Doctorado-ML/fimdlp
1
1
Fork 0
mirror of https://github.com/Doctorado-ML/FImdlp.git synced 2025-08-17 08:25:51 +00:00
Code Issues Packages Projects Releases Wiki Activity

Refactor some methods

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2022-12-02 23:36:18 +01:00
parent f28465a64f
commit b1f5d337fc
5 changed files with 29 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
sample.py
View File

@@ -66,28 +66,28 @@ features = data.feature_names
# test.transform(X)
# test.get_cut_points()
test = CFImdlp(debug=True, proposed=False)
test = CFImdlp(debug=False, proposed=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]
# 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]
# # To check
# indices2 = np.argsort(X)
# Xs = np.array(X)[indices2]
# ys = np.array(y)[indices2]
# test.fit(X[:, 0], y)
test.fit(X, y)
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}]"
)
# 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(Xs, ys)
@@ -102,13 +102,13 @@ print(test.get_discretized_values())
# 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)
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])
print(test.fit(X[:, 0], y).transform(X[:, 0]))