Hello again,
I've added some extra code to plot 6 smaller graphs, which should change the
initialization of the centroids.
The title of the plots should be the sum of the squared distance of each
initialization (which should tell me when they converge).
The smaller graphs appear (unlabelled) however, my data is not being plotted.
Can someone tell me where I'm going wrong?
n_iter = 9
fig, ax = plt.subplots(3, 3, figsize=(16, 16))
ax = np.ravel(ax)
centers = []
for i in range(n_iter):
# Run local implementation of kmeans
max_iter=3,
random_state=np.random.randint(0, 1000, size=1)
clusters = km.clusters
ax[i].scatter([km.labels == 0, 0], [km.labels == 0, 1],
c='green', label='cluster 1')
ax[i].scatter([km.labels == 1, 0], [km.labels == 1, 1],
c='blue', label='cluster 2')
ax[i].scatter(clusters[:, 0], clusters[:, 1],
c='r', marker='*', s=300, label='centroid')
ax[i].set_xlim([-2, 2])
ax[i].set_ylim([-2, 2])
ax[i].legend(loc='lower right')
ax[i].set_title(f'{km.error:.4f}')
ax[i].set_aspect('equal')
plt.tight_layout();
Thanks...
________________________________
From: Stephen Malcolm <stephen_malc...@hotmail.com>
Sent: 04 October 2020 21:14
To: scikit-learn@python.org <scikit-learn@python.org>
Subject: Rerunning Kmeans with Python
Hello all,
I've written some code to run Kmeans on a data set (please see below).
And I've plotted the results, with my two clusters/ centroids.
However, I've to re-run Kmeans several times and pull up different plots
(showing the different centroid positions).
Can someone point me in the right direction how to write this extra code to
perform this task?
Then I've to conclude if Kmeans is stable. I believe this is the lowest sum of
squared errors?
Thanking you in advance.
#pandas used to read dataset and return the data
#numpy and matplotlib to represent and visualize the data
#sklearn to implement kmeans algorithm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
#import the data
data = pd.read_csv('file.csv')
#extract values
x=data['V1']
y=data['V2']
V1_V2 = np.column_stack ((V1, V2))
km_res = KMeans (n_clusters= 2).fit(V1_V2)
y_kmeans = km_res.predict(V1_V2)
plt.scatter(V1, V2, c=y_kmeans, cmap='viridis', s = 50, alpha = 0.5)
plt.xlabel('V1')
plt.ylabel('V2')
plt.title('Visualization of raw data');
clusters = km_res.cluster_centers_
plt.scatter(clusters[:,0], clusters[:,1], c='blue', s=150)
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