y_p :::python 在这个（或任何）脚本中做了什么？

Question

我正在尝试完成 Sebastian Raschka 关于特征缩放的教程，但我无法运行下面的代码，因为它在第三行（以“python”结尾的行）抛出错误。

from matplotlib import pyplot as plt

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10,5))

y_p    :::python
    # Standardization

    x = [1,4,5,6,6,2,3]
    mean = sum(x)/len(x)
    std_dev = (1/len(x) * sum([ (x_i - mean)**2 for x_i in x]))**0.5

    z_scores = [(x_i - mean)/std_dev for x_i in x]

    # Min-Max scaling

    minmax = [(x_i - min(x)) / (min(x) - max(x)) for x_i in x]os = [0 for i in range(len(x))]

ax1.scatter(z_scores, y_pos, color='g')
ax1.set_title('Python standardization', color='g')

ax2.scatter(minmax, y_pos, color='g')
ax2.set_title('Python Min-Max scaling', color='g')

ax3.scatter(z_scores_np, y_pos, color='b')
ax3.set_title('Python NumPy standardization', color='b')
The-effect-of-standardization
ax4.scatter(np_minmax, y_pos, color='b')
ax4.set_title('Python NumPy Min-Max scaling', color='b')

plt.tight_layout()

for ax in (ax1, ax2, ax3, ax4):
    ax.get_yaxis().set_visible(False)
    ax.grid()

plt.show()

那么，y_p :::python 做了什么？

Answer 1

答案是它不是有效的 Python 代码。

您应该查看 ipython 笔记本，我相信您从中获得了该代码的某些部分。

http://nbviewer.jupyter.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb

相关的sn-p是

from matplotlib import pyplot as plt

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10,5))

y_pos = [0 for i in range(len(x))]

ax1.scatter(z_scores, y_pos, color='g')
ax1.set_title('Python standardization', color='g')