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26 lines
656 B
Python
26 lines
656 B
Python
import numpy as np
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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]]
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m_ = np.array(m)
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x_ = np.mean(m_[:,0])
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y_ = np.mean(m_[:,1])
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u = np.array([x_,y_])
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r = [np.sqrt(np.var(m_[:,0])),np.sqrt(np.var(m_[:,1]))]
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x__ = (m_[:,0] - x_ )/r[0]
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y__ = (m_[:,1] - y_ )/r[1]
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nm = np.matrix([x__,y__])
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cx = np.cov(nm)
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print cx.shape
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x = np.array([1.9,3])
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n = 2
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a = 1/ np.sqrt((2*np.pi**k)*np.det(cx))
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b = np.exp(() )
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#from scipy.stats import multivariate_normal
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#print multivariate_normal.pdf(x,u,cx)
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#-- We are ready to perform anomaly detection ...
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