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Construct is a Python library for the construction and deconstruction of data structures in a declarative fashion. In this context, construction, or building, refers to the process of converting (serializing) a programmatic object into a binary representation.
In computational number theory, the index calculus algorithm is a probabilistic algorithm for computing discrete logarithms. Dedicated to the discrete logarithm in ( Z / q Z ) ∗ {\displaystyle (\mathbb {Z} /q\mathbb {Z} )^{*}} where q {\displaystyle q} is a prime, index calculus leads to a family of algorithms adapted to finite fields and to ...
Decision tree learning is a method commonly used in data mining. [3] The goal is to create a model that predicts the value of a target variable based on several input variables. A decision tree is a simple representation for classifying examples.
Finding an entry in the auxiliary index would tell us which block to search in the main database; after searching the auxiliary index, we would have to search only that one block of the main database—at a cost of one more disk read. In the above example the index would hold 10,000 entries and would take at most 14 comparisons to return a result.
Final suffix tree using Ukkonen's algorithm (example). To better illustrate how a suffix tree is constructed using Ukkonen's algorithm, we can consider the string S = xabxac. Start with an empty root node. Construct for S[1] by adding the first character of the string. Rule 2 applies, which creates a new leaf node.
A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables.
In computer science, Thompson's construction algorithm, also called the McNaughton–Yamada–Thompson algorithm, [1] is a method of transforming a regular expression into an equivalent nondeterministic finite automaton (NFA). [2] This NFA can be used to match strings against the regular expression. This algorithm is credited to Ken Thompson.
A recent study suggests that this claim is generally unjustified, and proposes two methods for minimum sample size estimation in PLS-PM. [ 13 ] [ 14 ] Another point of contention is the ad hoc way in which PLS-PM has been developed and the lack of analytic proofs to support its main feature: the sampling distribution of PLS-PM weights.