def __init__(self, ticker_len, start_amount, histdepth):
self.in_shapes = []
self.out_shapes = []
self.trade_hist = {}
self.polo = Poloniex()
self.hist_depth = histdepth
self.ticker_len = ticker_len
self.end_ts = datetime.now()+timedelta(seconds=(ticker_len*24))
self.start_amount = start_amount
self.hs = HistWorker()
self.refresh_data()
self.inputs = self.hs.hist_shaped.shape[0]*(self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
#self.make_shapes()
self.folio = CryptoFolio(start_amount, list(self.hs.currentHists.keys()))
self.leaf_names = []
for ix in range(1,self.inputs+1):
sign = sign *-1
self.in_shapes.append((0.0-(sign*.005*ix), -1.0, 0.0+(sign*.005*ix)))
self.out_shapes.append((0.0, 1.0, 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
for l in range(len(self.in_shapes[0])):
self.leaf_names.append('leaf_one_'+str(l))
self.leaf_names.append('leaf_two_'+str(l))
self.load_net()
print(self.hs.coin_dict)
self.poloTrader()
def __init__(self, args, substrate=None):
_GymNeatConfig.__init__(self, args, layout=(5, 1))
subs = self.params['Substrate']
actfun = subs['function']
inp = eval(subs['input'])
hid = eval(subs['hidden']) if substrate is None else substrate
out = eval(subs['output'])
self.substrate = Substrate(inp, out, hid)
self.actfun = actfun
# For recurrent nets
self.activations = len(self.substrate.hidden_coordinates) + 2
# Output of CPPN is recurrent, so negate indices
self.node_names = {j: self.node_names[k]
for j, k in enumerate(self.node_names)}
# CPPN itself always has the same input and output nodes
self.cppn_node_names = {-1: 'x1',
-2: 'y1',
-3: 'x2',
-4: 'y2',
-5: 'bias',
0: 'weight'}
def __init__(self, ticker_len, start_amount, histdepth, base_sym):
self.trade_hist = {}
self.polo = Poloniex()
self.hd = histdepth
self.ticker_len = ticker_len
self.end_ts = datetime.now() + timedelta(seconds=(ticker_len * 24))
self.start_amount = start_amount
self.hs = HistWorker()
self.hs.combine_live_usd_frames()
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0]) - 1
self.but_target = .1
self.inputs = self.hs.hist_shaped.shape[0] * (
self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
self.folio = CryptoFolio(start_amount,
list(self.hs.currentHists.keys()), base_sym)
self.base_sym = base_sym
sign = 1
for ix in range(1, self.outputs + 1):
sign = sign * -1
self.out_shapes.append((0.0 - (sign * .005 * ix), 0.0, -1.0))
for ix2 in range(1, (self.inputs // self.outputs) + 1):
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.load_net()
print(self.hs.coin_dict)
self.poloTrader()
def __init__(self, hist_depth):
self.hs = HistWorker()
self.hs.combine_binance_frames()
self.hd = hist_depth
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0])
self.but_target = .1
self.inputs = self.hs.hist_shaped.shape[0] * (
self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
sign = 1
for ix in range(1, self.outputs + 1):
sign = sign * -1
self.out_shapes.append((0.0 - (sign * .005 * ix), -1.0, -1.0))
for ix2 in range(1, (self.inputs // self.outputs) + 1):
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 1.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.epoch_len = 144
#self.node_names = ['x1', 'y1', 'z1', 'x2', 'y2', 'z2', 'weight']
self.leaf_names = []
#num_leafs = 2**(len(self.node_names)-1)//2
for l in range(len(self.in_shapes[0])):
self.leaf_names.append('leaf_one_' + str(l))
self.leaf_names.append('leaf_two_' + str(l))
def reset_substrate(self, input_row):
current_inputs = self.substrate.input_coordinates
new_input = []
for ix, t in enumerate(current_inputs):
t = list(t)
offset = input_row[ix] * .5
t[2] = t[2] + .5
new_input.append(tuple(t))
#self.in_shapes = new_input
self.substrate = Substrate(new_input, self.out_shapes)
def set_substrate(self):
sign = 1
x_increment = 1.0 / self.outputs
y_increment = 1.0 / len(self.hs.hist_shaped[0][0])
for ix in range(self.outputs):
self.out_shapes.append((1.0 - (ix * x_increment), 0.0, -1.0))
for ix2 in range(len(self.hs.hist_shaped[0][0])):
self.in_shapes.append((-1.0 + (ix * x_increment),
1.0 - (ix2 * y_increment), 1.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
def make_shapes(self):
sign = 1
self.out_shapes = []
self.in_shapes = []
for ix in range(1, self.outputs + 1):
sign = sign * -1
self.out_shapes.append((0.0 - (sign * .005 * ix), 0.0, -1.0))
for ix2 in range(1, (self.inputs // self.outputs) + 1):
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
def set_substrate(self):
sign = 1
x_increment = 1.0 / self.outputs
y_increment = 1.0 / len(self.hs.hist_shaped[0][0])
for ix in range(self.outputs):
self.out_shapes.append((1.0-(ix*x_increment), 0.0, -1.0))
for ix2 in range(self.inputs//self.outputs):
if(ix2 >= len(self.tree.cs)-1):
treex = ix2 - len(self.tree.cs)-1
else:
treex = ix2
center = self.tree.cs[treex]
self.in_shapes.append((center.coord[0]+(ix*x_increment), center.coord[1] - (ix2*y_increment), center.coord[2]+.5))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
def __init__(self, hist_depth):
self.hs = HistWorker()
self.hd = hist_depth
self.end_idx = len(self.hs.currentHists["XCP"])
self.but_target = .1
self.inputs = self.hs.hist_shaped.shape[0]*(self.hs.hist_shaped[0].shape[1]-1)
self.outputs = self.hs.hist_shaped.shape[0]
sign = 1
for ix in range(1,self.outputs+1):
sign = sign *-1
self.out_shapes.append((sign/ix, .0, 1.0*sign))
for ix2 in range(1,len(self.hs.hist_shaped[0][0])):
self.in_shapes.append((-sign/ix, (sign/ix2), 1.0*sign))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.epoch_len = 360
def make_config(args):
cfg = _GymNeatConfig.load(args, '-hyper')
subs = cfg['Substrate']
actfun = subs['function']
inp = eval(subs['input'])
hid = eval(subs['hidden'])
out = eval(subs['output'])
substrate = Substrate(inp, out, hid)
# Load rest of config from file
config = _GymHyperConfig(args, substrate, actfun)
evalfun = _GymHyperConfig.eval_genome
return config, evalfun
def __init__(self, hist_depth):
self.hs = HistWorker()
self.hd = hist_depth
self.end_idx = len(self.hs.currentHists["DASH"])
self.but_target = .1
self.inputs = self.hs.hist_shaped.shape[0]*(self.hs.hist_shaped[0].shape[1]-1) * self.hd
self.outputs = self.hs.hist_shaped.shape[0]
sign = 1
for ix in range(self.outputs):
sign = sign *-1
self.out_shapes.append((sign*ix, 1))
for ix2 in range(len(self.hs.hist_shaped[0][0])-1):
for ix3 in range(self.hd):
self.in_shapes.append((sign*ix, ((1+ix2)*.1)))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.epoch_len = 55
def __init__(self, genome, config):
self.genome = genome
self.config = config
# Network input, hidden and output coordinates.
input_coordinates = []
for i in range(0, 5883):
input_coordinates.append((-1. + (2. * i / 3.), -1.))
hidden_coordinates = [[(-0.5, 0.5), (0.5, 0.5)],
[(-0.5, -0.5), (0.5, -0.5)]]
output_coordinates = [(-1., 1.), (1., 1.), (-1., 1.)]
activations = len(hidden_coordinates) + 2
sub = Substrate(input_coordinates, output_coordinates,
hidden_coordinates)
self.sub = sub
activations = len(hidden_coordinates) + 2
self.activations = activations
def poloTrader(self):
end_prices = {}
active = self.get_one_bar_input_2d()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network()
net.reset()
for n in range(network.activations):
out = net.activate(active)
#print(len(out))
rng = len(out)
#rng = iter(shuffle(rng))
for x in np.random.permutation(rng):
sym = self.coin_dict[x]
#print(out[x])
try:
if (out[x] < -.5):
print("selling: ", sym)
self.folio.sell_coin(
sym, self.currentHists[sym]['close'][self.end_idx])
elif (out[x] > .5):
print("buying: ", sym)
self.folio.buy_coin(
sym, self.currentHists[sym]['close'][self.end_idx])
except:
print('error', sym)
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hist_shaped[x][len(self.hist_shaped[x]) -
1][2]
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(3):
time.sleep(self.ticker_len / 4)
p_vals = self.get_current_balance()
print("current value: ", p_vals[0], "current btc holdings: ",
p_vals[1])
#print(self.folio.ledger)
time.sleep(self.ticker_len / 4)
self.pull_polo()
self.poloTrader()
def make_config(args):
# Load config from file
cfg = _GymNeatConfig.load(args, '-eshyper')
subs = cfg['Substrate']
actfun = subs['function']
inp = eval(subs['input'])
out = eval(subs['output'])
# Get substrate from -hyper.cfg file named by Gym environment
substrate = Substrate(inp, out)
# Load rest of config from file
config = _GymEsHyperConfig(args, substrate, actfun, cfg['ES'])
evalfun = _GymEsHyperConfig.eval_genome
return config, evalfun
def refresh(self):
self.in_shapes = []
self.out_shapes = []
self.hs = HistWorker()
self.hs.get_kraken_train()
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0])
self.but_target = .1
self.inputs = self.hs.hist_shaped.shape[0] * (
self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
sign = 1
for ix in range(1, self.outputs + 1):
sign = sign * -1
self.out_shapes.append((0.0 - (sign * .005 * ix), 0.0, -1.0))
for ix2 in range(1, (self.inputs // self.outputs) + 1):
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
def refresh(self):
self.in_shapes = []
self.out_shapes = [(0.0, -1.0, -1.0)]
self.hs = HistWorker()
self.hs.get_binance_train()
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0])
self.but_target = .1
print(self.hs.hist_shaped.shape)
self.num_syms = self.hs.hist_shaped.shape[0]
self.inputs = self.hs.hist_shaped[0].shape[1] + 1
self.outputs = 1
sign = 1
for ix2 in range(1, self.inputs + 1):
sign *= -1
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 0.0))
self.substrate = Substrate(self.in_shapes, self.out_shapes)
self.set_leaf_names()
def refresh(self):
self.in_shapes = []
self.out_shapes = [(0.0, -1.0, -1.0)]
self.hs = HistWorker()
self.hs.get_kraken_train()
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0])
self.but_target = .1
print(self.hs.hist_shaped.shape)
self.num_syms = self.hs.hist_shaped.shape[0]
# add one to number of symbols to account for current position size
# input we pass for context
self.inputs = self.hs.hist_shaped[0].shape[1] + 1
self.outputs = 1
sign = 1
for ix2 in range(1,self.inputs+1):
sign *= -1
self.in_shapes.append((0.0+(sign*.01*ix2), 0.0-(sign*.01*ix2), 0.0))
self.substrate = Substrate(self.in_shapes, self.out_shapes)
self.set_leaf_names()
def refresh(self, reload_data=False):
print("refreshing")
self.in_shapes = []
self.out_shapes = []
self.hs = HistWorker()
if(reload_data != False):
self.hs.pull_robinhood_train_data()
self.hs.get_robinhood_train()
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0])
self.but_target = 1.0
self.inputs = self.hs.hist_shaped.shape[0]*(self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
sign = 1
for ix in range(1,self.outputs+1):
sign = sign *-1
self.out_shapes.append((0.0-(sign*.005*ix), 0.0, -1.0))
for ix2 in range(1,(self.inputs//self.outputs)+1):
self.in_shapes.append((0.0+(sign*.01*ix2), 0.0-(sign*.01*ix2), 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
def poloTrader(self):
end_prices = {}
active = self.get_one_bar_input_2d()
self.load_net()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network_nd('paper_net.png')
net.reset()
for n in range(1, self.hist_depth+1):
out = net.activate(active[self.hist_depth-n])
#print(len(out))
rng = len(out)
#rng = iter(shuffle(rng))
self.reset_tickers()
for x in np.random.permutation(rng):
sym = self.hs.coin_dict[x]
#print(out[x])
try:
if(out[x] < -.5):
print("selling: ", sym)
p = self.get_price('BTC_'+sym)
price = p -(p*.01)
self.sell_coin('BTC_'+sym, price)
elif(out[x] > .5):
print("buying: ", sym)
self.target_percent = .1 + out[x] - .45
p = self.get_price('BTC_'+sym)
price = p*1.01
self.buy_coin('BTC_'+sym, price)
except:
print('error', sym)
#skip the hold case because we just dont buy or sell hehe
if datetime.now() >= self.end_ts:
return
else:
time.sleep(self.ticker_len)
self.refresh_data()
#self.closeOrders()
self.poloTrader()
def __init__(self, args, substrate=None):
_GymNeatConfig.__init__(self, args, layout=(5, 1))
# Attempt to get substrate info from environment
if hasattr(self.env, 'get_substrate'):
actfun, inp, hid, out = self.env.get_substrate()
# Default to substrate info from config file
else:
subs = self.params['Substrate']
inp = eval(subs['input'])
hid = eval(subs['hidden']) if substrate is None else substrate
out = eval(subs['output'])
actfun = subs['function']
self.substrate = Substrate(inp, out, hid)
self.actfun = actfun
# For recurrent nets
self.activations = len(self.substrate.hidden_coordinates) + 2
# Output of CPPN is recurrent, so negate indices
self.node_names = {
j: self.node_names[k]
for j, k in enumerate(self.node_names)
}
# CPPN itself always has the same input and output nodes
self.cppn_node_names = {
-1: 'x1',
-2: 'y1',
-3: 'x2',
-4: 'y2',
-5: 'bias',
0: 'weight'
}
def poloTrader(self):
end_prices = {}
active = self.get_one_bar_input_2d()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network()
net.reset()
for n in range(network.activations):
out = net.activate(active)
#print(len(out))
rng = len(out)
#rng = iter(shuffle(rng))
for x in np.random.permutation(rng):
sym = self.coin_dict[x]
#print(out[x])
try:
if (out[x] < -.5):
print("selling: ", sym)
self.sell_coin(
sym,
self.get_price(sym),
)
elif (out[x] > .5):
print("buying: ", sym)
self.buy_coin(sym, self.get_price(sym))
except:
print('error', sym)
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.get_price(sym)
if datetime.now() >= self.end_ts:
return
else:
time.sleep(self.ticker_len)
self.reset_tickers
self.pull_polo()
self.poloTrader()
import neat
import logging
import cPickle as pickle
import gym
from pureples.shared.visualize import draw_net
from pureples.shared.substrate import Substrate
from pureples.shared.gym_runner import run_es
from pureples.es_hyperneat.es_hyperneat import ESNetwork
# Network input and output coordinates.
input_coordinates = [(-0.33, -1.), (0.33, -1.)]
output_coordinates = [(-0.5, 1.), (0., 1.), (0.5, 1.)]
sub = Substrate(input_coordinates, output_coordinates)
# ES-HyperNEAT specific parameters.
params = {
"initial_depth": 0,
"max_depth": 1,
"variance_threshold": 0.03,
"band_threshold": 0.3,
"iteration_level": 1,
"division_threshold": 0.5,
"max_weight": 8.0,
"activation": "sigmoid"
}
# Config for CPPN.
config = neat.config.Config(neat.genome.DefaultGenome,
neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet,
import logging
import pickle
import gym
import neat
from pureples.shared.visualize import draw_net
from pureples.shared.substrate import Substrate
from pureples.shared.gym_runner import run_hyper
from pureples.hyperneat.hyperneat import create_phenotype_network
# Network input and output coordinates.
input_coordinates = [(-0.33, -1.), (0.33, -1.)]
OUTPUT_COORDINATES = [(-0.5, 1.), (0., 1.), (0.5, 1.)]
HIDDEN_COORDINATES = [[(-0.5, 0.5), (0.5, 0.5)], [(0.0, 0.0)],
[(-0.5, -0.5), (0.5, -0.5)]]
SUBSTRATE = Substrate(input_coordinates, OUTPUT_COORDINATES,
HIDDEN_COORDINATES)
ACTIVATIONS = len(HIDDEN_COORDINATES) + 2
# Config for CPPN.
CONFIG = neat.config.Config(neat.genome.DefaultGenome,
neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet,
neat.stagnation.DefaultStagnation,
'config_cppn_mountain_car')
def run(gens, env):
"""
Run the pole balancing task using the Gym environment
Returns the winning genome and the statistics of the run.
"""
def poloTrader(self):
try:
trade_df = pd.read_json("./live_hist/json_hist.json")
except Exception as e:
trade_df = pd.DataFrame()
end_prices = {}
active = self.get_one_bar_input_2d()
self.load_net()
sub = Substrate(self.in_shapes, self.out_shapes)
net = ESNetwork(sub, self.cppn, self.params, self.hd)
network = net.create_phenotype_network_nd('paper_net.png')
sell_syms = []
buy_syms = []
buy_signals = []
sell_signals = []
for n in range(1, self.hd):
network.activate(active[self.hd - n])
out = network.activate(active[0])
self.reset_tickers()
for x in range(len(out)):
sym = self.hs.coin_dict[x]
end_prices[sym] = self.get_price(self.base_sym + "_" + sym)
if (out[x] > .5):
buy_signals.append(out[x])
buy_syms.append(sym)
if (out[x] < -.5):
sell_signals.append(out[x])
sell_syms.append(sym)
#rng = iter(shuffle(rng))
sorted_buys = np.argsort(buy_signals)[::-1]
sorted_sells = np.argsort(sell_signals)
for x in sorted_sells:
try:
sym = sell_syms[x]
p = end_prices[sym]
print("selling: ", sym)
self.folio.sell_coin(sym, p)
except Exception as e:
print("error placing order")
for x in sorted_buys:
try:
sym = buy_syms[x]
p = end_prices[sym]
print("buying: ", sym)
self.folio.buy_coin(sym, p)
except Exception as e:
print("error placing order")
'''
self.trade_hist["date"] = datetime.now()
self.trade_hist["portfoliovalue"] = self.folio.get_total_btc_value_no_sell(end_prices)[0]
self.trade_hist["portfolio"] = self.folio.ledger
self.trade_hist["percentchange"] = ((self.trade_hist["portfoliovalue"] - self.folio.start)/self.folio.start)*100
trade_df.append(self.trade_hist)
trade_df.to_json("./live_hist/json_hist.json")
if(self.trade_hist["portfoliovalue"] > self.folio.start *1.1):
self.folio.start = self.folio.get_total_btc_value(end_prices)[0]
'''
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(2):
p_vals = self.get_current_balance()
print("current value: ", p_vals[0], "current holdings: ",
p_vals[1])
time.sleep(self.ticker_len / 2)
self.refresh_data()
self.poloTrader()
def poloTrader(self):
try:
trade_df = pd.read_json("./live_hist/json_hist.json")
except:
trade_df = pd.DataFrame()
end_prices = {}
active = self.get_one_bar_input_2d()
self.load_net()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network_nd()
net.reset()
signals = []
net.reset()
for n in range(1, self.hist_depth+1):
out = net.activate(active[self.hist_depth-n])
for x in range(len(out)):
signals.append(out[x])
#rng = iter(shuffle(rng))
sorted_shit = np.argsort(signals)[::-1]
self.reset_tickers()
sym = ""
for x in sorted_shit:
sym = self.hs.coin_dict[x]
#print(out[x])
try:
if(out[x] < -.5):
p = self.get_price('BTC_'+sym)
print("selling: ", sym)
self.folio.sell_coin(sym, p)
elif(out[x] > .5):
p = self.get_price('BTC_'+sym)
print("buying: ", sym)
self.folio.buy_coin(sym, p)
except:
print("error buying or selling")
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hs.currentHists[sym]["close"].iloc[-1]
#self.trade_hist["date"] = self.hs.currentHists[sym]["date"].iloc[-1]
self.trade_hist["date"] = time.time()
self.trade_hist["portfoliovalue"] = self.folio.get_total_btc_value_no_sell(end_prices)[0]
self.trade_hist["portfolio"] = self.folio.ledger
self.trade_hist["percentchange"] = ((self.trade_hist["portfoliovalue"] - self.folio.start)/self.folio.start)*100
print(self.trade_hist)
print(self.folio.ledger)
self.db.insert(self.trade_hist)
self.trade_hist = {}
'''
if(self.trade_hist["portfoliovalue"] > self.folio.start *1.1):
self.folio.start = self.folio.get_total_btc_value(end_prices)[0]
'''
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(2):
time.sleep(self.ticker_len/2)
#print(self.folio.ledger)
self.refresh_data
self.poloTrader()
from pureples.shared.substrate import Substrate
from pureples.shared.visualize import draw_net
from pureples.es_hyperneat.es_hyperneat import ESNetwork
# S, M or L; Small, Medium or Large (logic implemented as "Not 'S' or 'M' then Large").
VERSION = "S"
VERSION_TEXT = "small" if VERSION == "S" else "medium" if VERSION == "M" else "large"
# Network inputs and expected outputs.
XOR_INPUTS = [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0)]
XOR_OUTPUTS = [(0.0, ), (1.0, ), (1.0, ), (0.0, )]
# Network coordinates and the resulting substrate.
INPUT_COORDINATES = [(-1.0, -1.0), (0.0, -1.0), (1.0, -1.0)]
OUTPUT_COORDINATES = [(0.0, 1.0)]
SUBSTRATE = Substrate(INPUT_COORDINATES, OUTPUT_COORDINATES)
def params(version):
"""
ES-HyperNEAT specific parameters.
"""
return {
"initial_depth": 0 if version == "S" else 1 if version == "M" else 2,
"max_depth": 1 if version == "S" else 2 if version == "M" else 3,
"variance_threshold": 0.03,
"band_threshold": 0.3,
"iteration_level": 1,
"division_threshold": 0.5,
"max_weight": 5.0,
"activation": "sigmoid"
# Network input and output coordinates.
# input_coordinates = [(-0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.), (0.33, -1.)]
# output_coordinates = [(-0.5, 1.),(-0.5, 1.),(-0.5, 1.)]
#
# sub = Substrate(input_coordinates, output_coordinates)
# Network input and output coordinates.
# input_coordinates = [ (-0.8, -1.), (-0.7, -1.), (-0.6, -1.), (-0.5, -1.), (-0.4, -1.), (-0.3, -1.), (-0.2, -1.), (-0.1, -1.), (-0.05, -1.), (0.01, -1.), (0.01, -1.), (0.05, -1.), (0.1, -1.), (0.2, -1.), (0.3, -1.), (0.4, -1.), (0.5, -1.), (0.6, -1.), (0.7, -1.), (0.8, -1.), (0.9, -1.)]
input_coordinates = [(-0.5, -1.)]
output_coordinates = [(0.5, 1.)]
# hidden_coordinates = [[(0.0, 0.0)]]
sub = Substrate(input_coordinates, output_coordinates) #,hidden_coordinates)
# activations = len(hidden_coordinates) + 2
# ES-HyperNEAT specific parameters.
params = {
"initial_depth": 0,
"max_depth": 100,
"variance_threshold": 0.9,
"band_threshold": 0.3,
"iteration_level": 10,
"division_threshold": 0.5,
"max_weight": 8.0,
"activation": "sigmoid"
}
# Config for CPPN.
