import utils import numpy as np from utils import save_obj, load_obj import plots from sklearn import preprocessing x, y = utils.load_excel_data("4clstrain1200.xlsx") y = y - np.ones(shape=(np.shape(y))) y = y.astype(int) class_num = 4 print(y[0]) print(min(y)) print(max(y)) print(x[0]) dim = np.shape(x)[1] number_of_circles = 7 plots.plot_classification_data(x, y, [0, 1, 2, 3]) x = preprocessing.scale(x) x, y = utils.unison_shuffled_copies(x, y) x, x_validation, x_test, y, y_validation, y_test = utils.split_data(x, y, 1, 0) plots.plot_classification_data(x, y, [0, 1, 2, 3]) import ES import RBF
import RBF import utils from utils import save_obj, load_obj import plots x, y = utils.load_excel_data("regdata1500.xlsx") from sklearn import preprocessing ind = load_obj("IND_REG") W = load_obj("W_REG") x = preprocessing.scale(x) y = preprocessing.scale(y) print(RBF.evaluator(RBF.regression_loss, x_train=None, y_train=None, x_test=x, y_test=y, W=W, individual=ind)) y_out = RBF.get_y_regression(individual=ind, x_train=None, y_train=None, x_test=x, W=W) plots.plot_regression_result(y_correct=y, y_model=y_out)
import RBF import utils from utils import save_obj, load_obj import plots import matplotlib.pyplot as plt import numpy as np from sklearn import preprocessing x_train, y_train = utils.load_excel_data("2clstrain1200.xlsx") x, y = utils.load_excel_data("2clstest4000.xlsx") plots.plot_classification_data(x, y, [-1, 1]) number_of_circles = 7 dim = len(x[0]) print("dim", len(x[0])) ind = load_obj("IND_2CLS") W = RBF.get_W(x_train, y_train, ind, number_of_circles) W = load_obj("W_2CLS") print(ind) x = preprocessing.scale(x) x_train = preprocessing.scale(x_train) # preprocessing.scale(y) print( RBF.evaluator(RBF.binary_classification_loss, x_train=x_train, y_train=y_train, x_test=x,
import utils import numpy as np from utils import save_obj, load_obj # x, y = utils.regression_data2(1000, dim) x_train, y_train = utils.load_excel_data("regdata1500.xlsx") dim = np.shape(x_train)[1] number_of_circles = 20 import plots from sklearn import preprocessing # min_max_scaler = preprocessing.MinMaxScaler() # x = min_max_scaler.fit_transform(x) x_train = preprocessing.scale(x_train) y_train = preprocessing.scale(y_train) print(np.shape(x_train)) print(np.shape(y_train)) x_train, y_train = utils.unison_shuffled_copies(x_train, y_train) x_train, x_validation, x_test, y_train, y_validation, y_test = utils.split_data( x_train, y_train, 0.6, 0) # plots.scatter_plot(x[:, 0], x[:, 1], y) import ES import RBF best = ES.find_circle_coordinates(MU=10,