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))
