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- from __future__ import division
- import numpy as np
- from utils.ml import AnomalyDetection
- mo = [[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.transpose(np.array(mo))
- xu_ = np.mean(m[0,:])
- yu_ = np.mean(m[1,:])
- xr_ = np.sqrt(np.var(m[0,:]))
- yr_ = np.sqrt(np.var(m[1,:]))
- #
- # -- normalizing the matrix before computing covariance
- #
- mn = np.array([list( (m[0,:]-xu_)/xr_),list( (m[1,:]-yu_)/yr_)])
- cx = np.cov(mn)
- n = m.shape[0]
- test=[2.4,3.1]
- x = np.array(test)
- u = np.array([xu_,yu_])
- d = np.matrix(x - u)
- d.shape = (n,1)
- a = (2*(np.pi)**(n/2))*np.linalg.det(cx)**0.5
- b = np.exp((-0.5*np.transpose(d)) * (np.linalg.inv(cx)*d))
- print u.shape
- print cx.shape
- from scipy.stats import multivariate_normal
- xo= multivariate_normal.pdf(x,u,cx)
- yo= (b/a)[0,0]
- e= np.float64(0.05)
- print [yo,yo < e]
- print [xo,xo < e]
- ml = AnomalyDetection()
- end = int(len(mo)*.7)
- mu,sigma = ml.gParameters(mo)
- r = ml.gPx(mu,sigma,[test],0.05)
- for i in range(0,len(r)) :
- print ' *** ', mo[(i+end)],r[i]
- #for row in np.transpose(m):
- # print ",".join([str(value) for value in row])
- #-- We are ready to perform anomaly detection ...
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