如何使用数组为散点图中的簇着色?
How to color clusters in scatter plot using an array?
我正在使用 sklearn
noisy_circles
function 和 link 中一样的 1500 点来创建 2 个圆圈。
我有一个点引用数组,其中每个值都应该是不同的簇颜色:
nbrs_array = [ 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 973, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
973, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 984, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 992, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 972, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 992, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 984, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 972, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88]
数组中有 5 个唯一值,因此应该有 5 种不同的颜色。但是当我使用以下方法绘制它时:
plt.figure(figsize=(10,10))
plt.scatter(x,y, c=nbrs_array)
plt.show()
输出为
像这样的东西应该可以完成工作:
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
from matplotlib.colors import Normalize, to_hex
from sklearn import datasets
def get_colors(arr, cmap='viridis'):
cmap = cm.get_cmap(cmap)
n = len(np.unique(arr))
colornorm = Normalize(vmin=1, vmax=n)
hex_map = dict()
for i, cl in enumerate(np.unique(arr)):
hex_map[cl] = to_hex(cmap(colornorm(i + 1)))
colors = list(map(lambda x: hex_map[x], arr))
return colors
n_samples = 1500
data, _ = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)
x, y = data[:, 0], data[:, 1]
nbrs_array = [...]
plt.figure(figsize=(10, 10))
plt.scatter(x, y, c=get_colors(nbrs_array))
plt.show()
我正在使用 sklearn
noisy_circles
function 和 link 中一样的 1500 点来创建 2 个圆圈。
我有一个点引用数组,其中每个值都应该是不同的簇颜色:
nbrs_array = [ 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 973, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
973, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
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88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
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88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 984, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
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88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 992, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 972, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
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88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 992, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 984, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 972, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88,
88, 88, 88, 88, 88]
数组中有 5 个唯一值,因此应该有 5 种不同的颜色。但是当我使用以下方法绘制它时:
plt.figure(figsize=(10,10))
plt.scatter(x,y, c=nbrs_array)
plt.show()
输出为
像这样的东西应该可以完成工作:
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
from matplotlib.colors import Normalize, to_hex
from sklearn import datasets
def get_colors(arr, cmap='viridis'):
cmap = cm.get_cmap(cmap)
n = len(np.unique(arr))
colornorm = Normalize(vmin=1, vmax=n)
hex_map = dict()
for i, cl in enumerate(np.unique(arr)):
hex_map[cl] = to_hex(cmap(colornorm(i + 1)))
colors = list(map(lambda x: hex_map[x], arr))
return colors
n_samples = 1500
data, _ = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)
x, y = data[:, 0], data[:, 1]
nbrs_array = [...]
plt.figure(figsize=(10, 10))
plt.scatter(x, y, c=get_colors(nbrs_array))
plt.show()