public MulticlassSupportVectorMachine<Polynomial> v3_1_0()
{
ksvm = new MulticlassSupportVectorMachine<Polynomial>(
inputs: 2, kernel: new Polynomial(2), classes: 10);
smo = new MulticlassSupportVectorLearning<Polynomial>()
{
Model = ksvm
};
smo.Learn(problem.Training.Inputs, problem.Testing.Output);
return ksvm;
}
public void SparseLinearTest()
{
MulticlassSupportVectorMachine<Linear> svm1;
MulticlassSupportVectorMachine<Linear, Sparse<double>> svm2;
{
Accord.Math.Random.Generator.Seed = 0;
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.iris_scale));
var reader = new SparseReader(file, Encoding.Default);
var samples = reader.ReadDenseToEnd();
double[][] x = samples.Item1;
int[] y = samples.Item2.ToMulticlass();
var learner = new MulticlassSupportVectorLearning<Linear>()
{
Learner = (p) => new LinearDualCoordinateDescent<Linear>()
};
svm1 = learner.Learn(x, y);
}
{
Accord.Math.Random.Generator.Seed = 0;
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.iris_scale));
// Create a new Sparse Sample Reader to read any given file,
// passing the correct dense sample size in the constructor
var reader = new SparseReader(file, Encoding.Default);
var samples = reader.ReadSparseToEnd();
Sparse<double>[] x = samples.Item1;
int[] y = samples.Item2.ToMulticlass();
var learner = new MulticlassSupportVectorLearning<Linear, Sparse<double>>()
{
Learner = (p) => new LinearDualCoordinateDescent<Linear, Sparse<double>>()
};
svm2 = learner.Learn(x, y);
}
Assert.AreEqual(svm1.Models.Length, svm2.Models.Length);
for (int i = 0; i < svm1.Models.Length; i++)
{
var ma = svm1[i].Value;
var mb = svm2[i].Value;
Assert.IsTrue(ma.Weights.IsEqual(mb.Weights));
Assert.AreEqual(ma.SupportVectors.Length, mb.SupportVectors.Length);
for (int j = 0; j < ma.SupportVectors.Length; j++)
{
double[] expected = ma.SupportVectors[j];
double[] actual = mb.SupportVectors[j].ToDense(4);
Assert.IsTrue(expected.IsEqual(actual, 1e-5));
}
}
}
public void multiclass_precomputed_matrix_smo()
{
#region doc_precomputed
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
double[][] trainInputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] trainOutputs = // those are the training set class labels
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// Let's chose a kernel function
Polynomial kernel = new Polynomial(2);
// Get the kernel matrix for the training set
double[][] K = kernel.ToJagged(trainInputs);
// Create a pre-computed kernel
var pre = new Precomputed(K);
// Create a one-vs-one learning algorithm using SMO
var teacher = new MulticlassSupportVectorLearning<Precomputed, int>()
{
Learner = (p) => new SequentialMinimalOptimization<Precomputed, int>()
{
Kernel = pre
}
};
#if DEBUG
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
// Learn a machine
var machine = teacher.Learn(pre.Indices, trainOutputs);
// Compute the machine's prediction for the training set
int[] trainPrediction = machine.Decide(pre.Indices);
// Evaluate prediction error for the training set using mean accuracy (mAcc)
double trainingError = new ZeroOneLoss(trainOutputs).Loss(trainPrediction);
// Now let's compute the machine's prediction for a test set
double[][] testInputs = // test-set inputs
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
};
int[] testOutputs = // those are the test set class labels
{
0, 0, 1, 2,
};
// Compute precomputed matrix between train and testing
pre.Values = kernel.ToJagged2(trainInputs, testInputs);
// Update the kernel
machine.Kernel = pre;
// Compute the machine's prediction for the test set
int[] testPrediction = machine.Decide(pre.Indices);
// Evaluate prediction error for the training set using mean accuracy (mAcc)
double testError = new ZeroOneLoss(testOutputs).Loss(testPrediction);
#endregion
Assert.AreEqual(0, trainingError);
Assert.AreEqual(0, testError);
// Create a one-vs-one learning algorithm using SMO
var teacher2 = new MulticlassSupportVectorLearning<Polynomial>()
{
Learner = (p) => new SequentialMinimalOptimization<Polynomial>()
{
Kernel = kernel
}
};
#if DEBUG
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
// Learn a machine
var expected = teacher2.Learn(trainInputs, trainOutputs);
Assert.AreEqual(4, expected.NumberOfInputs);
Assert.AreEqual(3, expected.NumberOfOutputs);
Assert.AreEqual(0, machine.NumberOfInputs);
Assert.AreEqual(3, machine.NumberOfOutputs);
var machines = Enumerable.Zip(machine, expected, (a,b) => Tuple.Create(a.Value, b.Value));
foreach (var pair in machines)
{
var a = pair.Item1;
var e = pair.Item2;
Assert.AreEqual(0, a.NumberOfInputs);
Assert.AreEqual(2, a.NumberOfOutputs);
Assert.AreEqual(4, e.NumberOfInputs);
Assert.AreEqual(2, e.NumberOfOutputs);
Assert.IsTrue(a.Weights.IsEqual(e.Weights));
}
}
public void learn_test()
{
#region doc_learn
// Generate always same random numbers
Accord.Math.Random.Generator.Seed = 0;
// The following is a simple auto association function in which
// the last column of each input correspond to its own class. This
// problem should be easily solved using a Linear kernel.
// Sample input data
double[][] inputs =
{
new double[] { 1, 2, 0 },
new double[] { 6, 2, 3 },
new double[] { 1, 1, 1 },
new double[] { 7, 6, 2 },
};
// Output for each of the inputs
int[] outputs = { 0, 3, 1, 2 };
// Create the multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning<Linear>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization<Linear>()
{
// If you would like to use other kernels, simply replace
// the generic parameter to the desired kernel class, such
// as for example, Polynomial or Gaussian:
Kernel = new Linear() // use the Linear kernel
}
};
// Estimate the multi-class support vector machine using one-vs-one method
MulticlassSupportVectorMachine<Linear> ovo = teacher.Learn(inputs, outputs);
// Obtain class predictions for each sample
int[] predicted = ovo.Decide(inputs);
// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);
#endregion
Assert.AreEqual(0, error);
Assert.IsTrue(predicted.IsEqual(outputs));
Assert.IsTrue(ovo.Scores(inputs[0]).IsEqual(new double[] { 0.62, -0.25, -0.59, -0.62 }, 1e-2));
Assert.IsTrue(ovo.Scores(inputs[1]).IsEqual(new double[] { -0.62, -0.57, -0.13, 0.62 }, 1e-2));
Assert.IsTrue(ovo.Scores(inputs[2]).IsEqual(new double[] { -0.25, 0.63, -0.63, -0.51 }, 1e-2));
}
public void multiclass_calibration_generic_kernel()
{
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
double[][] inputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] outputs = // those are the class labels
{
0, 0, 0, 0, 0,
1, 1, 1,
2, 2, 2, 2,
};
// Create the multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning<IKernel>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization<IKernel>()
{
UseKernelEstimation = false,
Kernel = Gaussian.FromGamma(0.5)
}
};
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
// Create the multi-class learning algorithm for the machine
var calibration = new MulticlassSupportVectorLearning<IKernel>(machine)
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new ProbabilisticOutputCalibration<IKernel>(param.Model)
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(inputs, outputs);
// Obtain class predictions for each sample
int[] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[] scores = machine.Score(inputs);
// Get log-likelihoods (should be same as scores)
double[][] logl = machine.LogLikelihoods(inputs);
// Get probability for each sample
double[][] prob = machine.Probabilities(inputs);
// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
//string str = logl.ToCSharp();
double[] expectedScores =
{
1.87436400885238, 1.81168086449304, 1.74038320983522,
1.87436400885238, 1.81168086449304, 1.55446926953952,
1.67016543853596, 1.67016543853596, 1.83135194001403,
1.83135194001403, 1.59836868669125, 2.0618816310294
};
double[][] expectedLogL =
{
new double[] { 1.87436400885238, -1.87436400885238, -1.7463646841257 },
new double[] { 1.81168086449304, -1.81168086449304, -1.73142460658826 },
new double[] { 1.74038320983522, -1.58848669816072, -1.74038320983522 },
new double[] { 1.87436400885238, -1.87436400885238, -1.7463646841257 },
new double[] { 1.81168086449304, -1.81168086449304, -1.73142460658826 },
new double[] { -1.55446926953952, 1.55446926953952, -0.573599079216229 },
new double[] { -0.368823000428743, 1.67016543853596, -1.67016543853596 },
new double[] { -0.368823000428743, 1.67016543853596, -1.67016543853596 },
new double[] { -1.83135194001403, -1.20039293330558, 1.83135194001403 },
new double[] { -1.83135194001403, -1.20039293330558, 1.83135194001403 },
new double[] { -0.894598978116595, -1.59836868669125, 1.59836868669125 },
new double[] { -1.87336852014759, -2.0618816310294, 2.0618816310294 }
};
double[][] expectedProbs =
{
new double[] { 0.95209908906855, 0.0224197237689656, 0.0254811871624848 },
new double[] { 0.947314032745205, 0.0252864560196241, 0.0273995112351714 },
new double[] { 0.937543314993345, 0.0335955309754816, 0.028861154031173 },
new double[] { 0.95209908906855, 0.0224197237689656, 0.0254811871624848 },
new double[] { 0.947314032745205, 0.0252864560196241, 0.0273995112351714 },
new double[] { 0.0383670466237636, 0.859316640577158, 0.102316312799079 },
new double[] { 0.111669460983068, 0.857937888238824, 0.0303926507781076 },
new double[] { 0.111669460983068, 0.857937888238824, 0.0303926507781076 },
new double[] { 0.0238971617859334, 0.0449126146360623, 0.931190223578004 },
new double[] { 0.0238971617859334, 0.0449126146360623, 0.931190223578004 },
new double[] { 0.0735735561383806, 0.0363980776342206, 0.890028366227399 },
new double[] { 0.0188668069460003, 0.0156252941482294, 0.96550789890577 }
};
// Must be exactly the same as test above
Assert.AreEqual(0, error);
Assert.AreEqual(0.5, ((Gaussian)machine[0].Value.Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[1].Value.Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[2].Value.Kernel).Gamma);
Assert.AreEqual(1.0231652126930515, loss);
Assert.IsTrue(predicted.IsEqual(outputs));
Assert.IsTrue(expectedScores.IsEqual(scores, 1e-10));
Assert.IsTrue(expectedLogL.IsEqual(logl, 1e-10));
Assert.IsTrue(expectedProbs.IsEqual(prob, 1e-10));
}
public void multiclass_linear_smo_new_usage()
{
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
double[][] inputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] outputs = // those are the class labels
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// Create a one-vs-one learning algorithm using LIBLINEAR's L2-loss SVC dual
var teacher = new MulticlassSupportVectorLearning<Linear>();
teacher.Learner = (p) => new SequentialMinimalOptimization<Linear>()
{
UseComplexityHeuristic = true
};
#if DEBUG
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
for (int i = 0; i < inputs.Length; i++)
{
double actual = machine.Decide(inputs[i]);
double expected = outputs[i];
Assert.AreEqual(expected, actual);
}
}
public void multiclass_linear_new_usage()
{
#region doc_learn_ldcd
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
double[][] inputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] outputs = // those are the class labels
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// Create a one-vs-one multi-class SVM learning algorithm
var teacher = new MulticlassSupportVectorLearning<Linear>()
{
// using LIBLINEAR's L2-loss SVC dual for each SVM
Learner = (p) => new LinearDualCoordinateDescent()
{
Loss = Loss.L2
}
};
// Configure parallel execution options
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
// Obtain class predictions for each sample
int[] predicted = machine.Decide(inputs);
// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);
#endregion
Assert.AreEqual(0, error);
Assert.IsTrue(predicted.IsEqual(outputs));
}
public void multiclass_gaussian_new_usage()
{
#region doc_learn_gaussian
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
double[][] inputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] outputs = // those are the class labels
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// Create the multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning<Gaussian>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization<Gaussian>()
{
// Estimate a suitable guess for the Gaussian kernel's parameters.
// This estimate can serve as a starting point for a grid search.
UseKernelEstimation = true
}
};
// Configure parallel execution options
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
// Obtain class predictions for each sample
int[] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[] scores = machine.Score(inputs);
// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);
#endregion
// Get log-likelihoods (should be same as scores)
double[][] logl = machine.LogLikelihoods(inputs);
// Get probability for each sample
double[][] prob = machine.Probabilities(inputs);
// Compute classification error
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
string str = scores.ToCSharp();
double[] expectedScores =
{
1.00888999727541, 1.00303259868784, 1.00068403386636, 1.00888999727541,
1.00303259868784, 1.00831890183328, 1.00831890183328, 0.843757409449037,
0.996768862332386, 0.996768862332386, 1.02627325826713, 1.00303259868784,
0.996967401312164, 0.961947708617365, 1.02627325826713
};
double[][] expectedLogL =
{
new double[] { 1.00888999727541, -1.00888999727541, -1.00135670089335 },
new double[] { 1.00303259868784, -0.991681098166717, -1.00303259868784 },
new double[] { 1.00068403386636, -0.54983354268499, -1.00068403386636 },
new double[] { 1.00888999727541, -1.00888999727541, -1.00135670089335 },
new double[] { 1.00303259868784, -0.991681098166717, -1.00303259868784 },
new double[] { -1.00831890183328, 1.00831890183328, -0.0542719287771535 },
new double[] { -1.00831890183328, 1.00831890183328, -0.0542719287771535 },
new double[] { -0.843757409449037, 0.843757409449037, -0.787899083913034 },
new double[] { -0.178272229157676, 0.996768862332386, -0.996768862332386 },
new double[] { -0.178272229157676, 0.996768862332386, -0.996768862332386 },
new double[] { -1.02627325826713, -1.00323113766761, 1.02627325826713 },
new double[] { -1.00303259868784, -0.38657999872922, 1.00303259868784 },
new double[] { -0.996967401312164, -0.38657999872922, 0.996967401312164 },
new double[] { -0.479189991343958, -0.961947708617365, 0.961947708617365 },
new double[] { -1.02627325826713, -1.00323113766761, 1.02627325826713 }
};
double[][] expectedProbs =
{
new double[] { 0.789324598208647, 0.104940932711551, 0.105734469079803 },
new double[] { 0.78704862182644, 0.107080012017624, 0.105871366155937 },
new double[] { 0.74223157627093, 0.157455631737191, 0.100312791991879 },
new double[] { 0.789324598208647, 0.104940932711551, 0.105734469079803 },
new double[] { 0.78704862182644, 0.107080012017624, 0.105871366155937 },
new double[] { 0.0900153422818135, 0.676287261796794, 0.233697395921392 },
new double[] { 0.0900153422818135, 0.676287261796794, 0.233697395921392 },
new double[] { 0.133985810363445, 0.72433118122885, 0.141683008407705 },
new double[] { 0.213703968297751, 0.692032433073136, 0.0942635986291124 },
new double[] { 0.213703968297751, 0.692032433073136, 0.0942635986291124 },
new double[] { 0.10192623206507, 0.104302095948601, 0.79377167198633 },
new double[] { 0.0972161784678357, 0.180077937396817, 0.722705884135347 },
new double[] { 0.0981785890979593, 0.180760971768703, 0.721060439133338 },
new double[] { 0.171157270099157, 0.105617610634377, 0.723225119266465 },
new double[] { 0.10192623206507, 0.104302095948601, 0.79377167198633 }
};
Assert.AreEqual(0, error);
Assert.AreEqual(4.5289447815997672, loss, 1e-10);
Assert.IsTrue(predicted.IsEqual(outputs));
Assert.IsTrue(expectedScores.IsEqual(scores, 1e-10));
Assert.IsTrue(expectedLogL.IsEqual(logl, 1e-10));
Assert.IsTrue(expectedProbs.IsEqual(prob, 1e-10));
}
public void kaggle_digits_with_compress()
{
string root = Environment.CurrentDirectory;
var training = Properties.Resources.trainingsample;
var validation = Properties.Resources.validationsample;
var tset = readData(training);
var observations = tset.Item1;
var labels = tset.Item2;
var teacher = new MulticlassSupportVectorLearning<Linear>();
var svm = teacher.Learn(observations, labels);
Assert.AreEqual(50, svm.Models[0][0].SupportVectors.Length);
Assert.AreEqual(127, svm.Models[1][0].SupportVectors.Length);
svm.Compress();
Assert.AreEqual(1, svm.Models[0][0].SupportVectors.Length);
Assert.AreEqual(1, svm.Models[1][0].SupportVectors.Length);
{
var trainingLoss = new ZeroOneLoss(labels)
{
Mean = true
};
double error = trainingLoss.Loss(svm.Decide(observations));
Assert.AreEqual(0.054, error);
}
{
var vset = readData(validation);
var validationData = vset.Item1;
var validationLabels = vset.Item2;
var validationLoss = new ZeroOneLoss(validationLabels)
{
Mean = true
};
double val = validationLoss.Loss(svm.Decide(validationData));
Assert.AreEqual(0.082, val);
}
}
public void multiclass_new_usage_method_polynomial()
{
double[][] inputs =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] outputs =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
var learner = new MulticlassSupportVectorLearning<Polynomial>()
{
Learner = (p) => new SequentialMinimalOptimization<Polynomial>()
{
Model = p.Model,
Complexity = 1,
Kernel = new Polynomial(2)
}
};
learner.ParallelOptions.MaxDegreeOfParallelism = 1;
MulticlassSupportVectorMachine<Polynomial> msvm = learner.Learn(inputs, outputs);
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
int[] evals = { 8, 8, 7, 7, 7, 7, 6, 6 };
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
msvm.Method = MulticlassComputeMethod.Elimination;
double actual = msvm.Decide(inputs[i]);
Assert.AreEqual(expected, actual);
Assert.AreEqual(evals[i], msvm.GetLastKernelEvaluations());
}
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
msvm.Method = MulticlassComputeMethod.Voting;
double actual = msvm.Decide(inputs[i]);
Assert.AreEqual(expected, actual);
Assert.AreEqual(8, msvm.GetLastKernelEvaluations());
}
}
public void multiclass_new_usage_method_linear()
{
double[][] inputs =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] outputs =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
var learner = new MulticlassSupportVectorLearning<Linear>();
learner.Learner = (_) => new LinearCoordinateDescent()
{
Complexity = 1
};
learner.ParallelOptions.MaxDegreeOfParallelism = 1;
MulticlassSupportVectorMachine<Linear> msvm = learner.Learn(inputs, outputs);
// Linear machines in compact form do not require kernel evaluations
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
msvm.Method = MulticlassComputeMethod.Elimination;
double actual = msvm.Decide(inputs[i]);
Assert.AreEqual(expected, actual);
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
}
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
msvm.Method = MulticlassComputeMethod.Voting;
double actual = msvm.Decide(inputs[i]);
Assert.AreEqual(expected, actual);
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
}
}
/// <summary>
/// Creates a Support Vector Machine and estimate
/// its parameters using a learning algorithm.
/// </summary>
///
private void btnRunTraining_Click(object sender, EventArgs e)
{
if (dgvTrainingSource.Rows.Count == 0)
{
MessageBox.Show("Please load the training data before clicking this button");
return;
}
lbStatus.Text = "Gathering data. This may take a while...";
Application.DoEvents();
// Extract inputs and outputs
int rows = dgvTrainingSource.Rows.Count;
double[][] input = new double[rows][];
int[] output = new int[rows];
for (int i = 0; i < rows; i++)
{
input[i] = (double[])dgvTrainingSource.Rows[i].Cells["colTrainingFeatures"].Value;
output[i] = (int)dgvTrainingSource.Rows[i].Cells["colTrainingLabel"].Value;
}
// Create the chosen kernel function
// using the user interface parameters
//
IKernel kernel = createKernel();
// Extract training parameters from the interface
double complexity = (double)numComplexity.Value;
double tolerance = (double)numTolerance.Value;
int cacheSize = (int)numCache.Value;
SelectionStrategy strategy = (SelectionStrategy)cbStrategy.SelectedItem;
// Create the learning algorithm using the machine and the training data
var ml = new MulticlassSupportVectorLearning<IKernel>()
{
// Configure the learning algorithm
Learner = (param) => new SequentialMinimalOptimization<IKernel>()
{
Complexity = complexity,
Tolerance = tolerance,
CacheSize = cacheSize,
Strategy = strategy,
Kernel = kernel
}
};
lbStatus.Text = "Training the classifiers. This may take a (very) significant amount of time...";
Application.DoEvents();
Stopwatch sw = Stopwatch.StartNew();
// Train the machines. It should take a while.
ksvm = ml.Learn(input, output);
// If we created a linear machine, compress the support vectors
// into one single parameter vector for increased performance:
if (ksvm.Kernel is Linear)
{
ksvm.Compress();
}
sw.Stop();
double error = new ZeroOneLoss(output)
{
Mean = true
}.Loss(ksvm.Decide(input));
lbStatus.Text = String.Format(
"Training complete ({0}ms, {1}er). Click Classify to test the classifiers.",
sw.ElapsedMilliseconds, error);
// Update the interface status
btnClassifyVoting.Enabled = true;
btnClassifyElimination.Enabled = true;
btnCalibration.Enabled = true;
// Populate the information tab with the machines
dgvMachines.Rows.Clear();
int k = 1;
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < i; j++, k++)
{
var machine = ksvm[i, j];
int sv = machine.SupportVectors == null ? 0 : machine.SupportVectors.Length;
int c = dgvMachines.Rows.Add(k, i + "-vs-" + j, sv, machine.Threshold);
dgvMachines.Rows[c].Tag = machine;
}
}
// approximate size in bytes =
// number of support vectors * number of doubles in a support vector * size of double
int bytes = ksvm.SupportVectorUniqueCount * 1024 * sizeof(double);
float megabytes = bytes / (1024 * 1024);
lbSize.Text = String.Format("{0} ({1} MB)", ksvm.SupportVectorUniqueCount, megabytes);
}
/// <summary>
/// Calibrates the current Support Vector Machine to produce
/// probabilistic outputs using ProbabilisticOutputLearning.
/// </summary>
///
private void btnRunCalibration_Click(object sender, EventArgs e)
{
if (ksvm == null)
{
MessageBox.Show("Please train the machines first.");
return;
}
// Extract inputs and outputs
int rows = dgvTrainingSource.Rows.Count;
double[][] input = new double[rows][];
int[] output = new int[rows];
for (int i = 0; i < rows; i++)
{
input[i] = (double[])dgvTrainingSource.Rows[i].Cells["colTrainingFeatures"].Value;
output[i] = (int)dgvTrainingSource.Rows[i].Cells["colTrainingLabel"].Value;
}
// Create the calibration algorithm using the training data
var ml = new MulticlassSupportVectorLearning<IKernel>()
{
Model = ksvm,
// Configure the calibration algorithm
Learner = (p) => new ProbabilisticOutputCalibration<IKernel>()
{
Model = p.Model
}
};
lbStatus.Text = "Calibrating the classifiers. This may take a (very) significant amount of time...";
Application.DoEvents();
Stopwatch sw = Stopwatch.StartNew();
// Train the machines. It should take a while.
ml.Learn(input, output);
sw.Stop();
double error = new ZeroOneLoss(output).Loss(ksvm.Decide(input));
lbStatus.Text = String.Format(
"Calibration complete ({0}ms, {1}er). Click Classify to test the classifiers.",
sw.ElapsedMilliseconds, error);
btnClassifyVoting.Enabled = true;
}