dariocazzani

import numpy as np
import random
from termcolor import colored

"""
https://www.slickcharts.com/sp500/returns
"""
SP500_history = list(reversed([-4.38, 21.83, 11.96, 1.38, 13.69, 32.39, 16, 2.11, 15.06, 26.46, -37, 5.49, 15.79, 4.91, 10.88, 28.68, -22.1, -11.89, -9.1, 21.04, 28.58, 33.36, 22.96, 37.58, 1.32, 10.08, 7.62, 30.47, -3.1, 31.69, 16.61, 5.25, 18.67, 31.73, 6.27, 22.56, 21.55, -4.91, 32.42, 18.44, 6.56, -7.18, 23.84, 37.2, -26.47, -14.66, 18.98, 14.31, 4.01, -8.5, 11.06, 23.98, -10.06, 12.45, 16.48, 22.8, -8.73, 26.89, 0.47, 11.96, 43.36, -10.78, 6.56, 31.56, 52.62, -0.99, 18.37, 24.02, 31.71, 18.79, 5.5, 5.71, -8.07, 36.44, 19.75, 25.9, 20.34, -11.59, -9.78, -0.41, 31.12, -35.03, 33.92, 47.67, -1.44, 53.99, -8.19, -43.34, -24.9, -8.42, 43.61, 37.49, 11.62]))
initial_withdraw = 1.
increase = 1

dariocazzani

How to Install Wine 2.0 Stable in Ubuntu 16.04, 14.04, 16.10

http://ubuntuhandbook.org/index.php/2017/01/install-wine-2-0-ubuntu-16-04-14-04-16-10/

download steam

curl -o ~/Downloads/SteamSetup.exe http://media.steampowered.com/client/installer/SteamSetup.exe

dariocazzani

from gym.envs.box2d.car_dynamics import Car
from gym.envs.box2d import CarRacing
[...]
position = np.random.randint(len(env.track))        
env.car = Car(env.world, *env.track[position][1:4])

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with mp.Pool(mp.cpu_count()) as p:        
    p.map(simulate_batch, range(_NUM_BATCHES))

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def train():
	es = cma.CMAEvolutionStrategy(_NUM_PARAMS * [0], 0.1, {'popsize': 16})
	rewards_through_gens = []
	generation = 1
	try:
		while not es.stop():
			solutions = es.ask()
			with mp.Pool(mp.cpu_count()) as p:
				rewards = list(tqdm.tqdm(p.imap(play, list(solutions)), total=len(solutions)))

dariocazzani

def decide_action(sess, network, observation, params):
	observation = normalize_observation(observation)
	embedding = sess.run(network.z, feed_dict={network.image: observation[None, :,  :,  :]})
	weights, bias = get_weights_bias(params)

	action = np.zeros(_NUM_ACTIONS)
	prediction = np.matmul(np.squeeze(embedding), weights) + bias
	prediction = np.tanh(prediction)

	action[0] = prediction[0]

dariocazzani

class Network(object):
	# Create model
	def __init__(self):
		self.image = tf.placeholder(tf.float32, [None, 96, 96, 3], name='image')
		self.resized_image = tf.image.resize_images(self.image, [64, 64])
		tf.summary.image('resized_image', self.resized_image, 20)

		self.z_mu, self.z_logvar = self.encoder(self.resized_image)
		self.z = self.sample_z(self.z_mu, self.z_logvar)
		self.reconstructions = self.decoder(self.z)

dariocazzani

from __future__ import division

import numpy as np
import pandas as pd
import copy
from Engine import Engine

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split

dariocazzani

from __future__ import division

import numpy as np
import sys
from operator import methodcaller
from Engine import Engine

# data from https://people.sc.fsu.edu/~jburkardt/datasets/knapsack_01/knapsack_01.html
capacity = 6404180
weights = [382745, 799601, 909247, 729069, 467902, 44328, 34610, 698150, 823460,\

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def run(self):
    now = time.time()
    try:
        for i in range(self.iterations):
             self.__produce_next_gen()
             self.__mutation()
    except KeyboardInterrupt:
        print('Quitting...')
    run_time = time.time() - now
    print('Run time: {:.2f}'.format(run_time))