Functional programming
The functional paradigm from the ground up: pure functions, immutability and statelessness, function types, first-class and higher-order functions, partial application and composition, map, filter and fold, list processing by head and tail, and why the style suits big data, ending with a robot that finds its way out using a pipeline of pure functions.
Start in the simulator- A13.1 The functional paradigm Side effects, pure functions and referential transparency, immutability and statelessness, with the robot's side…
- A13.2 Function types and function application Functions as mappings, function type f: A to B, domain and co-domain, and function application with arguments from a…
- A13.3 First-class objects and higher-order functions Functions as values in variables, lists and dictionaries, passed as arguments and returned as results, lambdas,…
- A13.4 Partial application and composition Why every Haskell function takes one argument, partial function application, functools.partial, and composing…
- A13.5 Map, filter and fold The three higher-order functions, foldl and foldr, and a pipeline over a log of sensor readings.
- A13.6 List processing Lists as a head and a tail, the empty list, prepend and append, and recursion over a list to drive a route there and…
- A13.7 Functional programming and big data Volume, velocity and variety, why big data needs distributed processing, and how immutability, statelessness and…
- A13.8 Project: the way out Sweep the bay, decide with a pipeline of pure functions built by composition, map, filter and fold, then drive out.