Python KNN:TypeError:在0-d数组上迭代
我正在使用（）中的KNN代码处理我自己的数据。（我没有使用Iris数据集。）我已经为这篇文章缩减了数据量，这样我就可以在这里包含这两个数组Python KNN:TypeError:在0-d数组上迭代,python,pandas,scikit-learn,knn,Python,Pandas,Scikit Learn,Knn,我正在使用（）中的KNN代码处理我自己的数据。（我没有使用Iris数据集。）我已经为这篇文章缩减了数据量，这样我就可以在这里包含这两个数组 working_df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 475 entries, 236582 to 237060 Data columns (total 4 columns): Salinity 475 non-null floa
working_df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 475 entries, 236582 to 237060
Data columns (total 4 columns):
Salinity 475 non-null float64
Temperature 475 non-null float64
Calculated Chlorophyll 475 non-null float64
Station_group 475 non-null object
ValueError: RGBA values should be within 0-1 range
plt.scatter（X[：，0]，X[：，1]，c=y，cmap=cmap\u bold，edgecolor='k'，s=20）看起来您的CMAP中只有3种颜色，但有6个类需要为每个类指定一种颜色。尝试在列出的颜色映射中列出6种颜色，而不是3种颜色。绘制数据时会出现错误：
plt.scatter（X[：，0]，X[：，1]，c=y，cmap=cmap\u bold，edgecolor='k'，s=20）c=y的值错误和类型错误。类型错误发生在plt.pcolormesh（xx，yy，Z，cmap=cmap_light）
我不熟悉pcolormesh
，所以我无法帮助。@Chris-抱歉；我误解了。感谢您注意到c=y的值错误（它可能在该调用中的任何地方）。
ValueError: RGBA values should be within 0-1 range
TypeError: iteration over a 0-d array
n_neighbors = 3
h = .02 # step size in the mesh
# Create color maps
cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])
for weights in ['uniform', 'distance']:
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X, y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
edgecolor='k', s=20)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("3-Class classification (k = %i, weights = '%s')"
% (n_neighbors, weights))
plt.show()
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