oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/artifitial1.data", usecols=range(1), delimiter=",")
classes = np.loadtxt("Datasets/artifitial1.data", usecols=-1, delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(np.array(list([np.float32(y)]) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
oDataSet.labels = np.array([classes]).T
for j in range(20):
slices = KFold(n_splits=K_FOLD)
oData = Data(1, 31, samples=50)
indices = np.arange(oDataSet.attributes.shape[0])
np.random.shuffle(indices)
oData.Testing_indexes = indices[int(oDataSet.attributes.shape[0] * 0.85):]
oData.Training_indexes = indices[:int(oDataSet.attributes.shape[0] * 0.85)]
grid_result = np.zeros((len(GRID), K_FOLD))
for g1, g_param in enumerate(GRID):
k_slice = 0
for train, test in slices.split(oData.Training_indexes):
mpl = multi_Layered_perceptron_linear(LEARNING_RATE, (oDataSet.attributes.shape[1], g_param, 1))
mpl.train_regression(oDataSet.attributes[oData.Training_indexes[train]],
oDataSet.labels[oData.Training_indexes[train]], epochs)
y_pred = []
y_true = []
for i in test:
usecols=range(34),
dtype=int,
delimiter=",")
classes = np.loadtxt("Datasets/dermatology.data",
dtype=int,
usecols=-1,
delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent(
np.unique(classes, return_counts=True)[1], 0.8)
perc = Layered_perceptron_Logistic(learning_rate,
len(oDataSet.labelsNames))
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros(
(len(oDataSet.labelsNames), len(oDataSet.labelsNames)))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
predicted = perc.predict(data)
oData.confusion_matrix[int(oDataSet.labels[i]), predicted] += 1
print(oData)
oDataSet.append(oData)
learning_rate = 0.01
epochs = 5000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/artifitial1.data", usecols=range(2), delimiter=",")
classes = np.loadtxt("Datasets/artifitial1.data", dtype=int, usecols=-1, delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
# oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent([50, 50, 50], 0.8)
perc = Layered_perceptron(learning_rate, len(oDataSet.labelsNames))
perc.train(oDataSet.attributes[oData.Training_indexes], oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros((len(oDataSet.labelsNames), len(oDataSet.labelsNames)))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
predicted = perc.predict(data)
oData.confusion_matrix[int(oDataSet.labels[i]), predicted] += 1
print(oData)
oDataSet.append(oData)
oExp.add_data_set(oDataSet,
description=" Experimento Artificial LP 20 realizaçoes.".format())
oExp.save("Objects/EXP01_1_LP_20.gzip".format())
dtype=int,
delimiter=",")
classes = np.loadtxt("Datasets/breast-cancer-wisconsin.data",
dtype=int,
usecols=-1,
delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(10):
slices = KFold(n_splits=K_FOLD, shuffle=True)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent(
np.unique(classes, return_counts=True)[1], 0.8)
grid_result = np.zeros((len(GRID_NEURON), len(GRID_B), K_FOLD))
for g1, g_param in enumerate(GRID_NEURON):
for g2, g2_param in enumerate(GRID_B):
k_slice = 0
for train, test in slices.split(oData.Training_indexes):
K.clear_session()
model = Sequential()
rbflayer = RBFLayer(
g_param,
initializer=InitCentersRandom(
oDataSet.attributes[oData.Training_indexes[train]]),
betas=g2_param,
oDataSet = DataSet()
base = np.loadtxt("Datasets/iris_3.data", usecols=range(4), delimiter=",")
classes = np.loadtxt("Datasets/iris_3.data",
dtype=object,
usecols=-1,
delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(2, 31, samples=50)
oData.random_training_test_by_percent([100, 50], 0.8)
perc = Perceptron(learning_rate)
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros((2, 2))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
oData.confusion_matrix[int(oDataSet.labels[i]),
perc.predict(data)] += 1
oDataSet.append(oData)
oExp.add_data_set(
oDataSet, description=" Experimento iris PS 20 realizaçoes.".format())
oExp.save("Objects/EXP01_3_PS_20.gzip".format())
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/dt_1.txt", usecols=range(1), delimiter=" ")
classes = np.loadtxt("Datasets/dt_1.txt", usecols=-1, delimiter=" ")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list([np.float32(y)]) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
classes = np.array([classes]).T
for j in range(20):
print(j)
oData = Data(2, 31, samples=50)
indices = np.arange(oDataSet.attributes.shape[0])
np.random.shuffle(indices)
oData.Testing_indexes = indices[int(oDataSet.attributes.shape[0] * 0.85):]
oData.Training_indexes = indices[:int(oDataSet.attributes.shape[0] * 0.85)]
perc = Perceptron_Adaline(learning_rate)
perc.train(oDataSet.attributes[oData.Training_indexes],
classes[oData.Training_indexes].copy(), epochs)
oData.model = perc
ert = 0
plotar = []
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
predict = perc.predict(data)[0, 0]
plotar.append([classes[i, 0], predict])
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(np.array(list([np.float32(y)]) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
oDataSet.labels = np.array([classes]).T
for j in range(20):
experiment = Experiment(api_key="9F7edG4BHTWFJJetI2XctSUzM",
project_name="mest-rn-t6-artifitial1",
workspace="lukkascost",
)
experiment.set_name("REALIZACAO_{:02d}".format(j + 1))
slices = KFold(n_splits=K_FOLD, shuffle=True)
oData = Data(1, 31, samples=50)
indices = np.arange(oDataSet.attributes.shape[0])
np.random.shuffle(indices)
oData.Testing_indexes = indices[int(oDataSet.attributes.shape[0] * 0.85):]
oData.Training_indexes = indices[:int(oDataSet.attributes.shape[0] * 0.85)]
grid_result = np.zeros((len(GRID_NEURON), len(GRID_B), K_FOLD))
for g1, g_param in enumerate(GRID_NEURON):
for g2, g2_param in enumerate(GRID_B):
k_slice = 0
for train, test in slices.split(oData.Training_indexes):
model = Sequential()
rbflayer = RBFLayer(g_param,
initializer=InitCentersRandom(oDataSet.attributes[oData.Training_indexes[train]]),
betas=g2_param,