# Centering (center)¶

Centers an observation sequence about the mean of its observations – that is, given:

$\begin{split}O=\begin{pmatrix} o_1^{(1)} & o_2^{(1)} & \cdots & o_D^{(1)} \\ o_1^{(2)} & o_2^{(2)} & \cdots & o_D^{(2)} \\ \vdots & \vdots & \ddots & \vdots \\ o_1^{(T)} & o_2^{(T)} & \cdots & o_D^{(T)} \end{pmatrix} \qquad \boldsymbol{\mu}=\begin{pmatrix} \overline{o_1} & \overline{o_2} & \cdots & \overline{o_D} \end{pmatrix}\end{split}$

Where $$\overline{o_d}$$ represents the mean of the $$d^\text{th}$$ feature of $$O$$.

We subtract $$\boldsymbol{\mu}$$ from each observation, or row in $$O$$. This centers the observations.

For further information, please see the preprocessing tutorial notebook.

## Example¶

 1 2 3 4 5 6 7 8 import numpy as np from sequentia.preprocessing import center # Create some sample data X = [np.random.random((10 * i, 3)) for i in range(1, 4)] # Center the data X = center(X) 

## API reference¶

sequentia.preprocessing.center(X)[source]

Centers an observation sequence (or multiple sequences) by centering observations around the mean.

Parameters: X: numpy.ndarray or List[numpy.ndarray] An individual observation sequence or a list of multiple observation sequences. centered: numpy.ndarray or List[numpy.ndarray] The centered input observation sequence(s).