from sklearn.datasets import load_iris
from fimdlp.mdlp import FImdlp
from fimdlp.cppfimdlp import CFImdlp
import numpy as np


data = load_iris()
X = data.data
y = data.target
features = data.feature_names
test = FImdlp()
test.fit(X, y, features=features)
# test.transform(X)

test = CFImdlp(debug=False)
# k = test.cut_points(X[:, 0], y)
# print(k)
# k = test.cut_points_ant(X[:, 0], y)
# print(k)
# test.debug_points(X[:, 0], y)
X = [5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9]
indices = [4, 3, 6, 8, 2, 1, 5, 0, 9, 7]
y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
# test.fit(X[:, 0], y)
test.fit(X, y)
result = test.get_cut_points()
for item in result:
    print(
        f"Class={item['classNumber']} - ({item['start']:3d}, {item['end']:3d})"
        f" -> ({item['fromValue']:3.1f}, {item['toValue']:3.1f}]"
    )
print(test.get_discretized_values())
# print(test.transform(X))
# print(X)
# print(indices)
# print(np.array(X)[indices])

# X = np.array(
#     [
#         [5.1, 3.5, 1.4, 0.2],
#         [5.2, 3.0, 1.4, 0.2],
#         [5.3, 3.2, 1.3, 0.2],
#         [5.3, 3.1, 1.5, 0.2],
#     ]
# )
# y = np.array([0, 0, 0, 1])
# test.fit(X, y).transform(X)