from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score
from sklearn.model_selection import KFold, train_test_split
from liu_ebrb import LiuEBRBClassifier
from process_data import process_to_pieces
import random
import pandas as pd
from random_process import random_array
df = pd.read_csv('newdata.csv', names=[' HR', ' PULSE', ' RESP', ' SpO2', 'Class'])
X = df.drop(['Class'], axis=1).values
y = df["Class"].values
A, D = process_to_pieces(X, y, 3, 4)
ebrb = LiuEBRBClassifier(A, D)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
print(X_train)
ebrb = ebrb.fit(X_train, y_train)
y_predict = ebrb.predict(X_test)
print(y_predict)
print(accuracy_score(y_predict, y_test))
