Add python implementation of the algorithm

2025-08-18 08:55:51 +00:00 · 2022-12-07 01:27:28 +01:00
parent 7f4b09d2d6
commit 89c7366c4e
5 changed files with 212 additions and 73 deletions
--- a/sample.py
+++ b/sample.py
@@ -61,69 +61,69 @@ 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.get_cut_points()
-for proposal in [True, False]:
-    X = data.data
-    y = data.target
-    print("*** Proposal: ", proposal)
-    test = CFImdlp(debug=True, proposal=proposal)
-    test.fit(X[:, 0], y)
-    result = test.get_cut_points()
-    for item in result:
-        print(
-            f"Class={item['classNumber']} - ({item['start']:3d}, "
-            f"{item['end']:3d}) -> ({item['fromValue']:3.1f}, "
-            f"{item['toValue']:3.1f}]"
-        )
-    print(test.get_discretized_values())
-    print("+" * 40)
-    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],
-            [5.4, 3.1, 1.5, 0.2],
-        ]
-    )
-    y = np.array([0, 0, 0, 1])
-    print(test.fit(X[:, 0], y).transform(X[:, 0]))
-    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("*" * 40)
-# print(Xs, ys)
-# print("**********************")
-# test = [(0, 3), (4, 4), (5, 5), (6, 8), (9, 9)]
-# print(ys)
-# for start, end in test:
-#     print("Testing ", start, end, ys[:end], ys[end:])
-#     print("Information gain: ", information_gain(ys, ys[:end], ys[end:]))
-# print(test.transform(X))
-# print(X)
-# print(indices)
-# print(np.array(X)[indices])
+test = FImdlp()
+test.fit(X, y)
+test.transform(X)
+print(test.get_cut_points())
+# for proposal in [True, False]:
+#     X = data.data
+#     y = data.target
+#     print("*** Proposal: ", proposal)
+#     test = CFImdlp(debug=True, proposal=proposal)
+#     test.fit(X[:, 0], y)
+#     result = test.get_cut_points()
+#     for item in result:
+#         print(
+#             f"Class={item['classNumber']} - ({item['start']:3d}, "
+#             f"{item['end']:3d}) -> ({item['fromValue']:3.1f}, "
+#             f"{item['toValue']:3.1f}]"
+#         )
+#     print(test.get_discretized_values())
+#     print("+" * 40)
+#     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],
+#             [5.4, 3.1, 1.5, 0.2],
+#         ]
+#     )
+#     y = np.array([0, 0, 0, 1])
+#     print(test.fit(X[:, 0], y).transform(X[:, 0]))
+#     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("*" * 40)
+# # print(Xs, ys)
+# # print("**********************")
+# # test = [(0, 3), (4, 4), (5, 5), (6, 8), (9, 9)]
+# # print(ys)
+# # for start, end in test:
+# #     print("Testing ", start, end, ys[:end], ys[end:])
+# #     print("Information gain: ", information_gain(ys, ys[:end], ys[end:]))
+# # print(test.transform(X))
+# # print(X)
+# # print(indices)
+# # print(np.array(X)[indices])


-# # 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]
-# y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
-# indices = [4, 3, 6, 8, 2, 1, 5, 0, 9, 7]
-# clf = CFImdlp(debug=True, proposal=False)
-# clf.fit(X, y)
-# print(clf.get_cut_points())
-# y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
-# # To check
-# indices2 = np.argsort(X)
-# Xs = np.array(X)[indices2]
-# ys = np.array(y)[indices2]
-kdd_JapaneseVowels
+# # # 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]
+# # y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
+# # indices = [4, 3, 6, 8, 2, 1, 5, 0, 9, 7]
+# # clf = CFImdlp(debug=True, proposal=False)
+# # clf.fit(X, y)
+# # print(clf.get_cut_points())
+# # y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
+# # # To check
+# # indices2 = np.argsort(X)
+# # Xs = np.array(X)[indices2]
+# # ys = np.array(y)[indices2]
+# kdd_JapaneseVowels