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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.
The get and set accessors are called as methods using the parameter list of the indexer declaration, but the set accessor still has the implicit value parameter. Example 1 [ edit ]
The PAPRIKA method pertains to value models for ranking particular alternatives that are known to decision-makers (e.g. as in the job candidates example above) and also to models for ranking potentially all hypothetically possible alternatives in a pool that is changing over time (e.g. patients presenting for medical care).
The following list contains syntax examples of how a range of element of an array can be accessed. In the following table: first – the index of the first element in the slice; last – the index of the last element in the slice; end – one more than the index of last element in the slice; len – the length of the slice (= end - first)
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.
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.
In computer science theory – particularly formal language theory – Glushkov's construction algorithm, invented by Victor Mikhailovich Glushkov, transforms a given regular expression into an equivalent nondeterministic finite automaton (NFA).
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.