def detectProcessAnomalies(passedData, username):
# Get the trained data set
trainedData = DatabaseInteractionClass.getProcessLearnedData(username)
#scaledTrainingData = []
#scaledPassedData = []
#for point in trainedData:
# scaledTrainingData.append(AnomalyDetectionClass.scalePoint(point))
#for point in passedData:
# scaledPassedData.append(AnomalyDetectionClass.scalePoint(point))
print "in detect process anomalies"
print "training data:"
print trainedData
print "input data:"
print passedData
npTrainedData = np.array(trainedData)
#npTrainedData = np.array(scaledTrainingData)
# Generate the model, and fit the trained data to it
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.00005)
clf.fit(npTrainedData)
# This should return an array of 1's and -1's (in float64 form), the -1's corresponding to failures
return clf.predict(passedData)
print DIM.isLearningMode("zlbales")
print DIM.isLearningMode("cbcullen")
#integer version of Zach's IP (129.186.251.4) at time of writing test
print DIM.getLatLongFromIP(2176514820)
#integer version of Google's IP address for DNS server (8.8.8.8)
print DIM.getLatLongFromIP((8 * 256 * 256 * 256) + (8 * 256 * 256) + (8 * 256) + 8)
print DIM.setScore("zlbales", 99, 10)
print DIM.setScore("zlbales", 98, 20)
print DIM.setScore("zlbales", 97, 30)
print DIM.setScore("zlbales", 100, 0)
print DIM.getProcessLearnedData("zlbales")
print DIM.getProcessLearnedData("cbcullen")
print DIM.getProcessLearnedData("unknown")
print DIM.getFileLearnedData("zlbales")
print DIM.getFileLearnedData("cbcullen")
print DIM.getFileLearnedData("unknown")
print DIM.getNetworkLearnedData("zlbales")
print DIM.getNetworkLearnedData("cbcullen")
print DIM.getNetworkLearnedData("unknown")
vector = []
for i in range(1,88):
vector.append(i)
print DIM.insertFileLearningData("zlbales",-1,vector)
