Function: Decision Tree

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © RicardoSantos

//@version=4
study(title='Function: Decision Tree')

//| -------------------------------------------------------------------------------|
var float[] seed = array.new_float(size=1, initial_value=42.0)                  //-|
f_prng(_range)=>                                                              //---|
//| pseudo random function:                                                 // O O |
    var float _return = 1.0                                                 //- V -|
    float _seed = array.get(id=seed, index=0)                                //NNNN|
    float _reseed =  271.828                                                  // A |
    _return := ((3.1416 * _return % _seed) * _reseed) % _range                // V |
    array.set(id=seed, index=0, value=(1 + (_seed * 1.618) % _range) + _return) //||
    _return                                                                    // ||
//| -------------------------------------------------------------------------------|


f_decision_tree(_node_weights, _depth)=>
    int[] _path = array.new_int(size=0, initial_value=0)
    int _nweights = array.size(id=_node_weights)
    int _sumweights = ceil(array.sum(id=_node_weights))
    if _nweights > 0 and _depth > 0 and _sumweights == 1
        for _d = 1 to _depth
            for _w = 0 to 999
                int _select_weight_index = int(f_prng(_nweights))
                float _rng = f_prng(1.0)
                float _weight = array.get(id=_node_weights, index=_select_weight_index)
                if _weight >= _rng
                    array.push(id=_path, value=_select_weight_index)
                    break
    _path

//| Array to contain weights, sum of array values must be 1.
var float[] node_weights = array.new_float(size=0, initial_value=0)
if barstate.isfirst
    array.push(id=node_weights, value=0.5)
    array.push(id=node_weights, value=0.5)

//| Input Parameters:
int depth = input(defval=1, minval=1)
int paths = input(defval=2, minval=1)

string t = ''

for _p = 1 to paths
    _path = f_decision_tree(node_weights, depth)

    t := t + '\nPath ' + tostring(_p, '#') + ': ' + tostring(_path)

var label la = label.new(bar_index, 0.0, '')
label.set_xy(id=la, x=bar_index, y=0.0)
label.set_text(id=la, text=t)