n    var changed = false; // add dimensions when needed\n\n    while (newSize.length < size.length) {\n      newSize.push(0);\n      changed = true;\n    } // enlarge size when needed\n\n\n    for (var i = 0, ii = size.length; i < ii; i++) {\n      if (size[i] > newSize[i]) {\n        newSize[i] = size[i];\n        changed = true;\n      }\n    }\n\n    if (changed) {\n      // resize only when size is changed\n      _resize(matrix, newSize, defaultValue);\n    }\n  }\n  /**\n   * Create a clone of the matrix\n   * @memberof DenseMatrix\n   * @return {DenseMatrix} clone\n   */\n\n\n  DenseMatrix.prototype.clone = function () {\n    var m = new DenseMatrix({\n      data: (0, _object.clone)(this._data),\n      size: (0, _object.clone)(this._size),\n      datatype: this._datatype\n    });\n    return m;\n  };\n  /**\n   * Retrieve the size of the matrix.\n   * @memberof DenseMatrix\n   * @returns {number[]} size\n   */\n\n\n  DenseMatrix.prototype.size = function () {\n    return this._size.slice(0); // return a clone of _size\n  };\n  /**\n   * Create a new matrix with the results of the callback function executed on\n   * each entry of the matrix.\n   * @memberof DenseMatrix\n   * @param {Function} callback   The callback function is invoked with three\n   *                              parameters: the value of the element, the index\n   *                              of the element, and the Matrix being traversed.\n   *\n   * @return {DenseMatrix} matrix\n   */\n\n\n  DenseMatrix.prototype.map = function (callback) {\n    // matrix instance\n    var me = this;\n\n    var recurse = function recurse(value, index) {\n      if ((0, _is.isArray)(value)) {\n        return value.map(function (child, i) {\n          return recurse(child, index.concat(i));\n        });\n      } else {\n        return callback(value, index, me);\n      }\n    }; // determine the new datatype when the original matrix has datatype defined\n    // TODO: should be done in matrix constructor instead\n\n\n    var data = recurse(this._data, []);\n    var datatype = this._datatype !== undefined ? (0, _array.getArrayDataType)(data, _is.typeOf) : undefined;\n    return new DenseMatrix(data, datatype);\n  };\n  /**\n   * Execute a callback function on each entry of the matrix.\n   * @memberof DenseMatrix\n   * @param {Function} callback   The callback function is invoked with three\n   *                              parameters: the value of the element, the index\n   *                              of the element, and the Matrix being traversed.\n   */\n\n\n  DenseMatrix.prototype.forEach = function (callback) {\n    // matrix instance\n    var me = this;\n\n    var recurse = function recurse(value, index) {\n      if ((0, _is.isArray)(value)) {\n        value.forEach(function (child, i) {\n          recurse(child, index.concat(i));\n        });\n      } else {\n        callback(value, index, me);\n      }\n    };\n\n    recurse(this._data, []);\n  };\n  /**\n   * Create an Array with a copy of the data of the DenseMatrix\n   * @memberof DenseMatrix\n   * @returns {Array} array\n   */\n\n\n  DenseMatrix.prototype.toArray = function () {\n    return (0, _object.clone)(this._data);\n  };\n  /**\n   * Get the primitive value of the DenseMatrix: a multidimensional array\n   * @memberof DenseMatrix\n   * @returns {Array} array\n   */\n\n\n  DenseMatrix.prototype.valueOf = function () {\n    return this._data;\n  };\n  /**\n   * Get a string representation of the matrix, with optional formatting options.\n   * @memberof DenseMatrix\n   * @param {Object | number | Function} [options]  Formatting options. See\n   *                                                lib/utils/number:format for a\n   *                                                description of the available\n   *                                                options.\n   * @returns {string} str\n   */\n\n\n  DenseMatrix.prototype.format = function (options) {\n    return (0, _string.format)(this._data, options);\n  };\n  /**\n   * Get a string representation of the matrix\n   * @memberof DenseMatrix\n   * @returns {string} str\n   */\n\n\n  DenseMatrix.prototype.toString = function () {\n    return (0, _string.format)(this._data);\n  };\n  /**\n   * Get a JSON representation of the matrix\n   * @memberof DenseMatrix\n   * @returns {Object}\n   */\n\n\n  DenseMatrix.prototype.toJSON = function () {\n    return {\n      mathjs: 'DenseMatrix',\n      data: this._data,\n      size: this._size,\n      datatype: this._datatype\n    };\n  };\n  /**\n   * Get the kth Matrix diagonal.\n   *\n   * @memberof DenseMatrix\n   * @param {number | BigNumber} [k=0]     The kth diagonal where the vector will retrieved.\n   *\n   * @returns {Matrix}                     The matrix with the diagonal values.\n   */\n\n\n  DenseMatrix.prototype.diagonal = function (k) {\n    // validate k if any\n    if (k) {\n      // convert BigNumber to a number\n      if ((0, _is.isBigNumber)(k)) {\n        k = k.toNumber();\n      } // is must be an integer\n\n\n      if (!(0, _is.isNumber)(k) || !(0, _number.isInteger)(k)) {\n        throw new TypeError('The parameter k must be an integer number');\n      }\n    } else {\n      // default value\n      k = 0;\n    }\n\n    var kSuper = k > 0 ? k : 0;\n    var kSub = k < 0 ? -k : 0; // rows & columns\n\n    var rows = this._size[0];\n    var columns = this._size[1]; // number diagonal values\n\n    var n = Math.min(rows - kSub, columns - kSuper); // x is a matrix get diagonal from matrix\n\n    var data = []; // loop rows\n\n    for (var i = 0; i < n; i++) {\n      data[i] = this._data[i + kSub][i + kSuper];\n    } // create DenseMatrix\n\n\n    return new DenseMatrix({\n      data: data,\n      size: [n],\n      datatype: this._datatype\n    });\n  };\n  /**\n   * Create a diagonal matrix.\n   *\n   * @memberof DenseMatrix\n   * @param {Array} size                     The matrix size.\n   * @param {number | Matrix | Array } value The values for the diagonal.\n   * @param {number | BigNumber} [k=0]       The kth diagonal where the vector will be filled in.\n   * @param {number} [defaultValue]          The default value for non-diagonal\n   * @param {string} [datatype]              The datatype for the diagonal\n   *\n   * @returns {DenseMatrix}\n   */\n\n\n  DenseMatrix.diagonal = function (size, value, k, defaultValue) {\n    if (!(0, _is.isArray)(size)) {\n      throw new TypeError('Array expected, size parameter');\n    }\n\n    if (size.length !== 2) {\n      throw new Error('Only two dimensions matrix are supported');\n    } // map size & validate\n\n\n    size = size.map(function (s) {\n      // check it is a big number\n      if ((0, _is.isBigNumber)(s)) {\n        // convert it\n        s = s.toNumber();\n      } // validate arguments\n\n\n      if (!(0, _is.isNumber)(s) || !(0, _number.isInteger)(s) || s < 1) {\n        throw new Error('Size values must be positive integers');\n      }\n\n      return s;\n    }); // validate k if any\n\n    if (k) {\n      // convert BigNumber to a number\n      if ((0, _is.isBigNumber)(k)) {\n        k = k.toNumber();\n      } // is must be an integer\n\n\n      if (!(0, _is.isNumber)(k) || !(0, _number.isInteger)(k)) {\n        throw new TypeError('The parameter k must be an integer number');\n      }\n    } else {\n      // default value\n      k = 0;\n    }\n\n    var kSuper = k > 0 ? k : 0;\n    var kSub = k < 0 ? -k : 0; // rows and columns\n\n    var rows = size[0];\n    var columns = size[1]; // number of non-zero items\n\n    var n = Math.min(rows - kSub, columns - kSuper); // value extraction function\n\n    var _value; // check value\n\n\n    if ((0, _is.isArray)(value)) {\n      // validate array\n      if (value.length !== n) {\n        // number of values in array must be n\n        throw new Error('Invalid value array length');\n      } // define function\n\n\n      _value = function _value(i) {\n        // return value @ i\n        return value[i];\n      };\n    } else if ((0, _is.isMatrix)(value)) {\n      // matrix size\n      var ms = value.size(); // validate matrix\n\n      if (ms.length !== 1 || ms[0] !== n) {\n        // number of values in array must be n\n        throw new Error('Invalid matrix length');\n      } // define function\n\n\n      _value = function _value(i) {\n        // return value @ i\n        return value.get([i]);\n      };\n    } else {\n      // define function\n      _value = function _value() {\n        // return value\n        return value;\n      };\n    } // discover default value if needed\n\n\n    if (!defaultValue) {\n      // check first value in array\n      defaultValue = (0, _is.isBigNumber)(_value(0)) ? _value(0).mul(0) // trick to create a BigNumber with value zero\n      : 0;\n    } // empty array\n\n\n    var data = []; // check we need to resize array\n\n    if (size.length > 0) {\n      // resize array\n      data = (0, _array.resize)(data, size, defaultValue); // fill diagonal\n\n      for (var d = 0; d < n; d++) {\n        data[d + kSub][d + kSuper] = _value(d);\n      }\n    } // create DenseMatrix\n\n\n    return new DenseMatrix({\n      data: data,\n      size: [rows, columns]\n    });\n  };\n  /**\n   * Generate a matrix from a JSON object\n   * @memberof DenseMatrix\n   * @param {Object} json  An object structured like\n   *                       `{\"mathjs\": \"DenseMatrix\", data: [], size: []}`,\n   *                       where mathjs is optional\n   * @returns {DenseMatrix}\n   */\n\n\n  DenseMatrix.fromJSON = function (json) {\n    return new DenseMatrix(json);\n  };\n  /**\n   * Swap rows i and j in Matrix.\n   *\n   * @memberof DenseMatrix\n   * @param {number} i       Matrix row index 1\n   * @param {number} j       Matrix row index 2\n   *\n   * @return {Matrix}        The matrix reference\n   */\n\n\n  DenseMatrix.prototype.swapRows = function (i, j) {\n    // check index\n    if (!(0, _is.isNumber)(i) || !(0, _number.isInteger)(i) || !(0, _is.isNumber)(j) || !(0, _number.isInteger)(j)) {\n      throw new Error('Row index must be positive integers');\n    } // check dimensions\n\n\n    if (this._size.length !== 2) {\n      throw new Error('Only two dimensional matrix is supported');\n    } // validate index\n\n\n    (0, _array.validateIndex)(i, this._size[0]);\n    (0, _array.validateIndex)(j, this._size[0]); // swap rows\n\n    DenseMatrix._swapRows(i, j, this._data); // return current instance\n\n\n    return this;\n  };\n  /**\n   * Swap rows i and j in Dense Matrix data structure.\n   *\n   * @param {number} i       Matrix row index 1\n   * @param {number} j       Matrix row index 2\n   * @param {Array} data     Matrix data\n   */\n\n\n  DenseMatrix._swapRows = function (i, j, data) {\n    // swap values i <-> j\n    var vi = data[i];\n    data[i] = data[j];\n    data[j] = vi;\n  };\n  /**\n   * Preprocess data, which can be an Array or DenseMatrix with nested Arrays and\n   * Matrices. 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{ isArray, isBigNumber, isIndex, isMatrix, isNumber, isString, typeOf } from '../../utils/is';\nimport { arraySize, getArrayDataType, reshape, resize, unsqueeze, validate, validateIndex } from '../../utils/array';\nimport { format } from '../../utils/string';\nimport { isInteger } from '../../utils/number';\nimport { clone, deepStrictEqual } from '../../utils/object';\nimport { DimensionError } from '../../error/DimensionError';\nimport { factory } from '../../utils/factory';\nvar name = 'DenseMatrix';\nvar dependencies = ['Matrix'];\nexport var createDenseMatrixClass = /* #__PURE__ */factory(name, dependencies, function (_ref) {\n  var Matrix = _ref.Matrix;\n\n  /**\n   * Dense Matrix implementation. A regular, dense matrix, supporting multi-dimensional matrices. This is the default matrix type.\n   * @class DenseMatrix\n   */\n  function DenseMatrix(data, datatype) {\n    if (!(this instanceof DenseMatrix)) {\n      throw new SyntaxError('Constructor must be called with the new operator');\n    }\n\n    if (datatype && !isString(datatype)) {\n      throw new Error('Invalid datatype: ' + datatype);\n    }\n\n    if (isMatrix(data)) {\n      // check data is a DenseMatrix\n      if (data.type === 'DenseMatrix') {\n        // clone data & size\n        this._data = clone(data._data);\n        this._size = clone(data._size);\n        this._datatype = datatype || data._datatype;\n      } else {\n        // build data from existing matrix\n        this._data = data.toArray();\n        this._size = data.size();\n        this._datatype = datatype || data._datatype;\n      }\n    } else if (data && isArray(data.data) && isArray(data.size)) {\n      // initialize fields from JSON representation\n      this._data = data.data;\n      this._size = data.size; // verify the dimensions of the array\n\n      validate(this._data, this._size);\n      this._datatype = datatype || data.datatype;\n    } else if (isArray(data)) {\n      // replace nested Matrices with Arrays\n      this._data = preprocess(data); // get the dimensions of the array\n\n      this._size = arraySize(this._data); // verify the dimensions of the array, TODO: compute size while processing array\n\n      validate(this._data, this._size); // data type unknown\n\n      this._datatype = datatype;\n    } else if (data) {\n      // unsupported type\n      throw new TypeError('Unsupported type of data (' + typeOf(data) + ')');\n    } else {\n      // nothing provided\n      this._data = [];\n      this._size = [0];\n      this._datatype = datatype;\n    }\n  }\n\n  DenseMatrix.prototype = new Matrix();\n  /**\n   * Create a new DenseMatrix\n   */\n\n  DenseMatrix.prototype.createDenseMatrix = function (data, datatype) {\n    return new DenseMatrix(data, datatype);\n  };\n  /**\n   * Attach type information\n   */\n\n\n  DenseMatrix.prototype.type = 'DenseMatrix';\n  DenseMatrix.prototype.isDenseMatrix = true;\n  /**\n   * Get the matrix type\n   *\n   * Usage:\n   *    const matrixType = matrix.getDataType()  // retrieves the matrix type\n   *\n   * @memberOf DenseMatrix\n   * @return {string}   type information; if multiple types are found from the Matrix, it will return \"mixed\"\n   */\n\n  DenseMatrix.prototype.getDataType = function () {\n    return getArrayDataType(this._data, typeOf);\n  };\n  /**\n   * Get the storage format used by the matrix.\n   *\n   * Usage:\n   *     const format = matrix.storage()  // retrieve storage format\n   *\n   * @memberof DenseMatrix\n   * @return {string}           The storage format.\n   */\n\n\n  DenseMatrix.prototype.storage = function () {\n    return 'dense';\n  };\n  /**\n   * Get the datatype of the data stored in the matrix.\n   *\n   * Usage:\n   *     const format = matrix.datatype()   // retrieve matrix datatype\n   *\n   * @memberof DenseMatrix\n   * @return {string}           The datatype.\n   */\n\n\n  DenseMatrix.prototype.datatype = function () {\n    return this._datatype;\n  };\n  /**\n   * Create a new DenseMatrix\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @param {string} [datatype]\n   */\n\n\n  DenseMatrix.prototype.create = function (data, datatype) {\n    return new DenseMatrix(data, datatype);\n  };\n  /**\n   * Get a subset of the matrix, or replace a subset of the matrix.\n   *\n   * Usage:\n   *     const subset = matrix.subset(index)               // retrieve subset\n   *     const value = matrix.subset(index, replacement)   // replace subset\n   *\n   * @memberof DenseMatrix\n   * @param {Index} index\n   * @param {Array | Matrix | *} [replacement]\n   * @param {*} [defaultValue=0]      Default value, filled in on new entries when\n   *                                  the matrix is resized. If not provided,\n   *                                  new matrix elements will be filled with zeros.\n   */\n\n\n  DenseMatrix.prototype.subset = function (index, replacement, defaultValue) {\n    switch (arguments.length) {\n      case 1:\n        return _get(this, index);\n      // intentional fall through\n\n      case 2:\n      case 3:\n        return _set(this, index, replacement, defaultValue);\n\n      default:\n        throw new SyntaxError('Wrong number of arguments');\n    }\n  };\n  /**\n   * Get a single element from the matrix.\n   * @memberof DenseMatrix\n   * @param {number[]} index   Zero-based index\n   * @return {*} value\n   */\n\n\n  DenseMatrix.prototype.get = function (index) {\n    if (!isArray(index)) {\n      throw new TypeError('Array expected');\n    }\n\n    if (index.length !== this._size.length) {\n      throw new DimensionError(index.length, this._size.length);\n    } // check index\n\n\n    for (var x = 0; x < index.length; x++) {\n      validateIndex(index[x], this._size[x]);\n    }\n\n    var data = this._data;\n\n    for (var i = 0, ii = index.length; i < ii; i++) {\n      var indexI = index[i];\n      validateIndex(indexI, data.length);\n      data = data[indexI];\n    }\n\n    return data;\n  };\n  /**\n   * Replace a single element in the matrix.\n   * @memberof DenseMatrix\n   * @param {number[]} index   Zero-based index\n   * @param {*} value\n   * @param {*} [defaultValue]        Default value, filled in on new entries when\n   *                                  the matrix is resized. If not provided,\n   *                                  new matrix elements will be left undefined.\n   * @return {DenseMatrix} self\n   */\n\n\n  DenseMatrix.prototype.set = function (index, value, defaultValue) {\n    if (!isArray(index)) {\n      throw new TypeError('Array expected');\n    }\n\n    if (index.length < this._size.length) {\n      throw new DimensionError(index.length, this._size.length, '<');\n    }\n\n    var i, ii, indexI; // enlarge matrix when needed\n\n    var size = index.map(function (i) {\n      return i + 1;\n    });\n\n    _fit(this, size, defaultValue); // traverse over the dimensions\n\n\n    var data = this._data;\n\n    for (i = 0, ii = index.length - 1; i < ii; i++) {\n      indexI = index[i];\n      validateIndex(indexI, data.length);\n      data = data[indexI];\n    } // set new value\n\n\n    indexI = index[index.length - 1];\n    validateIndex(indexI, data.length);\n    data[indexI] = value;\n    return this;\n  };\n  /**\n   * Get a submatrix of this matrix\n   * @memberof DenseMatrix\n   * @param {DenseMatrix} matrix\n   * @param {Index} index   Zero-based index\n   * @private\n   */\n\n\n  function _get(matrix, index) {\n    if (!isIndex(index)) {\n      throw new TypeError('Invalid index');\n    }\n\n    var isScalar = index.isScalar();\n\n    if (isScalar) {\n      // return a scalar\n      return matrix.get(index.min());\n    } else {\n      // validate dimensions\n      var size = index.size();\n\n      if (size.length !== matrix._size.length) {\n        throw new DimensionError(size.length, matrix._size.length);\n      } // validate if any of the ranges in the index is out of range\n\n\n      var min = index.min();\n      var max = index.max();\n\n      for (var i = 0, ii = matrix._size.length; i < ii; i++) {\n        validateIndex(min[i], matrix._size[i]);\n        validateIndex(max[i], matrix._size[i]);\n      } // retrieve submatrix\n      // TODO: more efficient when creating an empty matrix and setting _data and _size manually\n\n\n      return new DenseMatrix(_getSubmatrix(matrix._data, index, size.length, 0), matrix._datatype);\n    }\n  }\n  /**\n   * Recursively get a submatrix of a multi dimensional matrix.\n   * Index is not checked for correct number or length of dimensions.\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @param {Index} index\n   * @param {number} dims   Total number of dimensions\n   * @param {number} dim    Current dimension\n   * @return {Array} submatrix\n   * @private\n   */\n\n\n  function _getSubmatrix(data, index, dims, dim) {\n    var last = dim === dims - 1;\n    var range = index.dimension(dim);\n\n    if (last) {\n      return range.map(function (i) {\n        validateIndex(i, data.length);\n        return data[i];\n      }).valueOf();\n    } else {\n      return range.map(function (i) {\n        validateIndex(i, data.length);\n        var child = data[i];\n        return _getSubmatrix(child, index, dims, dim + 1);\n      }).valueOf();\n    }\n  }\n  /**\n   * Replace a submatrix in this matrix\n   * Indexes are zero-based.\n   * @memberof DenseMatrix\n   * @param {DenseMatrix} matrix\n   * @param {Index} index\n   * @param {DenseMatrix | Array | *} submatrix\n   * @param {*} defaultValue          Default value, filled in on new entries when\n   *                                  the matrix is resized.\n   * @return {DenseMatrix} matrix\n   * @private\n   */\n\n\n  function _set(matrix, index, submatrix, defaultValue) {\n    if (!index || index.isIndex !== true) {\n      throw new TypeError('Invalid index');\n    } // get index size and check whether the index contains a single value\n\n\n    var iSize = index.size();\n    var isScalar = index.isScalar(); // calculate the size of the submatrix, and convert it into an Array if needed\n\n    var sSize;\n\n    if (isMatrix(submatrix)) {\n      sSize = submatrix.size();\n      submatrix = submatrix.valueOf();\n    } else {\n      sSize = arraySize(submatrix);\n    }\n\n    if (isScalar) {\n      // set a scalar\n      // check whether submatrix is a scalar\n      if (sSize.length !== 0) {\n        throw new TypeError('Scalar expected');\n      }\n\n      matrix.set(index.min(), submatrix, defaultValue);\n    } else {\n      // set a submatrix\n      // validate dimensions\n      if (iSize.length < matrix._size.length) {\n        throw new DimensionError(iSize.length, matrix._size.length, '<');\n      }\n\n      if (sSize.length < iSize.length) {\n        // calculate number of missing outer dimensions\n        var i = 0;\n        var outer = 0;\n\n        while (iSize[i] === 1 && sSize[i] === 1) {\n          i++;\n        }\n\n        while (iSize[i] === 1) {\n          outer++;\n          i++;\n        } // unsqueeze both outer and inner dimensions\n\n\n        submatrix = unsqueeze(submatrix, iSize.length, outer, sSize);\n      } // check whether the size of the submatrix matches the index size\n\n\n      if (!deepStrictEqual(iSize, sSize)) {\n        throw new DimensionError(iSize, sSize, '>');\n      } // enlarge matrix when needed\n\n\n      var size = index.max().map(function (i) {\n        return i + 1;\n      });\n\n      _fit(matrix, size, defaultValue); // insert the sub matrix\n\n\n      var dims = iSize.length;\n      var dim = 0;\n\n      _setSubmatrix(matrix._data, index, submatrix, dims, dim);\n    }\n\n    return matrix;\n  }\n  /**\n   * Replace a submatrix of a multi dimensional matrix.\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @param {Index} index\n   * @param {Array} submatrix\n   * @param {number} dims   Total number of dimensions\n   * @param {number} dim\n   * @private\n   */\n\n\n  function _setSubmatrix(data, index, submatrix, dims, dim) {\n    var last = dim === dims - 1;\n    var range = index.dimension(dim);\n\n    if (last) {\n      range.forEach(function (dataIndex, subIndex) {\n        validateIndex(dataIndex);\n        data[dataIndex] = submatrix[subIndex[0]];\n      });\n    } else {\n      range.forEach(function (dataIndex, subIndex) {\n        validateIndex(dataIndex);\n\n        _setSubmatrix(data[dataIndex], index, submatrix[subIndex[0]], dims, dim + 1);\n      });\n    }\n  }\n  /**\n   * Resize the matrix to the given size. Returns a copy of the matrix when\n   * `copy=true`, otherwise return the matrix itself (resize in place).\n   *\n   * @memberof DenseMatrix\n   * @param {number[]} size           The new size the matrix should have.\n   * @param {*} [defaultValue=0]      Default value, filled in on new entries.\n   *                                  If not provided, the matrix elements will\n   *                                  be filled with zeros.\n   * @param {boolean} [copy]          Return a resized copy of the matrix\n   *\n   * @return {Matrix}                 The resized matrix\n   */\n\n\n  DenseMatrix.prototype.resize = function (size, defaultValue, copy) {\n    // validate arguments\n    if (!isArray(size)) {\n      throw new TypeError('Array expected');\n    } // matrix to resize\n\n\n    var m = copy ? this.clone() : this; // resize matrix\n\n    return _resize(m, size, defaultValue);\n  };\n\n  function _resize(matrix, size, defaultValue) {\n    // check size\n    if (size.length === 0) {\n      // first value in matrix\n      var v = matrix._data; // go deep\n\n      while (isArray(v)) {\n        v = v[0];\n      }\n\n      return v;\n    } // resize matrix\n\n\n    matrix._size = size.slice(0); // copy the array\n\n    matrix._data = resize(matrix._data, matrix._size, defaultValue); // return matrix\n\n    return matrix;\n  }\n  /**\n   * Reshape the matrix to the given size. Returns a copy of the matrix when\n   * `copy=true`, otherwise return the matrix itself (reshape in place).\n   *\n   * NOTE: This might be better suited to copy by default, instead of modifying\n   *       in place. For now, it operates in place to remain consistent with\n   *       resize().\n   *\n   * @memberof DenseMatrix\n   * @param {number[]} size           The new size the matrix should have.\n   * @param {boolean} [copy]          Return a reshaped copy of the matrix\n   *\n   * @return {Matrix}                 The reshaped matrix\n   */\n\n\n  DenseMatrix.prototype.reshape = function (size, copy) {\n    var m = copy ? this.clone() : this;\n    m._data = reshape(m._data, size);\n    m._size = size.slice(0);\n    return m;\n  };\n  /**\n   * Enlarge the matrix when it is smaller than given size.\n   * If the matrix is larger or equal sized, nothing is done.\n   * @memberof DenseMatrix\n   * @param {DenseMatrix} matrix           The matrix to be resized\n   * @param {number[]} size\n   * @param {*} defaultValue          Default value, filled in on new entries.\n   * @private\n   */\n\n\n  function _fit(matrix, size, defaultValue) {\n    var // copy the array\n    newSize = matrix._size.slice(0);\n\n    var changed = false; // add dimensions when needed\n\n    while (newSize.length < size.length) {\n      newSize.push(0);\n      changed = true;\n    } // enlarge size when needed\n\n\n    for (var i = 0, ii = size.length; i < ii; i++) {\n      if (size[i] > newSize[i]) {\n        newSize[i] = size[i];\n        changed = true;\n      }\n    }\n\n    if (changed) {\n      // resize only when size is changed\n      _resize(matrix, newSize, defaultValue);\n    }\n  }\n  /**\n   * Create a clone of the matrix\n   * @memberof DenseMatrix\n   * @return {DenseMatrix} clone\n   */\n\n\n  DenseMatrix.prototype.clone = function () {\n    var m = new DenseMatrix({\n      data: clone(this._data),\n      size: clone(this._size),\n      datatype: this._datatype\n    });\n    return m;\n  };\n  /**\n   * Retrieve the size of the matrix.\n   * @memberof DenseMatrix\n   * @returns {number[]} size\n   */\n\n\n  DenseMatrix.prototype.size = function () {\n    return this._size.slice(0); // return a clone of _size\n  };\n  /**\n   * Create a new matrix with the results of the callback function executed on\n   * each entry of the matrix.\n   * @memberof DenseMatrix\n   * @param {Function} callback   The callback function is invoked with three\n   *                              parameters: the value of the element, the index\n   *                              of the element, and the Matrix being traversed.\n   *\n   * @return {DenseMatrix} matrix\n   */\n\n\n  DenseMatrix.prototype.map = function (callback) {\n    // matrix instance\n    var me = this;\n\n    var recurse = function recurse(value, index) {\n      if (isArray(value)) {\n        return value.map(function (child, i) {\n          return recurse(child, index.concat(i));\n        });\n      } else {\n        return callback(value, index, me);\n      }\n    }; // determine the new datatype when the original matrix has datatype defined\n    // TODO: should be done in matrix constructor instead\n\n\n    var data = recurse(this._data, []);\n    var datatype = this._datatype !== undefined ? getArrayDataType(data, typeOf) : undefined;\n    return new DenseMatrix(data, datatype);\n  };\n  /**\n   * Execute a callback function on each entry of the matrix.\n   * @memberof DenseMatrix\n   * @param {Function} callback   The callback function is invoked with three\n   *                              parameters: the value of the element, the index\n   *                              of the element, and the Matrix being traversed.\n   */\n\n\n  DenseMatrix.prototype.forEach = function (callback) {\n    // matrix instance\n    var me = this;\n\n    var recurse = function recurse(value, index) {\n      if (isArray(value)) {\n        value.forEach(function (child, i) {\n          recurse(child, index.concat(i));\n        });\n      } else {\n        callback(value, index, me);\n      }\n    };\n\n    recurse(this._data, []);\n  };\n  /**\n   * Create an Array with a copy of the data of the DenseMatrix\n   * @memberof DenseMatrix\n   * @returns {Array} array\n   */\n\n\n  DenseMatrix.prototype.toArray = function () {\n    return clone(this._data);\n  };\n  /**\n   * Get the primitive value of the DenseMatrix: a multidimensional array\n   * @memberof DenseMatrix\n   * @returns {Array} array\n   */\n\n\n  DenseMatrix.prototype.valueOf = function () {\n    return this._data;\n  };\n  /**\n   * Get a string representation of the matrix, with optional formatting options.\n   * @memberof DenseMatrix\n   * @param {Object | number | Function} [options]  Formatting options. See\n   *                                                lib/utils/number:format for a\n   *                                                description of the available\n   *                                                options.\n   * @returns {string} str\n   */\n\n\n  DenseMatrix.prototype.format = function (options) {\n    return format(this._data, options);\n  };\n  /**\n   * Get a string representation of the matrix\n   * @memberof DenseMatrix\n   * @returns {string} str\n   */\n\n\n  DenseMatrix.prototype.toString = function () {\n    return format(this._data);\n  };\n  /**\n   * Get a JSON representation of the matrix\n   * @memberof DenseMatrix\n   * @returns {Object}\n   */\n\n\n  DenseMatrix.prototype.toJSON = function () {\n    return {\n      mathjs: 'DenseMatrix',\n      data: this._data,\n      size: this._size,\n      datatype: this._datatype\n    };\n  };\n  /**\n   * Get the kth Matrix diagonal.\n   *\n   * @memberof DenseMatrix\n   * @param {number | BigNumber} [k=0]     The kth diagonal where the vector will retrieved.\n   *\n   * @returns {Matrix}                     The matrix with the diagonal values.\n   */\n\n\n  DenseMatrix.prototype.diagonal = function (k) {\n    // validate k if any\n    if (k) {\n      // convert BigNumber to a number\n      if (isBigNumber(k)) {\n        k = k.toNumber();\n      } // is must be an integer\n\n\n      if (!isNumber(k) || !isInteger(k)) {\n        throw new TypeError('The parameter k must be an integer number');\n      }\n    } else {\n      // default value\n      k = 0;\n    }\n\n    var kSuper = k > 0 ? k : 0;\n    var kSub = k < 0 ? -k : 0; // rows & columns\n\n    var rows = this._size[0];\n    var columns = this._size[1]; // number diagonal values\n\n    var n = Math.min(rows - kSub, columns - kSuper); // x is a matrix get diagonal from matrix\n\n    var data = []; // loop rows\n\n    for (var i = 0; i < n; i++) {\n      data[i] = this._data[i + kSub][i + kSuper];\n    } // create DenseMatrix\n\n\n    return new DenseMatrix({\n      data: data,\n      size: [n],\n      datatype: this._datatype\n    });\n  };\n  /**\n   * Create a diagonal matrix.\n   *\n   * @memberof DenseMatrix\n   * @param {Array} size                     The matrix size.\n   * @param {number | Matrix | Array } value The values for the diagonal.\n   * @param {number | BigNumber} [k=0]       The kth diagonal where the vector will be filled in.\n   * @param {number} [defaultValue]          The default value for non-diagonal\n   * @param {string} [datatype]              The datatype for the diagonal\n   *\n   * @returns {DenseMatrix}\n   */\n\n\n  DenseMatrix.diagonal = function (size, value, k, defaultValue) {\n    if (!isArray(size)) {\n      throw new TypeError('Array expected, size parameter');\n    }\n\n    if (size.length !== 2) {\n      throw new Error('Only two dimensions matrix are supported');\n    } // map size & validate\n\n\n    size = size.map(function (s) {\n      // check it is a big number\n      if (isBigNumber(s)) {\n        // convert it\n        s = s.toNumber();\n      } // validate arguments\n\n\n      if (!isNumber(s) || !isInteger(s) || s < 1) {\n        throw new Error('Size values must be positive integers');\n      }\n\n      return s;\n    }); // validate k if any\n\n    if (k) {\n      // convert BigNumber to a number\n      if (isBigNumber(k)) {\n        k = k.toNumber();\n      } // is must be an integer\n\n\n      if (!isNumber(k) || !isInteger(k)) {\n        throw new TypeError('The parameter k must be an integer number');\n      }\n    } else {\n      // default value\n      k = 0;\n    }\n\n    var kSuper = k > 0 ? k : 0;\n    var kSub = k < 0 ? -k : 0; // rows and columns\n\n    var rows = size[0];\n    var columns = size[1]; // number of non-zero items\n\n    var n = Math.min(rows - kSub, columns - kSuper); // value extraction function\n\n    var _value; // check value\n\n\n    if (isArray(value)) {\n      // validate array\n      if (value.length !== n) {\n        // number of values in array must be n\n        throw new Error('Invalid value array length');\n      } // define function\n\n\n      _value = function _value(i) {\n        // return value @ i\n        return value[i];\n      };\n    } else if (isMatrix(value)) {\n      // matrix size\n      var ms = value.size(); // validate matrix\n\n      if (ms.length !== 1 || ms[0] !== n) {\n        // number of values in array must be n\n        throw new Error('Invalid matrix length');\n      } // define function\n\n\n      _value = function _value(i) {\n        // return value @ i\n        return value.get([i]);\n      };\n    } else {\n      // define function\n      _value = function _value() {\n        // return value\n        return value;\n      };\n    } // discover default value if needed\n\n\n    if (!defaultValue) {\n      // check first value in array\n      defaultValue = isBigNumber(_value(0)) ? _value(0).mul(0) // trick to create a BigNumber with value zero\n      : 0;\n    } // empty array\n\n\n    var data = []; // check we need to resize array\n\n    if (size.length > 0) {\n      // resize array\n      data = resize(data, size, defaultValue); // fill diagonal\n\n      for (var d = 0; d < n; d++) {\n        data[d + kSub][d + kSuper] = _value(d);\n      }\n    } // create DenseMatrix\n\n\n    return new DenseMatrix({\n      data: data,\n      size: [rows, columns]\n    });\n  };\n  /**\n   * Generate a matrix from a JSON object\n   * @memberof DenseMatrix\n   * @param {Object} json  An object structured like\n   *                       `{\"mathjs\": \"DenseMatrix\", data: [], size: []}`,\n   *                       where mathjs is optional\n   * @returns {DenseMatrix}\n   */\n\n\n  DenseMatrix.fromJSON = function (json) {\n    return new DenseMatrix(json);\n  };\n  /**\n   * Swap rows i and j in Matrix.\n   *\n   * @memberof DenseMatrix\n   * @param {number} i       Matrix row index 1\n   * @param {number} j       Matrix row index 2\n   *\n   * @return {Matrix}        The matrix reference\n   */\n\n\n  DenseMatrix.prototype.swapRows = function (i, j) {\n    // check index\n    if (!isNumber(i) || !isInteger(i) || !isNumber(j) || !isInteger(j)) {\n      throw new Error('Row index must be positive integers');\n    } // check dimensions\n\n\n    if (this._size.length !== 2) {\n      throw new Error('Only two dimensional matrix is supported');\n    } // validate index\n\n\n    validateIndex(i, this._size[0]);\n    validateIndex(j, this._size[0]); // swap rows\n\n    DenseMatrix._swapRows(i, j, this._data); // return current instance\n\n\n    return this;\n  };\n  /**\n   * Swap rows i and j in Dense Matrix data structure.\n   *\n   * @param {number} i       Matrix row index 1\n   * @param {number} j       Matrix row index 2\n   * @param {Array} data     Matrix data\n   */\n\n\n  DenseMatrix._swapRows = function (i, j, data) {\n    // swap values i <-> j\n    var vi = data[i];\n    data[i] = data[j];\n    data[j] = vi;\n  };\n  /**\n   * Preprocess data, which can be an Array or DenseMatrix with nested Arrays and\n   * Matrices. Replaces all nested Matrices with Arrays\n   * @memberof DenseMatrix\n   * @param {Array} data\n   * @return {Array} data\n   */\n\n\n  function preprocess(data) {\n    for (var i = 0, ii = data.length; i < ii; i++) {\n      var elem = data[i];\n\n      if (isArray(elem)) {\n        data[i] = preprocess(elem);\n      } else if (elem && elem.isMatrix === true) {\n        data[i] = preprocess(elem.valueOf());\n      }\n    }\n\n    return data;\n  }\n\n  return DenseMatrix;\n}, {\n  isClass: true\n});"},"lineCount":null}},"error":null,"hash":"3cdea50b3496e98f60a567004d1541fc","cacheData":{"env":{}}}