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How to use the transpose function from mathjs

Find comprehensive JavaScript mathjs.transpose code examples handpicked from public code repositorys.

mathjs.transpose is a function that transposes a matrix by flipping its rows and columns, effectively rotating it by 90 degrees.

helper.js

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  // Check if its Square
  if ((math.size(givenMatrix))[0] !== (math.size(givenMatrix))[1]) {
    return false;
  }

  const conjugateTranspose = math.transpose(math.conj(givenMatrix));
  const inverse = math.inv(givenMatrix);

  return math.deepEqual(inverse, conjugateTranspose);
};

+ 5 other calls in file

helper.js

GitHub: adamisntdead/qics

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  // Check if its Square
  if (math.size(givenMatrix)[0] !== math.size(givenMatrix)[1]) {
    return false;
  }

  var conjugateTranspose = math.transpose(math.conj(givenMatrix));
  var inverse = math.inv(givenMatrix);

  return math.deepEqual(inverse, conjugateTranspose);
};

How does mathjs.transpose work?

mathjs.transpose is a function provided by the mathjs library, which is used for mathematical operations in JavaScript.

When mathjs.transpose is called on a matrix, it creates a new matrix with its rows and columns swapped.

For example, given the following matrix:

css
[ 1, 2, 3 ]
[ 4, 5, 6 ]
[ 7, 8, 9 ]

mathjs.transpose would return:

css
[ 1, 4, 7 ]
[ 2, 5, 8 ]
[ 3, 6, 9 ]

As you can see, the rows and columns of the original matrix have been flipped, effectively rotating the matrix by 90 degrees.

mathjs.transpose can be useful for a variety of applications, including data analysis, image processing, and linear algebra.

gltf.controller.js

GitHub: MediaComem/smapshot-api

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let ratio = maxSize / dist;
let scaledXYZ = math.multiply(xyzCam, ratio);
// Insert the computed coordinates in the collada template file
// ------------------------------------------------------------
// create the coordinate string for collada
scaledXYZ = math.transpose(scaledXYZ)
let coordString = "";
const dim0 = math.subset(math.size(scaledXYZ), math.index([0]));
const dim1 = math.subset(math.size(scaledXYZ), math.index([1]));
for (let i = 0; i < dim1; i += 1) {

3d_charts.js

GitHub: luobogao/meditation-mapping

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matrix.forEach(row => {
    m.push([row.x, row.y, row.z, 1])
})

var cameraMatrix = [[10, 0, 0, 0], [0, 10, 0, 0], [0, 0, 1, 0]]
var projection = math.transpose(math.multiply(cameraMatrix, math.transpose(m)))
//console.log(projection)
for (let i = 0; i < matrix.length; i++) {
    var p = projection[i]
    matrix[i].xp = matrix[i].x // p[0]

+ 196 other calls in file

Ai Example

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const math = require("mathjs");

const matrix = math.matrix([
  [1, 2, 3],
  [4, 5, 6],
  [7, 8, 9],
]);

const transposedMatrix = math.transpose(matrix);

console.log(transposedMatrix.toArray());

In this example, we're using the mathjs library to create a matrix and then using math.transpose to transpose it. We're then using the toArray function to convert the transposed matrix back into a standard JavaScript array, and using console.log to output it to the console. When we run this code, we get the following output: css Copy code

analysis.js

GitHub: luobogao/meditation-mapping

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}


export function prepareDataset(rows) {
    switch (modelType) {
        case "covariance":
            return math.transpose(subtract_means(rows))


        case "cosine":
            return math.transpose(unit_scaling(rows))
    }

+ 15 other calls in file

createNetwork.js

GitHub: rosberg03/neural-network

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function BP2(W, D, Z) {
    // Denna kodrad formatterar endast matriserna rätt i JavaScript
    const matrixW = Array.isArray(W[0]) ? W : [W]; 
    // ...och har inget med matematiken att göra.


    const transposedW = transpose(matrixW);
    const sigprimedZ = Z.map(sigprime);
    return dotMultiply(multiply(transposedW, D), sigprimedZ);
}

index.js

GitHub: Kirigaya-Kazuton/IA

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// multiplicação matricial: (1*2)+(2*3)=8
console.log('multiplicação matricial:', math.multiply([1, 2], [2, 3]));
// subtração 5 - 2 e 3 -1
console.log('subtração:', math.subtract([5, 3], [2, 1]));
// transposição [1, 3], [2, 4]
console.log('transposição:', math.transpose([[1, 2], [3, 4]]));
// seleção aleatória
console.log('seleção aleatória:', math.random([2, 2]));
// adição 3, 5
console.log('adição:', math.add([1, 2], [2, 3]));

+ 2 other calls in file

LM.js

GitHub: mljs/optimization

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// J = rank-1 update to Jacobian Matrix J(i,j)=dy(i)/dp(j)	i=1:n; j=1:m
//console.log(p+" X "+ p_old)
var h  = math.subtract(p, p_old);

//console.log("hhh "+h);
var h_t = math.transpose(h);
var hh = math.multiply(h_t,h);
var hhh = math.map(h_t,function(value){
    return value / hh;
});

+ 5 other calls in file

testLM.js

GitHub: mljs/optimization

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var t = new Array(Npnt);//[1:Npnt]';				  // independent variable


for(var i=0;i<Npnt;i++)
    t[i]=[i+1];
//t = math.transpose(math.matrix(t));
// true value of parameters ...
var p_true = [];
if(example_number == 1) p_true  = [ [20],   [10],   [1],  [50] ];
if(example_number == 2) p_true  = [ [20],  [-24],  [30], [-40] ];

lorenz.js

GitHub: tkraus7/cprng_based_on_gls

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var Plotly = require('plotly.js-dist-min')


/* Single iteration of the GLS. */
var m = math.matrix()
var lorenz_generalized = function (dydt, y, t) {
    temp = math.transpose(math.multiply(y[0], math.matrix([[0], [-y[2]], [y[1]]])))
    res = math.add(math.multiply(m, y), temp).valueOf()[0]
    dydt[0] = res[0]
    dydt[1] = res[1]
    dydt[2] = res[2]

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