Update README.md

Author: iqiukp

README.md

@@ -1,2 +1,167 @@
-# SVDD
- Python code for abnormal detection or fault detection using  Support Vector Data Description (SVDD)
+# Support Vector Data Description (SVDD)
+
+Python Code for abnormal detection or fault detection using SVDD.
+
+Version 1.0, 1-DEC-2019
+    
+Email: iqiukp@outlook.com
+
+-------------------------------------------------------------------
+
+## Main features
+
+* SVDD model for training dataset containing only positive training data. (SVDD)
+* SVDD model for training dataset containing both positive training data and negative training data. (nSVDD)
+* Multiple kinds of kernel functions.
+* Visualization module including ROC curve plotting, test result plotting, and decision boundary.
+
+-------------------------------------------------------------------
+
+## Requirements
+
+* matplotlib
+* cvxopt
+* scipy
+* numpy
+* scikit_learn
+
+-------------------------------------------------------------------
+
+## About SVDD model
+
+Two types of SVDD models are built according to the following references:
+
+[1]    Tax D M J, Duin R P W. Support vector data description[J]. Machine learning, 2004, 54(1): 45-66.
+
+-------------------------------------------------------------------
+
+## A simple application for decision boundary (using differnent kernel functions)
+
+```
+
+# -*- coding: utf-8 -*-
+
+import sys
+sys.path.append("..")
+from src.svdd import SVDD
+from src.visualize import Visualization as draw
+from data import PrepareData as load
+
+# load banana-shape data
+trainData, testData, trainLabel, testLabel = load.banana()
+
+
+# kernel list
+kernelList = {"1": {"type": 'gauss', "width": 1/24},
+              "2": {"type": 'linear', "offset": 0},
+              "3": {"type": 'ploy', "degree": 2, "offset": 0},
+              "4": {"type": 'tanh', "gamma": 1e-4, "offset": 0},
+              "5": {"type": 'lapl', "width": 1/12}
+              }
+
+
+for i in range(len(kernelList)):
+
+    # set SVDD parameters
+    parameters = {"positive penalty": 0.9,
+                  "negative penalty": 0.8,
+                  "kernel": kernelList.get(str(i+1)),
+                  "option": {"display": 'on'}}
+    
+    # construct an SVDD model
+    svdd = SVDD(parameters)
+    
+    # train SVDD model
+    svdd.train(trainData, trainLabel)
+      
+    # test SVDD model
+    distance, accuracy = svdd.test(testData, testLabel)
+    
+    # visualize the results
+    # draw.testResult(svdd, distance)
+    # draw.testROC(testLabel, distance)
+    draw.boundary(svdd, trainData, trainLabel)
+
+```
+
+* gaussian kernel function
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/kernel_gauss.png" width="720">
+</p>
+
+* linear kernel function
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/kernel_linear.png" width="720">
+</p>
+
+* polynomial kernel function
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/kernel_ploy.png" width="720">
+</p>
+
+* sigmoid kernel function
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/kernel_tanh.png" width="720">
+</p>
+
+* laplacian kernel function
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/kernel_lapl.png" width="720">
+</p>
+
+
+## A simple application for abnormal detection or fault detection
+
+```
+
+# -*- coding: utf-8 -*-
+
+import sys
+sys.path.append("..")
+from src.svdd import SVDD
+from src.visualize import Visualization as draw
+from data import PrepareData as load
+
+# load banana-shape data
+trainData, testData, trainLabel, testLabel = load.iris()
+
+# set SVDD parameters
+parameters = {"positive penalty": 0.9,
+              "negative penalty": 0.8,
+              "kernel": {"type": 'gauss', "width": 1/24},
+              "option": {"display": 'on'}}
+
+
+# construct an SVDD model
+svdd = SVDD(parameters)
+
+# train SVDD model
+svdd.train(trainData, trainLabel)
+
+
+# test SVDD model
+distance, accuracy = svdd.test(testData, testLabel)
+
+# visualize the results
+draw.testResult(svdd, distance)
+draw.testROC(testLabel, distance)
+
+```
+
+* test result
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/hybrid_result.png" width="720">
+</p>
+
+* ROC curve
+
+<p align="middle">
+  <img src="https://github.com/iqiukp/SVDD/blob/master/imgs/hybrid_roc.png" width="480">
+</p>
+