/// <summary>
/// Tests an entire batch of files. See comments atop MainForm.TestBatch_Click().
/// </summary>
/// <param name="subject">Subject identification.</param>
/// <param name="speed">"fast", "medium", or "slow"</param>
/// <param name="categories">A list of gesture categories that each contain lists of prototypes (examples) within that gesture category.</param>
/// <param name="dir">The directory into which to write the output files.</param>
/// <param name="protractor">If true, uses Protractor instead of Golden Section Search.</param>
/// <returns>The two filenames of the output file if successful; null otherwise. The main results are in string[0],
/// while the detailed recognition results are in string[1].</returns>
public string[] TestBatch(string subject, string speed, List <Category> categories, string dir, bool protractor)
{
StreamWriter mw = null; // main results writer
StreamWriter dw = null; // detailed results writer
string[] filenames = new string[2];
try
{
// set up a main results file and detailed results file
int start = Environment.TickCount;
filenames[0] = String.Format("{0}\\$1({1})_main_{2}.txt", dir, protractor ? "protractor" : "gss", start); // main results (small file)
filenames[1] = String.Format("{0}\\$1({1})_nbest_{2}.txt", dir, protractor ? "protractor" : "gss", start); // recognition details (large file)
mw = new StreamWriter(filenames[0], false, Encoding.UTF8);
mw.WriteLine("Subject = {0}, Recognizer = $1, Search = {1}, Speed = {2}, StartTime(ms) = {3}", subject, protractor ? "protractor" : "gss", speed, start);
mw.WriteLine("Subject Recognizer Search Speed NumTraining GestureType RecognitionRate\n");
dw = new StreamWriter(filenames[1], false, Encoding.UTF8);
dw.WriteLine("Subject = {0}, Recognizer = $1, Search = {1}, Speed = {2}, StartTime(ms) = {3}", subject, protractor ? "protractor" : "gss", speed, start);
dw.WriteLine("Correct? NumTrain Tested 1stCorrect Pts Ms Angle : (NBestNames) [NBestScores]\n");
// determine the number of gesture categories and the number of examples in each one
int numCategories = categories.Count;
int numExamples = categories[0].NumExamples;
double totalTests = (numExamples - 1) * NumRandomTests;
// outermost loop: trains on N=1..9, tests on 10-N (for e.g., numExamples = 10)
for (int n = 1; n <= numExamples - 1; n++)
{
// storage for the final avg results for each category for this N
double[] results = new double[numCategories];
// run a number of tests at this particular N number of training examples
for (int r = 0; r < NumRandomTests; r++)
{
_gestures.Clear(); // clear any (old) loaded prototypes
// load (train on) N randomly selected gestures in each category
for (int i = 0; i < numCategories; i++)
{
int[] chosen = RandomEx.Array(0, numExamples - 1, n, true); // select N unique indices
for (int j = 0; j < chosen.Length; j++)
{
Unistroke p = categories[i][chosen[j]]; // get the prototype from this category at chosen[j]
_gestures.Add(p.Name, p); // load the randomly selected test gestures into the recognizer
}
}
// testing loop on all unloaded gestures in each category. creates a recognition
// rate (%) by averaging the binary outcomes (correct, incorrect) for each test.
for (int i = 0; i < numCategories; i++)
{
// pick a random unloaded gesture in this category for testing.
// instead of dumbly picking, first find out what indices aren't
// loaded, and then randomly pick from those.
int[] notLoaded = new int[numExamples - n];
for (int j = 0, k = 0; j < numExamples; j++)
{
Unistroke g = categories[i][j];
if (!_gestures.ContainsKey(g.Name))
{
notLoaded[k++] = j; // jth gesture in categories[i] is not loaded
}
}
int chosen = RandomEx.Integer(0, notLoaded.Length - 1); // index
Unistroke p = categories[i][notLoaded[chosen]]; // gesture to test
Debug.Assert(!_gestures.ContainsKey(p.Name));
// do the recognition!
List <PointF> testPts = GeotrigEx.RotatePoints( // spin gesture randomly
TimePointF.ConvertList(p.RawPoints),
GeotrigEx.Degrees2Radians(RandomEx.Integer(0, 359))
);
NBestList result = this.Recognize(TimePointF.ConvertList(testPts), protractor);
string category = Category.ParseName(result.Name);
int correct = (category == categories[i].Name) ? 1 : 0;
dw.WriteLine("{0} {1} {2} {3} {4} {5} {6:F1}{7} : ({8}) [{9}]",
correct, // Correct?
n, // NumTrain
p.Name, // Tested
FirstCorrect(p.Name, result.Names), // 1stCorrect
p.RawPoints.Count, // Pts
p.Duration, // Ms
Math.Round(result.Angle, 1), (char)176, // Angle tweaking :
result.NamesString, // (NBestNames)
result.ScoresString); // [NBestScores]
results[i] += correct;
}
// provide feedback as to how many tests have been performed thus far.
double testsSoFar = ((n - 1) * NumRandomTests) + r;
//TODO
//ProgressChangedEvent(this, new ProgressEventArgs(testsSoFar / totalTests)); // callback
}
//
// now create the final results for this N and write them to a file
//
for (int i = 0; i < numCategories; i++)
{
results[i] /= (double)NumRandomTests; // normalize by the number of tests at this N
Category c = (Category)categories[i];
// Subject Recognizer Search Speed NumTraining GestureType RecognitionRate
mw.WriteLine("{0} $1 {1} {2} {3} {4} {5:F3}",
subject,
protractor ? "protractor" : "gss",
speed,
n,
c.Name,
Math.Round(results[i], 3)
);
}
}
// time-stamp the end of the processing
int end = Environment.TickCount;
mw.WriteLine("\nEndTime(ms) = {0}, Minutes = {1:F2}", end, Math.Round((end - start) / 60000.0, 2));
dw.WriteLine("\nEndTime(ms) = {0}, Minutes = {1:F2}", end, Math.Round((end - start) / 60000.0, 2));
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
filenames = null;
}
finally
{
if (mw != null)
{
mw.Close();
}
if (dw != null)
{
dw.Close();
}
}
return(filenames);
}
/// <summary>
/// Constructs a FittsSession instance. A FittsSession contains conditions for a Fitts' law study,
/// which, in turn, contain a set of trials. A constructed instance contains a list of conditions
/// in sequence, which themselves contain a list of trials.
/// </summary>
/// <param name="o">The options that configure this session obtained from the OptionsForm dialog.</param>
public SessionData(OptionsForm.Options o)
{
//
// Set the condition variables that define this test.
//
_subject = o.Subject;
_circular = o.Is2D;
_a = o.A;
_w = o.W;
_mtpct = o.MTPct;
_intercept = o.Intercept;
_slope = o.Slope;
_screen = Screen.PrimaryScreen.Bounds;
_conditions = new List <ConditionData>();
//
// Create the order of conditions. Nesting is mt%[a[w]]].
//
int[] a_order, w_order, mt_order;
if (o.Randomize) // randomize the order of conditions
{
a_order = RandomEx.Array(0, o.A.Length - 1, o.A.Length, true);
w_order = RandomEx.Array(0, o.W.Length - 1, o.W.Length, true);
if (o.MTPct != null)
{
mt_order = RandomEx.Array(0, o.MTPct.Length - 1, o.MTPct.Length, true);
while (o.MTPct[mt_order[0]] < StatsEx.Mean(o.MTPct)) // enforce that the first MT% condition is >avg(MT%)
{
mt_order = RandomEx.Array(0, o.MTPct.Length - 1, o.MTPct.Length, true);
}
}
else
{
mt_order = null;
}
}
else // in-order arrays
{
a_order = new int[o.A.Length];
for (int i = 0; i < o.A.Length; i++)
{
a_order[i] = i;
}
w_order = new int[o.W.Length];
for (int i = 0; i < o.W.Length; i++)
{
w_order[i] = i;
}
mt_order = (o.MTPct != null) ? new int[o.MTPct.Length] : null;
for (int i = 0; o.MTPct != null && i < o.MTPct.Length; i++)
{
mt_order[i] = i;
}
}
//
// Create the ordered condition list for the first block of all conditions (i.e., one time through).
//
int n = 0;
for (int i = 0; o.MTPct == null || i < o.MTPct.Length; i++)
{
for (int j = 0; j < o.A.Length; j++)
{
for (int k = 0; k < o.W.Length; k++)
{
double pct = (o.MTPct != null) ? o.MTPct[mt_order[i]] : -1.0; // MT%
double fitts = o.Intercept + o.Slope * Math.Log((double)o.A[a_order[j]] / o.W[w_order[k]] + 1.0, 2.0);
long mt = (o.MTPct != null) ? (long)fitts : -1L; // Fitts' law predicted movement time
ConditionData cd = new ConditionData(0, n++, o.A[a_order[j]], o.W[w_order[k]], pct, mt, o.Trials, o.Practice, o.Is2D);
_conditions.Add(cd);
}
}
if (o.MTPct == null)
{
break;
}
}
//
// Now possibly replicate the created order multiple times.
//
int nConditions = _conditions.Count; // number of conditions in one block
for (int b = 1; b < o.Blocks; b++)
{
for (int c = 0; c < nConditions; c++)
{
ConditionData fx = _conditions[c];
ConditionData fc = new ConditionData(b, c, fx.A, fx.W, fx.MTPct, fx.MTPred, o.Trials, o.Practice, o.Is2D);
_conditions.Add(fc);
}
}
}
