def main():
#make a neural network with set architecture
arch = (2,4,1)
nn = Neural_Network(arch)
#XOR input data
X_train = np.array( [ [0,0], [0,1], [1,0], [1,1] ] )
#XOR output data
y_train = np.array( [[0],[1],[1],[0]] )
#set max iterations, learning rate, and convergence threshold
iters, lr, threshold = 5000, 1, 0.00001
#train the network
J_Hist = nn.train(X_train, y_train, alpha = lr, maxIter = iters, convergenceThreshold = threshold)
#forward propagate to get a prediction from the network
result = nn.forwardProp(X_train)
#print some nice information
print("\nUnfiltered Prediction:\n", result)
print("Final Prediction:\n", result >= 0.5, '\n')
print("Random init cost: ", round(J_Hist[0], 5), ", Final cost: ", round(J_Hist[-1], 5))
print("Cost reduction from random init: ", round(J_Hist[0] - J_Hist[-1], 5), '\n')
#set up subplots for the cost history and decision boundary
figure, plots = plt.subplots(ncols=2)
figure.suptitle('Neural Network Learning of XOR') #supertitle
figure.tight_layout(pad=2.5, w_pad=1.5, h_pad=0) #fix margins
drawCostHistory(J_Hist, plots[0])
drawDecisionBoundary(nn, plots[1], seperation_coefficient = 50, square_size = 1, allowNegatives = False)
#show the cool graphs :)
plt.show()
def programWorkStation(train_file):
image_values = read_mat(train_file)[0] # images
normalized_images = normalize(image_values) # normalized images
expected_classes = read_mat(train_file)[1] # expected flower types
expected_outputs = expectedOutputs(expected_classes) # flatten outputs
X = normalized_images #normalized input images
size_of_one_image = len(normalized_images[0])
size_of_input = size_of_one_image
# parameters of neural network
hidden_node_number = 100
hidden_layer_number = 2
size_of_output = 5
learning_rate = 0.005
epoch_size = 300
batch_size = 20
# neural network object is created here.
Beauty_Neural_Network = Neural_Network(size_of_input, hidden_node_number,
size_of_output, hidden_layer_number)
deneme_input = X
size_den_inp = len(deneme_input)
den_expected = expected_outputs
# run the code according to epoch and batch sizes.
epochProcess(epoch_size, batch_size, learning_rate, Beauty_Neural_Network,
size_den_inp, deneme_input, den_expected)
def train(self, training_data):
"""
Data should be nx(m+1) numpy matrix where n is the
number of examples and m is the number of features
(recall that the first element of the vector is the label).
I recommend implementing the specific algorithms in a
seperate module and then determining which method to call
based on classifier_type. E.g. if you had a module called
neural_nets:
if self.classifier_type == 'neural_net':
import neural_nets
neural_nets.train_neural_net(self.params, training_data)
Note that your training algorithms should be modifying the parameters
so make sure that your methods are actually modifying self.params
You should print the accuracy, precision, and recall on the training data.
"""
if self.classifier_type == 'neural_network':
#change num_input, num_output based upon the data
self.nn = Neural_Network("neural_network",weights = [], num_input=self.params['num_input'], num_hidden=1000, num_output=self.params['num_output'], alt_weight=self.params['one']=='1', momentum=self.params['two']=='1')
self.nn.train(training_data)
elif self.classifier_type == 'naive_bayes':
self.nb = Naive_Bayes("naive_bayes")
self.nb.train(training_data)
elif self.classifier_type =='decision_tree':
self.dt = Decision_Tree("decision_tree", pruning=self.params['one']=='1',
info_gain_ratio=self.params['two']=='1')
self.dt.train(training_data)
def train(args):
try:
inputs = []
targets = []
y = range(args.start,args.end+1)
if(args.db == 'u'):
dl = Data_Loader()
i,t = dl.getTargets(y)
elif(args.db == 'b'):
dl = Data_Loader()
i,t = dl.getBalancedTargets(y)
elif(args.db == 'p'):
dl = Data_Loader('playoffTeams.csv')
i,t = dl.getTargets(y)
elif(args.db == 'o'):
dl = Data_Loader('balancedData.csv')
i,t = dl.getTargets(y)
elif(args.db == 's'):
dl = Data_Loader()
i,t = dl.getBLSmoteTargets(y,.25)
#i,t = dl.getSmoteTargets(y)
inputs += i
targets += t
#create NN
# if file already exists, build on that training
if (os.path.exists(args.file)):
print "file exists"
nn = Neural_Network.createFromFile(args.file)
pass
else:
print "file does not exist"
nn = Neural_Network.createWithRandomWeights(len(inputs[0]),args.nodes,len(targets[0]))
#train NN with the given data
print 'Beginning Training...'
nn = nn.train(args.epochs,inputs,targets,args.learn_rate)
nn.saveToFile(args.file)
print "Neural Network saved to %s" % (args.file)
except Exception as e:
print "invalid formatting, consult neural_main.py t --help \n Error: %s" % e
def compute(self, simulation, closest_rsu):
neural_net = Neural_Network()
X = self.training_data.pop()
y = self.training_label.pop()
# print(X)
# print(y)
with autograd.record():
output = self.net(X)
if cfg['attack'] == 'label' and len(
closest_rsu.accumulative_gradients
) < cfg['num_faulty_grads']:
loss = neural_net.loss(output, 9 - y)
else:
loss = neural_net.loss(output, y)
loss.backward()
grad_collect = []
for param in self.net.collect_params().values():
if param.grad_req != 'null':
grad_collect.append(param.grad().copy())
self.gradients = grad_collect
def b_2(plot=False, units=[5], eeta=0.1, threshold=1e-6):
print("\nNeural_Network")
model = Neural_Network(len(train_data[0]), units, activation="sigmoid")
print(model)
model.train(train_data,
train_labels,
max_iter=5000,
eeta=eeta,
batch_size=len(train_data),
threshold=threshold,
decay=False)
pred = model.predict(train_data)
train_acc = accuracy_score(train_labels, pred) * 100
print("Train Set Accuracy: ", train_acc)
pred = model.predict(test_data)
test_acc = accuracy_score(test_labels, pred) * 100
print("Test Set Accuracy: ", test_acc)
if plot:
plot_decision_boundary(
model.predict, np.array(train_data), np.array(train_labels),
"Neural_Network Train Set\n Units in Hidden layers: %s\nAccuracy: %f"
% (str(model.hidden_layer_sizes), train_acc))
plot_decision_boundary(
model.predict, np.array(test_data), np.array(test_labels),
"Neural_Network Test Set\n Units in Hidden layers: %s\nAccuracy: %f"
% (str(model.hidden_layer_sizes), test_acc))
def b_3(plot=False):
units = [1, 2, 3, 10, 20, 40]
lrs = [0.09, 0.09, 0.1, 0.1, 0.1, 0.01]
# lrs = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
for unit, lr in zip(units, lrs):
print("\nNeural_Network")
model = Neural_Network(len(train_data[0]), [unit],
activation="sigmoid")
print(model)
model.train(train_data,
train_labels,
max_iter=10000,
eeta=lr,
batch_size=len(train_data),
threshold=1e-6,
decay=False)
pred = model.predict(train_data)
train_acc = accuracy_score(train_labels, pred) * 100
print("Train Set Accuracy: ", train_acc)
pred = model.predict(test_data)
test_acc = accuracy_score(test_labels, pred) * 100
print("Test Set Accuracy: ", test_acc)
if plot:
plot_decision_boundary(
model.predict, np.array(test_data), np.array(test_labels),
"Neural_Network Test Set\n Units in Hidden layers: %s\nAccuracy: %f"
% (str(model.hidden_layer_sizes), test_acc))
def test_xor():
X = np.array(([3, 5], [5, 1], [10, 2]), dtype=float)
y = np.array(([75], [82], [93]), dtype=float)
X = X / np.amax(X, axis=0)
y = y / 100 # Max test score is 100
X = np.array(([1, 1], [0, 1], [0, 0], [1, 0]), dtype=float)
y = np.array(([0], [1], [0], [1]), dtype=float)
NN = Neural_Network()
train(NN, X, y)
X = np.array(([1, 1]), dtype=float)
yHat = NN.forward(X)
print('estimate for {}: {}'.format(X, yHat))
X = np.array(([0, 1]), dtype=float)
yHat = NN.forward(X)
print('estimate for {}: {}'.format(X, yHat))
X = np.array(([1, 0]), dtype=float)
yHat = NN.forward(X)
print('estimate for {}: {}'.format(X, yHat))
X = np.array(([0, 0]), dtype=float)
yHat = NN.forward(X)
print('estimate for {}: {}'.format(X, yHat))
def test_train_ocr():
X1 = np.array(([3, 5], [5, 1], [10, 2]), dtype=float)
y1 = np.array(([75], [82], [93]), dtype=float)
a, b, c, c_to_recognized = alphabet()
inputLayerSize = len(a[0])
hiddenLayerSize = 3 * inputLayerSize
outputLayerSize = 1
NN = Neural_Network(inputLayerSize=inputLayerSize,
hiddenLayerSize=hiddenLayerSize,
outputLayerSize=outputLayerSize)
X = np.array((a[0], b[0], c[0]), dtype=float)
y = np.array((a[1], b[1], c[1]), dtype=float)
train(NN, X, y)
X = np.array((c[0]), dtype=float)
yHat = NN.forward(X)
print('estimate for good C: {}'.format(yHat))
X = np.array((c_to_recognized), dtype=float)
yHat = NN.forward(X)
print('estimate for bad C: {}'.format(yHat))
def cross_validate(args):
# import some functions
encode = Data_Loader().encode
find_error = NFL_Predictor().compareVector
try:
nn = Neural_Network.createFromFile(args.file)
print "Loaded Neural Network with %i hidden nodes" % len(nn.hidden_nodes)
totalCorrect = 0.0
total_tested = 0.0
for y in range(args.start,args.end+1):
classRight = [0, 0, 0, 0, 0, 0]
correct = incorrect = 0
if(args.db == 'u'):
dl = Data_Loader()
teams = dl.getAllTeams(y)
elif(args.db == 'b'):
dl = Data_Loader()
teams = dl.getAllTeams(y)
elif(args.db == 'p'):
dl = Data_Loader('playoffTeams.csv')
teams = dl.getAllTeams(y)
elif(args.db == 'o'):
dl = Data_Loader('balancedData.csv')
teams = dl.getAllTeams(y)
total_tested += len(teams)
total_error = 0.0
for t in teams:
t.result = nn.feed_forward(t.stats)
error = (find_error(t.result, encode(t.classification)))
total_error += error**2
if error < .08:
correct += 1
classRight
if args.v:
print "team %s, results %s, class %s, error %s" % (t.name, t.result, encode(t.classification), error)
if not args.q:
print "%d \t within threshold: %d/%d \t error: %s" % (y, correct, len(teams), str(total_error))
totalCorrect += correct
print "totalCorrect: %i/%i, %.2f%%" % (totalCorrect, total_tested, (totalCorrect/total_tested)*100)
except Exception as e:
print "invalid formatting, consult neural_main.py c --help \nError: %s" % e
def __init__(self, init_NN=True) -> None:
if init_NN:
self.NN: Neural_Network = Neural_Network(SHAPE)
else:
self.NN: Neural_Network = None
self.size: int = int()
self.time: int = int()
##### simulation variables #####
self.pos: List[int] = [frame_x//2, frame_y//2]
self.body: List[List[int]] = [[self.pos[0]-10*i, self.pos[1]] for i in range(3)]
self.length: int = 3
# controls
self.direction: str = 'RIGHT'
self.change_to: str = self.direction
self.food_pos: List[int] = [r.randrange(1, (frame_x//10)) * 10, r.randrange(1, (frame_y//10)) * 10]
#self.food_spawn: bool = True
self.dead: bool = False
##### data fed to neural network #####
self.current_frame: List[List[int]] = None
self.framebuffer: List[List[int]] = None
self.reset_framebuffer()
def classify(self, show_output=False):
"""Send the preprocessed images to the NN classifier"""
print('{0} Numbers to be classified'.format(len(self.cropped_images)))
return_list = []
self.apply_cropping(show_output=show_output)
net = Neural_Network()
net.load_state_dict(torch.load(TENSOR_LOCATION))
net.eval()
for image in self.cropped_images:
image = Image.fromarray(image)
# Resizes the number and adds a 10 px border
transfrom = transforms.Compose([
transforms.Grayscale(),
transforms.Resize(self.output_size - self.border_size),
transforms.CenterCrop(self.output_size),
transforms.ToTensor(),
])
img_tensor = transfrom(image)
if show_output:
plt.imshow(np.array(img_tensor)[0, :, :],
cmap=plt.cm.gray_r,
interpolation='nearest')
plt.title('Image used for classification')
plt.show()
img_tensor.unsqueeze_(0)
outputs = net.forward(Variable(img_tensor))
dummy, predicted_labels = torch.max(outputs.data, 1)
return_list.append(int(predicted_labels.numpy().max()))
print('Classified: {0}'.format(predicted_labels.numpy().max()))
return return_list
def predict(args):
try:
nn = Neural_Network.createFromFile(args.file)
dl = Data_Loader()
#team = dl.getTeam(args.team, args.year)
team = dl.getTeam(args.team, args.year)
result = nn.feed_forward(team.stats)
print "\n\nPredicting the %i %s..." % (args.year, team_dict.teams[args.team])
print "RESULTS: %.3f\n" % result[0]
if args.show_expected:
print "EXPECTED: %s" % (dl.encode(team.classification))[0]
results_graph = "\t|" + "".join(repeat("-",int(result[0]/.8*50))) + "|" + "".join(repeat("-",(int((1-result[0]/.8)*50)))) + "|"
print " Not in playoffs" + "".join(repeat(" ",35)) + "Super Bowl Champs"
print results_graph
post_processor = NFL_Predictor(nn)
similar_teams = post_processor.compareWithPastTeams(dl.getEveryTeam(), team, 16)
print"\nThe 15 most similar teams throughout history:"
del similar_teams[0]
for t in similar_teams:
print "%s \tScore: %f" % t
except Exception as e:
print "invalid formatting, consult neural_main.py t --help \n Error: %s" % e
def c_2(plot=False, units=[100], activation="sigmoid", eeta=0.1):
print("\nNeural_Network MNIST")
model = Neural_Network(len(mnist_trd[0]), units, activation=activation)
print(model)
model.train(mnist_trd,
mnist_trl,
max_iter=300,
eeta=eeta,
batch_size=100,
decay=True,
threshold=1e-3)
pred = model.predict(mnist_trd)
train_acc = accuracy_score(mnist_trl, pred) * 100
print("Train Set Accuracy: ", train_acc)
pred = model.predict(mnist_ted)
test_acc = accuracy_score(mnist_tel, pred) * 100
print("Test Set Accuracy: ", test_acc)
################################################################################
##
## Copyright 2016 Udara Karunarathna (IT13021030) and Supun Sudaraka (IT13019914). All rights reserved.
##
################################################################################
import numpy as np
from scipy import optimize
from StringIO import StringIO
import matplotlib.pyplot as plot
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from neural_network import Neural_Network
from trainer import Trainer
NeuralNetwork = Neural_Network()
#Pass to Trainer Data:
X,Y = NeuralNetwork.readInputFile("input.txt")
Y = np.reshape(Y, (3,1))
print ("Input : ")
print (X)
print ("Results : ")
print (Y)
#normalized training data
X = NeuralNetwork.normalize(X)
Y = Y/100
print ("Input : ")
print (X)
def get_model(weights=[], bias=[]):
return Neural_Network(9, 6, 3, weights, bias)
class Classifier:
def __init__(self, classifier_type, **kwargs):
"""
Initializer. Classifier_type should be a string which refers
to the specific algorithm the current classifier is using.
Use keyword arguments to store parameters
specific to the algorithm being used. E.g. if you were
making a neural net with 30 input nodes, hidden layer with
10 units, and 3 output nodes your initalization might look
something like this:
neural_net = Classifier(weights = [], num_input=30, num_hidden=10, num_output=3)
Here I have the weight matrices being stored in a list called weights (initially empty).
"""
self.classifier_type = classifier_type
self.params = kwargs
"""
The kwargs you inputted just becomes a dictionary, so we can save
that dictionary to be used in other methods.
"""
def train(self, training_data):
"""
Data should be nx(m+1) numpy matrix where n is the
number of examples and m is the number of features
(recall that the first element of the vector is the label).
I recommend implementing the specific algorithms in a
seperate module and then determining which method to call
based on classifier_type. E.g. if you had a module called
neural_nets:
if self.classifier_type == 'neural_net':
import neural_nets
neural_nets.train_neural_net(self.params, training_data)
Note that your training algorithms should be modifying the parameters
so make sure that your methods are actually modifying self.params
You should print the accuracy, precision, and recall on the training data.
"""
if self.classifier_type == 'neural_network':
#change num_input, num_output based upon the data
self.nn = Neural_Network("neural_network",weights = [], num_input=self.params['num_input'], num_hidden=1000, num_output=self.params['num_output'], alt_weight=self.params['one']=='1', momentum=self.params['two']=='1')
self.nn.train(training_data)
elif self.classifier_type == 'naive_bayes':
self.nb = Naive_Bayes("naive_bayes")
self.nb.train(training_data)
elif self.classifier_type =='decision_tree':
self.dt = Decision_Tree("decision_tree", pruning=self.params['one']=='1',
info_gain_ratio=self.params['two']=='1')
self.dt.train(training_data)
def predict(self, data):
"""
Predict class of a single data vector
Data should be 1x(m+1) numpy matrix where m is the number of features
(recall that the first element of the vector is the label).
I recommend implementing the specific algorithms in a
seperate module and then determining which method to call
based on classifier_type.
This method should return the predicted label.
"""
def test(self, test_data):
"""
Data should be nx(m+1) numpy matrix where n is the
number of examples and m is the number of features
(recall that the first element of the vector is the label).
You should print the accuracy, precision, and recall on the test data.
"""
#pdb.set_trace()
#Accuracy, Recall, and Precision
relevant_and_retrieved, relevant, retrieved, total, hit = 0, 0, 0, 0, 0
for person in test_data:
predict = 0
if self.classifier_type == 'neural_network':
predict = self.nn.predict(person)
elif self.classifier_type == 'naive_bayes':
predict = self.nb.predict(person)
elif self.classifier_type == 'decision_tree':
predict = self.dt.predict(person)
if predict == person[0]:
if predict == 0:
relevant_and_retrieved += 1
hit += 1
if person[0] == 0:
relevant += 1
if predict == 0:
retrieved += 1
total += 1
accuracy = hit/float(total)
recall = relevant_and_retrieved/float(relevant)
precision = relevant_and_retrieved/float(retrieved)
print "Accuracy: ", accuracy
print "Precision ", precision
print "Recall: " , recall
import time
from neural_network import Neural_Network, X, y
import numpy as np
weightsToTry = np.linspace(-5, 5, 1000)
costs = np.zeros(1000)
NN = Neural_Network()
startTime = time.clock()
for i in range(1000):
NN.W1[0, 0] = weightsToTry[i]
yHat = NN.forward(X)
costs[i] = 0.5 * sum((y - yHat) ** 2)
endTime = time.clock()
print(endTime)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import numpy
from neural_network import Neural_Network
from plotter import Plotter
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
#writing a circle of pixels to the array according to middle coordinates of mouse position
plotter=Plotter()
NN=Neural_Network()
if input("Wanna train model? y/N ") =='y':
layers = [800,400]
NN.initialize(layers)
NN.train(20)
if input("Wanna save model? Y/n ") !='n' :
NN.save_model()
elif input("Wanna load from file? Y/n ")!='n' :
NN.load_model()
else :
print("Exit")
[1, 1, 0], [1, 1, 1]),
dtype=float) # 7x3 Tensor
# y = our output of our neural network. This is a supervised method.
y = np.array(([1], [0], [0], [0], [0], [0], [0], [1]), dtype=float)
# what value we want to predict
xPredicted = np.array(([0, 0, 1]), dtype=float)
# Normalize xPredicted
X = X / np.amax(X, axis=0) # maximum of X input array
# maximum of xPredicted (our input data for the prediction)
xPredicted = xPredicted / np.amax(xPredicted, axis=0)
# set up our Loss file for graphing
lossFile = open("SumSquaredLossList.csv", "w")
myNeuralNetwork = Neural_Network(hidden_layer_size=10)
# trainingEpochs = 1000
trainingEpochs = 100000
for i in range(trainingEpochs):
# train myNeuralNetwork 1,000 times print ("Epoch # " + str(i) + "\n")
print("Network Input : \n" + str(X))
print("Expected Output of XOR Gate Neural Network: \n" + str(y))
print("Actual Output from XOR Gate Neural Network: \n" +
str(myNeuralNetwork.feedForward(X))) # mean sum squared loss
Loss = np.mean(np.square(y - myNeuralNetwork.feedForward(X)))
myNeuralNetwork.saveSumSquaredLossList(i, Loss)
print("Sum Squared Loss: \n" + str(Loss))
print("\n")
myNeuralNetwork.trainNetwork(X, y)
myNeuralNetwork.saveWeights()
from neural_network import Neural_Network
import load_data as ld
import pdb
nb = Neural_Network("neural_network",weights = [], num_input=16, num_hidden=1000, num_output=2) #neural_net = Classifier(weights = [], num_input=30, num_hidden=10, num_output=3)
data = ld.load_congress_data(.85)
#data = ld.load_iris(.75)
#data = ld.load_monks(3)
classify = nb.train(data[0])
#nb.train(iris[0])
#pdb.set_trace()
#nb.test(congress[1])
tot, hit = 0, 0
ones = 0
zeros = 0
twos = 0
for person in data[1]:
predict = nb.predict(person)
if predict == person[0]:
hit += 1
tot += 1
if predict == 1:
ones += 1
elif predict == 0:
zeros += 1
def initialize_network_for_validation(network_file_lines,
initial_weights_file_lines,
dataset_file_lines,
isTest,
network=None):
#print("[main] Inicializando rede")
#print("network_file_lines", network_file_lines)
#print("initial_weights_file_lines", initial_weights_file_lines)
#print("dataset_file_lines", dataset_file_lines)
if (network == None):
#primeira linha é o fator de regularização
network_lambda = float(network_file_lines[0])
#ada linha sendo uma camada e o valor da linha sendo a quantidade de neurônios
layers_size = []
for neurons in network_file_lines[1:]:
#print("[main] camada com", neurons, "neuronio")
layers_size.append(int(neurons))
layers = [] # camadas
# faz a leitura dos pesos no arquivo de pesos iniciais passados por linha de comando
if (len(initial_weights_file_lines) > 0):
#print("initial weghts vector is not null")
for line in initial_weights_file_lines:
neurons = line.split(';')
v_neurons = []
for neuron in neurons:
weights = neuron.split(',')
v_weights = []
for weight in weights: #pesos de cada neurônio
v_weights.append(float(weight))
v_neurons.append(v_weights)
layers.append(
np.array(v_neurons)
) #cada camada tem seus neurônios que contém seus pesos
else:
#cria pesos inicias randomicamente entre -1 e 1
#print("initial weights vector is null")
#print("layer_sizes", layers_size)
for i, layer in enumerate(layers_size[:-1]):
v_neurons = []
for i in range(layers_size[i + 1]):
weights_v = []
for y in range(layer + 1): #bias
weights_v.append(random.triangular(-1, 1, 0))
v_neurons.append(weights_v)
layers.append(np.array(v_neurons))
instances = []
for instance in dataset_file_lines:
instances.append(instance)
#print("[main] Fator de regularizacao:", network_lambda)
#print("[main] Quantidade de camadas:", len(layers))
#estrutura geral da rede
neural_network = Neural_Network(network_lambda, layers_size, layers)
if (isTest):
networkPlus = Neural_Network(network_lambda, layers_size, layers)
networkMinus = Neural_Network(network_lambda, layers_size, layers)
networkClean = Neural_Network(network_lambda, layers_size, layers)
back_propagation.gradient_verification(network, dataset_file_lines,
isTest, alpha, networkPlus,
networkMinus, networkClean,
0.000001)
#chama algoritmo de bajpropagation passando a rede e as instancias de treinamento
errorReg, network, fx, D = back_propagation.execute(
neural_network, dataset_file_lines, isTest, alpha)
else:
errorReg, network, fx, D = back_propagation.execute(
network, dataset_file_lines, isTest, alpha)
return errorReg, network, fx
DL = Data_Loader()
'''
nn = Neural_Network.createWithRandomWeights(66,40,6)
# train! with learning rate proportional to # of teams in the situations
inputs = []
targets = []
for y in range(2005,2007):
i,t = DL.getTargets(y)
inputs += i
targets += t
#print targets
nn = nn.train(10000,inputs,targets,1.5)
nn.saveToFile("predictortest.txt")
'''
nn = Neural_Network.createFromFile("predictortest.txt")
teams_2011 = DL.getAllTeams(2011)
pats_2011 = filter(lambda t: t.name == "nwe", teams_2011)[0]
all_other_teams = filter(lambda t: t.name != "nwe", teams_2011)
predictor = NFL_Predictor(nn)
similar = predictor.compareWithPastTeams(all_other_teams, pats_2011, 3)
for t,d in similar:
print t.name + " " + str(d) + "\n"
