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Commit 568bb8ca authored by Amir MOHAMMADI's avatar Amir MOHAMMADI
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Drop bob.db.iris

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...@@ -56,7 +56,6 @@ test: ...@@ -56,7 +56,6 @@ test:
- sphinx {{ sphinx }} - sphinx {{ sphinx }}
- sphinx_rtd_theme {{ sphinx_rtd_theme }} - sphinx_rtd_theme {{ sphinx_rtd_theme }}
- matplotlib {{ matplotlib }} - matplotlib {{ matplotlib }}
- bob.db.iris
about: about:
home: https://www.idiap.ch/software/bob/ home: https://www.idiap.ch/software/bob/
......
...@@ -5,26 +5,18 @@ import numpy ...@@ -5,26 +5,18 @@ import numpy
from matplotlib.lines import Line2D from matplotlib.lines import Line2D
from matplotlib.patches import Ellipse from matplotlib.patches import Ellipse
from sklearn.datasets import load_iris
import bob.db.iris
from bob.learn.em import GMMMachine from bob.learn.em import GMMMachine
logger = logging.getLogger("bob.learn.em") logger = logging.getLogger("bob.learn.em")
logger.setLevel("DEBUG") logger.setLevel("DEBUG")
data_per_class = bob.db.iris.data() iris_data = load_iris()
setosa = numpy.column_stack( data = iris_data.data
(data_per_class["setosa"][:, 0], data_per_class["setosa"][:, 3]) setosa = data[iris_data.target == 0]
) versicolor = data[iris_data.target == 1]
versicolor = numpy.column_stack( virginica = data[iris_data.target == 2]
(data_per_class["versicolor"][:, 0], data_per_class["versicolor"][:, 3])
)
virginica = numpy.column_stack(
(data_per_class["virginica"][:, 0], data_per_class["virginica"][:, 3])
)
data = numpy.vstack((setosa, versicolor, virginica))
# Two clusters with a feature dimensionality of 3 # Two clusters with a feature dimensionality of 3
machine = GMMMachine( machine = GMMMachine(
......
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy
import bob.db.iris from sklearn.datasets import load_iris
from bob.learn.em import KMeansMachine from bob.learn.em import KMeansMachine
data_per_class = bob.db.iris.data() iris_data = load_iris()
setosa = numpy.column_stack( data = iris_data.data
(data_per_class["setosa"][:, 0], data_per_class["setosa"][:, 3]) setosa = data[iris_data.target == 0]
) versicolor = data[iris_data.target == 1]
versicolor = numpy.column_stack( virginica = data[iris_data.target == 2]
(data_per_class["versicolor"][:, 0], data_per_class["versicolor"][:, 3])
)
virginica = numpy.column_stack(
(data_per_class["virginica"][:, 0], data_per_class["virginica"][:, 3])
)
data = numpy.vstack((setosa, versicolor, virginica))
# Training KMeans # Training KMeans
# 3 clusters with a feature dimensionality of 2 # 3 clusters with a feature dimensionality of 2
......
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