Build sklearn transformer

sample.py

@@ -1,14 +1,9 @@
-import numpy as np
 from sklearn.datasets import load_iris
-from fimdlp import CFImdlp
+from fimdlp.mdlp import FImdlp
 
 data = load_iris()
 X = data.data
 y = data.target
 features = data.feature_names
-test = CFImdlp()
-print("Cut points for each feature in Iris dataset:")
-for i in range(0, X.shape[1]):
-    data = np.sort(X[:, i])
-    Xcutpoints = test.cut_points(data, y)
-    print(f"{features[i]:20s}: {Xcutpoints}")
+test = FImdlp()
+Xcutpoints = test.fit(X, y, features=features).transform(X)