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  • Implementing __concat__

    - by Casebash
    I tried to implement __concat__, but it didn't work >>> class lHolder(): ... def __init__(self,l): ... self.l=l ... def __concat__(self, l2): ... return self.l+l2 ... def __iter__(self): ... return self.l.__iter__() ... >>> lHolder([1])+[2] Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: unsupported operand type(s) for +: 'lHolder' and 'list' How can I fix this?

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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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  • PyGTK/GIO: monitor directory for changes recursively

    - by detly
    Take the following demo code (from the GIO answer to this question), which uses a GIO FileMonitor to monitor a directory for changes: import gio def directory_changed(monitor, file1, file2, evt_type): print "Changed:", file1, file2, evt_type gfile = gio.File(".") monitor = gfile.monitor_directory(gio.FILE_MONITOR_NONE, None) monitor.connect("changed", directory_changed) import glib ml = glib.MainLoop() ml.run() After running this code, I can then create and modify child nodes and be notified of the changes. However, this only works for immediate children (I am aware that the docs don't say otherwise). The last of the following shell commands will not result in a notification: touch one mkdir two touch two/three Is there an easy way to make it recursive? I'd rather not manually code something that looks for directory creation and adds a monitor, removing them on deletion, etc. The intended use is for a VCS file browser extension, to be able to cache the statuses of files in a working copy and update them individually on changes. So there might by anywhere from tens to thousands (or more) directories to monitor. I'd like to just find the root of the working copy and add the file monitor there. I know about pyinotify, but I'm avoiding it so that this works under non-Linux kernels such as FreeBSD or... others. As far as I'm aware, the GIO FileMonitor uses inotify underneath where available, and I can understand not emphasising the implementation to maintain some degree of abstraction, but it suggested to me that it should be possible. (In case it matters, I originally posted this on the PyGTK mailing list.)

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  • Django select max id

    - by pistacchio
    Hi, given a standard model (called Image) with an autoset 'id', how do I get the max id? So far I've tried: max_id = Image.objects.all().aggregate(Max('id')) but I get a 'id__max' Key error. Trying max_id = Image.objects.order_by('id')[0].id gives a 'argument 2 to map() must support iteration' exception Any help?

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  • Django Haystack exact filtering

    - by blackrobot
    I have a haystack search which has the following SearchIndex: class GrantIndex(indexes.SearchIndex): """ This provides the search index for the Grant application. """ text = indexes.CharField(document=True, use_template=True) year = indexes.IntegerField(model_attr='year__year') date = indexes.DateField(model_attr='date') program = indexes.CharField(model_attr='program__area') grantee = indexes.CharField(model_attr='grantee') amount = indexes.IntegerField(model_attr='amount') site.register(Grant, GrantIndex) If I want to search filtering out any programs that ARE NOT 'Health', I run the following query: from haystack.query import SearchQuerySet sqs = SearchQuerySet() sqs = sqs.filter(program='Health') Unfortunately, this also produces objects from the program 'Health\Other' and 'Health\Cardiovascular'. How do I stop the search from allowing those other programs in? I run Ubuntu 9.10 with Xapian as my search back-end.

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  • Programming an Event listener for files in a directory on Linux

    - by Epitaph
    On Ubuntu linux, when you watch a flash video, it gets saved temporarily in the /tmp as flv files while the video buffers. I use vlc to directly play these files. Currently, I have scripted a shortcut that directly scans and opens the latest file in /tmp with vlc, when clicked. But, I want to program a Java application that will continually monitor this /tmp directory for any new flv files, and open it in vlc automatically. I know I can use Runtime.exec() to open the VLC application with the flv files. But, I DO NOT want to run a while(true) loop (with sleep) to scan for files. How can I make use of Event Handling (Java or any other language) on Linux to complete this task?

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  • Increasing coverage with try-except-finally and a context-manager

    - by Daan Timmer
    This is the flow that I have in my program 277: try: 278: with open(r"c:\afile.txt", "w") as aFile: ...: pass # write data 329: except IOError as ex: ...: print ex 332: finally: 333: if os.path.exists(r"c:\afile.txt"): 334: shutil.copy(r"c:\afile.txt", r"c:\dest.txt") I've got all paths covered except for from line 278 to line 333 I got a normal happy-flow. I stubbed __builtin__.open to raise IOError when the open is called with said file name But how do I go from 278 to 333. Is this even possible? Additional information: - using coverage.py 3.4 (only listing 3.5, we can't currently upgrade to 3.5)

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  • OperationalError: foreign key mismatch

    - by Niek de Klein
    I have two tables that I'm filling, 'msrun' and 'feature'. 'feature' has a foreign key pointing to the 'msrun_name' column of the 'msrun' table. Inserting in the tables works fine. But when I try to delete from the 'feature' table I get the following error: pysqlite2.dbapi2.OperationalError: foreign key mismatch From the rules of foreign keys in the manual of SQLite: - The parent table does not exist, or - The parent key columns named in the foreign key constraint do not exist, or - The parent key columns named in the foreign key constraint are not the primary key of the parent table and are not subject to a unique constraint using collating sequence specified in the CREATE TABLE, or - The child table references the primary key of the parent without specifying the primary key columns and the number of primary key columns in the parent do not match the number of child key columns. I can see nothing that I'm violating. My create tables look like this: DROP TABLE IF EXISTS `msrun`; -- ----------------------------------------------------- -- Table `msrun` -- ----------------------------------------------------- CREATE TABLE IF NOT EXISTS `msrun` ( `msrun_name` VARCHAR(40) PRIMARY KEY NOT NULL , `description` VARCHAR(500) NOT NULL ); DROP TABLE IF EXISTS `feature`; -- ----------------------------------------------------- -- Table `feature` -- ----------------------------------------------------- CREATE TABLE IF NOT EXISTS `feature` ( `feature_id` VARCHAR(40) PRIMARY KEY NOT NULL , `intensity` DOUBLE NOT NULL , `overallquality` DOUBLE NOT NULL , `charge` INT NOT NULL , `content` VARCHAR(45) NOT NULL , `msrun_msrun_name` VARCHAR(40) NOT NULL , CONSTRAINT `fk_feature_msrun1` FOREIGN KEY (`msrun_msrun_name` ) REFERENCES `msrun` (`msrun_name` ) ON DELETE NO ACTION ON UPDATE NO ACTION); CREATE UNIQUE INDEX `id_UNIQUE` ON `feature` (`feature_id` ASC); CREATE INDEX `fk_feature_msrun1` ON `feature` (`msrun_msrun_name` ASC); As far as I can see the parent table exists, the foreign key is pointing to the right parent key, the parent key is a primary key and the foreign key specifies the primary key column. The script that produces the error: from pysqlite2 import dbapi2 as sqlite import parseFeatureXML connection = sqlite.connect('example.db') cursor = connection.cursor() cursor.execute("PRAGMA foreign_keys=ON") inputValues = ('example', 'description') cursor.execute("INSERT INTO `msrun` VALUES(?, ?)", inputValues) featureXML = parseFeatureXML.Reader('../example_scripts/example_files/input/featureXML_example.featureXML') for feature in featureXML.getSimpleFeatureInfo(): inputValues = (featureXML['id'], featureXML['intensity'], featureXML['overallquality'], featureXML['charge'], featureXML['content'], 'example') # insert the values into msrun using ? for sql injection safety cursor.execute("INSERT INTO `feature` VALUES(?,?,?,?,?,?)", inputValues) connection.commit() for feature in featureXML.getSimpleFeatureInfo(): cursor.execute("DELETE FROM `feature` WHERE feature_id = ?", (str(featureXML['id']),))

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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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  • 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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  • how to load an image to a grid using pygame, instead of just using a fill color?

    - by yao jiang
    I am trying to create a "map of a city" using pygame. I want to be able to put images of buildings in specific grid coords rather than just filling them in with a color. This is how I am creating this map grid: def clear(): for r in range(rows): for c in range(rows): if r%3 == 1 and c%3 == 1: color = brown; grid[r][c] = 1; else: color = white; grid[r][c] = 0; pygame.draw.rect(screen, color, [(margin+width)*c+margin, (margin+height)*r+margin, width, height]) pygame.display.flip(); Now how do I put images of buildings in those brown colored grids at those specific locations? I've tried some of the samples online but can't seem to get them to work. Any help is appreciated. If anyone have a good source for free sprites that I can use for pygame, please let me know. Thanks!

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  • Using numpy.apply

    - by andylei
    What's wrong with this snippet of code? import numpy as np from scipy import stats d = np.arange(10.0) cutoffs = [stats.scoreatpercentile(d, pct) for pct in range(0, 100, 20)] f = lambda x: np.sum(x > cutoffs) fv = np.vectorize(f) # why don't these two lines output the same values? [f(x) for x in d] # => [0, 1, 2, 2, 3, 3, 4, 4, 5, 5] fv(d) # => array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) Any ideas?

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  • openerp client customization

    - by iamgopal
    openerp client seems to be nice and working , i would like to hack it and use it as a front end to my open erp solution. but the documentation regarding client side design or customization is poor on openerp site , is there any good reference or documentation available for further digging in to openerp client side coding ? or more : if any similar client solution available that can be plug in to any back end system. ( i.e. rich internet client )

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  • PyQt: Get the position of QGraphicsWidgets in a QGraphicsGridLayout

    - by Chris Phillips
    I have a fairly simple PyQt application in which I'm placing instances of a QGraphicsWidget in a QGraphicsGridLayout and want to connect the widgets with lines drawn with a QGraphicsPath. Unfortunately, no matter what I try, I always get (0, 0) back as the position for both the start and end widgets. I'm constructing the graph with a recursive function that adds widgets to the scene and layout. Once the recursive function is complete, the layout is added to a new widget, which is added to the scene to show everything. The edges are added to the scene as widgets are created. How do I get a non-zero position of any of the widgets in the grid layout?

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  • Is this the correct way to convert a UTC datetime string into localtime?

    - by Steve
    Is this the correct way to convert a UTC string into local time allowing for daylight savings? It looks ok to me but you never know :) import time UTC_STRING = "2010-03-25 02:00:00" stamp = time.mktime(time.strptime(UTC_STRING,"%Y-%m-%d %H:%M:%S")) stamp -= time.timezone now = time.localtime() if now[8] == 1: stamp += 60*60 elif now[8] == -1: stamp -= 60*60 print 'UTC: ', time.gmtime(stamp) print 'Local: ', time.localtime(stamp) --- Results from New Zealand (GMT+12 dst=1) --- UTC: (2010, 3, 25, 2, 0, 0, 3, 84, 0) Local: (2010, 3, 25, 15, 0, 0, 3, 84, 1)

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  • Making HTTP POST request

    - by infrared
    I'm trying to make a POST request to retrieve information about a book. Here is the code that returns HTTP code: 302, Moved import httplib, urllib params = urllib.urlencode({ 'isbn' : '9780131185838', 'catalogId' : '10001', 'schoolStoreId' : '15828', 'search' : 'Search' }) headers = {"Content-type": "application/x-www-form-urlencoded", "Accept": "text/plain"} conn = httplib.HTTPConnection("bkstr.com:80") conn.request("POST", "/webapp/wcs/stores/servlet/BuybackSearch", params, headers) response = conn.getresponse() print response.status, response.reason data = response.read() conn.close() When I try from a browser, from this page: http://www.bkstr.com/webapp/wcs/stores/servlet/BuybackMaterialsView?langId=-1&catalogId=10001&storeId=10051&schoolStoreId=15828 , it works. What am I missing in my code? Thanks EDIT: Here's what I get when I call print response.msg 302 Moved Date: Tue, 07 Sep 2010 16:54:29 GMT Vary: Host,Accept-Encoding,User-Agent Location: http://www.bkstr.com/webapp/wcs/stores/servlet/BuybackSearch X-UA-Compatible: IE=EmulateIE7 Content-Length: 0 Content-Type: text/plain; charset=utf-8 Seems that the location points to the same url I'm trying to access in the first place? EDIT2: I've tried using urllib2 as suggested here. Here is the code: import urllib, urllib2 url = 'http://www.bkstr.com/webapp/wcs/stores/servlet/BuybackSearch' values = {'isbn' : '9780131185838', 'catalogId' : '10001', 'schoolStoreId' : '15828', 'search' : 'Search' } data = urllib.urlencode(values) req = urllib2.Request(url, data) response = urllib2.urlopen(req) print response.geturl() print response.info() the_page = response.read() print the_page And here is the output: http://www.bkstr.com/webapp/wcs/stores/servlet/BuybackSearch Date: Tue, 07 Sep 2010 16:58:35 GMT Pragma: No-cache Cache-Control: no-cache Expires: Thu, 01 Jan 1970 00:00:00 GMT Set-Cookie: JSESSIONID=0001REjqgX2axkzlR6SvIJlgJkt:1311s25dm; Path=/ Vary: Accept-Encoding,User-Agent X-UA-Compatible: IE=EmulateIE7 Content-Length: 0 Connection: close Content-Type: text/html; charset=utf-8 Content-Language: en-US Set-Cookie: TSde3575=225ec58bcb0fdddfad7332c2816f1f152224db2f71e1b0474c866f3b; Path=/

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  • Modify Django settings variables in a middleware

    - by jack
    I set a variable MAX_REQUEST = 100 in settings.py I write a middleware which may lower this value for request origining from a proxy ip address by the following code: settings.MAX_REQUEST = 10 However, looks like the above modification affects all legitimate users. Is it normal?

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  • 404 not found in telnet, works fine in browser

    - by Viranch Mehta
    i am having a very irritating problem, when i open a url ( http://celebs.widewallpapers.net/md/a/adriana-lima/1440/Adriana-Lima-1440x900-002.jpg ) in browser, it works fine.. but when i try to access it by telnet on bash, i get 404 not found!! my exact terminal: $ telnet celebs.widewallpapers.net 80 HEAD /md/a/adriana-lima/1440/Adriana-Lima-1440x900-002.jpg HTTP/1.0 [enter] [enter] HTTP/1.1 404 Not Found Server: nginx Date: Sun, 23 May 2010 21:36:05 GMT Content-Type: text/html; charset=windows-1251 Content-Length: 166 Connection: close please help me with this as i m trying to make a C batch-downloader, which is almost working as same as the telnet.

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  • Issue reading packets from a pcap file. dpkt module. What gives?

    - by Chris
    I am running the following test script to try to read packets from a sample .pcap file I have downloaded. It won't seem to run. I have all of the modules, but no examples seem to be running. import socket import dpkt import sys pcapReader = dpkt.pcap.Reader(file("test1.pcap", "rb")) for ts, data in pcapReader: ether = dpkt.ethernet.Ethernet(data) if ether.type != dpkt.ethernet.ETH_TYPE_IP: raise ip = ether.data src = socket.inet_ntoa(ip.src) dst = socket.inet_ntoa(ip.dst) print "%s -> %s" % (src, dst) For some reason, this is not being interpreted properly. When running it, I get KeyError: 138 module body in test.py at line 4 function __init__ in pcap.py at line 105 Program exited. Why is this? What's wrong?

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  • Unexplained file not found for an existing file

    - by knishua
    Following is the error that occurs in this part of the code. Although the path is valid, a RuntimeError occurs—strange. What is happening, and how can I get this to work? for root,dirs,files in os.walk(self.path): for f in files : if (f.split('.')[1] == "mb"): z = utils.executeInMainThreadWithResult(self.contains,(f.split('.')[0])) if not (isinstance(z,NoneType)): cmds.symbolButton(self.arSubCategory + f.split('.')[0], image=(z[1].replace("\\","/")), width = 35,height = 70, c = "h.imp_file(" + "\"" + root.replace("\\","/") + "/" + f + "\"" + ")") def contains(self,imageName): print 'imageName : ',imageName,'\n' for root, dirs, files in os.walk(self.path+"images"): for g in files: x = re.search(imageName,g) if not (isinstance(x, NoneType)): print 'g ',root+"/"+g.replace("\\","/"),'\n' return (1,(root+"/"+g)) Error: # z is (1, 'T:/Reference_Library/Reference_work/Char_models/Workfiles/images\\rboxdisk1\\female\\highpoly/granny01_highpoly.jpg') Error: File not found: T:/Reference_Library/Reference_work/Char_models/Workfiles/images/rboxdisk1/female/highpoly/granny01_highpoly.jpg Traceback (most recent call last): File "<maya console>", line 115, in <module> File "<maya console>", line 65, in showWindowanimLibrary RuntimeError: File not found: T:/Reference_Library/Reference_work/Char_models/Workfiles/images/rboxdisk1/female/highpoly/granny01_highpoly.jpg

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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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  • PyOpenGL - passing transformation matrix into shader

    - by M-V
    I am having trouble passing projection and modelview matrices into the GLSL shader from my PyOpenGL code. My understanding is that OpenGL matrices are column major, but when I pass in projection and modelview matrices as shown, I don't see anything. I tried the transpose of the matrices, and it worked for the modelview matrix, but the projection matrix doesn't work either way. Here is the code: import OpenGL from OpenGL.GL import * from OpenGL.GL.shaders import * from OpenGL.GLU import * from OpenGL.GLUT import * from OpenGL.GLUT.freeglut import * from OpenGL.arrays import vbo import numpy, math, sys strVS = """ attribute vec3 aVert; uniform mat4 uMVMatrix; uniform mat4 uPMatrix; uniform vec4 uColor; varying vec4 vCol; void main() { // option #1 - fails gl_Position = uPMatrix * uMVMatrix * vec4(aVert, 1.0); // option #2 - works gl_Position = vec4(aVert, 1.0); // set color vCol = vec4(uColor.rgb, 1.0); } """ strFS = """ varying vec4 vCol; void main() { // use vertex color gl_FragColor = vCol; } """ # particle system class class Scene: # initialization def __init__(self): # create shader self.program = compileProgram(compileShader(strVS, GL_VERTEX_SHADER), compileShader(strFS, GL_FRAGMENT_SHADER)) glUseProgram(self.program) self.pMatrixUniform = glGetUniformLocation(self.program, 'uPMatrix') self.mvMatrixUniform = glGetUniformLocation(self.program, "uMVMatrix") self.colorU = glGetUniformLocation(self.program, "uColor") # attributes self.vertIndex = glGetAttribLocation(self.program, "aVert") # color self.col0 = [1.0, 1.0, 0.0, 1.0] # define quad vertices s = 0.2 quadV = [ -s, s, 0.0, -s, -s, 0.0, s, s, 0.0, s, s, 0.0, -s, -s, 0.0, s, -s, 0.0 ] # vertices self.vertexBuffer = glGenBuffers(1) glBindBuffer(GL_ARRAY_BUFFER, self.vertexBuffer) vertexData = numpy.array(quadV, numpy.float32) glBufferData(GL_ARRAY_BUFFER, 4*len(vertexData), vertexData, GL_STATIC_DRAW) # render def render(self, pMatrix, mvMatrix): # use shader glUseProgram(self.program) # set proj matrix glUniformMatrix4fv(self.pMatrixUniform, 1, GL_FALSE, pMatrix) # set modelview matrix glUniformMatrix4fv(self.mvMatrixUniform, 1, GL_FALSE, mvMatrix) # set color glUniform4fv(self.colorU, 1, self.col0) #enable arrays glEnableVertexAttribArray(self.vertIndex) # set buffers glBindBuffer(GL_ARRAY_BUFFER, self.vertexBuffer) glVertexAttribPointer(self.vertIndex, 3, GL_FLOAT, GL_FALSE, 0, None) # draw glDrawArrays(GL_TRIANGLES, 0, 6) # disable arrays glDisableVertexAttribArray(self.vertIndex) class Renderer: def __init__(self): pass def reshape(self, width, height): self.width = width self.height = height self.aspect = width/float(height) glViewport(0, 0, self.width, self.height) glEnable(GL_DEPTH_TEST) glDisable(GL_CULL_FACE) glClearColor(0.8, 0.8, 0.8,1.0) glutPostRedisplay() def keyPressed(self, *args): sys.exit() def draw(self): glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) # build projection matrix fov = math.radians(45.0) f = 1.0/math.tan(fov/2.0) zN, zF = (0.1, 100.0) a = self.aspect pMatrix = numpy.array([f/a, 0.0, 0.0, 0.0, 0.0, f, 0.0, 0.0, 0.0, 0.0, (zF+zN)/(zN-zF), -1.0, 0.0, 0.0, 2.0*zF*zN/(zN-zF), 0.0], numpy.float32) # modelview matrix mvMatrix = numpy.array([1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.5, 0.0, -5.0, 1.0], numpy.float32) # render self.scene.render(pMatrix, mvMatrix) # swap buffers glutSwapBuffers() def run(self): glutInitDisplayMode(GLUT_RGBA) glutInitWindowSize(400, 400) self.window = glutCreateWindow("Minimal") glutReshapeFunc(self.reshape) glutDisplayFunc(self.draw) glutKeyboardFunc(self.keyPressed) # Checks for key strokes self.scene = Scene() glutMainLoop() glutInit(sys.argv) prog = Renderer() prog.run() When I use option #2 in the shader without either matrix, I get the following output: What am I doing wrong?

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  • How do I automatically rebuild the Sphinx index under django-sphinx?

    - by Apreche
    I just setup django-sphinx, and it is working beautifully. I am now able to search my model and get amazing results. The one problem is that I have to build the index by hand using the indexer command. That means every time I add new content, I have to manually hit the command line to rebuild the search index. That is just not acceptable. I could make a cron job that automatically runs the indexer command every so often, but that's far from optimal. New data won't be indexed until the cron runs again. In addition, the indexer will run unnecessarily most times as my site doesn't have data being added very often. How do I set it up so that the Sphinx index will automatically rebuild itself whenever data is added to or modified in a searchable django model?

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