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mutest, sigma2test = estimateGaussian(Xtest) | |
ptest = multivariateGaussian(Xtest, mutest, sigma2test) | |
pvaltest = multivariateGaussian(Xvaltest, mutest, sigma2test) | |
F1test, epsilontest = selectThreshHold(yvaltest, pvaltest) | |
print('\nBest epsilon and F1 are\n',epsilontest, F1test) |
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outliersTest = ptest < epsilontest | |
listOfOl = findIndices(outliersTest) | |
print('\n\n Outliers are:\n',listOfOl) | |
print('\n\nNumber of outliers are: ',len(listOfOl)) |
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mutest, sigma2test = estimateGaussian(Xtest) | |
ptest = multivariateGaussian(Xtest, mutest, sigma2test) | |
pvaltest = multivariateGaussian(Xvaltest, mutest, sigma2test) | |
F1test, epsilontest = selectThreshHold(yvaltest, pvaltest) | |
print('\nBest epsilon and F1 are\n',epsilontest, F1test) |
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newDataset = sio.loadmat('anomalyDataTest.mat') | |
Xtest = newDataset['X'] | |
Xvaltest = newDataset['Xval'] | |
yvaltest = newDataset['yval'] |
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plt.scatter(X[:, 0], X[:, 1], marker = "x") | |
plt.xlabel('Latency(ms)') | |
plt.ylabel('Throughput(mb/s)') | |
plt.scatter(X[listOfOutliers,0], X[listOfOutliers, 1], facecolors = 'none', edgecolors = 'r') | |
plt.show() |
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count_outliers = len(listOfOutliers) | |
print('\n\nNumber of outliers:', count_outliers) | |
print('\n',listOfOutliers) |
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def findIndices(binVec): | |
l = [] | |
for i in range(len(binVec)): | |
if binVec[i] == 1: | |
l.append(i) | |
return l |
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def selectThreshHold(yval, pval): | |
F1 = 0 | |
bestF1 = 0 | |
bestEpsilon = 0 | |
stepsize = (np.max(pval) - np.min(pval))/1000 | |
epsVec = np.arange(np.min(pval), np.max(pval), stepsize) | |
noe = len(epsVec) |
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def multivariateGaussian(X, mu, sigma2): | |
n = np.size(sigma2, 1) | |
m = np.size(sigma2, 0) | |
#print(m,n) | |
if n == 1 or m == 1: | |
# print('Yes!') | |
sigma2 = np.diag(sigma2[0, :]) | |
#print(sigma2) | |
X = X - mu |
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def estimateGaussian(X): | |
n = np.size(X, 1) | |
m = np.size(X, 0) | |
mu = np.zeros((n, 1)) | |
sigma2 = np.zeros((n, 1)) | |
mu = np.reshape((1/m)*np.sum(X, 0), (1, n)) | |
sigma2 = np.reshape((1/m)*np.sum(np.power((X - mu),2), 0),(1, n)) | |
return mu, sigma2 |
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