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  • Optimising RSS parsing on App Engine to avoid high CPU warnings

    - by Danny Tuppeny
    I'm pulling some RSS feeds into a datastore in App Engine to serve up to an iPhone app. I use cron to schedule updating the RSS every x minutes. Each task only parses one RSS feed (which has 15-20 items). I frequently get warnings about high CPU usage in the App Engine dashboard, so I'm looking for ways to optimise my code. Currently, I use minidom (since it's already there on App Engine), but I suspect it's not very efficient! Here's the code: dom = minidom.parseString(urlfetch.fetch(url).content) if dom: items = [] for node in dom.getElementsByTagName('item'): item = RssItem( key_name = self.getText(node.getElementsByTagName('guid')[0].childNodes), title = self.getText(node.getElementsByTagName('title')[0].childNodes), description = self.getText(node.getElementsByTagName('description')[0].childNodes), modified = datetime.now(), link = self.getText(node.getElementsByTagName('link')[0].childNodes), categories = [self.getText(category.childNodes) for category in node.getElementsByTagName('category')] ); items.append(item); db.put(items); def getText(self, nodelist): rc = '' for node in nodelist: if node.nodeType == node.TEXT_NODE: rc = rc + node.data return rc There isn't much going on, but the scripts often take 2-6 seconds CPU time, which seems a bit excessive for looping through 20ish items and reading a few attributes. What can I do to make this faster? Is there anything particularly bad in the above code, or should I change to another way of parsing? Are there are any libraries (that work on App Engine) that would be better, or would I be better parsing the RSS myself?

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  • How To Create Per-Request Singleton in Pylons?

    - by dave mankoff
    In our Pylons based web-app, we're creating a class that essentially provides some logging functionality. We need a new instance of this class for each http request that comes in, but only one per request. What is the proper way to go about this? Should we just create the object in middleware and store in in request.environ? Is there a more appropriate way to go about this?

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  • Using sub filters/queries in Google App Engine

    - by fredrik
    Hi, I'm trying to use figure out how to sub query a query that uses a filter. From what I've figured out so far while using .filter() it changes the original query, that leads to a second .filter() would also have to match the first filter. I would like to make something like this: modules = data.Modules.all().filter('page = ', page.key()) modules.filter('name = ', 'Test') modules.filter('name = ', 'Test2') I can't get the "Test2" filter to work. The only solution I have at the moment is to make all new queries. data.Modules.all().filter('page = ', page.key()).filter('name = ', "Test").get() data.Modules.all().filter('page = ', page.key()).filter('name = ', "Test2").get() Or write the same as an GQL. But for me it seams quite stupid way to go. I've looked at using ancestors, but I don't quite understand it and honestly don't know if that's the way to go. Any ideas? ..fredrik

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  • Should we have a database independent SQL like query language in Django? [closed]

    - by Yugal Jindle
    Note : I know we have Django ORM already that keeps things database independent and converts to the database specific SQL queries. Once things starts getting complicated it is preferred to write raw SQL queries for better efficiency. When you write raw sql queries your code gets trapped with the database you are using. I also understand its important to use the full power of your database that can-not be achieved with the django orm alone. My Question : Until I use any database specific feature, why should one be trapped with the database. For instance : We have a query with multiple joins and we decided to write a raw sql query. Now, that makes my website postgres specific. Even when I have not used any postgres specific feature. I feel there should be some fake sql language which can translate to any database's sql query. Even Django's ORM can be built over it. So, that if you go out of ORM but not database specific - you can still remain database independent. I asked the same question to Jacob Kaplan Moss (In person) : He advised me to stay with the database that I like and endure its whole power, to which I agree. But my point was not that we should be database independent. My point is we should be database independent until we use a database specific feature. Please explain, why should be there a fake sql layer over the actual sql ?

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  • Multi choice form field in Django

    - by Dingo
    Hi! I'am developing application on app-engine-path. I would like to make form with multichoice (acceptably languages for user). Code look like this: Language settings: settings.LANGUAGES = ((u"cs", u"Ceština"), (u"en", u"English")) Form model: class UserForm(forms.ModelForm): first_name = forms.CharField(max_length=100) last_name = forms.CharField(max_length=100) languages = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple, choices=settings.LANGUAGES) The form is rendered o.k. (all languages have checkbox. IDs, NAMEs is ok.) But if I save some languages for user, those languages don't check checkboxes. User model look like this class User(User): #... languages = db.StringListProperty() #... and view: def edit_profile(request): user = request.user if request.method == 'POST': form = UserForm(request.POST) if form.is_valid(): # ... else: form = UserForm(instance=user) data = {"user":user, "form": form} return render_to_response(request, 'user_profile/user_profile.html', data)

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  • store/load numpy array from binary files

    - by Javier
    Dear all, I would like to store and load numpy arrays from binary files. For that purposes, I created two small functions. Each binary file should contain the dimensionality of the given matrix. def saveArrayToFile(data, fileName): with open(fileName, 'w') as file: a = array.array('f') nSamples, ndim = data.shape a.extend([nSamples, ndim]) # write number of elements and dimensions a.fromstring(data.tostring()) a.tofile(file) def readArrayFromFile(fileName): _featDesc = np.fromfile(fileName, 'f') _ndesc = int(_featDesc[0]) _ndim = int(_featDesc[1]) _featDesc = _featDesc[2:] _featDesc = _featDesc.reshape([_ndesc, _ndim]) return _featDesc, _ndesc, _ndim An example on how to use the functions is: myarr=np.array([[7, 4],[3, 9],[1, 3]]) saveArrayToFile(myarr,'myfile.txt') _featDesc, _ndesc, _ndim = readArrayFromFile('myfile.txt') However, an error message of 'ValueError: total size of new array must be unchanged' is shown. My arrays can be of size MxN and MxM. Any suggestions are more than welcomed. I think the problem might be in the saveArrayToFile function. Best wishes, Javier

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  • Pretty-printing of numpy.array

    - by camillio
    Hello, I'm curious, whether there is any way to print formated numpy.arrays, e.g., in the way similar to this: x = 1.23456 print '%.3f' % x If I want to print the numpy.array of floats, it prints several decimals, often in 'scientific' format, which is rather hard to read even for low-dimensional arrays. However, numpy.array apparently has to be printed as a string, i.e., with %s. Is there any solution ready for this purpose? Many thanks in advance :-)

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  • [numpy] storing record arrays in object arrays

    - by Peter Prettenhofer
    I'd like to convert a list of record arrays -- dtype is (uint32, float32) -- into a numpy array of dtype np.object: X = np.array(instances, dtype = np.object) where instances is a list of arrays with data type np.dtype([('f0', '<u4'), ('f1', '<f4')]). However, the above statement results in an array whose elements are also of type np.object: X[0] array([(67111L, 1.0), (104242L, 1.0)], dtype=object) Does anybody know why? The following statement should be equivalent to the above but gives the desired result: X = np.empty((len(instances),), dtype = np.object) X[:] = instances X[0] array([(67111L, 1.0), (104242L, 1.0), dtype=[('f0', '<u4'), ('f1', '<f4')]) thanks & best regards, peter

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  • Rebuilding website from Django 0.96 to Django 1.2

    - by Neytiri
    I've got a website done in Django 0.96 (done in 2007), and now we are thinking about rebuilding it (not just migrating) for Django 1.2 . Can anyone point me to the new (and worth the while) widgets, plugins and other stuff for Django 1.2 (released in april 2010). I've heard of "South" and of a widget for debugging (can't remember the name), but I'm a little lost here.

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  • Django1.1 model field value preprocessing before returning

    - by Satoru.Logic
    Hi, all. I have a model class like this: class Note(models.Model): author = models.ForeignKey(User, related_name='notes') content = NoteContentField(max_length=256) NoteContentField is a custom sub-class of CharField that override the to_python method in purpose of doing some twitter-text-conversion processing. class NoteContentField(models.CharField): __metaclass__ = models.SubfieldBase def to_python(self, value): value = super(NoteContentField, self).to_python(value) from ..utils import linkify return mark_safe(linkify(value)) However, this doesn't work. When I save a Note object like this: note = Note(author=request.use, content=form.cleaned_data['content']) note.save() The conversed value is saved into the database, which is not what I wanna see. What I'm trying to do is to save the raw content into the database, and only make the conversion when the content attribute is later accessed. Would you please tell me what's wrong with this? Thanks to Pierre and Daniel. I have figured out what's wrong. I thought the text-conversion code should be in either to_python or get_db_prep_value, and that's wrong. I should override both of them, make to_python do the conversion and get_db_prep_value return the unconversed value: from ..utils import linkify class NoteContentField(models.CharField): __metaclass__ = models.SubfieldBase def to_python(self, value): self._raw_value = super(NoteContentField, self).to_python(value) return mark_safe(linkify(self._raw_value)) def get_db_prep_value(self, value): return self._raw_value I wonder if there is a better way to implement this?

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  • find the colour name from a hexadecimal colour code

    - by sree01
    Hi , i want to find the name of a colour from the hexadecimal colour code. When i get a hex colour code i want to find the most matching colour name. for example for the code #c06040 , how to find out if it is a shade of brown, blue or yellow ?. so that i can find the colour of an object in the image without human intervention. Is there any relation between the hexadecimal code of the shades of a colour? please give some sample code if there is any.

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  • PyML 0.7.2 - How to prevent accuracy from dropping after storing/loading a classifier?

    - by Michael Aaron Safyan
    This is a followup from "Save PyML.classifiers.multi.OneAgainstRest(SVM()) object?". The solution to that question was close, but not quite right, (the SparseDataSet is broken, so attempting to save/load with that dataset container type will fail, no matter what. Also, PyML is inconsistent in terms of whether labels should be numbers or strings... it turns out that the oneAgainstRest function is actually not good enough, because the labels need to be strings and simultaneously convertible to floats, because there are places where it is assumed to be a string and elsewhere converted to float) and so after a great deal of hacking and such I was finally able to figure out a way to save and load my multi-class classifier without it blowing up with an error.... however, although it is no longer giving me an error message, it is still not quite right as the accuracy of the classifier drops significantly when it is saved and then reloaded (so I'm still missing a piece of the puzzle). I am currently using the following custom mutli-class classifier for training, saving, and loading: class SVM(object): def __init__(self,features_or_filename,labels=None,kernel=None): if isinstance(features_or_filename,str): filename=features_or_filename; if labels!=None: raise ValueError,"Labels must be None if loading from a file."; with open(os.path.join(filename,"uniquelabels.list"),"rb") as uniquelabelsfile: self.uniquelabels=sorted(list(set(pickle.load(uniquelabelsfile)))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; self.classifiers=[]; for classidx, classname in enumerate(self.uniquelabels): self.classifiers.append(PyML.classifiers.svm.loadSVM(os.path.join(filename,str(classname)+".pyml.svm"),datasetClass = PyML.VectorDataSet)); else: features=features_or_filename; if labels==None: raise ValueError,"Labels must not be None when training."; self.uniquelabels=sorted(list(set(labels))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; points = [[float(xij) for xij in xi] for xi in features]; self.classifiers=[PyML.SVM(kernel) for label in self.uniquelabels]; for i in xrange(len(self.uniquelabels)): currentlabel=self.uniquelabels[i]; currentlabels=['+1' if k==currentlabel else '-1' for k in labels]; currentdataset=PyML.VectorDataSet(points,L=currentlabels,positiveClass='+1'); self.classifiers[i].train(currentdataset,saveSpace=False); def accuracy(self,pts,labels): logger=logging.getLogger("ml"); correct=0; total=0; classindexes=[self.labeltoindex[label] for label in labels]; h=self.hypotheses(pts); for idx in xrange(len(pts)): if h[idx]==classindexes[idx]: logger.info("RIGHT: Actual \"%s\" == Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])); correct+=1; else: logger.info("WRONG: Actual \"%s\" != Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])) total+=1; return float(correct)/float(total); def prediction(self,pt): h=self.hypothesis(pt); if h!=None: return self.uniquelabels[h]; return h; def predictions(self,pts): h=self.hypotheses(self,pts); return [self.uniquelabels[x] if x!=None else None for x in h]; def hypothesis(self,pt): bestvalue=None; bestclass=None; dataset=PyML.VectorDataSet([pt]); for classidx, classifier in enumerate(self.classifiers): val=classifier.decisionFunc(dataset,0); if (bestvalue==None) or (val>bestvalue): bestvalue=val; bestclass=classidx; return bestclass; def hypotheses(self,pts): bestvalues=[None for pt in pts]; bestclasses=[None for pt in pts]; dataset=PyML.VectorDataSet(pts); for classidx, classifier in enumerate(self.classifiers): for ptidx in xrange(len(pts)): val=classifier.decisionFunc(dataset,ptidx); if (bestvalues[ptidx]==None) or (val>bestvalues[ptidx]): bestvalues[ptidx]=val; bestclasses[ptidx]=classidx; return bestclasses; def save(self,filename): if not os.path.exists(filename): os.makedirs(filename); with open(os.path.join(filename,"uniquelabels.list"),"wb") as uniquelabelsfile: pickle.dump(self.uniquelabels,uniquelabelsfile,pickle.HIGHEST_PROTOCOL); for classidx, classname in enumerate(self.uniquelabels): self.classifiers[classidx].save(os.path.join(filename,str(classname)+".pyml.svm")); I am using the latest version of PyML (0.7.2, although PyML.__version__ is 0.7.0). When I construct the classifier with a training dataset, the reported accuracy is ~0.87. When I then save it and reload it, the accuracy is less than 0.001. So, there is something here that I am clearly not persisting correctly, although what that may be is completely non-obvious to me. Would you happen to know what that is?

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  • Iterating through nested dictionaries

    - by Framester
    I want to write an iterator for my 'toy' Trie implementation. Adding already works like this: class Trie: def __init__(self): self.root = dict() pass def add(self, string, value): global nops current_dict = self.root for letter in s: nops += 1 current_dict = current_dict.setdefault(letter, {}) current_dict = current_dict.setdefault('value', value) pass The output of the adding looks like that: trie = Trie() trie.add("hello",1) trie.add("world",2) trie.add("worlds",12) print trie.root {'h': {'e': {'l': {'l': {'o': {'value': 1}}}}}, 'w': {'o': {'r': {'l': {'d': {'s': {'value': 2}, 'value': 2}}}}}} I know, that I need a __iter__ and next method. def __iter__(self): self.root.__iter__() pass def next(self): print self.root.next() But AttributeError: 'dict' object has no attribute 'next'. How should I do it? [Update] In the perfect world I would like the output to be one dict with all the words/entries with their corresponding values.

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  • How to chroot Django

    - by Brian M. Hunt
    Can one run Django in a chroot? Notably, what's necessary in order to set up (for example) /var/www as a chroot'd directory and then have Django run in that chroot'd directory? Thank you - I'm grateful for any input.

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  • Add data to Django form class using modelformset_factory

    - by dean
    I have a problem where I need to display a lot of forms for detail data for a hierarchical data set. I want to display some relational fields as labels for the forms and I'm struggling with a way to do this in a more robust way. Here is the code... class Category(models.Model): name = models.CharField(max_length=160) class Item(models.Model): category = models.ForeignKey('Category') name = models.CharField(max_length=160) weight = models.IntegerField(default=0) class Meta: ordering = ('category','weight','name') class BudgetValue(models.Model): value = models.IntegerField() plan = models.ForeignKey('Plan') item = models.ForeignKey('Item') I use the modelformset_factory to create a formset of budgetvalue forms for a particular plan. What I'd like is item name and category name for each BudgetValue. When I iterate through the forms each one will be labeled properly. class BudgetValueForm(forms.ModelForm): item = forms.ModelChoiceField(queryset=Item.objects.all(),widget=forms.HiddenInput()) plan = forms.ModelChoiceField(queryset=Plan.objects.all(),widget=forms.HiddenInput()) category = "" < assign dynamically on form creation > item = "" < assign dynamically on form creation > class Meta: model = BudgetValue fields = ('item','plan','value') What I started out with is just creating a dictionary of budgetvalue.item.category.name, budgetvalue.item.name, and the form for each budget value. This gets passed to the template and I render it as I intended. I'm assuming that the ordering of the forms in the formset and the querset used to genererate the formset keep the budgetvalues in the same order and the dictionary is created correctly. That is the budgetvalue.item.name is associated with the correct form. This scares me and I'm thinking there has to be a better way. Any help would be greatly appreciated.

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  • Problem trying to achieve a join using the `comments` contrib in Django

    - by NiKo
    Hi, Django rookie here. I have this model, comments are managed with the django_comments contrib: class Fortune(models.Model): author = models.CharField(max_length=45, blank=False) title = models.CharField(max_length=200, blank=False) slug = models.SlugField(_('slug'), db_index=True, max_length=255, unique_for_date='pub_date') content = models.TextField(blank=False) pub_date = models.DateTimeField(_('published date'), db_index=True, default=datetime.now()) votes = models.IntegerField(default=0) comments = generic.GenericRelation( Comment, content_type_field='content_type', object_id_field='object_pk' ) I want to retrieve Fortune objects with a supplementary nb_comments value for each, counting their respectve number of comments ; I try this query: >>> Fortune.objects.annotate(nb_comments=models.Count('comments')) From the shell: >>> from django_fortunes.models import Fortune >>> from django.db.models import Count >>> Fortune.objects.annotate(nb_comments=Count('comments')) [<Fortune: My first fortune, from NiKo>, <Fortune: Another One, from Dude>, <Fortune: A funny one, from NiKo>] >>> from django.db import connection >>> connection.queries.pop() {'time': '0.000', 'sql': u'SELECT "django_fortunes_fortune"."id", "django_fortunes_fortune"."author", "django_fortunes_fortune"."title", "django_fortunes_fortune"."slug", "django_fortunes_fortune"."content", "django_fortunes_fortune"."pub_date", "django_fortunes_fortune"."votes", COUNT("django_comments"."id") AS "nb_comments" FROM "django_fortunes_fortune" LEFT OUTER JOIN "django_comments" ON ("django_fortunes_fortune"."id" = "django_comments"."object_pk") GROUP BY "django_fortunes_fortune"."id", "django_fortunes_fortune"."author", "django_fortunes_fortune"."title", "django_fortunes_fortune"."slug", "django_fortunes_fortune"."content", "django_fortunes_fortune"."pub_date", "django_fortunes_fortune"."votes" LIMIT 21'} Below is the properly formatted sql query: SELECT "django_fortunes_fortune"."id", "django_fortunes_fortune"."author", "django_fortunes_fortune"."title", "django_fortunes_fortune"."slug", "django_fortunes_fortune"."content", "django_fortunes_fortune"."pub_date", "django_fortunes_fortune"."votes", COUNT("django_comments"."id") AS "nb_comments" FROM "django_fortunes_fortune" LEFT OUTER JOIN "django_comments" ON ("django_fortunes_fortune"."id" = "django_comments"."object_pk") GROUP BY "django_fortunes_fortune"."id", "django_fortunes_fortune"."author", "django_fortunes_fortune"."title", "django_fortunes_fortune"."slug", "django_fortunes_fortune"."content", "django_fortunes_fortune"."pub_date", "django_fortunes_fortune"."votes" LIMIT 21 Can you spot the problem? Django won't LEFT JOIN the django_comments table with the content_type data (which contains a reference to the fortune one). This is the kind of query I'd like to be able to generate using the ORM: SELECT "django_fortunes_fortune"."id", "django_fortunes_fortune"."author", "django_fortunes_fortune"."title", COUNT("django_comments"."id") AS "nb_comments" FROM "django_fortunes_fortune" LEFT OUTER JOIN "django_comments" ON ("django_fortunes_fortune"."id" = "django_comments"."object_pk") LEFT OUTER JOIN "django_content_type" ON ("django_comments"."content_type_id" = "django_content_type"."id") GROUP BY "django_fortunes_fortune"."id", "django_fortunes_fortune"."author", "django_fortunes_fortune"."title", "django_fortunes_fortune"."slug", "django_fortunes_fortune"."content", "django_fortunes_fortune"."pub_date", "django_fortunes_fortune"."votes" LIMIT 21 But I don't manage to do it, so help from Django veterans would be much appreciated :) Hint: I'm using Django 1.2-DEV Thanks in advance for your help.

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  • user inheritance in django

    - by amateur
    Hi guys, I saw a couple of ways extending user information of users and decided to adopt the model inheritance method. for instance, I have : class Parent(User): contact_means = models.IntegerField() is_staff = False objects = userManager() Now it is done, I've downloaded django_registration to help me out with sending emails to new users. The thing is, instead of using registration forms to register new user, I want to to invoke the email sending/acitvation capability of django_registration. So my workflow is: 1. add new Parent object in admin page. 2. send email My problem is, the django-registration creates a new registration profile together with a new user in the user table. how do I tweak this such that I am able to add the user entry into the custom user table. I have tried to create a modelAdmin and alter the save_model method to launch the create_inactive_user from django_registration, however I do not how to save the user object generated from django_registration into my Parent table when I have using model inheritance and I do not have a Foreign key attribute in my parent model.

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  • Django models avaoid duplicates

    - by Hulk
    In models, class Getdata(models.Model): title = models.CharField(max_length=255) state = models.CharField(max_length=2, choices=STATE, default="0") name = models.ForeignKey(School) created_by = models.ForeignKey(profile) def __unicode__(self): return self.id() In templates <form> <input type="submit" save the data/> </form> If the user clicks on the save button and the above data is saved in the table how to avoid the duplicates,i.e, if the user again clicks on the same submit button there should not be another entry for the same values.Or is it some this that has to be handeled in views Thanks..

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  • What algorithms are suitable for this simple machine learning problem?

    - by user213060
    I have a what I think is a simple machine learning question. Here is the basic problem: I am repeatedly given a new object and a list of descriptions about the object. For example: new_object: 'bob' new_object_descriptions: ['tall','old','funny']. I then have to use some kind of machine learning to find previously handled objects that had similar descriptions, for example, past_similar_objects: ['frank','steve','joe']. Next, I have an algorithm that can directly measure whether these objects are indeed similar to bob, for example, correct_objects: ['steve','joe']. The classifier is then given this feedback training of successful matches. Then this loop repeats with a new object. a Here's the pseudo-code: Classifier=new_classifier() while True: new_object,new_object_descriptions = get_new_object_and_descriptions() past_similar_objects = Classifier.classify(new_object,new_object_descriptions) correct_objects = calc_successful_matches(new_object,past_similar_objects) Classifier.train_successful_matches(object,correct_objects) But, there are some stipulations that may limit what classifier can be used: There will be millions of objects put into this classifier so classification and training needs to scale well to millions of object types and still be fast. I believe this disqualifies something like a spam classifier that is optimal for just two types: spam or not spam. (Update: I could probably narrow this to thousands of objects instead of millions, if that is a problem.) Again, I prefer speed when millions of objects are being classified, over accuracy. What are decent, fast machine learning algorithms for this purpose?

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  • Cookies with urllib

    - by CMC
    This will probably seem like a really simple question, and I am quite confused as to why this is so difficult for me. I would like to write a function that takes three inputs: [url, data, cookies] that will use urllib (not urllib2) to get the contents of the requested url. I figured it'd be simple, so I wrote the following: def fetch(url, data = None, cookies = None): if isinstance(data, dict): data = urllib.urlencode(data) if isinstance(cookies, dict): # TODO: find a better way to do this cookies = "; ".join([str(key) + "=" + str(cookies[key]) for key in cookies]) opener = urllib.FancyURLopener() opener.addheader("Cookie", cookies) obj = opener.open(url, data) result = obj.read() obj.close() return result This doesn't work, as far as I can tell (can anyone confirm that?) and I'm stumped.

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  • Socket Lose Connection

    - by Dave Dixon
    I know Twisted can do this well but what about just plain socket? How'd you tell if you randomly lost your connection in socket? Like, If my internet was to go out of a second and come back on.

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