why does numpy normalize these equivalent arrays differently? : <p>The two arrays <code>raw_document_scores</code> and <code>raw_doc_scores1 are equal</code>, evidenced by <code>raw_document_scores==raw_doc_scores1</code> returning an array of all trues. However, when I check if </p>

<pre><code>raw_document_scores/np.linalg.norm(raw_document_scores, 1, axis=1).reshape(-1,1) \
== raw_doc_scores1/np.linalg.norm(raw_doc_scores1, 1, axis=1).reshape(-1,1)
</code></pre>

<p>I get false for some entries. I subtracted the two arrays to see how much each entry differs by, and it's less than e-18, so they are approximately equal. However, why are they not exactly equal, if the input arrays are exactly equal? Are the first two arrays not actually equal, and python/numpy is lying when it says all their entries are equal? Here is an image to show what happened.</p>

<p><a href="https://i.stack.imgur.com/7mYLJ.png" rel="nofollow noreferrer">difference</a></p>
