mirror of
https://github.com/Doctorado-ML/FImdlp.git
synced 2025-08-17 00:15:52 +00:00
48 lines
1.1 KiB
Python
48 lines
1.1 KiB
Python
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)
|