how to plot two cluster using dictionary file-CodePudding

I have a dictionary file saved in .npy file that contain two cluster ids that i want to plot in a scatter plot(for the id values saved under key '0' one cluster and the id values saved under key '1' is another cluster)

My script:

import numpy as np
import matplotlib.pyplot as plt
data=np.load("dict.npy",allow_pickle=True)
print(data)
array({0: array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  90,  91,
        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 125,
       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
       139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
       152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
       165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
       178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
       191, 192, 193, 194, 195, 196, 197, 198, 199]), 1: array([ 89, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,
       116, 117, 118, 119, 120, 121, 122, 123, 124])}, dtype=object)

CodePudding user response：

An example as you have request:

#you will need these libraries:

import numpy as np
from sklearn.cluster import KMeans

from matplotlib import pyplot as plt

Then generate some random 2D data, just for this example:

#the data you want to cluster
X = np.random.multivariate_normal(mean=[1,2], cov=[[.5, .25], [.25,.75]], size=1800)
plt.scatter(*X.T, alpha=.25, color="k")

Finally run the clustering and see the result:

X_cluster = KMeans(n_clusters=2).fit_predict(X)
for c in set(X_cluster):
    plt.scatter(*X[X_cluster==c].T, alpha=.25)

CodePudding user response：

plt.figure(figsize=(7,7))
for cluster in data:
   plt.scatter(X[data[cluster],0], X[data[cluster],1])
plt.show()

where X is the dateset that you have used for the clustering and has shape (N,2) (N is the number of samples)