import numpy as np
import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtrain, Ytrain = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xtest, Ytest = mnist.test.next_batch(200) #200 for testing
# tf Graph Input
xtrain = tf.placeholder("float", [None, 784])
xtest = tf.placeholder("float", [784])
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtrain, tf.negative(xtest))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.argmin(distance, 0)
accuracy = 0.
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
   sess.run(init)
   # loop over test data
   for i in range(len(Xtest)):
       # Get nearest neighbor
       nn_index = sess.run(pred, feed_dict={xtrain: Xtrain, xtest: Xtest[i, :]})
     # Get nearest neighbor class label and compare it to its true label
       print "Test", i, "Prediction:", np.argmax(Ytrain[nn_index]), \
           "True Class:", np.argmax(Ytest[i])
       # Calculate accuracy
       if np.argmax(Ytrain[nn_index]) == np.argmax(Ytest[i]):
           accuracy += 1./len(Xtest)
   print "Accuracy:", accuracy