Jussi Kujala jukujala

## fit_lognorm_vs_many_data_distributions.py
"""

Generate data from different distributions, fit a log-normal and
show how much fitted mean overvaluates data mean.
Overvaluation 0.05 = 5% higher fitted log-norm mean than data mean.

Copy-paste of output:

 * log normal data
   - mean(empirical data): 4.22, mean(fitted long-norm): 4.26, overvaluation: 0.01

## python_blackjack_simulator.py
""" Blackjack simulator, works for RL
  * Copied and modified from:
    https://gist.github.com/mjhea0/5680216
  * See function game(get_player_action) for instructions.
  * Requires Python 3.7
  * Handles 10M simulations in < 30 mins at Macbook Pro
  * Interactive play: python blackjack_simu.py
  * Might not implement blackjack rules perfectly
"""
import random

## moneyh.py
import numpy as np
from scipy.optimize import minimize_scalar

def moneyh(R):
  """ save 1$ every month for 28 month

  Args:
    R: annualised yearly interest rate

  Returns:
	"""

	Generate data from different distributions, fit a log-normal and
	show how much fitted mean overvaluates data mean.
	Overvaluation 0.05 = 5% higher fitted log-norm mean than data mean.

	Copy-paste of output:

	* log normal data
	- mean(empirical data): 4.22, mean(fitted long-norm): 4.26, overvaluation: 0.01
	""" Blackjack simulator, works for RL
	* Copied and modified from:
	https://gist.github.com/mjhea0/5680216
	* See function game(get_player_action) for instructions.
	* Requires Python 3.7
	* Handles 10M simulations in < 30 mins at Macbook Pro
	* Interactive play: python blackjack_simu.py
	* Might not implement blackjack rules perfectly
	"""
	import random
	import numpy as np
	from scipy.optimize import minimize_scalar

	def moneyh(R):
	""" save 1$ every month for 28 month

	Args:
	R: annualised yearly interest rate

	Returns: