EuroPython 2020: Talk voting is open
Talk voting is your chance to tell us what you’d like to see at EuroPython 2020. We will leave talk voting open until: Sunday, April 26 23:59:59 CEST In order to vote, please log in to the website and then navigate to the talk voting page: * EuroPython 2020 Talk Voting * https://ep2020.europython.eu/events/talk-voting/ How talk voting works - The talk voting page lists all submitted proposals, including talks, helpdesks and posters. The proposals are sorted in random order. In order to vote, have a look at the title/abstract and then indicate your personal interest in attending this session. We have simplified the voting process and you may choose between these four options: - must see - want to see - maybe - not interested The talks you haven’t voted for are marked “No vote”. Your votes are automatically saved to the backend without the need to click on a save or submit button. Who can participate? Any registered attendee of the current EuroPython (*) as well as any attendee of one of the past EuroPython conferences going back to 2015 can vote. If you have submitted a proposal this year, you are also eligible to vote. Talk Selection -- After the talk voting phase, the EuroPython Program Workgroup (WG) will use the votes to select the talks and build a schedule. The talk voting is a good and strong indicator what attendees are interested to see. Submissions are also selected based on editorial criteria to e.g. increase diversity, give a chance to less mainstream topics as well as avoid too much of the same topic. In general, the Program WG will try to give as many speakers a chance to talk as possible. If speakers have submitted multiple talks, the one with the highest rating will most likely get selected. (*) We will start ticket sales in the coming days to give you a chance to participate in talk voting as well. Help spread the word Please help us spread this message by sharing it on your social networks as widely as possible. Thank you ! Link to the blog post: https://blog.europython.eu/post/615363800716263424/europython-2020-talk-voting-is-open Tweet: https://twitter.com/europython/status/1249994570236076032 Thanks, -- EuroPython 2020 Team https://ep2020.europython.eu/ https://www.europython-society.org/ -- https://mail.python.org/mailman/listinfo/python-list
To apply pca for a large csv
Hello all, i have a csv of 1 gb which consists of 25000 columns and 2 rows. I want to apply pca so i have seen sciki-learn had inbuilt fucntionality to use that. But i have seen to do eo you have to load data in data frame. But my machine is i5 with 8 gb of ram which fails to load all this data in data frame and shows memory error. Is there any alternative way that still i could aaply PCA on the same machine to the same rata set -- https://mail.python.org/mailman/listinfo/python-list
Re: To apply pca for a large csv
On Tue, Apr 14, 2020 at 9:41 PM Rahul Gupta wrote: > > Hello all, i have a csv of 1 gb which consists of 25000 columns and 2 > rows. I want to apply pca so i have seen sciki-learn had inbuilt > fucntionality to use that. But i have seen to do eo you have to load data in > data frame. But my machine is i5 with 8 gb of ram which fails to load all > this data in data frame and shows memory error. Is there any alternative way > that still i could aaply PCA on the same machine to the same rata set > Are you running a 32-bit or 64-bit version of Python? ChrisA -- https://mail.python.org/mailman/listinfo/python-list
Strong AI Steps for Coding AI Mind in Python
1. Code the MainLoop module -- http://ai.neocities.org/MainLoop.html Code the MainLoop in Python. Use either an actual loop with subroutine calls, or make a ringlet of perhaps object-oriented module stubs, each calling the next stub. Provide the ESCAPE key or other mechanisms for the user to stop the AI. 2. Code the Sensorium module or subroutine -- http://ai.neocities.org/Sensorium.html Start a subroutine or module that is able to sense something coming in from the outside world, i.e., a key-press on the keyboard. 3. Stub in the EnThink module for English thinking -- http://ai.neocities.org/EnThink.html 4. Initiate the AudInput module for keyboard or acoustic input. Drop any [ESCAPE] mechanism down by one tier, into the AudInput module, but do not eliminate or bypass the quite essential Sensorium module, because another programmer may wish to specialize in implementing some elaborate sensory modality among your sensory input stubs. Code the AudInput module initially to deal with ASCII keyboard input. If you are an expert at speech recognition, extrapolate backwards from the storage requirements (space and format) of the acoustic input of real phonemes in your AudInput system, so that the emerging robot Mind may be ready in advance for the switch from hearing by keyboard to hearing by microphone or artificial ear. 5. The TabulaRasa loop. Before you can create an auditory memory AudMem subroutine for storing input from the keyboard, you may need to code a "TabulaRasa" loop that will fill the mental memory of the AI with blank engrams, thus reserving the memory space and preventing error messages about unavailable locations in the AI memory. 6. MindBoot English +/- Russian bootstrap -- http://ai.neocities.org/MindBoot.html The knowledge base (MindBoot) module makes it possible for the Strong AI Mind to begin thinking immediately when you launch the more advanced AI program. Here we stub in the EnBoot subroutine with an English word or two before the AudMem module begins to store new words coming from the AudInput module. The EnBoot stub shows us that the first portion of the AI mental memory is reserved for the innate concepts and the English words that express each concept. If you use the same Unicode that Perl enjoys to create a Strong AI Mind in Arabic, Chinese, Hungarian, Indonesian, Japanese, Korean, Swahili, Urdu or any other natural human language, you will need to create a bootstrap module for your chosen human language. 7. AudMem (Auditory Memory) -- http://ai.neocities.org/AudMem.html Into the auditory array that was filled with blank spaces by the TabulaRasa sequence and primed with some bootstrap content by the EnBoot or MindBoot sequence, insert some new memories with the AudMem auditory memory module. Modify the AudInput module to prompt for English words and modify the EnThink module to display words stored in memory as if they were a thought being generated in English (or in your chosen natural human language). 8. Speech Module -- http://ai.neocities.org/Speech.html The Speech module fetches characters from a starting point in auditory memory and displays the characters on-screen until a blank space occurs to signify the end of the word stored in memory. 9. NewConcept Module -- http://ai.neocities.org/NewConcept.html The NewConcept module creates a new concept for any unrecognized word in the input stream, even a misspelled word entered by mistake. In Symbolic AI, each word of natural language is the symbol of a concept, and as such is the key to accessing the concept. Of course, a recognized image may also grant access to a concept. 10. EnParser English Parsing Module -- http://ai.neocities.org/EnParser.html The EnParser (English parser) module does not so much determine the part of speech of a word of input, but more importantly it assigns to an input word its grammatical role in the complete phrase being processed during Natural Language Understanding. 12. AudRecog auditory Recognition Module -- http://ai.neocities.org/AudRecog.html The AudRecog module for auditory recognition recognizes various forms of a word, such as singular or plural nouns, or verbs with various inflected endings. 13. OldConcept Module -- http://ai.neocities.org/OldConcept.html If the AudRecog module recognizes a particular word, then the AudInput module calls the OldConcept module to create a new instance of the previously known concept. If a word is not recognized, AudInput calls the NewConcept module to create a new concept for the word as a symbol. 14. SpreadAct Spreading Activation Module -- http://ai.neocities.org/Spreadact.html The SpreadAct module for Spreading Activation performs both simple spreading activation between concepts and also an extremely sophisticated role of responding to various input queries posed by human users. 15. EnNounPhrase English Noun-Phrase Module -- http://ai.neocities.org/EnNounPh
Re: Strong AI Steps for Coding AI Mind in Python
On Tue, Apr 14, 2020 at 8:21 AM wrote: > > 1. Code the MainLoop module -- http://ai.neocities.org/MainLoop.html > > Code the MainLoop in Python. Use either an actual loop with subroutine calls, > or make a ringlet of perhaps object-oriented module stubs, each calling the > next stub. Provide the ESCAPE key or other mechanisms for the user to stop > the AI. > > 2. Code the Sensorium module or subroutine -- > http://ai.neocities.org/Sensorium.html > > Start a subroutine or module that is able to sense something coming in from > the outside world, i.e., a key-press on the keyboard. > > 3. Stub in the EnThink module for English thinking -- > http://ai.neocities.org/EnThink.html > > 4. Initiate the AudInput module for keyboard or acoustic input. > > Drop any [ESCAPE] mechanism down by one tier, into the AudInput module, but > do not eliminate or bypass the quite essential Sensorium module, because > another programmer may wish to specialize in implementing some elaborate > sensory modality among your sensory input stubs. Code the AudInput module > initially to deal with ASCII keyboard input. If you are an expert at speech > recognition, extrapolate backwards from the storage requirements (space and > format) of the acoustic input of real phonemes in your AudInput system, so > that the emerging robot Mind may be ready in advance for the switch from > hearing by keyboard to hearing by microphone or artificial ear. > > 5. The TabulaRasa loop. > > Before you can create an auditory memory AudMem subroutine for storing input > from the keyboard, you may need to code a "TabulaRasa" loop that will fill > the mental memory of the AI with blank engrams, thus reserving the memory > space and preventing error messages about unavailable locations in the AI > memory. > > 6. MindBoot English +/- Russian bootstrap -- > http://ai.neocities.org/MindBoot.html > > The knowledge base (MindBoot) module makes it possible for the Strong AI Mind > to begin thinking immediately when you launch the more advanced AI program. > Here we stub in the EnBoot subroutine with an English word or two before the > AudMem module begins to store new words coming from the AudInput module. The > EnBoot stub shows us that the first portion of the AI mental memory is > reserved for the innate concepts and the English words that express each > concept. If you use the same Unicode that Perl enjoys to create a Strong AI > Mind in Arabic, Chinese, Hungarian, Indonesian, Japanese, Korean, Swahili, > Urdu or any other natural human language, you will need to create a bootstrap > module for your chosen human language. > > 7. AudMem (Auditory Memory) -- http://ai.neocities.org/AudMem.html > > Into the auditory array that was filled with blank spaces by the TabulaRasa > sequence and primed with some bootstrap content by the EnBoot or MindBoot > sequence, insert some new memories with the AudMem auditory memory module. > Modify the AudInput module to prompt for English words and modify the EnThink > module to display words stored in memory as if they were a thought being > generated in English (or in your chosen natural human language). > > > 8. Speech Module -- http://ai.neocities.org/Speech.html > > The Speech module fetches characters from a starting point in auditory memory > and displays the characters on-screen until a blank space occurs to signify > the end of the word stored in memory. > > > 9. NewConcept Module -- http://ai.neocities.org/NewConcept.html > > The NewConcept module creates a new concept for any unrecognized word in the > input stream, even a misspelled word entered by mistake. In Symbolic AI, each > word of natural language is the symbol of a concept, and as such is the key > to accessing the concept. Of course, a recognized image may also grant access > to a concept. > > > > 10. EnParser English Parsing Module -- http://ai.neocities.org/EnParser.html > > The EnParser (English parser) module does not so much determine the part of > speech of a word of input, but more importantly it assigns to an input word > its grammatical role in the complete phrase being processed during Natural > Language Understanding. > > > > 12. AudRecog auditory Recognition Module -- > http://ai.neocities.org/AudRecog.html > > The AudRecog module for auditory recognition recognizes various forms of a > word, such as singular or plural nouns, or verbs with various inflected > endings. > > > > 13. OldConcept Module -- http://ai.neocities.org/OldConcept.html > > If the AudRecog module recognizes a particular word, then the AudInput module > calls the OldConcept module to create a new instance of the previously known > concept. If a word is not recognized, AudInput calls the NewConcept module to > create a new concept for the word as a symbol. > > > > 14. SpreadAct Spreading Activation Module -- > http://ai.neocities.org/Spreadact.html > > The SpreadAct module for Spreading Activation performs both simple spreading > act
Installation of Camelot
I am a new Python user and using "Installation of Camelot" site I have python 3.7 64bit and Anaconda installed site states The easiest way to install Camelot is to install it with conda, which is a package manager and environment management system for the Anaconda distribution. The dependencies Tkinter and ghostscript can be installed using your system’s package manager. You can run one of the following, based on your OS. anaconda navigator shows I have TK installed but is it Tkinter? then I did with Juypter $ apt install python3-tk ghostscript File "", line 1 $ apt install python3-tk ghostscript ^ SyntaxError: invalid syntax what am I missing? any help will be appreciated -- https://mail.python.org/mailman/listinfo/python-list
Re: Installation of Camelot
On 2020-04-14 16:20, anson freer wrote: I am a new Python user and using "Installation of Camelot" site I have python 3.7 64bit and Anaconda installed site states The easiest way to install Camelot is to install it with conda, which is a package manager and environment management system for the Anaconda distribution. The dependencies Tkinter and ghostscript can be installed using your system’s package manager. You can run one of the following, based on your OS. anaconda navigator shows I have TK installed but is it Tkinter? then I did with Juypter $ apt install python3-tk ghostscript File "", line 1 $ apt install python3-tk ghostscript ^ SyntaxError: invalid syntax what am I missing? any help will be appreciated That _exactly_ did you type and where did you type it? Did you type the "$"? That's the command-line prompt for Linux. -- https://mail.python.org/mailman/listinfo/python-list
Re: Java JMS and python
Found this, it's old, but it's the code. I hope it helps. https://github.com/springpython/springpython Julio El mar., 14 de abr. de 2020 a la(s) 00:30, Sam (pyt...@net153.net) escribió: > > On 4/13/20 9:51 PM, Julio Oña wrote: > > Hi > > > > There is a tool for that (I didn't use it): > > https://docs.spring.io/spring-python/1.2.x/sphinx/html/jms.html > > > > Hope it works for you. > > Julio > > > > El lun., 13 de abr. de 2020 a la(s) 22:44, Chris Angelico > > (ros...@gmail.com) escribió: > >> > >> On Tue, Apr 14, 2020 at 11:20 AM Sam wrote: > >>> > >>> Hi, > >>> > >>> We are not a java shop and we are trying to interface with an API that > >>> is "JMS only". We asked if it supported activeMQ or STOMP and they > >>> replied that it is Sun JMS only. So what does that mean if we want to > >>> communicate with it from python or similar? Curious if > >>> anyone else has been down this path... > >>> > >> > >> I don't know what JMS is, but have you tried searching PyPI for it? > >> > >> Worst case, most of these sorts of protocols (if I'm reading you > >> correctly) are built on top of things that Python *does* understand > >> (TCP/IP, or HTTP, or somesuch), so you should be able to reimplement > >> the protocol yourself. But try PyPI first. > >> > >> ChrisA > >> -- > >> https://mail.python.org/mailman/listinfo/python-list > > > > > I had high hopes for the spring for python project.. but looks like it > is dead? Most docs for it point to dates of 2009 and all the source > links point to dead websites. > > Regards, > Sam > -- > https://mail.python.org/mailman/listinfo/python-list -- https://mail.python.org/mailman/listinfo/python-list
Re: To apply pca for a large csv
64 bit version -- https://mail.python.org/mailman/listinfo/python-list
speech reconition
speech recognition is working really slowly in my vs code what will i do can someone please help me -- https://mail.python.org/mailman/listinfo/python-list
Re: i am want to read data from the csv that i wrote using python csv module but apart from filed names and row count i am unable to read rest of the data
On 12 Apr 2020 12:30, Peter Otten <__pete...@web.de> wrote: Rahul Gupta wrote: > for line in enumerate(csv_reader): > print(line[csv_reader.fieldnames[1]]) enumerate() generates (index, line) tuples that you need to unpack: for index, line in enumerate(csv_reader): print(line[csv_reader.fieldnames[1]]) ==》 Shouldn't that be, e.g: print( line[csv_reader.fieldnames[1].index()] ) Or maybe: cols = csv_reader.fieldnames print( [[val for val, col in zip(record, cols) if col in ['somecol']] for record in csv_reader]) ? -- https://mail.python.org/mailman/listinfo/python-list
Re: To apply pca for a large csv
On Tue, 14 Apr 2020 at 12:42, Rahul Gupta wrote: > > Hello all, i have a csv of 1 gb which consists of 25000 columns and 2 > rows. I want to apply pca so i have seen sciki-learn had inbuilt > fucntionality to use that. But i have seen to do eo you have to load data in > data frame. But my machine is i5 with 8 gb of ram which fails to load all > this data in data frame and shows memory error. Is there any alternative way > that still i could aaply PCA on the same machine to the same rata set Do you know how to compute a covariance matrix "manually"? If so then it can be done while reading the data line by line without reading all of the data into memory at once. The problem though is that your 25000 columns mean that the matrix itself will fill most of your memory (25000**2*8 bytes == 5 GB using double precision floating point). You can make life much easier for yourself by choosing a subset of the columns that you are likely to be interested in and reducing the size of your dataset before you begin. -- Oscar -- https://mail.python.org/mailman/listinfo/python-list
