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 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 __init__(self, layer_nodes=None, len_sequence=None):
self.len_sequence = len_sequence
self.recurrent_size = 2
if layer_nodes:
layer_nodes[0] += self.recurrent_size
layer_nodes[-1] += self.recurrent_size
self.nn = [
NeuralNetwork(layer_nodes=layer_nodes)
for _ in range(self.len_sequence)
]
def get_model(self, weight):
model = NeuralNetwork(self.nn_nodes)
nn_weight = []
for i in range(1, len(self.nn_nodes)):
layer_weight = []
for _ in range(self.nn_nodes[i]):
layer_weight.append(weight[:self.nn_nodes[i - 1]])
weight = weight[self.nn_nodes[i - 1]:]
nn_weight.append(layer_weight)
model.set_weight(nn_weight)
return model
__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
