wisnunugroho21

find_grade <- function(score) {

    if (score >= 90 && score <= 100) {
        return("A")
    } else if (score >= 85 && score <= 89) {
        return("A-")
    } else if (score >= 80 && score <= 84) {
        return("B+")
    } else if (score >= 75 && score <= 79) {
        return("B")

wisnunugroho21

#install library gapminder dan tidyverse jika belum diinstall 
#install.packages("gapminder")
#install.packages("tidyverse)

library(tidyverse)
library(gapminder)

gapminder_subset <- gapminder %>%
    filter(year == 2007)

wisnunugroho21

getNumberFromArray <- function(x, idx) {
    return(x[idx])
}

milisecondToTime <- function(x) {

    x <- x / 1000
    result = as.POSIXct(x, origin = "1970-01-01")
    return(result)
}

wisnunugroho21

import torch
from torch.nn.functional import softmax

def masked_softmax(input: torch.Tensor, bool_mask: torch.Tensor, dim: int = -1, dtype: torch.dtype = None) -> torch.Tensor:
    min_type_value = torch.finfo(input.dtype).min
    masked_value = input.masked_fill(bool_mask, min_type_value)

    return softmax(masked_value, dim = dim, dtype = dtype)

## Example 

wisnunugroho21

# Pytorch implementation of Hungarian Algorithm
# Inspired from here : https://python.plainenglish.io/hungarian-algorithm-introduction-python-implementation-93e7c0890e15

# Despite my effort to parallelize the code, there is still some sequential workflows in this code

from typing import Tuple
import torch
from torch import Tensor

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')