我想添加一个“球体”到我的数据集群

Question

我想添加一种“球体”到我的数据集群。

我的数据集群是这样的，它没有 “” 球体”。

这是我的代码

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import style 
style.use('ggplot') 
import pandas as pd 
from sklearn.cluster import KMeans 

MY_FILE='total_watt.csv' 
date = [] 
consumption = [] 

df = pd.read_csv(MY_FILE, parse_dates=[0], index_col=[0]) 
df = df.resample('1D', how='sum') 
df = df.dropna() 

date = df.index.tolist() 
date = [x.strftime('%Y-%m-%d') for x in date] 
from sklearn.preprocessing import LabelEncoder 

encoder = LabelEncoder() 
date_numeric = encoder.fit_transform(date) 
consumption = df[df.columns[0]].values 

X = np.array([date_numeric, consumption]).T 

kmeans = KMeans(n_clusters=3) 
kmeans.fit(X) 

centroids = kmeans.cluster_centers_ 
labels = kmeans.labels_ 

print(centroids) 
print(labels) 

fig, ax = plt.subplots(figsize=(10,8)) 
rect = fig.patch 
rect.set_facecolor('#2D2B2B') 



colors = ["b.","r.","g."] 

for i in range(len(X)): 
    print("coordinate:",encoder.inverse_transform(X[i,0].astype(int)), X[i,1], "label:", labels[i]) 
    ax.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10) 
ax.scatter(centroids[:, 0],centroids[:, 1], marker = "x", s=150, linewidths = 5, zorder = 10) 
a = np.arange(0, len(X), 5) 
ax.set_xticks(a) 
ax.set_xticklabels(encoder.inverse_transform(a.astype(int))) 
ax.tick_params(axis='x', colors='lightseagreen') 
ax.tick_params(axis='y', colors='lightseagreen') 
plt.scatter(centroids[:, 0],centroids[:, 1], marker = "x", s=100, c="black", linewidths = 5, zorder = 10) 
ax.set_title('Energy consumptions Clusters (high/medium/low)', color='gold') 
ax.set_xlabel('time', color='gold') 
ax.set_ylabel('date(year 2011)', color='gold') 


plt.show() 

“球体” 是周围环境的情节（集聚区），如图所示。

我试图谷歌它。

但是当我键入“matplotlib球”，我不能得到任何结果..

Answer 1

在您的文章中示例图表看起来像Generalized Gaussian Mixture导致每个球是高斯2-d密度。

我会立即写一个示例代码来演示如何在数据集上使用GMM并进行这种绘图。

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import style 
style.use('ggplot') 
import pandas as pd 
# code changes here 
# =========================================== 
from sklearn.mixture import GMM 
# =========================================== 
from sklearn.preprocessing import LabelEncoder 

# replace it with you file path 
MY_FILE='/home/Jian/Downloads/total_watt.csv' 

df = pd.read_csv(MY_FILE, parse_dates=[0], index_col=[0]) 
df = df.resample('1D', how='sum') 
df = df.dropna() 

date = df.index.tolist() 
date = [x.strftime('%Y-%m-%d') for x in date] 

encoder = LabelEncoder() 
date_numeric = encoder.fit_transform(date) 
consumption = df[df.columns[0]].values 

X = np.array([date_numeric, consumption]).T 


# code changes here 
# =========================================== 
gmm = GMM(n_components=3, random_state=0) 
gmm.fit(X) 
y_pred = gmm.predict(X) 

# the center is given by mean 
gmm.means_ 

# =========================================== 

import matplotlib as mpl 
fig, ax = plt.subplots(figsize=(10,8)) 

for i, color in enumerate('rgb'): 
    # sphere background 
    width, height = 2 * 1.96 * np.sqrt(np.diagonal(gmm._get_covars()[i])) 
    ell = mpl.patches.Ellipse(gmm.means_[i], width, height, color=color) 
    ell.set_alpha(0.1) 
    ax.add_artist(ell) 
    # data points 
    X_data = X[y_pred == i] 
    ax.scatter(X_data[:,0], X_data[:,1], color=color) 
    # center 
    ax.scatter(gmm.means_[i][0], gmm.means_[i][1], marker='x', s=100, c=color) 


ax.set_title('Energy consumptions Clusters (high/medium/low)', color='gold') 
ax.set_xlabel('time', color='gold') 
ax.set_ylabel('date(year 2011)', color='gold') 
a = np.arange(0, len(X), 5) 
ax.set_xticks(a) 
ax.set_xticklabels(encoder.inverse_transform(a.astype(int))) 
ax.tick_params(axis='x', colors='lightseagreen') 
ax.tick_params(axis='y', colors='lightseagreen')