1pha

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Distribution Analysis\n",
    "\n",
    "약물 투여 여부를 중심으로 연구를 진행하게 되었다. 기존 근시 환자의 내방 이후 최종 검진 시 SE값을 예측하는 예측모델에 그쳤다면, 여기에 더 나아가 약물사용이 어떻게 모델 예측에 영향력을 미치는지 살펴보는 방향을 진행한다.   "
   ]

1pha

1pha

import multiprocessing as mp

def add(val):
	list_proxy.append(val)
	print(list_proxy)


if __name__=="__main__":
	man = mp.Manager()
	list_proxy = man.list()

1pha

# Explanations will further be attached.
from dataclasses import dataclass, field

@dataclass
class A:
    d: int = 3
    _e: int = 5
    _g: list = field(default_factory=list)
    def __post_init__(self):
        self._f = 6

1pha

# Reference
# https://discuss.pytorch.org/t/how-do-i-check-the-number-of-parameters-of-a-model/4325/8
def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

1pha

from io import BytesIO, StringIO
from urllib.request import urlopen
from zipfile import ZipFile

import pandas as pd

def download_and_unzip(url, extract_to='.'):
    http_response = urlopen(url)
    zipfile = ZipFile(BytesIO(http_response.read()))
    return zipfile

1pha

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np

class MyDataset(Dataset):
    def __init__(self):
        x = np.random.randint(10, size=[1000, 3])  # 1000 3-dim samples
        self.x = [x[i].tolist() for i in range(1000)]
        y = np.random.randint(low=0, high=2, size=(1000,))
        self.y = [y[i] for i in range(1000)]

1pha

import os
import random
import numpy as np

import torch

def seed_everything(seed=42):
    
    os.environ['PYTHONHASHSEED'] = str(seed)
    random.seed(seed)

1pha

def recursive_dfs(v, discovered=[]):

    discovered.append(v)    
    for w in graph[v]: # for the graph of adjacent matrix
        if w not in discovered:
            discovered = recursive_dfs(w, discovered)
    return discovered # will return the nodes searched in order


def iterative_dfs(start_v):

1pha

import os

def kill_desktop_ini(root_dir):

    for (root, dirs, files) in os.walk(root_dir):

        if len(files) > 0:
            for file_name in files:
                if file_name.endswith('.ini'):
                    os.remove(f'{root}/{file_name}')