dsapandora

(define (domain n64-zelda-majora)

    (:requirements :typing :fluents :durative-actions)

    (:types
        item cycevt location object
    )

    (:constants
        bow ocarina powder-keg goron-mask eponas-song song-of-time - item

dsapandora

#include <future>
#include <iostream>
#include <vector>

// for use of std::accumulate
#if _MSC_VER
#include <numeric>
#else
#include <algorithm> 
#endif

dsapandora

# This can be used to get the branch for any case needed.
# Eliminate the white spaces in the beginning and the end of the string

BRANCH=`git branch | grep "*" | cut -d "*" -f2 | sed 's/^[ \t]*//;s/[ \t]*$//'` 

dsapandora

using UnityEngine;
using System.Collections;

public class follow_camera: MonoBehaviour {

    public GameObject player;       //Public variable to store a reference to the player game object


    private Vector3 offset;         //Private variable to store the offset distance between the player and camera

dsapandora

using CnControls; //Free Packege CN control
using UnityEngine;

namespace CustomJoystick
{
//Requieres to work
  [RequireComponent(typeof (Animator))]
  [RequireComponent(typeof (CapsuleCollider))]
  [RequireComponent(typeof (Rigidbody))]

dsapandora

speed = (transform.position - lastPosition).magnitude;

dsapandora

	GameObject FindClosestEnemy()
{
    GameObject[] gos;
    gos = GameObject.FindGameObjectsWithTag("Enemy");
    GameObject closest = null;
    float distance = Mathf.Infinity;
    Vector3 position = transform.position;
    foreach (GameObject go in gos)
    {
        Vector3 diff = go.transform.position - position;

dsapandora

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>

#define LEARNING_RATE    0.1
#define MAX_ITERATION    100

float randomFloat()
{

dsapandora

import java.util.*;
import java.io.*;
import java.text.*;
import java.math.*;

class Perceptron
{
	static int MAX_ITER = 100;
	static double LEARNING_RATE = 0.1;           
	static int NUM_INSTANCES = 100;

dsapandora

import numpy as np


class Perceptron:
    def __init__(self, n_inputs, learning_rate):
        """
        :param n_inputs: the number of inputs of the perceptron
        :param learning_rate: the learning rate
        """
        self.n_inputs = n_inputs