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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])
- n = 2
- a = 1/ np.sqrt((2*np.pi**k)*np.det(cx))
- b = np.exp(() )
- #from scipy.stats import multivariate_normal
- #print multivariate_normal.pdf(x,u,cx)
- #-- We are ready to perform anomaly detection ...
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