我想将水平值而不是垂直值标准化。代码读取代码后提供的csv文件,并输出具有规范化值的新csv文件。如何使其水平标准化?鉴于以下代码:
代码
#norm_code.py
#normalization = x-min/max-min
import numpy as np
from sklearn import preprocessing
all_data=np.loadtxt(open("c:/Python27/test.csv","r"),
delimiter=",",
skiprows=0,
dtype=np.float64)
x=all_data[:]
print('total number of samples (rows):', x.shape[0])
print('total number of features (columns):', x.shape[1])
minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit(x)
X_minmax=minmax_scale.transform(x)
with open('test_norm.csv',"w") as f:
f.write("\n".join(",".join(map(str, x)) for x in (X_minmax)))
test.csv
1 2 0 4 3
3 2 1 1 0
2 1 1 0 1
答案 0 :(得分:4)
您可以简单地对转置进行操作,并对结果进行转置:
minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit(x.T)
X_minmax=minmax_scale.transform(x.T).T
答案 1 :(得分:2)
Oneliner在不使用sklearn的情况下回答:
X_minmax = np.transpose( (x-np.min(x,axis=1))/(np.max(x, axis=1)-np.min(x,axis=1)))
这比使用预处理的MinMaxScaler快约8倍。