Algorithms For Dummies

 

   

  

Algorithms For Dummies

MrJob package, 255

MRP (Material Requirements Planning)  

software, 290

MST (Minimum Spanning Tree), 31–32, 183–185

Mueller, John Paul (author)

blog for, 4

Machine Learning For Dummies, 16, 25, 373

MATLAB For Dummies, 48

Python for Data Science For Dummies, 91

multicore processing units, 250–252

multiply

 function, 97

N

naive, 311

Napoleon For Dummies (Markham), 28

Nash, John (economist), 28, 403

Nash Equilibrium, 28

National Security Agency (NSA), 230

navigating graphs, 203–206

nesting, 82–83

.NET (website), 47

networks

clustering in groups, 198–201

importance of, 156–161

leveraging, 18

NetworkX

about, 202, 220

adding a graph to a matrix, 170–171

website, 162, 169

networkx

 package, 205

neural networks, 17

Newton, Issac (inventor), 27

node_color

 argument, 163

normal distribution, 326

Norvig, Peter (author)

Artificial Intelligence: A Modern Approach, 301

notebooks

creating, 61–63

exporting, 63

importing, 64–65

removing, 63–64

NP-complete problems, 288–290

n-queens problem, 345, 373

NSA (National Security Agency), 230

numbers

multiplying large, 402

working with, 68–74

numeric format, putting graphs in, 169–172

numpy array

 function, 93

NumPy matrix, 93, 96, 169, 170–171

numpy multiply

 function, 98

numpy random.permutation

 function, 101

O

objective function, 343, 344, 360

occupancy grid maps, 375

oceanography, big data and, 230

one-way functions, 401

open addressing, 150

open category, 199

operations, distributing, 252–254

operator precedence, 73–74

optimal replacement strategy, 291

ord()

 command, 74

ordering in stacks, 121–123

ordinate, 39

outbound links, 212

out_degree_centrality()

 function, 168

output, 164, 166

overhead, parallelism and, 252

P

Page, Larry (author)

“The Anatomy of a LargeScale Hypertextual Web 

Search Engine,” 210

“PageRank: Bringing Order to the Web,” 213

PageRank algorithm, 207–221

“PageRank: Bringing Order to the Web” (Brin and 

Page), 213

PageRank_naive

 function, 219

PageRank_teleporting

 function, 220

Papadimitriou, Christos H. (professor), 350

paradigms, 283

parallel paradigm, 250–252

parallelizing operations

about, 249–250

managing large amounts of data, 250–258

MapReduce algorithm, 258–264

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Index 

     413

parity, 404

partial values, 149

path finding algorithms, 377–387

path planning, 374

pathfinding, 374

pathing, 374

pattern analysis, 396–397

.pdf file extensions, 209

PEPs (Python Enhanced Proposals), 47

Perlin, Ken (professor), 26

permutations, distinguishing, 100–101

permutations

 function, 102

pesudo-random numbers, 393

physics, big data and, 229

PID (proportional integrative derivative) algorithm, 25

piling data in order, 121–125

Pisano, Leonardo (mathematician), 303. See also 

Fibonacci sequence

pixel, 268

placeholder, 86

Plato (philosopher), 28

Pledge algorithm, 377

plotting, 155

plus (+) sign, 88

polynomial time, 289

positional arguments, 79

prefix-free codes, 294

prime numbers, 27

Prim’s algorithm, 186, 187–189

Prim’s MST, 31–32

print()

 function, 79, 81, 82, 139

priority queues, 186–187

probability, 323–325

problem depth, 33

problem instance, 33

problem solution, 23

problem space, 33–34

problem-solving, 15–19, 399–403

processed vertex, 174

product recommendation, 221

production, optimizing, 365–369

Project Gutenberg organization (website), 259

proportional integrative derivative (PID) algorithm, 25

pseudo-random number generation, algorithms  

for, 14

PSF (Python Software Foundation), 46

PuLP, 364–365

pure heuristic search, 35

Pyrrhic victory, 31

Python

about, 43–45, 67–68

benefits of, 45–48

conditional statements, 81–85

dates, 76

distributions, 48–51

downloading datasets and example code, 58–66

functions, 77–81

implementing a script, 213–216

implementing code, 350–353

indexing data using dictionaries, 90

installing on Linux, 51–52

installing on MacOS, 52–53

installing on Windows, 54–57

iterators, 89–90

loop statements, 81–85

manipulating data. See manipulating data

storing data using sets, lists and tuples, 85–89

strings, 74–75

working with numbers and logic, 68–74

Python Enhanced Proposals (PEPs), 47

Python for Data Science For Dummies (Mueller and 

Massaron), 91

Python Software Foundation (PSF), 46

pythonxy, 50–51

Q

quadratic complexity O(n

2

), 41

queues, 87, 123, 186–187

QuickSelect algorithm, 330–333

Quicksort algorithm, 140–142, 323, 336–337, 392

R

R programming language, 48

Rabin, Michael (mathematician), 322

RAM (Random Access Machine), 37–38

RAM (Random-Access Memory), 37

Random Access Machine (RAM), 37–38

random numbers, 393

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414