#!/usr/bin/env python
# coding: utf-8

# <br>
# # ** 6 Predict the TimeSeries Data**
# 시계열 데이터 예측

# <br><br>
# ## **1 은닉 마르코프 모델을 활용 (HMM)**
# <img src="https://i.stack.imgur.com/rx8kj.png" width='400' align='left'>

# <br>
# ### **01 Pattern 이 분명한 데이터를 활용하여 은닉 마르코프 모델 생성 (HMM)**
# 은닉 마르코프 모델의 용도를 확인해본다

# In[1]:


import numpy as np
from hmmlearn.hmm import GaussianHMM

data = np.loadtxt('./data/hmm_data.txt', delimiter=',')
X    = np.column_stack([data[:, 2]])
x    = [x[0] for x in X]

get_ipython().run_line_magic('matplotlib', 'inline')
import pandas as pd
pd.Series(x).plot(figsize=(25,2))


# In[2]:


import warnings
warnings.filterwarnings("ignore")
num_components = 5
hmm = GaussianHMM(n_components    = num_components,
                  covariance_type = 'diag',
                  n_iter          = 1000)
hmm.fit(X)


# In[3]:


hmm


# In[4]:


hmm.means_


# In[5]:


for i in range(hmm.n_components):
    print('''Hidden state {:2} -  Mean : {:.3f} \\ Variance : {:.3f}'''.format(
        i + 1, hmm.means_[i][0], np.diag(hmm.covars_[i])[0]))    


# In[6]:


hidden_states = hmm.predict(X[-100:])
pd.Series(hidden_states).plot(figsize=(15,3), grid=True)


# <br>
# ### **02 주가 데이터를 활용한 은닉 마르코프 모델 생성 (HMM)**
# by Stock Price Data

# In[7]:


# from pandas_datareader import get_data_yahoo
# price = get_data_yahoo('005930.KS','2017-06-01')
# price = price.fillna('ffill')
# price = price.dropna()
# price.tail(3)


# In[8]:


# 저장된 CSV 파일을 사용한다

import pandas as pd
price = pd.read_csv('./data/stock.csv')
price = price.set_index('Date')
price.head(3)
price.index = pd.DatetimeIndex(price.index)
price.info()


# In[9]:


import numpy as np
price_array = np.asarray(price.Close)
price_array.shape


# In[10]:


price_array = price_array.reshape(1, -1)
price_array.shape


# In[11]:


price_array.max()


# In[12]:


import warnings
warnings.filterwarnings("ignore")

num_components = 100
hmm            = GaussianHMM(n_components    = num_components,
                             covariance_type = 'diag',   
                             n_iter          = 1000)
hmm.fit(price_array.T)                                                


# In[13]:


hidden_states = hmm.predict(price_array.T[:15])
pd.Series(hidden_states).plot(figsize=(10,2), grid=True)


# In[ ]: