I meant... helium
This implementation takes some of the ideas from the Norvig's implementation (the aima-python lib), but it's made with a more "pythonic" approach, and more emphasis on creating a stable, modern, and maintainable version. We are testing the majority of the lib, it's available via pip install, has a standard repository and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.
This new release adds a few statistical classification methods to SimpleAI with
the intention of start replicating the machine learning aspects of aima-python, also includes lots of tests for the classifiers, documentation, and a few sample uses of the classifiers.
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I count four in Spanish: R (alveolar tap or flap), RR (alveolar trill), L (alveolar lateral approximant) and LL (palatal lateral approximant). And three in English.
The only "new thing" is a database abstraction layer that they should have already been using to begin with. Who in this day still writes their software heavily coupled to a single database rather than using a thin abstraction layer?
we did, it's desktopcouch. Turned out to be too thin.
Our structured data sync service is CouchDB, except for tomboy notes. Syncing files is a completely separate stack.
Wiley Coyote... Super Genius.
Though, his reliance on ACME for equipment, should be reconsidered.
I always thought that Wiley Coyote depicts very well the agony of working as an engineer. The laws of nature seem to work against you. Murphy's laws are against you. The tools/equipment do not behave according to the specs, and tend to fail at the worst possible time. Good ideas fail because of implementation details or even bad luck. Yet, you cannot let the problem go, you have to fix it! One last try, ok, maybe another one!
NP is short for Natalie Portman, and the car analogy follows:
Adleman's chief scientist, Nickolas Chelyapov, offered this illustration: Imagine that a fussy customer walks onto a million-car auto square and gives the dealer a complicated list of criteria for the car he wants.
"First," he says, "I want it to be either a Cadillac or a convertible or red." Second, "if it is a Cadillac, then it has to have four seats or a locking gas cap." Third, "If it is a convertible, it should not be a Cadillac or it should have two seats."
The customer rattles off a list of 24 such conditions, and the salesman has to find the one car in stock that meets all the requirements. (Adleman and his team chose a problem they knew had exactly one solution.) The salesman will have to run through the customer's entire list for each of the million cars in turn -- a hopeless task unless he can move and think at superhuman speed.
This serial method is the way a digital electronic computer solves such a problem.
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