我正在尝试解决形状不兼容的问题。我有4个功能，如果我正确理解.shape，x的形状应该是（n_samples，n_features）。但我怎么能纠正这个？
以下是发生错误的行：
plot_surface(est, X_train[:, 0], X_train[:, 1], ax=ax, threshold=0.5, contourf=True)
以下是每个变量的信息：
est：
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=1, p=2,
weights='uniform')
X_train：
[[-1. -1. -1. -1. -1. -1. -1. -1. -1. -1.]
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
这是完整的堆栈跟踪：
Traceback (most recent call last):
File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\pydevd.py", line 1596, in <module>
globals = debugger.run(setup['file'], None, None, is_module)
File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\pydevd.py", line 974, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/Thomas/Desktop/!UFV/CIS480/project/NHL-Predictor.py", line 229, in <module>
plot_datasets(est)
File "C:/Users/Thomas/Desktop/!UFV/CIS480/project/NHL-Predictor.py", line 195, in plot_datasets
plot_surface(est, X_train[:, 0], X_train[:, 1], ax=ax, threshold=0.5, contourf=True)
File "C:/Users/Thomas/Desktop/!UFV/CIS480/project/NHL-Predictor.py", line 153, in plot_surface
pred = est.predict_proba(X_pred)[:, 1]
File "C:\Users\Thomas\Anaconda3\lib\site-packages\sklearn\neighbors\classification.py", line 190, in predict_proba
neigh_dist, neigh_ind = self.kneighbors(X)
File "C:\Users\Thomas\Anaconda3\lib\site-packages\sklearn\neighbors\base.py", line 353, in kneighbors
n_jobs=n_jobs, squared=True)
File "C:\Users\Thomas\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py", line 1240, in pairwise_distances
return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
File "C:\Users\Thomas\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py", line 1083, in _parallel_pairwise
return func(X, Y, **kwds)
File "C:\Users\Thomas\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py", line 222, in euclidean_distances
X, Y = check_pairwise_arrays(X, Y)
File "C:\Users\Thomas\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py", line 122, in check_pairwise_arrays
X.shape[1], Y.shape[1]))
ValueError: Incompatible dimension for X and Y matrices: X.shape[1] == 2 while Y.shape[1] == 10
Process finished with exit code 1
~~~~~ UPDATE：~~~~~
以下是包含导致错误的语句的整个函数：
def plot_datasets(est):
"""Plotsthe decision surface of ``est`` on each of the three datasets. """
fig, axes = plt.subplots(1, 3, figsize=(10, 4))
for (name, ds), ax in zip(datasets.items(), axes):
X_train = ds['X_train']
y_train = ds['y_train']
X_test = ds['X_test']
y_test = ds['y_test']
# plot test lighter than training
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
# plot limits
ax.set_xlim(X_train[:, 0].min(), X_train[:, 0].max())
ax.set_ylim(X_train[:, 1].min(), X_train[:, 1].max())
# no ticks
ax.set_xticks(())
ax.set_yticks(())
ax.set_ylabel('$x_1$')
ax.set_xlabel('$x_0$')
ax.set_title(name)
if est is not None:
est.fit(X_train, y_train)
#X.reshape(10, 1)
plot_surface(est, X_train[:, 0], X_train[:, 1], ax=ax, #ERROR IS CAUSED HERE
threshold=0.5, contourf=True)
err = (y_test != est.predict(X_test)).mean()
ax.text(0.88, 0.02, '%.2f' % err, transform=ax.transAxes)
plt.show()
fig.subplots_adjust(left=.02, right=.98)
该函数中重要变量的一些值：
X_train：
[[ 0. 0. -0.70710678 -1. ]
[-0.52223297 0. 1.41421356 1.4 ]
[-1.04446594 0. 1.41421356 1. ]
[-0.52223297 0. -0.70710678 -1.4 ]
[ 0. 0. -0.70710678 -0.2 ]
y_train：
[0 1 1 0 0 1]
X_test：
[[ 1.04446594 0. 1.41421356 -1.8 ]
[-1.04446594 0. 1.41421356 -0.2 ]
[ 0.52223297 0. 1.41421356 0.6 ]
[ 1.04446594 0. -0.70710678 -0.6 ]]
y_test：
[0 1 1 0]
这是plot_surface：
def plot_surface(est, x_1, x_2, ax=None, threshold=0.0, contourf=False):
"""Plots the decision surface of ``est`` on features ``x1`` and ``x2``. """
xx1, xx2 = np.meshgrid(np.linspace(x_1.min(), x_1.max(), 100),
np.linspace(x_2.min(), x_2.max(), 100))
# plot the hyperplane by evaluating the parameters on the grid
X_pred = np.c_[xx1.ravel(), xx2.ravel()] # convert 2d grid into seq of points
if hasattr(est, 'predict_proba'): # check if ``est`` supports probabilities
# take probability of positive class
pred = est.predict_proba(X_pred)[:, 1]
else:
pred = est.predict(X_pred)
Z = pred.reshape((100, 100)) # reshape seq to grid
if ax is None:
ax = plt.gca()
# plot line via contour plot
if contourf:
ax.contourf(xx1, xx2, Z, levels=np.linspace(0, 1.0, 10), cmap=plt.cm.RdBu, alpha=0.6)
ax.contour(xx1, xx2, Z, levels=[threshold], colors='black')
ax.set_xlim((x_1.min(), x_1.max()))
ax.set_ylim((x_2.min(), x_2.max()))