Quality-Diversity Algorithms: A new approach based on MAP-Elites applied toRobotNavigation

Evolutionary Algorithms have taken an important place in many application fields, including robotics. In this article, we will, first, present the navigation problem in robotics. After that, we will show why it’s better to look for diversity than for quality and experiment with some well-known methods to do this kind of thing. Finally, we will present a new intuition for associating quality with diversity to outperform both quality and diversity-oriented algorithms.

Navigation Task

In robotics, it is common that agents are roughly represented as several sensors that collect sensory information about the environment and several actors that can take values in a discrete or continuous range.

Representative diagram of the relation between an agent and his environnement (by me)

In this blog post, we are interested in the task of navigation. This task consists of an agent with proximity sensors to move in an environment to reach a goal.

Our experiment sensors are range finders and radars arranged around the agent, as presented in this illustration taken from [3].

The actor is the motor that can give an impulse in the forward or reverse direction and an impulse to the left or the right, both represented by velocities taking their values in the real interval [-2,2].

The environment is a maze with a single goal that the agent has to reach by minimising distance travelled and collisions.

In their article, Lehman and Staley[3] used two mazes, one they referred to as “medium,” but it was more a “standard” maze. The second they considered as “hard” because of the deceptive behaviour that results from following the distance to the goal.

#robotics #evolutionary-algorithms #neural-networks #artificial-intelligence #algorithms

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Buddha Community

Quality-Diversity Algorithms: A new approach based on MAP-Elites applied toRobotNavigation

Quality-Diversity Algorithms: A new approach based on MAP-Elites applied toRobotNavigation

Evolutionary Algorithms have taken an important place in many application fields, including robotics. In this article, we will, first, present the navigation problem in robotics. After that, we will show why it’s better to look for diversity than for quality and experiment with some well-known methods to do this kind of thing. Finally, we will present a new intuition for associating quality with diversity to outperform both quality and diversity-oriented algorithms.

Navigation Task

In robotics, it is common that agents are roughly represented as several sensors that collect sensory information about the environment and several actors that can take values in a discrete or continuous range.

Representative diagram of the relation between an agent and his environnement (by me)

In this blog post, we are interested in the task of navigation. This task consists of an agent with proximity sensors to move in an environment to reach a goal.

Our experiment sensors are range finders and radars arranged around the agent, as presented in this illustration taken from [3].

The actor is the motor that can give an impulse in the forward or reverse direction and an impulse to the left or the right, both represented by velocities taking their values in the real interval [-2,2].

The environment is a maze with a single goal that the agent has to reach by minimising distance travelled and collisions.

In their article, Lehman and Staley[3] used two mazes, one they referred to as “medium,” but it was more a “standard” maze. The second they considered as “hard” because of the deceptive behaviour that results from following the distance to the goal.

#robotics #evolutionary-algorithms #neural-networks #artificial-intelligence #algorithms

Edward Jackson

Edward Jackson

1653377002

PySpark Cheat Sheet: Spark in Python

This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning.

Apache Spark is generally known as a fast, general and open-source engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It allows you to speed analytic applications up to 100 times faster compared to technologies on the market today. You can interface Spark with Python through "PySpark". This is the Spark Python API exposes the Spark programming model to Python. 

Even though working with Spark will remind you in many ways of working with Pandas DataFrames, you'll also see that it can be tough getting familiar with all the functions that you can use to query, transform, inspect, ... your data. What's more, if you've never worked with any other programming language or if you're new to the field, it might be hard to distinguish between RDD operations.

Let's face it, map() and flatMap() are different enough, but it might still come as a challenge to decide which one you really need when you're faced with them in your analysis. Or what about other functions, like reduce() and reduceByKey()

PySpark cheat sheet

Even though the documentation is very elaborate, it never hurts to have a cheat sheet by your side, especially when you're just getting into it.

This PySpark cheat sheet covers the basics, from initializing Spark and loading your data, to retrieving RDD information, sorting, filtering and sampling your data. But that's not all. You'll also see that topics such as repartitioning, iterating, merging, saving your data and stopping the SparkContext are included in the cheat sheet. 

Note that the examples in the document take small data sets to illustrate the effect of specific functions on your data. In real life data analysis, you'll be using Spark to analyze big data.

PySpark is the Spark Python API that exposes the Spark programming model to Python.

Initializing Spark 

SparkContext 

>>> from pyspark import SparkContext
>>> sc = SparkContext(master = 'local[2]')

Inspect SparkContext 

>>> sc.version #Retrieve SparkContext version
>>> sc.pythonVer #Retrieve Python version
>>> sc.master #Master URL to connect to
>>> str(sc.sparkHome) #Path where Spark is installed on worker nodes
>>> str(sc.sparkUser()) #Retrieve name of the Spark User running SparkContext
>>> sc.appName #Return application name
>>> sc.applicationld #Retrieve application ID
>>> sc.defaultParallelism #Return default level of parallelism
>>> sc.defaultMinPartitions #Default minimum number of partitions for RDDs

Configuration 

>>> from pyspark import SparkConf, SparkContext
>>> conf = (SparkConf()
     .setMaster("local")
     .setAppName("My app")
     . set   ("spark. executor.memory",   "lg"))
>>> sc = SparkContext(conf = conf)

Using the Shell 

In the PySpark shell, a special interpreter-aware SparkContext is already created in the variable called sc.

$ ./bin/spark-shell --master local[2]
$ ./bin/pyspark --master local[s] --py-files code.py

Set which master the context connects to with the --master argument, and add Python .zip..egg or.py files to the

runtime path by passing a comma-separated list to  --py-files.

Loading Data 

Parallelized Collections 

>>> rdd = sc.parallelize([('a',7),('a',2),('b',2)])
>>> rdd2 = sc.parallelize([('a',2),('d',1),('b',1)])
>>> rdd3 = sc.parallelize(range(100))
>>> rdd = sc.parallelize([("a",["x","y","z"]),
               ("b" ["p","r,"])])

External Data 

Read either one text file from HDFS, a local file system or any Hadoop-supported file system URI with textFile(), or read in a directory of text files with wholeTextFiles(). 

>>> textFile = sc.textFile("/my/directory/•.txt")
>>> textFile2 = sc.wholeTextFiles("/my/directory/")

Retrieving RDD Information 

Basic Information 

>>> rdd.getNumPartitions() #List the number of partitions
>>> rdd.count() #Count RDD instances 3
>>> rdd.countByKey() #Count RDD instances by key
defaultdict(<type 'int'>,{'a':2,'b':1})
>>> rdd.countByValue() #Count RDD instances by value
defaultdict(<type 'int'>,{('b',2):1,('a',2):1,('a',7):1})
>>> rdd.collectAsMap() #Return (key,value) pairs as a dictionary
   {'a': 2, 'b': 2}
>>> rdd3.sum() #Sum of RDD elements 4950
>>> sc.parallelize([]).isEmpty() #Check whether RDD is empty
True

Summary 

>>> rdd3.max() #Maximum value of RDD elements 
99
>>> rdd3.min() #Minimum value of RDD elements
0
>>> rdd3.mean() #Mean value of RDD elements 
49.5
>>> rdd3.stdev() #Standard deviation of RDD elements 
28.866070047722118
>>> rdd3.variance() #Compute variance of RDD elements 
833.25
>>> rdd3.histogram(3) #Compute histogram by bins
([0,33,66,99],[33,33,34])
>>> rdd3.stats() #Summary statistics (count, mean, stdev, max & min)

Applying Functions 

#Apply a function to each RFD element
>>> rdd.map(lambda x: x+(x[1],x[0])).collect()
[('a' ,7,7, 'a'),('a' ,2,2, 'a'), ('b' ,2,2, 'b')]
#Apply a function to each RDD element and flatten the result
>>> rdd5 = rdd.flatMap(lambda x: x+(x[1],x[0]))
>>> rdd5.collect()
['a',7 , 7 ,  'a' , 'a' , 2,  2,  'a', 'b', 2 , 2, 'b']
#Apply a flatMap function to each (key,value) pair of rdd4 without changing the keys
>>> rdds.flatMapValues(lambda x: x).collect()
[('a', 'x'), ('a', 'y'), ('a', 'z'),('b', 'p'),('b', 'r')]

Selecting Data

Getting

>>> rdd.collect() #Return a list with all RDD elements 
[('a', 7), ('a', 2), ('b', 2)]
>>> rdd.take(2) #Take first 2 RDD elements 
[('a', 7),  ('a', 2)]
>>> rdd.first() #Take first RDD element
('a', 7)
>>> rdd.top(2) #Take top 2 RDD elements 
[('b', 2), ('a', 7)]

Sampling

>>> rdd3.sample(False, 0.15, 81).collect() #Return sampled subset of rdd3
     [3,4,27,31,40,41,42,43,60,76,79,80,86,97]

Filtering

>>> rdd.filter(lambda x: "a" in x).collect() #Filter the RDD
[('a',7),('a',2)]
>>> rdd5.distinct().collect() #Return distinct RDD values
['a' ,2, 'b',7]
>>> rdd.keys().collect() #Return (key,value) RDD's keys
['a',  'a',  'b']

Iterating 

>>> def g (x): print(x)
>>> rdd.foreach(g) #Apply a function to all RDD elements
('a', 7)
('b', 2)
('a', 2)

Reshaping Data 

Reducing

>>> rdd.reduceByKey(lambda x,y : x+y).collect() #Merge the rdd values for each key
[('a',9),('b',2)]
>>> rdd.reduce(lambda a, b: a+ b) #Merge the rdd values
('a', 7, 'a' , 2 , 'b' , 2)

 

Grouping by

>>> rdd3.groupBy(lambda x: x % 2) #Return RDD of grouped values
          .mapValues(list)
          .collect()
>>> rdd.groupByKey() #Group rdd by key
          .mapValues(list)
          .collect() 
[('a',[7,2]),('b',[2])]

Aggregating

>> seqOp = (lambda x,y: (x[0]+y,x[1]+1))
>>> combOp = (lambda x,y:(x[0]+y[0],x[1]+y[1]))
#Aggregate RDD elements of each partition and then the results
>>> rdd3.aggregate((0,0),seqOp,combOp) 
(4950,100)
#Aggregate values of each RDD key
>>> rdd.aggregateByKey((0,0),seqop,combop).collect() 
     [('a',(9,2)), ('b',(2,1))]
#Aggregate the elements of each partition, and then the results
>>> rdd3.fold(0,add)
     4950
#Merge the values for each key
>>> rdd.foldByKey(0, add).collect()
[('a' ,9), ('b' ,2)]
#Create tuples of RDD elements by applying a function
>>> rdd3.keyBy(lambda x: x+x).collect()

Mathematical Operations 

>>>> rdd.subtract(rdd2).collect() #Return each rdd value not contained in rdd2
[('b' ,2), ('a' ,7)]
#Return each (key,value) pair of rdd2 with no matching key in rdd
>>> rdd2.subtractByKey(rdd).collect()
[('d', 1)1
>>>rdd.cartesian(rdd2).collect() #Return the Cartesian product of rdd and rdd2

Sort 

>>> rdd2.sortBy(lambda x: x[1]).collect() #Sort RDD by given function
[('d',1),('b',1),('a',2)]
>>> rdd2.sortByKey().collect() #Sort (key, value) ROD by key
[('a' ,2), ('b' ,1), ('d' ,1)]

Repartitioning 

>>> rdd.repartition(4) #New RDD with 4 partitions
>>> rdd.coalesce(1) #Decrease the number of partitions in the RDD to 1

Saving 

>>> rdd.saveAsTextFile("rdd.txt")
>>> rdd.saveAsHadoopFile("hdfs:// namenodehost/parent/child",
               'org.apache.hadoop.mapred.TextOutputFormat')

Stopping SparkContext 

>>> sc.stop()

Execution 

$ ./bin/spark-submit examples/src/main/python/pi.py

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#pyspark #cheatsheet #spark #python

Justen  Hintz

Justen Hintz

1663559281

To-do List App with HTML, CSS and JavaScript

Learn how to create a to-do list app with local storage using HTML, CSS and JavaScript. Build a Todo list application with HTML, CSS and JavaScript. Learn the basics to JavaScript along with some more advanced features such as LocalStorage for saving data to the browser.

HTML:

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>To Do List With Local Storage</title>
    <!-- Font Awesome Icons -->
    <link
      rel="stylesheet"
      href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.2.0/css/all.min.css"
    />
    <!-- Google Fonts -->
    <link
      href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500&display=swap"
      rel="stylesheet"
    />
    <!-- Stylesheet -->
    <link rel="stylesheet" href="style.css" />
  </head>
  <body>
    <div class="container">
      <div id="new-task">
        <input type="text" placeholder="Enter The Task Here..." />
        <button id="push">Add</button>
      </div>
      <div id="tasks"></div>
    </div>
    <!-- Script -->
    <script src="script.js"></script>
  </body>
</html>

CSS:

* {
  padding: 0;
  margin: 0;
  box-sizing: border-box;
}
body {
  background-color: #0b87ff;
}
.container {
  width: 90%;
  max-width: 34em;
  position: absolute;
  transform: translate(-50%, -50%);
  top: 50%;
  left: 50%;
}
#new-task {
  position: relative;
  background-color: #ffffff;
  padding: 1.8em 1.25em;
  border-radius: 0.3em;
  box-shadow: 0 1.25em 1.8em rgba(1, 24, 48, 0.15);
  display: grid;
  grid-template-columns: 9fr 3fr;
  gap: 1em;
}
#new-task input {
  font-family: "Poppins", sans-serif;
  font-size: 1em;
  border: none;
  border-bottom: 2px solid #d1d3d4;
  padding: 0.8em 0.5em;
  color: #111111;
  font-weight: 500;
}
#new-task input:focus {
  outline: none;
  border-color: #0b87ff;
}
#new-task button {
  font-family: "Poppins", sans-serif;
  font-weight: 500;
  font-size: 1em;
  background-color: #0b87ff;
  color: #ffffff;
  outline: none;
  border: none;
  border-radius: 0.3em;
  cursor: pointer;
}
#tasks {
  background-color: #ffffff;
  position: relative;
  padding: 1.8em 1.25em;
  margin-top: 3.8em;
  width: 100%;
  box-shadow: 0 1.25em 1.8em rgba(1, 24, 48, 0.15);
  border-radius: 0.6em;
}
.task {
  background-color: #ffffff;
  padding: 0.3em 0.6em;
  margin-top: 0.6em;
  display: flex;
  align-items: center;
  border-bottom: 2px solid #d1d3d4;
  cursor: pointer;
}
.task span {
  font-family: "Poppins", sans-serif;
  font-size: 0.9em;
  font-weight: 400;
}
.task button {
  color: #ffffff;
  padding: 0.8em 0;
  width: 2.8em;
  border-radius: 0.3em;
  border: none;
  outline: none;
  cursor: pointer;
}
.delete {
  background-color: #fb3b3b;
}
.edit {
  background-color: #0b87ff;
  margin-left: auto;
  margin-right: 3em;
}
.completed {
  text-decoration: line-through;
}

Javascript:

//Initial References
const newTaskInput = document.querySelector("#new-task input");
const tasksDiv = document.querySelector("#tasks");
let deleteTasks, editTasks, tasks;
let updateNote = "";
let count;

//Function on window load
window.onload = () => {
  updateNote = "";
  count = Object.keys(localStorage).length;
  displayTasks();
};

//Function to Display The Tasks
const displayTasks = () => {
  if (Object.keys(localStorage).length > 0) {
    tasksDiv.style.display = "inline-block";
  } else {
    tasksDiv.style.display = "none";
  }

  //Clear the tasks
  tasksDiv.innerHTML = "";

  //Fetch All The Keys in local storage
  let tasks = Object.keys(localStorage);
  tasks = tasks.sort();

  for (let key of tasks) {
    let classValue = "";

    //Get all values
    let value = localStorage.getItem(key);
    let taskInnerDiv = document.createElement("div");
    taskInnerDiv.classList.add("task");
    taskInnerDiv.setAttribute("id", key);
    taskInnerDiv.innerHTML = `<span id="taskname">${key.split("_")[1]}</span>`;
    //localstorage would store boolean as string so we parse it to boolean back
    let editButton = document.createElement("button");
    editButton.classList.add("edit");
    editButton.innerHTML = `<i class="fa-solid fa-pen-to-square"></i>`;
    if (!JSON.parse(value)) {
      editButton.style.visibility = "visible";
    } else {
      editButton.style.visibility = "hidden";
      taskInnerDiv.classList.add("completed");
    }
    taskInnerDiv.appendChild(editButton);
    taskInnerDiv.innerHTML += `<button class="delete"><i class="fa-solid fa-trash"></i></button>`;
    tasksDiv.appendChild(taskInnerDiv);
  }

  //tasks completed
  tasks = document.querySelectorAll(".task");
  tasks.forEach((element, index) => {
    element.onclick = () => {
      //local storage update
      if (element.classList.contains("completed")) {
        updateStorage(element.id.split("_")[0], element.innerText, false);
      } else {
        updateStorage(element.id.split("_")[0], element.innerText, true);
      }
    };
  });

  //Edit Tasks
  editTasks = document.getElementsByClassName("edit");
  Array.from(editTasks).forEach((element, index) => {
    element.addEventListener("click", (e) => {
      //Stop propogation to outer elements (if removed when we click delete eventually rhw click will move to parent)
      e.stopPropagation();
      //disable other edit buttons when one task is being edited
      disableButtons(true);
      //update input value and remove div
      let parent = element.parentElement;
      newTaskInput.value = parent.querySelector("#taskname").innerText;
      //set updateNote to the task that is being edited
      updateNote = parent.id;
      //remove task
      parent.remove();
    });
  });

  //Delete Tasks
  deleteTasks = document.getElementsByClassName("delete");
  Array.from(deleteTasks).forEach((element, index) => {
    element.addEventListener("click", (e) => {
      e.stopPropagation();
      //Delete from local storage and remove div
      let parent = element.parentElement;
      removeTask(parent.id);
      parent.remove();
      count -= 1;
    });
  });
};

//Disable Edit Button
const disableButtons = (bool) => {
  let editButtons = document.getElementsByClassName("edit");
  Array.from(editButtons).forEach((element) => {
    element.disabled = bool;
  });
};

//Remove Task from local storage
const removeTask = (taskValue) => {
  localStorage.removeItem(taskValue);
  displayTasks();
};

//Add tasks to local storage
const updateStorage = (index, taskValue, completed) => {
  localStorage.setItem(`${index}_${taskValue}`, completed);
  displayTasks();
};

//Function To Add New Task
document.querySelector("#push").addEventListener("click", () => {
  //Enable the edit button
  disableButtons(false);
  if (newTaskInput.value.length == 0) {
    alert("Please Enter A Task");
  } else {
    //Store locally and display from local storage
    if (updateNote == "") {
      //new task
      updateStorage(count, newTaskInput.value, false);
    } else {
      //update task
      let existingCount = updateNote.split("_")[0];
      removeTask(updateNote);
      updateStorage(existingCount, newTaskInput.value, false);
      updateNote = "";
    }
    count += 1;
    newTaskInput.value = "";
  }
});

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Build a Todo list app in HTML, CSS & JavaScript | JavaScript for Beginners tutorial

Build a Todo List App in HTML, CSS & JavaScript with LocalStorage | JavaScript for Beginners

To Do List using HTML CSS JavaScript | To Do List JavaScript

Create A Todo List App in HTML CSS & JavaScript | Todo App in JavaScript

#html #css #javascript

Mobile App Development Companies in New York 2020 – TopDevelopers.co

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#mobile app development service providers in new york #top mobile app development companies in new york #new york based top mobile app development firms #best mobile app developers at new york #new york state #top mobile app developers

Top 10+ iPhone App Development Companies in New York City 2020 – TopDevelopers.co

Profusely examined top iPhone App Development Companies in New York City with ratings & reviews to help find the best iPhone App Developers to build your solution.

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