Volume Of a Sphere

Patterns

Applicable neurons

• Multiple (x × y)
• Division (x ÷ y)
• 3
• 4
• Pi (π) - 3.141592653589793
• Rounding to whole ten thousands of something
• Square - content
• Rectangle - content
• Rectangular cuboid - Surface Area
• minutes into seconds
• \\D
• right bottom

Algorithm

Test

/**
 * 3: 
 *
 * @return {Array}
 */
function neuron503()
{
return [3];
}

/**
 * 4: 
 *
 * @return {Array}
 */
function neuron504()
{
return [4];
}

/**
 * Pi (π) - 3.141592653589793: 
 *
 * @return {Array}
 */
function neuron560()
{
math.config({number: 'BigNumber', precision: 64}); return[math.PI]; /* return [3.141592653589793] */
}

/**
 * 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()];
}

/**
 * 0: 
 *
 * @return {Array}
 */
function neuron500()
{
return [0];
}

/**
 * 1: 
 *
 * @return {Array}
 */
function neuron501()
{
return [1];
}

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

/**
 * Connect - four inputs: 
 * 
 * @param x1 string A
 * @param x2 string B
 * @param x3 string C
 * @param x4 string D
 * @return {Array}
 */
function neuron565(x1, x2, x3, x4)
{
  var outputs = [];
  outputs[0] = x1;
  outputs[1] = x2;
  outputs[2] = x3;
  outputs[3] = x4;

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

  arr = neuron520(outputs[1], outputs[4]);
  outputs[5] = arr[0];

  arr = neuron520(outputs[0], outputs[5]);
  outputs[6] = arr[0];

  return[outputs[6]];
}


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

/**
 * Connect - five inputs: 
 * 
 * @param x1 1
 * @param x2 2
 * @param x3 3
 * @param x4 4
 * @param x5 5
 * @return {Array}
 */
function neuron651(x1, x2, x3, x4, x5)
{
  var outputs = [];
  outputs[0] = x1;
  outputs[1] = x2;
  outputs[2] = x3;
  outputs[3] = x4;
  outputs[4] = x5;

  arr = neuron565(outputs[1], outputs[2], outputs[3], outputs[4]);
  outputs[5] = arr[0];

  arr = neuron520(outputs[0], outputs[5]);
  outputs[6] = arr[0];

  return[outputs[6]];
}


/**
 * 10000: 
 * 
 * @return {Array}
 */
function neuron706()
{
  var outputs = [];

  arr = neuron500();
  outputs[0] = arr[0];

  arr = neuron501();
  outputs[1] = arr[0];

  arr = neuron651(outputs[1], outputs[0], outputs[0], outputs[0], outputs[0]);
  outputs[2] = arr[0];

  return[outputs[2]];
}


/**
 * 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()];
}

/**
 * Round to an integer: round(x)
 *
 * @param x1 Value
 * @return {Array}
 */
function neuron620(x1)
{
return[Math.round(Number(x1))]
}

/**
 * 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()];
}

/**
 * Rounding to whole ten thousands of something: 
 * 
 * @param x1 Value
 * @return {Array}
 */
function neuron705(x1)
{
  var outputs = [];
  outputs[0] = x1;

  arr = neuron706();
  outputs[1] = arr[0];

  arr = neuron3(outputs[1], outputs[0]);
  outputs[2] = arr[0];

  arr = neuron620(outputs[2]);
  outputs[3] = arr[0];

  arr = neuron17(outputs[3], outputs[1]);
  outputs[4] = arr[0];

  return[outputs[4]];
}


/**
 * Volume Of a Sphere: 
 * 
 * @param x1 Radius
 * @return {Array}
 */
function neuron543(x1)
{
  var outputs = [];
  outputs[0] = x1;

  arr = neuron503();
  outputs[1] = arr[0];

  arr = neuron504();
  outputs[2] = arr[0];

  arr = neuron560();
  outputs[3] = arr[0];

  arr = neuron3(outputs[0], outputs[2]);
  outputs[4] = arr[0];

  arr = neuron17(outputs[3], outputs[1]);
  outputs[5] = arr[0];

  arr = neuron3(outputs[0], outputs[4]);
  outputs[6] = arr[0];

  arr = neuron3(outputs[6], outputs[0]);
  outputs[7] = arr[0];

  arr = neuron3(outputs[5], outputs[7]);
  outputs[8] = arr[0];

  arr = neuron705(outputs[8]);
  outputs[9] = arr[0];

  return[outputs[9]];
}