The phrase "assigns the input" is correct and usable in written English.
It can be used in contexts related to programming, data processing, or any situation where input is being allocated or designated for a specific purpose.
Example: "The function assigns the input to the variable for further processing."
Alternatives: "allocates the input" or "designates the input".
Based on these assignments, the sampler optimally assigns the input sequences to the N subgroups (the rows) and, at the same time, finds M patterns that optimally differentiate the foreground from the background (based on the hyperpartition specification) for each of the M functionally-divergent categories (the columns).
In the case of the 0/1 loss function, the Bayes decision rule simplifies to the MAP decision rule [51], which assigns the input face represented by F to the subject ω ℓ if begin{array}rcl@ p omega_{ell}|mathbf{F}) > p omega_{m}|mathbf{F})hspace{1cm} forall {mneqell} end{array} (23).
Our conclusion is that different methods can be used to assign the input p-values to the list of analyzed transcripts and to define seeds and candidates, but it is advisable to base the choice on the dataset characteristics.
Given this input, the mcBPPS sampler runs (in parallel) multiple BPPS procedures (one for each constraint category) both to optimally assign the input sequences to the subgroups (and, by implication, to the foreground and background partitions for each category) and to define, for each category, a pattern that optimally differentiates the foreground from the background sequences.
This combination rule, selected among other criteria after preliminary tests, assigns to the input image the label corresponding to the class receiving the majority of votes [ 25].
The left part of Figure 2 shows one of the candidate structures, tree t1, which this grammar assigns to the input sequence ACCAAAGG.
Weights are assigned to the input links to simulate the action of the neurotransmitters.
Initially, a small value is assigned to the input current I.
Step1 Representation of stochastic model inputs: For each uncertain input, corresponding srv is assigned and the input random variable is expressed in terms of the srv.
However, the use of RMR or similar classification systems in providing an accurate estimation of TBM penetration rate has had limited success due to the nature of the weights assigned to the input parameters.
Then, a series of ensemble models are built for a given number of neurons in which the number of descriptors is progressively increased, selecting them based on the ranking assigned by the Input Gradient procedure.
