using NumSharp;

using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow;

namespace TensorFlowNET.Examples.Utility

{

public class DataSet

{

private int _num_examples;

public int num_examples => _num_examples;

private int _epochs_completed;

public int epochs_completed => _epochs_completed;

private int _index_in_epoch;

public int index_in_epoch => _index_in_epoch;

private NDArray _images;

public NDArray images => _images;

private NDArray _labels;

public NDArray labels => _labels;

public DataSet(NDArray images, NDArray labels, TF_DataType dtype, bool reshape)

{

_num_examples = images.shape[0];

images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]);

images.astype(dtype.as_numpy_datatype());

images = np.multiply(images, 1.0f / 255.0f);

labels.astype(dtype.as_numpy_datatype());

_images = images;

_labels = labels;

_epochs_completed = 0;

_index_in_epoch = 0;

}

public (NDArray, NDArray) next_batch(int batch_size, bool fake_data = false, bool shuffle = true)

{

var start = _index_in_epoch;

// Shuffle for the first epoch

if(_epochs_completed == 0 && start == 0 && shuffle)

{

var perm0 = np.arange(_num_examples);

np.random.shuffle(perm0);

_images = images[perm0];

_labels = labels[perm0];

}

// Go to the next epoch

if (start + batch_size > _num_examples)

{

// Finished epoch

_epochs_completed += 1;

// Get the rest examples in this epoch

var rest_num_examples = _num_examples - start;

//var images_rest_part = _images[np.arange(start, _num_examples)];

//var labels_rest_part = _labels[np.arange(start, _num_examples)];

// Shuffle the data

if (shuffle)

{

var perm = np.arange(_num_examples);

np.random.shuffle(perm);

_images = images[perm];

_labels = labels[perm];

}

start = 0;

_index_in_epoch = batch_size - rest_num_examples;

var end = _index_in_epoch;

var images_new_part = _images[np.arange(start, end)];

var labels_new_part = _labels[np.arange(start, end)];

/*return (np.concatenate(new float[][] { images_rest_part.Data<float>(), images_new_part.Data<float>() }, axis: 0),

np.concatenate(new float[][] { labels_rest_part.Data<float>(), labels_new_part.Data<float>() }, axis: 0));*/

return (images_new_part, labels_new_part);

}

else

{

_index_in_epoch += batch_size;

var end = _index_in_epoch;

return (_images[np.arange(start, end)], _labels[np.arange(start, end)]);

}

}

}

}