using System;

using System.Collections.Generic;

using System.IO;

using System.Text;

using Tensorflow.Keras.Utils;

using Tensorflow.NumPy;

using System.Linq;

namespace Tensorflow.Keras.Datasets

{

/// <summary>

/// This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment

/// (positive/negative). Reviews have been preprocessed, and each review is

/// encoded as a list of word indexes(integers).

/// </summary>

public class Imdb

{

string origin_folder = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/";

string file_name = "imdb.npz";

string dest_folder = "imdb";

/// <summary>

/// Loads the [IMDB dataset](https://ai.stanford.edu/~amaas/data/sentiment/).

/// </summary>

/// <param name="path"></param>

/// <param name="num_words"></param>

/// <param name="skip_top"></param>

/// <param name="maxlen"></param>

/// <param name="seed"></param>

/// <param name="start_char"></param>

/// <param name="oov_char"></param>

/// <param name="index_from"></param>

/// <returns></returns>

public DatasetPass load_data(string path = "imdb.npz",

int num_words = -1,

int skip_top = 0,

int maxlen = -1,

int seed = 113,

int start_char = 1,

int oov_char= 2,

int index_from = 3)

{

if (maxlen == -1) throw new InvalidArgumentError("maxlen must be assigned.");

var dst = Download();

var lines = File.ReadAllLines(Path.Combine(dst, "imdb_train.txt"));

var x_train_string = new string[lines.Length];

var y_train = np.zeros(new int[] { lines.Length }, np.int64);

for (int i = 0; i < lines.Length; i++)

{

y_train[i] = long.Parse(lines[i].Substring(0, 1));

x_train_string[i] = lines[i].Substring(2);

}

var x_train = keras.preprocessing.sequence.pad_sequences(PraseData(x_train_string), maxlen: maxlen);

File.ReadAllLines(Path.Combine(dst, "imdb_test.txt"));

var x_test_string = new string[lines.Length];

var y_test = np.zeros(new int[] { lines.Length }, np.int64);

for (int i = 0; i < lines.Length; i++)

{

y_test[i] = long.Parse(lines[i].Substring(0, 1));

x_test_string[i] = lines[i].Substring(2);

}

var x_test = keras.preprocessing.sequence.pad_sequences(PraseData(x_test_string), maxlen: maxlen);

return new DatasetPass

{

Train = (x_train, y_train),

Test = (x_test, y_test)

};

}

(NDArray, NDArray) LoadX(byte[] bytes)

{

var y = np.Load_Npz<byte[]>(bytes);

return (y["x_train.npy"], y["x_test.npy"]);

}

(NDArray, NDArray) LoadY(byte[] bytes)

{

var y = np.Load_Npz<long[]>(bytes);

return (y["y_train.npy"], y["y_test.npy"]);

}

string Download()

{

var dst = Path.Combine(Path.GetTempPath(), dest_folder);

Directory.CreateDirectory(dst);

Web.Download(origin_folder + file_name, dst, file_name);

return dst;

// return Path.Combine(dst, file_name);

}

protected IEnumerable<int[]> PraseData(string[] x)

{

var data_list = new List<int[]>();

for (int i = 0; i < len(x); i++)

{

var list_string = x[i];

var cleaned_list_string = list_string.Replace("[", "").Replace("]", "").Replace(" ", "");

string[] number_strings = cleaned_list_string.Split(',');

int[] numbers = new int[number_strings.Length];

for (int j = 0; j < number_strings.Length; j++)

{

numbers[j] = int.Parse(number_strings[j]);

}

data_list.Add(numbers);

}

return data_list;

}

}

}