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pagetribe .

Stir it up - News Mixer - 0 views

shared by pagetribe . on 23 Dec 08 - Cached
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    click on title to see how comments are applied. It also uses a comment per facebook login to see what comments your friends have left.
pagetribe .

iiNews May - 0 views

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    Job hunting online with Sandy Lim This month, we cover job hunting online, with a look at pay scales in the current job market; advice on preparing a quality resume; and a neat community forum for small business owners and freelancers. [Paycheck] The Great Australian Pay Check The Great Australian Pay Check susses out pay scales, perks, work-life balance and job satisfaction across the Aussie job market - letting you find out where you fit in without having to ask. Great for planning your next career move. [careerone] Career One Resume Advice Get good recommendations for the Australian style of resume writing, common Gen Y and migrant job search issues, and writing for specific recruitment audiences. When you're done, there's also cover letter & interview advice and a redundancy survival guide. [flying solo] Flying Solo If being your own boss is more your style, check out the Flying Solo community for articles on growing and promoting your business, how to work and & network smarter, and keeping your work-life balance in check. They've also got a discussion forum for specific advice and general banter. Still confused? All you internet first-timers can cut your teeth on our Broadband for Beginners workshops, where we also cover employment & social networking.
Executive Brief

Succeeding with Scrum: Start by Creating an Effective Product Vision - 0 views

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    An effective product vision ensures the success of SCRUM by guiding your team and aligning stakeholders and customers. Learn how to craft this all-important unifying product vision to succeed with Scrum!
anonymous

Fix Slow Running Computer Now - 1 views

My computer is running so slow so I contact Fix Slow Computers Online. They offer online computer support services to fix slow computers. They have the best computer tech specialists who know how t...

fix slow computers

started by anonymous on 12 May 11 no follow-up yet
shalani mujer

They Effectively Fixed My laptop - 2 views

I love to surf the internet using my laptop, then one day it just stopped running. I did not know what to do since the blue screen error did not disappear though I have tried rebooting my laptop. ...

PC technical support

started by shalani mujer on 10 Nov 11 no follow-up yet
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 .

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.
hansel molly

Great Remote Computer Support Services - 1 views

Computer Support Professional offers unrivaled online computer support services that gave me the assurance that my computer is in good hands. Every time I needed the help of their computer support ...

computer support

started by hansel molly on 26 May 11 no follow-up yet
seth kutcher

The Best Remote PC Support I Ever Had - 1 views

The Remote PC Support Now excellent remote PC support services are the best. They have skilled computer tech professionals who can fix your PC while you wait or just go back to work or just simpl...

remote PC support

started by seth kutcher on 12 Sep 11 no follow-up yet
pagetribe .

The Espresso Guide ™ - 0 views

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    cappuccino
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