def test__back_propagation(self):
data = [[1, 1, 2]]
target = [1, 0, 0]
nn = NeuralNetwork()
nn.create_network(len(data[0]), 2, (1,), weights=[[-.1, .2, .1, -.4], [-.15, -.2, .3]])
r = nn._feed_forward(data[0])
uw = nn._back_propagation(r, target[0])
def run_iris():
# Load iris data set
iris = datasets.load_iris()
n_inputs = len(iris.data[0])
n_outputs = len(iris.target_names)
network = NeuralNetwork()
network.create_network(n_inputs, n_outputs, (3, 4))
data_scaled = preprocessing.scale(iris.data)
print("Their neural network results: {}".format(cross_val_score(nn, data_scaled, iris.target, 3)))
print("My neural network results: {}".format(cross_val_score(network, data_scaled, iris.target, 3)))
def run_diabetes():
data = []
target = []
# Read data
with open('pima-indians-diabetes.data') as diabetes_file:
diabetes_reader = csv.reader(diabetes_file, quoting=csv.QUOTE_NONNUMERIC)
for row in diabetes_reader:
data.append(row[:8])
target.append(int(row[8]))
n_inputs = len(data[0])
n_outputs = len(set(target))
network = NeuralNetwork()
network.create_network(n_inputs, n_outputs, (3, 4))
data_scaled = preprocessing.scale(np.array(data))
print("Neural network results accuracy: {}".format(cross_val_score(network, data_scaled, np.array(target), 3)))
def test__back_propagation(self):
data = [[1, 1, 2]]
target = [1, 0, 0]
nn = NeuralNetwork()
nn.create_network(len(data[0]),
2, (1, ),
weights=[[-.1, .2, .1, -.4], [-.15, -.2, .3]])
r = nn._feed_forward(data[0])
uw = nn._back_propagation(r, target[0])
#!/usr/bin/env python3
import sys
import random
from NeuralNetwork.neural_network import NeuralNetwork
import json
import numpy as np
with open('C:\\Users\\dbari\Documents\\GitHub\\Connect4-py\\options.json',
'r') as file:
options = json.load(file)
ann = NeuralNetwork(options)
class Settings():
def __init__(self):
self.timebank = None
self.time_per_move = None
self.player_names = None
self.your_bot = None
self.your_botid = None
self.field_width = None
self.field_height = None
class Field():
def __init__(self):
self.position = []
self.field_state = None
def update_field(self, celltypes, settings):
