By Allen B. Downey

Expand your Python abilities via operating with information constructions and algorithms in a fresh context—through an eye-opening exploration of complexity technology. 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 chain of routines, case reports, and easy-to-understand explanations.

You’ll paintings with graphs, set of rules research, scale-free networks, and mobile automata, utilizing complicated gains that make Python one of these robust language. excellent as a textual content for classes on Python programming and algorithms, Think Complexity also will support self-learners achieve priceless event with issues and ideas they may not stumble upon otherwise.
* paintings with NumPy arrays and SciPy equipment, simple sign processing and speedy Fourier rework, and hash tables
* examine summary types of complicated actual platforms, together with strength legislation, fractals and red noise, and Turing machines
* Get starter code and suggestions that can assist you re-implement and expand unique experiments in complexity
* discover the philosophy of technological know-how, together with the character of medical legislation, idea selection, realism and instrumentalism, and different themes
* research case experiences of complicated platforms submitted by means of scholars and readers

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Extra info for Think Complexity: Complexity Science and Computational Modeling

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Since then, people have used it to describe phenomena in the natural and social sciences including sizes of cities and towns, sand particles and meteorites, forest fires and earthquakes. 4. Pareto distributions 45 The parameters xm and α determine the location and shape of the distribution. xm is the minimum possible quantity. Values from a Pareto distribution often have these properties: Long tail: Pareto distributions contain many small values and a few very large ones. 80/20 rule: The large values in a Pareto distribution are so large that they make up a disproportionate share of the total.

2. What is the order of growth of (n2 + n) · (n + 1)? Before you start multiplying, remember that you only need the leading term. 3. If f is in O( g), for some unspecified function g, what can we say about a f + b? 4. If f 1 and f 2 are in O( g), what can we say about f 1 + f 2 ? 5. If f 1 is in O( g) and f 2 is in O(h), what can we say about f 1 + f 2 ? 6. If f 1 is in O( g) and f 2 is O(h), what can we say about f 1 ∗ f 2 ? Programmers who care about performance often find this kind of analysis hard to swallow.

3. Write a function called ❜✐s❡❝t✐♦♥ that takes a sorted list and a target value and returns the index of the value in the list, if it’s there, or ◆♦♥❡ if it’s not. Or you could read the documentation of the ❜✐s❡❝t module and use that! Bisection search can be much faster than linear search, but it requires the sequence to be in order, which might require extra work. There is another data structure, called a hashtable that is even faster—it can do a search in constant time—and it doesn’t require the items to be sorted.

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