We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
BM25 : Incorrect scoring function
https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/summarization/bm25.py
**Instead of "len(document)" it should be the length of the _index_ document of the corpus.**
  
```
 def get_score(self, document, index, average_idf): 
           # in this line it should be the length of the index document in the corpus
            score += (idf * self.f[index][word] * (PARAM_K1 + 1)
                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))
```

BM25 : Incorrect scoring function
https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/summarization/bm25.py
**Instead of "len(document)" it should be the length of the _index_ document of the corpus.**
  
```
 def get_score(self, document, index, average_idf): 
           # in this line it should be the length of the index document in the corpus
            score += (idf * self.f[index][word] * (PARAM_K1 + 1)
                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))
```


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `gensim/summarization/bm25.py`
Content:
```
1 #!/usr/bin/env python
2 # -*- coding: utf-8 -*-
3 #
4 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
5 
6 """This module contains function of computing rank scores for documents in
7 corpus and helper class `BM25` used in calculations. Original alhorithm
8 descibed in [1]_, also you may check Wikipedia page [2]_.
9 
10 
11 .. [1] Robertson, Stephen; Zaragoza, Hugo (2009).  The Probabilistic Relevance Framework: BM25 and Beyond,
12        http://www.staff.city.ac.uk/~sb317/papers/foundations_bm25_review.pdf
13 .. [2] Okapi BM25 on Wikipedia, https://en.wikipedia.org/wiki/Okapi_BM25
14 
15 
16 
17 Examples
18 --------
19 >>> from gensim.summarization.bm25 import get_bm25_weights
20 >>> corpus = [
21 ...     ["black", "cat", "white", "cat"],
22 ...     ["cat", "outer", "space"],
23 ...     ["wag", "dog"]
24 ... ]
25 >>> result = get_bm25_weights(corpus)
26 
27 
28 Data:
29 -----
30 .. data:: PARAM_K1 - Free smoothing parameter for BM25.
31 .. data:: PARAM_B - Free smoothing parameter for BM25.
32 .. data:: EPSILON - Constant used for negative idf of document in corpus.
33 
34 """
35 
36 
37 import math
38 from six import iteritems
39 from six.moves import xrange
40 
41 
42 PARAM_K1 = 1.5
43 PARAM_B = 0.75
44 EPSILON = 0.25
45 
46 
47 class BM25(object):
48     """Implementation of Best Matching 25 ranking function.
49 
50     Attributes
51     ----------
52     corpus_size : int
53         Size of corpus (number of documents).
54     avgdl : float
55         Average length of document in `corpus`.
56     corpus : list of list of str
57         Corpus of documents.
58     f : list of dicts of int
59         Dictionary with terms frequencies for each document in `corpus`. Words used as keys and frequencies as values.
60     df : dict
61         Dictionary with terms frequencies for whole `corpus`. Words used as keys and frequencies as values.
62     idf : dict
63         Dictionary with inversed terms frequencies for whole `corpus`. Words used as keys and frequencies as values.
64 
65     """
66 
67     def __init__(self, corpus):
68         """
69         Parameters
70         ----------
71         corpus : list of list of str
72             Given corpus.
73 
74         """
75         self.corpus_size = len(corpus)
76         self.avgdl = sum(float(len(x)) for x in corpus) / self.corpus_size
77         self.corpus = corpus
78         self.f = []
79         self.df = {}
80         self.idf = {}
81         self.initialize()
82 
83     def initialize(self):
84         """Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies."""
85         for document in self.corpus:
86             frequencies = {}
87             for word in document:
88                 if word not in frequencies:
89                     frequencies[word] = 0
90                 frequencies[word] += 1
91             self.f.append(frequencies)
92 
93             for word, freq in iteritems(frequencies):
94                 if word not in self.df:
95                     self.df[word] = 0
96                 self.df[word] += 1
97 
98         for word, freq in iteritems(self.df):
99             self.idf[word] = math.log(self.corpus_size - freq + 0.5) - math.log(freq + 0.5)
100 
101     def get_score(self, document, index, average_idf):
102         """Computes BM25 score of given `document` in relation to item of corpus selected by `index`.
103 
104         Parameters
105         ----------
106         document : list of str
107             Document to be scored.
108         index : int
109             Index of document in corpus selected to score with `document`.
110         average_idf : float
111             Average idf in corpus.
112 
113         Returns
114         -------
115         float
116             BM25 score.
117 
118         """
119         score = 0
120         for word in document:
121             if word not in self.f[index]:
122                 continue
123             idf = self.idf[word] if self.idf[word] >= 0 else EPSILON * average_idf
124             score += (idf * self.f[index][word] * (PARAM_K1 + 1)
125                       / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))
126         return score
127 
128     def get_scores(self, document, average_idf):
129         """Computes and returns BM25 scores of given `document` in relation to
130         every item in corpus.
131 
132         Parameters
133         ----------
134         document : list of str
135             Document to be scored.
136         average_idf : float
137             Average idf in corpus.
138 
139         Returns
140         -------
141         list of float
142             BM25 scores.
143 
144         """
145         scores = []
146         for index in xrange(self.corpus_size):
147             score = self.get_score(document, index, average_idf)
148             scores.append(score)
149         return scores
150 
151 
152 def get_bm25_weights(corpus):
153     """Returns BM25 scores (weights) of documents in corpus.
154     Each document has to be weighted with every document in given corpus.
155 
156     Parameters
157     ----------
158     corpus : list of list of str
159         Corpus of documents.
160 
161     Returns
162     -------
163     list of list of float
164         BM25 scores.
165 
166     Examples
167     --------
168     >>> from gensim.summarization.bm25 import get_bm25_weights
169     >>> corpus = [
170     ...     ["black", "cat", "white", "cat"],
171     ...     ["cat", "outer", "space"],
172     ...     ["wag", "dog"]
173     ... ]
174     >>> result = get_bm25_weights(corpus)
175 
176     """
177     bm25 = BM25(corpus)
178     average_idf = sum(float(val) for val in bm25.idf.values()) / len(bm25.idf)
179 
180     weights = []
181     for doc in corpus:
182         scores = bm25.get_scores(doc, average_idf)
183         weights.append(scores)
184 
185     return weights
186
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/gensim/summarization/bm25.py b/gensim/summarization/bm25.py
--- a/gensim/summarization/bm25.py
+++ b/gensim/summarization/bm25.py
@@ -4,7 +4,7 @@
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
 """This module contains function of computing rank scores for documents in
-corpus and helper class `BM25` used in calculations. Original alhorithm
+corpus and helper class `BM25` used in calculations. Original algorithm
 descibed in [1]_, also you may check Wikipedia page [2]_.
 
 
@@ -61,7 +61,8 @@
         Dictionary with terms frequencies for whole `corpus`. Words used as keys and frequencies as values.
     idf : dict
         Dictionary with inversed terms frequencies for whole `corpus`. Words used as keys and frequencies as values.
-
+    doc_len : list of int
+        List of document lengths.
     """
 
     def __init__(self, corpus):
@@ -78,12 +79,14 @@
         self.f = []
         self.df = {}
         self.idf = {}
+        self.doc_len = []
         self.initialize()
 
     def initialize(self):
         """Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies."""
         for document in self.corpus:
             frequencies = {}
+            self.doc_len.append(len(document))
             for word in document:
                 if word not in frequencies:
                     frequencies[word] = 0
@@ -122,7 +125,7 @@
                 continue
             idf = self.idf[word] if self.idf[word] >= 0 else EPSILON * average_idf
             score += (idf * self.f[index][word] * (PARAM_K1 + 1)
-                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))
+                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * self.doc_len[index] / self.avgdl)))
         return score
 
     def get_scores(self, document, average_idf):

{"golden_diff": "diff --git a/gensim/summarization/bm25.py b/gensim/summarization/bm25.py\n--- a/gensim/summarization/bm25.py\n+++ b/gensim/summarization/bm25.py\n@@ -4,7 +4,7 @@\n # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html\n \n \"\"\"This module contains function of computing rank scores for documents in\n-corpus and helper class `BM25` used in calculations. Original alhorithm\n+corpus and helper class `BM25` used in calculations. Original algorithm\n descibed in [1]_, also you may check Wikipedia page [2]_.\n \n \n@@ -61,7 +61,8 @@\n         Dictionary with terms frequencies for whole `corpus`. Words used as keys and frequencies as values.\n     idf : dict\n         Dictionary with inversed terms frequencies for whole `corpus`. Words used as keys and frequencies as values.\n-\n+    doc_len : list of int\n+        List of document lengths.\n     \"\"\"\n \n     def __init__(self, corpus):\n@@ -78,12 +79,14 @@\n         self.f = []\n         self.df = {}\n         self.idf = {}\n+        self.doc_len = []\n         self.initialize()\n \n     def initialize(self):\n         \"\"\"Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies.\"\"\"\n         for document in self.corpus:\n             frequencies = {}\n+            self.doc_len.append(len(document))\n             for word in document:\n                 if word not in frequencies:\n                     frequencies[word] = 0\n@@ -122,7 +125,7 @@\n                 continue\n             idf = self.idf[word] if self.idf[word] >= 0 else EPSILON * average_idf\n             score += (idf * self.f[index][word] * (PARAM_K1 + 1)\n-                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))\n+                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * self.doc_len[index] / self.avgdl)))\n         return score\n \n     def get_scores(self, document, average_idf):\n", "issue": "BM25 : Incorrect scoring function\nhttps://github.com/RaRe-Technologies/gensim/blob/develop/gensim/summarization/bm25.py\r\n**Instead of \"len(document)\" it should be the length of the _index_ document of the corpus.**\r\n  \r\n```\r\n def get_score(self, document, index, average_idf): \r\n           # in this line it should be the length of the index document in the corpus\r\n            score += (idf * self.f[index][word] * (PARAM_K1 + 1)\r\n                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))\r\n```\r\n\nBM25 : Incorrect scoring function\nhttps://github.com/RaRe-Technologies/gensim/blob/develop/gensim/summarization/bm25.py\r\n**Instead of \"len(document)\" it should be the length of the _index_ document of the corpus.**\r\n  \r\n```\r\n def get_score(self, document, index, average_idf): \r\n           # in this line it should be the length of the index document in the corpus\r\n            score += (idf * self.f[index][word] * (PARAM_K1 + 1)\r\n                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))\r\n```\r\n\n", "before_files": [{"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html\n\n\"\"\"This module contains function of computing rank scores for documents in\ncorpus and helper class `BM25` used in calculations. Original alhorithm\ndescibed in [1]_, also you may check Wikipedia page [2]_.\n\n\n.. [1] Robertson, Stephen; Zaragoza, Hugo (2009).  The Probabilistic Relevance Framework: BM25 and Beyond,\n       http://www.staff.city.ac.uk/~sb317/papers/foundations_bm25_review.pdf\n.. [2] Okapi BM25 on Wikipedia, https://en.wikipedia.org/wiki/Okapi_BM25\n\n\n\nExamples\n--------\n>>> from gensim.summarization.bm25 import get_bm25_weights\n>>> corpus = [\n...     [\"black\", \"cat\", \"white\", \"cat\"],\n...     [\"cat\", \"outer\", \"space\"],\n...     [\"wag\", \"dog\"]\n... ]\n>>> result = get_bm25_weights(corpus)\n\n\nData:\n-----\n.. data:: PARAM_K1 - Free smoothing parameter for BM25.\n.. data:: PARAM_B - Free smoothing parameter for BM25.\n.. data:: EPSILON - Constant used for negative idf of document in corpus.\n\n\"\"\"\n\n\nimport math\nfrom six import iteritems\nfrom six.moves import xrange\n\n\nPARAM_K1 = 1.5\nPARAM_B = 0.75\nEPSILON = 0.25\n\n\nclass BM25(object):\n    \"\"\"Implementation of Best Matching 25 ranking function.\n\n    Attributes\n    ----------\n    corpus_size : int\n        Size of corpus (number of documents).\n    avgdl : float\n        Average length of document in `corpus`.\n    corpus : list of list of str\n        Corpus of documents.\n    f : list of dicts of int\n        Dictionary with terms frequencies for each document in `corpus`. Words used as keys and frequencies as values.\n    df : dict\n        Dictionary with terms frequencies for whole `corpus`. Words used as keys and frequencies as values.\n    idf : dict\n        Dictionary with inversed terms frequencies for whole `corpus`. Words used as keys and frequencies as values.\n\n    \"\"\"\n\n    def __init__(self, corpus):\n        \"\"\"\n        Parameters\n        ----------\n        corpus : list of list of str\n            Given corpus.\n\n        \"\"\"\n        self.corpus_size = len(corpus)\n        self.avgdl = sum(float(len(x)) for x in corpus) / self.corpus_size\n        self.corpus = corpus\n        self.f = []\n        self.df = {}\n        self.idf = {}\n        self.initialize()\n\n    def initialize(self):\n        \"\"\"Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies.\"\"\"\n        for document in self.corpus:\n            frequencies = {}\n            for word in document:\n                if word not in frequencies:\n                    frequencies[word] = 0\n                frequencies[word] += 1\n            self.f.append(frequencies)\n\n            for word, freq in iteritems(frequencies):\n                if word not in self.df:\n                    self.df[word] = 0\n                self.df[word] += 1\n\n        for word, freq in iteritems(self.df):\n            self.idf[word] = math.log(self.corpus_size - freq + 0.5) - math.log(freq + 0.5)\n\n    def get_score(self, document, index, average_idf):\n        \"\"\"Computes BM25 score of given `document` in relation to item of corpus selected by `index`.\n\n        Parameters\n        ----------\n        document : list of str\n            Document to be scored.\n        index : int\n            Index of document in corpus selected to score with `document`.\n        average_idf : float\n            Average idf in corpus.\n\n        Returns\n        -------\n        float\n            BM25 score.\n\n        \"\"\"\n        score = 0\n        for word in document:\n            if word not in self.f[index]:\n                continue\n            idf = self.idf[word] if self.idf[word] >= 0 else EPSILON * average_idf\n            score += (idf * self.f[index][word] * (PARAM_K1 + 1)\n                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * len(document) / self.avgdl)))\n        return score\n\n    def get_scores(self, document, average_idf):\n        \"\"\"Computes and returns BM25 scores of given `document` in relation to\n        every item in corpus.\n\n        Parameters\n        ----------\n        document : list of str\n            Document to be scored.\n        average_idf : float\n            Average idf in corpus.\n\n        Returns\n        -------\n        list of float\n            BM25 scores.\n\n        \"\"\"\n        scores = []\n        for index in xrange(self.corpus_size):\n            score = self.get_score(document, index, average_idf)\n            scores.append(score)\n        return scores\n\n\ndef get_bm25_weights(corpus):\n    \"\"\"Returns BM25 scores (weights) of documents in corpus.\n    Each document has to be weighted with every document in given corpus.\n\n    Parameters\n    ----------\n    corpus : list of list of str\n        Corpus of documents.\n\n    Returns\n    -------\n    list of list of float\n        BM25 scores.\n\n    Examples\n    --------\n    >>> from gensim.summarization.bm25 import get_bm25_weights\n    >>> corpus = [\n    ...     [\"black\", \"cat\", \"white\", \"cat\"],\n    ...     [\"cat\", \"outer\", \"space\"],\n    ...     [\"wag\", \"dog\"]\n    ... ]\n    >>> result = get_bm25_weights(corpus)\n\n    \"\"\"\n    bm25 = BM25(corpus)\n    average_idf = sum(float(val) for val in bm25.idf.values()) / len(bm25.idf)\n\n    weights = []\n    for doc in corpus:\n        scores = bm25.get_scores(doc, average_idf)\n        weights.append(scores)\n\n    return weights\n", "path": "gensim/summarization/bm25.py"}], "after_files": [{"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html\n\n\"\"\"This module contains function of computing rank scores for documents in\ncorpus and helper class `BM25` used in calculations. Original algorithm\ndescibed in [1]_, also you may check Wikipedia page [2]_.\n\n\n.. [1] Robertson, Stephen; Zaragoza, Hugo (2009).  The Probabilistic Relevance Framework: BM25 and Beyond,\n       http://www.staff.city.ac.uk/~sb317/papers/foundations_bm25_review.pdf\n.. [2] Okapi BM25 on Wikipedia, https://en.wikipedia.org/wiki/Okapi_BM25\n\n\n\nExamples\n--------\n>>> from gensim.summarization.bm25 import get_bm25_weights\n>>> corpus = [\n...     [\"black\", \"cat\", \"white\", \"cat\"],\n...     [\"cat\", \"outer\", \"space\"],\n...     [\"wag\", \"dog\"]\n... ]\n>>> result = get_bm25_weights(corpus)\n\n\nData:\n-----\n.. data:: PARAM_K1 - Free smoothing parameter for BM25.\n.. data:: PARAM_B - Free smoothing parameter for BM25.\n.. data:: EPSILON - Constant used for negative idf of document in corpus.\n\n\"\"\"\n\n\nimport math\nfrom six import iteritems\nfrom six.moves import xrange\n\n\nPARAM_K1 = 1.5\nPARAM_B = 0.75\nEPSILON = 0.25\n\n\nclass BM25(object):\n    \"\"\"Implementation of Best Matching 25 ranking function.\n\n    Attributes\n    ----------\n    corpus_size : int\n        Size of corpus (number of documents).\n    avgdl : float\n        Average length of document in `corpus`.\n    corpus : list of list of str\n        Corpus of documents.\n    f : list of dicts of int\n        Dictionary with terms frequencies for each document in `corpus`. Words used as keys and frequencies as values.\n    df : dict\n        Dictionary with terms frequencies for whole `corpus`. Words used as keys and frequencies as values.\n    idf : dict\n        Dictionary with inversed terms frequencies for whole `corpus`. Words used as keys and frequencies as values.\n    doc_len : list of int\n        List of document lengths.\n    \"\"\"\n\n    def __init__(self, corpus):\n        \"\"\"\n        Parameters\n        ----------\n        corpus : list of list of str\n            Given corpus.\n\n        \"\"\"\n        self.corpus_size = len(corpus)\n        self.avgdl = sum(float(len(x)) for x in corpus) / self.corpus_size\n        self.corpus = corpus\n        self.f = []\n        self.df = {}\n        self.idf = {}\n        self.doc_len = []\n        self.initialize()\n\n    def initialize(self):\n        \"\"\"Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies.\"\"\"\n        for document in self.corpus:\n            frequencies = {}\n            self.doc_len.append(len(document))\n            for word in document:\n                if word not in frequencies:\n                    frequencies[word] = 0\n                frequencies[word] += 1\n            self.f.append(frequencies)\n\n            for word, freq in iteritems(frequencies):\n                if word not in self.df:\n                    self.df[word] = 0\n                self.df[word] += 1\n\n        for word, freq in iteritems(self.df):\n            self.idf[word] = math.log(self.corpus_size - freq + 0.5) - math.log(freq + 0.5)\n\n    def get_score(self, document, index, average_idf):\n        \"\"\"Computes BM25 score of given `document` in relation to item of corpus selected by `index`.\n\n        Parameters\n        ----------\n        document : list of str\n            Document to be scored.\n        index : int\n            Index of document in corpus selected to score with `document`.\n        average_idf : float\n            Average idf in corpus.\n\n        Returns\n        -------\n        float\n            BM25 score.\n\n        \"\"\"\n        score = 0\n        for word in document:\n            if word not in self.f[index]:\n                continue\n            idf = self.idf[word] if self.idf[word] >= 0 else EPSILON * average_idf\n            score += (idf * self.f[index][word] * (PARAM_K1 + 1)\n                      / (self.f[index][word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * self.doc_len[index] / self.avgdl)))\n        return score\n\n    def get_scores(self, document, average_idf):\n        \"\"\"Computes and returns BM25 scores of given `document` in relation to\n        every item in corpus.\n\n        Parameters\n        ----------\n        document : list of str\n            Document to be scored.\n        average_idf : float\n            Average idf in corpus.\n\n        Returns\n        -------\n        list of float\n            BM25 scores.\n\n        \"\"\"\n        scores = []\n        for index in xrange(self.corpus_size):\n            score = self.get_score(document, index, average_idf)\n            scores.append(score)\n        return scores\n\n\ndef get_bm25_weights(corpus):\n    \"\"\"Returns BM25 scores (weights) of documents in corpus.\n    Each document has to be weighted with every document in given corpus.\n\n    Parameters\n    ----------\n    corpus : list of list of str\n        Corpus of documents.\n\n    Returns\n    -------\n    list of list of float\n        BM25 scores.\n\n    Examples\n    --------\n    >>> from gensim.summarization.bm25 import get_bm25_weights\n    >>> corpus = [\n    ...     [\"black\", \"cat\", \"white\", \"cat\"],\n    ...     [\"cat\", \"outer\", \"space\"],\n    ...     [\"wag\", \"dog\"]\n    ... ]\n    >>> result = get_bm25_weights(corpus)\n\n    \"\"\"\n    bm25 = BM25(corpus)\n    average_idf = sum(float(val) for val in bm25.idf.values()) / len(bm25.idf)\n\n    weights = []\n    for doc in corpus:\n        scores = bm25.get_scores(doc, average_idf)\n        weights.append(scores)\n\n    return weights\n", "path": "gensim/summarization/bm25.py"}]}