mhjensen
35 lines
875 B
Plaintext
35 lines
875 B
Plaintext
#The covariance matrix and its eigenvalues the hard way
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from random import random, seed
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import numpy as np
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def covariance(x, y, n):
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sum = 0.0
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mean_x = np.mean(x)
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mean_y = np.mean(y)
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for i in range(0, n):
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sum += (x[(i)]-mean_x)*(y[i]-mean_y)
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return sum/n
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n = 10
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x = np.random.normal(size=n)
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y = np.random.normal(size=n)
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z = x*x*x+y*y 0.5*np.random.normal(size=n)
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covxx = covariance(x,x,n)
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covxy = covariance(x,y,n)
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covxz = covariance(x,z,n)
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covyy = covariance(y,y,n)
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covyz = covariance(y,z,n)
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covzz = covariance(z,z,n)
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SigmaCov = np.array([ [covxx, covxy, covxz], [covxy, covyy, covyz], [covxz, covyz, covzz]])
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print(SigmaCov)
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EigValues, EigVectors = np.linalg.eig(SigmaCov)
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# sort eigenvectors and eigenvalues
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permute = EigValues.argsort()
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EigValues = EigValues[permute]
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EigVectors = EigVectors[:,permute]
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print(EigValues)
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print(EigVectors)
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