ltrgoddard

# Takes an ordered vector of numeric values and returns a small bar chart made
# out of Unicode block elements. Works well inside dplyr mutate() or summarise()
# calls on grouped data frames.

sparkbar <- function(values) {
  span <- max(values) - min(values)
  if(span > 0 & !is.na(span)) {
    steps <- round(values / (span /  7))
    blocks <- c('▁', '▂', '▃', '▄', '▅', '▆', '▇', '█')
    paste(sapply(steps - (min(steps) - 1), function(i) blocks[i]), collapse = '')

muety

# Inspired by https://medium.com/@tuzzer/cart-pole-balancing-with-q-learning-b54c6068d947

import gym
import numpy as np
import math
from collections import deque

class QCartPoleSolver():
    def __init__(self, buckets=(1, 1, 6, 12,), n_episodes=1000, n_win_ticks=195, min_alpha=0.1, min_epsilon=0.1, gamma=1.0, ada_divisor=25, max_env_steps=None, quiet=False, monitor=False):
        self.buckets = buckets # down-scaling feature space to discrete range

manashmandal

# Day 0: Mean, Median, and Mode

# Selecting standard input
con <- file('stdin', open='r')

# We don't need the first input 
data_line <- readLines(con)[[2]]

# splitting the data into individual string
split_data <- strsplit(data_line, " ")