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.load("Objects/EXP01_3_PS_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
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,
description=" Experimento Dermatologia LP 20 realizaçoes.".format())
oExp.save("Objects/EXP01_4_LP_20.gzip".format())
oExp = Experiment.load("Objects/EXP01_4_LP_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
ax1 = fig.add_subplot(111)
# ax2 = ax1.twiny()
p = [oDataSet.attributes[0], oDataSet.attributes[-1]]
res = []
for i in p:
data = np.matrix(np.hstack(([-1], i))).T
predict = perc.predict(data)[0, 0]
res.append([i, predict])
res = np.array(res)
ax1.plot(base[[0, -1]], res[:, 1])
p = [base[0], base[-1]]
res = []
for i in p:
predict = 2 * i + 3
res.append([i, predict])
res = np.array(res)
ax1.plot(res[:, 0], res[:, 1])
plt.show()
oData.params = {
"MSE": ert / oData.Testing_indexes.shape[0],
"RMSE": np.sqrt(ert / oData.Testing_indexes.shape[0])
}
print(oData.params)
oDataSet.append(oData)
oExp.add_data_set(oDataSet)
oExp.save("Objects/EXP01_DT1_20.gzip")
