from elm import ELM
from sklearn.preprocessing import normalize
from sklearn.datasets import fetch_openml as fetch_mldata
from sklearn.model_selection import train_test_split

db_name = 'australian'

data_set = fetch_mldata(db_name)
data_set.data = normalize(data_set.data)
data_set.target = [1 if i == 1 else -1 for i in  data_set.target.astype(int)]

X_train, X_test, y_train, y_test = train_test_split(
    data_set.data, data_set.target, 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))

from elm import ELM
from sklearn.preprocessing import normalize, LabelEncoder
from sklearn.datasets import fetch_openml as fetch_mldata
from sklearn.model_selection import KFold, cross_val_score

db_name = 'iris'
hid_nums = [10, 20, 30]

print(db_name)
data_set = fetch_mldata(db_name)
data_set.data = normalize(data_set.data)
data_set.target = LabelEncoder().fit_transform(data_set.target)

for hid_num in hid_nums:
    print(hid_num, end=' ')
    e = ELM(hid_num)

    ave = 0
    for i in range(10):
        cv = KFold(n_splits=5, shuffle=True)
        scores = cross_val_score(e, data_set.data, data_set.target, cv=cv, scoring='accuracy', n_jobs=-1)
        ave += scores.mean()

    ave /= 10

    print("Accuracy: %0.3f " % (ave))