class CarGenetic(RaceGame):
def __init__(self):
self.delta_angle = [
-0.035, -0.03, -0.025, -0.02, -0.015, -0.01, -0.005, 0, 0.005,
0.01, 0.015, 0.02, 0.025, 0.03, 0.035
]
def start(self):
self.len_delta = len(self.delta_angle)
self.nn_nodes = [5, 8, 5, self.len_delta]
self.model = NeuralNetwork(self.nn_nodes)
self.step = 0
self.max_step = 0
self.training_group = []
self.best_traning = []
RaceGame.__init__(self)
def load(self):
self.model = NeuralNetwork()
self.model.load('car_model.dat')
self.step = 0
self.max_step = 0
self.training_group = []
self.best_traning = []
RaceGame.__init__(self)
def update(self):
RaceGame.update(self)
if self.is_broken:
self.training_car()
self.car.restart()
self.training_group = []
self.step = 0
else:
input = self.collision_sight + [self.car.angle]
id_output = self.model.train(input)
self.training_group.append((input, id_output))
self.step += 1
if self.step == 300:
self.model.save('car_model.dat')
self.car.update(delta_angle=self.delta_angle[id_output])
def training_car(self):
num_last_step = 5
if self.step > self.max_step:
self.max_step = self.step
self.best_traning = self.training_group[:-num_last_step]
for train_data in self.best_traning:
self.model.train(train_data[0], expected_id=train_data[1])
for train_data in self.training_group[-num_last_step:]:
self.model.train(train_data[0], unexpected_id=train_data[1])
class CarGenetic(RaceGame):
def __init__(self):
self.delta_angle = [-0.035, -0.03, -0.025, -0.02, -0.015, -0.01, -0.005, 0,
0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035]
def start(self):
self.len_delta = len(self.delta_angle)
self.nn_nodes = [5, 8, 5, self.len_delta]
self.model = NeuralNetwork(self.nn_nodes)
self.step = 0
self.max_step = 0
self.training_group = []
self.best_traning = []
RaceGame.__init__(self)
def load(self):
self.model = NeuralNetwork()
self.model.load('car_model.dat')
self.step = 0
self.max_step = 0
self.training_group = []
self.best_traning = []
RaceGame.__init__(self)
def update(self):
RaceGame.update(self)
if self.is_broken:
self.training_car()
self.car.restart()
self.training_group = []
self.step = 0
else:
input = self.collision_sight + [self.car.angle]
id_output = self.model.train(input)
self.training_group.append((input, id_output))
self.step += 1
if self.step == 300:
self.model.save('car_model.dat')
self.car.update(delta_angle=self.delta_angle[id_output])
def training_car(self):
num_last_step = 5
if self.step > self.max_step:
self.max_step = self.step
self.best_traning = self.training_group[:-num_last_step]
for train_data in self.best_traning:
self.model.train(train_data[0], expected_id=train_data[1])
for train_data in self.training_group[-num_last_step:]:
self.model.train(train_data[0], unexpected_id=train_data[1])
__author__ = 'sunary'
from neural_network.model_nn import NeuralNetwork
import pandas as pd
import datetime
model = NeuralNetwork([50, 20, 10, 2])
model.load('numer_ai.dat')
def train(file='/Users/sunary/Downloads/numerai_datasets/numerai_training_data.csv'):
df = pd.read_csv(file)
for _round in range(100):
print 'Round {}: {}'.format(_round, datetime.datetime.now())
for index, row in df.iterrows():
model.train(row[:50].tolist(), int(row['target']))
model.save('numer_ai.dat')
def test(file='/Users/sunary/Downloads/numerai_datasets/numerai_training_data.csv'):
df = pd.read_csv(file)
accuracy = 0
for index, row in df.iterrows():
predict_id = model.train(row[:50].tolist())
if predict_id == int(row['target']):
accuracy += 1
