book

The Nature of Code

Name: The Nature of Code
Author: Daniel Shiffman
ISBN: 9781718503700

by Daniel Shiffman

September 2024

Intermediate to advanced

640 pages

15h 48m

English

No Starch Press

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Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Technical Reviewer
Brief Contents
Contents in Detail
Acknowledgments
Introduction
What Is This Book?A Word About p5.jsWhat Do You Need to Know?How Are You Reading This Book?The Coding Train ConnectionAdditional ResourcesThe “Story” of This BookPart 1: Inanimate ObjectsPart 2: It’s Alive!Part 3: IntelligenceUsing This Book as a SyllabusHow to Read the CodeFull ExamplesComplete SnippetsContext-Free CodeSnipped CodeExercisesSolutionsThe Ecosystem ProjectGetting Help and Submitting Feedback

0 Randomness
Random WalksThe Random Walker ClassExample 0.1: A Traditional Random WalkExample 0.2: A Random-Number DistributionProbability and Nonuniform DistributionsExample 0.3: A Walker That Tends to Move to the RightA Normal Distribution of Random NumbersExample 0.4: A Gaussian DistributionA Custom Distribution of Random NumbersExample 0.5: An Accept-Reject DistributionA Smoother Approach with Perlin NoiseNoise RangesExample 0.6: A Perlin Noise WalkerTwo-Dimensional NoiseThe Ecosystem Project
1 Vectors
The Point of VectorsExample 1.1: Bouncing Ball with No VectorsVectors in p5.jsVector AdditionExample 1.2: Bouncing Ball with Vectors!More Vector MathVector SubtractionExample 1.3: Vector SubtractionVector Multiplication and DivisionExample 1.4: Multiplying a VectorVector MagnitudeExample 1.5: Vector MagnitudeNormalizing VectorsExample 1.6: Normalizing a VectorMotion with VectorsExample 1.7: Motion 101 (Velocity)AccelerationAlgorithm 1: Constant AccelerationExample 1.8: Motion 101 (Velocity and Constant Acceleration)Algorithm 2: Random AccelerationExample 1.9: Motion 101 (Velocity and Random Acceleration)Static vs. Nonstatic MethodsAlgorithm 3: Interactive MotionExample 1.10: Accelerating Toward the MouseThe Ecosystem Project
2 Forces
Forces and Newton’s Laws of MotionNewton’s First LawNewton’s Third LawNewton’s Second LawForce AccumulationFactoring In MassCreating ForcesExample 2.1: ForcesExample 2.2: Forces Acting on Two ObjectsExample 2.3: Gravity Scaled by MassModeling a ForceFrictionExample 2.4: Including FrictionAir and Fluid ResistanceExample 2.5: Fluid ResistanceGravitational AttractionExample 2.6: AttractionExample 2.7: Attraction with Many MoversThe n-Body ProblemExample 2.8: Two-Body AttractionExample 2.9: n BodiesThe Ecosystem Project
3 Oscillation
AnglesAngular MotionExample 3.1: Angular Motion Using rotate()Example 3.2: Forces with (Arbitrary) Angular MotionTrigonometry FunctionsPointing in the Direction of MovementExample 3.3: Pointing in the Direction of MotionPolar vs. Cartesian CoordinatesExample 3.4: Polar to CartesianProperties of OscillationExample 3.5: Simple Harmonic Motion IOscillation with Angular VelocityExample 3.6: Simple Harmonic Motion IIExample 3.7: Oscillator ObjectsWavesExample 3.8: Static WaveExample 3.9: The WaveSpring ForcesExample 3.10: A Spring ConnectionThe PendulumExample 3.11: Swinging PendulumThe Ecosystem Project
4 Particle Systems
Why Particle Systems MatterA Single ParticleExample 4.1: A Single ParticleAn Array of ParticlesExample 4.2: An Array of ParticlesA Particle EmitterExample 4.3: A Single Particle EmitterA System of EmittersExample 4.4: A System of SystemsInheritance and PolymorphismInheritance BasicsPolymorphism BasicsParticles with Inheritance and PolymorphismExample 4.5: A Particle System with Inheritance and PolymorphismParticle Systems with ForcesExample 4.6: A Particle System with ForcesParticle Systems with RepellersExample 4.7: A Particle System with a RepellerImage Textures and Additive BlendingExample 4.8: An Image-Texture Particle SystemExample 4.9: Additive BlendingThe Ecosystem Project
5 Autonomous Agents
Forces from WithinVehicles and SteeringThe Steering ForceExample 5.1: Seeking a TargetThe Arrive BehaviorExample 5.2: Arriving at a TargetYour Own BehaviorsExample 5.3: “Stay Within Walls” Steering BehaviorFlow FieldsExample 5.4: Flow-Field FollowingPath FollowingThe Dot ProductSimple Path FollowingExample 5.5: Creating a Path ObjectExample 5.6: Simple Path FollowingPath Following with Multiple SegmentsExample 5.7: Path Made of Multiple Line SegmentsExample 5.8: Path FollowingComplex SystemsImplementing Group Behaviors (or: Let’s Not Run Into Each Other)Example 5.9: SeparationCombining BehaviorsExample 5.10: Combining Steering Behaviors (Seek and Separate)FlockingExample 5.11: FlockingAlgorithmic Efficiency (or: Why Does My Sketch Run So Slowly?)Spatial SubdivisionsExample 5.12: Bin-Lattice Spatial SubdivisionExample 5.13: QuadtreeMore Optimization TricksExample 5.14: Sin/Cos Lookup TableThe Ecosystem Project
6 Physics Libraries
Why Use a Physics Library?Importing the Matter.js LibraryMatter.js OverviewEngineBodiesRenderExample 6.1: Matter.js Default Render and RunnerMatter.js with p5.jsExample 6.2: A Comfortable and Cozy p5.js Sketch That Needs a Little Matter.jsStep 1: Add Matter.js to the p5.js SketchStep 2: Link Every Box Object with a Matter.js BodyStep 3: Draw the BodyStatic Matter.js BodiesExample 6.3: Falling Boxes Hitting BoundariesPolygons and Groups of ShapesExample 6.4: Polygon ShapesExample 6.5: Multiple Shapes on One BodyMatter.js ConstraintsDistance ConstraintsExample 6.6: Matter.js PendulumRevolute ConstraintsExample 6.7: Spinning WindmillMouse ConstraintsExample 6.8: MouseConstraint DemonstrationAdding More ForcesExample 6.9: Attraction with Matter.jsCollision EventsExample 6.10: Collision EventsA Brief Interlude: Integration MethodsVerlet Physics with Toxiclibs.jsVectorsThe Physics WorldParticlesSpringsExample 6.11: Simple Spring with Toxiclibs.jsSoft-Body SimulationsA StringExample 6.12: Soft Swinging PendulumA Soft-Body CharacterExample 6.13: Soft-Body CharacterA Force-Directed GraphExample 6.14: ClusterAttraction and Repulsion BehaviorsExample 6.15: Attraction (and Repulsion) BehaviorsThe Ecosystem Project
7 Cellular Automata
What Is a Cellular Automaton?Elementary Cellular AutomataDefining RulesetsProgramming an Elementary CADrawing an Elementary CAExample 7.1: Wolfram Elementary Cellular AutomataWolfram ClassificationClass 1: UniformityClass 2: RepetitionClass 3: RandomClass 4: ComplexityThe Game of LifeThe Rules of the GameThe ImplementationExample 7.2: Game of LifeObject-Oriented CellsExample 7.3: Object-Oriented Game of LifeVariations on Traditional CANonrectangular GridsProbabilisticContinuousImage ProcessingHistoricalMoving CellsNestingThe Ecosystem Project
8 Fractals
What Is a Fractal?RecursionImplementing Recursive FunctionsExample 8.1: Recursive Circles OnceExample 8.2: Recursive Circles TwiceExample 8.3: Recursive Circles Four TimesDrawing the Cantor Set with RecursionExample 8.4: The Cantor SetThe Koch CurveExample 8.5: The Koch CurveTreesThe Deterministic VersionExample 8.6: A Recursive TreeThe Stochastic VersionExample 8.7: A Stochastic TreeL-systemsExample 8.8: Simple L-system Sentence GenerationExample 8.9: An L-systemThe Ecosystem Project
9 Evolutionary Computing
Genetic Algorithms: Inspired by Actual EventsWhy Use Genetic Algorithms?How Genetic Algorithms WorkStep 1: Population CreationStep 2: SelectionStep 3: ReproductionStep 4: Repetition!Coding the Genetic AlgorithmStep 1: InitializationStep 2: SelectionStep 3: Reproduction (Crossover and Mutation)Putting It All TogetherExample 9.1: Genetic Algorithm for Evolving ShakespeareCustomizing Genetic AlgorithmsKey 1: The Global VariablesKey 2: The Fitness FunctionKey 3: The Genotype and PhenotypeEvolving Forces: Smart RocketsDeveloping the RocketsManaging the PopulationExample 9.2: Smart RocketsMaking ImprovementsExample 9.3: Smarter RocketsInteractive SelectionExample 9.4: Interactive SelectionEcosystem SimulationGenotype and PhenotypeSelection and ReproductionExample 9.5: An Evolving EcosystemThe Ecosystem Project
10 Neural Networks
Introducing Artificial Neural NetworksHow Neural Networks WorkMachine Learning LibrariesThe PerceptronPerceptron StepsPutting It All TogetherSimple Pattern Recognition Using a PerceptronThe Perceptron CodeExample 10.1: The PerceptronPutting the “Network” in Neural NetworkMachine Learning with ml5.jsThe Machine Learning Life CycleClassification and RegressionNetwork Designml5.js SyntaxBuilding a Gesture ClassifierCollecting and Preparing the DataChoosing a ModelTraining the ModelEvaluating the ModelTuning the ParametersDeploying the ModelExample 10.2: Gesture ClassifierThe Ecosystem Project
11 Neuroevolution
Reinforcement LearningEvolving Neural Networks Is NEAT!Coding Flappy BirdExample 11.1: Flappy Bird CloneNeuroevolutionary Flappy BirdThe Bird BrainVariation: A Flock of Happy BirdsSelection: Flappy Bird FitnessHeredity: Baby BirdsExample 11.2: Flappy Bird with NeuroevolutionSteering the Neuroevolutionary WayExample 11.3: Smart Rockets with NeuroevolutionResponding to ChangeSpeeding Up TimeExample 11.4: Dynamic Neuroevolutionary SteeringA Neuroevolutionary EcosystemSensing the EnvironmentExample 11.5: A Bloop with SensorsLearning from the SensorsExample 11.6: A Neuroevolutionary EcosystemThe Ecosystem ProjectThe End
Appendix: Creature Design
Image Credits
Index

Content preview from The Nature of Code

THE NATURE OF CODE

SIMULATING NATURAL SYSTEMS WITH JAVASCRIPT

DANIEL SHIFFMAN

San Francisco

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The Nature of Code

by Daniel Shiffman

THE NATURE OF CODE

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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