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

# In[1]:


from time import time
from sklearn.cluster import k_means
from elm import ELMClassifier, ELMRegressor, GenELMClassifier, GenELMRegressor
from random_layer import RandomLayer, MLPRandomLayer, RBFRandomLayer, GRBFRandomLayer
from sklearn.neighbors import KDTree
from sklearn.pipeline import Pipeline
from sklearn.linear_model import Ridge


# In[1]:


import pandas as pd
import numpy as np
#dataset link: http://climate.weather.gc.ca/
data = pd.read_csv('winter_processed.csv')
data = np.array(data)
data


# In[3]:


#This is to convert Time Series to 0-mean and unit-std deviation series.
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(data)


# In[4]:


from sklearn.cross_validation import train_test_split

scaler.mean_
scaler.scale_
data_scaled = scaler.transform(data)
#splitting data into test and train parts
d_train,d_test = train_test_split(data_scaled, test_size=0.2, random_state=42)


# In[5]:


#memory order
m = 16
#auto corrected input vector for training (we are taking 16 lagged values as features for each data point)
x_train = np.array([d_train[i][0] for i in range(m)])
y_train = np.array(d_train[16][0])

for i in range(1,1135):
    l = np.array([d_train[j][0] for j in range(i,i+m)])
    x_train = np.vstack([x_train,l])
    y_train = np.vstack([y_train,d_train[i+m]])
    
#auto corrected input vector for test
x_test = np.array([d_test[i][0] for i in range(m)])
y_test = np.array(d_test[16][0])

for i in range(1,266):
    l = np.array([d_test[j][0] for j in range(i,i+m)])
    x_test = np.vstack([x_test,l])
    y_test = np.vstack([y_test,d_test[i+m]])


# In[ ]:





# In[6]:


from sklearn import pipeline
import matplotlib.pyplot as plt
#parameters
n_hidden = 500
alpha = 0.7
rbf_width = 0.1
activation_func = 'sigmoid'
regressor = None
random_state = 0
ridge_alpha = 0.001
rl = RandomLayer( n_hidden = n_hidden, alpha = alpha, 
    rbf_width = rbf_width, activation_func = activation_func )

ridge = Ridge( alpha = ridge_alpha )

elmr = pipeline.Pipeline( [( 'rl', rl ), ( 'ridge', ridge )] )  

elmr.fit( x_train, y_train )
#p = elmr.predict( x_test )
print ("Training Score: ",elmr.score(x_train, y_train),"Testing Score: ", -elmr.score(x_test, y_test))


# In[7]:


err_train = elmr.predict(x_train) - y_train
err_test = elmr.predict(x_test) - y_test


# In[8]:


tree = KDTree(x_train,leaf_size=200)
dist, ind = tree.query(x_test,k=150)
print(ind.shape)


# In[9]:


err_dict = np.zeros(shape=(len(x_test),150))
j = 0
for tst_pattern in (ind):
    err_dict[j] = [err_train[i] for i in ind[j]]
    j+=1


# In[10]:


U = []
L = []
U = [y_test[i] + max(err_dict[i]) for i in range(len(err_dict))]
L = [y_test[i] + min(err_dict[i]) for i in range(len(err_dict))]


# In[11]:


x_test.shape, y_test.shape


# In[ ]:





# In[12]:


count = 0
i = 0
for val in y_test:
    if val >= L[i] and val <= U[i]:
        count += 1
    i+= 1
picp = count/len(y_test)


# In[13]:


y_min = np.amin(y_test)
y_max = np.amax(y_test)
S = 0

for i in range(len(L)):
    S+= (abs(U[i]-L[i]))

S /= (y_max-y_min)


# In[14]:


S /= len(y_test)
S


# In[15]:


print("PICP (Probability Coverage) value: ",picp,"NMPIW (Measure of PI widths) value: ",S)


# In[19]:


get_ipython().run_line_magic('matplotlib', 'inline')
from matplotlib import pyplot as plt
plt.plot(L,'r') # plotting t,a separately 
plt.plot(elmr.predict(x_test),'b') # plotting t,b separately 
plt.plot(U,'g') # plotting t,c separately 
plt.xlabel("Time (Hours)")
plt.ylabel("Wind Speed (KM/Hr)")
plt.show()


# In[ ]: