优达机器学习：特征缩放

练习：最小值/最大值重缩放器编码

import numpy as npdef featureScaling(arr):    arr = np.array(arr)    max = np.max(arr)    min = np.min(arr)    res = []    for item in arr:        data = float(item-min)/(max-min)        res.append(data)    return res# tests of your feature scaler--line below is input datadata = [115, 140, 175]print featureScaling(data)

练习：需要重缩放的算法练习

• 使用 RBF 核函数的 SVN
• K-均值聚类

练习：缩放类型

• MinMaxScaler

练习：计算重缩放特征

• salary : 0.17962406631
• stock : 0.0290205889347

import numpy as npstocklist = []for item in data_dict:    stock = data_dict[item]['exercised_stock_options']    if stock != 'NaN':        stocklist.append( stock )stocklist = np.array(stocklist)print (1000000.0 - np.min(stocklist)) / (np.max(stocklist) - np.min(stocklist))salarylist = []for item in data_dict:    salary = data_dict[item]['salary']    if salary != 'NaN':        salarylist.append( salary )print (200000.0 - np.min(salarylist)) / (np.max(salarylist) - np.min(salarylist))

很奇怪的是，使用sklearn里面的MinMaxScaler的缩放率计算有误差，代码如下

import numpy as npstocklist = []for item in data_dict:    stock = data_dict[item]['exercised_stock_options']    if stock != 'NaN':        stocklist.append( stock )stocklist = np.array(stocklist)from sklearn import preprocessingmin_max_scaler = preprocessing.MinMaxScaler()min_max_scaler.fit_transform(stocklist)print 1000000 * min_max_scaler.scale_salarylist = []for item in data_dict:    salary = data_dict[item]['salary']    if salary != 'NaN':        salarylist.append( salary )salarylist = np.array(salarylist)min_max_scaler.fit_transform(salarylist)print 200000 * min_max_scaler.scale_print np.max(salarylist)print np.min(salarylist)

练习：何时部署特征缩放

• 重要