scikit-learn · njali2001 · Sep 14, 2017 · Sep 14, 2017 · Sep 14, 2017 · Sep 14, 2017
diff --git a/build_tools/travis/install.sh b/build_tools/travis/install.sh
@@ -56,6 +56,14 @@ if [[ "$DISTRIB" == "conda" ]]; then
     # Install nose-timer via pip
     pip install nose-timer
 
+    pip install matplotlib
+
+    pip install seaborn
+
+    pip install statsmodels
+
+
+
 elif [[ "$DISTRIB" == "ubuntu" ]]; then
     # At the time of writing numpy 1.9.1 is included in the travis
     # virtualenv but we want to use the numpy installed through apt-get

diff --git a/examples/som/README.txt b/examples/som/README.txt
@@ -0,0 +1,6 @@
+.. _som_examples:
+
+Self Organized Map
+--------------
+
+Examples concerning the :mod:`sklearn.som` module.
diff --git a/examples/som/build.py b/examples/som/build.py
@@ -0,0 +1,20 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
diff --git a/examples/som/convergence.py b/examples/som/convergence.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# map quality
+som.convergence(m)
diff --git a/examples/som/embed.py b/examples/som/embed.py
@@ -0,0 +1,26 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display the embedding accuracy of the map
+som.embed(m)
+
+# display the embedding accuracies of the individual features
+som.embed(m, verb=True)
diff --git a/examples/som/marginal.py b/examples/som/marginal.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display marginal distribution of 3rd dimension
+som.marginal(m, 2)
diff --git a/examples/som/neuron.py b/examples/som/neuron.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display the neuron at position (4,4)
+som.neuron(m, 3, 3)
diff --git a/examples/som/projection.py b/examples/som/projection.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display the label association for the map
+som.projection(m)
diff --git a/examples/som/significance.py b/examples/som/significance.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display the relative feature significance graphically
+som.significance(m)
diff --git a/examples/som/starburst.py b/examples/som/starburst.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display the starburst for the map
+som.starburst(m)
diff --git a/examples/som/topo.py b/examples/som/topo.py
@@ -0,0 +1,23 @@
+import som
+import pandas as pd
+from sklearn import datasets
+
+# import iris datasets
+iris = datasets.load_iris()
+
+# initialize label, if there is no label set labels = None
+labels = iris.target
+
+# initialize data
+data = pd.DataFrame(iris.data[:, :4])
+data.columns = iris.feature_names
+
+# som written entirely in python, som_f written by fortran,
+# which is much faster than python
+algorithm = "som"
+
+# Build a map
+m = som.build(data, labels, algorithm=algorithm)
+
+# display estimated topographical accuracy of the map
+som.topo(m)