using NumSharp;

using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

using System.Text.RegularExpressions;

using System.Threading.Tasks;

namespace Tensorflow.Keras

{

public class Sequence

{

/// <summary>

/// Pads sequences to the same length.

/// https://keras.io/preprocessing/sequence/

/// https://faroit.github.io/keras-docs/1.2.0/preprocessing/sequence/

/// </summary>

/// <param name="sequences">List of lists, where each element is a sequence.</param>

/// <param name="maxlen">Int, maximum length of all sequences.</param>

/// <param name="dtype">Type of the output sequences.</param>

/// <param name="padding">String, 'pre' or 'post':</param>

/// <param name="truncating">String, 'pre' or 'post'</param>

/// <param name="value">Float or String, padding value.</param>

/// <returns></returns>

public NDArray pad_sequences(NDArray sequences,

int? maxlen = null,

string dtype = "int32",

string padding = "pre",

string truncating = "pre",

object value = null)

{

int[] length = new int[sequences.size];

if (maxlen == null)

maxlen = length.Max();

if (value == null)

value = 0f;

var nd = new NDArray(np.int32, new Shape(sequences.size, maxlen.Value));

for (int i = 0; i < nd.shape[0]; i++)

{

switch(sequences[i])

{

default:

throw new NotImplementedException("pad_sequences");

}

}

return nd;

}

}

}