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Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.
Consider minimizing the function () = ‖ ~ ~ ‖. Since this is a convex function , a sufficient condition for optimality is that the gradient is zero ( ∇ F ( x ) = 0 {\displaystyle \nabla F(x)=0} ) which gives rise to the equation
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop.All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels. [102]
Convolutional code with any code rate can be designed based on polynomial selection; [15] however, in practice, a puncturing procedure is often used to achieve the required code rate. Puncturing is a technique used to make a m/n rate code from a "basic" low-rate (e.g., 1/n) code. It is achieved by deleting of some bits in the encoder output.
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
A projection mapping is displayed on the surface of the Tokyo Metropolitan Government building to celebrate the New Year in Tokyo, Japan Jan. 1, 2025.
These solutions verify the constraints of their linear program and, by duality, have the same value of objective function (=) which we will call . This optimal value is a function of the different coefficients of the primal problem: z ∗ = z ∗ ( c , A , b ) {\displaystyle z^{*}=z^{*}(c,A,b)} .