rtkclouds

// Importing necessary libraries and modules
let _ = require('lodash') // lodash for utility functions
let tf = require('@tensorflow/tfjs-node') // TensorFlow.js for machine learning
let colors = require('colors') // colors for terminal output

// Importing functions from Algernon library
const algernon = require('algernon-js');
let {
    generateBacktrackingRaw,
    solveAStarRaw,

rtkclouds

// Importing necessary libraries: lodash, TensorFlow.js, and colors
let _ = require('lodash');
let tf = require('@tensorflow/tfjs-node');
let colors=require('colors');

// Function to generate a dataset of binary arrays with a given size
function buildDataset(sp) {
    let s = []
    for (let samples = 0; samples < sp; samples++) {
        // Initialize an array with a single 0 element

rtkclouds

import torch
import torch.nn as nn
import torch.nn.functional as F

class IdPredictPyTorch(nn.Module):
    def __init__(self, input_shape, predict_steps=30, groups=512, layers=20, groups_size=20, no_skip=False):
        super(IdPredictPyTorch, self).__init__()
        self.predict_steps = predict_steps
        self.groups = groups
        self.layers = layers

rtkclouds

import torch
import torch.nn as nn
import torch.nn.functional as F

class IdNorm(nn.Module):
    def __init__(self, output_dim, depth):
        super(IdNorm, self).__init__()
        self.depth = depth
        self.output_dim = output_dim
        self.embedding = nn.Embedding(output_dim, depth)

rtkclouds

const _ = require('lodash');

const fs = require('fs');
let tf=require('@tensorflow/tfjs-node-gpu')
global.tf=tf


let cl={}
let a1 = new TextEncoder()
let b1 = new TextDecoder()

rtkclouds

import os
import numpy as np

# Assuming 'word2vec_function' is the Python equivalent of your 'word2vec' function
# and 'gpt_encode' is a function that encodes a string similarly to 'gpt.encode'

def word2vec_function(input_path, output_path, options, callback):
    # This function should perform the word2vec operation and write the output to 'output_path'
    # Then call 'callback' with the result
    pass

rtkclouds

/*

The modified code introduces a grouping concept for evaluating actions based on random data. 
Initially, random vectors of actions and positions are generated. 
The data corresponding to these positions are then collected from a predefined window. 
This data is divided into groups, and the average of each group is calculated. 
The action to be evaluated is selected based on the cumulative modular sum of this data.
 The action evaluation takes into account the group averages, offering a more nuanced approach compared to a direct evaluation. 
The code runs multiple iterations to determine the best action based on this evaluation method
*/

rtkclouds

const n = 10; // Number of possible actions
const z = 20; // Size of the window
const s = 5;  // Size of the action vector
const k = 3;  // Size of the position vector

// Generates a random action vector
function generateRandomActions() {
    let actions = [];
    for (let i = 0; i < s; i++) {
        actions.push(Math.floor(Math.random() * n));

rtkclouds

import torch
import torch.nn as nn
import math

class IdNorm(nn.Module):
    def __init__(self, cluster_size=128):
        super(IdNorm, self).__init__()
        self.cluster_size = cluster_size
        self.n = None  # Será definido no método build
        self.embs = nn.ModuleList()

rtkclouds

class Rezero(layers.Layer):
    def __init__(self):
        super().__init__()
        self.alpha1 = tf.Variable(0.0, trainable=True)

    def call(self, inputs, training):
        return self.alpha1*inputs
class CustomRezero(tf.keras.layers.Layer):