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In PL/I the original term for a union was cell, [5] which is still accepted as a synonym for union by several compilers. The union declaration is similar to the structure definition, where elements at the same level within the union declaration occupy the same storage. Elements of the union can be any data type, including structures and array.
After some operations of Union, some sets are grouped together. The operation Union(x, y) replaces the set containing x and the set containing y with their union. Union first uses Find to determine the roots of the trees containing x and y. If the roots are the same, there is nothing more to do. Otherwise, the two trees must be merged.
A segment can have one (and in the case of the inpage attribute, two) of five alignment attributes: […] Byte, which means a segment can be located at any address. […] Word, which means a segment can only be located at an address that is a multiple of two, starting from address 0H.
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
For example, the union of three sets A, B, and C contains all elements of A, all elements of B, and all elements of C, and nothing else. Thus, x is an element of A ∪ B ∪ C if and only if x is in at least one of A, B, and C. A finite union is the union of a finite number of sets; the phrase does not imply that the union set is a finite set ...
For example, for A the first of these cells gives the sum of the probabilities for A being red, regardless of which possibility for B in the column above the cell occurs, as 2 / 3 . Thus the marginal probability distribution for A {\displaystyle A} gives A {\displaystyle A} 's probabilities unconditional on B {\displaystyle B} , in a ...
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the " amount of information " (in units such as shannons ( bits ), nats or hartleys ) obtained about one random variable by observing the other random ...
The region quadtree represents a partition of space in two dimensions by decomposing the region into four equal quadrants, subquadrants, and so on with each leaf node containing data corresponding to a specific subregion. Each node in the tree either has exactly four children, or has no children (a leaf node).