text: #!/usr/bin/env python
  # encoding:utf-8
  # @Time   : 2019/11/29
  # @Author : 胡茂海
  # @Site   : 
  # @File   : test.py
  
  
  from FeatureProcess import *
  import pandas as pd
  import numpy as np
  
  # data = pd.read_csv('titanic.csv')
  # y = data['Survived']
  # x = data[[c for c in data.columns if c !='Survived']]
  # print(x)
  
  
  fsd = FeaturesStandard('StandardScaler')
  data = [[0, 0], [0, 0], [1, 1], [1, 1]]
  scr=fsd.fit(data)
  print(scr.mean_)
  print(scr.transform(data))
  print('--------------------')
  
  
  data =pd.read_csv('../data/titanic.csv')
  x = data['Age'].values
  y = data['Survived'].values
  fsd = FeaturesStandard('CutBins')
  scr=fsd.fit(y,x)
  print(scr.transform(x))
  print('--------------------')
  
  
  
  fe = FeaturesEncoder()
  X = [['Male', 1], ['Female', 3], ['Female', 2]]
  enc =fe.fit(X)
  print(enc.categories_)
  print(enc.transform([['Female', 1], ['Male', 4]]).toarray())
  print(enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]]))
  print(enc.get_feature_names(['gender', 'group']))
  
  
  fd = FeaturesDecomposition()
  X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
  p = fd.fit(X)
  print(p.explained_variance_ratio_)
  print(p.singular_values_)
  
  fs = FeaturesSelection()
  X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]
  fs.fit(X)
  X = fs.transform(X)
  print(X)
  
  
  