getSnen

Built-in function: Calculates the maximum number of samples of each subset in each neuron within the SOM network based on the sampling ratio specified by the user.

Providing six different algorithms that can be used to split the
available data into training, test and validation subsets with similar distribution
for hydrological model developments. The dataSplit() function will help you divide
the data according to specific requirements, and you can refer to the par.default()
function to set the parameters for data splitting. The getAUC() function will help
you measure the similarity of distribution features between the data subsets.
For more information about the data splitting algorithms, please refer to:
Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>,
Zheng et al. (2022) <doi:10.1029/2021WR031818>.

Junyi Chen

DSAM

Data Splitting Algorithms for Model Developments

Feifei Zheng

getSnen function

<dl><dt>som.info</dt>
<dd>The list contains information about the SOM network, including the total number of neurons, the number of rows, and the set of data points within each neuron.</dd>
<dt>control</dt>
<dd>User-defined parameter list, where each parameter definition refers to the par.default function.</dd></dl>

Arguments

Get sampling number of each SOM neuron — getSnen

<dl>

<dt>som.info</dt>
<dd>The list contains information about the SOM network, including the total number of neurons, the number of rows, and the set of data points within each neuron.</dd>


<dt>control</dt>
<dd>User-defined parameter list, where each parameter definition refers to the par.default function.</dd>

</dl>

getSnen: Get sampling number of each SOM neuron

Description

Usage

Value

Arguments