import pandas as pd
import numpy as np
import matplotlib.pylab as plt

x=np.linspace(0,50,100)
ts1=pd.Series(3.1*np.sin(x/1.5)+3.5)
ts2=pd.Series(2.2*np.sin(x/3.5+2.4)+3.2)
ts3=pd.Series(0.04*x+3.0)

ts1.plot()
ts2.plot()
ts3.plot()

plt.ylim(-2,10)
plt.legend(['ts1','ts2','ts3'])
plt.show()

def euclid_dist(t1,t2):
    return sqrt(sum((t1-t2)**2))

print euclid_dist(ts1,ts2)

print euclid_dist(ts1,ts3)

def DTWDistance(s1, s2):
    DTW={}
    
    for i in range(len(s1)):
        DTW[(i, -1)] = float('inf')
    for i in range(len(s2)):
        DTW[(-1, i)] = float('inf')
    DTW[(-1, -1)] = 0

    for i in range(len(s1)):
        for j in range(len(s2)):
            dist= (s1[i]-s2[j])**2
            DTW[(i, j)] = dist + min(DTW[(i-1, j)],DTW[(i, j-1)], DTW[(i-1, j-1)])
		
    return sqrt(DTW[len(s1)-1, len(s2)-1])

print DTWDistance(ts1,ts2)

print DTWDistance(ts1,ts3)

def DTWDistance(s1, s2,w):
    DTW={}
    
    w = max(w, abs(len(s1)-len(s2)))
    
    for i in range(-1,len(s1)):
        for j in range(-1,len(s2)):
            DTW[(i, j)] = float('inf')
    DTW[(-1, -1)] = 0
  
    for i in range(len(s1)):
        for j in range(max(0, i-w), min(len(s2), i+w)):
            dist= (s1[i]-s2[j])**2
            DTW[(i, j)] = dist + min(DTW[(i-1, j)],DTW[(i, j-1)], DTW[(i-1, j-1)])
		
    return sqrt(DTW[len(s1)-1, len(s2)-1])

print DTWDistance(ts1,ts2,10)

print DTWDistance(ts1,ts3,10)

def LB_Keogh(s1,s2,r):
    LB_sum=0
    for ind,i in enumerate(s1):
        
        lower_bound=min(s2[(ind-r if ind-r>=0 else 0):(ind+r)])
        upper_bound=max(s2[(ind-r if ind-r>=0 else 0):(ind+r)])
        
        if i>upper_bound:
            LB_sum=LB_sum+(i-upper_bound)**2
        elif i<lower_bound:
            LB_sum=LB_sum+(i-lower_bound)**2
    
    return sqrt(LB_sum)

print LB_Keogh(ts1,ts2,20)

print LB_Keogh(ts1,ts3,20)

from sklearn.metrics import classification_report

def knn(train,test,w):
    preds=[]
    for ind,i in enumerate(test):
        min_dist=float('inf')
        closest_seq=[]
        #print ind
        for j in train:
            if LB_Keogh(i[:-1],j[:-1],5)<min_dist:
                dist=DTWDistance(i[:-1],j[:-1],w)
                if dist<min_dist:
                    min_dist=dist
                    closest_seq=j
        preds.append(closest_seq[-1])
    return classification_report(test[:,-1],preds)

train = np.genfromtxt('datasets/train.csv', delimiter='\t')
test = np.genfromtxt('datasets/test.csv', delimiter='\t')
print knn(train,test,4)

import random

def k_means_clust(data,num_clust,num_iter,w=5):
    centroids=random.sample(data,num_clust)
    counter=0
    for n in range(num_iter):
        counter+=1
        print counter
        assignments={}
        #assign data points to clusters
        for ind,i in enumerate(data):
            min_dist=float('inf')
            closest_clust=None
            for c_ind,j in enumerate(centroids):
                if LB_Keogh(i,j,5)<min_dist:
                    cur_dist=DTWDistance(i,j,w)
                    if cur_dist<min_dist:
                        min_dist=cur_dist
                        closest_clust=c_ind
            if closest_clust in assignments:
                assignments[closest_clust].append(ind)
            else:
                assignments[closest_clust]=[]
    
        #recalculate centroids of clusters
        for key in assignments:
            clust_sum=0
            for k in assignments[key]:
                clust_sum=clust_sum+data[k]
            centroids[key]=[m/len(assignments[key]) for m in clust_sum]
    
    return centroids
        

train = np.genfromtxt('datasets/train.csv', delimiter='\t')
test = np.genfromtxt('datasets/test.csv', delimiter='\t')
data=np.vstack((train[:,:-1],test[:,:-1]))

import matplotlib.pylab as plt

centroids=k_means_clust(data,4,10,4)
for i in centroids:
    
    plt.plot(i)

plt.show()