using NumSharp;

using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow;

using TensorFlowNET.Examples.Utility;

namespace TensorFlowNET.Examples

{

/// <summary>

/// A nearest neighbor learning algorithm example

/// This example is using the MNIST database of handwritten digits

/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py

/// </summary>

public class NearestNeighbor : Python, IExample

{

public int Priority => 5;

public bool Enabled { get; set; } = true;

public string Name => "Nearest Neighbor";

Datasets mnist;

NDArray Xtr, Ytr, Xte, Yte;

public int? TrainSize = null;

public int ValidationSize = 5000;

public int? TestSize = null;

public bool Run()

{

// tf Graph Input

var xtr = tf.placeholder(tf.float32, new TensorShape(-1, 784));

var xte = tf.placeholder(tf.float32, new TensorShape(784));

// Nearest Neighbor calculation using L1 Distance

// Calculate L1 Distance

var distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices: 1);

// Prediction: Get min distance index (Nearest neighbor)

var pred = tf.arg_min(distance, 0);

float accuracy = 0f;

// Initialize the variables (i.e. assign their default value)

var init = tf.global_variables_initializer();

with(tf.Session(), sess =>

{

// Run the initializer

sess.run(init);

PrepareData();

foreach(int i in range(Xte.shape[0]))

{

// Get nearest neighbor

long nn_index = sess.run(pred, new FeedItem(xtr, Xtr), new FeedItem(xte, Xte[i]));

// Get nearest neighbor class label and compare it to its true label

int index = (int)nn_index;

print($"Test {i} Prediction: {np.argmax(Ytr[(NDArray)index])} True Class: {np.argmax(Yte[i] as NDArray)}");

// Calculate accuracy

if (np.argmax(Ytr[(NDArray)index]) == np.argmax(Yte[i] as NDArray))

accuracy += 1f/ Xte.shape[0];

}

print($"Accuracy: {accuracy}");

});

return accuracy > 0.9;

}

public void PrepareData()

{

mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size:ValidationSize, test_size:TestSize);

// In this example, we limit mnist data

(Xtr, Ytr) = mnist.train.next_batch(TrainSize==null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)

(Xte, Yte) = mnist.test.next_batch(TestSize==null ? 200 : TestSize.Value / 100); // 200 for testing

}

}

}