[英]Normalise 2D Numpy Array: Zero Mean Unit Variance
I have a 2D Numpy array, in which I want to normalise each column to zero mean and unit variance. 我有一个2D Numpy数组,其中我想将每列归一化为零均值和单位方差。 Since I'm primarily used to C++, the method in which I'm doing is to use loops to iterate over elements in a column and do the necessary operations, followed by repeating this for all columns.
由于我主要习惯于C ++,我所使用的方法是使用循环迭代列中的元素并执行必要的操作,然后对所有列重复此操作。 I wanted to know about a pythonic way to do so.
我想知道一个pythonic的方法。
Let class_input_data
be my 2D array. 让
class_input_data
成为我的2D数组。 I can get the column mean as: 我可以得到列的意思是:
column_mean = numpy.sum(class_input_data, axis = 0)/class_input_data.shape[0]
I then subtract the mean from all columns by: 然后我通过以下方法减去所有列的均值:
class_input_data = class_input_data - column_mean
By now, the data should be zero mean. 到目前为止,数据应为零均值。 However, the value of:
但是,价值:
numpy.sum(class_input_data, axis = 0)
isn't equal to 0, implying that I have done something wrong in my normalisation. 不等于0,暗示我在规范化中做错了。 By isn't equal to 0, I don't mean very small numbers which can be attributed to floating point inaccuracies.
By不等于0,我不是指可归因于浮点不准确的非常小的数字。
Something like: 就像是:
import numpy as np
eg_array = 5 + (np.random.randn(10, 10) * 2)
normed = (eg_array - eg_array.mean(axis=0)) / eg_array.std(axis=0)
normed.mean(axis=0)
Out[14]:
array([ 1.16573418e-16, -7.77156117e-17, -1.77635684e-16,
9.43689571e-17, -2.22044605e-17, -6.09234885e-16,
-2.22044605e-16, -4.44089210e-17, -7.10542736e-16,
4.21884749e-16])
normed.std(axis=0)
Out[15]: array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.