{"version":3,"file":"ml-pls.js","sources":["../node_modules/is-any-array/src/index.js","../node_modules/ml-array-max/lib-es6/index.js","../node_modules/ml-array-min/lib-es6/index.js","../node_modules/ml-array-rescale/lib-es6/index.js","../node_modules/ml-matrix/src/inspect.js","../node_modules/ml-matrix/src/mathOperations.js","../node_modules/ml-matrix/src/util.js","../node_modules/ml-matrix/src/stat.js","../node_modules/ml-matrix/src/matrix.js","../node_modules/ml-matrix/src/wrap/WrapperMatrix2D.js","../node_modules/ml-matrix/src/dc/lu.js","../node_modules/ml-matrix/src/dc/util.js","../node_modules/ml-matrix/src/dc/qr.js","../node_modules/ml-matrix/src/dc/svd.js","../node_modules/ml-matrix/src/decompositions.js","../node_modules/ml-matrix/src/dc/nipals.js","../src/util/utils.js","../src/PLS.js","../src/KOPLS.js","../node_modules/ml-confusion-matrix/src/index.js","../node_modules/ml-cross-validation/src/getFolds.js","../node_modules/ml-array-sum/lib-es6/index.js","../node_modules/ml-array-mean/lib-es6/index.js","../node_modules/ml-roc-multiclass/lib-esm/getAuc.js","../node_modules/ml-roc-multiclass/lib-esm/utilities/getClasses.js","../node_modules/ml-roc-multiclass/lib-esm/utilities/getClassesPairs.js","../node_modules/ml-roc-multiclass/lib-esm/utilities/getNumericalTargets.js","../node_modules/ml-roc-multiclass/lib-esm/utilities/getSelectedResults.js","../node_modules/ml-roc-multiclass/lib-esm/utilities/getThresholds.js","../node_modules/ml-roc-multiclass/lib-esm/getRocCurve.js","../src/oplsNipals.js","../src/util/tss.js","../src/OPLS.js"],"sourcesContent":["const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n if (!isArray(input)) {\n throw new TypeError('input must be an array');\n }\n\n if (input.length === 0) {\n throw new TypeError('input must not be empty');\n }\n\n var _options$fromIndex = options.fromIndex,\n fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n _options$toIndex = options.toIndex,\n toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n throw new Error('fromIndex must be a positive integer smaller than length');\n }\n\n if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n }\n\n var maxValue = input[fromIndex];\n\n for (var i = fromIndex + 1; i < toIndex; i++) {\n if (input[i] > maxValue) maxValue = input[i];\n }\n\n return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\nfunction min(input) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n if (!isArray(input)) {\n throw new TypeError('input must be an array');\n }\n\n if (input.length === 0) {\n throw new TypeError('input must not be empty');\n }\n\n var _options$fromIndex = options.fromIndex,\n fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n _options$toIndex = options.toIndex,\n toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n throw new Error('fromIndex must be a positive integer smaller than length');\n }\n\n if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n }\n\n var minValue = input[fromIndex];\n\n for (var i = fromIndex + 1; i < toIndex; i++) {\n if (input[i] < minValue) minValue = input[i];\n }\n\n return minValue;\n}\n\nexport default min;\n","import isArray from 'is-any-array';\nimport max from 'ml-array-max';\nimport min from 'ml-array-min';\n\nfunction rescale(input) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n if (!isArray(input)) {\n throw new TypeError('input must be an array');\n } else if (input.length === 0) {\n throw new TypeError('input must not be empty');\n }\n\n var output;\n\n if (options.output !== undefined) {\n if (!isArray(options.output)) {\n throw new TypeError('output option must be an array if specified');\n }\n\n output = options.output;\n } else {\n output = new Array(input.length);\n }\n\n var currentMin = min(input);\n var currentMax = max(input);\n\n if (currentMin === currentMax) {\n throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');\n }\n\n var _options$min = options.min,\n minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,\n _options$max = options.max,\n maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;\n\n if (minValue >= maxValue) {\n throw new RangeError('min option must be smaller than max option');\n }\n\n var factor = (maxValue - minValue) / (currentMax - currentMin);\n\n for (var i = 0; i < input.length; i++) {\n output[i] = (input[i] - currentMin) * factor + minValue;\n }\n\n return output;\n}\n\nexport { rescale as default };\n","const indent = ' '.repeat(2);\nconst indentData = ' '.repeat(4);\n\nexport function inspectMatrix() {\n return inspectMatrixWithOptions(this);\n}\n\nexport function inspectMatrixWithOptions(matrix, options = {}) {\n const { maxRows = 15, maxColumns = 10, maxNumSize = 8 } = options;\n return `${matrix.constructor.name} {\n${indent}[\n${indentData}${inspectData(matrix, maxRows, maxColumns, maxNumSize)}\n${indent}]\n${indent}rows: ${matrix.rows}\n${indent}columns: ${matrix.columns}\n}`;\n}\n\nfunction inspectData(matrix, maxRows, maxColumns, maxNumSize) {\n const { rows, columns } = matrix;\n const maxI = Math.min(rows, maxRows);\n const maxJ = Math.min(columns, maxColumns);\n const result = [];\n for (let i = 0; i < maxI; i++) {\n let line = [];\n for (let j = 0; j < maxJ; j++) {\n line.push(formatNumber(matrix.get(i, j), maxNumSize));\n }\n result.push(`${line.join(' ')}`);\n }\n if (maxJ !== columns) {\n result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;\n }\n if (maxI !== rows) {\n result.push(`... ${rows - maxRows} more rows`);\n }\n return result.join(`\\n${indentData}`);\n}\n\nfunction formatNumber(num, maxNumSize) {\n const numStr = String(num);\n if (numStr.length <= maxNumSize) {\n return numStr.padEnd(maxNumSize, ' ');\n }\n const precise = num.toPrecision(maxNumSize - 2);\n if (precise.length <= maxNumSize) {\n return precise;\n }\n const exponential = num.toExponential(maxNumSize - 2);\n const eIndex = exponential.indexOf('e');\n const e = exponential.slice(eIndex);\n return exponential.slice(0, maxNumSize - e.length) + e;\n}\n","export function installMathOperations(AbstractMatrix, Matrix) {\n AbstractMatrix.prototype.add = function add(value) {\n if (typeof value === 'number') return this.addS(value);\n return this.addM(value);\n };\n\n AbstractMatrix.prototype.addS = function addS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) + value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.addM = function addM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) + matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.add = function add(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.add(value);\n };\n\n AbstractMatrix.prototype.sub = function sub(value) {\n if (typeof value === 'number') return this.subS(value);\n return this.subM(value);\n };\n\n AbstractMatrix.prototype.subS = function subS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) - value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.subM = function subM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) - matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.sub = function sub(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.sub(value);\n };\n AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;\n AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;\n AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;\n AbstractMatrix.subtract = AbstractMatrix.sub;\n\n AbstractMatrix.prototype.mul = function mul(value) {\n if (typeof value === 'number') return this.mulS(value);\n return this.mulM(value);\n };\n\n AbstractMatrix.prototype.mulS = function mulS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) * value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.mulM = function mulM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) * matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.mul = function mul(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.mul(value);\n };\n AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;\n AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;\n AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;\n AbstractMatrix.multiply = AbstractMatrix.mul;\n\n AbstractMatrix.prototype.div = function div(value) {\n if (typeof value === 'number') return this.divS(value);\n return this.divM(value);\n };\n\n AbstractMatrix.prototype.divS = function divS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) / value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.divM = function divM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) / matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.div = function div(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.div(value);\n };\n AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;\n AbstractMatrix.prototype.divideS = AbstractMatrix.prototype.divS;\n AbstractMatrix.prototype.divideM = AbstractMatrix.prototype.divM;\n AbstractMatrix.divide = AbstractMatrix.div;\n\n AbstractMatrix.prototype.mod = function mod(value) {\n if (typeof value === 'number') return this.modS(value);\n return this.modM(value);\n };\n\n AbstractMatrix.prototype.modS = function modS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) % value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.modM = function modM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) % matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.mod = function mod(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.mod(value);\n };\n AbstractMatrix.prototype.modulus = AbstractMatrix.prototype.mod;\n AbstractMatrix.prototype.modulusS = AbstractMatrix.prototype.modS;\n AbstractMatrix.prototype.modulusM = AbstractMatrix.prototype.modM;\n AbstractMatrix.modulus = AbstractMatrix.mod;\n\n AbstractMatrix.prototype.and = function and(value) {\n if (typeof value === 'number') return this.andS(value);\n return this.andM(value);\n };\n\n AbstractMatrix.prototype.andS = function andS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) & value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.andM = function andM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) & matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.and = function and(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.and(value);\n };\n\n AbstractMatrix.prototype.or = function or(value) {\n if (typeof value === 'number') return this.orS(value);\n return this.orM(value);\n };\n\n AbstractMatrix.prototype.orS = function orS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) | value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.orM = function orM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) | matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.or = function or(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.or(value);\n };\n\n AbstractMatrix.prototype.xor = function xor(value) {\n if (typeof value === 'number') return this.xorS(value);\n return this.xorM(value);\n };\n\n AbstractMatrix.prototype.xorS = function xorS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) ^ value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.xorM = function xorM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) ^ matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.xor = function xor(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.xor(value);\n };\n\n AbstractMatrix.prototype.leftShift = function leftShift(value) {\n if (typeof value === 'number') return this.leftShiftS(value);\n return this.leftShiftM(value);\n };\n\n AbstractMatrix.prototype.leftShiftS = function leftShiftS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) << value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.leftShiftM = function leftShiftM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) << matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.leftShift = function leftShift(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.leftShift(value);\n };\n\n AbstractMatrix.prototype.signPropagatingRightShift = function signPropagatingRightShift(value) {\n if (typeof value === 'number') return this.signPropagatingRightShiftS(value);\n return this.signPropagatingRightShiftM(value);\n };\n\n AbstractMatrix.prototype.signPropagatingRightShiftS = function signPropagatingRightShiftS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) >> value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.signPropagatingRightShiftM = function signPropagatingRightShiftM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) >> matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.signPropagatingRightShift = function signPropagatingRightShift(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.signPropagatingRightShift(value);\n };\n\n AbstractMatrix.prototype.rightShift = function rightShift(value) {\n if (typeof value === 'number') return this.rightShiftS(value);\n return this.rightShiftM(value);\n };\n\n AbstractMatrix.prototype.rightShiftS = function rightShiftS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) >>> value);\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.rightShiftM = function rightShiftM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) >>> matrix.get(i, j));\n }\n }\n return this;\n };\n\n AbstractMatrix.rightShift = function rightShift(matrix, value) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.rightShift(value);\n };\n AbstractMatrix.prototype.zeroFillRightShift = AbstractMatrix.prototype.rightShift;\n AbstractMatrix.prototype.zeroFillRightShiftS = AbstractMatrix.prototype.rightShiftS;\n AbstractMatrix.prototype.zeroFillRightShiftM = AbstractMatrix.prototype.rightShiftM;\n AbstractMatrix.zeroFillRightShift = AbstractMatrix.rightShift;\n\n AbstractMatrix.prototype.not = function not() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, ~(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.not = function not(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.not();\n };\n\n AbstractMatrix.prototype.abs = function abs() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.abs(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.abs = function abs(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.abs();\n };\n\n AbstractMatrix.prototype.acos = function acos() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.acos(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.acos = function acos(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.acos();\n };\n\n AbstractMatrix.prototype.acosh = function acosh() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.acosh(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.acosh = function acosh(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.acosh();\n };\n\n AbstractMatrix.prototype.asin = function asin() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.asin(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.asin = function asin(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.asin();\n };\n\n AbstractMatrix.prototype.asinh = function asinh() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.asinh(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.asinh = function asinh(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.asinh();\n };\n\n AbstractMatrix.prototype.atan = function atan() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.atan(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.atan = function atan(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.atan();\n };\n\n AbstractMatrix.prototype.atanh = function atanh() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.atanh(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.atanh = function atanh(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.atanh();\n };\n\n AbstractMatrix.prototype.cbrt = function cbrt() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.cbrt(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.cbrt = function cbrt(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.cbrt();\n };\n\n AbstractMatrix.prototype.ceil = function ceil() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.ceil(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.ceil = function ceil(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.ceil();\n };\n\n AbstractMatrix.prototype.clz32 = function clz32() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.clz32(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.clz32 = function clz32(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.clz32();\n };\n\n AbstractMatrix.prototype.cos = function cos() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.cos(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.cos = function cos(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.cos();\n };\n\n AbstractMatrix.prototype.cosh = function cosh() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.cosh(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.cosh = function cosh(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.cosh();\n };\n\n AbstractMatrix.prototype.exp = function exp() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.exp(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.exp = function exp(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.exp();\n };\n\n AbstractMatrix.prototype.expm1 = function expm1() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.expm1(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.expm1 = function expm1(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.expm1();\n };\n\n AbstractMatrix.prototype.floor = function floor() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.floor(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.floor = function floor(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.floor();\n };\n\n AbstractMatrix.prototype.fround = function fround() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.fround(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.fround = function fround(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.fround();\n };\n\n AbstractMatrix.prototype.log = function log() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.log(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.log = function log(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.log();\n };\n\n AbstractMatrix.prototype.log1p = function log1p() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.log1p(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.log1p = function log1p(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.log1p();\n };\n\n AbstractMatrix.prototype.log10 = function log10() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.log10(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.log10 = function log10(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.log10();\n };\n\n AbstractMatrix.prototype.log2 = function log2() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.log2(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.log2 = function log2(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.log2();\n };\n\n AbstractMatrix.prototype.round = function round() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.round(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.round = function round(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.round();\n };\n\n AbstractMatrix.prototype.sign = function sign() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.sign(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.sign = function sign(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.sign();\n };\n\n AbstractMatrix.prototype.sin = function sin() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.sin(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.sin = function sin(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.sin();\n };\n\n AbstractMatrix.prototype.sinh = function sinh() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.sinh(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.sinh = function sinh(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.sinh();\n };\n\n AbstractMatrix.prototype.sqrt = function sqrt() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.sqrt(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.sqrt = function sqrt(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.sqrt();\n };\n\n AbstractMatrix.prototype.tan = function tan() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.tan(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.tan = function tan(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.tan();\n };\n\n AbstractMatrix.prototype.tanh = function tanh() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.tanh(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.tanh = function tanh(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.tanh();\n };\n\n AbstractMatrix.prototype.trunc = function trunc() {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.trunc(this.get(i, j)));\n }\n }\n return this;\n };\n\n AbstractMatrix.trunc = function trunc(matrix) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.trunc();\n };\n\n AbstractMatrix.pow = function pow(matrix, arg0) {\n const newMatrix = new Matrix(matrix);\n return newMatrix.pow(arg0);\n };\n\n AbstractMatrix.prototype.pow = function pow(value) {\n if (typeof value === 'number') return this.powS(value);\n return this.powM(value);\n };\n\n AbstractMatrix.prototype.powS = function powS(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.pow(this.get(i, j), value));\n }\n }\n return this;\n };\n\n AbstractMatrix.prototype.powM = function powM(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (this.rows !== matrix.rows ||\n this.columns !== matrix.columns) {\n throw new RangeError('Matrices dimensions must be equal');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, Math.pow(this.get(i, j), matrix.get(i, j)));\n }\n }\n return this;\n };\n}\n","/**\n * @private\n * Check that a row index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkRowIndex(matrix, index, outer) {\n let max = outer ? matrix.rows : matrix.rows - 1;\n if (index < 0 || index > max) {\n throw new RangeError('Row index out of range');\n }\n}\n\n/**\n * @private\n * Check that a column index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkColumnIndex(matrix, index, outer) {\n let max = outer ? matrix.columns : matrix.columns - 1;\n if (index < 0 || index > max) {\n throw new RangeError('Column index out of range');\n }\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkRowVector(matrix, vector) {\n if (vector.to1DArray) {\n vector = vector.to1DArray();\n }\n if (vector.length !== matrix.columns) {\n throw new RangeError(\n 'vector size must be the same as the number of columns',\n );\n }\n return vector;\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkColumnVector(matrix, vector) {\n if (vector.to1DArray) {\n vector = vector.to1DArray();\n }\n if (vector.length !== matrix.rows) {\n throw new RangeError('vector size must be the same as the number of rows');\n }\n return vector;\n}\n\nexport function checkIndices(matrix, rowIndices, columnIndices) {\n return {\n row: checkRowIndices(matrix, rowIndices),\n column: checkColumnIndices(matrix, columnIndices),\n };\n}\n\nexport function checkRowIndices(matrix, rowIndices) {\n if (typeof rowIndices !== 'object') {\n throw new TypeError('unexpected type for row indices');\n }\n\n let rowOut = rowIndices.some((r) => {\n return r < 0 || r >= matrix.rows;\n });\n\n if (rowOut) {\n throw new RangeError('row indices are out of range');\n }\n\n if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);\n\n return rowIndices;\n}\n\nexport function checkColumnIndices(matrix, columnIndices) {\n if (typeof columnIndices !== 'object') {\n throw new TypeError('unexpected type for column indices');\n }\n\n let columnOut = columnIndices.some((c) => {\n return c < 0 || c >= matrix.columns;\n });\n\n if (columnOut) {\n throw new RangeError('column indices are out of range');\n }\n if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);\n\n return columnIndices;\n}\n\nexport function checkRange(matrix, startRow, endRow, startColumn, endColumn) {\n if (arguments.length !== 5) {\n throw new RangeError('expected 4 arguments');\n }\n checkNumber('startRow', startRow);\n checkNumber('endRow', endRow);\n checkNumber('startColumn', startColumn);\n checkNumber('endColumn', endColumn);\n if (\n startRow > endRow ||\n startColumn > endColumn ||\n startRow < 0 ||\n startRow >= matrix.rows ||\n endRow < 0 ||\n endRow >= matrix.rows ||\n startColumn < 0 ||\n startColumn >= matrix.columns ||\n endColumn < 0 ||\n endColumn >= matrix.columns\n ) {\n throw new RangeError('Submatrix indices are out of range');\n }\n}\n\nexport function newArray(length, value = 0) {\n let array = [];\n for (let i = 0; i < length; i++) {\n array.push(value);\n }\n return array;\n}\n\nfunction checkNumber(name, value) {\n if (typeof value !== 'number') {\n throw new TypeError(`${name} must be a number`);\n }\n}\n\nexport function checkNonEmpty(matrix) {\n if (matrix.isEmpty()) {\n throw new Error('Empty matrix has no elements to index');\n }\n}\n","import { newArray } from './util';\n\nexport function sumByRow(matrix) {\n let sum = newArray(matrix.rows);\n for (let i = 0; i < matrix.rows; ++i) {\n for (let j = 0; j < matrix.columns; ++j) {\n sum[i] += matrix.get(i, j);\n }\n }\n return sum;\n}\n\nexport function sumByColumn(matrix) {\n let sum = newArray(matrix.columns);\n for (let i = 0; i < matrix.rows; ++i) {\n for (let j = 0; j < matrix.columns; ++j) {\n sum[j] += matrix.get(i, j);\n }\n }\n return sum;\n}\n\nexport function sumAll(matrix) {\n let v = 0;\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n v += matrix.get(i, j);\n }\n }\n return v;\n}\n\nexport function productByRow(matrix) {\n let sum = newArray(matrix.rows, 1);\n for (let i = 0; i < matrix.rows; ++i) {\n for (let j = 0; j < matrix.columns; ++j) {\n sum[i] *= matrix.get(i, j);\n }\n }\n return sum;\n}\n\nexport function productByColumn(matrix) {\n let sum = newArray(matrix.columns, 1);\n for (let i = 0; i < matrix.rows; ++i) {\n for (let j = 0; j < matrix.columns; ++j) {\n sum[j] *= matrix.get(i, j);\n }\n }\n return sum;\n}\n\nexport function productAll(matrix) {\n let v = 1;\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n v *= matrix.get(i, j);\n }\n }\n return v;\n}\n\nexport function varianceByRow(matrix, unbiased, mean) {\n const rows = matrix.rows;\n const cols = matrix.columns;\n const variance = [];\n\n for (let i = 0; i < rows; i++) {\n let sum1 = 0;\n let sum2 = 0;\n let x = 0;\n for (let j = 0; j < cols; j++) {\n x = matrix.get(i, j) - mean[i];\n sum1 += x;\n sum2 += x * x;\n }\n if (unbiased) {\n variance.push((sum2 - (sum1 * sum1) / cols) / (cols - 1));\n } else {\n variance.push((sum2 - (sum1 * sum1) / cols) / cols);\n }\n }\n return variance;\n}\n\nexport function varianceByColumn(matrix, unbiased, mean) {\n const rows = matrix.rows;\n const cols = matrix.columns;\n const variance = [];\n\n for (let j = 0; j < cols; j++) {\n let sum1 = 0;\n let sum2 = 0;\n let x = 0;\n for (let i = 0; i < rows; i++) {\n x = matrix.get(i, j) - mean[j];\n sum1 += x;\n sum2 += x * x;\n }\n if (unbiased) {\n variance.push((sum2 - (sum1 * sum1) / rows) / (rows - 1));\n } else {\n variance.push((sum2 - (sum1 * sum1) / rows) / rows);\n }\n }\n return variance;\n}\n\nexport function varianceAll(matrix, unbiased, mean) {\n const rows = matrix.rows;\n const cols = matrix.columns;\n const size = rows * cols;\n\n let sum1 = 0;\n let sum2 = 0;\n let x = 0;\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < cols; j++) {\n x = matrix.get(i, j) - mean;\n sum1 += x;\n sum2 += x * x;\n }\n }\n if (unbiased) {\n return (sum2 - (sum1 * sum1) / size) / (size - 1);\n } else {\n return (sum2 - (sum1 * sum1) / size) / size;\n }\n}\n\nexport function centerByRow(matrix, mean) {\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n matrix.set(i, j, matrix.get(i, j) - mean[i]);\n }\n }\n}\n\nexport function centerByColumn(matrix, mean) {\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n matrix.set(i, j, matrix.get(i, j) - mean[j]);\n }\n }\n}\n\nexport function centerAll(matrix, mean) {\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n matrix.set(i, j, matrix.get(i, j) - mean);\n }\n }\n}\n\nexport function getScaleByRow(matrix) {\n const scale = [];\n for (let i = 0; i < matrix.rows; i++) {\n let sum = 0;\n for (let j = 0; j < matrix.columns; j++) {\n sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);\n }\n scale.push(Math.sqrt(sum));\n }\n return scale;\n}\n\nexport function scaleByRow(matrix, scale) {\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n matrix.set(i, j, matrix.get(i, j) / scale[i]);\n }\n }\n}\n\nexport function getScaleByColumn(matrix) {\n const scale = [];\n for (let j = 0; j < matrix.columns; j++) {\n let sum = 0;\n for (let i = 0; i < matrix.rows; i++) {\n sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);\n }\n scale.push(Math.sqrt(sum));\n }\n return scale;\n}\n\nexport function scaleByColumn(matrix, scale) {\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n matrix.set(i, j, matrix.get(i, j) / scale[j]);\n }\n }\n}\n\nexport function getScaleAll(matrix) {\n const divider = matrix.size - 1;\n let sum = 0;\n for (let j = 0; j < matrix.columns; j++) {\n for (let i = 0; i < matrix.rows; i++) {\n sum += Math.pow(matrix.get(i, j), 2) / divider;\n }\n }\n return Math.sqrt(sum);\n}\n\nexport function scaleAll(matrix, scale) {\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n matrix.set(i, j, matrix.get(i, j) / scale);\n }\n }\n}\n","import rescale from 'ml-array-rescale';\n\nimport { inspectMatrix, inspectMatrixWithOptions } from './inspect';\nimport { installMathOperations } from './mathOperations';\nimport {\n sumByRow,\n sumByColumn,\n sumAll,\n productByRow,\n productByColumn,\n productAll,\n varianceByRow,\n varianceByColumn,\n varianceAll,\n centerByRow,\n centerByColumn,\n centerAll,\n scaleByRow,\n scaleByColumn,\n scaleAll,\n getScaleByRow,\n getScaleByColumn,\n getScaleAll,\n} from './stat';\nimport {\n checkRowVector,\n checkRowIndex,\n checkColumnIndex,\n checkColumnVector,\n checkRange,\n checkIndices,\n checkNonEmpty,\n} from './util';\n\nexport class AbstractMatrix {\n static from1DArray(newRows, newColumns, newData) {\n let length = newRows * newColumns;\n if (length !== newData.length) {\n throw new RangeError('data length does not match given dimensions');\n }\n let newMatrix = new Matrix(newRows, newColumns);\n for (let row = 0; row < newRows; row++) {\n for (let column = 0; column < newColumns; column++) {\n newMatrix.set(row, column, newData[row * newColumns + column]);\n }\n }\n return newMatrix;\n }\n\n static rowVector(newData) {\n let vector = new Matrix(1, newData.length);\n for (let i = 0; i < newData.length; i++) {\n vector.set(0, i, newData[i]);\n }\n return vector;\n }\n\n static columnVector(newData) {\n let vector = new Matrix(newData.length, 1);\n for (let i = 0; i < newData.length; i++) {\n vector.set(i, 0, newData[i]);\n }\n return vector;\n }\n\n static zeros(rows, columns) {\n return new Matrix(rows, columns);\n }\n\n static ones(rows, columns) {\n return new Matrix(rows, columns).fill(1);\n }\n\n static rand(rows, columns, options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { random = Math.random } = options;\n let matrix = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n matrix.set(i, j, random());\n }\n }\n return matrix;\n }\n\n static randInt(rows, columns, options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { min = 0, max = 1000, random = Math.random } = options;\n if (!Number.isInteger(min)) throw new TypeError('min must be an integer');\n if (!Number.isInteger(max)) throw new TypeError('max must be an integer');\n if (min >= max) throw new RangeError('min must be smaller than max');\n let interval = max - min;\n let matrix = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n let value = min + Math.round(random() * interval);\n matrix.set(i, j, value);\n }\n }\n return matrix;\n }\n\n static eye(rows, columns, value) {\n if (columns === undefined) columns = rows;\n if (value === undefined) value = 1;\n let min = Math.min(rows, columns);\n let matrix = this.zeros(rows, columns);\n for (let i = 0; i < min; i++) {\n matrix.set(i, i, value);\n }\n return matrix;\n }\n\n static diag(data, rows, columns) {\n let l = data.length;\n if (rows === undefined) rows = l;\n if (columns === undefined) columns = rows;\n let min = Math.min(l, rows, columns);\n let matrix = this.zeros(rows, columns);\n for (let i = 0; i < min; i++) {\n matrix.set(i, i, data[i]);\n }\n return matrix;\n }\n\n static min(matrix1, matrix2) {\n matrix1 = this.checkMatrix(matrix1);\n matrix2 = this.checkMatrix(matrix2);\n let rows = matrix1.rows;\n let columns = matrix1.columns;\n let result = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));\n }\n }\n return result;\n }\n\n static max(matrix1, matrix2) {\n matrix1 = this.checkMatrix(matrix1);\n matrix2 = this.checkMatrix(matrix2);\n let rows = matrix1.rows;\n let columns = matrix1.columns;\n let result = new this(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));\n }\n }\n return result;\n }\n\n static checkMatrix(value) {\n return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);\n }\n\n static isMatrix(value) {\n return value != null && value.klass === 'Matrix';\n }\n\n get size() {\n return this.rows * this.columns;\n }\n\n apply(callback) {\n if (typeof callback !== 'function') {\n throw new TypeError('callback must be a function');\n }\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n callback.call(this, i, j);\n }\n }\n return this;\n }\n\n to1DArray() {\n let array = [];\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n array.push(this.get(i, j));\n }\n }\n return array;\n }\n\n to2DArray() {\n let copy = [];\n for (let i = 0; i < this.rows; i++) {\n copy.push([]);\n for (let j = 0; j < this.columns; j++) {\n copy[i].push(this.get(i, j));\n }\n }\n return copy;\n }\n\n toJSON() {\n return this.to2DArray();\n }\n\n isRowVector() {\n return this.rows === 1;\n }\n\n isColumnVector() {\n return this.columns === 1;\n }\n\n isVector() {\n return this.rows === 1 || this.columns === 1;\n }\n\n isSquare() {\n return this.rows === this.columns;\n }\n\n isEmpty() {\n return this.rows === 0 || this.columns === 0;\n }\n\n isSymmetric() {\n if (this.isSquare()) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j <= i; j++) {\n if (this.get(i, j) !== this.get(j, i)) {\n return false;\n }\n }\n }\n return true;\n }\n return false;\n }\n\n isEchelonForm() {\n let i = 0;\n let j = 0;\n let previousColumn = -1;\n let isEchelonForm = true;\n let checked = false;\n while (i < this.rows && isEchelonForm) {\n j = 0;\n checked = false;\n while (j < this.columns && checked === false) {\n if (this.get(i, j) === 0) {\n j++;\n } else if (this.get(i, j) === 1 && j > previousColumn) {\n checked = true;\n previousColumn = j;\n } else {\n isEchelonForm = false;\n checked = true;\n }\n }\n i++;\n }\n return isEchelonForm;\n }\n\n isReducedEchelonForm() {\n let i = 0;\n let j = 0;\n let previousColumn = -1;\n let isReducedEchelonForm = true;\n let checked = false;\n while (i < this.rows && isReducedEchelonForm) {\n j = 0;\n checked = false;\n while (j < this.columns && checked === false) {\n if (this.get(i, j) === 0) {\n j++;\n } else if (this.get(i, j) === 1 && j > previousColumn) {\n checked = true;\n previousColumn = j;\n } else {\n isReducedEchelonForm = false;\n checked = true;\n }\n }\n for (let k = j + 1; k < this.rows; k++) {\n if (this.get(i, k) !== 0) {\n isReducedEchelonForm = false;\n }\n }\n i++;\n }\n return isReducedEchelonForm;\n }\n\n echelonForm() {\n let result = this.clone();\n let h = 0;\n let k = 0;\n while (h < result.rows && k < result.columns) {\n let iMax = h;\n for (let i = h; i < result.rows; i++) {\n if (result.get(i, k) > result.get(iMax, k)) {\n iMax = i;\n }\n }\n if (result.get(iMax, k) === 0) {\n k++;\n } else {\n result.swapRows(h, iMax);\n let tmp = result.get(h, k);\n for (let j = k; j < result.columns; j++) {\n result.set(h, j, result.get(h, j) / tmp);\n }\n for (let i = h + 1; i < result.rows; i++) {\n let factor = result.get(i, k) / result.get(h, k);\n result.set(i, k, 0);\n for (let j = k + 1; j < result.columns; j++) {\n result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n }\n }\n h++;\n k++;\n }\n }\n return result;\n }\n\n reducedEchelonForm() {\n let result = this.echelonForm();\n let m = result.columns;\n let n = result.rows;\n let h = n - 1;\n while (h >= 0) {\n if (result.maxRow(h) === 0) {\n h--;\n } else {\n let p = 0;\n let pivot = false;\n while (p < n && pivot === false) {\n if (result.get(h, p) === 1) {\n pivot = true;\n } else {\n p++;\n }\n }\n for (let i = 0; i < h; i++) {\n let factor = result.get(i, p);\n for (let j = p; j < m; j++) {\n let tmp = result.get(i, j) - factor * result.get(h, j);\n result.set(i, j, tmp);\n }\n }\n h--;\n }\n }\n return result;\n }\n\n set() {\n throw new Error('set method is unimplemented');\n }\n\n get() {\n throw new Error('get method is unimplemented');\n }\n\n repeat(options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { rows = 1, columns = 1 } = options;\n if (!Number.isInteger(rows) || rows <= 0) {\n throw new TypeError('rows must be a positive integer');\n }\n if (!Number.isInteger(columns) || columns <= 0) {\n throw new TypeError('columns must be a positive integer');\n }\n let matrix = new Matrix(this.rows * rows, this.columns * columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n }\n }\n return matrix;\n }\n\n fill(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, value);\n }\n }\n return this;\n }\n\n neg() {\n return this.mulS(-1);\n }\n\n getRow(index) {\n checkRowIndex(this, index);\n let row = [];\n for (let i = 0; i < this.columns; i++) {\n row.push(this.get(index, i));\n }\n return row;\n }\n\n getRowVector(index) {\n return Matrix.rowVector(this.getRow(index));\n }\n\n setRow(index, array) {\n checkRowIndex(this, index);\n array = checkRowVector(this, array);\n for (let i = 0; i < this.columns; i++) {\n this.set(index, i, array[i]);\n }\n return this;\n }\n\n swapRows(row1, row2) {\n checkRowIndex(this, row1);\n checkRowIndex(this, row2);\n for (let i = 0; i < this.columns; i++) {\n let temp = this.get(row1, i);\n this.set(row1, i, this.get(row2, i));\n this.set(row2, i, temp);\n }\n return this;\n }\n\n getColumn(index) {\n checkColumnIndex(this, index);\n let column = [];\n for (let i = 0; i < this.rows; i++) {\n column.push(this.get(i, index));\n }\n return column;\n }\n\n getColumnVector(index) {\n return Matrix.columnVector(this.getColumn(index));\n }\n\n setColumn(index, array) {\n checkColumnIndex(this, index);\n array = checkColumnVector(this, array);\n for (let i = 0; i < this.rows; i++) {\n this.set(i, index, array[i]);\n }\n return this;\n }\n\n swapColumns(column1, column2) {\n checkColumnIndex(this, column1);\n checkColumnIndex(this, column2);\n for (let i = 0; i < this.rows; i++) {\n let temp = this.get(i, column1);\n this.set(i, column1, this.get(i, column2));\n this.set(i, column2, temp);\n }\n return this;\n }\n\n addRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) + vector[j]);\n }\n }\n return this;\n }\n\n subRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) - vector[j]);\n }\n }\n return this;\n }\n\n mulRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) * vector[j]);\n }\n }\n return this;\n }\n\n divRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) / vector[j]);\n }\n }\n return this;\n }\n\n addColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) + vector[i]);\n }\n }\n return this;\n }\n\n subColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) - vector[i]);\n }\n }\n return this;\n }\n\n mulColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) * vector[i]);\n }\n }\n return this;\n }\n\n divColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) / vector[i]);\n }\n }\n return this;\n }\n\n mulRow(index, value) {\n checkRowIndex(this, index);\n for (let i = 0; i < this.columns; i++) {\n this.set(index, i, this.get(index, i) * value);\n }\n return this;\n }\n\n mulColumn(index, value) {\n checkColumnIndex(this, index);\n for (let i = 0; i < this.rows; i++) {\n this.set(i, index, this.get(i, index) * value);\n }\n return this;\n }\n\n max() {\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(0, 0);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n if (this.get(i, j) > v) {\n v = this.get(i, j);\n }\n }\n }\n return v;\n }\n\n maxIndex() {\n checkNonEmpty(this);\n let v = this.get(0, 0);\n let idx = [0, 0];\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n if (this.get(i, j) > v) {\n v = this.get(i, j);\n idx[0] = i;\n idx[1] = j;\n }\n }\n }\n return idx;\n }\n\n min() {\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(0, 0);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n if (this.get(i, j) < v) {\n v = this.get(i, j);\n }\n }\n }\n return v;\n }\n\n minIndex() {\n checkNonEmpty(this);\n let v = this.get(0, 0);\n let idx = [0, 0];\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n if (this.get(i, j) < v) {\n v = this.get(i, j);\n idx[0] = i;\n idx[1] = j;\n }\n }\n }\n return idx;\n }\n\n maxRow(row) {\n checkRowIndex(this, row);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(row, 0);\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) > v) {\n v = this.get(row, i);\n }\n }\n return v;\n }\n\n maxRowIndex(row) {\n checkRowIndex(this, row);\n checkNonEmpty(this);\n let v = this.get(row, 0);\n let idx = [row, 0];\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) > v) {\n v = this.get(row, i);\n idx[1] = i;\n }\n }\n return idx;\n }\n\n minRow(row) {\n checkRowIndex(this, row);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(row, 0);\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) < v) {\n v = this.get(row, i);\n }\n }\n return v;\n }\n\n minRowIndex(row) {\n checkRowIndex(this, row);\n checkNonEmpty(this);\n let v = this.get(row, 0);\n let idx = [row, 0];\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) < v) {\n v = this.get(row, i);\n idx[1] = i;\n }\n }\n return idx;\n }\n\n maxColumn(column) {\n checkColumnIndex(this, column);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(0, column);\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) > v) {\n v = this.get(i, column);\n }\n }\n return v;\n }\n\n maxColumnIndex(column) {\n checkColumnIndex(this, column);\n checkNonEmpty(this);\n let v = this.get(0, column);\n let idx = [0, column];\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) > v) {\n v = this.get(i, column);\n idx[0] = i;\n }\n }\n return idx;\n }\n\n minColumn(column) {\n checkColumnIndex(this, column);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(0, column);\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) < v) {\n v = this.get(i, column);\n }\n }\n return v;\n }\n\n minColumnIndex(column) {\n checkColumnIndex(this, column);\n checkNonEmpty(this);\n let v = this.get(0, column);\n let idx = [0, column];\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) < v) {\n v = this.get(i, column);\n idx[0] = i;\n }\n }\n return idx;\n }\n\n diag() {\n let min = Math.min(this.rows, this.columns);\n let diag = [];\n for (let i = 0; i < min; i++) {\n diag.push(this.get(i, i));\n }\n return diag;\n }\n\n norm(type = 'frobenius') {\n let result = 0;\n if (type === 'max') {\n return this.max();\n } else if (type === 'frobenius') {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n result = result + this.get(i, j) * this.get(i, j);\n }\n }\n return Math.sqrt(result);\n } else {\n throw new RangeError(`unknown norm type: ${type}`);\n }\n }\n\n cumulativeSum() {\n let sum = 0;\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n sum += this.get(i, j);\n this.set(i, j, sum);\n }\n }\n return this;\n }\n\n dot(vector2) {\n if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n let vector1 = this.to1DArray();\n if (vector1.length !== vector2.length) {\n throw new RangeError('vectors do not have the same size');\n }\n let dot = 0;\n for (let i = 0; i < vector1.length; i++) {\n dot += vector1[i] * vector2[i];\n }\n return dot;\n }\n\n mmul(other) {\n other = Matrix.checkMatrix(other);\n\n let m = this.rows;\n let n = this.columns;\n let p = other.columns;\n\n let result = new Matrix(m, p);\n\n let Bcolj = new Float64Array(n);\n for (let j = 0; j < p; j++) {\n for (let k = 0; k < n; k++) {\n Bcolj[k] = other.get(k, j);\n }\n\n for (let i = 0; i < m; i++) {\n let s = 0;\n for (let k = 0; k < n; k++) {\n s += this.get(i, k) * Bcolj[k];\n }\n\n result.set(i, j, s);\n }\n }\n return result;\n }\n\n strassen2x2(other) {\n other = Matrix.checkMatrix(other);\n let result = new Matrix(2, 2);\n const a11 = this.get(0, 0);\n const b11 = other.get(0, 0);\n const a12 = this.get(0, 1);\n const b12 = other.get(0, 1);\n const a21 = this.get(1, 0);\n const b21 = other.get(1, 0);\n const a22 = this.get(1, 1);\n const b22 = other.get(1, 1);\n\n // Compute intermediate values.\n const m1 = (a11 + a22) * (b11 + b22);\n const m2 = (a21 + a22) * b11;\n const m3 = a11 * (b12 - b22);\n const m4 = a22 * (b21 - b11);\n const m5 = (a11 + a12) * b22;\n const m6 = (a21 - a11) * (b11 + b12);\n const m7 = (a12 - a22) * (b21 + b22);\n\n // Combine intermediate values into the output.\n const c00 = m1 + m4 - m5 + m7;\n const c01 = m3 + m5;\n const c10 = m2 + m4;\n const c11 = m1 - m2 + m3 + m6;\n\n result.set(0, 0, c00);\n result.set(0, 1, c01);\n result.set(1, 0, c10);\n result.set(1, 1, c11);\n return result;\n }\n\n strassen3x3(other) {\n other = Matrix.checkMatrix(other);\n let result = new Matrix(3, 3);\n\n const a00 = this.get(0, 0);\n const a01 = this.get(0, 1);\n const a02 = this.get(0, 2);\n const a10 = this.get(1, 0);\n const a11 = this.get(1, 1);\n const a12 = this.get(1, 2);\n const a20 = this.get(2, 0);\n const a21 = this.get(2, 1);\n const a22 = this.get(2, 2);\n\n const b00 = other.get(0, 0);\n const b01 = other.get(0, 1);\n const b02 = other.get(0, 2);\n const b10 = other.get(1, 0);\n const b11 = other.get(1, 1);\n const b12 = other.get(1, 2);\n const b20 = other.get(2, 0);\n const b21 = other.get(2, 1);\n const b22 = other.get(2, 2);\n\n const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n const m2 = (a00 - a10) * (-b01 + b11);\n const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n const m5 = (a10 + a11) * (-b00 + b01);\n const m6 = a00 * b00;\n const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n const m8 = (-a00 + a20) * (b02 - b12);\n const m9 = (a20 + a21) * (-b00 + b02);\n const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n const m13 = (a02 - a22) * (b11 - b21);\n const m14 = a02 * b20;\n const m15 = (a21 + a22) * (-b20 + b21);\n const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n const m17 = (a02 - a12) * (b12 - b22);\n const m18 = (a11 + a12) * (-b20 + b22);\n const m19 = a01 * b10;\n const m20 = a12 * b21;\n const m21 = a10 * b02;\n const m22 = a20 * b01;\n const m23 = a22 * b22;\n\n const c00 = m6 + m14 + m19;\n const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n const c11 = m2 + m4 + m5 + m6 + m20;\n const c12 = m14 + m16 + m17 + m18 + m21;\n const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n const c21 = m12 + m13 + m14 + m15 + m22;\n const c22 = m6 + m7 + m8 + m9 + m23;\n\n result.set(0, 0, c00);\n result.set(0, 1, c01);\n result.set(0, 2, c02);\n result.set(1, 0, c10);\n result.set(1, 1, c11);\n result.set(1, 2, c12);\n result.set(2, 0, c20);\n result.set(2, 1, c21);\n result.set(2, 2, c22);\n return result;\n }\n\n mmulStrassen(y) {\n y = Matrix.checkMatrix(y);\n let x = this.clone();\n let r1 = x.rows;\n let c1 = x.columns;\n let r2 = y.rows;\n let c2 = y.columns;\n if (c1 !== r2) {\n // eslint-disable-next-line no-console\n console.warn(\n `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n );\n }\n\n // Put a matrix into the top left of a matrix of zeros.\n // `rows` and `cols` are the dimensions of the output matrix.\n function embed(mat, rows, cols) {\n let r = mat.rows;\n let c = mat.columns;\n if (r === rows && c === cols) {\n return mat;\n } else {\n let resultat = AbstractMatrix.zeros(rows, cols);\n resultat = resultat.setSubMatrix(mat, 0, 0);\n return resultat;\n }\n }\n\n // Make sure both matrices are the same size.\n // This is exclusively for simplicity:\n // this algorithm can be implemented with matrices of different sizes.\n\n let r = Math.max(r1, r2);\n let c = Math.max(c1, c2);\n x = embed(x, r, c);\n y = embed(y, r, c);\n\n // Our recursive multiplication function.\n function blockMult(a, b, rows, cols) {\n // For small matrices, resort to naive multiplication.\n if (rows <= 512 || cols <= 512) {\n return a.mmul(b); // a is equivalent to this\n }\n\n // Apply dynamic padding.\n if (rows % 2 === 1 && cols % 2 === 1) {\n a = embed(a, rows + 1, cols + 1);\n b = embed(b, rows + 1, cols + 1);\n } else if (rows % 2 === 1) {\n a = embed(a, rows + 1, cols);\n b = embed(b, rows + 1, cols);\n } else if (cols % 2 === 1) {\n a = embed(a, rows, cols + 1);\n b = embed(b, rows, cols + 1);\n }\n\n let halfRows = parseInt(a.rows / 2, 10);\n let halfCols = parseInt(a.columns / 2, 10);\n // Subdivide input matrices.\n let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n // Compute intermediate values.\n let m1 = blockMult(\n AbstractMatrix.add(a11, a22),\n AbstractMatrix.add(b11, b22),\n halfRows,\n halfCols,\n );\n let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n let m6 = blockMult(\n AbstractMatrix.sub(a21, a11),\n AbstractMatrix.add(b11, b12),\n halfRows,\n halfCols,\n );\n let m7 = blockMult(\n AbstractMatrix.sub(a12, a22),\n AbstractMatrix.add(b21, b22),\n halfRows,\n halfCols,\n );\n\n // Combine intermediate values into the output.\n let c11 = AbstractMatrix.add(m1, m4);\n c11.sub(m5);\n c11.add(m7);\n let c12 = AbstractMatrix.add(m3, m5);\n let c21 = AbstractMatrix.add(m2, m4);\n let c22 = AbstractMatrix.sub(m1, m2);\n c22.add(m3);\n c22.add(m6);\n\n // Crop output to the desired size (undo dynamic padding).\n let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n resultat = resultat.setSubMatrix(c11, 0, 0);\n resultat = resultat.setSubMatrix(c12, c11.rows, 0);\n resultat = resultat.setSubMatrix(c21, 0, c11.columns);\n resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);\n return resultat.subMatrix(0, rows - 1, 0, cols - 1);\n }\n return blockMult(x, y, r, c);\n }\n\n scaleRows(options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { min = 0, max = 1 } = options;\n if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n if (min >= max) throw new RangeError('min must be smaller than max');\n let newMatrix = new Matrix(this.rows, this.columns);\n for (let i = 0; i < this.rows; i++) {\n const row = this.getRow(i);\n if (row.length > 0) {\n rescale(row, { min, max, output: row });\n }\n newMatrix.setRow(i, row);\n }\n return newMatrix;\n }\n\n scaleColumns(options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { min = 0, max = 1 } = options;\n if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n if (min >= max) throw new RangeError('min must be smaller than max');\n let newMatrix = new Matrix(this.rows, this.columns);\n for (let i = 0; i < this.columns; i++) {\n const column = this.getColumn(i);\n if (column.length) {\n rescale(column, {\n min: min,\n max: max,\n output: column,\n });\n }\n newMatrix.setColumn(i, column);\n }\n return newMatrix;\n }\n\n flipRows() {\n const middle = Math.ceil(this.columns / 2);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < middle; j++) {\n let first = this.get(i, j);\n let last = this.get(i, this.columns - 1 - j);\n this.set(i, j, last);\n this.set(i, this.columns - 1 - j, first);\n }\n }\n return this;\n }\n\n flipColumns() {\n const middle = Math.ceil(this.rows / 2);\n for (let j = 0; j < this.columns; j++) {\n for (let i = 0; i < middle; i++) {\n let first = this.get(i, j);\n let last = this.get(this.rows - 1 - i, j);\n this.set(i, j, last);\n this.set(this.rows - 1 - i, j, first);\n }\n }\n return this;\n }\n\n kroneckerProduct(other) {\n other = Matrix.checkMatrix(other);\n\n let m = this.rows;\n let n = this.columns;\n let p = other.rows;\n let q = other.columns;\n\n let result = new Matrix(m * p, n * q);\n for (let i = 0; i < m; i++) {\n for (let j = 0; j < n; j++) {\n for (let k = 0; k < p; k++) {\n for (let l = 0; l < q; l++) {\n result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n }\n }\n }\n }\n return result;\n }\n\n kroneckerSum(other) {\n other = Matrix.checkMatrix(other);\n if (!this.isSquare() || !other.isSquare()) {\n throw new Error('Kronecker Sum needs two Square Matrices');\n }\n let m = this.rows;\n let n = other.rows;\n let AxI = this.kroneckerProduct(Matrix.eye(n, n));\n let IxB = Matrix.eye(m, m).kroneckerProduct(other);\n return AxI.add(IxB);\n }\n\n transpose() {\n let result = new Matrix(this.columns, this.rows);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n result.set(j, i, this.get(i, j));\n }\n }\n return result;\n }\n\n sortRows(compareFunction = compareNumbers) {\n for (let i = 0; i < this.rows; i++) {\n this.setRow(i, this.getRow(i).sort(compareFunction));\n }\n return this;\n }\n\n sortColumns(compareFunction = compareNumbers) {\n for (let i = 0; i < this.columns; i++) {\n this.setColumn(i, this.getColumn(i).sort(compareFunction));\n }\n return this;\n }\n\n subMatrix(startRow, endRow, startColumn, endColumn) {\n checkRange(this, startRow, endRow, startColumn, endColumn);\n let newMatrix = new Matrix(\n endRow - startRow + 1,\n endColumn - startColumn + 1,\n );\n for (let i = startRow; i <= endRow; i++) {\n for (let j = startColumn; j <= endColumn; j++) {\n newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n }\n }\n return newMatrix;\n }\n\n subMatrixRow(indices, startColumn, endColumn) {\n if (startColumn === undefined) startColumn = 0;\n if (endColumn === undefined) endColumn = this.columns - 1;\n if (\n startColumn > endColumn ||\n startColumn < 0 ||\n startColumn >= this.columns ||\n endColumn < 0 ||\n endColumn >= this.columns\n ) {\n throw new RangeError('Argument out of range');\n }\n\n let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n for (let i = 0; i < indices.length; i++) {\n for (let j = startColumn; j <= endColumn; j++) {\n if (indices[i] < 0 || indices[i] >= this.rows) {\n throw new RangeError(`Row index out of range: ${indices[i]}`);\n }\n newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n }\n }\n return newMatrix;\n }\n\n subMatrixColumn(indices, startRow, endRow) {\n if (startRow === undefined) startRow = 0;\n if (endRow === undefined) endRow = this.rows - 1;\n if (\n startRow > endRow ||\n startRow < 0 ||\n startRow >= this.rows ||\n endRow < 0 ||\n endRow >= this.rows\n ) {\n throw new RangeError('Argument out of range');\n }\n\n let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n for (let i = 0; i < indices.length; i++) {\n for (let j = startRow; j <= endRow; j++) {\n if (indices[i] < 0 || indices[i] >= this.columns) {\n throw new RangeError(`Column index out of range: ${indices[i]}`);\n }\n newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n }\n }\n return newMatrix;\n }\n\n setSubMatrix(matrix, startRow, startColumn) {\n matrix = Matrix.checkMatrix(matrix);\n if (matrix.isEmpty()) {\n return this;\n }\n let endRow = startRow + matrix.rows - 1;\n let endColumn = startColumn + matrix.columns - 1;\n checkRange(this, startRow, endRow, startColumn, endColumn);\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n this.set(startRow + i, startColumn + j, matrix.get(i, j));\n }\n }\n return this;\n }\n\n selection(rowIndices, columnIndices) {\n let indices = checkIndices(this, rowIndices, columnIndices);\n let newMatrix = new Matrix(rowIndices.length, columnIndices.length);\n for (let i = 0; i < indices.row.length; i++) {\n let rowIndex = indices.row[i];\n for (let j = 0; j < indices.column.length; j++) {\n let columnIndex = indices.column[j];\n newMatrix.set(i, j, this.get(rowIndex, columnIndex));\n }\n }\n return newMatrix;\n }\n\n trace() {\n let min = Math.min(this.rows, this.columns);\n let trace = 0;\n for (let i = 0; i < min; i++) {\n trace += this.get(i, i);\n }\n return trace;\n }\n\n clone() {\n let newMatrix = new Matrix(this.rows, this.columns);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n newMatrix.set(row, column, this.get(row, column));\n }\n }\n return newMatrix;\n }\n\n sum(by) {\n switch (by) {\n case 'row':\n return sumByRow(this);\n case 'column':\n return sumByColumn(this);\n case undefined:\n return sumAll(this);\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n product(by) {\n switch (by) {\n case 'row':\n return productByRow(this);\n case 'column':\n return productByColumn(this);\n case undefined:\n return productAll(this);\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n mean(by) {\n const sum = this.sum(by);\n switch (by) {\n case 'row': {\n for (let i = 0; i < this.rows; i++) {\n sum[i] /= this.columns;\n }\n return sum;\n }\n case 'column': {\n for (let i = 0; i < this.columns; i++) {\n sum[i] /= this.rows;\n }\n return sum;\n }\n case undefined:\n return sum / this.size;\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n variance(by, options = {}) {\n if (typeof by === 'object') {\n options = by;\n by = undefined;\n }\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { unbiased = true, mean = this.mean(by) } = options;\n if (typeof unbiased !== 'boolean') {\n throw new TypeError('unbiased must be a boolean');\n }\n switch (by) {\n case 'row': {\n if (!Array.isArray(mean)) {\n throw new TypeError('mean must be an array');\n }\n return varianceByRow(this, unbiased, mean);\n }\n case 'column': {\n if (!Array.isArray(mean)) {\n throw new TypeError('mean must be an array');\n }\n return varianceByColumn(this, unbiased, mean);\n }\n case undefined: {\n if (typeof mean !== 'number') {\n throw new TypeError('mean must be a number');\n }\n return varianceAll(this, unbiased, mean);\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n standardDeviation(by, options) {\n if (typeof by === 'object') {\n options = by;\n by = undefined;\n }\n const variance = this.variance(by, options);\n if (by === undefined) {\n return Math.sqrt(variance);\n } else {\n for (let i = 0; i < variance.length; i++) {\n variance[i] = Math.sqrt(variance[i]);\n }\n return variance;\n }\n }\n\n center(by, options = {}) {\n if (typeof by === 'object') {\n options = by;\n by = undefined;\n }\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { center = this.mean(by) } = options;\n switch (by) {\n case 'row': {\n if (!Array.isArray(center)) {\n throw new TypeError('center must be an array');\n }\n centerByRow(this, center);\n return this;\n }\n case 'column': {\n if (!Array.isArray(center)) {\n throw new TypeError('center must be an array');\n }\n centerByColumn(this, center);\n return this;\n }\n case undefined: {\n if (typeof center !== 'number') {\n throw new TypeError('center must be a number');\n }\n centerAll(this, center);\n return this;\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n scale(by, options = {}) {\n if (typeof by === 'object') {\n options = by;\n by = undefined;\n }\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n let scale = options.scale;\n switch (by) {\n case 'row': {\n if (scale === undefined) {\n scale = getScaleByRow(this);\n } else if (!Array.isArray(scale)) {\n throw new TypeError('scale must be an array');\n }\n scaleByRow(this, scale);\n return this;\n }\n case 'column': {\n if (scale === undefined) {\n scale = getScaleByColumn(this);\n } else if (!Array.isArray(scale)) {\n throw new TypeError('scale must be an array');\n }\n scaleByColumn(this, scale);\n return this;\n }\n case undefined: {\n if (scale === undefined) {\n scale = getScaleAll(this);\n } else if (typeof scale !== 'number') {\n throw new TypeError('scale must be a number');\n }\n scaleAll(this, scale);\n return this;\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n toString(options) {\n return inspectMatrixWithOptions(this, options);\n }\n}\n\nAbstractMatrix.prototype.klass = 'Matrix';\nif (typeof Symbol !== 'undefined') {\n AbstractMatrix.prototype[\n Symbol.for('nodejs.util.inspect.custom')\n ] = inspectMatrix;\n}\n\nfunction compareNumbers(a, b) {\n return a - b;\n}\n\n// Synonyms\nAbstractMatrix.random = AbstractMatrix.rand;\nAbstractMatrix.randomInt = AbstractMatrix.randInt;\nAbstractMatrix.diagonal = AbstractMatrix.diag;\nAbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;\nAbstractMatrix.identity = AbstractMatrix.eye;\nAbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;\nAbstractMatrix.prototype.tensorProduct =\n AbstractMatrix.prototype.kroneckerProduct;\n\nexport default class Matrix extends AbstractMatrix {\n constructor(nRows, nColumns) {\n super();\n if (Matrix.isMatrix(nRows)) {\n // eslint-disable-next-line no-constructor-return\n return nRows.clone();\n } else if (Number.isInteger(nRows) && nRows >= 0) {\n // Create an empty matrix\n this.data = [];\n if (Number.isInteger(nColumns) && nColumns >= 0) {\n for (let i = 0; i < nRows; i++) {\n this.data.push(new Float64Array(nColumns));\n }\n } else {\n throw new TypeError('nColumns must be a positive integer');\n }\n } else if (Array.isArray(nRows)) {\n // Copy the values from the 2D array\n const arrayData = nRows;\n nRows = arrayData.length;\n nColumns = nRows ? arrayData[0].length : 0;\n if (typeof nColumns !== 'number') {\n throw new TypeError(\n 'Data must be a 2D array with at least one element',\n );\n }\n this.data = [];\n for (let i = 0; i < nRows; i++) {\n if (arrayData[i].length !== nColumns) {\n throw new RangeError('Inconsistent array dimensions');\n }\n this.data.push(Float64Array.from(arrayData[i]));\n }\n } else {\n throw new TypeError(\n 'First argument must be a positive number or an array',\n );\n }\n this.rows = nRows;\n this.columns = nColumns;\n }\n\n set(rowIndex, columnIndex, value) {\n this.data[rowIndex][columnIndex] = value;\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.data[rowIndex][columnIndex];\n }\n\n removeRow(index) {\n checkRowIndex(this, index);\n this.data.splice(index, 1);\n this.rows -= 1;\n return this;\n }\n\n addRow(index, array) {\n if (array === undefined) {\n array = index;\n index = this.rows;\n }\n checkRowIndex(this, index, true);\n array = Float64Array.from(checkRowVector(this, array));\n this.data.splice(index, 0, array);\n this.rows += 1;\n return this;\n }\n\n removeColumn(index) {\n checkColumnIndex(this, index);\n for (let i = 0; i < this.rows; i++) {\n const newRow = new Float64Array(this.columns - 1);\n for (let j = 0; j < index; j++) {\n newRow[j] = this.data[i][j];\n }\n for (let j = index + 1; j < this.columns; j++) {\n newRow[j - 1] = this.data[i][j];\n }\n this.data[i] = newRow;\n }\n this.columns -= 1;\n return this;\n }\n\n addColumn(index, array) {\n if (typeof array === 'undefined') {\n array = index;\n index = this.columns;\n }\n checkColumnIndex(this, index, true);\n array = checkColumnVector(this, array);\n for (let i = 0; i < this.rows; i++) {\n const newRow = new Float64Array(this.columns + 1);\n let j = 0;\n for (; j < index; j++) {\n newRow[j] = this.data[i][j];\n }\n newRow[j++] = array[i];\n for (; j < this.columns + 1; j++) {\n newRow[j] = this.data[i][j - 1];\n }\n this.data[i] = newRow;\n }\n this.columns += 1;\n return this;\n }\n}\n\ninstallMathOperations(AbstractMatrix, Matrix);\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix2D extends AbstractMatrix {\n constructor(data) {\n super();\n this.data = data;\n this.rows = data.length;\n this.columns = data[0].length;\n }\n\n set(rowIndex, columnIndex, value) {\n this.data[rowIndex][columnIndex] = value;\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.data[rowIndex][columnIndex];\n }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class LuDecomposition {\n constructor(matrix) {\n matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n let lu = matrix.clone();\n let rows = lu.rows;\n let columns = lu.columns;\n let pivotVector = new Float64Array(rows);\n let pivotSign = 1;\n let i, j, k, p, s, t, v;\n let LUcolj, kmax;\n\n for (i = 0; i < rows; i++) {\n pivotVector[i] = i;\n }\n\n LUcolj = new Float64Array(rows);\n\n for (j = 0; j < columns; j++) {\n for (i = 0; i < rows; i++) {\n LUcolj[i] = lu.get(i, j);\n }\n\n for (i = 0; i < rows; i++) {\n kmax = Math.min(i, j);\n s = 0;\n for (k = 0; k < kmax; k++) {\n s += lu.get(i, k) * LUcolj[k];\n }\n LUcolj[i] -= s;\n lu.set(i, j, LUcolj[i]);\n }\n\n p = j;\n for (i = j + 1; i < rows; i++) {\n if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n p = i;\n }\n }\n\n if (p !== j) {\n for (k = 0; k < columns; k++) {\n t = lu.get(p, k);\n lu.set(p, k, lu.get(j, k));\n lu.set(j, k, t);\n }\n\n v = pivotVector[p];\n pivotVector[p] = pivotVector[j];\n pivotVector[j] = v;\n\n pivotSign = -pivotSign;\n }\n\n if (j < rows && lu.get(j, j) !== 0) {\n for (i = j + 1; i < rows; i++) {\n lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n }\n }\n }\n\n this.LU = lu;\n this.pivotVector = pivotVector;\n this.pivotSign = pivotSign;\n }\n\n isSingular() {\n let data = this.LU;\n let col = data.columns;\n for (let j = 0; j < col; j++) {\n if (data.get(j, j) === 0) {\n return true;\n }\n }\n return false;\n }\n\n solve(value) {\n value = Matrix.checkMatrix(value);\n\n let lu = this.LU;\n let rows = lu.rows;\n\n if (rows !== value.rows) {\n throw new Error('Invalid matrix dimensions');\n }\n if (this.isSingular()) {\n throw new Error('LU matrix is singular');\n }\n\n let count = value.columns;\n let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n let columns = lu.columns;\n let i, j, k;\n\n for (k = 0; k < columns; k++) {\n for (i = k + 1; i < columns; i++) {\n for (j = 0; j < count; j++) {\n X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n }\n }\n }\n for (k = columns - 1; k >= 0; k--) {\n for (j = 0; j < count; j++) {\n X.set(k, j, X.get(k, j) / lu.get(k, k));\n }\n for (i = 0; i < k; i++) {\n for (j = 0; j < count; j++) {\n X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n }\n }\n }\n return X;\n }\n\n get determinant() {\n let data = this.LU;\n if (!data.isSquare()) {\n throw new Error('Matrix must be square');\n }\n let determinant = this.pivotSign;\n let col = data.columns;\n for (let j = 0; j < col; j++) {\n determinant *= data.get(j, j);\n }\n return determinant;\n }\n\n get lowerTriangularMatrix() {\n let data = this.LU;\n let rows = data.rows;\n let columns = data.columns;\n let X = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n if (i > j) {\n X.set(i, j, data.get(i, j));\n } else if (i === j) {\n X.set(i, j, 1);\n } else {\n X.set(i, j, 0);\n }\n }\n }\n return X;\n }\n\n get upperTriangularMatrix() {\n let data = this.LU;\n let rows = data.rows;\n let columns = data.columns;\n let X = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n if (i <= j) {\n X.set(i, j, data.get(i, j));\n } else {\n X.set(i, j, 0);\n }\n }\n }\n return X;\n }\n\n get pivotPermutationVector() {\n return Array.from(this.pivotVector);\n }\n}\n","export function hypotenuse(a, b) {\n let r = 0;\n if (Math.abs(a) > Math.abs(b)) {\n r = b / a;\n return Math.abs(a) * Math.sqrt(1 + r * r);\n }\n if (b !== 0) {\n r = a / b;\n return Math.abs(b) * Math.sqrt(1 + r * r);\n }\n return 0;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class QrDecomposition {\n constructor(value) {\n value = WrapperMatrix2D.checkMatrix(value);\n\n let qr = value.clone();\n let m = value.rows;\n let n = value.columns;\n let rdiag = new Float64Array(n);\n let i, j, k, s;\n\n for (k = 0; k < n; k++) {\n let nrm = 0;\n for (i = k; i < m; i++) {\n nrm = hypotenuse(nrm, qr.get(i, k));\n }\n if (nrm !== 0) {\n if (qr.get(k, k) < 0) {\n nrm = -nrm;\n }\n for (i = k; i < m; i++) {\n qr.set(i, k, qr.get(i, k) / nrm);\n }\n qr.set(k, k, qr.get(k, k) + 1);\n for (j = k + 1; j < n; j++) {\n s = 0;\n for (i = k; i < m; i++) {\n s += qr.get(i, k) * qr.get(i, j);\n }\n s = -s / qr.get(k, k);\n for (i = k; i < m; i++) {\n qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n }\n }\n }\n rdiag[k] = -nrm;\n }\n\n this.QR = qr;\n this.Rdiag = rdiag;\n }\n\n solve(value) {\n value = Matrix.checkMatrix(value);\n\n let qr = this.QR;\n let m = qr.rows;\n\n if (value.rows !== m) {\n throw new Error('Matrix row dimensions must agree');\n }\n if (!this.isFullRank()) {\n throw new Error('Matrix is rank deficient');\n }\n\n let count = value.columns;\n let X = value.clone();\n let n = qr.columns;\n let i, j, k, s;\n\n for (k = 0; k < n; k++) {\n for (j = 0; j < count; j++) {\n s = 0;\n for (i = k; i < m; i++) {\n s += qr.get(i, k) * X.get(i, j);\n }\n s = -s / qr.get(k, k);\n for (i = k; i < m; i++) {\n X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n }\n }\n }\n for (k = n - 1; k >= 0; k--) {\n for (j = 0; j < count; j++) {\n X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n }\n for (i = 0; i < k; i++) {\n for (j = 0; j < count; j++) {\n X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n }\n }\n }\n\n return X.subMatrix(0, n - 1, 0, count - 1);\n }\n\n isFullRank() {\n let columns = this.QR.columns;\n for (let i = 0; i < columns; i++) {\n if (this.Rdiag[i] === 0) {\n return false;\n }\n }\n return true;\n }\n\n get upperTriangularMatrix() {\n let qr = this.QR;\n let n = qr.columns;\n let X = new Matrix(n, n);\n let i, j;\n for (i = 0; i < n; i++) {\n for (j = 0; j < n; j++) {\n if (i < j) {\n X.set(i, j, qr.get(i, j));\n } else if (i === j) {\n X.set(i, j, this.Rdiag[i]);\n } else {\n X.set(i, j, 0);\n }\n }\n }\n return X;\n }\n\n get orthogonalMatrix() {\n let qr = this.QR;\n let rows = qr.rows;\n let columns = qr.columns;\n let X = new Matrix(rows, columns);\n let i, j, k, s;\n\n for (k = columns - 1; k >= 0; k--) {\n for (i = 0; i < rows; i++) {\n X.set(i, k, 0);\n }\n X.set(k, k, 1);\n for (j = k; j < columns; j++) {\n if (qr.get(k, k) !== 0) {\n s = 0;\n for (i = k; i < rows; i++) {\n s += qr.get(i, k) * X.get(i, j);\n }\n\n s = -s / qr.get(k, k);\n\n for (i = k; i < rows; i++) {\n X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n }\n }\n }\n }\n return X;\n }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class SingularValueDecomposition {\n constructor(value, options = {}) {\n value = WrapperMatrix2D.checkMatrix(value);\n\n if (value.isEmpty()) {\n throw new Error('Matrix must be non-empty');\n }\n\n let m = value.rows;\n let n = value.columns;\n\n const {\n computeLeftSingularVectors = true,\n computeRightSingularVectors = true,\n autoTranspose = false,\n } = options;\n\n let wantu = Boolean(computeLeftSingularVectors);\n let wantv = Boolean(computeRightSingularVectors);\n\n let swapped = false;\n let a;\n if (m < n) {\n if (!autoTranspose) {\n a = value.clone();\n // eslint-disable-next-line no-console\n console.warn(\n 'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n );\n } else {\n a = value.transpose();\n m = a.rows;\n n = a.columns;\n swapped = true;\n let aux = wantu;\n wantu = wantv;\n wantv = aux;\n }\n } else {\n a = value.clone();\n }\n\n let nu = Math.min(m, n);\n let ni = Math.min(m + 1, n);\n let s = new Float64Array(ni);\n let U = new Matrix(m, nu);\n let V = new Matrix(n, n);\n\n let e = new Float64Array(n);\n let work = new Float64Array(m);\n\n let si = new Float64Array(ni);\n for (let i = 0; i < ni; i++) si[i] = i;\n\n let nct = Math.min(m - 1, n);\n let nrt = Math.max(0, Math.min(n - 2, m));\n let mrc = Math.max(nct, nrt);\n\n for (let k = 0; k < mrc; k++) {\n if (k < nct) {\n s[k] = 0;\n for (let i = k; i < m; i++) {\n s[k] = hypotenuse(s[k], a.get(i, k));\n }\n if (s[k] !== 0) {\n if (a.get(k, k) < 0) {\n s[k] = -s[k];\n }\n for (let i = k; i < m; i++) {\n a.set(i, k, a.get(i, k) / s[k]);\n }\n a.set(k, k, a.get(k, k) + 1);\n }\n s[k] = -s[k];\n }\n\n for (let j = k + 1; j < n; j++) {\n if (k < nct && s[k] !== 0) {\n let t = 0;\n for (let i = k; i < m; i++) {\n t += a.get(i, k) * a.get(i, j);\n }\n t = -t / a.get(k, k);\n for (let i = k; i < m; i++) {\n a.set(i, j, a.get(i, j) + t * a.get(i, k));\n }\n }\n e[j] = a.get(k, j);\n }\n\n if (wantu && k < nct) {\n for (let i = k; i < m; i++) {\n U.set(i, k, a.get(i, k));\n }\n }\n\n if (k < nrt) {\n e[k] = 0;\n for (let i = k + 1; i < n; i++) {\n e[k] = hypotenuse(e[k], e[i]);\n }\n if (e[k] !== 0) {\n if (e[k + 1] < 0) {\n e[k] = 0 - e[k];\n }\n for (let i = k + 1; i < n; i++) {\n e[i] /= e[k];\n }\n e[k + 1] += 1;\n }\n e[k] = -e[k];\n if (k + 1 < m && e[k] !== 0) {\n for (let i = k + 1; i < m; i++) {\n work[i] = 0;\n }\n for (let i = k + 1; i < m; i++) {\n for (let j = k + 1; j < n; j++) {\n work[i] += e[j] * a.get(i, j);\n }\n }\n for (let j = k + 1; j < n; j++) {\n let t = -e[j] / e[k + 1];\n for (let i = k + 1; i < m; i++) {\n a.set(i, j, a.get(i, j) + t * work[i]);\n }\n }\n }\n if (wantv) {\n for (let i = k + 1; i < n; i++) {\n V.set(i, k, e[i]);\n }\n }\n }\n }\n\n let p = Math.min(n, m + 1);\n if (nct < n) {\n s[nct] = a.get(nct, nct);\n }\n if (m < p) {\n s[p - 1] = 0;\n }\n if (nrt + 1 < p) {\n e[nrt] = a.get(nrt, p - 1);\n }\n e[p - 1] = 0;\n\n if (wantu) {\n for (let j = nct; j < nu; j++) {\n for (let i = 0; i < m; i++) {\n U.set(i, j, 0);\n }\n U.set(j, j, 1);\n }\n for (let k = nct - 1; k >= 0; k--) {\n if (s[k] !== 0) {\n for (let j = k + 1; j < nu; j++) {\n let t = 0;\n for (let i = k; i < m; i++) {\n t += U.get(i, k) * U.get(i, j);\n }\n t = -t / U.get(k, k);\n for (let i = k; i < m; i++) {\n U.set(i, j, U.get(i, j) + t * U.get(i, k));\n }\n }\n for (let i = k; i < m; i++) {\n U.set(i, k, -U.get(i, k));\n }\n U.set(k, k, 1 + U.get(k, k));\n for (let i = 0; i < k - 1; i++) {\n U.set(i, k, 0);\n }\n } else {\n for (let i = 0; i < m; i++) {\n U.set(i, k, 0);\n }\n U.set(k, k, 1);\n }\n }\n }\n\n if (wantv) {\n for (let k = n - 1; k >= 0; k--) {\n if (k < nrt && e[k] !== 0) {\n for (let j = k + 1; j < n; j++) {\n let t = 0;\n for (let i = k + 1; i < n; i++) {\n t += V.get(i, k) * V.get(i, j);\n }\n t = -t / V.get(k + 1, k);\n for (let i = k + 1; i < n; i++) {\n V.set(i, j, V.get(i, j) + t * V.get(i, k));\n }\n }\n }\n for (let i = 0; i < n; i++) {\n V.set(i, k, 0);\n }\n V.set(k, k, 1);\n }\n }\n\n let pp = p - 1;\n let iter = 0;\n let eps = Number.EPSILON;\n while (p > 0) {\n let k, kase;\n for (k = p - 2; k >= -1; k--) {\n if (k === -1) {\n break;\n }\n const alpha =\n Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n e[k] = 0;\n break;\n }\n }\n if (k === p - 2) {\n kase = 4;\n } else {\n let ks;\n for (ks = p - 1; ks >= k; ks--) {\n if (ks === k) {\n break;\n }\n let t =\n (ks !== p ? Math.abs(e[ks]) : 0) +\n (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n if (Math.abs(s[ks]) <= eps * t) {\n s[ks] = 0;\n break;\n }\n }\n if (ks === k) {\n kase = 3;\n } else if (ks === p - 1) {\n kase = 1;\n } else {\n kase = 2;\n k = ks;\n }\n }\n\n k++;\n\n switch (kase) {\n case 1: {\n let f = e[p - 2];\n e[p - 2] = 0;\n for (let j = p - 2; j >= k; j--) {\n let t = hypotenuse(s[j], f);\n let cs = s[j] / t;\n let sn = f / t;\n s[j] = t;\n if (j !== k) {\n f = -sn * e[j - 1];\n e[j - 1] = cs * e[j - 1];\n }\n if (wantv) {\n for (let i = 0; i < n; i++) {\n t = cs * V.get(i, j) + sn * V.get(i, p - 1);\n V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));\n V.set(i, j, t);\n }\n }\n }\n break;\n }\n case 2: {\n let f = e[k - 1];\n e[k - 1] = 0;\n for (let j = k; j < p; j++) {\n let t = hypotenuse(s[j], f);\n let cs = s[j] / t;\n let sn = f / t;\n s[j] = t;\n f = -sn * e[j];\n e[j] = cs * e[j];\n if (wantu) {\n for (let i = 0; i < m; i++) {\n t = cs * U.get(i, j) + sn * U.get(i, k - 1);\n U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));\n U.set(i, j, t);\n }\n }\n }\n break;\n }\n case 3: {\n const scale = Math.max(\n Math.abs(s[p - 1]),\n Math.abs(s[p - 2]),\n Math.abs(e[p - 2]),\n Math.abs(s[k]),\n Math.abs(e[k]),\n );\n const sp = s[p - 1] / scale;\n const spm1 = s[p - 2] / scale;\n const epm1 = e[p - 2] / scale;\n const sk = s[k] / scale;\n const ek = e[k] / scale;\n const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n const c = sp * epm1 * (sp * epm1);\n let shift = 0;\n if (b !== 0 || c !== 0) {\n if (b < 0) {\n shift = 0 - Math.sqrt(b * b + c);\n } else {\n shift = Math.sqrt(b * b + c);\n }\n shift = c / (b + shift);\n }\n let f = (sk + sp) * (sk - sp) + shift;\n let g = sk * ek;\n for (let j = k; j < p - 1; j++) {\n let t = hypotenuse(f, g);\n if (t === 0) t = Number.MIN_VALUE;\n let cs = f / t;\n let sn = g / t;\n if (j !== k) {\n e[j - 1] = t;\n }\n f = cs * s[j] + sn * e[j];\n e[j] = cs * e[j] - sn * s[j];\n g = sn * s[j + 1];\n s[j + 1] = cs * s[j + 1];\n if (wantv) {\n for (let i = 0; i < n; i++) {\n t = cs * V.get(i, j) + sn * V.get(i, j + 1);\n V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));\n V.set(i, j, t);\n }\n }\n t = hypotenuse(f, g);\n if (t === 0) t = Number.MIN_VALUE;\n cs = f / t;\n sn = g / t;\n s[j] = t;\n f = cs * e[j] + sn * s[j + 1];\n s[j + 1] = -sn * e[j] + cs * s[j + 1];\n g = sn * e[j + 1];\n e[j + 1] = cs * e[j + 1];\n if (wantu && j < m - 1) {\n for (let i = 0; i < m; i++) {\n t = cs * U.get(i, j) + sn * U.get(i, j + 1);\n U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));\n U.set(i, j, t);\n }\n }\n }\n e[p - 2] = f;\n iter = iter + 1;\n break;\n }\n case 4: {\n if (s[k] <= 0) {\n s[k] = s[k] < 0 ? -s[k] : 0;\n if (wantv) {\n for (let i = 0; i <= pp; i++) {\n V.set(i, k, -V.get(i, k));\n }\n }\n }\n while (k < pp) {\n if (s[k] >= s[k + 1]) {\n break;\n }\n let t = s[k];\n s[k] = s[k + 1];\n s[k + 1] = t;\n if (wantv && k < n - 1) {\n for (let i = 0; i < n; i++) {\n t = V.get(i, k + 1);\n V.set(i, k + 1, V.get(i, k));\n V.set(i, k, t);\n }\n }\n if (wantu && k < m - 1) {\n for (let i = 0; i < m; i++) {\n t = U.get(i, k + 1);\n U.set(i, k + 1, U.get(i, k));\n U.set(i, k, t);\n }\n }\n k++;\n }\n iter = 0;\n p--;\n break;\n }\n // no default\n }\n }\n\n if (swapped) {\n let tmp = V;\n V = U;\n U = tmp;\n }\n\n this.m = m;\n this.n = n;\n this.s = s;\n this.U = U;\n this.V = V;\n }\n\n solve(value) {\n let Y = value;\n let e = this.threshold;\n let scols = this.s.length;\n let Ls = Matrix.zeros(scols, scols);\n\n for (let i = 0; i < scols; i++) {\n if (Math.abs(this.s[i]) <= e) {\n Ls.set(i, i, 0);\n } else {\n Ls.set(i, i, 1 / this.s[i]);\n }\n }\n\n let U = this.U;\n let V = this.rightSingularVectors;\n\n let VL = V.mmul(Ls);\n let vrows = V.rows;\n let urows = U.rows;\n let VLU = Matrix.zeros(vrows, urows);\n\n for (let i = 0; i < vrows; i++) {\n for (let j = 0; j < urows; j++) {\n let sum = 0;\n for (let k = 0; k < scols; k++) {\n sum += VL.get(i, k) * U.get(j, k);\n }\n VLU.set(i, j, sum);\n }\n }\n\n return VLU.mmul(Y);\n }\n\n solveForDiagonal(value) {\n return this.solve(Matrix.diag(value));\n }\n\n inverse() {\n let V = this.V;\n let e = this.threshold;\n let vrows = V.rows;\n let vcols = V.columns;\n let X = new Matrix(vrows, this.s.length);\n\n for (let i = 0; i < vrows; i++) {\n for (let j = 0; j < vcols; j++) {\n if (Math.abs(this.s[j]) > e) {\n X.set(i, j, V.get(i, j) / this.s[j]);\n }\n }\n }\n\n let U = this.U;\n\n let urows = U.rows;\n let ucols = U.columns;\n let Y = new Matrix(vrows, urows);\n\n for (let i = 0; i < vrows; i++) {\n for (let j = 0; j < urows; j++) {\n let sum = 0;\n for (let k = 0; k < ucols; k++) {\n sum += X.get(i, k) * U.get(j, k);\n }\n Y.set(i, j, sum);\n }\n }\n\n return Y;\n }\n\n get condition() {\n return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n }\n\n get norm2() {\n return this.s[0];\n }\n\n get rank() {\n let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n let r = 0;\n let s = this.s;\n for (let i = 0, ii = s.length; i < ii; i++) {\n if (s[i] > tol) {\n r++;\n }\n }\n return r;\n }\n\n get diagonal() {\n return Array.from(this.s);\n }\n\n get threshold() {\n return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n }\n\n get leftSingularVectors() {\n return this.U;\n }\n\n get rightSingularVectors() {\n return this.V;\n }\n\n get diagonalMatrix() {\n return Matrix.diag(this.s);\n }\n}\n","import LuDecomposition from './dc/lu';\nimport QrDecomposition from './dc/qr';\nimport SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\nimport WrapperMatrix2D from './wrap/WrapperMatrix2D';\n\nexport function inverse(matrix, useSVD = false) {\n matrix = WrapperMatrix2D.checkMatrix(matrix);\n if (useSVD) {\n return new SingularValueDecomposition(matrix).inverse();\n } else {\n return solve(matrix, Matrix.eye(matrix.rows));\n }\n}\n\nexport function solve(leftHandSide, rightHandSide, useSVD = false) {\n leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n if (useSVD) {\n return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n } else {\n return leftHandSide.isSquare()\n ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n : new QrDecomposition(leftHandSide).solve(rightHandSide);\n }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class nipals {\n constructor(X, options = {}) {\n X = WrapperMatrix2D.checkMatrix(X);\n let { Y } = options;\n const {\n scaleScores = false,\n maxIterations = 1000,\n terminationCriteria = 1e-10,\n } = options;\n\n let u;\n if (Y) {\n if (Array.isArray(Y) && typeof Y[0] === 'number') {\n Y = Matrix.columnVector(Y);\n } else {\n Y = WrapperMatrix2D.checkMatrix(Y);\n }\n if (Y.rows !== X.rows) {\n throw new Error('Y should have the same number of rows as X');\n }\n u = Y.getColumnVector(0);\n } else {\n u = X.getColumnVector(0);\n }\n\n let diff = 1;\n let t, q, w, tOld;\n\n for (\n let counter = 0;\n counter < maxIterations && diff > terminationCriteria;\n counter++\n ) {\n w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));\n w = w.div(w.norm());\n\n t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));\n\n if (counter > 0) {\n diff = t.clone().sub(tOld).pow(2).sum();\n }\n tOld = t.clone();\n\n if (Y) {\n q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n q = q.div(q.norm());\n\n u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));\n } else {\n u = t;\n }\n }\n\n if (Y) {\n let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n p = p.div(p.norm());\n let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n let yResidual = Y.clone().sub(\n t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()),\n );\n\n this.t = t;\n this.p = p.transpose();\n this.w = w.transpose();\n this.q = q;\n this.u = u;\n this.s = t.transpose().mmul(t);\n this.xResidual = xResidual;\n this.yResidual = yResidual;\n this.betas = residual;\n } else {\n this.w = w.transpose();\n this.s = t.transpose().mmul(t).sqrt();\n if (scaleScores) {\n this.t = t.clone().div(this.s.get(0, 0));\n } else {\n this.t = t;\n }\n this.xResidual = X.sub(t.mmul(w.transpose()));\n }\n }\n}\n","import { Matrix } from 'ml-matrix';\n\n/**\n * @private\n * Function that given vector, returns its norm\n * @param {Vector} X\n * @return {number} Norm of the vector\n */\nexport function norm(X) {\n return Math.sqrt(X.clone().apply(pow2array).sum());\n}\n\n/**\n * @private\n * Function that pow 2 each element of a Matrix or a Vector,\n * used in the apply method of the Matrix object\n * @param {number} i - index i.\n * @param {number} j - index j.\n * @return {Matrix} The Matrix object modified at the index i, j.\n * */\nexport function pow2array(i, j) {\n this.set(i, j, this.get(i, j) ** 2);\n}\n\n/**\n * @private\n * Function that normalize the dataset and return the means and\n * standard deviation of each feature.\n * @param {Matrix} dataset\n * @return {object} dataset normalized, means and standard deviations\n */\nexport function featureNormalize(dataset) {\n let means = dataset.mean('column');\n let std = dataset.standardDeviation('column', {\n mean: means,\n unbiased: true,\n });\n let result = Matrix.checkMatrix(dataset).subRowVector(means);\n return { result: result.divRowVector(std), means: means, std: std };\n}\n\n/**\n * @private\n * Function that initialize an array of matrices.\n * @param {Array} array\n * @param {boolean} isMatrix\n * @return {Array} array with the matrices initialized.\n */\nexport function initializeMatrices(array, isMatrix) {\n if (isMatrix) {\n for (let i = 0; i < array.length; ++i) {\n for (let j = 0; j < array[i].length; ++j) {\n let elem = array[i][j];\n array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;\n }\n }\n } else {\n for (let i = 0; i < array.length; ++i) {\n array[i] = new Matrix(array[i]);\n }\n }\n\n return array;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport * as Utils from './util/utils';\n\n/**\n * @class PLS\n */\nexport class PLS {\n /**\n * Constructor for Partial Least Squares (PLS)\n * @param {object} options\n * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)\n * @param {number} [options.tolerance=1e-5]\n * @param {boolean} [options.scale=true] - rescale dataset using mean.\n * @param {object} model - for load purposes.\n */\n constructor(options, model) {\n if (options === true) {\n this.meanX = model.meanX;\n this.stdDevX = model.stdDevX;\n this.meanY = model.meanY;\n this.stdDevY = model.stdDevY;\n this.PBQ = Matrix.checkMatrix(model.PBQ);\n this.R2X = model.R2X;\n this.scale = model.scale;\n this.scaleMethod = model.scaleMethod;\n this.tolerance = model.tolerance;\n } else {\n let { tolerance = 1e-5, scale = true } = options;\n this.tolerance = tolerance;\n this.scale = scale;\n this.latentVectors = options.latentVectors;\n }\n }\n\n /**\n * Fits the model with the given data and predictions, in this function is calculated the\n * following outputs:\n *\n * T - Score matrix of X\n * P - Loading matrix of X\n * U - Score matrix of Y\n * Q - Loading matrix of Y\n * B - Matrix of regression coefficient\n * W - Weight matrix of X\n *\n * @param {Matrix|Array} trainingSet\n * @param {Matrix|Array} trainingValues\n */\n train(trainingSet, trainingValues) {\n trainingSet = Matrix.checkMatrix(trainingSet);\n trainingValues = Matrix.checkMatrix(trainingValues);\n\n if (trainingSet.length !== trainingValues.length) {\n throw new RangeError(\n 'The number of X rows must be equal to the number of Y rows',\n );\n }\n\n this.meanX = trainingSet.mean('column');\n this.stdDevX = trainingSet.standardDeviation('column', {\n mean: this.meanX,\n unbiased: true,\n });\n this.meanY = trainingValues.mean('column');\n this.stdDevY = trainingValues.standardDeviation('column', {\n mean: this.meanY,\n unbiased: true,\n });\n\n if (this.scale) {\n trainingSet = trainingSet\n .clone()\n .subRowVector(this.meanX)\n .divRowVector(this.stdDevX);\n trainingValues = trainingValues\n .clone()\n .subRowVector(this.meanY)\n .divRowVector(this.stdDevY);\n }\n\n if (this.latentVectors === undefined) {\n this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);\n }\n\n let rx = trainingSet.rows;\n let cx = trainingSet.columns;\n let ry = trainingValues.rows;\n let cy = trainingValues.columns;\n\n let ssqXcal = trainingSet.clone().mul(trainingSet).sum(); // for the r²\n let sumOfSquaresY = trainingValues.clone().mul(trainingValues).sum();\n\n let tolerance = this.tolerance;\n let n = this.latentVectors;\n let T = Matrix.zeros(rx, n);\n let P = Matrix.zeros(cx, n);\n let U = Matrix.zeros(ry, n);\n let Q = Matrix.zeros(cy, n);\n let B = Matrix.zeros(n, n);\n let W = P.clone();\n let k = 0;\n let t;\n let w;\n let q;\n let p;\n\n while (Utils.norm(trainingValues) > tolerance && k < n) {\n let transposeX = trainingSet.transpose();\n let transposeY = trainingValues.transpose();\n\n let tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));\n let uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));\n\n let t1 = trainingSet.getColumnVector(tIndex);\n let u = trainingValues.getColumnVector(uIndex);\n t = Matrix.zeros(rx, 1);\n\n while (Utils.norm(t1.clone().sub(t)) > tolerance) {\n w = transposeX.mmul(u);\n w.div(Utils.norm(w));\n t = t1;\n t1 = trainingSet.mmul(w);\n q = transposeY.mmul(t1);\n q.div(Utils.norm(q));\n u = trainingValues.mmul(q);\n }\n\n t = t1;\n let num = transposeX.mmul(t);\n let den = t.transpose().mmul(t).get(0, 0);\n p = num.div(den);\n let pnorm = Utils.norm(p);\n p.div(pnorm);\n t.mul(pnorm);\n w.mul(pnorm);\n\n num = u.transpose().mmul(t);\n den = t.transpose().mmul(t).get(0, 0);\n let b = num.div(den).get(0, 0);\n trainingSet.sub(t.mmul(p.transpose()));\n trainingValues.sub(t.clone().mul(b).mmul(q.transpose()));\n\n T.setColumn(k, t);\n P.setColumn(k, p);\n U.setColumn(k, u);\n Q.setColumn(k, q);\n W.setColumn(k, w);\n\n B.set(k, k, b);\n k++;\n }\n\n k--;\n T = T.subMatrix(0, T.rows - 1, 0, k);\n P = P.subMatrix(0, P.rows - 1, 0, k);\n U = U.subMatrix(0, U.rows - 1, 0, k);\n Q = Q.subMatrix(0, Q.rows - 1, 0, k);\n W = W.subMatrix(0, W.rows - 1, 0, k);\n B = B.subMatrix(0, k, 0, k);\n\n this.ssqYcal = sumOfSquaresY;\n this.E = trainingSet;\n this.F = trainingValues;\n this.T = T;\n this.P = P;\n this.U = U;\n this.Q = Q;\n this.W = W;\n this.B = B;\n this.PBQ = P.mmul(B).mmul(Q.transpose());\n this.R2X = t\n .transpose()\n .mmul(t)\n .mmul(p.transpose().mmul(p))\n .div(ssqXcal)\n .get(0, 0);\n }\n\n /**\n * Predicts the behavior of the given dataset.\n * @param {Matrix|Array} dataset - data to be predicted.\n * @return {Matrix} - predictions of each element of the dataset.\n */\n predict(dataset) {\n let X = Matrix.checkMatrix(dataset);\n if (this.scale) {\n X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);\n }\n let Y = X.mmul(this.PBQ);\n Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);\n return Y;\n }\n\n /**\n * Returns the explained variance on training of the PLS model\n * @return {number}\n */\n getExplainedVariance() {\n return this.R2X;\n }\n\n /**\n * Export the current model to JSON.\n * @return {object} - Current model.\n */\n toJSON() {\n return {\n name: 'PLS',\n R2X: this.R2X,\n meanX: this.meanX,\n stdDevX: this.stdDevX,\n meanY: this.meanY,\n stdDevY: this.stdDevY,\n PBQ: this.PBQ,\n tolerance: this.tolerance,\n scale: this.scale,\n };\n }\n\n /**\n * Load a PLS model from a JSON Object\n * @param {object} model\n * @return {PLS} - PLS object from the given model\n */\n static load(model) {\n if (model.name !== 'PLS') {\n throw new RangeError(`Invalid model: ${model.name}`);\n }\n return new PLS(true, model);\n }\n}\n\n/**\n * @private\n * Function that returns the index where the sum of each\n * column vector is maximum.\n * @param {Matrix} data\n * @return {number} index of the maximum\n */\nfunction maxSumColIndex(data) {\n return Matrix.rowVector(data.sum('column')).maxIndex()[0];\n}\n","import { Matrix, SingularValueDecomposition, inverse } from 'ml-matrix';\n\nimport { initializeMatrices } from './util/utils';\n\n/**\n * @class KOPLS\n */\nexport class KOPLS {\n /**\n * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)\n * @param {object} options\n * @param {number} [options.predictiveComponents] - Number of predictive components to use.\n * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.\n * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).\n * @param {object} model - for load purposes.\n */\n constructor(options, model) {\n if (options === true) {\n this.trainingSet = new Matrix(model.trainingSet);\n this.YLoadingMat = new Matrix(model.YLoadingMat);\n this.SigmaPow = new Matrix(model.SigmaPow);\n this.YScoreMat = new Matrix(model.YScoreMat);\n this.predScoreMat = initializeMatrices(model.predScoreMat, false);\n this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);\n this.YOrthEigen = model.YOrthEigen;\n this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);\n this.toNorm = initializeMatrices(model.toNorm, false);\n this.TURegressionCoeff = initializeMatrices(\n model.TURegressionCoeff,\n false,\n );\n this.kernelX = initializeMatrices(model.kernelX, true);\n this.kernel = model.kernel;\n this.orthogonalComp = model.orthogonalComp;\n this.predictiveComp = model.predictiveComp;\n } else {\n if (options.predictiveComponents === undefined) {\n throw new RangeError('no predictive components found!');\n }\n if (options.orthogonalComponents === undefined) {\n throw new RangeError('no orthogonal components found!');\n }\n if (options.kernel === undefined) {\n throw new RangeError('no kernel found!');\n }\n\n this.orthogonalComp = options.orthogonalComponents;\n this.predictiveComp = options.predictiveComponents;\n this.kernel = options.kernel;\n }\n }\n\n /**\n * Train the K-OPLS model with the given training set and labels.\n * @param {Matrix|Array} trainingSet\n * @param {Matrix|Array} trainingValues\n */\n train(trainingSet, trainingValues) {\n trainingSet = Matrix.checkMatrix(trainingSet);\n trainingValues = Matrix.checkMatrix(trainingValues);\n\n // to save and compute kernel with the prediction dataset.\n this.trainingSet = trainingSet.clone();\n\n let kernelX = this.kernel.compute(trainingSet);\n\n let Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);\n let temp = kernelX;\n kernelX = new Array(this.orthogonalComp + 1);\n for (let i = 0; i < this.orthogonalComp + 1; i++) {\n kernelX[i] = new Array(this.orthogonalComp + 1);\n }\n kernelX[0][0] = temp;\n\n let result = new SingularValueDecomposition(\n trainingValues.transpose().mmul(kernelX[0][0]).mmul(trainingValues),\n {\n computeLeftSingularVectors: true,\n computeRightSingularVectors: false,\n },\n );\n let YLoadingMat = result.leftSingularVectors;\n let Sigma = result.diagonalMatrix;\n\n YLoadingMat = YLoadingMat.subMatrix(\n 0,\n YLoadingMat.rows - 1,\n 0,\n this.predictiveComp - 1,\n );\n Sigma = Sigma.subMatrix(\n 0,\n this.predictiveComp - 1,\n 0,\n this.predictiveComp - 1,\n );\n\n let YScoreMat = trainingValues.mmul(YLoadingMat);\n\n let predScoreMat = new Array(this.orthogonalComp + 1);\n let TURegressionCoeff = new Array(this.orthogonalComp + 1);\n let YOrthScoreMat = new Array(this.orthogonalComp);\n let YOrthLoadingVec = new Array(this.orthogonalComp);\n let YOrthEigen = new Array(this.orthogonalComp);\n let YOrthScoreNorm = new Array(this.orthogonalComp);\n\n let SigmaPow = Matrix.pow(Sigma, -0.5);\n // to avoid errors, check infinity\n\n function removeInfinity(i, j) {\n if (this.get(i, j) === Infinity) {\n this.set(i, j, 0);\n }\n }\n\n SigmaPow.apply(removeInfinity);\n\n for (let i = 0; i < this.orthogonalComp; ++i) {\n predScoreMat[i] = kernelX[0][i]\n .transpose()\n .mmul(YScoreMat)\n .mmul(SigmaPow);\n\n let TpiPrime = predScoreMat[i].transpose();\n TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i]))\n .mmul(TpiPrime)\n .mmul(YScoreMat);\n\n result = new SingularValueDecomposition(\n TpiPrime.mmul(\n Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)),\n ).mmul(predScoreMat[i]),\n {\n computeLeftSingularVectors: true,\n computeRightSingularVectors: false,\n },\n );\n let CoTemp = result.leftSingularVectors;\n let SoTemp = result.diagonalMatrix;\n\n YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);\n YOrthEigen[i] = SoTemp.get(0, 0);\n\n YOrthScoreMat[i] = Matrix.sub(\n kernelX[i][i],\n predScoreMat[i].mmul(TpiPrime),\n )\n .mmul(predScoreMat[i])\n .mmul(YOrthLoadingVec[i])\n .mul(Math.pow(YOrthEigen[i], -0.5));\n\n let toiPrime = YOrthScoreMat[i].transpose();\n YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));\n\n YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);\n\n let ITo = Matrix.sub(\n Identity,\n YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()),\n );\n\n kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);\n kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);\n }\n\n let lastScoreMat = (predScoreMat[this.orthogonalComp] = kernelX[0][\n this.orthogonalComp\n ]\n .transpose()\n .mmul(YScoreMat)\n .mmul(SigmaPow));\n\n let lastTpPrime = lastScoreMat.transpose();\n TURegressionCoeff[this.orthogonalComp] = inverse(\n lastTpPrime.mmul(lastScoreMat),\n )\n .mmul(lastTpPrime)\n .mmul(YScoreMat);\n\n this.YLoadingMat = YLoadingMat;\n this.SigmaPow = SigmaPow;\n this.YScoreMat = YScoreMat;\n this.predScoreMat = predScoreMat;\n this.YOrthLoadingVec = YOrthLoadingVec;\n this.YOrthEigen = YOrthEigen;\n this.YOrthScoreMat = YOrthScoreMat;\n this.toNorm = YOrthScoreNorm;\n this.TURegressionCoeff = TURegressionCoeff;\n this.kernelX = kernelX;\n }\n\n /**\n * Predicts the output given the matrix to predict.\n * @param {Matrix|Array} toPredict\n * @return {{y: Matrix, predScoreMat: Array, predYOrthVectors: Array}} predictions\n */\n predict(toPredict) {\n let KTestTrain = this.kernel.compute(toPredict, this.trainingSet);\n\n let temp = KTestTrain;\n KTestTrain = new Array(this.orthogonalComp + 1);\n for (let i = 0; i < this.orthogonalComp + 1; i++) {\n KTestTrain[i] = new Array(this.orthogonalComp + 1);\n }\n KTestTrain[0][0] = temp;\n\n let YOrthScoreVector = new Array(this.orthogonalComp);\n let predScoreMat = new Array(this.orthogonalComp);\n\n let i;\n for (i = 0; i < this.orthogonalComp; ++i) {\n predScoreMat[i] = KTestTrain[i][0]\n .mmul(this.YScoreMat)\n .mmul(this.SigmaPow);\n\n YOrthScoreVector[i] = Matrix.sub(\n KTestTrain[i][i],\n predScoreMat[i].mmul(this.predScoreMat[i].transpose()),\n )\n .mmul(this.predScoreMat[i])\n .mmul(this.YOrthLoadingVec[i])\n .mul(Math.pow(this.YOrthEigen[i], -0.5));\n\n YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);\n\n let scoreMatPrime = this.YOrthScoreMat[i].transpose();\n KTestTrain[i + 1][0] = Matrix.sub(\n KTestTrain[i][0],\n YOrthScoreVector[i]\n .mmul(scoreMatPrime)\n .mmul(this.kernelX[0][i].transpose()),\n );\n\n let p1 = Matrix.sub(\n KTestTrain[i][0],\n KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime),\n );\n let p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);\n let p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);\n\n KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);\n }\n\n predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n let prediction = predScoreMat[i]\n .mmul(this.TURegressionCoeff[i])\n .mmul(this.YLoadingMat.transpose());\n\n return {\n prediction: prediction,\n predScoreMat: predScoreMat,\n predYOrthVectors: YOrthScoreVector,\n };\n }\n\n /**\n * Export the current model to JSON.\n * @return {object} - Current model.\n */\n toJSON() {\n return {\n name: 'K-OPLS',\n YLoadingMat: this.YLoadingMat,\n SigmaPow: this.SigmaPow,\n YScoreMat: this.YScoreMat,\n predScoreMat: this.predScoreMat,\n YOrthLoadingVec: this.YOrthLoadingVec,\n YOrthEigen: this.YOrthEigen,\n YOrthScoreMat: this.YOrthScoreMat,\n toNorm: this.toNorm,\n TURegressionCoeff: this.TURegressionCoeff,\n kernelX: this.kernelX,\n trainingSet: this.trainingSet,\n orthogonalComp: this.orthogonalComp,\n predictiveComp: this.predictiveComp,\n };\n }\n\n /**\n * Load a K-OPLS with the given model.\n * @param {object} model\n * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).\n * @return {KOPLS}\n */\n static load(model, kernel) {\n if (model.name !== 'K-OPLS') {\n throw new RangeError(`Invalid model: ${model.name}`);\n }\n\n if (!kernel) {\n throw new RangeError('You must provide a kernel for the model!');\n }\n\n model.kernel = kernel;\n return new KOPLS(true, model);\n }\n}\n","/**\n * Constructs a confusion matrix\n * @class ConfusionMatrix\n * @example\n * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])\n * @param {Array>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns\n * the predicted label.\n * @param {Array} labels - Labels of the confusion matrix, a 1D Array\n */\nexport default class ConfusionMatrix {\n constructor(matrix, labels) {\n if (matrix.length !== matrix[0].length) {\n throw new Error('Confusion matrix must be square');\n }\n if (labels.length !== matrix.length) {\n throw new Error(\n 'Confusion matrix and labels should have the same length',\n );\n }\n this.labels = labels;\n this.matrix = matrix;\n }\n\n /**\n * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in\n * the correct order!\n * @param {Array} actual - The predicted labels of the classification\n * @param {Array} predicted - The actual labels of the classification. Has to be of same length as\n * predicted.\n * @param {object} [options] - Additional options\n * @param {Array} [options.labels] - The list of labels that should be used. If not provided the distinct set\n * of labels present in predicted and actual is used. Labels are compared using the strict equality operator\n * '==='\n * @return {ConfusionMatrix} - Confusion matrix\n */\n static fromLabels(actual, predicted, options = {}) {\n if (predicted.length !== actual.length) {\n throw new Error('predicted and actual must have the same length');\n }\n let distinctLabels;\n if (options.labels) {\n distinctLabels = new Set(options.labels);\n } else {\n distinctLabels = new Set([...actual, ...predicted]);\n }\n distinctLabels = Array.from(distinctLabels);\n if (options.sort) {\n distinctLabels.sort(options.sort);\n }\n\n // Create confusion matrix and fill with 0's\n const matrix = Array.from({ length: distinctLabels.length });\n for (let i = 0; i < matrix.length; i++) {\n matrix[i] = new Array(matrix.length);\n matrix[i].fill(0);\n }\n\n for (let i = 0; i < predicted.length; i++) {\n const actualIdx = distinctLabels.indexOf(actual[i]);\n const predictedIdx = distinctLabels.indexOf(predicted[i]);\n if (actualIdx >= 0 && predictedIdx >= 0) {\n matrix[actualIdx][predictedIdx]++;\n }\n }\n\n return new ConfusionMatrix(matrix, distinctLabels);\n }\n\n /**\n * Get the confusion matrix\n * @return {Array >}\n */\n getMatrix() {\n return this.matrix;\n }\n\n getLabels() {\n return this.labels;\n }\n\n /**\n * Get the total number of samples\n * @return {number}\n */\n getTotalCount() {\n let predicted = 0;\n for (let i = 0; i < this.matrix.length; i++) {\n for (let j = 0; j < this.matrix.length; j++) {\n predicted += this.matrix[i][j];\n }\n }\n return predicted;\n }\n\n /**\n * Get the total number of true predictions\n * @return {number}\n */\n getTrueCount() {\n let count = 0;\n for (let i = 0; i < this.matrix.length; i++) {\n count += this.matrix[i][i];\n }\n return count;\n }\n\n /**\n * Get the total number of false predictions.\n * @return {number}\n */\n getFalseCount() {\n return this.getTotalCount() - this.getTrueCount();\n }\n\n /**\n * Get the number of true positive predictions.\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getTruePositiveCount(label) {\n const index = this.getIndex(label);\n return this.matrix[index][index];\n }\n\n /**\n * Get the number of true negative predictions\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getTrueNegativeCount(label) {\n const index = this.getIndex(label);\n let count = 0;\n for (let i = 0; i < this.matrix.length; i++) {\n for (let j = 0; j < this.matrix.length; j++) {\n if (i !== index && j !== index) {\n count += this.matrix[i][j];\n }\n }\n }\n return count;\n }\n\n /**\n * Get the number of false positive predictions.\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getFalsePositiveCount(label) {\n const index = this.getIndex(label);\n let count = 0;\n for (let i = 0; i < this.matrix.length; i++) {\n if (i !== index) {\n count += this.matrix[i][index];\n }\n }\n return count;\n }\n\n /**\n * Get the number of false negative predictions.\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getFalseNegativeCount(label) {\n const index = this.getIndex(label);\n let count = 0;\n for (let i = 0; i < this.matrix.length; i++) {\n if (i !== index) {\n count += this.matrix[index][i];\n }\n }\n return count;\n }\n\n /**\n * Get the number of real positive samples.\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getPositiveCount(label) {\n return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);\n }\n\n /**\n * Get the number of real negative samples.\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getNegativeCount(label) {\n return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);\n }\n\n /**\n * Get the index in the confusion matrix that corresponds to the given label\n * @param {any} label - The label to search for\n * @throws if the label is not found\n * @return {number}\n */\n getIndex(label) {\n const index = this.labels.indexOf(label);\n if (index === -1) throw new Error('The label does not exist');\n return index;\n }\n\n /**\n * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.\n * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number} - The true positive rate [0-1]\n */\n getTruePositiveRate(label) {\n return this.getTruePositiveCount(label) / this.getPositiveCount(label);\n }\n\n /**\n * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.\n * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getTrueNegativeRate(label) {\n return this.getTrueNegativeCount(label) / this.getNegativeCount(label);\n }\n\n /**\n * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)\n * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getPositivePredictiveValue(label) {\n const TP = this.getTruePositiveCount(label);\n return TP / (TP + this.getFalsePositiveCount(label));\n }\n\n /**\n * Negative predictive value\n * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getNegativePredictiveValue(label) {\n const TN = this.getTrueNegativeCount(label);\n return TN / (TN + this.getFalseNegativeCount(label));\n }\n\n /**\n * False negative rate a.k.a. miss rate.\n * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getFalseNegativeRate(label) {\n return 1 - this.getTruePositiveRate(label);\n }\n\n /**\n * False positive rate a.k.a. fall-out rate.\n * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getFalsePositiveRate(label) {\n return 1 - this.getTrueNegativeRate(label);\n }\n\n /**\n * False discovery rate (FDR)\n * {@link https://en.wikipedia.org/wiki/False_discovery_rate}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getFalseDiscoveryRate(label) {\n const FP = this.getFalsePositiveCount(label);\n return FP / (FP + this.getTruePositiveCount(label));\n }\n\n /**\n * False omission rate (FOR)\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getFalseOmissionRate(label) {\n const FN = this.getFalseNegativeCount(label);\n return FN / (FN + this.getTruePositiveCount(label));\n }\n\n /**\n * F1 score\n * {@link https://en.wikipedia.org/wiki/F1_score}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getF1Score(label) {\n const TP = this.getTruePositiveCount(label);\n return (\n (2 * TP) /\n (2 * TP +\n this.getFalsePositiveCount(label) +\n this.getFalseNegativeCount(label))\n );\n }\n\n /**\n * Matthews correlation coefficient (MCC)\n * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getMatthewsCorrelationCoefficient(label) {\n const TP = this.getTruePositiveCount(label);\n const TN = this.getTrueNegativeCount(label);\n const FP = this.getFalsePositiveCount(label);\n const FN = this.getFalseNegativeCount(label);\n return (\n (TP * TN - FP * FN) /\n Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))\n );\n }\n\n /**\n * Informedness\n * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getInformedness(label) {\n return (\n this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1\n );\n }\n\n /**\n * Markedness\n * @param {any} label - The label that should be considered \"positive\"\n * @return {number}\n */\n getMarkedness(label) {\n return (\n this.getPositivePredictiveValue(label) +\n this.getNegativePredictiveValue(label) -\n 1\n );\n }\n\n /**\n * Get the confusion table.\n * @param {any} label - The label that should be considered \"positive\"\n * @return {Array >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]\n */\n getConfusionTable(label) {\n return [\n [this.getTruePositiveCount(label), this.getFalseNegativeCount(label)],\n [this.getFalsePositiveCount(label), this.getTrueNegativeCount(label)],\n ];\n }\n\n /**\n * Get total accuracy.\n * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])\n */\n getAccuracy() {\n let correct = 0;\n let incorrect = 0;\n for (let i = 0; i < this.matrix.length; i++) {\n for (let j = 0; j < this.matrix.length; j++) {\n if (i === j) correct += this.matrix[i][j];\n else incorrect += this.matrix[i][j];\n }\n }\n return correct / (correct + incorrect);\n }\n\n /**\n * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.\n * @param {any} actual - The true label\n * @param {any} predicted - The predicted label\n * @return {number} - The element in the confusion matrix\n */\n getCount(actual, predicted) {\n const actualIndex = this.getIndex(actual);\n const predictedIndex = this.getIndex(predicted);\n return this.matrix[actualIndex][predictedIndex];\n }\n\n /**\n * Compute the general prediction accuracy\n * @deprecated Use getAccuracy\n * @return {number} - The prediction accuracy ([0-1]\n */\n get accuracy() {\n return this.getAccuracy();\n }\n\n /**\n * Compute the number of predicted observations\n * @deprecated Use getTotalCount\n * @return {number}\n */\n get total() {\n return this.getTotalCount();\n }\n}\n","/**\n * get folds indexes\n * @param {Array} features\n * @param {Number} k - number of folds, a\n */\nexport function getFolds(features, k = 5) {\n let N = features.length;\n let allIdx = new Array(N);\n for (let i = 0; i < N; i++) {\n allIdx[i] = i;\n }\n\n let l = Math.floor(N / k);\n // create random k-folds\n let current = [];\n let folds = [];\n while (allIdx.length) {\n let randi = Math.floor(Math.random() * allIdx.length);\n current.push(allIdx[randi]);\n allIdx.splice(randi, 1);\n if (current.length === l) {\n folds.push(current);\n current = [];\n }\n }\n // we push the remaining to the last fold so that the total length is\n // preserved. Otherwise the Q2 will fail.\n if (current.length) current.forEach((e) => folds[k - 1].push(e));\n folds = folds.slice(0, k);\n\n let foldsIndex = folds.map((x, idx) => ({\n testIndex: x,\n trainIndex: [].concat(...folds.filter((el, idx2) => idx2 !== idx)),\n }));\n return foldsIndex;\n}\n","import isArray from 'is-any-array';\n\nfunction sum(input) {\n if (!isArray(input)) {\n throw new TypeError('input must be an array');\n }\n\n if (input.length === 0) {\n throw new TypeError('input must not be empty');\n }\n\n var sumValue = 0;\n\n for (var i = 0; i < input.length; i++) {\n sumValue += input[i];\n }\n\n return sumValue;\n}\n\nexport default sum;\n","import sum from 'ml-array-sum';\n\nfunction mean(input) {\n return sum(input) / input.length;\n}\n\nexport default mean;\n","import mean from 'ml-array-mean';\n/**\n * Returns the Area under the curve.\n * @param curves Object containing the true positivie and false positive rate vectors.\n * @return Area under the curve.\n */\nexport function getAuc(curves) {\n const auc = [];\n for (const curve of curves) {\n let area = 0;\n const x = curve.specificities;\n const y = curve.sensitivities;\n for (let i = 1; i < x.length; i++) {\n area += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);\n }\n area = area > 0.5 ? area : 1 - area;\n auc.push(area);\n }\n return mean(auc);\n}\n//# sourceMappingURL=getAuc.js.map","/**\n * @param array Array containing category metadata\n * @return Class object.\n */\nexport function getClasses(array) {\n let nbClasses = 0;\n const classes = [{ name: array[0], value: 0, ids: [] }];\n for (const element of array) {\n const currentClass = classes.some((item) => item.name === element);\n if (!currentClass) {\n nbClasses++;\n classes.push({ name: element, value: nbClasses, ids: [] });\n }\n }\n for (const category of classes) {\n const label = category.name;\n const indexes = [];\n for (let j = 0; j < array.length; j++) {\n if (array[j] === label)\n indexes.push(j);\n }\n category.ids = indexes;\n }\n return classes;\n}\n//# sourceMappingURL=getClasses.js.map","/**\n * Returns an array of pairs of classes.\n * @param list Array containing a list of classes.\n * @return Array with pairs of classes.\n */\nexport function getClassesPairs(list) {\n const pairs = [];\n for (let i = 0; i < list.length - 1; i++) {\n for (let j = i + 1; j < list.length; j++) {\n pairs.push([list[i], list[j]]);\n }\n }\n return pairs;\n}\n//# sourceMappingURL=getClassesPairs.js.map","import max from 'ml-array-max';\nimport min from 'ml-array-min';\n/**\n * Returns the array of metadata numerically categorized.\n * @param targets Array containing the categories.\n * @param tests Array containing the result of prediction.\n * @param pair Object with information about two classes.\n * @param [options={}]\n * @return Array containing the categories assinged as numbers.\n */\nexport function getNumericalTargets(targets, tests, pair, options = {}) {\n const { dx = 0.001 } = options;\n const boundaries = [min(tests) - dx, max(tests) + dx];\n const results = [];\n for (let i = 0; i < targets.length; i++) {\n for (let j = 0; j < pair.length; j++) {\n if (targets[i] === pair[j].name) {\n results[i] = boundaries[j];\n break;\n }\n }\n }\n return results;\n}\n//# sourceMappingURL=getNumericalTargets.js.map","/**\n * Returns the array of metadata numerically categorized.\n * @param array Array containing the categories.\n * @param pair Object containing information about the two classes to deal with.\n * @return Array containing the prediction values arranged for the corresponding classes.\n */\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport function getSelectedResults(array, pair) {\n const results = [];\n for (const item of pair) {\n for (const id of item.ids) {\n const result = array[id];\n results.push(result);\n }\n }\n return results;\n}\n//# sourceMappingURL=getSelectedResults.js.map","/**\n * Returns an array of thresholds to build the curve.\n * @param predictions Array of predictions.\n * @return An array of thresholds.\n */\nexport function getThresholds(predictions) {\n const uniques = [...new Set(predictions)].sort((a, b) => a - b);\n const thresholds = [uniques[0]];\n for (let i = 0; i < uniques.length - 1; i++) {\n const half = (uniques[i + 1] - uniques[i]) / 2;\n thresholds.push(uniques[i] + half);\n }\n thresholds.push(uniques[uniques.length - 1] + 0.001);\n return thresholds;\n}\n//# sourceMappingURL=getThresholds.js.map","import { getClasses } from './utilities/getClasses';\nimport { getClassesPairs } from './utilities/getClassesPairs';\nimport { getNumericalTargets } from './utilities/getNumericalTargets';\nimport { getSelectedResults } from './utilities/getSelectedResults';\nimport { getThresholds } from './utilities/getThresholds';\n/**\n * Returns a ROC (Receiver Operating Characteristic) curve for a given response and prediction vectors.\n * @param responses Array containing category metadata.\n * @param predictions Array containing the results of regression.\n * @return sensitivities and specificities as a object.\n */\nexport function getRocCurve(responses, predictions) {\n const classes = getClasses(responses);\n const pairsOfClasses = getClassesPairs(classes);\n const curves = [];\n for (const pairs of pairsOfClasses) {\n const tests = getSelectedResults(predictions, pairs);\n const targets = getSelectedResults(responses, pairs);\n const numericalTargets = getNumericalTargets(targets, tests, pairs);\n const curve = { sensitivities: [], specificities: [] };\n const limits = getThresholds(tests);\n for (const limit of limits) {\n let truePositives = 0;\n let falsePositives = 0;\n let trueNegatives = 0;\n let falseNegatives = 0;\n for (let j = 0; j < numericalTargets.length; j++) {\n if (tests[j] > limit && numericalTargets[j] > limit)\n truePositives++;\n if (tests[j] >= limit && numericalTargets[j] <= limit)\n falsePositives++;\n if (tests[j] < limit && numericalTargets[j] < limit)\n trueNegatives++;\n if (tests[j] <= limit && numericalTargets[j] >= limit)\n falseNegatives++;\n }\n curve.sensitivities.push(truePositives / (truePositives + falseNegatives));\n curve.specificities.push(trueNegatives / (falsePositives + trueNegatives));\n }\n curves.push(curve);\n }\n return curves;\n}\n//# sourceMappingURL=getRocCurve.js.map","import { Matrix, NIPALS } from 'ml-matrix';\n\nimport { norm } from './util/utils.js';\n\n/**\n * OPLS loop\n * @param {Array|Matrix} data matrix with features\n * @param {Array|Matrix} labels an array of labels (dependent variable)\n * @param {Object} [options={}] an object with options\n * @return {Object} an object with model (filteredX: err,\n loadingsXOrtho: pOrtho,\n scoresXOrtho: tOrtho,\n weightsXOrtho: wOrtho,\n weightsPred: w,\n loadingsXpred: p,\n scoresXpred: t,\n loadingsY:)\n */\nexport function oplsNipals(data, labels, options = {}) {\n const { numberOSC = 100, limit = 10e-10 } = options;\n\n data = Matrix.checkMatrix(data);\n labels = Matrix.checkMatrix(labels);\n let tW = [];\n if (labels.columns > 1) {\n const wh = getWh(data, labels);\n const ssWh = wh.norm() ** 2;\n let ssT = ssWh;\n let pcaW;\n let count = 0;\n do {\n if (count === 0) {\n pcaW = new NIPALS(wh);\n tW.push(pcaW.t);\n } else {\n let data = pcaW.xResidual;\n pcaW = new NIPALS(data);\n tW.push(pcaW.t);\n }\n ssT = wh.norm() ** 2;\n count++;\n } while (ssT / ssWh > limit);\n }\n\n let u = labels.getColumnVector(0);\n let diff = 1;\n let t, c, w, uNew;\n for (let i = 0; i < numberOSC && diff > limit; i++) {\n w = u.transpose().mmul(data).div(u.transpose().mmul(u).get(0, 0));\n w = w.transpose().div(norm(w));\n\n t = data.mmul(w).div(w.transpose().mmul(w).get(0, 0)); // t_h paso 3\n\n // calc loading\n c = t.transpose().mmul(labels).div(t.transpose().mmul(t).get(0, 0));\n\n // calc new u and compare with one in previus iteration (stop criterion)\n uNew = labels.mmul(c.transpose());\n uNew = uNew.div(c.transpose().mmul(c).get(0, 0));\n\n if (i > 0) {\n diff = uNew.clone().sub(u).pow(2).sum() / uNew.clone().pow(2).sum();\n }\n u = uNew.clone();\n }\n\n // calc loadings\n let wOrtho;\n let p = t.transpose().mmul(data).div(t.transpose().mmul(t).get(0, 0));\n if (labels.columns > 1) {\n for (let i = 0; i < tW.length; i++) {\n let tw = tW[i].transpose();\n p = p.sub(\n tw.mmul(p.transpose()).mmul(tw), // .div(tw.mmul(tw.transpose()).get(0, 0))\n );\n }\n wOrtho = p.clone();\n } else {\n wOrtho = p\n .clone()\n .sub(\n w\n .transpose()\n .mmul(p.transpose())\n .div(w.transpose().mmul(w).get(0, 0))\n .mmul(w.transpose()),\n );\n }\n\n wOrtho.div(norm(wOrtho));\n\n let tOrtho = data\n .mmul(wOrtho.transpose())\n .div(wOrtho.mmul(wOrtho.transpose()).get(0, 0));\n\n // orthogonal loadings\n let pOrtho = tOrtho\n .transpose()\n .mmul(data)\n .div(tOrtho.transpose().mmul(tOrtho).get(0, 0));\n\n // filtered data\n let err = data.clone().sub(tOrtho.mmul(pOrtho));\n return {\n filteredX: err,\n weightsXOrtho: wOrtho,\n loadingsXOrtho: pOrtho,\n scoresXOrtho: tOrtho,\n weightsXPred: w,\n loadingsXpred: p,\n scoresXpred: t,\n loadingsY: c,\n };\n}\n\nfunction getWh(xValue, yValue) {\n let result = new Matrix(xValue.columns, yValue.columns);\n for (let i = 0; i < yValue.columns; i++) {\n let yN = yValue.getColumnVector(i).transpose();\n let component = yN.mmul(xValue).div(yN.mmul(yN.transpose()).get(0, 0));\n result.setColumn(i, component.getRow(0));\n }\n return result;\n}\n","import { Matrix } from 'ml-matrix';\n\n/**\n * Get total sum of square\n * @param {Array} x an array\n * @return {Number} - the sum of the squares\n */\nexport function tss(x) {\n return Matrix.mul(x, x).sum();\n}\n","import ConfusionMatrix from 'ml-confusion-matrix';\nimport { getFolds } from 'ml-cross-validation';\nimport { Matrix, NIPALS } from 'ml-matrix';\nimport { getRocCurve, getAuc } from 'ml-roc-multiclass';\n\nimport { oplsNipals } from './oplsNipals.js';\nimport { tss } from './util/tss.js';\n\n/**\n * Creates new OPLS (orthogonal partial latent structures) from features and labels.\n * @param {Array} data - matrix containing data (X).\n * @param {Array} labels - 1D Array containing metadata (Y).\n * @param {Object} [options={}]\n * @param {boolean} [options.center = true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale = true] - should the data be scaled (divide by the standard deviation).\n * @param {Array} [options.cvFolds = []] - Allows to provide folds as array of objects with the arrays trainIndex and testIndex as properties.\n * @param {number} [options.nbFolds = 7] - Allows to generate the defined number of folds with the training and test set choosen randomly from the data set.\n * */\n\nexport class OPLS {\n constructor(data, labels, options = {}) {\n if (data === true) {\n const opls = options;\n this.center = opls.center;\n this.scale = opls.scale;\n this.means = opls.means;\n this.meansY = opls.meansY;\n this.stdevs = opls.stdevs;\n this.stdevs = opls.stdevsY;\n this.model = opls.model;\n this.tCV = opls.tCV;\n this.tOrthCV = opls.tOrthCV;\n this.yHatCV = opls.yHatCV;\n this.mode = opls.mode;\n return;\n }\n\n const features = new Matrix(data);\n // set default values\n // cvFolds allows to define folds for testing purpose\n const { center = true, scale = true, cvFolds = [], nbFolds = 7 } = options;\n\n let group;\n if (typeof labels[0] === 'number') {\n // numeric labels: OPLS regression is used\n this.mode = 'regression';\n group = Matrix.from1DArray(labels.length, 1, labels);\n } else if (typeof labels[0] === 'string') {\n // non-numeric labels: OPLS-DA is used\n this.mode = 'discriminantAnalysis';\n group = Matrix.checkMatrix(createDummyY(labels)).transpose();\n }\n\n // getting center and scale the features (all)\n this.center = center;\n if (this.center) {\n this.means = features.mean('column');\n this.meansY = group.mean('column');\n } else {\n this.stdevs = null;\n }\n this.scale = scale;\n if (this.scale) {\n this.stdevs = features.standardDeviation('column');\n this.stdevsY = group.standardDeviation('column');\n } else {\n this.means = null;\n }\n\n // check and remove for features with sd = 0 TODO here\n // check opls.R line 70\n\n const folds = cvFolds.length > 0 ? cvFolds : getFolds(labels, nbFolds);\n const Q2 = [];\n const aucResult = [];\n this.model = [];\n this.tCV = [];\n this.tOrthCV = [];\n this.yHatCV = [];\n const oplsCV = [];\n\n let modelNC = [];\n\n // this code could be made more efficient by reverting the order of the loops\n // this is a legacy loop to be consistent with R code from MetaboMate package\n // this allows for having statistic (R2) from CV to decide wether to continue\n // with more latent structures\n let overfitted = false;\n let nc = 0;\n let value;\n\n do {\n const yHatk = new Matrix(group.rows, 1);\n const tPredk = new Matrix(group.rows, 1);\n const tOrthk = new Matrix(group.rows, 1);\n oplsCV[nc] = [];\n for (let f = 0; f < folds.length; f++) {\n const trainTest = this._getTrainTest(features, group, folds[f]);\n const testXk = trainTest.testFeatures;\n const Xk = trainTest.trainFeatures;\n const Yk = trainTest.trainLabels;\n\n // determine center and scale of training set\n const dataCenter = Xk.mean('column');\n const dataSD = Xk.standardDeviation('column');\n\n // center and scale training set\n if (center) {\n Xk.center('column');\n Yk.center('column');\n }\n\n if (scale) {\n Xk.scale('column');\n Yk.scale('column');\n }\n\n // perform opls\n let oplsk;\n if (nc === 0) {\n oplsk = oplsNipals(Xk, Yk);\n } else {\n oplsk = oplsNipals(oplsCV[nc - 1][f].filteredX, Yk);\n }\n\n // store model for next component\n oplsCV[nc][f] = oplsk;\n const plsCV = new NIPALS(oplsk.filteredX, { Y: Yk });\n\n // scaling the test dataset with respect to the train\n testXk.center('column', { center: dataCenter });\n testXk.scale('column', { scale: dataSD });\n\n const Eh = testXk;\n // removing the orthogonal components from PLS\n let scores;\n for (let idx = 0; idx < nc + 1; idx++) {\n scores = Eh.mmul(oplsCV[idx][f].weightsXOrtho.transpose()); // ok\n Eh.sub(scores.mmul(oplsCV[idx][f].loadingsXOrtho));\n }\n\n // prediction\n const tPred = Eh.mmul(plsCV.w.transpose());\n const yHatComponents = tPred\n .mmul(plsCV.betas)\n .mmul(plsCV.q.transpose()); // ok\n const yHat = new Matrix(yHatComponents.rows, 1);\n for (let i = 0; i < yHatComponents.rows; i++) {\n yHat.setRow(i, [yHatComponents.getRowVector(i).sum()]);\n }\n // adding all prediction from all folds\n for (let i = 0; i < folds[f].testIndex.length; i++) {\n yHatk.setRow(folds[f].testIndex[i], [yHat.get(i, 0)]);\n tPredk.setRow(folds[f].testIndex[i], [tPred.get(i, 0)]);\n tOrthk.setRow(folds[f].testIndex[i], [scores.get(i, 0)]);\n }\n } // end of loop over folds\n\n this.tCV.push(tPredk);\n this.tOrthCV.push(tOrthk);\n this.yHatCV.push(yHatk);\n\n // calculate Q2y for all the prediction (all folds)\n // ROC for DA is not implemented (check opls.R line 183) TODO\n const tssy = tss(group.center('column').scale('column'));\n let press = 0;\n for (let i = 0; i < group.columns; i++) {\n press += tss(group.getColumnVector(i).sub(yHatk));\n }\n const Q2y = 1 - press / group.columns / tssy;\n Q2.push(Q2y);\n if (this.mode === 'regression') {\n value = Q2y;\n } else if (this.mode === 'discriminantAnalysis') {\n const rocCurve = getRocCurve(labels, yHatk.to1DArray());\n const areaUnderCurve = getAuc(rocCurve);\n aucResult.push(areaUnderCurve);\n value = areaUnderCurve;\n }\n\n // calculate the R2y for the complete data\n if (nc === 0) {\n modelNC = this._predictAll(features, group);\n } else {\n modelNC = this._predictAll(modelNC.xRes, group, {\n scale: false,\n center: false,\n });\n }\n\n // adding the predictive statistics from CV\n let listOfValues;\n modelNC.Q2y = Q2;\n if (this.mode === 'regression') {\n listOfValues = Q2;\n } else {\n listOfValues = aucResult;\n modelNC.auc = aucResult;\n }\n modelNC.value = value;\n\n if (nc > 0) {\n overfitted = listOfValues[nc - 1] >= value ? true : false;\n }\n this.model.push(modelNC);\n // store the model for each component\n nc++;\n // console.warn(`OPLS iteration over # of Components: ${nc + 1}`);\n } while (!overfitted); // end of loop over nc\n // store scores from CV\n const tCV = this.tCV;\n const tOrthCV = this.tOrthCV;\n const yHatCV = this.yHatCV;\n const m = this.model[nc - 1];\n const XOrth = m.XOrth;\n const FeaturesCS = features.center('column').scale('column');\n let labelsCS;\n if (this.mode === 'regression') {\n labelsCS = group.center('column').scale('column');\n } else {\n labelsCS = group;\n }\n const Xres = FeaturesCS.clone().sub(XOrth);\n const plsCall = new NIPALS(Xres, { Y: labelsCS });\n const E = Xres.clone().sub(plsCall.t.mmul(plsCall.p));\n\n const R2x = this.model.map((x) => x.R2x);\n const R2y = this.model.map((x) => x.R2y);\n\n this.output = {\n Q2y: Q2,\n auc: aucResult,\n R2x,\n R2y,\n tPred: m.plsC.t,\n pPred: m.plsC.p,\n wPred: m.plsC.w,\n betasPred: m.plsC.betas,\n Qpc: m.plsC.q,\n tCV,\n tOrthCV,\n yHatCV,\n oplsCV,\n tOrth: m.tOrth,\n pOrth: m.pOrth,\n wOrth: m.wOrth,\n XOrth,\n yHat: m.totalPred,\n Yres: m.plsC.yResidual,\n E,\n folds,\n };\n }\n\n /**\n * get access to all the computed elements\n * Mainly for debug and testing\n * @return {Object} output object\n */\n getLogs() {\n return this.output;\n }\n\n getScores() {\n const scoresX = this.tCV.map((x) => x.to1DArray());\n const scoresY = this.tOrthCV.map((x) => x.to1DArray());\n return { scoresX, scoresY };\n }\n\n /**\n * Load an OPLS model from JSON\n * @param {Object} model\n * @return {OPLS}\n */\n static load(model) {\n if (typeof model.name !== 'string') {\n throw new TypeError('model must have a name property');\n }\n if (model.name !== 'OPLS') {\n throw new RangeError(`invalid model: ${model.name}`);\n }\n return new OPLS(true, [], model);\n }\n\n /**\n * Export the current model to a JSON object\n * @return {Object} model\n */\n toJSON() {\n return {\n name: 'OPLS',\n center: this.center,\n scale: this.scale,\n means: this.means,\n stdevs: this.stdevs,\n model: this.model,\n tCV: this.tCV,\n tOrthCV: this.tOrthCV,\n yHatCV: this.yHatCV,\n };\n }\n\n /**\n * Predict scores for new data\n * @param {Matrix} features - a matrix containing new data\n * @param {Object} [options={}]\n * @param {Array} [options.trueLabel] - an array with true values to compute confusion matrix\n * @param {Number} [options.nc] - the number of components to be used\n * @return {Object} - predictions\n */\n predict(features, options = {}) {\n const {\n trueLabels = [],\n center = this.center,\n scale = this.scale,\n } = options;\n\n let labels;\n if (typeof trueLabels[0] === 'number') {\n labels = Matrix.from1DArray(trueLabels.length, 1, trueLabels);\n } else if (typeof trueLabels[0] === 'string') {\n labels = Matrix.checkMatrix(createDummyY(trueLabels)).transpose();\n }\n\n features = new Matrix(features);\n\n // scaling the test dataset with respect to the train\n if (center) {\n features.center('column', { center: this.means });\n if (labels?.rows > 0) {\n labels.center('column', { center: this.meansY });\n }\n }\n if (scale) {\n features.scale('column', { scale: this.stdevs });\n if (labels?.rows > 0) {\n labels.scale('column', { scale: this.stdevsY });\n }\n }\n\n const nc =\n this.mode === 'regression'\n ? this.model[0].Q2y.length\n : this.model[0].auc.length;\n\n const Eh = features.clone();\n // removing the orthogonal components from PLS\n let tOrth;\n let wOrth;\n let pOrth;\n let yHat;\n let tPred;\n for (let idx = 0; idx < nc; idx++) {\n const model = this.model[idx];\n wOrth = model.wOrth.transpose();\n pOrth = model.pOrth;\n tOrth = Eh.mmul(wOrth);\n Eh.sub(tOrth.mmul(pOrth));\n // prediction\n tPred = Eh.mmul(model.plsC.w.transpose());\n yHat = tPred.mmul(model.plsC.betas).mmul(model.plsC.q);\n }\n\n if (labels?.rows > 0) {\n if (this.mode === 'regression') {\n const tssy = tss(labels);\n const press = tss(labels.clone().sub(yHat));\n const Q2y = 1 - press / tssy;\n\n return { tPred, tOrth, yHat, Q2y };\n } else if (this.mode === 'discriminantAnalysis') {\n const confusionMatrix = ConfusionMatrix.fromLabels(\n trueLabels,\n yHat.to1DArray(),\n );\n\n const rocCurve = getRocCurve(trueLabels, yHat.to1DArray());\n const auc = getAuc(rocCurve);\n return { tPred, tOrth, yHat, confusionMatrix, auc };\n }\n } else {\n return { tPred, tOrth, yHat };\n }\n }\n\n _predictAll(features, labels, options = {}) {\n // cannot use the global this.center here\n // since it is used in the NC loop and\n // centering and scaling should only be\n // performed once\n const { center = true, scale = true } = options;\n\n if (center) {\n features.center('column');\n labels.center('column');\n }\n\n if (scale) {\n features.scale('column');\n labels.scale('column');\n // reevaluate tssy and tssx after scaling\n // must be global because re-used for next nc iteration\n // tssx is only evaluate the first time\n this.tssy = tss(labels);\n this.tssx = tss(features);\n }\n\n const oplsC = oplsNipals(features, labels);\n const plsC = new NIPALS(oplsC.filteredX, { Y: labels });\n\n const tPred = oplsC.filteredX.mmul(plsC.w.transpose());\n const yHatComponents = tPred.mmul(plsC.betas).mmul(plsC.q.transpose()); // ok\n const yHat = new Matrix(yHatComponents.rows, 1);\n for (let i = 0; i < yHatComponents.rows; i++) {\n yHat.setRow(i, [yHatComponents.getRowVector(i).sum()]);\n }\n let rss = 0;\n for (let i = 0; i < labels.columns; i++) {\n rss += tss(labels.getColumnVector(i).sub(yHat));\n }\n const R2y = 1 - rss / labels.columns / this.tssy;\n const xEx = plsC.t.mmul(plsC.p);\n const rssx = tss(xEx);\n const R2x = rssx / this.tssx;\n\n return {\n R2y,\n R2x,\n xRes: oplsC.filteredX,\n tOrth: oplsC.scoresXOrtho,\n pOrth: oplsC.loadingsXOrtho,\n wOrth: oplsC.weightsXOrtho,\n tPred: tPred,\n totalPred: yHat,\n XOrth: oplsC.scoresXOrtho.mmul(oplsC.loadingsXOrtho),\n oplsC,\n plsC,\n };\n }\n /**\n *\n * @param {*} X - dataset matrix object\n * @param {*} group - labels matrix object\n * @param {*} index - train and test index (output from getFold())\n */\n _getTrainTest(X, group, index) {\n const testFeatures = new Matrix(index.testIndex.length, X.columns);\n const testLabels = new Matrix(index.testIndex.length, group.columns);\n index.testIndex.forEach((el, idx) => {\n testFeatures.setRow(idx, X.getRow(el));\n testLabels.setRow(idx, group.getRow(el));\n });\n\n const trainFeatures = new Matrix(index.trainIndex.length, X.columns);\n const trainLabels = new Matrix(index.trainIndex.length, group.columns);\n index.trainIndex.forEach((el, idx) => {\n trainFeatures.setRow(idx, X.getRow(el));\n trainLabels.setRow(idx, group.getRow(el));\n });\n\n return {\n trainFeatures,\n testFeatures,\n trainLabels,\n testLabels,\n };\n }\n}\n\nfunction createDummyY(array) {\n const features = [...new Set(array)];\n const result = [];\n if (features.length > 2) {\n for (let i = 0; i < features.length; i++) {\n const feature = [];\n for (let j = 0; j < array.length; j++) {\n const point = features[i] === array[j] ? 1 : -1;\n feature.push(point);\n }\n result.push(feature);\n }\n return result;\n } else {\n const result = [];\n for (let j = 0; j < array.length; j++) {\n const point = features[0] === array[j] ? 2 : 1;\n result.push(point);\n }\n return [result];\n 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