Introduction to computation and programming using Python:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
2013
|
| Ausgabe: | rev. and expanded ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | "This book is based on an MIT course that has been offered twice a year since 2006"--Preface |
| Beschreibung: | XIV, 298 S. Ill., graph. Darst. |
| ISBN: | 9780262525008 0262525003 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | Titel: Introduction to computation and programming using Python
Autor: Guttag, John V
Jahr: 2013
CONTENTS
PREFACE.......................................................................................................xiii
ACKNOWLEDGMENTS.....................................................................................xv
1 GETTING STARTED....................................................................................1
2 INTRODUCTION TO PYTHON......................................................................7
2.1 The Basic Elements of Python...............................................................8
2.1.1 Objects, Expressions, and Numerical Types....................................9
2.1.2 Variables and Assignment............................................................11
2.1.3 IDLE............................................................................................13
2.2 Branching Programs........................................................................... 14
2.3 Strings and Input...............................................................................16
2.3.1 Input............................................................................................18
2.4 Iteration..............................................................................................18
3 SOME SIMPLE NUMERICAL PROGRAMS..................................................21
3.1 Exhaustive Enumeration....................................................................21
3.2 For Loops............................................................................................23
3.3 Approximate Solutions and Bisection Search......................................25
3.4 A Few Words About Using Floats........................................................29
3.5 Newton-Raphson................................................................................32
4 FUNCTIONS, SCOPING, and ABSTRACTION.............................................34
4.1 Functions and Scoping.......................................................................35
4.1.1 Function Definitions.....................................................................35
4.1.2 Keyword Arguments and Default Values.......................................36
4.1.3 Scoping........................................................................................37
4.2 Specifications.....................................................................................41
4.3 Recursion...........................................................................................44
4.3.1 Fibonacci Numbers......................................................................45
4.3.2 Palindromes.................................................................................48
4.4 Global Variables.................................................................................50
4.5 Modules..............................................................................................51
4.6 Files...................................................................................................53
5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS.. 56
5.1 Tuples................................................................................................56
5.1.1 Sequences and Multiple Assignment.............................................57
5.2 Lists and Mutability............................................................................58
5.2.1 Cloning........................................................................................63
5.2.2 List Comprehension.....................................................................63
5.3 Functions as Objects..........................................................................64
5.4 Strings, Tuples, and Lists...................................................................66
5.5 Dictionaries........................................................................................67
6 TESTING AND DEBUGGING......................................................................70
6.1 Testing................................................................................................70
6.1.1 Black-Box Testing........................................................................71
6.1.2 Glass-Box Testing........................................................................73
6.1.3 Conducting Tests.........................................................................74
6.2 Debugging..........................................................................................76
6.2.1 Learning to Debug........................................................................78
6.2.2 Designing the Experiment............................................................79
6.2.3 When the Going Gets Tough.........................................................81
6.2.4 And When You Have Found The Bug..........................................82
7 EXCEPTIONS AND ASSERTIONS..............................................................84
7.1 Handling Exceptions...........................................................................84
7.2 Exceptions as a Control Flow Mechanism...........................................87
7.3 Assertions...........................................................................................90
8 CLASSES AND OBJECT-ORIENTED PROGRAMMING...............................91
8.1 Abstract Data Types and Classes........................................................91
8.1.1 Designing Programs Using Abstract Data Types............................96
8.1.2 Using Classes to Keep Track of Students and Faculty...................96
8.2 Inheritance.........................................................................................99
8.2.1 Multiple Levels of Inheritance..................................................... 101
8.2.2 The Substitution Principle..........................................................102
8.3 Encapsulation and Information Hiding.............................................. 103
8.3.1 Generators.................................................................................106
8.4 Mortgages, an Extended Example.....................................................108
ix
9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY............113
9.1 Thinking About Computational Complexity.......................................113
9.2 Asymptotic Notation..........................................................................116
9.3 Some Important Complexity Classes.................................................118
9.3.1 Constant Complexity..................................................................118
9.3.2 Logarithmic Complexity..............................................................118
9.3.3 Linear Complexity......................................................................119
9.3.4 Log-Linear Complexity................................................................120
9.3.5 Polynomial Complexity...............................................................120
9.3.6 Exponential Complexity..............................................................121
9.3.7 Comparisons of Complexity Classes............................................123
10 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES.........................125
10.1 Search Algorithms..........................................................................126
10.1.1 Linear Search and Using Indirection to Access Elements..........126
10.1.2 Binaiy Search and Exploiting Assumptions..............................128
10.2 Sorting Algorithms..........................................................................131
10.2.1 Merge Sort................................................................................132
10.2.2 Exploiting Functions as Parameters..........................................135
10.2.3 Sorting in Python.....................................................................136
10.3 Hash Tables....................................................................................137
11 PLOTTING AND MORE ABOUT CLASSES................................................141
11.1 Plotting Using PyLab.......................................................................141
11.2 Plotting Mortgages, an Extended Example.......................................146
12 STOCHASTIC PROGRAMS, PROBABILITY, AND STATISTICS...................152
12.1 Stochastic Programs.......................................................................153
12.2 Inferential Statistics and Simulation...............................................155
12.3 Distributions.................................................................................. 166
12.3.1 Normal Distributions and Confidence Levels.............................168
12.3.2 Uniform Distributions..............................................................170
12.3.3 Exponential and Geometric Distributions................................. 171
12.3.4 Benford s Distribution..............................................................173
12.4 How Often Does the Better Team Win?............................................ 174
12.5 Hashing and Collisions................................................................... 177
X
13 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION................. 179
13.1 The Drunkard s Walk......................................................................179
13.2 Biased Random Walks.................................................................... 186
13.3 Treacherous Fields.......................................................................... 191
14 MONTE CARLO SIMULATION.................................................................. 193
14.1 Pascal s Problem............................................................................. 194
14.2 Pass or Don t Pass?......................................................................... 195
14.3 Using Table Lookup to Improve Performance................................... 199
14.4 Finding jc........................................................................................200
14.5 Some Closing Remarks About Simulation Models............................204
15 UNDERSTANDING EXPERIMENTAL DATA..............................................207
15.1 The Behavior of Springs..................................................................207
15.1.1 Using Linear Regression to Find a Fit.......................................210
15.2 The Behavior of Projectiles..............................................................214
15.2.1 Coefficient of Determination.....................................................216
15.2.2 Using a Computational Model...................................................217
15.3 Fitting Exponentially Distributed Data............................................218
15.4 When Theory Is Missing..................................................................221
16 LIES, DAMNED LIES, AND STATISTICS..................................................222
16.1 Garbage In Garbage Out (GIGO)......................................................222
16.2 Pictures Can Be Deceiving..............................................................223
16.3 Cum Hoc Ergo Propter Hoc...............................................................225
16.4 Statistical Measures Don t Tell the Whole Story...............................226
16.5 Sampling Bias.................................................................................228
16.6 Context Matters..............................................................................229
16.7 Beware of Extrapolation..................................................................229
16.8 The Texas Sharpshooter Fallacy......................................................230
16.9 Percentages Can Confuse................................................................232
16.10 Just Beware..................................................................................233
17 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS..............................234
17.1 Knapsack Problems........................................................................234
17.1.1 Greedy Algorithms....................................................................235
17.1.2 An Optimal Solution to the 0/1 Knapsack Problem...................238
xi
17.2 Graph Optimization Problems.........................................................240
17.2.1 Some Classic Graph-Theoretic Problems...................................244
17.2.2 The Spread of Disease and Min Cut..........................................245
17.2.3 Shortest Path: Depth-First Search and Breadth-First Search.... 246
18 DYNAMIC PROGRAMMING.....................................................................252
18.1 Fibonacci Sequences, Revisited.......................................................252
18.2 Dynamic Programming and the 0/1 Knapsack Problem...................254
18.3 Dynamic Programming and Divide-and-Conquer.............................261
19 A QUICK LOOK AT MACHINE LEARNING................................................262
19.1 Feature Vectors..............................................................................264
19.2 Distance Metrics.............................................................................266
19.3 Clustering.......................................................................................270
19.4 Types Example and Cluster.............................................................272
19.5 K-means Clustering........................................................................274
19.6 A Contrived Example......................................................................276
19.7 A Less Contrived Example...............................................................280
19.8 Wrapping Up...................................................................................286
PYTHON 2.7 QUICK REFERENCE.................................................................287
INDEX..........................................................................................................289
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| spelling | Guttag, John V. Verfasser aut Introduction to computation and programming using Python John V. Guttag rev. and expanded ed. Cambridge, Mass. [u.a.] MIT Press 2013 XIV, 298 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier "This book is based on an MIT course that has been offered twice a year since 2006"--Preface Informatik (DE-588)4026894-9 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Python Programmiersprache (DE-588)4434275-5 s DE-604 Informatik (DE-588)4026894-9 s 2\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025871219&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
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| title | Introduction to computation and programming using Python |
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