Added Example to README

README.rst

@@ -34,6 +34,35 @@ Sacred achieves this through the following main mechanisms:
 -  **Automatic seeding** helps controlling the randomness in your experiments,
    such that the results remain reproducible.
 
+Example
+-------
++------------------------------------------------+--------------------------------------------+
+| **Script to train an SVM on the Iris Dataset** | **The same Script as a Sacred Experiment** |
++------------------------------------------------+--------------------------------------------+
+| .. code:: python                               | .. code:: python                           |
+|                                                |                                            |
+|  from numpy.random import permutation          |   from numpy.random import permutation     |
+|  from sklearn import svm, datasets             |   from sklearn import svm, datasets        |
+|                                                |   from sacred import Experiment            |
+|                                                |   ex = Experiment('iris_rbf_svm')          |
+|                                                |                                            |
+|                                                |   @ex.config                               |
+|                                                |   def cfg():                               |
+|  C = 1.0                                       |       C = 1.0                              |
+|  gamma = 0.7                                   |       gamma = 0.7                          |
+|                                                |                                            |
+|                                                |   @ex.automain                             |
+|                                                |   def run(C, gamma):                       |
+|  iris = datasets.load_iris()                   |       iris = datasets.load_iris()          |
+|  perm = permutation(iris.target.size)          |       per = permutation(iris.target.size)  |
+|  iris.data = iris.data[perm]                   |       iris.data = iris.data[per]           |
+|  iris.target = iris.target[perm]               |       iris.target = iris.target[per]       |
+|  clf = svm.SVC(C, 'rbf', gamma=gamma)          |       clf = svm.SVC(C, 'rbf', gamma=gamma) |
+|  clf.fit(iris.data[:90],                       |       clf.fit(iris.data[:90],              |
+|          iris.target[:90])                     |               iris.target[:90])            |
+|  print(clf.score(iris.data[90:],               |       return clf.score(iris.data[90:],     |
+|                  iris.target[90:]))            |                        iris.target[90:])   |
++------------------------------------------------+--------------------------------------------+
 
 Documentation
 -------------