public void DecodeTest()
{
#region doc_decode
// In this example, we will reproduce the example on the Viterbi algorithm
// available on Wikipedia: http://en.wikipedia.org/wiki/Viterbi_algorithm
// Create the transition matrix A
double[,] transition =
{
{ 0.7, 0.3 },
{ 0.4, 0.6 }
};
// Create the emission matrix B
double[,] emission =
{
{ 0.1, 0.4, 0.5 },
{ 0.6, 0.3, 0.1 }
};
// Create the initial probabilities pi
double[] initial = { 0.6, 0.4 };
// Create a new hidden Markov model
var hmm = new HiddenMarkovModel(transition, emission, initial);
// After that, one could, for example, query the probability
// of a sequence occurring. We will consider the sequence
int[] sequence = new int[] { 0, 1, 2 };
// And now we will evaluate its likelihood
double logLikelihood = hmm.LogLikelihood(sequence);
// At this point, the log-likelihood of the sequence
// occurring within the model is -3.3928721329161653.
// We can also get the Viterbi path of the sequence
int[] path = hmm.Decode(sequence);
// And the likelihood along the Viterbi path is
double viterbi; hmm.Decode(sequence, out viterbi);
// At this point, the state path will be 1-0-0 and the
// log-likelihood will be -4.3095199438871337
#endregion
Assert.AreEqual(-3.3928721329161653, logLikelihood);
Assert.AreEqual(-4.3095199438871337, viterbi);
Assert.AreEqual(path[0], 1);
Assert.AreEqual(path[1], 0);
Assert.AreEqual(path[2], 0);
}
/// <summary>
/// Gets the probability mass function (pmf) for
/// this distribution evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <returns>
/// The probability of <c>x</c> occurring
/// in the current distribution.
/// </returns>
///
/// <remarks>
/// The Probability Mass Function (PMF) describes the
/// probability that a given value <c>x</c> will occur.
/// </remarks>
///
public override double ProbabilityMassFunction(int[] x)
{
return(Math.Exp(model.LogLikelihood(x)));
}
/// <summary>
/// Gets the probability mass function (pmf) for
/// this distribution evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <returns>
/// The probability of <c>x</c> occurring
/// in the current distribution.
/// </returns>
///
/// <remarks>
/// The Probability Mass Function (PMF) describes the
/// probability that a given value <c>x</c> will occur.
/// </remarks>
///
protected internal override double InnerProbabilityMassFunction(int[] x)
{
return(Math.Exp(model.LogLikelihood(x)));
}
private void Video1_Proccess1()
{
//if (_capture1 != null && _capture1.Ptr != IntPtr.Zero)
//{
int war_at_frame = 0;
bool warning = false;
while (camera_1.frameNum < total_frames1 - 10)
{
//Console.WriteLine(camera_1.frameNum);
if (camera_1.frameNum % 20 == 0)
{
count = 0;
}
abnormal_vote = 0;
normal_vote = 0;
try
{
double[] fe = F_E.extract(vid1, camera_1.frameNum);
if (fe[0] == null || fe[1] == null)
{
fe[0] = 240;
fe[0] = 170;
}
int[] fff = new int[] { (int)fe[0], (int)fe[1] };
//int knn_answer = knn.Decide(fe);
int RF_answer = RF.Decide(fe);
bool LR_answer = LR.Decide(fe);
//bool SVM_answer = SVM.Decide(fe);
int NB_answer = NB.Decide(fff);
double fl1 = HMM.LogLikelihood(fff);
if (chocking || lying)
{
Console.WriteLine(fl1);
if (fl1.CompareTo(-8.3) == 1)
{
hmm_count++;
}
}
else if (violence)
{
if (RF_answer == 1)
{
abnormal_vote += 0.978546619845336;
}
else
{
normal_vote += 0.978546619845336;
}
if (LR_answer)
{
abnormal_vote += 0.8428031393318365;
}
else
{
normal_vote += 0.8428031393318365;
}
if (NB_answer == 1)
{
abnormal_vote += 0.8746569953754341;
}
else
{
normal_vote += 0.8746569953754341;
}
if (abnormal_vote.CompareTo(normal_vote) == 1)
{
count++;
}
}
if (hmm_count >= 2 || count >= 4)
{
if (count >= 4)
{
count = 0;
}
if (hmm_count >= 2)
{
hmm_count = 0;
}
this.pictureBox3.Invoke((MethodInvoker) delegate
{
// Running on the UI thread
pictureBox3.Image = Properties.Resources.warning;
});
if (alarm)
{
wplayer.URL = "D:\\2\\Real-Time Abnormal Event Detection And Tracking In Video\\Alarm.mp3";
wplayer.controls.play();
}
//pictureBox3.Image = Properties.Resources.warning;
warning = true;
war_at_frame = camera_1.frameNum;
Media.Crop_video(vid1, (int)camera_1.frameNum / (fbs + 5), 30);
Media.thumbnail(vid1, (int)camera_1.frameNum / (fbs + 5));
Image image = Image.FromFile(@"D:\2\Real-Time Abnormal Event Detection And Tracking In Video\croped_videos\crop" + Media.num.ToString() + ".jpg");
dataGridView1.Rows.Add(image, @"D:\2\Real-Time Abnormal Event Detection And Tracking In Video\croped_videos\crop" + Media.num.ToString() + ".mpg");
Media.num++;
}
if (warning && camera_1.frameNum >= (war_at_frame + 10))
{
this.pictureBox3.Invoke((MethodInvoker) delegate
{
// Running on the UI thread
pictureBox3.Image = Properties.Resources._checked;
});
//pictureBox3.Image = Properties.Resources._checked;
warning = false;
}
}
catch (Exception e)
{
Console.WriteLine("1--- ", e.Message);
}
}
}
public void LearnTest3()
{
#region doc_learn
// We will create a Hidden Markov Model to detect
// whether a given sequence starts with a zero.
int[][] sequences = new int[][]
{
new int[] { 0, 1, 1, 1, 1, 0, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 0, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
};
// Create a new Hidden Markov Model with 3 states for
// an output alphabet of two characters (zero and one)
var hmm = new HiddenMarkovModel(states: 3, symbols: 2);
// Create the learning algorithm
var teacher = new BaumWelchLearning(hmm)
{
Tolerance = 0.0001, // until log-likelihood changes less than 0.0001
Iterations = 0 // and use as many iterations as needed
};
// Estimate the model
teacher.Learn(sequences);
// Now we can calculate the probability that the given
// sequences originated from the model. We can compute
// those probabilities using the Viterbi algorithm:
double vl1; hmm.Decode(new int[] { 0, 1 }, out vl1); // -0.69317855
double vl2; hmm.Decode(new int[] { 0, 1, 1, 1 }, out vl2); // -2.16644878
// Sequences which do not start with zero have much lesser probability.
double vl3; hmm.Decode(new int[] { 1, 1 }, out vl3); // -11.3580034
double vl4; hmm.Decode(new int[] { 1, 0, 0, 0 }, out vl4); // -38.6759130
// Sequences which contains few errors have higher probability
// than the ones which do not start with zero. This shows some
// of the temporal elasticity and error tolerance of the HMMs.
double vl5; hmm.Decode(new int[] { 0, 1, 0, 1, 1, 1, 1, 1, 1 }, out vl5); // -8.22665
double vl6; hmm.Decode(new int[] { 0, 1, 1, 1, 1, 1, 1, 0, 1 }, out vl6); // -8.22665
// Additionally, we can also compute the probability
// of those sequences using the forward algorithm:
double fl1 = hmm.LogLikelihood(new int[] { 0, 1 }); // -0.000031369
double fl2 = hmm.LogLikelihood(new int[] { 0, 1, 1, 1 }); // -0.087005121
// Sequences which do not start with zero have much lesser probability.
double fl3 = hmm.LogLikelihood(new int[] { 1, 1 }); // -10.66485629
double fl4 = hmm.LogLikelihood(new int[] { 1, 0, 0, 0 }); // -36.61788687
// Sequences which contains few errors have higher probability
// than the ones which do not start with zero. This shows some
// of the temporal elasticity and error tolerance of the HMMs.
double fl5 = hmm.LogLikelihood(new int[] { 0, 1, 0, 1, 1, 1, 1, 1, 1 }); // -3.3744416
double fl6 = hmm.LogLikelihood(new int[] { 0, 1, 1, 1, 1, 1, 1, 0, 1 }); // -3.3744416
#endregion
Assert.AreEqual(-0.69317855044301457, vl1, 1e-4);
Assert.AreEqual(-2.166448784882073, vl2, 1e-4);
Assert.AreEqual(-11.358003471944887, vl3, 1e-4);
Assert.AreEqual(-38.675913006221506, vl4, 1e-4);
Assert.AreEqual(-8.22664996599565, vl5, 1e-4);
Assert.AreEqual(-8.2266499659956516, vl6, 1e-4);
Assert.IsTrue(vl1 > vl3 && vl1 > vl4);
Assert.IsTrue(vl2 > vl3 && vl2 > vl4);
Assert.AreEqual(-0.000031369883069287674, fl1, 1e-4);
Assert.AreEqual(-0.087005121634496585, fl2, 1e-4);
Assert.AreEqual(-10.664856291384941, fl3, 1e-4);
Assert.AreEqual(-36.617886878165528, fl4, 1e-4);
Assert.AreEqual(-3.3744415883604058, fl5, 1e-4);
Assert.AreEqual(-3.3744426259067066, fl6, 1e-4);
}
public void learn_test()
{
#region doc_learn
Accord.Math.Random.Generator.Seed = 0;
// Create continuous sequences. In the sequences below, there
// seems to be two states, one for values between 0 and 1 and
// another for values between 5 and 7. The states seems to be
// switched on every observation.
double[][] sequences = new double[][]
{
new double[] { 0.1, 5.2, 0.3, 6.7, 0.1, 6.0 },
new double[] { 0.2, 6.2, 0.3, 6.3, 0.1, 5.0 },
new double[] { 0.1, 7.0, 0.1, 7.0, 0.2, 5.6 },
};
// Specify a initial normal distribution
var density = new NormalDistribution();
// Create a continuous hidden Markov Model with two states organized in a forward
// topology and an underlying univariate Normal distribution as probability density.
var model = new HiddenMarkovModel <NormalDistribution, double>(new Forward(2), density);
// Configure the learning algorithms to train the sequence classifier until the
// difference in the average log-likelihood changes only by as little as 0.0001
var teacher = new ViterbiLearning <NormalDistribution, double>(model)
{
Tolerance = 0.0001,
Iterations = 0,
};
// Fit the model
teacher.Learn(sequences);
// See the probability of the sequences learned
double a1 = model.LogLikelihood(new[] { 0.1, 5.2, 0.3, 6.7, 0.1, 6.0 }); // log(0.40)
double a2 = model.LogLikelihood(new[] { 0.2, 6.2, 0.3, 6.3, 0.1, 5.0 }); // log(0.46)
// See the probability of an unrelated sequence
double a3 = model.LogLikelihood(new[] { 1.1, 2.2, 1.3, 3.2, 4.2, 1.0 }); // log(1.42)
#endregion
a1 = Math.Exp(a1);
a2 = Math.Exp(a2);
a3 = Math.Exp(a3);
Assert.AreEqual(0.4048936808991913, a1, 1e-10);
Assert.AreEqual(0.4656014344844673, a2, 1e-10);
Assert.AreEqual(1.4232710878429383E-48, a3, 1e-10);
Assert.AreEqual(2, model.Emissions.Length);
var state1 = (model.Emissions[0] as NormalDistribution);
var state2 = (model.Emissions[1] as NormalDistribution);
Assert.AreEqual(0.16666666666666, state1.Mean, 1e-10);
Assert.AreEqual(6.11111111111111, state2.Mean, 1e-10);
Assert.IsFalse(Double.IsNaN(state1.Mean));
Assert.IsFalse(Double.IsNaN(state2.Mean));
Assert.AreEqual(0.007499999999999, state1.Variance, 1e-10);
Assert.AreEqual(0.538611111111111, state2.Variance, 1e-10);
Assert.IsFalse(Double.IsNaN(state1.Variance));
Assert.IsFalse(Double.IsNaN(state2.Variance));
Assert.AreEqual(2, model.LogTransitions.GetLength(0));
Assert.AreEqual(2, model.LogTransitions.Columns());
var A = model.LogTransitions.Exp();
Assert.AreEqual(0.090, A[0][0], 1e-3);
Assert.AreEqual(0.909, A[0][1], 1e-3);
Assert.AreEqual(0.875, A[1][0], 1e-3);
Assert.AreEqual(0.125, A[1][1], 1e-3);
Assert.IsFalse(A.HasNaN());
}
