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eVa's 1-core. eVa's cards. We are currently witnessing a re-architecture of the web, away from pages and destinations, towards completely personalised experiences built on an aggregation of many individual pieces of content. Content being broken down into individual components and re-aggregated is the result of the rise of mobile technologies, billions of screens of all shapes and sizes, and unprecedented access to data from all kinds of sources through APIs and SDKs.

This is driving the web away from many pages of content linked together, towards individual pieces of content aggregated together into one experience. The aggregation depends on: The person consuming the content and their interests, preferences, behaviour.Their location and environmental context.Their friends’ interests, preferences and behaviour.The targeting advertising eco-system. If the predominant medium of our time is set to be the portable screen (think phones and tablets), then the predominant design pattern is set to be cards. eVa's symmetry. A red–black tree is a data structure which is a type of self-balancing binary search tree. Balance is preserved by painting each node of the tree with one of two colors (typically called 'red' and 'black') in a way that satisfies certain properties, which collectively constrain how unbalanced the tree can become in the worst case. When the tree is modified, the new tree is subsequently rearranged and repainted to restore the coloring properties.

The properties are designed in such a way that this rearranging and recoloring can be performed efficiently. The balancing of the tree is not perfect but it is good enough to allow it to guarantee searching in O(log n) time, where n is the total number of elements in the tree. The insertion and deletion operations, along with the tree rearrangement and recoloring, are also performed in O(log n) time.[1] Tracking the color of each node requires only 1 bit of information per node because there are only two colors. History[edit] Terminology[edit] Notes.

eVa's block-1. eVa's block-2. eVa's block-3. eVa's random. 0inShare Le module STATISTICA Random Forest est intégré à l’outil de data mining STATISTICA Data Miner. Il reprend les travaux effectués par Breiman et répond aussi bien à des problématiques de classification que de régression. Une Forêt Aléatoire (Random Forest) est constituée d’un ensemble d’arbres simples de prévision, chacun étant capable de produire une réponse lorsqu’on lui présente un sous-ensemble de prédicteurs. Pour les problématiques de classification, la réponse prend la forme d’une classe qui associe un ensemble (classe) de valeurs indépendantes (prédicteur) à une des catégories présente dans la variable indépendante. Concernant la régression, l’arbre est une estimation de la variable dépendante en fonction des prédicteurs. Samuel DODE a rejoint StatSoft France il y a 5 ans. Il y occupe la fonction de Directeur Adjoint StatSoft France = Filiale française de StatSoft Inc, éditeur de la gamme de produit STATISTICA.

eVa's antipath. Bad things · writing tags: Andrew Koenig first coined the term "antipattern" in an article in JOOP[1], which is sadly not available on the internet. The essential idea (as I remember it [2]) was that an antipattern was something that seems like a good idea when you begin, but leads you into trouble. Since then the term has often been used just to indicate any bad idea, but I think the original focus is more useful. In the paper Koenig said An antipattern is just like a pattern, except that instead of a solution it gives something that looks superficially like a solution but isn't one. -- Andrew Koenig This is what makes a good antipattern something separate to just a bad thing to point and laugh at.

When writing a description of an antipattern it's valuable to describe how to get out of trouble if you've taken the bad path. It's useful to remember that the same solution can be a good pattern in some contexts and an antipattern in others. eVa's hash. A hash function that maps names to integers from 0 to 15. There is a collision between keys "John Smith" and "Sandra Dee". Uses[edit] Hash tables[edit] Thus, the hash function only hints at the record's location. Still, in a half-full table, a good hash function will typically narrow the search down to only one or two entries. People who write complete hash table implementations choose a specific hash function—such as a Jenkins hash or Zobrist hashing—and independently choose a hash-table collision resolution scheme—such as coalesced hashing, cuckoo hashing, or hopscotch hashing.

Caches[edit] Bloom filters[edit] Finding duplicate records[edit] Protecting data[edit] A hash value can be used to uniquely identify secret information. Finding similar records[edit] Hash functions can also be used to locate table records whose key is similar, but not identical, to a given key; or pairs of records in a large file which have similar keys. Finding similar substrings[edit] Geometric hashing[edit] . . And .