feat | feature engineering automation tool for learning data | Machine Learning library

In my React Native application I want to be able to search and display songs, I have tried implementing search function, but none seems to work. The code below only allows me to type one letter, and when I type one letter my android device keyboard disappears my list will still remain (no search will be triggered).

Please I need help to make the search feature work in order for my search songs to display when User searches for songs.

Here's my player list code:

I have a collection of strings such as Some song [FEAT. John Doe] and I'm trying to extract the 'featured' part. It could be identified by one of several different words "FEAT|FEAT\\.|Featuring" and may or may not be enclosed by brackets. I'm using a Regex for this and here is what I've got so far:

I used psutil and resource library and print the output to a file,

the output of the text file given below:

image of the output file

before argument parser:

For image clustering I was using a piece of code which worked perfectly.

I try to connect with an FTP server with apache-commons-net-3.7.2 (implicit TLS, double factor authentication with client cert + login/password).

I can authenticate myself, enter in passive mode, but the client doesn't succeed in connecting to the server in order to get data by the data socket.

I can connect myself, on the same computer, with WinSCP (same settings). I have activated WinSCP logs to see protocol details, and I have adjusted my source code with the same options. I can verify that my protocol is ok with a ProtocolCommandListener. I know that passive mode is required because WinSCP emits PASV command.

I can see that WinSCP connects to the data socket on port 62564 (I have replaced FTP IP address with XXX)

I am implementing a version of the Very Large Scale Relieff algorithm detailed here.

Simply put, Very Large Scale Relieff split the set of features N into several random subsets Ns where Ns << N. Then it calculates the Relieff weights for the features in the subset Ns. For each feature, the final weight will be the highest weight assigned among the different subsets were that particular feature appear.

I have ~80000 features for ~100 subjects. I can calculate 10000 subsets of 8000 features each in a reasonable amount of time (~5 minutes running on 25 cores) with the following code (that is scaled down to 100 features in order to be easier to profile):

Apologies for asking a fairly common question, I have been looking all over and can't find a solution that fixes my problem.

I am using Firesharp, and trying to deserialize a Json object that Firebase returns into a class with a nested list.

I'm trying to run k-fold cross validation on pipeline(Standardscaler,DecisionTreeClassifier).

First, I import the data.

I'm trying to check how duplicate commits are formed in GIT. In that process I got struck at one point.

Created Local Repo & file colors.txt