Users Guide

A description of the feature vector can be obtained using the attribute bob.db.iris.names.

>>> descriptor_labels = bob.db.iris.names
>>> descriptor_labels
['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']

The data (feature vectors) can be retrieved using the bob.db.iris.data() function. This returns a 3-key dictionary, with 3 numpy.ndarray as values, one for each of the three species of Iris flowers.

>>> data = bob.db.iris.data()
>>> type(data['setosa'])
<... 'numpy.ndarray'>
>>> data['setosa'].shape
(50, 4)
>>> list(data.keys()) 
['setosa', 'versicolor', 'virginica']

Each numpy.ndarray consists of 50 feature vectors of length four.

The database also contains statistics about the feature vectors, which can be obtained using the bob.db.iris.stats dictionary. A description of these statistics is provided by bob.db.iris.stat_names.

Classifying the Iris Flowers with LDA

As an exemplary use case, we provide a script iris_lda.py that computes a Linear Discriminant Analysis (LDA) using the bob.learn.linear.FisherLDATrainer using all data vectors. Afterward, it classifies all training data and plots histograms of the data projected on the first LDA component. A detailed explanation of this example script is given in Bob‘s main documentation page.