'int'对象没有属性'flatten'

Question

I am working on a code this gives me a error saying 'int' object has no attribute 'flatten' . I am using kmean algorithm for k=2 to cluster my data (passed as a list of data points).

import csv
import numpy as np
k=2
data=[23,43,67,89,1,10,101,243,34]
clusters = [[] for i in range(k)]
def kmeans(data, k):
    centroids = []
    centroids = randomize_centroids(data, centroids, k)  
    old_centroids = [[] for i in range(k)] 
    iterations = 0
    while not (has_converged(centroids, old_centroids, iterations)):
        iterations += 1
        clusters = euclidean_dist(data, centroids, clusters) # assign data points to clusters
        index =0   # recalculate centroids
        for cluster in clusters:
            old_centroids[index] = centroids[index]
            centroids[index] = np.mean(cluster, axis=0).tolist()
            index += 1
    print("The total number of data instances is: " + str(len(data)))
    print("The total number of iterations necessary is: " + str(iterations))
    print("The means of each cluster are: " + str(centroids))
    print("The clusters are as follows:")
    for cluster in clusters:
        print("Cluster with a size of " + str(len(cluster)) + " starts here:")
        print(np.array(cluster).tolist())
        print("Cluster ends here.")
# Calculates euclidean distance between
# a data point and all the available cluster
# centroids.    
def euclidean_dist(data, centroids, clusters):
    for instance in data:   # Find which centroid is the closestto the given data point.
        print("hello")
        mu_index = min([(i[0], np.linalg.norm(instance-centroids[i[0]])) \
                            for i in enumerate(centroids)], key=lambda t:t[1])[0]
        try:
            clusters[mu_index].append(instance)
        except KeyError:
            clusters[mu_index] = [instance]

    # If any cluster is empty then assign one point
    # from data set randomly so as to not have empty
    # clusters and 0 means.        
    for cluster in clusters:
        if not cluster:
           cluster.append(data[np.random.randint(0, len(data), size=1)].flatten().tolist())
    return clusters


# randomize initial centroids
def randomize_centroids(data, centroids, k):
    for cluster in range(0, k):
        print("hello")
        centroids.append(data[np.random.randint(0, len(data), size=1)].flatten().tolist())
    return centroids


# check if clusters have converged    
def has_converged(centroids, old_centroids, iterations):
    MAX_ITERATIONS = 1000
    if iterations > MAX_ITERATIONS:
        return True
    return old_centroids == centroids


if __name__=='__main__' :
    kmeans([23,43,67,89,1,10,101,243,34],2)
    print("hello")

Traceback (most recent call last):

File "C:\\Users\\shri ram\\Desktop\\summer_code_files\\ddpcr_analysis.py", line 66, in kmeans([23,43,7,89,1,10,101,243,34,2],2) File "C:\\Users\\shri ram\\Desktop\\summer_code_files\\ddpcr_analysis.py", line 13, in kmeans

clusters = euclidean_dist(data, centroids, clusters) # assign data points to clusters

UnboundLocalError: local variable 'clusters' referenced before assignment

Answer 1

As mentioned in comments, you tried apply flatten to one int element (not to numpy array). Try

centroids.append([data[np.random.randint(0, len(data), size=1)]])

instead of

centroids.append(data[np.random.randint(0, len(data), size=1)].flatten().tolist())

Also, you should move clusters initialization to kmeans function

...
def kmeans(data, k):
    clusters = [[] for i in range(k)]
    centroids = []
    centroids = randomize_centroids(data, centroids, k)  
    ...