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G(V, E): weighted directed graph, with set of vertices V and set of directed edges E, w(u, v): cost of directed edge from node u to node v (costs are non-negative). Links that do not satisfy constraints on the shortest path are removed from the graph s: the source node; t: the destination node; K: the number of shortest paths to find; p u: a ...
In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from the field of graph theory within mathematics. A graph data structure consists of a finite (and possibly mutable) set of vertices (also called nodes or points ), together with a set of unordered pairs of these ...
They developed a set of 8,869 semantic relations and 10,675 syntactic relations which they use as a benchmark to test the accuracy of a model. When assessing the quality of a vector model, a user may draw on this accuracy test which is implemented in word2vec, [ 28 ] or develop their own test set which is meaningful to the corpora which make up ...
Note that winsorizing is not equivalent to simply excluding data, which is a simpler procedure, called trimming or truncation, but is a method of censoring data.. In a trimmed estimator, the extreme values are discarded; in a winsorized estimator, the extreme values are instead replaced by certain percentiles (the trimmed minimum and maximum).
The basic definition of a dictionary does not mandate an order. To guarantee a fixed order of enumeration, ordered versions of the associative array are often used. There are two senses of an ordered dictionary: The order of enumeration is always deterministic for a given set of keys by sorting.
Data-driven programming is similar to event-driven programming, in that both are structured as pattern matching and resulting processing, and are usually implemented by a main loop, though they are typically applied to different domains.
Illustration of set type using a Bachman diagram. A Bachman diagram is a certain type of data structure diagram, [2] and is used to design the data with a network or relational "logical" model, separating the data model from the way the data is stored in the system.
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.