fetch_olivetti_faces#
- sklearn.datasets.fetch_olivetti_faces(*,data_home=None,shuffle=False,random_state=0,download_if_missing=True,return_X_y=False,n_retries=3,delay=1.0)[source]#
Load the Olivetti faces data-set from AT&T (classification).
Download it if necessary.
Classes
40
Samples total
400
Dimensionality
4096
Features
real, between 0 and 1
Read more in theUser Guide.
- Parameters:
- data_homestr or path-like, default=None
Specify another download and cache folder for the datasets. By defaultall scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
- shufflebool, default=False
If True the order of the dataset is shuffled to avoid havingimages of the same person grouped.
- random_stateint, RandomState instance or None, default=0
Determines random number generation for dataset shuffling. Pass an intfor reproducible output across multiple function calls.SeeGlossary.
- download_if_missingbool, default=True
If False, raise an OSError if the data is not locally availableinstead of trying to download the data from the source site.
- return_X_ybool, default=False
If True, returns
(data,target)instead of aBunchobject. Seebelow for more information about thedataandtargetobject.Added in version 0.22.
- n_retriesint, default=3
Number of retries when HTTP errors are encountered.
Added in version 1.5.
- delayfloat, default=1.0
Number of seconds between retries.
Added in version 1.5.
- Returns:
- data
Bunch Dictionary-like object, with the following attributes.
- data: ndarray, shape (400, 4096)
Each row corresponds to a ravelledface image of original size 64 x 64 pixels.
- imagesndarray, shape (400, 64, 64)
Each row is a face imagecorresponding to one of the 40 subjects of the dataset.
- targetndarray, shape (400,)
Labels associated to each face image.Those labels are ranging from 0-39 and correspond to theSubject IDs.
- DESCRstr
Description of the modified Olivetti Faces Dataset.
- (data, target)tuple if
return_X_y=True Tuple with the
dataandtargetobjects described above.Added in version 0.22.
- data
Examples
>>>fromsklearn.datasetsimportfetch_olivetti_faces>>>olivetti_faces=fetch_olivetti_faces()>>>olivetti_faces.data.shape(400, 4096)>>>olivetti_faces.target.shape(400,)>>>olivetti_faces.images.shape(400, 64, 64)
