/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
(function() {
var JavaWrapper = require(EclairJS_Globals.NAMESPACE + '/JavaWrapper');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @class
* @memberof module:eclairjs/mllib/linalg
* @type {{}}
*/
var Matrices = function(){};
/**
* Creates a column-major dense matrix.
*
* @param {number} numRows number of rows
* @param {number} numCols number of columns
* @param {number[]} values matrix entries in column major
* @returns {module:eclairjs/mllib/linalg.Matrix}
*/
Matrices.dense = function (numRows, numCols, values) {
throw "not implemented by ElairJS";
// var javaObject = org.apache.spark.mllib.linalg.Matrices.dense(numRows,numCols,values);
// return Utils.javaToJs(javaObject);
};
/**
* Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.
*
* @param {number} numRows number of rows
* @param {number} numCols number of columns
* @param {number[]} colPtrs the index corresponding to the start of a new column
* @param {number[]} rowIndices the row index of the entry
* @param {number[]} values non-zero matrix entries in column major
* @returns {module:eclairjs/mllib/linalg.Matrix}
*/
Matrices.sparse = function (numRows, numCols, colPtrs, rowIndices, values) {
throw "not implemented by ElairJS";
// var javaObject = org.apache.spark.mllib.linalg.Matrices.sparse(numRows,numCols,colPtrs,rowIndices,values);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a `Matrix` consisting of zeros.
* @param {number} numRows number of rows of the matrix
* @param {number} numCols number of columns of the matrix
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `numRows` x `numCols` and values of zeros
*/
Matrices.zeros = function (numRows, numCols) {
throw "not implemented by ElairJS";
// var javaObject = org.apache.spark.mllib.linalg.Matrices.zeros(numRows,numCols);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a `DenseMatrix` consisting of ones.
* @param {number} numRows number of rows of the matrix
* @param {number} numCols number of columns of the matrix
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `numRows` x `numCols` and values of ones
*/
Matrices.ones = function (numRows, numCols) {
throw "not implemented by ElairJS";
// var javaObject = org.apache.spark.mllib.linalg.Matrices.ones(numRows,numCols);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a dense Identity Matrix in `Matrix` format.
* @param {number} n number of rows and columns of the matrix
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `n` x `n` and values of ones on the diagonal
*/
Matrices.eye = function (n) {
throw "not implemented by ElairJS";
// var javaObject = org.apache.spark.mllib.linalg.Matrices.eye(n);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a sparse Identity Matrix in `Matrix` format.
* @param {number} n number of rows and columns of the matrix
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `n` x `n` and values of ones on the diagonal
*/
Matrices.speye = function (n) {
throw "not implemented by ElairJS";
// var javaObject = org.apache.spark.mllib.linalg.Matrices.speye(n);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a `DenseMatrix` consisting of `i.i.d.` uniform random numbers.
* @param {number} numRows number of rows of the matrix
* @param {number} numCols number of columns of the matrix
* @param {Random} rng a random number generator
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
*/
Matrices.rand = function (numRows, numCols, rng) {
throw "not implemented by ElairJS";
// var rng_uw = Utils.unwrapObject(rng);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.rand(numRows,numCols,rng_uw);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a `SparseMatrix` consisting of `i.i.d.` gaussian random numbers.
* @param {number} numRows number of rows of the matrix
* @param {number} numCols number of columns of the matrix
* @param {number} density the desired density for the matrix
* @param {Random} rng a random number generator
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
*/
Matrices.sprand = function (numRows, numCols, density, rng) {
throw "not implemented by ElairJS";
// var rng_uw = Utils.unwrapObject(rng);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.sprand(numRows,numCols,density,rng_uw);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a `DenseMatrix` consisting of `i.i.d.` gaussian random numbers.
* @param {number} numRows number of rows of the matrix
* @param {number} numCols number of columns of the matrix
* @param {Random} rng a random number generator
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
*/
Matrices.randn = function (numRows, numCols, rng) {
throw "not implemented by ElairJS";
// var rng_uw = Utils.unwrapObject(rng);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.randn(numRows,numCols,rng_uw);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a `SparseMatrix` consisting of `i.i.d.` gaussian random numbers.
* @param {number} numRows number of rows of the matrix
* @param {number} numCols number of columns of the matrix
* @param {number} density the desired density for the matrix
* @param {Random} rng a random number generator
* @returns {module:eclairjs/mllib/linalg.Matrix} `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
*/
Matrices.sprandn = function (numRows, numCols, density, rng) {
throw "not implemented by ElairJS";
// var rng_uw = Utils.unwrapObject(rng);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.sprandn(numRows,numCols,density,rng_uw);
// return Utils.javaToJs(javaObject);
};
/**
* Generate a diagonal matrix in `Matrix` format from the supplied values.
* @param {module:eclairjs/mllib/linalg.Vector} vector a `Vector` that will form the values on the diagonal of the matrix
* on the diagonal
* @returns {module:eclairjs/mllib/linalg.Matrix} Square `Matrix` with size `values.length` x `values.length` and `values`
*/
Matrices.diag = function (vector) {
throw "not implemented by ElairJS";
// var vector_uw = Utils.unwrapObject(vector);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.diag(vector_uw);
// return Utils.javaToJs(javaObject);
};
/**
* Horizontally concatenate a sequence of matrices. The returned matrix will be in the format
* the matrices are supplied in. Supplying a mix of dense and sparse matrices will result in
* a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
* @param {module:eclairjs/mllib/linalg.Matrix[]} matrices array of matrices
* @returns {module:eclairjs/mllib/linalg.Matrix} a single `Matrix` composed of the matrices that were horizontally concatenated
*/
Matrices.horzcat = function (matrices) {
throw "not implemented by ElairJS";
// var matrices_uw = Utils.unwrapObject(matrices);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.horzcat(matrices_uw);
// return Utils.javaToJs(javaObject);
};
/**
* Vertically concatenate a sequence of matrices. The returned matrix will be in the format
* the matrices are supplied in. Supplying a mix of dense and sparse matrices will result in
* a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
* @param {module:eclairjs/mllib/linalg.Matrix[]} matrices array of matrices
* @returns {module:eclairjs/mllib/linalg.Matrix} a single `Matrix` composed of the matrices that were vertically concatenated
*/
Matrices.vertcat = function (matrices) {
throw "not implemented by ElairJS";
// var matrices_uw = Utils.unwrapObject(matrices);
// var javaObject = org.apache.spark.mllib.linalg.Matrices.vertcat(matrices_uw);
// return Utils.javaToJs(javaObject);
};
module.exports = Matrix;
})();