# Mean Centerer

A transformer object that performs column-based mean centering on a NumPy array.

from mlxtend.preprocessing import MeanCenterer

## Example 1 - Centering a NumPy Array

Use the fit method to fit the column means of a dataset (e.g., the training dataset) to a new MeanCenterer object. Then, call the transform method on the same dataset to center it at the sample mean.

import numpy as np
from mlxtend.preprocessing import MeanCenterer
X_train = np.array(
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
mc = MeanCenterer().fit(X_train)
mc.transform(X_train)

array([[-3., -3., -3.],
[ 0.,  0.,  0.],
[ 3.,  3.,  3.]])


## API

MeanCenterer()

Column centering of vectors and matrices.

Attributes

• col_means : numpy.ndarray [n_columns]

NumPy array storing the mean values for centering after fitting the MeanCenterer object.

### Methods

fit(X)

Gets the column means for mean centering.

Parameters

• X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Array of data vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

self

fit_transform(X)

Fits and transforms an arry.

Parameters

• X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Array of data vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

• X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]

A copy of the input array with the columns centered.

transform(X)

Centers a NumPy array.

Parameters

• X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Array of data vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

• X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]

A copy of the input array with the columns centered.