The resulting image file will be rather large, but it will be just as crisp and clear as you expect. Strictly speaking, LaTeX source can be used to directly generate two formats: DVI using latex, the first one to be supported; PDF using pdflatex, more recent.
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Without getting too deeply mired into the linear algebra, you can see immediately that we’ve scaled down vectors such that each element is between [0, 1], without losing too much valuable information. Instead, let’s assume the documents are stored in a file on disk, one document per line. Let’s call this a first stab at representing documents quantitatively, just by their word counts.
Let’s nail down some basic concepts in Python first. Draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest Name Email. Run latex as usual to generate the dvi file. Make sure the Set Default Target Output control is set to 220 ppi.
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The more negative a term, the more frequent it is. We’re almost there. To get TF-IDF weighted word vectors, you have to perform the simple calculation of tf * idf. Corpus Streaming – One Document at a Time Note that corpus above resides fully in memory, as a plain Python list. In this simple example, it doesn’t matter much, but just to make things clear, let’s assume there are millions of documents in the corpus.
Now we have converted our IDF vector into a matrix of size BxB, where the diagonal is the IDF vector. Tags make it easier for you to find threads of interest.
Here comes the best part: you don’t even have to do this by hand. This results in the highest resolution (provided your images are higher resolution than 220 dpi), but it also results in the largest document file sizes. The easiest way to start is to think about word frequencies. I’m going to try and stay away from NLTK and Scikits-Learn for as much as I can.
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