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")