# -*- coding: utf-8 -*-
# pylint: disable=E1101
"""
Created on Fri Oct 20 16:06:09 2017
@author: wnma3
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from keras.layers.core import Activation, Dense
from keras.models import Sequential
from sklearn.cluster import KMeans
from sklearn.linear_model import LogisticRegression as LR
from sklearn.linear_model import RandomizedLogisticRegression as RLR
from sklearn.manifold import TSNE
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier as DTC
from sklearn.tree import export_graphviz
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import adfuller as ADF
"""
programmer_1--> Use random forest to calculate effective features , Use linear regression to calculate the correlation coefficient
programmer_2--> Use decision number model , Generate the decision tree process and save it as do
