预期的二维数组，执行聚类时出现一维数组错误

Question

我试图制作一个集群管道，但出现以下错误，这表明这里存在一些维度问题：

Traceback (most recent call last):
  File "Driver.py", line 57, in <module>
    labelIDs = faceClusterUtility.Cluster()
  File "/Users/aryansharma/Desktop/FaceRecognitionPipeline_GeeksForGeeks/FaceClusteringLibrary.py", line 241, in Cluster
    clt.fit(encodings)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/cluster/_dbscan.py", line 346, in fit
    X = self._validate_data(X, accept_sparse="csr")
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/base.py", line 566, in _validate_data
    X = check_array(X, **check_params)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/validation.py", line 773, in check_array
    "if it contains a single sample.".format(array)
ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
0it [00:02, ?it/s]

这是显示错误的代码部分。下面的函数通过加载嵌入来定义聚类，然后执行聚类。它应该输出集群标签。

def Cluster(self):
        InputEncodingFile = self.EncodingFilePath
        if not (os.path.isfile(InputEncodingFile) and os.access(InputEncodingFile, os.R_OK)):
            print('The input encoding file, ' + 
                    str(InputEncodingFile) + ' does not exists or unreadable')
            exit()

        NumberOfParallelJobs = -1

        # load the serialized face encodings + bounding box locations from
        # disk, then extract the set of encodings to so we can cluster on
        # them
        print("[INFO] Loading encodings")
        data = pickle.loads(open(InputEncodingFile, "rb").read())
        data = np.array(data)

        encodings = [d["encoding"] for d in data]

        # cluster the embeddings
        print("[INFO] Clustering")
        clt = DBSCAN(eps=0.5, metric="euclidean", n_jobs=NumberOfParallelJobs)
        clt.fit(encodings)

        # determine the total number of unique faces found in the dataset
        labelIDs = np.unique(clt.labels_)
        numUniqueFaces = len(np.where(labelIDs > -1)[0])
        print("[INFO] # unique faces: {}".format(numUniqueFaces))

        return clt.labels_