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Suppose I have two vectors of length 25, and I want to compute their covariance matrix. I try doing this with numpy.cov, but always end up with a 2x2 matrix.
>>> 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]]) Using the rowvar flag doesn't help either - I get exactly the same result.
>>> np.cov(x,y,rowvar=0) array([[ 0.77568388, 0.15568432], [ 0.15568432, 0.73839014]]) How can I get the 25x25 covariance matrix?
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In NumPy for computing the covariance matrix of two given arrays with help of numpy. cov(). In this, we will pass the two arrays and it will return the covariance matrix of two given arrays.
covMatrix = np. cov(data,bias=True) # bias = True ,to get the population covarince matrix based on N. In the above example, we create a dataset with A, B, C columns using numpy library. To get the population covariance matrix (based on N)we had mentioned ,bias = True in cov() function.
Covariance between 2 random variables is calculated by taking the product of the difference between the value of each random variable and its mean, summing all the products, and finally dividing it by the number of values in the dataset.
You have two vectors, not 25. The computer I'm on doesn't have python so I can't test this, but try:
z = zip(x,y) np.cov(z) Of course.... really what you want is probably more like:
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) This takes the covariance (I think/hope) of num_vects 1xn vectors
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Try this:
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) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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