python newbie here. I am trying to run this code but I get the error message that the object is not iterable. Would appreciate some advice on what I am doing wrong. Thanks.
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()
Here is the error code i get:
/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
new_centroid = np.average(in_bandwidth, axis=0)
Is assigning a scalar to new_centroid then you are trying to tuple(scalar) which is throwing the error.
tuple(2.)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-51-4406f9e676cf> in <module>() ----> 1 tuple(2.) TypeError: 'float' object is not iterable