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- using NumSharp.Core;
- using System;
- using System.Collections.Generic;
- using System.IO;
- using System.Linq;
- using System.Text;
- using System.Text.RegularExpressions;
-
- namespace TensorFlowNET.Examples.CnnTextClassification
- {
- public class DataHelpers
- {
- /// <summary>
- /// Loads MR polarity data from files, splits the data into words and generates labels.
- /// Returns split sentences and labels.
- /// </summary>
- /// <param name="positive_data_file"></param>
- /// <param name="negative_data_file"></param>
- /// <returns></returns>
- public static (string[], NDArray) load_data_and_labels(string positive_data_file, string negative_data_file)
- {
- Directory.CreateDirectory("CnnTextClassification");
- Utility.Web.Download(positive_data_file, "CnnTextClassification/rt-polarity.pos");
- Utility.Web.Download(negative_data_file, "CnnTextClassification/rt-polarity.neg");
-
- // Load data from files
- var positive_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.pos")
- .Select(x => x.Trim())
- .ToArray();
-
- var negative_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.neg")
- .Select(x => x.Trim())
- .ToArray();
-
- var x_text = new List<string>();
- x_text.AddRange(positive_examples);
- x_text.AddRange(negative_examples);
- x_text = x_text.Select(x => clean_str(x)).ToList();
-
- var positive_labels = positive_examples.Select(x => new int[2] { 0, 1 }).ToArray();
- var negative_labels = negative_examples.Select(x => new int[2] { 1, 0 }).ToArray();
- var y = np.concatenate(new int[][][] { positive_labels, negative_labels });
- return (x_text.ToArray(), y);
- }
-
- private static string clean_str(string str)
- {
- str = Regex.Replace(str, @"[^A-Za-z0-9(),!?\'\`]", " ");
- str = Regex.Replace(str, @"\'s", " \'s");
- return str;
- }
- }
- }
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