優達機械学習:フィーチャースケール

練習:最小/最大再スケーラエンコーディング

import numpy as np

def 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 data
data = [115, 140, 175]
print featureScaling(data)

練習:再スケールが必要なアルゴリズムの練習

練習:スケールタイプ

練習:再スケールフィーチャーの計算

import numpy as np
stocklist = []
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))

import numpy as np
stocklist = []
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 preprocessing
min_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)

練習:フィーチャーのスケールを配置するタイミング