# 8+2=16106

• n1
• n2

• Output

## Neuron type

Best algorithm has been found - locked

## Patterns

Pattern Input Output
1.
 n1: 8 n2: 2
 Output: 16106
2.
 n1: 5 n2: 4
 Output: 2091
3.
 n1: 9 n2: 6
 Output: 54153
4.
 n1: 20 n2: 3
 Output: 602317

## Applicable neurons

• Plus (x + y)
• Minus (x - y)
• Multiple (x × y)
• Division (x ÷ y)
• Connect - two inputs
• Connect - three inputs
• Absolute value
• Cube - surface area
• Get html between tags (text)
• x / 2y
• after decimal point
• Partner XY

## Algorithm

### Test

```/**
* Plus (x + y): The addition of two whole numbers is the total amount of those quantities combined.
*
* @param x1 first number
* @param x2 second number
* @return {Array}
*/
function neuron1(x1, x2)
{
math.config({number: 'BigNumber', precision: 64}); return [math.eval(Number(x1) + '+'+Number(x2)).toString()];
}

/**
* Minus (x - y):
*
* @param x1 Number X
* @param x2 Number Y
* @return {Array}
*/
function neuron2(x1, x2)
{
math.config({number: 'BigNumber', precision: 64}); return [math.eval(Number(x1) + '-'+Number(x2)).toString()];
}

/**
* Connect - two inputs:
*
* @param x1 Variable A
* @param x2 Variable B
* @return {Array}
*/
function neuron520(x1, x2)
{
return [x1.toString()+x2.toString()];
}

/**
* Multiple (x × y):
*
* @param x1 Number X
* @param x2 Number Y
* @return {Array}
*/
function neuron3(x1, x2)
{
math.config({number: 'BigNumber', precision: 64}); return [math.eval(Number(x1) + '*'+Number(x2)).toString()];
}

/**
* Division (x ÷ y): X / Y
*
* @param x1 first number
* @param x2 second number
* @return {Array}
*/
function neuron17(x1, x2)
{
math.config({number: 'BigNumber', precision: 64}); return [math.eval(Number(x1) + '/'+Number(x2)).toString()];
}

/**
* Multiple (x × y):
*
* @param x1 Number X
* @param x2 Number Y
* @return {Array}
*/
function neuron3(x1, x2)
{
math.config({number: 'BigNumber', precision: 64}); return [math.eval(Number(x1) + '*'+Number(x2)).toString()];
}

/**
* x to the 2 (x²): x squared
*
* @param x1 Number X
* @return {Array}
*/
function neuron7(x1)
{
var outputs = [];
outputs[0] = x1;

arr = neuron3(outputs[0], outputs[0]);
outputs[1] = arr[0];

return[outputs[1]];
}

/**
* character .:
*
* @return {Array}
*/
function neuron510()
{
return['.'];
}

/**
* 5:
*
* @return {Array}
*/
function neuron505()
{
return [5];
}

/**
* Connect - two inputs:
*
* @param x1 Variable A
* @param x2 Variable B
* @return {Array}
*/
function neuron520(x1, x2)
{
return [x1.toString()+x2.toString()];
}

/**
* Half (0.5):
*
* @return {Array}
*/
function neuron522()
{
var outputs = [];

arr = neuron510();
outputs[0] = arr[0];

arr = neuron505();
outputs[1] = arr[0];

arr = neuron520(outputs[0], outputs[1]);
outputs[2] = arr[0];

return[outputs[2]];
}

/**
* x to the a  (xª): value of the number x to be the power of a
*
* @param x1 x - The base
* @param x2 a - The exponent
* @return {Array}
*/
function neuron18(x1, x2)
{
return[Math.pow(Number(x1), Number(x2))];
}

/**
* Square root (√¯):
*
* @param x1 Number X
* @return {Array}
*/
function neuron554(x1)
{
var outputs = [];
outputs[0] = x1;

arr = neuron522();
outputs[1] = arr[0];

arr = neuron18(outputs[0], outputs[1]);
outputs[2] = arr[0];

return[outputs[2]];
}

/**
* Absolute value:
*
* @param x1 Number
* @return {Array}
*/
function neuron570(x1)
{
var outputs = [];
outputs[0] = x1;

arr = neuron7(outputs[0]);
outputs[1] = arr[0];

arr = neuron554(outputs[1]);
outputs[2] = arr[0];

return[outputs[2]];
}

/**
* 8+2=16106:
*
* @param x1 n1
* @param x2 n2
* @return {Array}
*/
function neuron832(x1, x2)
{
var outputs = [];
outputs[0] = x1;
outputs[1] = x2;

arr = neuron1(outputs[0], outputs[1]);
outputs[2] = arr[0];

arr = neuron2(outputs[0], outputs[1]);
outputs[3] = arr[0];

arr = neuron520(outputs[2], outputs[3]);
outputs[4] = arr[0];

arr = neuron3(outputs[1], outputs[0]);
outputs[5] = arr[0];

arr = neuron17(outputs[1], outputs[4]);
outputs[6] = arr[0];

arr = neuron2(outputs[1], outputs[6]);
outputs[7] = arr[0];

arr = neuron520(outputs[5], outputs[4]);
outputs[8] = arr[0];

arr = neuron3(outputs[5], outputs[4]);
outputs[9] = arr[0];

arr = neuron17(outputs[8], outputs[8]);
outputs[10] = arr[0];

arr = neuron570(outputs[3]);
outputs[11] = arr[0];

return[outputs[8]];
}

```