Question

在下面的plot_decision函数中，与matplotlib一起使用时，我以前在Jupyter Notebook中收到“内存错误”，因此我将分辨率从0.2降低到了“ 2”。现在我收到一个

“ TypeError：contourf的参数过多；请参阅help（contourf）”

在尝试绘图时。.我做了一些微不足道的调整尝试，希望能对您有所帮助。

###绘图决策功能

from matplotlib.colors import ListedColormap

def plot_decision(X, y, classifier, test_idx=None, resolution=2, figsize=(8,8)):

# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('#cc0000', '#003399', '#00cc00', '#999999', '#66ffff')
cmap = ListedColormap(colors[:len(np.unique(y))])

# get dimensions
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
x3_min, x3_max = X[:, 1].min() - 1, X[:, 1].max() + 1
x4_min, x4_max = X[:, 1].min() - 1, X[:, 1].max() + 1
x5_min, x5_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2, xx3, xx4, xx5 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution), np.arange(x3_min, x3_max, resolution), np.arange(x4_min, x4_max, resolution), np.arange(x5_min, x5_max, resolution))

xmin = xx1.min()
xmax = xx1.max()
ymin = xx2.min()
ymax = xx2.max()
ymin = xx3.min()
ymax = xx3.max()
ymin = xx4.min()
ymax = xx4.max()
ymin = xx5.min()
ymax = xx5.max()

# create the figure
fig, ax = plt.subplots(figsize=figsize)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

# plot the decision surface
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel(), xx3.ravel(), xx4.ravel(), xx5.ravel()]).T)
Z = Z.reshape(xx1.shape)
ax.contourf(xx1, xx2, xx3, xx4, xx5, Z, alpha=0.4, cmap=cmap, zorder=1)
#ax.contourf(xx1, xx2, xx3, xx4, xx5, VWXYZ, alpha=0.4, cmap=cmap, zorder=1)

# plot all samples
for idx, cl in enumerate(np.unique(y)):
    ax.scatter(x=X[y == cl, 0], 
               y=X[y == cl, 1],
               alpha=0.6, 
               c=cmap(idx),
               edgecolor='black',
               marker='o',#markers[idx],
               s=50,
               label=cl,
               zorder=3)

# highlight test samples
if test_idx:
    X_test, y_test = X[test_idx, :], y[test_idx]
    ax.scatter(X_test[:, 0],
               X_test[:, 1],
               c='w',
               alpha=1.0,
               edgecolor='black',
               linewidths=1,
               marker='o',
               s=150, 
               label='test set',
               zorder=2)

##### END PLOT决策功能 #####开始图功能

# visualize the model's decision regions to see how it separates the samples
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision(X=X_combined, y=y_combined, classifier=dtree)
plt.xlabel('Parameters')
plt.ylabel('Survivors')
plt.legend(loc='upper left')
plt.show()

感谢任何愿意提供帮助的人：）

Matplotlib“轮廓参数太多”错误

0 个答案: