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g)    Ndataziris.csv.gzc                 \   U S:X  aD  [         R                  " [        SS9nUSS2SS24   USS2S4   p2UR                  [        5      nX#4$ U S:X  aR  [         R                  " [        SS9nUSS2SS24   USS2S4   p2/ SQUS'   / S	QUS
'   UR                  [        5      nX#4$ [        S5      e)u\  Iris flower dataset.

Source : https://archive.ics.uci.edu/ml/datasets/Iris
Number of samples : 150
Class labels : {0, 1, 2}, distribution: [50, 50, 50]
    0 = setosa, 1 = versicolor, 2 = virginica.

Dataset Attributes:

    - 1) sepal length [cm]
    - 2) sepal width [cm]
    - 3) petal length [cm]
    - 4) petal width [cm]


Parameters
--------
version : string, optional (default: 'uci').
  Version to use {'uci', 'corrected'}. 'uci' loads the dataset
  as deposited on the UCI machine learning repository, and
  'corrected' provides the version that is consistent with
  Fisher's original paper. See Note for details.


Returns
--------
X, y : [n_samples, n_features], [n_class_labels]
    X is the feature matrix with 150 flower samples as rows,
    and 4 feature columns sepal length, sepal width,
    petal length, and petal width.
    y is a 1-dimensional array of the class labels {0, 1, 2}


Note
--------
The Iris dataset (originally collected by Edgar Anderson) and
available in UCI's machine learning repository is different from
the Iris dataset described in the original paper by  R.A. Fisher [1]).
Precisely, there are two data points (row number
34 and 37) in UCI's Machine Learning repository are different from the
origianlly published Iris dataset. Also, the original version of the Iris
Dataset, which can be loaded via `version='corrected'` is the same
as the one in R.

[1] . A. Fisher (1936). "The use of multiple measurements in taxonomic
problems". Annals of Eugenics. 7 (2): 179–188

Examples
-----------
For usage examples, please see
https://rasbt.github.io/mlxtend/user_guide/data/iris_data/

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