def main(): from sklearn import preprocessing from sklearn.datasets import fetch_mldata from sklearn.model_selection import train_test_split db_name = 'diabetes' data_set = fetch_mldata(db_name) data_set.data = preprocessing.normalize(data_set.data) X_train, X_test, y_train, y_test = train_test_split( data_set.data, data_set.target, test_size=0.4) mlelm = MLELM(hidden_units=(10, 30, 200)).fit(X_train, y_train) elm = ELM(200).fit(X_train, y_train) print("MLELM Accuracy %0.3f " % mlelm.score(X_test, y_test)) print("ELM Accuracy %0.3f " % elm.score(X_test, y_test))
def main(): from sklearn import preprocessing from sklearn.datasets import fetch_mldata from sklearn.model_selection import train_test_split db_name = 'diabetes' data_set = fetch_mldata(db_name) data_set.data = preprocessing.normalize(data_set.data) X_train, X_test, y_train, y_test = train_test_split(data_set.data, data_set.target, test_size=0.4) mlelm = MLELM(hidden_units=(10, 30, 200)).fit(X_train, y_train) elm = ELM(200).fit(X_train, y_train) print("MLELM Accuracy %0.3f " % mlelm.score(X_test, y_test)) print("ELM Accuracy %0.3f " % elm.score(X_test, y_test))
def main(): from sklearn import preprocessing from sklearn.datasets import fetch_openml as fetch_mldata from sklearn.model_selection import train_test_split db_name = 'diabetes' data_set = fetch_mldata(db_name) data_set.data = preprocessing.normalize(data_set.data) tmp = data_set.target tmpL = [1 if i == "tested_positive" else -1 for i in tmp] data_set.target = tmpL X_train, X_test, y_train, y_test = train_test_split(data_set.data, data_set.target, test_size=0.4, random_state=0) mlelm = MLELM(hidden_neurons=(10, 30, 200), random_state=0).fit(X_train, y_train) elm = ELM(hidden_neurons=200, random_state=0).fit(X_train, y_train) print("MLELM Accuracy %0.3f " % mlelm.score(X_test, y_test)) print("ELM Accuracy %0.3f " % elm.score(X_test, y_test))
from elm import ELM from sklearn.preprocessing import normalize from sklearn.datasets import fetch_mldata from sklearn.model_selection import train_test_split import tempfile import pandas as pd test_data_home = tempfile.mkdtemp() db_name = 'australian' data_set = pd.read_csv('australian.csv') data_set_data = data_set.iloc[:, :-1] df_norm = (data_set_data - data_set_data.mean()) / (data_set_data.max() - data_set_data.min()) print(df_norm) y = data_set['class-label'] print(y) X_train, X_test, y_train, y_test = train_test_split( df_norm, y, test_size=0.4) elm = ELM(hid_num=10).fit(X_train, y_train) print("ELM Accuracy %0.3f " % elm.score(X_test, y_test))