def support_vector_regression(input_dict): """ Epsilon-Support Vector Regression, using the RBF kernel. """ clf = r.svr() output_dict = {} output_dict['out'] = clf return output_dict
def do_regressions(x_train, y_train, x_test, y_test): print("\nLinear regression:") since = time.time() mse_lr, r2_lr = regression.linear_regression(x_train, y_train, x_test, y_test) print('\tMean squared error linear regression: %.2f' % mse_lr) print('\tCoefficient of determination linear regression: %.2f' % r2_lr) print("\tExecution time:", time.time() - since, "s") print("\nSVR:") since = time.time() mse_svr, r2_svr = regression.svr(x_train, y_train, x_test, y_test) print('\tMean squared error SVR: %.2f' % mse_svr) print('\tCoefficient of determination SVR: %.2f' % r2_svr) print("\tExecution time:", time.time() - since, "s")
xte = te[:,0:len(te[0])-1] bte = standard_scaler.transform(xte) ate = min_max_scaler.transform(xte) yte = te[:,len(te[0])-1] mses = [] ffnnerr = 1e10 for i in range(0,10): ffnnerr = min(ffnnerr,regression.ffnnr(atr, ytr, ate, yte)) print("MIN err " + str(ffnnerr)) mses.append(regression.mlr(xtr, ytr, xte, yte)) mses.append(regression.knn(xtr, ytr, xte, yte)) mses.append(regression.rfr(xtr, ytr, xte, yte)) mses.append(regression.svr(xtr, ytr, xte, yte)) mses.append(ffnnerr) mses.append(regression.rbfnr(xtr, ytr, xte, yte)) import numpy as np import regression from sklearn import preprocessing path = "./Bank/Bank8FM/" tr = np.genfromtxt(path + "bank8FM.data", delimiter=' ') te = np.genfromtxt(path + "bank8FM.test", delimiter=' ') min_max_scaler = preprocessing.MinMaxScaler() standard_scaler = preprocessing.StandardScaler() xtr = tr[:,0:len(tr[0])-1] btr = standard_scaler.fit_transform(xtr) atr = min_max_scaler.fit_transform(xtr) ytr = tr[:,len(tr[0])-1]