我有一个二维的点数据集,我想使用K-means技术进行分类。
数据:
import numpy as np
x1 = np.array([3,1,1,2,1,6,6,6,5,6,7,8,9,8,9,9,8])
x2 = np.array([5,4,5,6,5,8,6,7,6,7,1,2,1,2,3,2,3])
X = np.array(list(zip(x1,x2))).reshape(len(x1), 2)
我想对从1到9的簇数进行求和,以测试散点图上的最终分布。因此,我计算了数据集的质心。
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
max_k = 10
K = range(1,max_k)
centroid = [sum(X)/len(X) for k in K]
sst = sum(np.min(cdist(X, centroid, "euclidean"), axis = 1))
然后使用
rgb
为每次迭代创建一个具有cm.Spectral
颜色的调色板。color_palette = [plt.cm.Spectral(float(k)/max_k) for k in K]
并在我遍历
k
的循环中使用它:from sklearn.cluster import KMeans
import pandas as pd
ssw = []
for k in K:
kmeanModel = KMeans(n_clusters=k).fit(X)
centers = pd.DataFrame(kmeanModel.cluster_centers_)
labels = kmeanModel.labels_
ssw_k = sum(np.min(cdist(X, kmeanModel.cluster_centers_), axis = 1))
ssw.append(ssw_k)
label_color = [color_palette[i] for i in labels]
plt.plot()
plt.xlim([0,10])
plt.ylim([0,10])
plt.title("Clustering for k = %s"%str(k))
plt.scatter(x1,x2, c=label_color)
plt.scatter(centers[0], centers[1], c=color_palette, marker = "x")
plt.show()
我正在用Python 3.7.3版本重现此代码,并且从这段代码的源头知道,它在较旧的版本中可以正常工作。来自
Spectral
的函数matplotlib.pyplot.cm
以小写(spectral
)编写时。结果是下一个。
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
4237 valid_shape = False
-> 4238 raise ValueError
4239 except ValueError:
ValueError:
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-26-2f513f9c616c> in <module>
24 plt.title("Clustering for k = %s"%str(k))
25 plt.scatter(x1,x2, c=label_color)
---> 26 plt.scatter(centers[0], centers[1], c=[i for i in color_palette], marker = "x")
27 plt.show()
~/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, data, **kwargs)
2860 vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths,
2861 verts=verts, edgecolors=edgecolors, **({"data": data} if data
-> 2862 is not None else {}), **kwargs)
2863 sci(__ret)
2864 return __ret
~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1808 "the Matplotlib list!)" % (label_namer, func.__name__),
1809 RuntimeWarning, stacklevel=2)
-> 1810 return func(ax, *args, **kwargs)
1811
1812 inner.__doc__ = _add_data_doc(inner.__doc__,
~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
4243 "acceptable for use with 'x' with size {xs}, "
4244 "'y' with size {ys}."
-> 4245 .format(nc=n_elem, xs=x.size, ys=y.size)
4246 )
4247 # Both the mapping *and* the RGBA conversion failed: pretty
ValueError: 'c' argument has 9 elements, which is not acceptable for use with 'x' with size 1, 'y' with size 1.
我希望每个组的中心都像组本身一样上色。
提前致谢。
最佳答案
尝试通过索引使用相应尺寸的调色板,该索引对应于x和y值的长度,如下所示。
附言:您的代码在matplotlib 2.2.2
中工作正常
for i, k in enumerate(K):
# rest of your code
plt.scatter(centers[0], centers[1], c=color_palette[0:i+1], marker = "x")
print (centers[0].values)
plt.show()
关于python - 如何将由pyplot.Spectral创建的调色板用于循环中的散点图?,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/56936255/