from core.layer import Layer, InputLayer, OutputLayer
from core.network import Network
with open('snek-learning.yaml', 'r') as file:
data = yaml.load(file)
#data = data[:1000]
learning_data = [{'inputs': list(row[:5]), 'outputs': [row[5]]} for row in data]
inputs = InputLayer([1, 1, 1, 1, 1])
middle = Layer(5)
middle3 = Layer(3)
output = OutputLayer([1])
network = Network([inputs, middle, middle3, output], 'snek')
network.connect()
network.load_learning_data(learning_data)
network.print_data()
folds = 4
result = [0]
i = 0
start_time = time()
while sum(result) / folds < 90:
results = network.learn_kfolds(folds, times=1)
scores = [(n, [round(x) for x in out] == check) for n, out, check in results]
stuff = Counter(scores)
result = [stuff[(i, True)] / (stuff[(i, False)] + stuff[(i, True)]) * 100 for i in range(folds)]
}
learning_data = []
with open('../data/training_data/iris.data', newline='') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
learning_data.append({
'inputs': [float(x) for x in row[0:4]],
'outputs': iris_map[row[4]]
})
inputs = InputLayer([1, 1, 1, 1])
middle = Layer(5)
middle2 = Layer(3)
output = OutputLayer([1, 1, 1])
network = Network([inputs, middle, middle2, output], 'iris')
network.connect()
network.load_learning_data(learning_data)
network.normalize_learning_data()
print("------- learn -------")
network.print_data()
folds = 4
results = network.learn_kfolds(folds, times=1000)
scores = [(n, [round(x) for x in out] == check) for n, out, check in results]
stuff = Counter(scores)
print("folds results: ", [stuff[(i, True)] / (stuff[(i, False)] + stuff[(i, True)]) * 100 for i in range(folds)])