我正在尝试使用kmeans对句子进行聚类 但没有正确输入cluster()的输入类型。

我尝试将列表Y与使用wordembedding def创建的sent_vectorizer一起使用，并且还尝试了dataframe版本的Y。

def sent_vectorizer(sent,model): #creates vectors for each tokenized sentence
    sent_vec =[]
    numw = 0
    for w in sent:
        try:
            if numw == 0:
                sent_vec = model[w]
            else:
                sent_vec = np.add(sent_vec,model[w]) #adds vectors of all words in a sentence over iterations
            numw+=1 #counts the number of words in all sentences
        except:
            pass

    return np.asarray(sent_vec) / numw

Y=[]
for sentence in all_words:
    Y.append(sent_vectorizer(sentence,model))   

print ("========================")
print (Y)

df_Y = pd.DataFrame(Y) 

NUM_CLUSTERS=3
kclusterer = KMeansClusterer(NUM_CLUSTERS,distance=nltk.cluster.util.cosine_distance,repeats=25,avoid_empty_clusters=True)
assigned_clusters = kclusterer.cluster(df_Y,assign_clusters=True)
print (assigned_clusters)

all_words有一个标记化句子的列表：

[['cloud','technologies'],['still','building','strong','foundation','game','changers','hyper','converged','technology','sd','wan','tying','colleges','together','one','seamless','security','plane','protecting','college','data'],['none'],['data','analytics'],['customer','experience','improvements'],['ar','vr'],['none','timeframe','longer','term','cloud','services','ai','technologies','changer'],['cloud','finance','integrated','ship','management'],['microservices','based','api','platform','omni','channels'],['moving','erp','cloud'],['online','learning','open','educational','resources'],'programs','meet','needs','today','learners'],['automation','existing','processes']]

错误回溯：

    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    <ipython-input-108-68f5fe386b54> in <module>
          1 NUM_CLUSTERS=3
          2 kclusterer = KMeansClusterer(NUM_CLUSTERS,avoid_empty_clusters=True)
    ----> 3 assigned_clusters = kclusterer.cluster(df_Y,assign_clusters=True)
          4 print (assigned_clusters)

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\nltk\cluster\util.py in cluster(self,vectors,assign_clusters,trace)
         60 
         61         # call abstract method to cluster the vectors
    ---> 62         self.cluster_vectorspace(vectors,trace)
         63 
         64         # assign the vectors to clusters

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\nltk\cluster\kmeans.py in cluster_vectorspace(self,trace)
         99             # effect the distance comparison)
        100             for means in meanss:
    --> 101                 means.sort(key=sum)
        102 
        103             # find the set of means that's minimally different from the others

    TypeError: 'float' object is not iterable ```

I also tried the following code and get error there as well:
    ```
    kmeans = cluster.KMeans(n_clusters=NUM_CLUSTERS)
    kmeans.fit(Y)
    ```
the error in this case is:
    ```
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-48-229383cd99be> in <module>
          1 kmeans = cluster.KMeans(n_clusters=NUM_CLUSTERS)
    ----> 2 kmeans.fit(Y)
          3 
          4 labels = kmeans.labels_
          5 centroids = kmeans.cluster_centers_

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\cluster\k_means_.py in fit(self,X,y,sample_weight)
        969                 tol=self.tol,random_state=random_state,copy_x=self.copy_x,970                 n_jobs=self.n_jobs,algorithm=self.algorithm,--> 971                 return_n_iter=True)
        972         return self
        973 

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\cluster\k_means_.py in k_means(X,n_clusters,sample_weight,init,precompute_distances,n_init,max_iter,verbose,tol,random_state,copy_x,n_jobs,algorithm,return_n_iter)
        309     order = "C" if copy_x else None
        310     X = check_array(X,accept_sparse='csr',dtype=[np.float64,np.float32],--> 311                     order=order,copy=copy_x)
        312     # verify that the number of samples given is larger than k
        313     if _num_samples(X) < n_clusters:

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array,accept_sparse,accept_large_sparse,dtype,order,copy,force_all_finite,ensure_2d,allow_nd,ensure_min_samples,ensure_min_features,warn_on_dtype,estimator)
        525             try:
        526                 warnings.simplefilter('error',ComplexWarning)
    --> 527                 array = np.asarray(array,dtype=dtype,order=order)
        528             except ComplexWarning:
        529                 raise ValueError("Complex data not supported\n"

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\numeric.py in asarray(a,order)
        536 
        537     """
    --> 538     return array(a,copy=False,order=order)
        539 
        540 

    ValueError: setting an array element with a sequence.