Extreme Learning Machine implemented in Python3 with scikit-learn
This module has 3-type ELM
- elm.py is general 3-step model ELM
- ecob_elm.py is equality constrained optimization based ELM
- ml_elm is multi layer ELM
- scikit-learn: 0.18.1
- numpy: 1.10.4 and up
pip install git+https://github.com/masaponto/python-elm
from elm import ELM
from sklearn.preprocessing import normalize
from sklearn.datasets import 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)
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
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import KFold, cross_val_score
db_names = ['australian', 'iris']
hid_nums = [10, 20, 30]
for db_name in db_names:
print(db_name)
data_set = fetch_mldata(db_name)
data_set.data = normalize(data_set.data)
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))MIT