Minesh A. Jethva minesh1291

Methods to improve Time series forecast (including ARIMA, Holt's winter)

By: Gurchetan Singh

	#!/bin/bash

	# Check if the script is run with root privileges
	if [ "$EUID" -ne 0 ]; then
	echo "Please run as root"
	exit
	fi

	# Determine the log file based on the distribution
	if [ -f /var/log/auth.log ]; then

	import json
	import numpy as np
	from pycocotools import mask
	from skimage import measure

	ground_truth_binary_mask = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
	[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
	[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
	[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],

	# I added all the imports by hand
	import torch
	from torchvision.datasets import ImageFolder
	from torchvision import transforms,models
	from torch import nn,optim

	# For all functions including this one, I wrote the name and docstring, and sometimes also the param names
	def finetune(folder, model):
	"""fine tune pytorch model using images from folder and report results on validation set"""
	if not os.path.exists(folder): raise ValueError(f"{folder} does not exist")

	"""
	An implementation of the pytorch Subset that returns an instance of the original dataset with a reduced number of items.
	This has two benefits:
	- It allows to stil access the attributes of the Dataset class, such as methods, or properties.
	- You can use the usual python index notation with slices to chunk the dataset, rather than creating a list of indices

	"""
	class Dataset(object):
	def __init__(self, iterable):
	self.items = iterable

	from sshtunnel import SSHTunnelForwarder
	from sqlalchemy import create_engine
	from sqlalchemy.orm import sessionmaker
	from functools import wraps

	# secrets.py contains credentials, etc.
	import secrets

	def get_engine_for_port(port):
	return create_engine('postgresql://{user}:{password}@{host}:{port}/{db}'.format(

	from keras.callbacks import Callback
	import keras.backend as K
	import numpy as np

	class SGDRScheduler(Callback):
	'''Cosine annealing learning rate scheduler with periodic restarts.

	# Usage
	```python
	schedule = SGDRScheduler(min_lr=1e-5,

	# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
	!pip install kaggle
	api_token = {"username":"USERNAME","key":"API_KEY"}
	import json
	import zipfile
	import os
	with open('/content/.kaggle/kaggle.json', 'w') as file:
	json.dump(api_token, file)
	!chmod 600 /content/.kaggle/kaggle.json
	!kaggle config path -p /content