Add FastLOF algorithm

docs/zreferences.bib

@@ -85,6 +85,16 @@ @article{goldstein2012histogram
   publisher={Citeseer}
 }
 
+@inproceedings{goldstein2012fastlof,
+  title={FastLOF: An expectation-maximization based local outlier detection algorithm},
+  author={Goldstein, Markus},
+  booktitle={2012 21st International Conference on Pattern Recognition (ICPR 2012)},
+  pages={2282--2285},
+  year={2012},
+  organization={IEEE},
+  doi={10.1109/ICPR.2012.3942}
+}
+
 @techreport{shyu2003novel,
   title={A novel anomaly detection scheme based on principal component classifier},
   author={Shyu, Mei-Ling and Chen, Shu-Ching and Sarinnapakorn, Kanoksri and Chang, LiWu},

examples/fastlof_example.py

@@ -0,0 +1,58 @@
+# -*- coding: utf-8 -*-
+"""Example of using FastLOF for outlier detection
+"""
+# Author: Alaa Abdelwahab
+# License: BSD 2 clause
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+# temporary solution for relative imports in case pyod is not installed
+# if pyod is installed, no need to use the following line
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+sys.path.append('.')
+
+from pyod.models.fastlof import FastLOF
+from pyod.utils.data import generate_data
+from pyod.utils.data import evaluate_print
+from pyod.utils.example import visualize
+
+if __name__ == "__main__":
+    contamination = 0.1  # percentage of outliers
+    n_train = 200  # number of training points
+    n_test = 100  # number of testing points
+
+    # Generate sample data
+    X_train, X_test, y_train, y_test = \
+        generate_data(n_train=n_train,
+                      n_test=n_test,
+                      n_features=2,
+                      contamination=contamination,
+                      random_state=42)
+
+    # train FastLOF detector
+    clf_name = 'FastLOF'
+    clf = FastLOF(contamination=contamination)
+    clf.fit(X_train)
+
+    # get the prediction labels and outlier scores of the training data
+    y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
+    y_train_scores = clf.decision_scores_  # raw outlier scores
+
+    # get the prediction on the test data
+    y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
+    y_test_scores = clf.decision_function(X_test)  # outlier scores
+
+    # evaluate and print the results
+    print("\nOn Training Data:")
+    evaluate_print(clf_name, y_train, y_train_scores)
+    print("\nOn Test Data:")
+    evaluate_print(clf_name, y_test, y_test_scores)
+
+    # visualize the results
+    visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
+              y_test_pred, show_figure=True, save_figure=False)