standardize()
In statistics, standardization is a widely used noramlization method. Standard score will be calculated after standardization.
Standard score is the number of standard deviations by which the value of a raw score is above or below the mean value of what is being observed or measured. Raw scores above the mean have positive standard scores, while those below the mean have negative standard scores.
Standard score can be calculated as following:
\[\frac{X-\mu}{\sigma}\]Import
import * as datacook from '@pipcook/datacook';
const { standardize } = datacook.Stat;
Syntax
standardize(xData: Tensor | RecursiveArray<number>, axis = -1): Tensor
Parameters
| Parameter | type | description |
|---|---|---|
| xData | Tensor | RecursiveArray<number> | input data |
axis optional | number | axis to compute, default=-1, which means calculation will be applied across all axes. If input data is one-dimensional, this parameter will have no effect |
Returns
<Tensor> data after standardization
Usage
const x = [ 1, 2, 3, 4, 5 ];
const y = standardize(x);
y.print();
/**
* Tensor
* [-1.2649111, -0.6324555, 0, 0.6324555, 1.2649111]
*/