dongho-jung

# input
# L -> the length of the log
# C -> the max number of times to cut
# xs -> the sorted list of possible positions to cut
L, _, C = map(int, input().split())
xs = sorted(map(int, input().split()))
min_val = L

def solve(start):
    upper_bound = L

dongho-jung

package main

import (
	"fmt"
)

type LinkedList struct {
	Head *Node
	Tail *Node
}

dongho-jung

package main

import (
        "github.com/fsnotify/fsnotify"
        "gopkg.in/yaml.v2"
        "io/ioutil"
        "log"
        "reflect"
        "time"
)

dongho-jung

package main

import (
	"errors"
	"fmt"
	"reflect"
	"strings"
)

var errNotStruct = errors.New("anyStruct must be struct")

dongho-jung

def get_powersets(total_set, idx=0, sets=None):
    if not sets:
        return get_subsets(total_set, idx, [[]])
    if len(total_set) > idx:
        return get_subsets(total_set, idx+1, sets + [s + [total_set[idx]] for s in sets])
    return sets

dongho-jung

[__import__('threading').Thread(target=(lambda x:lambda:[__import__('time').sleep(x),print(x)])(n)).start()for n in map(int,input().split())]

dongho-jung

import bpy
import math
import itertools
import numpy as np
import pickle


test_name = 'Beale' # Himmelblau, Matyas, Sphere, Rosenbrock, Rastrigin, Easom, Beale 
fps = 10

dongho-jung

def get_join_mask(*cols, unit=None):
    '''Return a boolean mask based on first column by extracting the index 
    having the same value appears in every columns. If unit is specified then 
    given columns are truncated with the unit. unit follows pandas.Series.dt accessor format.
    '''

    if unit:
        try:
            cols = map(lambda x: x.map(operator.methodcaller(unit)), cols)
        except Exception as e:

dongho-jung

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

cancer = load_breast_cancer()

# this call with stratify parameter will produce
# the proportion of values as same as cancer.target which is provided by stratify parameter
X_train, X_test, y_train, y_test = train_test_split(
    cancer.data, cancer.target, stratify=cancer.target

dongho-jung

-- below is postgresql
SELECT 	REGEXP_REPLACE(CASH_WORDS(level*100::MONEY),
		'dollars? and zero cents',
		''
	) x
FROM generate_series(1, 100, 1) t1(level);

-- below is oracle
SELECT TO_CHAR(TO_DATE(level, 'J'), 'JSP') x
FROM DUAL