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- >>> import numpy as np
- >>> x=np.random.normal(size=25)
- >>> y=np.random.normal(size=25)
- >>> np.cov(x,y)
- array([[ 0.77568388, 0.15568432],
- [ 0.15568432, 0.73839014]])
- >>> np.cov(x,y,rowvar=0)
- array([[ 0.77568388, 0.15568432],
- [ 0.15568432, 0.73839014]])
- z = zip(x,y)
- np.cov(z)
- n=100 # number of points in each vector
- num_vects=25
- vals=[]
- for _ in range(num_vects):
- vals.append(np.random.normal(size=n))
- np.cov(vals)
- import numpy as np
- x=np.random.normal(size=25)
- y=np.random.normal(size=25)
- z = np.vstack((x, y))
- c = np.cov(z.T)
- >> np.cov.__doc__
- def autocovariance(Xi, N, k):
- Xs=np.average(Xi)
- aCov = 0.0
- for i in np.arange(0, N-k):
- aCov = (Xi[(i+k)]-Xs)*(Xi[i]-Xs)+aCov
- return (1./(N))*aCov
- autocov[i]=(autocovariance(My_wector, N, h))
- np.cov(x,y, rowvar=0)
- np.cov((x,y), rowvar=0)
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