How can I specify two conditions to be placed on a one video to be downloaded?
i.e. Merging the two conditions stated below.
-f bestvideo[height=720]
-f bestvideo[ext=webm]
Using -f bestvideo[height=720][ext=webm] will download the format in the webm format that has a height of 720 px. This is explained in the README.
Related
Downloaded seven MP3 files from a website. exiftool says the Duration is two minutes.
Opened it in an audio editor and find that it is actually four minutes.
Opened a (non-downloaded) MP3 file in the same editor, duration different from two or four.
Copied all audio from the downloaded file and pasted over the other audio. Editor shows the other changing to four minutes.
exiftool shows the second file has a duration of four minutes.
Same behavior (different numbers) for the other six downloaded files. First one is the only one where the difference was approximately a factor of two (so it's not stereo vs. mono)
Is Duration an ID3 tag that can be falsified, as opposed to being measured from the actual audio?
There should be an (approx) in the exiftool output after the Duration value. Duration is not an embedded tag, it's a value that's calculated on the fly by exiftool. If you add the -G (-groupNames) option to your command, you'll see that it is part of the Composite tag group. If you check the listing there, you'll see the tags that exiftool uses to calculate the Duration. It's most likely the group that includes ID3Size and MPEG:AudioBitrate.
Exiftool doesn't read and parse the stream data, which the audio editor will do and get a more accurate result. Odds are there is something incorrect about the header for your file.
Related post on the exiftool forums.
I am fairly new to using newman and I am trying to figure out how exactly to create multiple iterations within one report.
I cannot find the htmlextra.js file anywhere locally on my laptop (Win 10) to just change that field stated on: https://hub.docker.com/r/dannydainton/htmlextra
image
Can anyone please help me out on how to add more than 1 iteration to a collection for the reporter?
Thank you very much and sorry to bother you all with this basic question, but I just cannot figure it out.
Iteration is set through newman and not htmlextra report , you can add iteration count through -n flag.
newman run collection.json -n 5 -r htmlextra
This will run collection 5 times
https://www.npmjs.com/package/newman will show all newman specific flag and
https://www.npmjs.com/package/newman-reporter-htmlextra shows all htmlextra specific flags
-n , --iteration-count Specifies the number of times the collection has to be run when used in conjunction with iteration
data file.
I was downloading a playlist from youtube using youtube-dl. I used autonumbering feature to number the videos which could be achieved by formatting the name of output file as follows -o "%(autonumber)s-%(title)s.%(ext)s". The download failed in between. Now I wish to start the autonumber from the video next to failed video and not 1. But autonumber resets itself to 1 everytime. How can I set it to a different number greater than 1 ?
For playlists you should use playlist_index instead: -o '%(playlist_index)s-%(title)s.%(ext)s'
Instead of %(playlist_index)s, you should use youtube-dl --autonumber-start 5 $URL -o "%(autonumber)s_stuff"
It will start %(autonumber)s at 5 in this case.
I am trying to use weka to classify text. What I do is this:
I create on big ARFF file with all of the data: all_of_it.arff.
I split that data into training and test:train.arff and test.arff
I do feature selection on the training set and output a new training file:train_fs.arff
I build a classifier with only those selected features.
And the problem is.....
I don't quite know how to standardize the test set to only use the features I selected from the training set. Something like create new test file from test.arff according to train_fs.arff
*I tried using
java -cp weka.jar weka.filters.unsupervised.attribute.Standardize -b -i train_fs.arff -o train2.arff -r test.arff -s test2.arff
but I got the infamous Src and Dest differ in # of attributes.
Is there any way to normalize/standardize the sets according to an arff file (namely my new training data with few features) I don't see how to do this with the Standardize or StringToWordVector filter.
Batch filtering is one solution to your problem.
Pros:
It will apply the same filter to your test dataset as you apply to your training dataset. When you perform feature selection, the two datasets will be compatible
Cons:
It is only availabe from the command line interface or Weka's Java API
The two datasets must be filtered at the same time
You can read more about Batch filtering here.
You may also want to look into InputMappedClassifier. It is a wrapper classifier that addresses incompatible training and testing data.
I am running a series of clustering analyses in weka and I have realized that automatizing it is the way to go if I want to get somewhere. I'll explain a bit how I am working.
I do all the pre-processing manually in R and save it as a csv file, importing it in weka and saving it again as an arff file.
I use weka's GUI, and in general I just open my data with in the arff file and go directly to the clustering tab and play around. (My experience using the CLI is limited).
I am trying to reproduce some results I've got by using the GUI, but now with commands in the CLI. The problem is that I usually ignore a list of attributes when clustering using the GUI. I cannot find a way of selecting a list of attributes to be ignored in the command line.
For example:
java weka.clusterers.XMeans \
-I 10 -M 1000 -J 1000 \
-L 2 -H 9 -B 1.0 -C 0.25 \
-D "weka.core.MinkowskiDistance -R first-last" -S 10 \
-t "/home/pedrosaurio/bigtable.arff"
My experience with weka is limited so I don't know if I am missing some basic understanding of how it works.
Data Preprocessing functions are called filters.
You need to use filters together with cluster algorithm.
See below example.
java weka.clusterers.FilteredClusterer \
-F weka.filters.unsupervised.attribute.Remove -V -R 1,5 \
-W weka.clusterers.XMeans -I 10 -M 1000 -J 1000 -L 2 -H 9 -B 1.0 -C 0.25 \
-D "weka.core.MinkowskiDistance -R first-last" -S 10 \
-t "/home/pedrosaurio/bigtable.arff"
Here we remove attributes 1-5 then use xmeans.
To ignore an attribute you have to do it from the distance function
Ignore attributes from command line (Matlab):
COLUMNS = '3-last'; % The indices start from 1, 'first' and 'last' are valid as well. E.g .: first-3,5,6-last
Df = weka.core.EuclideanDistance (); % Setup distance function.
Df.setAttributeIndices (COLUMNS); % Setup distance function.
Ignore attributes from GUI
Ignore attributes from GUI
I do not understand why when someone asks how to ignore attributes all the answers say how to modify the dataset, using a filter in the preprocess section.