`ValueError:太多值无法通过`scipy.stats.linregress`解包(预期为4) [英] `ValueError: too many values to unpack (expected 4)` with `scipy.stats.linregress`

问题描述

我知道，当将更多变量设置为值而不是函数返回时，会出现此错误消息( ValueError:太多的值无法解包(预期为4)).

I know that this error message (ValueError: too many values to unpack (expected 4)) appears when more variables are set to values than a function returns.

scipy.stats.linregress 根据scipy文档返回5个值(

scipy.stats.linregress returns 5 values according to the scipy documentation (http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html).

以下是一个简短的、可重现的示例，显示了对 linregress 的正常调用和失败调用:

Here is a short, reproducible example of a working call, and then a failed call, to linregress:

什么可以解释差异，为什么第二个却叫不好?

What could account for difference and why is the second one poorly called?

from scipy import stats
import numpy as np

if __name__ == '__main__':
    x = np.random.random(10)
    y = np.random.random(10)
    print(x,y)
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)


'''
Code above works
Code below fails
'''

    X = np.asarray([[-15.93675813],
 [-29.15297922],
 [ 36.18954863],
 [ 37.49218733],
 [-48.05882945],
 [ -8.94145794],
 [ 15.30779289],
 [-34.70626581],
 [  1.38915437],
 [-44.38375985],
 [  7.01350208],
 [ 22.76274892]])

    Y = np.asarray( [[  2.13431051],
 [  1.17325668],
 [ 34.35910918],
 [ 36.83795516],
 [  2.80896507],
 [  2.12107248],
 [ 14.71026831],
 [  2.61418439],
 [  3.74017167],
 [  3.73169131],
 [  7.62765885],
 [ 22.7524283 ]])

    print(X,Y) # The array initialization succeeds, if both arrays are print out


    for i in range(1,len(X)):
        slope, intercept, r_value, p_value, std_err = (stats.linregress(X[0:i,:], y = Y[0:i,:]))

推荐答案

您的问题源自切片 X 和 Y 数组.另外，您不需要 for 循环.请改用以下内容，它应该可以正常工作.

Your problem originates from slicing the X and Y arrays. Also you do not need the for loop. Use the following instead and it should work.

slope, intercept, r_value, p_value, std_err = stats.linregress(X[:,0], Y[:,0])

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