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Don't run block code in doc tests of word_pair_proximity_docids
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@ -12,7 +12,7 @@ different words in the documents.
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To compute it, we have access to (mainly) two inputs:
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* a list of sorted prefixes, such as:
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```
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```text
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c
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ca
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cat
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@ -25,7 +25,7 @@ different words from the database are included in this list.
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* a sorted list of word pairs and the distance between them (i.e. proximity),
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* associated with a roaring bitmap, such as:
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```
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```text
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good dog 3 -> docids1: [2, 5, 6]
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good doggo 1 -> docids2: [8]
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good dogma 1 -> docids3: [7, 19, 20]
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@ -37,7 +37,7 @@ I illustrate a simplified version of the algorithm to create the word-prefix
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pair-proximity database below:
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1. **Outer loop:** First, we iterate over each word pair and its proximity:
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```
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```text
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word1 : good
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word2 : dog
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proximity: 3
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@ -46,7 +46,7 @@ proximity: 3
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in the list of sorted prefixes. And we insert the key (`prefix`, `proximity`)
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and the value (`docids`) to a sorted map which we call the “batch”. For example,
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at the end of the first inner loop, we may have:
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```
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```text
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Outer loop 1:
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------------------------------
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word1 : good
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@ -63,7 +63,7 @@ batch: [
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]
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```
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3. For illustration purpose, let's run through a second iteration of the outer loop:
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```
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```text
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Outer loop 2:
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------------------------------
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word1 : good
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@ -87,7 +87,7 @@ of the elements inserted in the second iteration of the outer loop appear
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*before* elements from the first iteration.
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4. And a third:
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```
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```text
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Outer loop 3:
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------------------------------
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word1 : good
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@ -110,7 +110,7 @@ Notice that there were some conflicts which were resolved by merging the
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conflicting values together.
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5. On the fourth iteration of the outer loop, we have:
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```
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```text
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Outer loop 4:
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------------------------------
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word1 : good
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@ -131,7 +131,7 @@ is different than the previous `word1`.
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6. **Flushing the batch:** to flush the batch, we look at the `word1` and
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iterate over the elements of the batch in sorted order:
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```
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```text
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Flushing Batch loop 1:
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------------------------------
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word1 : good
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