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

# # Nearest Neighbor in TensorFlow
# 
# Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien
# 
# ## Setup
# 
# Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)
# 
# ### Updated for Python 3.6+ releases
# 
# Note: We dont need the seperate input_data.py file as tensorflow.examples has all that built in, which is neat :)

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import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)


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# In this example, we limit mnist data
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xte, Yte = mnist.test.next_batch(200) #200 for testing


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# Reshape images to 1D
Xtr = np.reshape(Xtr, newshape=(-1, 28*28))
Xte = np.reshape(Xte, newshape=(-1, 28*28))


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# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])


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# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)
# Predict: Get min distance index (Nearest neighbor)
pred = tf.argmin(distance, 0)

accuracy = 0.


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# Initializing the variables
init = tf.initialize_all_variables()


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# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # loop over test data
    for i in range(len(Xte)):
        # Get nearest neighbor
        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i,:]})
        # Get nearest neighbor class label and compare it to its true label
        print ("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
              "True Class:", np.argmax(Yte[i]))
        # Calculate accuracy
        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
            accuracy += 1./len(Xte)
    print ("Done!")
    print ("Accuracy:", accuracy)


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accuracy*100


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