NISH1001

MSSQL CLI

github...

UI

https://dbeaver.io/

sqlcmd -S <ip> -d <dbname> -U <username> -P <password> -I

NISH1001

#AUTHID,text,ext,neu,agr,con,opn
1997_504851.txt,"Well, right now I just woke up from a mid-day nap. It's sort of weird, but ever since I moved to Texas, I have had problems concentrating on things. I remember starting my homework in  10th grade as soon as the clock struck 4 and not stopping until it was done. Of course it was easier, but I still did it. But when I moved here, the homework got a little more challenging and there was a lot more busy work, and so I decided not to spend hours doing it, and just getting by. But the thing was that I always paid attention in class and just plain out knew the stuff, and now that I look back, if I had really worked hard and stayed on track the last two years without getting  lazy, I would have been a genius, but hey, that's all good. It's too late to correct the past, but I don't really know how to stay focused n the future. The one thing I know is that when  people say that b/c they live on campus they can't concentrate, it's b. s. For me it would be easier there, b

NISH1001

#!/bin/bash

echo "i am paradox"


#ffmpeg -i audio.mp3 -ignore_loop 0 -i test.gif -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" -shortest -strict -2 -c:v libx264 -threads 5 -c:a aac -b:a 192k -pix_fmt yuv420p -shortest final.mp4

# ffmpeg -i audio.mp3 -ignore_loop 0 -i test.gif -shortest -strict -2 -c:v libx264 -threads 5 -c:a aac -b:a 192k -pix_fmt yuv420p -shortest final.mp4

# ffmpeg -i audio.mp3 -ignore_loop 0 -i test.gif -vf "scale=trunc(iw/2)*1:trunc(ih/2)*2" -shortest -strict -2 -c:v libx264 -threads 5 -c:a aac -b:a 192k -pix_fmt yuv420p -shortest final.mp4

NISH1001

First get list of all the layers (after activation).

from keras.models import Model
layer_outputs = [layer.output for layer in model.layers]

Predict image and get corresponding activation in each layer

activation_model = Model(inputs=model.input, outputs=layer_outputs)

NISH1001

[('ENSMUSG00000117786', 0.19900803),
 ('ENSMUSG00000093954', 0.13898243),
 ('ENSMUSG00000028487', 0.13298473),
 ('ENSMUSG00000113575', 0.08847962),
 ('ENSMUSG00000056999', 0.043881908),
 ('ENSMUSG00000117465', 0.03738362),
 ('ENSMUSG00000067149', 0.03303743),
 ('ENSMUSG00000090352', 0.027412206),
 ('ENSMUSG00000094320', 0.020604169),
 ('ENSMUSG00000090015', 0.018502489),

NISH1001

[[{'condition': {'labels': ['condition_FLT', 'condition_GC'],
    'train_score': 1.0,
    'test_score': 1.0,
    'confusion_matrix': array([[696,   0],
           [  0, 671]]),
    'classification_report': {'condition_FLT': {'precision': 1.0,
      'recall': 1.0,
      'f1-score': 1.0,
      'support': 696},
     'condition_GC': {'precision': 1.0,

NISH1001

Vanilla Configuration

If you are not using any third party variants of bash like oh-my-bash, put the following command in your .bashrc:

export PROMPT_COMMAND='if [ "$(id -u)" -ne 0 ]; then echo "$(date "+%Y-%m-%d.%H:%M:%S") $(pwd) $(history 1)" >> ~/.logs/bash-history-$(date "+%Y-%m-%d").log; fi'

Close bashrc and open new terminal. Now, your history are saved in the folder ~/.logs according to timestamp.
Example file

/home/paradox/.logs/bash-history-2018-12-15.log

NISH1001

Check If archivelog mode is enabled

select log_mode from v$database;

Enable Archive mode

alter database archivelog;

NISH1001

#!/usr/bin/env python3

from tqdm import tqdm
import glob
import os
import sys

import cv2
import matplotlib.pyplot as plt
import numpy as np

NISH1001

#!/usr/bin/env python3


def fib(n):
    res = [1,1]
    for i in range(n):
        res.append(sum(res[-2:]))
    return res