def test_layers(self):
nn = NeuralNetwork.NeuralNetwork([1, 1, 1])
self.assertIs(type(nn.firstLayer), NeuronLayer.NeuronLayer)
self.assertIs(type(nn.firstLayer.nextLayer), OutputLayer.OutputLayer)
nn.firstLayer.neurons[0].setWeight(0, 0.5)
nn.firstLayer.neurons[0].setBias(1)
nn.firstLayer.nextLayer.neurons[0].setWeight(0, 0.5)
nn.firstLayer.nextLayer.neurons[0].setBias(1)
self.assertEqual(nn.firstLayer.getNeurons()[0].getWeights(), [0.5])
self.assertEqual(nn.firstLayer.getNeurons()[0].getBias(), 1)
self.assertIsNone(nn.firstLayer.getPreviousLayer())
self.assertIs(type(nn.firstLayer.getNextLayer()),
OutputLayer.OutputLayer)
nn.firstLayer.forwardFeed([1])
self.assertAlmostEqual(nn.firstLayer.getOutput()[0], 0.8, 1)
self.assertAlmostEqual(nn.firstLayer.getNextLayer().getOutput()[0],
0.8, 1)
self.assertIs(type(nn.firstLayer.getOutputLayer()),
OutputLayer.OutputLayer)
nn.firstLayer.getOutputLayer().backwardPropagateError([1.5])
self.assertAlmostEqual(nn.firstLayer.getNeurons()[0].getDelta(), 0.008,
3)
self.assertAlmostEqual(
nn.firstLayer.getNextLayer().getNeurons()[0].getDelta(), 0.1, 1)
nn.firstLayer.updateWeightsAndBias([1], 2.5)
self.assertAlmostEqual(nn.firstLayer.getNeurons()[0].getWeights()[0],
0.52, 2)
self.assertAlmostEqual(nn.firstLayer.getNeurons()[0].getBias(), 1.02,
2)
self.assertAlmostEqual(
nn.firstLayer.getNextLayer().getNeurons()[0].getWeights()[0], 0.72,
2)
self.assertAlmostEqual(
nn.firstLayer.getNextLayer().getNeurons()[0].getBias(), 1.27, 2)
def test_XOR_neural_network(self):
set = [[[1, 1], [0]], [[1, 0], [1]], [[0, 1], [1]], [[0, 0], [0]]]
epochs = 2000
learningRate = 0.5
nn = NeuralNetwork.NeuralNetwork([2, 3, 1])
for i in range(epochs):
for j in range(len(set)):
nn.train(set[j][0], set[j][1], learningRate)
nn.forwardFeed([1, 1])
self.assertAlmostEqual(0.0, nn.getOutput()[0], 0)
nn.forwardFeed([1, 0])
self.assertAlmostEqual(1.0, nn.getOutput()[0], 0)
nn.forwardFeed([0, 1])
self.assertAlmostEqual(1.0, nn.getOutput()[0], 0)
nn.forwardFeed([0, 0])
self.assertAlmostEqual(0.0, nn.getOutput()[0], 0)
y_train_batches = np.array_split(one_hot_encoded_y_train,
len(one_hot_encoded_y_train) // batch_size)
# # training data
# x_train = np.array([[[0,0]], [[0,1]], [[1,0]], [[1,1]]])
# one_hot_encoded_y_train = np.array([[[1, 0]], [[0, 1]], [[0, 1]], [[0, 1]]])
# y_train =[0, 1, 1, 1]
# this line is used to catch the errors arising from numpy.
np.seterr(all='raise')
input_number = x_train.shape[2]
output_number = 6
size_of_hidden_layer = 10
neural_network = NeuralNetwork(cross_entropy, cross_entropy_prime)
neural_network.add_layer(
FCLayer(input_number, size_of_hidden_layer, diminishing_factor=10))
neural_network.add_layer(ActivationLayer(swish, swish_prime))
neural_network.add_layer(FCLayer(size_of_hidden_layer, output_number))
neural_network.add_layer(ActivationLayer(softmax, softmax_prime))
neural_network.fit(x_train,
one_hot_encoded_y_train,
epoch_number=10,
initial_learning_rate=0.5,
decay=0.01)
out = neural_network.predict(x_train)
predictions = argmax(out)
print("confusion matrix:", confusion_matrix(y_train, predictions), sep="\n")
def constructNeuralNetwork(self, individualPos):
individual = self.getPopulation()[individualPos]
NN = NeuralNetwork.NeuralNetwork()
def main():
global SCREEN, FPSCLOCK
global NN, ITER, userControl
userControl = True
print("User control: " + str(userControl))
ITER = 0
#----------------REDES NEURONALES------------------#
NN = NeuralNetwork.NeuralNetwork([52, 6, 1])
#NN = NeuralNetwork.NeuralNetwork([2, 6, 1])
#--------------------------------------------------#
pygame.init()
FPSCLOCK = pygame.time.Clock()
SCREEN = pygame.display.set_mode((SCREENWIDTH, SCREENHEIGHT))
pygame.display.set_caption('Flappy Bird')
# numbers sprites for score display
IMAGES['numbers'] = (
pygame.image.load('assets/sprites/0.png').convert_alpha(),
pygame.image.load('assets/sprites/1.png').convert_alpha(),
pygame.image.load('assets/sprites/2.png').convert_alpha(),
pygame.image.load('assets/sprites/3.png').convert_alpha(),
pygame.image.load('assets/sprites/4.png').convert_alpha(),
pygame.image.load('assets/sprites/5.png').convert_alpha(),
pygame.image.load('assets/sprites/6.png').convert_alpha(),
pygame.image.load('assets/sprites/7.png').convert_alpha(),
pygame.image.load('assets/sprites/8.png').convert_alpha(),
pygame.image.load('assets/sprites/9.png').convert_alpha())
# game over sprite
IMAGES['gameover'] = pygame.image.load(
'assets/sprites/gameover.png').convert_alpha()
# message sprite for welcome screen
IMAGES['message'] = pygame.image.load(
'assets/sprites/message.png').convert_alpha()
# base (ground) sprite
IMAGES['base'] = pygame.image.load(
'assets/sprites/base.png').convert_alpha()
# sounds
if 'win' in sys.platform:
soundExt = '.wav'
else:
soundExt = '.ogg'
SOUNDS['die'] = pygame.mixer.Sound('assets/audio/die' + soundExt)
SOUNDS['hit'] = pygame.mixer.Sound('assets/audio/hit' + soundExt)
SOUNDS['point'] = pygame.mixer.Sound('assets/audio/point' + soundExt)
SOUNDS['swoosh'] = pygame.mixer.Sound('assets/audio/swoosh' + soundExt)
SOUNDS['wing'] = pygame.mixer.Sound('assets/audio/wing' + soundExt)
while True:
# select random background sprites
randBg = random.randint(0, len(BACKGROUNDS_LIST) - 1)
IMAGES['background'] = pygame.image.load(
BACKGROUNDS_LIST[randBg]).convert()
# select random player sprites
randPlayer = random.randint(0, len(PLAYERS_LIST) - 1)
IMAGES['player'] = (
pygame.image.load(PLAYERS_LIST[randPlayer][0]).convert_alpha(),
pygame.image.load(PLAYERS_LIST[randPlayer][1]).convert_alpha(),
pygame.image.load(PLAYERS_LIST[randPlayer][2]).convert_alpha(),
)
# select random pipe sprites
pipeindex = random.randint(0, len(PIPES_LIST) - 1)
IMAGES['pipe'] = (
pygame.transform.rotate(
pygame.image.load(PIPES_LIST[pipeindex]).convert_alpha(), 180),
pygame.image.load(PIPES_LIST[pipeindex]).convert_alpha(),
)
# hismask for pipes
HITMASKS['pipe'] = (
getHitmask(IMAGES['pipe'][0]),
getHitmask(IMAGES['pipe'][1]),
)
# hitmask for player
HITMASKS['player'] = (
getHitmask(IMAGES['player'][0]),
getHitmask(IMAGES['player'][1]),
getHitmask(IMAGES['player'][2]),
)
movementInfo = showWelcomeAnimation()
crashInfo = mainGame(movementInfo)
showGameOverScreen(crashInfo)
size = 857
filepath = os.path.dirname(os.getcwd()) + DEFAULT_DIR + DEFAULT_NAME
fp = open(filepath, "w")
config = 0
for epoch in epochs:
for lr in learning_rates:
for reg in regularizations:
for alpha in momentums:
mean_loss = 0
mean_validation = 0
for i in range(k):
model = NeuralNetwork()
model.add(InputLayer(10))
model.add(DenseLayer(50, fanin=10))
model.add(DenseLayer(30, fanin=50))
model.add(OutputLayer(2, fanin=30))
model.compile(size, epoch, lr / size, None, reg, alpha,
"mean_squared_error")
(train, val) = data.kfolds(index=i, k=k)
mean_loss = mean_loss + model.fit(train[0], train[1])[-1]
mean_validation = mean_validation + model.evaluate(
val[0], val[1])
fp.write("{}, {}, {}, {}, {}, {}, {}\n".format(
config, epoch, lr, reg, alpha, mean_loss / k,
mean_validation / k))
def plug(self, brain):
self.brain = NeuralNetwork(brain, False)
import numpy as np
import dataset as ds
from neural_networks import NeuralNetwork
from layers import InputLayer, OutputLayer, DenseLayer
import matplotlib.pyplot as plt
data = ds.MonksDataset()
my_model = NeuralNetwork()
my_model.add(InputLayer(17))
my_model.add(DenseLayer(10, fanin=17, activation="sigmoid"))
my_model.add(OutputLayer(1, fanin=10, activation="sigmoid"))
my_model.compile(122, 600, 0.075, None, 0.0001, 0, "mean_squared_error")
(loss, test_loss, accuracy, test_accuracy) = my_model.fit_monks(
data.train_data_patterns, data.train_data_targets, data.test_data_patterns,
data.test_data_targets)
print("Loss: {}".format(loss[-1]))
print("Test Loss: {}".format(test_loss[-1]))
print("Accuracy: {}".format(accuracy[-1]))
print("Test accuracy: {}".format(test_accuracy[-1]))
plot1 = plt.figure(1)
plt.plot(loss)
plt.plot(test_loss, "--")
plot2 = plt.figure(2)
def xor_network():
nn = NeuralNetwork(0.05, 2, 4, 1)
for _ in range(10000):
nn.train(xor)
return nn
# coding: utf-8
import bottle
import numpy as np
from neural_networks import NeuralNetwork
app = bottle.default_app()
app.iteration = 0
app.nn = NeuralNetwork(3, 3, 2)
@bottle.route('/')
def index():
return bottle.static_file('index.html', root="./front/")
@bottle.route('/json/color.get')
def color_get():
r, g, b = np.random.randint(0, 255, size=3)
app.iteration += 1
guess = app.nn.predict(np.array([[r, g, b]]))[0]
if guess[0] < guess[1]:
guess_label = "white"
else:
guess_label = "black"
ctx = {
'color': "#%02x%02x%02x" % (r, g, b),
'iteration': app.iteration,
import numpy as np import dataset as ds from neural_networks import NeuralNetwork from layers import InputLayer, OutputLayer, DenseLayer from functions._init_functions import init_functions from functions._activation_functions import activation_functions, activation_functions_derivatives from functions._loss_functions import loss_functions import plot as plt data = ds.MLCupDataset() data = ds.MLCupDataset() model = NeuralNetwork() model.add(InputLayer(10)) model.add(DenseLayer(50, fanin=10, activation="sigmoid")) model.add(DenseLayer(30, fanin=50, activation="sigmoid")) model.add(OutputLayer(2, fanin=30)) # configuration 322, line 324 model.compile(1143, 600, 0.03, None, 0.000008, 0.3, "mean_squared_error") loss = model.fit(data.train_data_patterns, data.train_data_targets) print(loss[-1]) plt.plot_loss(loss)
import numpy as np
import dataset as ds
from neural_networks import NeuralNetwork
from layers import InputLayer, OutputLayer, DenseLayer
import matplotlib.pyplot as plt
data = ds.MLCupDataset()
model = NeuralNetwork()
model.add(InputLayer(10))
model.add(DenseLayer(50, fanin=10, activation="sigmoid"))
model.add(DenseLayer(30, fanin=50, activation="sigmoid"))
model.add(OutputLayer(2, fanin=30))
# configuration 322, line 324
model.compile(1142, 600, 0.03/1142, None, 0.000008, 0.3, "mean_squared_error")
loss = model.fit(data.train_data_patterns, data.train_data_targets)
i = 0
final_loss = []
while i < len(loss):
final_loss.append((loss[i] + loss[i+1])/2)
i += 2
print(final_loss[-1])
print(model.evaluate(data.model_assessment_patterns, data.model_assessment_targets))
plt.plot(final_loss)
plt.show()
""" Method which generates sequence of numbers """
X = np.zeros([nums, 10, 20], dtype=float)
y = np.zeros([nums, 10, 20], dtype=float)
for i in range(nums):
start = np.random.randint(0, 10)
num_seq = np.arange(start, start + 10)
X[i] = to_categorical(num_seq, n_col=20)
y[i] = np.roll(X[i], -1, axis=0)
y[:, -1, 1] = 1 # Mark endpoint as 1
return X, y
if __name__ == '__main__':
X, y = gen_mult_ser(3000)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = NeuralNetwork(optimizer=optimizer, loss=CrossEntropy)
clf.add(RNN(10, activation="tanh", bptt_trunc=5, input_shape=(10, 61)))
clf.add(Activation('softmax'))
tmp_X = np.argmax(X_train[0], axis=1)
tmp_y = np.argmax(y_train[0], axis=1)
print("Number Series Problem:")
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y = [" + " ".join(tmp_y.astype("str")) + "]")
print()
train_err, _ = clf.fit(X_train, y_train, n_epochs=500, batch_size=512)
y_pred = np.argmax(clf.predict(X_test), axis=2)
y_test = np.argmax(y_test, axis=2)
accuracy = np.mean(accuracy_score(y_test, y_pred))
print(accuracy)
import random, time
from neural_networks import NeuralNetwork, DataSetParser
# Parameters.
epochs = 2000
learningRate = 0.1
neuralNetworkRepresentation = [7, 5, 3]
dataSetFileName = 'seeds.txt'
dataSetNumberOfParameters = 8
# Neural network construction.
nn = NeuralNetwork.NeuralNetwork(neuralNetworkRepresentation)
# Adapted Data Set.
parser = DataSetParser.DataSetParser(dataSetFileName,
dataSetNumberOfParameters)
parser.adaptData()
adaptedData = parser.getAdaptedData()
random.shuffle(adaptedData)
# Learning process.
start_time = time.time()
nn.learn(adaptedData, epochs, learningRate)
elapsed_time = time.time() - start_time
print('Learning elapsed time: ' + str(elapsed_time) + ' seconds.')
def randomSample(self):
"""Generate a neural network with random weights and bias according to the specified architecture."""
return NeuralNetwork.NeuralNetwork(self.getNeuralNetworkArchitecture())
def __init__(self, location, lenght, brain):
super(SmartSnake, self).__init__(location, lenght)
self.brain = NeuralNetwork(brain)
self.sight = Sight()
self.bind_predeath_event(self.punish_for_suicide)
# Parameters.
epochs = 2000
learningRate = 0.1
neuralNetworkRepresentation = [7, 5, 3]
dataSetFileName = 'seeds.txt'
dataSetNumberOfParameters = 8
# Neural network construction.
nn = NeuralNetwork.NeuralNetwork(neuralNetworkRepresentation)
# Adapted Data Set.
parser = DataSetParser.DataSetParser(dataSetFileName, dataSetNumberOfParameters)
parser.adaptData()
adaptedData = parser.getAdaptedData()
random.shuffle(adaptedData)
# Learning process.
start_time = time.time()
nn.learn(adaptedData, epochs, learningRate)
elapsed_time = time.time() - start_time
print('Learning elapsed time: '+str(elapsed_time) +' seconds.')
"""
NeuronsArray = [[[[1, 1], 1], [[1, 2], 1], [[1, 3], 1]], [[[4, 4, 4], 0.5]]]
print("init")
NN = NeuralNetwork.NeuralNetwork([1, 1, 1])
NN.setModelParameters(NeuronsArray)
NN2 = NeuralNetwork.NeuralNetwork([2, 3, 1])
print("initialized!")
print(NN.getOutputLayer().getPreviousLayer().getNeurons())
print(NN.getOutputLayer().getNeurons())
class SmartSnake(Snake):
def __init__(self, location, lenght, brain):
super(SmartSnake, self).__init__(location, lenght)
self.brain = NeuralNetwork(brain)
self.sight = Sight()
self.bind_predeath_event(self.punish_for_suicide)
def move(self, walls, food_location):
sight = self.sight(self.get_location(), food_location, self.get_body(),
self.direction)
# print(sight)
result = self.brain(sight)
desicion = self.change_direction(result)
self.reward_for_food_approach(food_location, desicion)
self.update_direction(desicion)
super(SmartSnake, self).move(walls, food_location)
def choose_direction(self, result):
if result == 0:
return 'u'
if result == 1:
return 'r'
if result == 2:
return 'd'
if result == 3:
return 'l'
def change_direction(self, direction):
if (self.direction == 'r'):
if (direction == 0):
return 'r'
if (direction == 1):
return 'u'
if (direction == 2):
return 'd'
if (self.direction == 'u'):
if (direction == 0):
return 'u'
if (direction == 1):
return 'l'
if (direction == 2):
return 'r'
if (self.direction == 'l'):
if (direction == 0):
return 'l'
if (direction == 1):
return 'd'
if (direction == 2):
return 'u'
if (self.direction == 'd'):
if (direction == 0):
return 'd'
if (direction == 1):
return 'r'
if (direction == 2):
return 'l'
def fit(self):
# if self.score < 5:
# return (DIE_OF_HUNGER - self.time_alive) ** 2 * pow(2, self.score)
# fitness = (DIE_OF_HUNGER - self.time_alive) ** 2
# fitness *= pow(2, 10)
return (self.score)
def mutate(self):
self.brain.mutate(MUTATION_PROBABILITY,
[replace_weight, scale, shift, swap_sign])
def reward_for_food_approach(self, food_location, decision):
location = self.get_location()
dist_to_food = abs(food_location[0][0] - location[0]) / self.get_grid(
) + abs(food_location[0][1] - location[1]) / self.get_grid()
future_location = self.calculate_move(decision)
future_dist_to_food = abs(
food_location[0][0] - future_location[0]) / self.get_grid() + abs(
food_location[0][1] - future_location[1]) / self.get_grid()
if (dist_to_food < future_dist_to_food):
self.score -= 1.5
else:
self.score += 1
def punish_for_suicide(self):
if self.time_alive < 10:
self.score -= 20
def plug(self, brain):
self.brain = NeuralNetwork(brain, False)
def get_brain(self):
return self.brain
def neural_network(x_train, x_test, y_train, y_test, x_pca, x_ica, x_kpca,
x_rp, x_kmeans, x_gmm, **kwargs):
"""Perform neural network experiment.
Args:
x_train (ndarray): training data.
x_test (ndarray): test data.
y_train (ndarray): training labels.
y_test (ndarray): test labels.
x_pca (ndarray): reduced dataset by PCA.
x_ica (ndarray): reduced dataset by ICA.
x_kpca (ndarray): reduced dataset by KPCA.
x_rp (ndarray): reduced dataset by RP.
x_kmeans (ndarray): clusters produced by k-Means.
x_gmm (ndarray): clusters produced by Gaussian Mixture Models.
kwargs (dict): additional arguments to pass:
- layer1_nodes (int): number of neurons in first layer.
- layer2_nodes (int): number of neurons in second layer.
- learning_rate (float): learning rate.
Returns:
None.
"""
print('\n--------------------------')
print('NN')
print('--------------------------')
# Declare Neural Network and perform experiments on the original dataset
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
nn.experiment(x_train, x_test, y_train, y_test)
print('\n--------------------------')
print('PCA + NN')
print('--------------------------')
# Declare Neural Network and perform experiments on the reduced dataset by PCA
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
nn.experiment(x_pca[0], x_pca[1], y_train, y_test)
print('\n--------------------------')
print('ICA + NN')
print('--------------------------')
# Declare Neural Network and perform experiments on the reduced dataset by ICA
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
nn.experiment(x_ica[0], x_ica[1], y_train, y_test)
print('\n--------------------------')
print('KPCA + NN')
print('--------------------------')
# Declare Neural Network and perform experiments on the reduced dataset by KPCA
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
nn.experiment(x_kpca[0], x_kpca[1], y_train, y_test)
print('\n--------------------------')
print('RP+ NN')
print('--------------------------')
# Declare Neural Network and perform experiments on the reduced dataset by RP
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
nn.experiment(x_rp[0], x_rp[1], y_train, y_test)
print('\n--------------------------')
print('KMEANS+ NN')
print('--------------------------')
# Declare Neural Network
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
# Augment the original dataset by adding clusters produced by k-Means as features
x_kmeans_normalized = (x_kmeans[0] - np.mean(x_kmeans[0])) / np.std(
x_kmeans[0])
x_kmeans_normalized = np.expand_dims(x_kmeans_normalized, axis=1)
x_train_new = np.append(x_train, x_kmeans_normalized, axis=1)
x_kmeans_normalized = (x_kmeans[1] - np.mean(x_kmeans[1])) / np.std(
x_kmeans[1])
x_kmeans_normalized = np.expand_dims(x_kmeans_normalized, axis=1)
x_test_new = np.append(x_test, x_kmeans_normalized, axis=1)
# Perform experiments on it
nn.experiment(x_train_new, x_test_new, y_train, y_test)
print('\n--------------------------')
print('GMM+ NN')
print('--------------------------')
# Declare Neural Network
nn = NeuralNetwork(layer1_nodes=kwargs['layer1_nodes'],
layer2_nodes=kwargs['layer2_nodes'],
learning_rate=kwargs['learning_rate'])
# Augment the original dataset by adding clusters produced by Gaussian Mixture Models as features
x_gmm_normalized = (x_gmm[0] - np.mean(x_gmm[0])) / np.std(x_gmm[0])
x_gmm_normalized = np.expand_dims(x_gmm_normalized, axis=1)
x_train_new = np.append(x_train, x_gmm_normalized, axis=1)
x_gmm_normalized = (x_gmm[1] - np.mean(x_gmm[1])) / np.std(x_gmm[1])
x_gmm_normalized = np.expand_dims(x_gmm_normalized, axis=1)
x_test_new = np.append(x_test, x_gmm_normalized, axis=1)
# Perform experiments on it
nn.experiment(x_train_new, x_test_new, y_train, y_test)
def experiment(x_train, x_test, y_train, y_test):
"""Perform experiment.
Args:
x_train (ndarray): training data.
x_test (ndarray): test data.
y_train (ndarray): training labels.
y_test (ndarray): test labels.
Returns:
None.
"""
# Array of training sizes to plot the learning curves over.
training_sizes = np.arange(20, int(len(x_train) * 0.9), 10)
# K-Nearest Neighbor
print('\n--------------------------')
knn = KNN(k=1, weights='uniform', p=2)
knn.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.3,
n_neighbors_range=np.arange(1, 50, 2),
p_range=np.arange(1, 20),
weight_functions=['uniform', 'distance'],
train_sizes=training_sizes)
# Support Vector Machines
print('\n--------------------------')
svm = SVM(c=1., kernel='rbf', degree=3, gamma=0.001, random_state=42)
svm.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.2,
C_range=[1, 5] + list(range(10, 100, 20)) +
list(range(100, 1000, 50)),
kernels=['linear', 'poly', 'rbf'],
gamma_range=np.logspace(-7, 0, 50),
poly_degrees=[2, 3, 4],
train_sizes=training_sizes)
# Decision Trees
print('\n--------------------------')
dt = DecisionTree(max_depth=1, min_samples_leaf=1, random_state=42)
dt.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.1,
max_depth_range=list(range(1, 50)),
min_samples_leaf_range=list(range(1, 30)),
train_sizes=training_sizes)
# AdaBoost
print('\n--------------------------')
boosted_dt = AdaBoost(n_estimators=50,
learning_rate=1.,
max_depth=3,
random_state=42)
boosted_dt.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.2,
max_depth_range=list(range(1, 30)),
n_estimators_range=[1, 3, 5, 8] +
list(range(10, 100, 5)) + list(range(100, 1000, 50)),
learning_rate_range=np.logspace(-6, 1, 50),
train_sizes=training_sizes)
# Neural Networks
print('\n--------------------------')
nn = NeuralNetwork(alpha=0.01,
layer1_nodes=50,
layer2_nodes=30,
learning_rate=0.001,
max_iter=100)
nn.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.1,
alpha_range=np.logspace(-5, 1, 30),
learning_rate_range=np.logspace(-4, 0, 50),
train_sizes=training_sizes)