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About this site - News Mixer - 0 views

  • Developers News Mixer is free and open source software, coded in Python with the Django Web development framework, and uses Facebook Connect for authentication. You can read more about our development process in our report, and find our source at Google Code.
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    Developers News Mixer is free and open source software, coded in Python with the Django Web development framework, and uses Facebook Connect for authentication. You can read more about our development process in our report, and find our source at Google Code.
pagetribe .

Django Tagging - 0 views

shared by pagetribe . on 18 Nov 08 - Cached
  • >>> tags = Tag.objects.usage_for_model(Widget, counts=True)
  • [('cheese', 1), ('house', 2), ('thing', 1), ('toast', 1)]
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    def usage_for_model(self, model, counts=False, min_count=None, filters=None): """ Obtain a list of tags associated with instances of the given Model class. If ``counts`` is True, a ``count`` attribute will be added to each tag, indicating how many times it has been used against the Model class in question. If ``min_count`` is given, only tags which have a ``count`` greater than or equal to ``min_count`` will be returned. Passing a value for ``min_count`` implies ``counts=True``. To limit the tags (and counts, if specified) returned to those used by a subset of the Model's instances, pass a dictionary of field lookups to be applied to the given Model as the ``filters`` argument. """ if filters is None: filters = {} if min_count is not None: counts = True model_table = qn(model._meta.db_table) model_pk = '%s.%s' % (model_table, qn(model._meta.pk.column)) query = """ SELECT DISTINCT %(tag)s.id, %(tag)s.name%(count_sql)s FROM %(tag)s INNER JOIN %(tagged_item)s ON %(tag)s.id = %(tagged_item)s.tag_id INNER JOIN %(model)s ON %(tagged_item)s.object_id = %(model_pk)s %%s WHERE %(tagged_item)s.content_type_id = %(content_type_id)s %%s GROUP BY %(tag)s.id, %(tag)s.name %%s ORDER BY %(tag)s.name ASC""" % { 'tag': qn(self.model._meta.db_table), 'count_sql': counts and (', COUNT(%s)' % model_pk) or '', 'tagged_item': qn(self._get_related_model_by_accessor('items')._meta.db_table), 'model': model_table, 'model_pk': model_pk, 'content_type_id': ContentType.objects.get_for_model(model).pk, } extra_joins = '' extra_criteria = '' min_count_sql
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    usage_for_model
pagetribe .

Screencast on many to many relationships between Django models - 0 views

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    A nice screencast on many to many relationships. Part of a 3 part series on developing a wiki in django.
pagetribe .

http://nltk.googlecode.com/svn/trunk/doc/book/ch01.html - 0 views

  • We can count how often a word occurs in a tex
  • Adding two lists creates a new list
  • count the occurrences of a particular word using text1.count('heaven')
  • ...18 more annotations...
  • By convention, m:n means elements m…n-1
  • A consequence of this last change is that the list only has four elements, and accessing a later value generates an error
  • We can join the words of a list to make a single string, or split a string into a list, as follows:
  • 'Monty Python'.split()
  • frequency distribution
  • frequency of each vocabulary item
  • find the 50 most frequent words
  • hese very long words are often hapaxes (i.e. unique) and perhaps it would be better to find frequently occurring long words.
  • Here are all words from the chat corpus that are longer than 7 characters, that occur more than 7 times:   >>> fdist5 = FreqDist(text5) >>> sorted([w for w in set(text5) if len(w) > 7 and fdist5[w] > 7]) ['#14-19teens', '#talkcity_adults', '((((((((((', '........', 'Question', 'actually', 'anything', 'computer', 'cute.-ass', 'everyone', 'football', 'innocent', 'listening', 'remember', 'seriously', 'something', 'together', 'tomorrow', 'watching'] >>>
  • The collocations() function does this for us
  • find bigrams that occur more often than we would expect based on the frequency of individual words.
  • fdist = FreqDist(samples) create a frequency distribution containing the given samples fdist.inc(sample) increment the count for this sample fdist['monstrous'] count of the number of times a given sample occurred fdist.freq('monstrous') frequency of a given sample fdist.N() total number of samples fdist.keys() the samples sorted in order of decreasing frequency for sample in fdist: iterate over the samples, in order of decreasing frequency fdist.max() sample with the greatest count fdist.tabulate() tabulate the frequency distribution fdist.plot() graphical plot of the frequency distribution fdist.plot(cumulative=True) cumulative plot of the frequency distribution fdist1 < fdist2 test if samples in fdist1 occur less frequently than in fdist2
  • it goes through each word in text1, assigning each one in turn to the variable w and performing the specified operation on the variable.
  • The above notation is called a "list comprehension"
  • [f(w) for ...] or [w.f() for ...],
  • Now that we are not double-counting words like This and this
  • by filtering out any non-alphabetic items:   >>> len(set([word.lower() for word in text1 if word.isalpha()]))
  • A collocation is a sequence of words which occur together unusually often. Thus red wine is a collocation, while the wine is not. A characteristic of collocations is that they are resistant to substitution with words that have similar senses — maroon wine sounds definitely odd.
pagetribe .

Hedged down: django - 0 views

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    Practical Django Projects conversion to 1.0 blog
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