By Allen B. Downey
Expand your Python abilities through operating with facts constructions and algorithms in a fresh context—through an eye-opening exploration of complexity technological know-how. even if you’re an intermediate-level Python programmer or a pupil of computational modeling, you’ll delve into examples of complicated platforms via a sequence of workouts, case reviews, and easy-to-understand explanations.
You’ll paintings with graphs, set of rules research, scale-free networks, and mobile automata, utilizing complicated positive aspects that make Python this type of robust language. excellent as a textual content for classes on Python programming and algorithms, Think Complexity also will aid self-learners achieve important event with subject matters and concepts they may not stumble upon otherwise.
* paintings with NumPy arrays and SciPy equipment, easy sign processing and quickly Fourier remodel, and hash tables
* learn summary types of advanced actual platforms, together with energy legislation, fractals and red noise, and Turing machines
* Get starter code and ideas that will help you re-implement and expand unique experiments in complexity
* discover the philosophy of technological know-how, together with the character of medical legislation, concept selection, realism and instrumentalism, and different issues
* study case stories of advanced platforms submitted by way of scholars and readers
Read Online or Download Think Complexity: Complexity Science and Computational Modeling PDF
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Additional info for Think Complexity: Complexity Science and Computational Modeling
7. To compute the average path length in a SmallWorldGraph, you probably ran Dijkstra’s single-source shortest path algorithm for each node in the graph. In effect, you solved the “all-pairs shortest path” problem, which finds the shortest path between all pairs of nodes. 1. Find an algorithm for the all-pairs shortest path problem and implement it. Compare the run time with your “all-source Dijkstra” algorithm. 2. Which algorithm gives better order-of-growth run time as a function of the number of vertices and edges?
1 Zipf’s Law Zipf’s law describes a relationship between the frequencies and ranks of words in natural languages; see ❤tt♣✿✴✴❡♥✳✇✐❦✐♣❡❞✐❛✳♦r❣✴✇✐❦✐✴❩✐♣❢✬s❴❧❛✇. The “frequency” of a word is the number of times it appears in a body of work. The “rank” of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc. Specifically, Zipf’s Law predicts that the frequency, f , of the word with rank r is: f = cr −s where s and c are parameters that depend on the language and the text.
Then ❣❡t could use a bisection search, which is O(log n). But inserting a new item in the middle of a list is linear, so this might not be the best option. There are other data structures (see ❤tt♣✿✴✴❡♥✳✇✐❦✐♣❡❞✐❛✳♦r❣✴ ✇✐❦✐✴❘❡❞✲❜❧❛❝❦❴tr❡❡) that can implement ❛❞❞ and ❣❡t in log time, but that’s still not as good as a hashtable, so let’s move on. One way to improve ▲✐♥❡❛r▼❛♣ is to break the list of key-value pairs into smaller lists. Here’s an implementation called ❇❡tt❡r▼❛♣, which is a list of 100 LinearMaps.