Only one carriage return (#2155)

* updating DIRECTORY.md

* touch

* fixup! Format Python code with psf/black push

* Update word_frequency_functions.py

* updating DIRECTORY.md

* Update word_frequency_functions.py

* Update lfu_cache.py

* Update sol1.py

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

machine_learning/word_frequency_functions.py

@@ -40,7 +40,7 @@ from math import log10
 """
 
 
-def term_frequency(term : str, document : str) -> int:
+def term_frequency(term: str, document: str) -> int:
     """
     Return the number of times a term occurs within
     a given document.
@@ -58,9 +58,7 @@ def term_frequency(term : str, document : str) -> int:
         str.maketrans("", "", string.punctuation)
     ).replace("\n", "")
     tokenize_document = document_without_punctuation.split(" ")  # word tokenization
-    return len(
-        [word for word in tokenize_document if word.lower() == term.lower()]
-    )
+    return len([word for word in tokenize_document if word.lower() == term.lower()])
 
 
 def document_frequency(term: str, corpus: str) -> int:
@@ -77,17 +75,18 @@ is the second document in the corpus.\\nTHIS is \
 the third document in the corpus.")
     (1, 3)
     """
-    corpus_without_punctuation = corpus.translate(
+    corpus_without_punctuation = corpus.lower().translate(
         str.maketrans("", "", string.punctuation)
     )  # strip all punctuation and replace it with ''
-    documents = corpus_without_punctuation.split("\n")
-    lowercase_documents = [document.lower() for document in documents]
-    return len(
-        [document for document in lowercase_documents if term.lower() in document]
-    ), len(documents)
+    docs = corpus_without_punctuation.split("\n")
+    term = term.lower()
+    return (
+        len([doc for doc in docs if term in doc]),
+        len(docs),
+    )
 
 
-def inverse_document_frequency(df : int, N: int) -> float:
+def inverse_document_frequency(df: int, N: int) -> float:
     """
     Return an integer denoting the importance
     of a word. This measure of importance is
@@ -116,7 +115,7 @@ def inverse_document_frequency(df : int, N: int) -> float:
     return round(log10(N / df), 3)
 
 
-def tf_idf(tf : int, idf: int) -> float:
+def tf_idf(tf: int, idf: int) -> float:
     """
     Combine the term frequency
     and inverse document frequency functions to