دانلود کتاب Modeling and Simulation in Python

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Cover Half Title Title Page Copyright Page Dedication Contents Preface Software CHAPTER 1: Introduction CHAPTER 2: Random Values 2.1. DEFINING RANDOM 2.2. REPLICATING RANDOM SEQUENCES 2.3. BIAS AND OFFSET 2.4. OTHER TYPES OF RANDOM VALUES 2.5. ALTERNATE DISTRIBUTIONS 2.6. CONFIRMING RANDOMNESS 2.7. SUMMARY CHAPTER 3: Application of Random Values 3.1. THE CARD GAME NAMED WAR 3.2. PYTHON IMPLEMENTATION 3.3. ALTERATIONS 3.3.1. One Ace 3.3.2. Four Aces 3.4. SUMMARY CHAPTER 4: The Monte Carlo Method 4.1. RANDOM VECTORS 4.2. ROLLING DICE 4.3. THE MONTE CARLO METHOD 4.3.1. Horizontal Barrier 4.3.2. Slanted Barrier 4.3.3. Integration 4.3.4. Square 4.3.5. Estimation of ˇ 4.4. HYPER-DIMENSIONAL BALL 4.5. PROPER SAMPLING 4.6. ESTIMATING THE AREA OF A STAR 4.6.1. Geometric Properties 4.6.2. Theoretic Ratio 4.6.3. Monte Carlo Estimate of the Star Area Ratio 4.6.3.1. Determining Side of Line 4.6.3.2. Python Script for Side of Line 4.7. UNEQUAL DISTRIBUTIONS 4.8. SUMMARY CHAPTER 5: Modeling Self-Organization 5.1. SCHELLING’S MODEL 5.2. MODELING IN PYTHON 5.3. TRIALS 5.4. ALTERATIONS TO THE ALGORITHM 5.5. SUMMARY CHAPTER 6: Hidden Markov Models 6.1. AN EMISSION HMM 6.2. A TRANSITION HMM 6.2.1. Data Structures for the Transition HMM 6.2.2. Constructing a Transition HMM 6.3. A RECURRENT HMM 6.4. CONSIDERATIONS 6.4.1. Assuming Data 6.4.2. Spurious Strings 6.4.3. Recurrent Probabilities 6.5. SUMMARY CHAPTER 7: Identification of Start Codons 7.1. BRIEF BIOLOGICAL BACKGROUND 7.2. IMPLEMENTATION INTO PYTHON 7.2.1. Data 7.2.2. Probabilities and Log Odds 7.2.3. Building the Matrices 7.2.4. A Query 7.2.5. Testing Queries 7.3. SUMMARY CHAPTER 8: HMM Application in Baseball 8.1. JUST ENOUGH BASEBALL 8.2. BASEBALL HMM 8.3. GATHERING DATA 8.4. COUNTING EVENTS 8.5. CREATING THE TRANSITION HMM 8.6. ANALYSIS 8.6.1. The User-Selected Inning 8.6.2. Rarest Transition 8.6.3. Unusual Inning 8.7. SUMMARY CHAPTER 9: Hidden Shakespeare Model 9.1. BUILDING THE HMM 9.2. CREATING NEW STRINGS 9.3. DISCOVERING A NEW SHAKESPEARE 9.4. STRUCTURE 9.5. MIDSUMMER MADNESS 9.6. SUMMARY CHAPTER 10: Connected Data 10.1. MOVIES DATABASE 10.2. PYTHON QUERIES 10.3. CONNECTIONS 10.4. FLOYD-WARSHALL ALGORITHM 10.4.1. The B and P Matrices 10.4.2. Creating the G and P Matrices 10.4.3. Creating the Matrices in Python 10.4.4. Finding the Shortest Path 10.5. APPLICATION OF FLOYD-WARSHALL TO ACTORS 10.6. BEYOND CONNECTIONS 10.7. SUMMARY CHAPTER 11: Gene Expression Arrays 11.1. THE DATA 11.2. READING THE SPREADSHEET 11.3. PROTOCOL 11.3.1. Background Subtraction 11.3.2. Ratios and Intensities 11.3.3. M and A 11.3.4. LOWESS 11.4. MULTIPLE FILES 11.5. SIMPLE ANALYSIS 11.6. SUMMARY CHAPTER 12: Simultaneous Equations 12.1. A LINEAR ALGEBRA APPROACH 12.2. IMPLEMENTATION INTO PYTHON 12.3. LIMITS OF COMPUTATION 12.3.1. Matrix Inversion Precision 12.3.2. Singularity 12.4. WORD PROBLEMS 12.4.1. Amusement Park Fees 12.4.2. Traffic Flow 12.5. APPLICATION TO KIRCHHOFF’S LAWS 12.5.1. The Junction Rule 12.5.2. The Loop Rule 12.5.3. Creating the Equations 12.5.4. Computing the Currents 12.6. SUMMARY CHAPTER 13: Simulations of Motion 13.1. LINEAR MOTION 13.2. CAR CRASH 13.3. ACCELERATED MOTION 13.4. VERTICAL MOTION 13.4.1. Going Up 13.4.2. Going Down 13.4.3. Going Up and Down 13.5. PROJECTILE MOTION 13.6. ENERGIES 13.6.1. Example 1 13.6.2. Example 2 13.7. AIM POINT FOR A FALLING OBJECT 13.7.1. Define the Variables 13.7.2. Point the Gun 13.7.3. Paintball Velocity 13.7.4. Time of Flight 13.7.5. Heights of Objects 13.8. ROCKET TEST 13.8.1. Creating the Simulation 13.8.2. Finding Correct Parameters 13.9. SUMMARY CHAPTER 14: Oscillations 14.1. SPRING THEORY 14.2. SPRING SIMULATION 14.3. CORRECTED SIMULATION 14.3.1. Average Acceleration 14.3.2. The Leapfrog Method 14.4. THE PENDULUM 14.5. SUMMARY CHAPTER 15: Coupled Differential Equations 15.1. SIMPLE EXAMPLE 15.2. TWO VARIABLES DEPENDENT ON TIME 15.3. DEPENDENT EQUATIONS 15.4. COUPLED EQUATIONS 15.5. HIV SIMULATION 15.6. THE SPRING MODEL 15.6.1. The Spring Model without Friction 15.6.2. The Spring Model with Friction 15.6.3. A Forced System 15.7. COUPLED SPRINGS 15.8. SUMMARY CHAPTER 16: Extraordinary Number of Solutions 16.1. THE GAME 16.2. BUILDING A SOLUTION 16.2.1. Cell Identities 16.2.2. Cell Representation 16.2.3. Puzzle Architecture 16.2.4. Creating a Puzzle 16.2.5. Presenting a Puzzle 16.2.6. Rule 1 16.2.7. Rule 2 16.3. ALTERNATE PUZZLE ARCHITECTURES 16.3.1. Larger Puzzles 16.3.2. Jigsaw Sudoku Puzzles 16.3.3. Rule 3 16.4. CREATING GAMES 16.4.1. Creating a Solved Puzzle 16.4.2. The Trivial Solution 16.4.3. Modifications to the Trivial Solution 16.4.4. Paring the Puzzle 16.5. SUMMARY CHAPTER 17: Agent Based Modeling − Virus Spread 17.1. THE ABM MODEL 17.1.1. Movement 17.1.2. Agent Collision 17.1.3. An Example 17.1.4. Alternate Computation 17.2. IMPLEMENTATION IN PYTHON 17.2.1. Agents as Objects 17.2.2. Iterations 17.2.3. Altering the Simulation 17.3. SPEED OF COMPUTATION 17.4. SUMMARY CHAPTER 18: Chess 18.1. THEORY 18.2. IMPLEMENTATION 18.2.1. Piece Values 18.2.2. Creating the Board 18.2.3. Moving 18.2.4. Individual Pieces 18.2.5. Assigning a Score to the State of the Game 18.2.5.1. Piece Values 18.2.5.2. Board Control 18.2.5.3. Pieces in Peril 18.2.5.4. Combining Scores 18.3. PLAYING THE GAME 18.3.1. Move Selection 18.3.2. Improving the Performance 18.4. SUMMARY Bibliography Index