talesa

Array

• Two Sum
• [ ] Best Time to Buy and Sell Stock
• [ ] Contains Duplicate
• Product of Array Except Self
• Maximum Subarray
• Maximum Product Subarray
• Find Minimum in Rotated Sorted Array

talesa

import argparse
from pathlib import Path
import shutil

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.tensorboard
import torchvision

talesa

sbatch --gres=gpu:1 --partition=ziz-gpu-small slurm.sh

talesa

import audioread as ar
import os

rootdir = '.'
audio_file_formats = ['wav', 'mp3', 'm4a']

for root, subdirs, files in os.walk(rootdir):
    for filename in files:
        if filename[-3:] in audio_file_formats:
            filepath = os.path.join(root, filename)

talesa

# lsf bkill all pending jobs
bkill `bjobs -p -o jobid | grep -v ^JOBID | tr '\n' ' '`

# lsf bkill jobs matching pattern
bkill `bjobs -w | grep MultipleGRUs2 | awk '{print $1}' |  tr '\n' ' '`

# something to do with the largest snapshots in the directory
du -sh * | sort -rh | head -n 5 | awk '{print $2"/*.pt"}' | xargs -I@ sh -c 'ls @'

talesa

class OrthogonalLinear(nn.Module):
    """ Implements a non-square linear with orthogonal colums """
    def __init__(self, input_size, output_size, lr_factor=0.1):
        super(OrthogonalLinear, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.max_size = max(self.input_size, self.output_size)
        
        self.log_orthogonal_kernel = nn.Parameter(torch.Tensor(self.max_size, self.max_size))
        self.log_orthogonal_kernel.register_hook(lambda: print("This should not be executed"))

talesa

t = (1, 2, [30, 40])
t[2] += [50, 60]
print(t)

# Options:
#     a) t becomes (1, 2, [30, 40, 50, 60]).
#     b) TypeError is raised with the message 'tuple' object does not support item assignment.
#     c) Neither.
#     d) Both a and b.

talesa

import multiprocessing
import subprocess
import os

def init(queue):
    global gpuid
    gpuid = queue.get()

gpus_list = list(range(8))
num_gpus = len(gpus_list)

talesa

filename = 'Network-publishing-database2_short2.csv'

import csv
import itertools
import numpy as np

with open(filename, newline='') as csvfile:
    rows = csv.reader(csvfile)
    rows = [[i for i in row if i!=''] for row in rows]

talesa

class OnlineStats:
    # https://www.johndcook.com/blog/standard_deviation/
    # Knuth TAOCP vol 2, 3rd edition, page 232

    def __init__(self):
        self.n = 0

    def push(self, x):
        self.n += 1