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())
oExp = Experiment.load("Objects/EXP01_1_LP_20.gzip".format())
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)
oExp.add_data_set(
oDataSet,
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/column_3C.dat", usecols=range(6), delimiter=" ")
classes = np.loadtxt("Datasets/column_3C.dat", 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()
oExp.add_data_set(oDataSet,
description=" Experimento COLUNA 3C LP 20 realizaçoes. com 30000 epocas".format())
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent([60, 150, 100], 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
oDataSet.append(oData)
oExp.experimentResults[0] = oDataSet
oExp.save("Objects/EXP02_3_LP_20.gzip".format())
oExp = Experiment.load("Objects/EXP02_3_LP_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
