Added style #12

12-inverse-multiplexer/README.md

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+Style #12
+==============================
+
+Constraints:
+
+- Input data is divided in chunks, similar to what an inverse multiplexer does to input signals
+
+- A map function applies a given worker function to each chunk of data, potentially in parallel
+
+- A reduce function takes the results of the many worker functions and recombines them into a coherent output
+
+Possible names:
+
+- Inverse multiplexer (check out electronics)
+- Map-reduce

12-inverse-multiplexer/tf-12.py

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+import sys, re, operator, string
+
+#
+# Functions for map reduce
+#
+def partition(data_str, nlines):
+    """ 
+    Generator function that partitions the input data_str (a big string)
+    into chunks of nlines.
+    """
+    lines = data_str.split('\n')
+    for i in xrange(0, len(lines), nlines):
+        yield '\n'.join(lines[i:i+nlines])
+
+def split_words(data_str):
+    """ 
+    Takes a string, filters non alphanumeric characters, normalizes to
+    lower case, scans for words, and filters the stop words. 
+    It returns a list of pairs (word, 1), one for each word in the input, so
+    [(w1, 1), (w2, 1), ..., (wn, 1)]
+    """
+    def _filter_chars(str_data):
+        """
+        Takes a string and returns a copy with all nonalphanumeric chars
+        replaced by white space
+        """
+        pattern = re.compile('[\W_]+')
+        return pattern.sub(' ', str_data)
+
+    def _normalize(str_data):
+        """
+        Takes a string and returns a copy with all characters in lower case
+        """
+        return str_data.lower()
+
+    def _scan(str_data):
+        """
+        Takes a string and scans for words, returning
+        a list of words.
+        """
+        return str_data.split()
+
+    def _remove_stop_words(word_list):
+        f = open('../stop_words.txt')
+        stop_words = f.read().split(',')
+        f.close()
+        # add single-letter words
+        stop_words.extend(list(string.ascii_lowercase))
+        return [w for w in word_list if not w in stop_words]
+
+    # The actual work of splitting the input into words
+    result = []
+    words = _remove_stop_words(_scan(_normalize(_filter_chars(data_str))))
+    for w in words:
+        result.append((w, 1))
+
+    return result
+
+def count_words(pairs_list_1, pairs_list_2):
+    """ 
+    Takes a two lists of pairs of the form
+    [(w1, 1), ...]
+    and returns a list of pairs [(w1, frequency), ...], 
+    where frequency is the sum of all the reported occurrences
+    """
+    mapping = dict((k, v) for k, v in pairs_list_1)
+    for p in pairs_list_2:
+        if p[0] in mapping:
+            mapping[p[0]] += p[1]
+        else:
+            mapping[p[0]] = 1
+
+    return mapping.items()
+
+#
+# Auxiliary functions
+#
+
+def read_file(path_to_file):
+    """
+    Takes a path to a file and returns the entire
+    contents of the file as a string
+    """
+    f = open(path_to_file)
+    data = f.read()
+    f.close()
+    return data
+
+def sort(word_freq):
+    """
+    Takes a collection of words and their frequencies
+    and returns a collection of pairs where the entries are
+    sorted by frequency 
+    """
+    return sorted(word_freq, key=operator.itemgetter(1), reverse=True)
+
+
+#
+# The main function
+#
+splits = map(split_words, partition(read_file(sys.argv[1]), 200))
+splits.insert(0, []) # Normalize input to reduce
+word_freqs = sort(reduce(count_words, splits))
+
+for tf in word_freqs[0:25]:
+    print tf[0], ' - ', tf[1]
+