1NNDTW.py

import numpy as np
#import math
import os
import psutil

import time
#from dtaidistance import dtw
#from tslearn.metrics import dtw_limited_warping_length
import sys


def dtw(A, B):
    N = len(A)
    M = len(B)
    d = np.zeros((N, M))
    for n in range(N):
        for m in range(M):
            d[n][m]=(A[n]-B[m])**2
    D=np.zeros(d.shape)
    D[0][0]=d[0][0]
    for n in range(1,N):
        D[n][0]=d[n][0]+D[n-1][0]
    for m in range(1,M):
        D[0][m]=d[0][m]+D[0][m-1]
    for n in range(1,N):
        for m in range(1,M):
            D[n][m]=d[n][m]+min(D[n-1][m],D[n-1][m-1],D[n][m-1])
    return D[N-1][M-1]


training = np.loadtxt(sys.argv[1], delimiter='\t')
testing = np.loadtxt(sys.argv[2],  delimiter='\t')

num_rows_test, num_columns_test = testing.shape
num_rows_train, num_columns_train = training.shape
training_noclass=training[:,1:]
testing_noclass=testing[:,1:]

predicted_label = None
correct = 0
start_time = time.time()
for i in range(num_rows_test):
    #print(i)
    least_distance = float('inf')
    for j in range(num_rows_train):
        dist = dtw(testing_noclass[i], training_noclass[j])
        if dist < least_distance:
            predicted_label = training[j][0]
            least_distance = dist
    if predicted_label == testing[i][0]:
        correct = correct + 1


accuracy = (correct/num_rows_test) * 100
print("Datasetname:",sys.argv[1])
print("Total Accuracy of oneNNDTW is:", accuracy)

print("Total Execution time oneNNDTW is:", time.time() - start_time)
process = psutil.Process(os.getpid())
memory = process.memory_full_info().uss
memory_in_KB = memory /(1024)
print("Total Memory of oneNNDTW inKB",memory_in_KB)  # in bytes

#dist = dtw.distance(testing[i], training[j])  # math.sqrt(squaring)