'numpy.float64' -Objekt ist nicht iterierbar - meanhift clustern

Question

Python-Neuling hier. Ich versuche, diesen Code auszuführen, aber ich erhalte die Fehlermeldung, dass das Objekt nicht iterierbar ist. Würde mich über einige Ratschläge freuen, was ich falsch mache. Vielen Dank.

import matplotlib.pyplot as plt 
import numpy as np 
import pandas as pd 

temp = pd.read_csv("file.csv", encoding='latin-1') 
xy = temp.ix[:,2:6] 
X = xy.values 

---

X 
array([[    nan,    nan], 
    [    nan,    nan], 
    [ 3.92144000e+00,    nan], 
    [ 4.42382000e+00,    nan], 
    [ 4.18931000e+00, 5.61562775e+02], 
    [    nan,    nan], 
    [ 4.33025000e+00, 6.73123391e+02], 
    [ 6.43775000e+00,    nan], 
    [ 3.12299000e+00, 2.21886627e+03], 
    [    nan,    nan], 
    [    nan,    nan]]) 

---

from itertools import cycle 
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk') 

class Mean_Shift: 
    def __init__(self, radius=4): 
     self.radius = radius 

    def fit(self, data): 
     centroids = {} 

     for i in range(len(data)): 
      centroids[i] = data[i] 

     while True: 
      new_centroids = [] 
      for i in centroids: 
       in_bandwidth = [] 
       centroid = centroids[i] 
       for featureset in data: 
        if np.linalg.norm(featureset-centroid) < self.radius: 
         in_bandwidth.append(featureset) 

       new_centroid = np.average(in_bandwidth, axis=0) 
       new_centroids.append(tuple(new_centroid)) 

      uniques = sorted(list(set(new_centroids))) 

      prev_centroids = dict(centroids) 

      centroids = {} 
      for i in range(len(uniques)): 
       centroids[i] = np.array(uniques[i]) 

      optimized = True 

      for i in centroids: 
       if not np.array_equal(centroids[i], prev_centroids[i]): 
        optimized = False 

       if not optimized: 
        break 

      if optimized: 
       break 

     self.centroids = centroids 

    def predict(self,data): 
     pass 


clf = Mean_Shift() 
clf.fit(X) 

centroids = clf.centroids 

plt.scatter(X[:,0],X[:,1],s=50) 

for c in centroids: 
plt.scatter(centroids[c][0], centroids[c][1], color = 'k', marker='*', s=150) 

plt.show() 

Hier ist der Fehlercode i erhalten:

/Users/carla/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice. 
    warnings.warn("Mean of empty slice.", RuntimeWarning) 
--------------------------------------------------------------------------- 
TypeError         Traceback (most recent call last) 
<ipython-input-11-e13932b6e72d> in <module>() 
50 
51 clf = Mean_Shift() 
---> 52 clf.fit(X) 
53 
54 centroids = clf.centroids 

<ipython-input-11-e13932b6e72d> in fit(self, data) 
22 
23     new_centroid = np.average(in_bandwidth, axis=0) 
---> 24     new_centroids.append(tuple(new_centroid)) 
25 
26    uniques = sorted(list(set(new_centroids))) 

TypeError: 'numpy.float64' object is not iterable 

Answer 1

new_centroid = np.average(in_bandwidth, axis=0) 

ist ein Skalar new_centroid zuweisen dann Sie TRYI sind ng zu tuple(scalar), die den Fehler wirft.

tuple(2.) 

--------------------------------------------------------------------------- 
TypeError         Traceback (most recent call last) 
<ipython-input-51-4406f9e676cf> in <module>() 
----> 1 tuple(2.) 

TypeError: 'float' object is not iterable