绘制逻辑回归：ValueError：x 和 y 必须具有相同的第一维答案

【问题标题】：plotting a logistic regression :ValueError: x and y must have same first dimension绘制逻辑回归：ValueError：x 和 y 必须具有相同的第一维
【发布时间】：2016-10-23 22:32:28
【问题描述】：

当我运行我的代码时，我收到一条错误消息：

ValueError: x 和 y 必须具有相同的第一维。

import numpy as np 
from matplotlib import pyplot as plt 
from sklearn.linear_model import LogisticRegression

x1 = np.array([0,0.6,1.2,1.5,1.8,2.5,3.3,3.9,4,4.9,5.1])
y1 = np.array([0,0,0,0,0,0,0,0,0,0,0])

x2 = np.array([3.2,3.8,4.5,5.2,5.8,6.4,6.7,7.1,7.6,8.1,8.5,9])
y2 = np.array([1,1,1,1,1,1,1,1,1,1,1])

X = np.array([[0],[0.6],[1.2],[1.5],[1.8],[2.5],[3.3],[3.9],[4],[4.9],[5.1],[3.2],[3.8],[4.5],[5.2],[5.8],[6.4],[6.7],[7.1],[7.6],[8.1],[8.5],[9]])

Y = np.array([0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1])

plt.plot(x1,y1,"ro",color="blue")
plt.plot(x2,y2,"ro",color="red")

plt.axis([-2,10,-0.5,2])

plt.show()

完整追溯

Traceback (most recent call last):
  File "ml2.py", line 16, in <module>
    plt.plot(x2,y2,"ro",color="red")
  File "/usr/lib/python2.7/dist-packages/matplotlib/pyplot.py", line 3154, in plot
    ret = ax.plot(*args, **kwargs)
  File "/usr/lib/python2.7/dist-packages/matplotlib/__init__.py", line 1814, in inner
    return func(ax, *args, **kwargs)
  File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_axes.py", line 1424, in plot
    for line in self._get_lines(*args, **kwargs):
  File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 386, in _grab_next_args
    for seg in self._plot_args(remaining, kwargs):
  File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 364, in _plot_args
    x, y = self._xy_from_xy(x, y)
  File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 223, in _xy_from_xy
    raise ValueError("x and y must have same first dimension")
ValueError: x and y must have same first dimension

【问题讨论】：

标签： python numpy matplotlib scikit-learn

【解决方案1】：

在调用plot 时，前两个参数 x 和 y 必须具有相同长度的列表或数组。

您的x2 和y2 的长度不同。

为了避免以后出现这个问题，有更好的方法来创建一个只填充一个值的数组，例如：

x2 = np.array([3.2,3.8,4.5,5.2,5.8,6.4,6.7,7.1,7.6,8.1,8.5,9])
y2 = np.full_like(x2, 1)

【讨论】：