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    <p>If you want to pass in the transpose, you'll need to set <code>rowvar</code> to zero.</p> <pre><code>In [10]: np.cov(someVec, rowvar=0) Out[10]: array(1.5833333333333333) In [11]: np.cov(someVec.T, rowvar=0) Out[11]: array(1.5833333333333333) </code></pre> <p>From the <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.cov.html" rel="nofollow">docs</a>:</p> <blockquote> <p><strong>rowvar</strong> : int, optional </p> <p>If rowvar is non-zero (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.</p> </blockquote> <p>If you want to find a full covariance matrix, you'll need more than one observation. With a single observation, and numpy's default estimator, <code>NaN</code> is exactly what you'd expect. If you would like to have normalization done by <code>N</code> instead of <code>(N-1)</code>, you can pass in a <code>1</code> to the bias. </p> <pre><code>In [12]: np.cov(someVec.T, bias=1) Out[12]: array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) </code></pre> <p>Again, from the docs.</p> <blockquote> <p><strong>bias</strong> : int, optional </p> <p>Default normalization is by (N - 1), where N is the number of observations given (unbiased estimate). If bias is 1, then normalization is by N. These values can be overridden by using the keyword ddof in numpy versions >= 1.5.</p> </blockquote>
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    1. COThank you, but this still returns a scalar, while what I need is a full-blown covariance matrix. See my response to NPE's comment above.
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    2. COOkay, it would have been good to make that clear in your question. Hopefully this is helpful.
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    3. COIt has occurred to me then that, with only one observation, I cannot hope to have a covariance matrix. However, the task still remains: I have a very high-dimensional vector (a bag-of-words representation of a document) and I want to reduce its dimensions in order to be able to compute similarities between itself and other, similar vectors. So it's only one observation, but has thousands of features. Would you happen to be able to suggest something? Thanks.
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