yashkumaratri

https://www.ucc-bsnl.co.in/

yashkumaratri

sudo lshw -C disk

sudo mkfs.ext4 /dev/sdb1

sudo mkdir /mnt/hdd1 

sudo mount /dev/sde /mnt/hdd1

sudo chmod -R 777 /mnt/hdd1/

yashkumaratri

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

yashkumaratri

Assuming the CSV looks something like :

"Distance","E","G","C","B"
"A","3","5","7","3"
"B","4","5","6","0"
"C","4","1","0","6"
"D","5","8","3","6"

Code:

yashkumaratri

#https://stackoverflow.com/questions/49457443/python-pdfminer-converts-pdf-file-into-one-chunk-of-string-with-no-spaces-betwee
#  
#According to this thread some pdfs mark the entire text as figure and by default PDFMiner doesn't try to perform layout analysis for figure text. To override this behavior the all_texts parameter needs to be set to True.
#


import io

import pdfminer
from pdfminer.converter import TextConverter

yashkumaratri

def make_heatmap(text, values, save=None, polarity=1):
    cell_height=.325
    cell_width=.15
    n_limit = 74
    text = list(map(lambda x: x.replace('\n', '\\n'), text))
    num_chars = len(text)
    total_chars = math.ceil(num_chars/float(n_limit))*n_limit
    mask = np.array([0]*num_chars + [1]*(total_chars-num_chars))
    text = np.array(text+[' ']*(total_chars-num_chars))
    values = np.array(values+[0]*(total_chars-num_chars))

yashkumaratri

For small files

wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rC6LWGNkHaZkuojCEWDqSKcDGwFMBTYZ' -O transformer_semeval.clf

For large files

wget --load-cookies cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rC6LWGNkHaZkuojCEWDqSKcDGwFMBTYZ' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1rC6LWGNkHaZkuojCEWDqSKcDGwFMBTYZ" -O transformer_semeval.clf

yashkumaratri

Learning Structured Representation for Text Classification
via Reinforcement Learning

https://github.com/keavil/AAAI18-code

yashkumaratri

def reduce_mem_usage(df):
    """ iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.        
    """
    start_mem = df.memory_usage().sum() / 1024**2
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
    
    for col in df.columns:
        col_type = df[col].dtype
        

yashkumaratri

import tensorflow as tf
import tensorflow_hub as hub
module_url = "https://tfhub.dev/google/universal-sentence-encoder-large/3"
# Import the Universal Sentence Encoder's TF Hub module
embed = hub.Module(module_url)

# Compute a representation for each message, showing various lengths supported.
messages = ["That band rocks!", "That song is really cool."]

with tf.Session() as session: