# -*- coding: utf-8 -*-
import inspect
import math
import numpy as np
from sklearn import preprocessing
def max_min_normalization(data_list):
"""
利用最大最小数将一组数据进行归一化输出
x_new = (x - min) / (max - min)
:param data_list:
:return:
"""
normalized_list = []
max_min_interval = max(data_list) - min(data_list)
for data in data_list:
data = float(data)
new_data = (data - min(data_list)) / max_min_interval
normalized_list.append(round(new_data, 3))
return normalized_list
def mean_normalization(data_list):
"""
利用平均数将一组数据进行标准化输出
标准化的结果不一定是在0,1之间
x_new = (x - mean) / (max - min)
:param data_list:
:return:
"""
normalized_list = []
mean = sum(data_list) / len(data_list)
max_min_interval = max(data_list) - min(data_list)
for data in data_list:
data = float(data)
new_data = (data - mean) / max_min_interval
normalized_list.append(round(new_data, 3))
return normalized_list
def zscores_normalization(data_list):
"""
利用z-scores方法针对数据进行标准化
:param data_list:
:return:
"""
normalized_list = []
mean = sum(data_list, 0.0) / len(data_list)
var_lst = []
for data in data_list:
var_lst.append((float(data) - mean) ** 2)
std_value = math.sqrt(sum(var_lst) / len(var_lst))
for data in data_list:
normalized_list.append(round((data - mean) / std_value, 3))
return normalized_list
def max_min_normalization_using_numpy(data_list):
"""
用数据处理包numpy归一化
:param data_list:
:return:
"""
normalized_list = []
max = np.max(data_list)
min = np.min(data_list)
for data in data_list:
new_data = (float(data) - min) / (max - min)
normalized_list.append(round(new_data, 3))
return normalized_list
def zscores_normalization_using_numpy(data_list):
"""
利用numpy中现有的方法计算标准差和平均数，然后用z-scores方法针对数据进行标准化
:param data_list:
:return:
"""
normalized_list = []
mean = np.mean(data_list)
std = np.std(data_list)
for data in data_list:
normalized_list.append(round((data - mean) / std, 3))
return normalized_list
def normalize_data_using_sk(data_list):
"""
利用sklearn学习库自带的归一方法实现
:param data_list:
:return:
"""
data_array = np.asarray(data_list, 'float').reshape(1, -1)
new_data = preprocessing.minmax_scale(data_array, axis=1)
return np.round(new_data, 3)[0, :]
if __name__ == '__main__':
data_list = np.random.randint(1, 20, 10)
data = globals().copy()
for key in data:
if inspect.isfunction(data[key]):
res = data[key](data_list)
print '%s:\n%s' % (key, res)
运行结果：
zscores_normalization_using_numpy:
[-1.528, 1.382, -0.255, 1.564, -0.073, 0.291, 0.837, -1.346, -0.8, -0.073]
max_min_normalization:
[0.0, 0.941, 0.412, 1.0, 0.471, 0.588, 0.765, 0.059, 0.235, 0.471]
normalize_data_using_sk:
[0. 0.941 0.412 1. 0.471 0.588 0.765 0.059 0.235 0.471]
max_min_normalization_using_numpy:
[0.0, 0.941, 0.412, 1.0, 0.471, 0.588, 0.765, 0.059, 0.235, 0.471]
mean_normalization:
[-0.471, 0.471, -0.059, 0.529, 0.0, 0.118, 0.294, -0.412, -0.235, 0.0]
zscores_normalization:
[-1.528, 1.382, -0.255, 1.564, -0.073, 0.291, 0.837, -1.346, -0.8, -0.073]