private string GetExternalPage(Post post)
{
WebClient client = new WebClient();
string htmlCode = client.DownloadString(post.Url);
return htmlCode;
}
private bool IsLinkOrImage(Post post)
{
if(post.IsSelf)
return false;
return true;
}
internal static string ToJson(Post post)
{
return JsonConvert.SerializeObject(post, Formatting.Indented);
}
/// <summary>
///
/// </summary>
/// <param name="session"></param>
/// <param name="post"></param>
/// <see cref="https://github.com/reddit/reddit/wiki/API"/>
public static void Submit(Session session, Post post, PostKind kind)
{
if (string.IsNullOrEmpty((kind == PostKind.Link ? post.Url : post.SelfText)))
throw new Exception("No link or self text added to the new post");
if (string.IsNullOrEmpty(post.SubReddit))
throw new Exception("No subreddit set");
if (string.IsNullOrEmpty(post.Title))
throw new Exception("No title provided");
var request = new Request
{
Url = "http://www.reddit.com/api/submit",
Method = "POST",
Cookie = session.Cookie,
Content = "uh=" + session.ModHash +
"&kind=" + (kind == PostKind.Link ? "link" : "self") +
"&url=" + (kind == PostKind.Link ? post.Url : post.SelfText) +
"&sr=" + post.SubReddit +
"&title=" + post.Title +
"&r=" + post.SubReddit +
"&renderstyle=html"
};
var json = string.Empty;
if (request.Execute(out json) != System.Net.HttpStatusCode.OK)
throw new Exception(json);
var o = JObject.Parse(json);
// Capcha
// o["jquery"][10][3].ToString()
// Error Message
// o["jquery"][12][3].ToString()
}
private void TestEmgu(Post post)
{
int K = 10;
//int trainSampleCount = 100;
int trainSampleCount = this.vectorTable[0].Length - 1;
int trainSampleColumns = this.vectorTable.Length - 2; //subtract two columns for the post id and IsImage
int scalingRatio = 10;
#region Generate the traning data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleColumns, trainSampleCount);
Matrix<float> trainClasses = new Matrix<float>(trainSampleColumns, 1);
Image<Bgr, Byte> img = new Image<Bgr, byte>(trainSampleCount, trainSampleCount);
Matrix<float> sample = new Matrix<float>(1, trainSampleCount);
for (int y = 1; y < this.vectorTable[0].Length - 1; y++) {
for (int x = 2; x < this.vectorTable.Length - 1; x++) {
trainData.Data.SetValue(Int32.Parse(this.vectorTable[x][y]) * scalingRatio, x - 2, y - 1);
}
}
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleColumns >> 1, 1);
//trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleColumns >> 1, trainSampleColumns, 1);
//trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix<float> results, neighborResponses;
results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);
KNearest knn = new KNearest(trainData, trainClasses, null, false, K);
for (int i = 0; i < img.Height; i++) {
for (int j = 0; j < img.Width; j++) {
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
//Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
// estimates the response and get the neighbors' labels
float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
int accuracy = 0;
// compute the number of neighbors representing the majority
for (int k = 0; k < K; k++) {
if (neighborResponses.Data[0, k] == response)
accuracy++;
}
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] =
response == 1 ?
(accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
(accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 90, 0));
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++) {
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
}
double poolSize = 2, threshold = 0.5, du;
du = Utilities.PrecisionUsingCosineSimilarity(WordCountTable, post, poolSize, threshold);
PointF newpoint = new PointF((float)du, (float)du);
img.Draw(new CircleF(newpoint, 2.0f), new Bgr(255, 0, 0), -1);
//Emgu.CV.UI.ImageViewer.Show(img);
//Emgu.CV.UI.ImageViewer imgviewer = new Emgu.CV.UI.ImageViewer(img);
//imgviewer.Show();
this.imageBox1.DisplayedImage = img;
//this.VisImg = new Emgu.CV.UI.ImageViewer(img);
}
private void CompareNewPost(Post post)
{
List<Post> postList = new List<Post>();
postList.Add(post);
string[][] wordCountTable = Utilities.GenerateCountVectorTable(postList);
}
