anomaly detection first stab

test/TestML.py

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+from utils import transport
+from utils.ml import ML
+import unittest
+import json
+import os
+
+
+path = os.environ['MONITOR_CONFIG_PATH']
+f = open(path)
+CONFIG = json.loads( f.read())
+f.close()
+factory = transport.DataSourceFactory()
+#greader = factory.instance(type=ref,args=p)
+
+class TestML(unittest.TestCase):
+	def setUp(self):
+		
+		ref = CONFIG['store']['class']['read']
+		p = CONFIG['store']['args']
+		p['qid'] = ['apps']
+		self.greader = factory.instance(type=ref,args=p)
+	def test_has_date(self):
+		r = self.greader.read()
+		
+		self.assertTrue(r)
+	def test_Filter(self):
+		r = self.greader.read()
+		r = r['apps']
+		x = ML.Filter('label','Google Chrome',r)
+		for row in x:
+			self.assertTrue(row['label'] == 'Google Chrome')
+	def test_Extract(self):
+		r = self.greader.read()
+		r = r['apps']
+		x = ML.Filter('label','Google Chrome',r)
+		x_ = ML.Extract(['cpu_usage','memory_usage'], x)
+		print x[0]
+		print x_
+		pass
+		
+
+if __name__ == '__main__' :
+	unittest.main()

test/demo.py

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+import numpy as np
+m = [[0.0, 4.5], [0.0, 4.5], [11.6, 4.4], [12.2, 4.3], [1.4, 3.9], [1.4, 3.9], [2.5, 3.8], [0.1, 3.8], [0.5, 5.1], [0.7, 5.2], [0.7, 5.1], [0.0, 4.6], [0.0, 4.6]]
+
+m_ = np.array(m)
+x_ = np.mean(m_[:,0])
+y_ = np.mean(m_[:,1])
+u = np.array([x_,y_])
+r = [np.sqrt(np.var(m_[:,0])),np.sqrt(np.var(m_[:,1]))]
+x__ = (m_[:,0] - x_ )/r[0]
+y__ = (m_[:,1] - y_ )/r[1]
+
+nm = np.matrix([x__,y__])
+
+
+cx = np.cov(nm)
+print cx.shape
+x = np.array([1.9,3])
+
+a = 1/ np.sqrt(2*np.pi)
+#from scipy.stats import multivariate_normal
+#print multivariate_normal.pdf(x,u,cx)
+
+
+#-- We are ready to perform anomaly detection ...