我的Juypter笔记本出现以下错误。我已经将mathplotlib更新到最新版本，但仍然收到错误
'c'参数看起来像一个单一的RGB或RGBA数字序列，应避免使用它，因为如果其长度与'x'和'y'匹配，则值映射将具有优先级。如果您确实想为所有点指定相同的RGB或RGBA值，请使用单行二维数组。
X=lab3_data
range_n_clusters = [2, 3, 4, 5, 6,7,8]
for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(18, 7)
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([0, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = cluster.KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = cm.nipy_spectral(float(i) / n_clusters)
ax1.fill_betweenx(np.arange(y_lower, y_upper),
0, ith_cluster_silhouette_values,
facecolor=color, edgecolor=color, alpha=0.7)
# Label the silhouette plots with their cluster numbers at the middle
ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")
# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
ax1.set_yticks([]) # Clear the yaxis labels / ticks
ax1.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])
# 2nd Plot showing the actual clusters formed
# append the cluster centers to the dataset
lab3_data_and_centers = np.r_[lab3_data,clusterer.cluster_centers_]
# project both th data and the k-Means cluster centers to a 2D space
XYcoordinates = manifold.MDS(n_components=2).fit_transform(lab3_data_and_centers)
# plot the transformed examples and the centers
# use the cluster assignment to colour the examples
# plot the transformed examples and the centers
# use the cluster assignment to colour the examples
clustering_scatterplot(points=XYcoordinates[:-n_clusters,:],
labels=cluster_labels,
centers=XYcoordinates[-n_clusters:,:],
title='MDS')
plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
"with n_clusters = %d" % n_clusters),
fontsize=14, fontweight='bold')
plt.show（）
答案 0 :(得分：9)
您还可以使用以下命令将c参数设为2D：
c=color.reshape(1,-1)
或
c=np.array([color])
或仅将原始颜色阵列更改为2D：
color = cm.nipy_spectral(float(i) / n_clusters).reshape(1,-1)
p.s .：由于我需要50点信誉才能发表评论，所以我只是打开一个新答案，尽管这只是D Adams使用内置numpy.atleast_2D（）解决方案下的评论。
答案 1 :(得分：4)
首先产生数据并定义颜色：
import numpy
import matplotlib
import matplotlib.pyplot
#Make the color you actually want:
Color = numpy.array([.5, .6, .7])
#Make some data:
Vals = numpy.random.uniform( size = (10, 3) )
PointCount = Vals.shape[0]
Xvals = Vals[:, 0]
Yvals = Vals[:, 1]
Zvals = Vals[:, 2]
第二次重现该问题：
#2D: Produce the warning
fig = matplotlib.pyplot.figure()
subplot = fig.add_subplot(111)
matplotlib.pyplot.scatter( Xvals, Yvals, c= Color, )
'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.
第一个解决方案是制作所需颜色的副本：
#Illustrate how to repeat a numpy array:
ValsCount = Vals.shape[0]
ColorsRepeated = numpy.repeat(numpy.atleast_2d(Color), ValsCount, axis = 0)
print ('ColorsRepeated')
print (ColorsRepeated)
#2D: Make scatter plot without color warning using repeat
fig = matplotlib.pyplot.figure()
subplot = fig.add_subplot(111)
matplotlib.pyplot.scatter( Xvals, Yvals, c= ColorsRepeated, )
#3D: Make scatter plot without color warning using repeat
fig = matplotlib.pyplot.figure()
subplot = fig.add_subplot(111, projection='3d')
matplotlib.pyplot.scatter( Xvals, Yvals, Zvals, c=ColorsRepeated, )
ColorsRepeated [[0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7] [0.5 0.6 0.7]]
另一种解决方案是使用matplotlib.pyplot.plot，它不会发出与matplotlib.pyplot.scatter相同的警告，您可以避免使用常规plot命令出现颜色重复问题，而不必连接点：
#2D: Make regular plot without using repeat
fig = matplotlib.pyplot.figure()
subplot = fig.add_subplot(111)
matplotlib.pyplot.plot( Xvals, Yvals, c= Color, marker = '.', linestyle = '', )
另一种解决方案是使彩色二维阵列具有一行，以解决matplotlib.pyplot.scatter的问题，但引发matplotlib.pyplot.plot的错误：
#2D: Use single row in 2D array to avoid warning
fig = matplotlib.pyplot.figure()
subplot = fig.add_subplot(111)
matplotlib.pyplot.scatter( Xvals, Yvals, c= numpy.atleast_2d(Color), )
使用常规的matplotlib.pyplot.plot命令和2D彩色单行会引发错误：
#2D: Try and fail to use a single row in 2D array in a regular plot
fig = matplotlib.pyplot.figure()
subplot = fig.add_subplot(111)
matplotlib.pyplot.plot( Xvals, Yvals, c= numpy.atleast_2d(Color), marker = '.', linestyle = '', )
ValueError: Invalid RGBA argument: array([[0.5, 0.6, 0.7]])
结论：
matplotlib.pyplot.plot和matplotlib.pyplot.scatter在颜色方面表现不同。 matplotlib.pyplot.plot需要一维数组。 matplotlib.pyplot.scatter需要2D数组。 2D数组可以是单行，也可以重复，也可以是每个数据点不同的颜色。如果matplotlib社区添加if语句为我们做重复并删除警告，那就太好了。
答案 2 :(得分：1)
在最新版本的matplotlib（3.0.3）中，参数'c'应该是二维数组。如果'c'的长度与'x'和'y'的长度匹配，则每个点的颜色对应于'c'的元素。如果要使每个点显示相同的颜色，则'c'应该是具有单个行的二维数组，例如c = np.array（[0.5，0.5，0.5]）。 最好的祝福！
答案 3 :(得分：0)
作为解决方法：
from matplotlib.axes._axes import _log as matplotlib_axes_logger
matplotlib_axes_logger.setLevel('ERROR')
答案 4 :(得分：0)
c=np.array([0.5, 0.5, 0.5]).reshape(1,-1)