qminer
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A C++ based data analytics platform for processing large-scale real-time streams containing structured and unstructured data
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<h1>Source: ladoc.js</h1>
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<pre class="prettyprint linenums"><code>/**
* Copyright (c) 2015, Jozef Stefan Institute, Quintelligence d.o.o. and contributors
* All rights reserved.
*
* This source code is licensed under the FreeBSD license found in the
* LICENSE file in the root directory of this source tree.
*/
/**
* Linear algebra module.
* @module la
* @example
* // import la module
* var la = require('qminer').la;
* // create a random matrix
* var mat = new la.Matrix({ rows: 10, cols: 5, random: true });
* // create a vector
* var vec = new la.Vector([1, 2, 3, 0, -1]);
* // multiply the matrix and vector
* var vec2 = mat.multiply(vec);
* // calculate the svd decomposition of the matrix
* var svd = la.svd(mat, 3);
*/
/**
* Computes the truncated SVD decomposition.
* @param {module:la.Matrix | module:la.SparseMatrix} mat - The matrix.
* @param {number} k - The number of singular vectors to be computed.
* @param {Object} [json] - The JSON object.
* @param {number} [json.iter = 100] - The number of iterations used for the algorithm.
* @param {number} [json.tol = 1e-6] - The tolerance number.
* @param {function} [callback] - The callback function, that takes the error parameters (err) and the result parameter (res).
* &lt;i>Only for the asynchronous function.&lt;/i>
* @returns {Object} The JSON object `svdRes` which contains the SVD decomposition U*S*V^T matrices:
* &lt;br>`svdRes.U` - The dense matrix of the decomposition. Type {@link module:la.Matrix}.
* &lt;br>`svdRes.V` - The dense matrix of the decomposition. Type {@link module:la.Matrix}.
* &lt;br>`svdRes.s` - The vector containing the singular values of the decomposition. Type {@link module:la.Vector}.
* @example &lt;caption>Asynchronous function&lt;/caption>
* // import the modules
* var la = require('qminer').la;
* // create a random matrix
* var A = new la.Matrix({ rows: 10, cols: 5, random: true });
* // set the parameters for the calculation
* var k = 2; // number of singular vectors
* var param = { iter: 1000, tol: 1e-4 };
* // calculate the svd
* la.svd(A, k, param, function (err, result) {
* if (err) { console.log(err); }
* // successful calculation
* var U = result.U;
* var V = result.V;
* var s = result.s;
* });
* @example &lt;caption>Synchronous function&lt;/caption>
* // import the modules
* var la = require('qminer').la;
* // create a random matrix
* var A = new la.Matrix({ rows: 10, cols: 5, random: true });
* // set the parameters for the calculation
* var k = 2; // number of singular vectors
* var param = { iter: 1000, tol: 1e-4 };
* // calculate the svd
* var result = la.svd(A, k, param);
* // successful calculation
* var U = result.U;
* var V = result.V;
* var s = result.s;
*/
exports.prototype.svd = function (mat, k, json) { return { U: Object.create(require('qminer').la.Matrix.prototype), V: Object.create(require('qminer').la.Matrix.prototype), s: Object.create(require('qminer').la.Vector.prototype) } }
/**
* Computes the QR decomposition.
* @param {module:la.Matrix} mat - The matrix.
* @param {number} [tol = 1e-6] - The tolerance number.
* @returns {Object} A JSON object `qrRes` which contains the decomposition matrices:
* &lt;br>`qrRes.Q` - The orthogonal matrix Q of the QR decomposition. Type {@link module:la.Matrix}.
* &lt;br>`qrRes.R` - The upper triangular matrix R of the QR decomposition. Type {@link module:la.Matrix}.
* @example
* // import la module
* var la = require('qminer').la;
* // create a random matrix
* var mat = new la.Matrix({ rows: 10, cols: 5, random: true });
* // calculate the QR decomposition of mat
* var qrRes = la.qr(mat);
*/
exports.prototype.qr = function (mat, tol) { return { Q: Object.create(require('qminer').la.Matrix.prototype), R: Object.create(require('qminer').la.Matrix.prototype) } }
/**
* Copyright (c) 2015, Jozef Stefan Institute, Quintelligence d.o.o. and contributors
* All rights reserved.
*
* This source code is licensed under the FreeBSD license found in the
* LICENSE file in the root directory of this source tree.
*/
/**
* Matrix constructor parameter object.
* @typedef {Object} matrixArg
* @property {number} rows - Number of rows.
* @property {number} cols - Number of columns.
* @property {boolean} [random=false] - Generate a random matrix with entries sampled from a uniform [0,1] distribution. If set to false, a zero matrix is created.
*/
/**
* Matrix class.
* @classdesc Represents a dense matrix (2d array), wraps a C++ object implemented in glib/base/ds.h.
* @class
* @param {(module:la~matrixArg | Array&lt;Array&lt;number>> | module:la.Matrix)} [arg] - Constructor arguments. There are three ways of constructing:
* &lt;br>1. Using the parameter object {@link module:la~matrixArg},
* &lt;br>2. using a nested array of matrix elements (row major). Example: [[1,2],[3,4]] has two rows, the first row is [1,2],
* &lt;br>3. using a dense matrix (copy constructor).
* @example
* // import la module
* var la = require('qminer').la;
* // create new matrix with matrixArg
* var mat = new la.Matrix({"rows": 3, "cols": 2, "random": true}); // creates a 3 x 2 matrix with random values
* // create a new matrix with nested arrays
* var mat2 = new la.Matrix([[1, 7, 4], [-10, 0, 3]]); // creates a 2 x 3 matrix with the designated values
*/
exports.Matrix = function(arg) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Returns an element of matrix.
* @param {number} rowIdx - Row index (zero based).
* @param {number} colIdx - Column index (zero based).
* @returns {number} The matrix element.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[2, 3], [-2, -2], [-3, 1]]);
* // get the value at the index (2, 1)
* var value = mat.at(2, 1); // returns the value 1
*/
exports.Matrix.prototype.at = function(rowIdx, colIdx) { return 0.0; }
/**
* Sets an element or a block of matrix.
* @param {number} rowIdx - Row index (zero based).
* @param {number} colIdx - Column index (zero based).
* @param {(number | module:la.Matrix)} arg - A number or a matrix. If arg is of type {@link module:la.Matrix}, it gets copied, where the argument's upper left corner, &lt;code>arg.at(0,0)&lt;/code>, gets copied to position (&lt;code>rowIdx&lt;/code>, &lt;code>colIdx&lt;/code>).
* @returns {module:la.Matrix} Self. The (&lt;code>rowIdx&lt;/code>, &lt;code>colIdx&lt;/code>) value/block is changed.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]);
* var arg = new la.Matrix([[10, 11], [12, 13]]);
* mat.put(0, 1, arg);
* // updates the matrix to
* // 1 10 11
* // 4 12 13
* // 7 8 9
*/
exports.Matrix.prototype.put = function(rowIdx, colIdx, arg) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Right-hand side multiplication of matrix with parameter.
* @param {(number | module:la.Vector | module:la.SparseVector | module:la.Matrix | module:la.SparseMatrix)} arg - Multiplication input. Supports scalar, vector and matrix input.
* @returns {(module:la.Matrix | module:la.Vector)}
* &lt;br>1. {@link module:la.Matrix}, if &lt;code>arg&lt;/code> is a number, {@link module:la.Matrix} or {@link module:la.SparseMatrix}.
* &lt;br>2. {@link module:la.Vector}, if &lt;code>arg&lt;/code> is a {@link module:la.Vector} or {@link module:la.SparseVector}.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[1, 2], [-1, 5]]);
* // create a new vector
* var vec = new la.Vector([1, -1]);
* //multiply mat and vec
* var vec2 = mat.multiply(vec); // returns vector [-1, -6]
*/
exports.Matrix.prototype.multiply = function(arg) { return (arg instanceof require('qminer').la.Vector | arg instanceof require('qminer').la.SparseVector) ? Object.create(require('qminer').la.Vector.prototype) : Object.create(require('qminer').la.Matrix.prototype); }
/**
* Matrix transpose and right-hand side multiplication of matrix with parameter.
* @param {(number | module:la.Vector | module:la.SparseVector | module:la.Matrix | module:la.SparseMatrix)} arg - Multiplication input. Supports scalar, vector and matrix input.
* @returns {(module:la.Matrix | module:la.Vector)}
* &lt;br>1. {@link module:la.Matrix}, if &lt;code>arg&lt;/code> is a number, {@link module:la.Matrix} or a {@link module:la.SparseMatrix}.
* &lt;br>2. {@link module:la.Vector}, if &lt;code>arg&lt;/code> is a {@link module:la.Vector} or a {@link module:la.SparseVector}.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[1, 2], [-1, 5]]);
* // create a new vector
* var vec = new la.Vector([1, -1]);
* //multiply mat and vec
* var vec2 = mat.multiplyT(vec); // returns vector [2, -3]
*/
exports.Matrix.prototype.multiplyT = function(arg) { return (arg instanceof require('qminer').la.Vector | arg instanceof require('qminer').la.SparseVector) ? Object.create(require('qminer').la.Vector.prototype) : Object.create(require('qminer').la.Matrix.prototype); }
/**
* Adds two matrices.
* @param {module:la.Matrix} mat - The second matrix.
* @returns {module:la.Matrix} The sum of the matrices.
* @example
* // import la module
* var la = require('qminer').la;
* // create two matrices
* var mat = new la.Matrix([[1, 2], [-1, 5]]);
* var mat2 = new la.Matrix([[1, -1], [3, 2]]);
* // add the matrices
* // the return matrix is
* // 2 1
* // 2 7
* var sum = mat.plus(mat2);
*/
exports.Matrix.prototype.plus = function(mat2) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Substracts two matrices.
* @param {module:la.Matrix} mat - The second matrix.
* @returns {module:la.Matrix} The difference of the matrices.
* @example
* // import la module
* var la = require('qminer').la;
* // create two matrices
* var mat = new la.Matrix([[1, 2], [-1, 5]]);
* var mat2 = new la.Matrix([[1, -1], [3, 2]]);
* // substract the matrices
* // the return matrix is
* // 0 3
* // -4 3
* var diff = mat.minus(mat2);
*/
exports.Matrix.prototype.minus = function(mat2) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Transposes the matrix.
* @returns {module:la.Matrix} Transposed matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a matrix
* var mat = new la.Matrix([[2, -5], [3, 1]]);
* // transpose the matrix
* // the return matrix is
* // 2 3
* // -5 1
* var trans = mat.transpose();
*/
exports.Matrix.prototype.transpose = function() { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Solves the linear system.
* @param {module:la.Vector} vec - The right-hand side of the equation.
* @returns {module:la.Vector} The solution of the linear system.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var M = new la.Matrix([[1, 2], [-1, -5]]);
* // create a new vector
* var b = new la.Vector([-1, -6]);
* // solve the linear system M*x = b
* var x = M.solve(b); // returns vector [1, -1]
*/
exports.Matrix.prototype.solve = function (vec) { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Calculates the matrix row norms.
* @returns {module:la.Vector} Vector, where the value at i-th index is the norm of the i-th row of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[3, 4], [4, 15/2]]);
* // get the row norms of the matrix
* var rowNorms = mat.rowNorms(); // returns the vector [5, 17/2]
*/
exports.Matrix.prototype.rowNorms = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Calculates the matrix column norms.
* @returns {module:la.Vector} Vector, where the value at i-th index is the norm of the i-th column of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[3, 4], [4, 15/2]]);
* // get the row norms of the matrix
* var rowNorms = mat.colNorms(); // returns the vector [5, 17/2]
*/
exports.Matrix.prototype.colNorms = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Normalizes each column of matrix.
* @returns {module:la.Matrix} Self. The columns of matrix are normalized.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[3, 4], [4, 15/2]]);
* // normalize the columns of the matrix
* // the matrix is going to be of the form:
* // 3/5 8/17
* // 4/5 15/17
* mat.normalizeCols();
*/
exports.Matrix.prototype.normalizeCols = function () { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Returns the matrix as string.
* @returns {string} Dense matrix as string.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[1, 2], [3, 5]]);
* // get matrix as string
* var text = mat.toString(); // returns `1 2 \n3 5 \n\n`
*/
exports.Matrix.prototype.toString = function () { return ""; }
/**
* Transforms the matrix from dense to sparse format.
* @returns {module:la.SparseMatrix} Sparse column matrix representation of dense matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [0, 3], [-4, 0]]);
* // transform the matrix into the sparse form
* var spMat = mat.sparse();
*/
exports.Matrix.prototype.sparse = function () { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns the frobenious norm of matrix.
* @returns {number} Frobenious norm of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 4]]);
* // get the frobenious norm of the matrix
* var frob = mat.frob(); // returns the value Math.sqrt(30)
*/
exports.Matrix.prototype.frob = function () { return 0.0; }
/**
* Gives the number of rows of matrix. Type `number`.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 1], [-4, 5]]);
* // get the number of rows
* var rowN = mat.rows; // returns 3
*/
exports.Matrix.prototype.rows = 0;
/**
* Gives the number of columns of matrix. Type `number`.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 1], [-4, 5]]);
* // get the number of cols
* var colsN = mat.cols; // returns 2
*/
exports.Matrix.prototype.cols = 0;
/**
* Gives the index of the maximum element in the given row.
* @param {number} rowIdx - Row index (zero based).
* @returns {number} Column index (zero based) of the maximum value in the &lt;code>rowIdx&lt;/code>-th row of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 1], [-4, 5]]);
* // get the column id of the maximum value of the second row
* var maxRow = mat.rowMaxIdx(1); // returns the value 0
*/
exports.Matrix.prototype.rowMaxIdx = function (rowIdx) { return 0; }
/**
* Gives the index of the maximum element in the given column.
* @param {number} colIdx - Column index (zero based).
* @returns {number} Row index (zero based) of the maximum value in &lt;code>colIdx&lt;/code>-th column of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 1], [-4, 5]]);
* // get the row id of the maximum value of the second column
* var maxRow = mat.colMaxIdx(1); // returns the value 2
*/
exports.Matrix.prototype.colMaxIdx = function (colIdx) { return 0; }
/**
* Returns the corresponding column of matrix as vector.
* @param {number} colIdx - Column index (zero based).
* @returns {module:la.Vector} The &lt;code>colIdx&lt;/code>-th column of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 1], [-4, 5]]);
* // get the second column of the matrix
* var col = mat.getCol(1);
*/
exports.Matrix.prototype.getCol = function (colIdx) { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Sets the column of the matrix.
* @param {number} colIdx - Column index (zero based).
* @param {module:la.Vector} vec - The new column of matrix.
* @returns {module:la.Matrix} Self. The &lt;code>colIdx&lt;/code>-th column is changed.
* @example
* // import la module
* var la = require('qminer').la;
* // create a matrix
* var mat = new la.Matrix([[1, -3, 2], [9, 2, -4], [-2, 3, 3]]);
* // create a vector
* var vec = new la.Vector([-3, 2, 2]);
* // set the first column of the matrix with the vector
* // the changed matrix is now
* // -3 -3 2
* // 2 2 -4
* // 2 3 3
* mat.setCol(0, vec);
*/
exports.Matrix.prototype.setCol = function (colIdx, vec) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Gets the submatrix from the column ids.
* @param {module:la.IntVector} intVec - The vector containing the column ids.
* @returns {module:la.Matrix} The submatrix containing the the columns of the original matrix.
* @example
* //import la module
* var la = require('qminer').la;
* // create a random matrix
* var mat = new la.Matrix({ rows: 10, cols: 10, random: true });
* // get the submatrix containing the 1, 2 and 4 column
* var submat = mat.getColSubmatrix(new la.IntVector([0, 1, 3]));
*/
exports.Matrix.prototype.getColSubmatrix = function (intVec) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Gets the submatrix from the column ids.
* @param {number} minRow - The minimum row index.
* @param {number} maxRow - The maximum row index.
* @param {number} minCol - The minimum column index.
* @param {number} maxCol - The maximum column index.
* @returns {module:la.Matrix} The submatrix of the original matrix.
* @example
* //import la module
* var la = require('qminer').la;
* // create a random matrix
* var mat = new la.Matrix({ rows: 10, cols: 10, random: true });
* // get the submatrix containing from the position (1, 2) to (7, 4)
* var submat = mat.getSubmatrix(1, 7, 2, 4);
*/
exports.Matrix.prototype.getSubmatrix = function (minRow, maxRow, minCol, maxCol) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Returns the corresponding row of matrix as vector.
* @param {number} rowIdx - Row index (zero based).
* @returns {module:la.Vector} The &lt;code>rowIdx&lt;/code>-th row of matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create the matrix
* var mat = new la.Matrix([[1, 2], [3, 1], [-4, 5]]);
* // get the first row of the matrix
* var row = mat.getRow(1);
*/
exports.Matrix.prototype.getRow = function (rowIdx) { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Sets the row of matrix.
* @param {number} rowIdx - Row index (zero based).
* @param {module:la.Vector} vec - The new row of matrix.
* @returns {module:la.Matrix} Self. The &lt;code>rowIdx&lt;/code>-th row is changed.
* @example
* // import la module
* var la = require('qminer').la;
* // create a matrix
* var mat = new la.Matrix([[1, -3, 2], [9, 2, -4], [-2, 3, 3]]);
* // create a vector
* var vec = new la.Vector([-3, 2, 2]);
* // set the first row of the matrix with the vector
* // the changed matrix is now
* // -3 2 2
* // 9 2 -4
* // -2 3 3
* mat.setRow(0, vec);
*/
exports.Matrix.prototype.setRow = function (rowIdx, vec) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Returns the diagonal elements of matrix.
* @returns {module:la.Vector} Vector containing the diagonal elements.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new matrix
* var mat = new la.Matrix([[1, -1, 0], [15, 8, 3], [0, 1, 0]]);
* // call diag function
* var vec = mat.diag(); // returns a vector [1, 8, 0]
*/
exports.Matrix.prototype.diag = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Saves the matrix as output stream.
* @param {module:fs.FOut} fout - Output stream.
* @returns {module:fs.FOut} The output stream &lt;code>fout&lt;/code>.
* @example
* // import the modules
* var fs = require('qminer').fs;
* var la = require('qminer').la;
* // create new matrix
* var mat = new la.Matrix([[1, 2], [3, 4]]);
* // open write stream
* var fout = fs.openWrite('mat.dat');
* // save matrix and close write stream
* mat.save(fout).close();
*/
exports.Matrix.prototype.save = function (fout) { return Object.create(require('qminer').fs.FOut.prototype); }
/**
* Loads the matrix from input stream.
* @param {module:fs.FIn} fin - Input stream.
* @returns {module:la.Matrix} Self. It is made out of the input stream &lt;code>fin&lt;/code>.
* @example
* // import the modules
* var fs = require('qminer').fs;
* var la = require('qminer').la;
* // create an empty matrix
* var mat = new la.Matrix();
* // open a read stream ('mat.dat' is pre-saved)
* var fin = fs.openRead('mat.dat');
* // load the matrix
* mat.load(fin);
*/
exports.Matrix.prototype.load = function (FIn) { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Sparse Vector.
* @classdesc Sparse vector is an array of (int,double) pairs that represent column indices and values.
* @class
* @param {(Array&lt;Array&lt;number>> | module:la.SparseVector)} [arg] - Constructor arguments. There are two ways of constructing:
* &lt;br>1. Using a nested array of vector elements. Example: `[[0, 2],[2, 3]]` has two nonzero values, first value is 2 at position 0, second value is 3 at position 2,
* &lt;br>2. using a sparse vector (copy constructor).
* @param {number} [dim] - Maximum length of sparse vector. &lt;i>It is only in combinantion with nested array of vector elements.&lt;/i>
* @example
* // import la module
* var la = require('qminer').la;
* // create new sparse vector with arrays
* var spVec = new la.SparseVector([[0, 1], [2, 3], [3, 6]]); // sparse vector [1, 0, 3, 6]
* // create new sparse vector with dim
* var spVec2 = new la.SparseVector([[0, 1], [2, 3], [3, 6]], 5); // largest index (zero based) is 4
*/
exports.SparseVector = function(arg, dim) { return Object.create(require('qminer').la.SparseVector.prototype); }
/**
* Returns an element of the sparse vector.
* @param {number} idx - Index (zero based).
* @returns {number} Sparse vector element.
* @example
* // import la module
* var la = require('qminer').la;
* // create a sparse vector
* var vec = new la.SparseVector([[0, 1], [3, 2], [4, -5]]);
* // get the value at the position 3
* vec.at(3); // returns the value 2
*/
exports.SparseVector.prototype.at = function (idx) { return 0.0; }
/**
* Puts a new element in sparse vector.
* @param {number} idx - Index (zero based).
* @param {number} num - Input value.
* @returns {module:la.SparseVector} Self. It puts/changes the values with the index `idx` to the value `num`.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var vec = new la.SparseVector([[0, 1], [3, 2], [4, -5]]);
* // set the new values at position 2
* vec.put(2, -4);
*/
exports.SparseVector.prototype.put = function (idx, num) { return Object.create(require('qminer').la.SparseVector.prototype); }
/**
* Returns the sum of all values in sparse vector.
* @returns {number} The sum of all values in sparse vector.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var vec = new la.SparseVector([[0, 1], [3, 2], [4, -5]]);
* // get the sum of the values in the vector
* vec.sum(); // returns -2
*/
exports.SparseVector.prototype.sum = function () { return 0.0; }
/**
* Returns the inner product of the parameter and the sparse vector.
* @param {(module:la.Vector | module:la.SparseVector)} arg - The inner product input.
* @returns {number} The inner product of the two vectors.
* @example
* // import la module
* var la = require('qminer').la;
* // create two vectors, one sparse and one dense
* var sparse = new la.SparseVector([[0, 1], [3, 2], [4, -5]]);
* var dense = new la.Vector([3, -4, 2, 0.5, -1]);
* // get the inner product of the vectors
* sparse.inner(dense); // returns the value 9
*/
exports.SparseVector.prototype.inner = function (arg) { return 0.0; }
/**
* Multiplies the sparse vector with a scalar.
* @param {number} num - The scalar.
* @returns {module:la.SparseVector} The product of `num` and sparse vector.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var spVec = new la.SparseVector([[0, 1], [2, 3], [3, 6]]);
* // multiply sparse vector with scalar 3.14
* var spVec2 = spVec.multiply(3.14); // returns sparse vector [3.14, 0, 9.42, 18.84]
*/
exports.SparseVector.prototype.multiply = function (num) { return Object.create(require('qminer').la.SparseVector.prototype); }
/**
* Normalizes the sparse vector.
* @returns {module:la.SparseVector} Self. The vector is normalized.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var spVec = new la.SparseVector([[0, 1], [2, 3], [3, 6]]);
* // normalize the sparse vector
* spVec.normalize();
*/
exports.SparseVector.prototype.normalize = function () { return Object.create(require('qminer').la.SparseVector.prototype); }
/**
* Returns the number of non-zero values. Type `number`.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var vec = new la.SparseVector([[0, 2], [3, 1], [7, 5], [11, 4]]);
* // check the number of nonzero values in sparse vector
* // returns 4
* var nonz = vec.nnz;
*/
exports.SparseVector.prototype.nnz = 0;
/**
* Returns the dimension of sparse vector. Type `number`.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector and designate the dimension of the vector
* var vec = new la.SparseVector([[0, 2], [3, 1], [7, 5], [11, 4]], 15);
* // get the dimension of the sparse vector
* // returns 15
* var dim = vec.dim;
*/
exports.SparseVector.prototype.dim = 0;
/**
* Returns the norm of sparse vector.
* @returns {number} Norm of sparse vector.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var vec = new la.SparseVector([[0, 2], [3, 1], [7, 5], [11, 4]]);
* // get the norm of the vector
* var norm = vec.norm();
*/
exports.SparseVector.prototype.norm = function () { return 0.0; }
/**
* Returns the dense vector representation of the sparse vector.
* @returns {module:la.Vector} The dense vector representation.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var vec = new la.SparseVector([[0, 2], [3, 1], [7, 5], [11, 4]]);
* // create a dense representation of the vector
* var dense = vec.full();
*/
exports.SparseVector.prototype.full = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Returns a dense vector of values of non-zero elements of sparse vector.
* @returns {module:la.Vector} A dense vector of values.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var vec = new la.SparseVector([[0, 2], [3, 1], [7, 5], [11, 4]]);
* // get the non-zero values of the sparse vector
* var valVec = vec.valVec();
*/
exports.SparseVector.prototype.valVec = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Returns a dense vector of indices (zero based) of non-zero elements of sparse vector.
* @returns {module:la.Vector} A dense vector of indeces.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sprase vector
* var vec = new la.SparseVector([[0, 2], [3, 1], [7, 5], [11, 4]]);
* // get the non-zero indeces of the sparse vector
* var idxVec = vec.idxVec();
*/
exports.SparseVector.prototype.idxVec = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Returns the string representation.
* @returns {string} The string representation of the sparse vector.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var spVec = new la.SparseVector([[0, 1], [2, 3]]);
* // get the string representation of the vector
* spVec.toString(); // returns the string '[(0, 1), (2, 3)]'
*/
exports.SparseVector.prototype.toString = function () { return ""; }
/**
* Sparse Matrix
* @classdesc Sparse Matrix is represented as a dense vector of sparse vectors which correspond to matrix columns.
* @class
* @param {(Array&lt;Array&lt;Array&lt;number>>> | module:la.SparseMatrix)} [arg] - Constructor arguments. There are two ways of constructing:
* &lt;br>1. using a nested array of sparse vectors (columns). A sparse vector is a nested array of pairs, first value is index, second is value. Example: [[[0, 2]], [[0, 1], [2, 3]]] has 2 columns.
* The second non-zero element in second column has a value 3 at index 2,
* &lt;br>2. using a sparse matrix (copy constructor).
* @param {number} [rows] - Maximal number of rows in sparse vector. &lt;i>It is only in combinantion with nested array of vector elements.&lt;/i>
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix with array
* var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
* // create a new sparse matrix with specified max rows
* var mat2 = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]], 3);
*/
exports.SparseMatrix = function(arg) { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns an element of the sparse matrix at the given location.
* @param {number} rowIdx - Row index (zero based).
* @param {number} colIdx - Column index (zero based).
* @returns {number} Matrix value.
* @example
* // import la module
* var la = require('qminer').la;
* // create a sparse matrix
* var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
* // get the value at the position (1, 1)
* mat.at(1, 1); // returns 3
*/
exports.SparseMatrix.prototype.at = function (rowIdx, colIdx) { return 0.0; }
/**
* Puts an element in sparse matrix.
* @param {number} rowIdx - Row index (zero based).
* @param {number} colIdx - Column index (zero based).
* @param {number} num - Element value.
* @returns {module:la.SparseMatrix} Self. The value at position (`rowIdx`, `colIdx`) is changed.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 3], [1, 2]], [[1, -2], [3, 4]], [[10, 8]]]);
* // set the value at position (2, 2) to -4
* mat.put(2, 2, -4);
*/
exports.SparseMatrix.prototype.put = function (rowIdx, colIdx, num) { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns the column of the sparse matrix.
* @param {number} colIdx - The column index (zero based).
* @returns {module:la.SparseVector} Sparse vector corresponding to the `colIdx`-th column of sparse matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a sparse matrix
* var mat = new la.SparseMatrix([[[0, 3], [1, 2]], [[1, -2], [3, 4]], [[10, 8]]]);
* // get the first column as a vector
* var first = mat.getCol(0); // returns the first column of the sparse matrix
*/
exports.SparseMatrix.prototype.getCol = function (colIdx) { return Object.create(require('qminer').la.SparseVector.prototype); }
/**
* Sets a column in sparse matrix.
* @param {number} colIdx - Column index (zero based).
* @param {module:la.SparseVector} spVec - The new column sparse vector.
* @returns {module:la.SparseMatrix} Self. The `colIdx`-th column has been replaced with `spVec`.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 3], [1, 2]], [[1, -2], [3, 4]], [[10, 8]]]);
* // create a new sparse vector to replace the third column
* var vec = new la.SparseVector([[0, 3], [2, -5]]);
* // set the third column of mat to vec
* mat.setCol(2, vec); // returns mat with the third column changed
*/
exports.SparseMatrix.prototype.setCol = function (colIdx, spVec) { return Object.create(require('qminer').la.SparseVector.prototype); }
/**
* Attaches a column to the sparse matrix.
* @param {module:la.SparseVector} spVec - Attached column as sparse vector.
* @returns {module:la.SparseMatrix} Self. The last column is now the added `spVec` and the number of columns is now bigger by one.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse vector
* var mat = new la.SparseMatrix([[[0, 2], [3, 5]], [[1, -3]]]);
* // create a new vector
* var vec = new la.SparseVector([[0, 2], [2, -3]]);
* // push the newly created vector to the matrix
* // the new matrix is going to be (in sparse form)
* // 2 0 2
* // 0 -3 0
* // 0 0 -3
* // 5 0 0
* mat.push(vec);
*/
exports.SparseMatrix.prototype.push = function (spVec) { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Multiplies argument with sparse maatrix.
* @param {(number | module:la.Vector | module:la.SparseVector | module:la.Matrix | module:la.SparseMatrix)} arg - Multiplication input.
* @returns {(module:la.Vector | module:la.Matrix)}
* &lt;br>1. {@link module:la.Matrix}, if `arg` is number, {@link module:la.Matrix} or {@link module:la.SparseMatrix}.
* &lt;br>2. {@link module:la.Vector}, if `arg` is {@link module:la.Vector} or {@link module:la.SparseVector}.
* @example
* // import la module
* var la = require('qminer').la;
* // create new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2], [3, 5]], [[1, -3]]]);
* // create a vector
* var vec = new la.Vector([3, -2]);
* // multiply the matrix and vector
* var vec2 = mat.multiply(vec);
*/
exports.SparseMatrix.prototype.multiply = function (arg) { return (arg instanceof require('qminer').la.Vector | arg instanceof require('qminer').la.SparseVector) ? Object.create(require('qminer').la.Vector.prototype) : Object.create(require('qminer').la.Matrix.prototype); }
/**
* Sparse matrix transpose and multiplies with argument.
* @param {(number | module:la.Vector | module:la.SparseVector | module:la.Matrix | module:la.SparseMatrix)} arg - Multiplication input.
* @returns {(module:la.Vector | module:la.Matrix)}
* &lt;br>1. {@link module:la.Matrix}, if `arg` is number, {@link module:la.Matrix} or {@link module:la.SparseMatrix}.
* &lt;br>2. {@link module:la.Vector}, if `arg` is {@link module:la.Vector} or {@link module:la.SparseVector}.
* @example
* // import la module
* var la = require('qminer').la;
* // create new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2], [3, 5]], [[1, -3]]]);
* // create a dense matrix
* var mat2 = new la.Matrix([[0, 1], [2, 3], [4, 5], [-1, 3]]);
* // transpose mat and multiply it with mat2
* var mat3 = mat.multiplyT(mat2);
*/
exports.SparseMatrix.prototype.multiplyT = function (arg) { return (arg instanceof require('qminer').la.Vector | arg instanceof require('qminer').la.SparseVector) ? Object.create(require('qminer').la.Vector.prototype) : Object.create(require('qminer').la.Matrix.prototype); }
/**
* Addition of two matrices.
* @param {module:la.SparseMatrix} mat - The second sparse matrix.
* @returns {module:la.SparseMatrix} Sum of the two sparse matrices.
* @example
* // import la module
* var la = require('qminer').la;
* // create two sparse matrices
* var mat = new la.SparseMatrix([[[0, 1], [3, 2]], [[1, -3]]]);
* var mat2 = new la.SparseMatrix([[[0, 3]],[[2, 1]]]);
* // get the sum of the two matrices
* // returns the sum ( insparse form)
* // 4 0
* // 0 -3
* // 0 1
* // 2 0
* var sum = mat.plus(mat2);
*/
exports.SparseMatrix.prototype.plus = function (spMat) { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Substraction of two matrices.
* @param {module:la.SparseMatrix} mat - The second sparse matrix.
* @returns {module:la.SparseMatrix} The difference of the two sparse matrices.
* @example
* // import la module
* var la = require('qminer').la;
* // create two sparse matrices
* var mat = new la.SparseMatrix([[[0, 1], [3, 2]], [[1, -3]]]);
* var mat2 = new la.SparseMatrix([[[0, 3]],[[2, 1]]]);
* // get the sum of the two matrices
* // returns the sum ( insparse form)
* // -2 0
* // 0 -3
* // 0 -1
* // 2 0
* var diff = mat.minus(mat2);
*/
exports.SparseMatrix.prototype.minus = function (spMat) { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns the transposed sparse matrix.
* @returns {module:la.SparseMatrix} Transposed sparse matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]);
* // transpose the sparse matrix
* // returns the transposed matrix (in sparse form)
* // 2 0 -3 0
* // 0 1 0 -2
* mat.transpose();
*/
exports.SparseMatrix.prototype.transpose = function () { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns a submatrix containing only selected columns.
* Columns are identified by a vector of ids.
* @param {module:la.IntVector} columnIdVec - Integer vector containing selected column ids.
* @returns {module:la.SparseMatrix} The submatrix containing the the columns of the original matrix.
* @example
* //import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]);
* // get the submatrix containing the 1, 2 and 4 column
* var submat = mat.getColSubmatrix(new la.IntVector([1]));
*/
exports.SparseMatrix.prototype.getColSubmatrix = function (columnIdVec) { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Clear content of the matrix and sets its row dimension to -1.
* @returns {module:la.SparseMatrix} Self. All the content has been cleared.
* @example
* //import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]);
* // clear the matrix
* mat.clear();
*/
exports.SparseMatrix.prototype.clear = function () { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns the vector of column norms of sparse matrix.
* @returns {module:la.Vector} Vector of column norms. Ihe i-th value of the return vector is the norm of i-th column of sparse matrix.
* @example
* //import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]);
* // get the column norms
* var norms = mat.colNorms();
*/
exports.SparseMatrix.prototype.colNorms = function () { return Object.create(require('qminer').la.Vector.prototype); }
/**
* Normalizes columns of sparse matrix.
* @returns {module:la.SparseMatrix} Self. The columns of the sparse matrix are normalized.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
* // normalize matrix columns
* // The new matrix elements are:
* // 1 0.316227
* // 0 0
* // 0 0.948683
* mat.normalizeCols();
*/
exports.SparseMatrix.prototype.normalizeCols = function () { return Object.create(require('qminer').la.SparseMatrix.prototype); }
/**
* Returns the dense representation of sparse matrix.
* @returns {module:la.Matrix} Dense representation of sparse matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
* // create a dense representation of sparse matrix
* // returns the dense matrix:
* // 2 1
* // 0 0
* // 0 3
* mat.full();
*/
exports.SparseMatrix.prototype.full = function () { return Object.create(require('qminer').la.Matrix.prototype); }
/**
* Returns the frobenious norm of sparse matrix.
* @returns {number} Frobenious norm of sparse matrix.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 1], [1, 3]], [[0, 2], [1, 4]]]);
* // get the frobenious norm of sparse matrix
* var norm = mat.frob(); // returns sqrt(30)
*/
exports.SparseMatrix.prototype.frob = function () { return 0.0; }
/**
* Gives the number of rows of sparse matrix. Type `number`.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
* // check the number of rows in sparse matrix
* mat.rows;
*/
exports.SparseMatrix.prototype.rows = 0;
/**
* Gives the number of columns of sparse matrix. Type `number`.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
* // check the number of columns in sparse matrix
* mat.cols;
*/
exports.SparseMatrix.prototype.cols = 0;
/**
* Prints sparse matrix on screen.
* @example
* // import la module
* var la = require('qminer').la;
* // create a new sparse matrix
* var spMat = new la.SparseMatrix([[[0, 1]], [[0, 3], [1, 8]]]);
* // print sparse matrix on screen
* // each row represents a nonzero element, where first value is row index, second
* // value is column index and third value is element value. For this matrix:
* // 0 0 1.000000
* // 0 1 3.000000
* // 1 1 8.000000
* spMat.print();
*/
exports.SparseMatrix.prototype.print = function () {}
/**
* Saves the sparse matrix as output stream.
* @param {module:fs.FOut} fout - Output stream.
* @param {boolean} [saveMatlab=false] - If true, saves using matlab three column text format. Otherwise, saves using binary format.
* @returns {module:fs.FOut} The output stream `fout`.
* @example
* //