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As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. [9]: 103–105 If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in ...
Temporary variables, along with XOR swaps and arithmetic operators, are one of three main ways to exchange the contents of two variables. To swap the contents of variables "a" and "b" one would typically use a temporary variable temp as follows, so as to preserve the data from a as it is being overwritten by b: temp := a a := b b := temp
In computer science, a data buffer (or just buffer) is a region of memory used to store data temporarily while it is being moved from one place to another. Typically, the data is stored in a buffer as it is retrieved from an input device (such as a microphone) or just before it is sent to an output device (such as speakers); however, a buffer may be used when data is moved between processes ...
Dynamic environment variables (also named internal variables or system information variables under DOS) are pseudo-environment variables supported by CMD.EXE when command-line extensions are enabled, and they expand to various discrete values whenever queried, that is, their values can change when queried multiple times even within the same ...
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.
The value of a computing variable is not necessarily part of an equation or formula as in mathematics. Variables in computer programming are frequently given long names to make them relatively descriptive of their use, whereas variables in mathematics often have terse, one- or two-character names for brevity in transcription and manipulation.
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.
Formally, the set of LTL formulas over AP is inductively defined as follows: if p ∈ AP then p is an LTL formula; if ψ and φ are LTL formulas then ¬ψ, φ ∨ ψ, X ψ, and φ U ψ are LTL formulas. [7] X is read as next and U is read as until. Other than these fundamental operators, there are additional logical and temporal operators ...