diff --git a/UCTB/preprocess/GraphGenerator.py b/UCTB/preprocess/GraphGenerator.py
index 5ba71c41..564051b0 100644
--- a/UCTB/preprocess/GraphGenerator.py
+++ b/UCTB/preprocess/GraphGenerator.py
@@ -235,9 +235,3 @@ def scaled_laplacian_STGCN(W):
     # lambda_max \approx 2.0, the largest eigenvalues of L.
     lambda_max = eigs(L, k=1, which='LR')[0][0].real
     return np.mat(2 * L / lambda_max - np.identity(n))
-
-if __name__ == '__main__':
-    am = np.array([[0,1,0,0,1,0],[1,0,1,0,1,0],[0,1,0,1,0,0],[0,0,1,0,1,1],[1,1,0,1,0,0],[0,0,0,1,0,0]],dtype=np.float32)
-    print(scaled_Laplacian_ASTGCN(am))
-    print(scaled_laplacian_STGCN(am))
-    print(GraphGenerator.adjacent_to_laplacian(am))
\ No newline at end of file
diff --git a/UCTB/preprocess/preprocessor.py b/UCTB/preprocess/preprocessor.py
index 533df36e..3308b4bd 100644
--- a/UCTB/preprocess/preprocessor.py
+++ b/UCTB/preprocess/preprocessor.py
@@ -7,7 +7,7 @@ class Normalizer(ABC):
     Normalizer is the base abstract class for many normalizers such as MaxMinNormalizer and ZscoreNormalizer.You can also build your own normalizer by inheriting this class.
 
     Args:
-        X: Data which normalizer extracts characteristics from.
+        X(ndarray): Data which normalizer extracts characteristics from.
     """
     @abstractmethod
     def __init__(self, X):
diff --git a/docs/.doctrees/UCTB.preprocess.doctree b/docs/.doctrees/UCTB.preprocess.doctree
index 7e7eaee0..b8e80674 100644
Binary files a/docs/.doctrees/UCTB.preprocess.doctree and b/docs/.doctrees/UCTB.preprocess.doctree differ
diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle
index 059d61f6..7ebba939 100644
Binary files a/docs/.doctrees/environment.pickle and b/docs/.doctrees/environment.pickle differ
diff --git a/docs/UCTB.preprocess.html b/docs/UCTB.preprocess.html
index 8a9b536e..83acd2a2 100644
--- a/docs/UCTB.preprocess.html
+++ b/docs/UCTB.preprocess.html
@@ -361,7 +361,7 @@ <h2><span class="section-number">5.2.1. </span>UCTB.preprocess.GraphGenerator mo
 <p>Normalizer is the base abstract class for many normalizers such as MaxMinNormalizer and ZscoreNormalizer.You can also build your own normalizer by inheriting this class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>X</strong> – Data which normalizer extracts characteristics from.</p>
+<dd class="field-odd"><p><strong>X</strong> (<em>ndarray</em>) – Data which normalizer extracts characteristics from.</p>
 </dd>
 </dl>
 </dd></dl>