public void multilabelSVM()
{
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
}
};
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
// Create the multi-class learning algorithm for the machine
var calibration = new MulticlassSupportVectorLearning <Gaussian>()
{
Model = machine, // We will start with an existing machine
// Configure the learning algorithm to use Platt's calibration
Learner = (param) => new ProbabilisticOutputCalibration <Gaussian>()
{
Model = param.Model // Start with an existing machine
}
};
// 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);
message += "SVM Validacja\n";
message += "error " + error.ToString() + "\n";
message += "loss " + loss.ToString() + "\n\n";
}
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_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 multilabel_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 MultilabelSupportVectorLearning <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 MultilabelSupportVectorLearning <IKernel>(machine)
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (p) => new ProbabilisticOutputCalibration <IKernel>(p.Model)
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(inputs, outputs);
// Obtain class predictions for each sample
bool[][] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[][] scores = machine.Scores(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 using mean accuracy (mAcc)
double error = new HammingLoss(outputs).Loss(predicted);
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
string a = scores.ToCSharp();
string b = logl.ToCSharp();
string c = prob.ToCSharp();
double[][] expectedScores =
{
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { 1.44477953581274, -1.98592298465108, -2.27356092239125 },
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { -2.40815576360914, 0.328362962196791, -0.932721757919691 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.14888646926108, -1.99399145231447, 1.33101148524982 },
new double[] { -2.12915064678299, -1.98592298465108, 1.3242171079396 },
new double[] { -1.47197826667149, -1.96368715704762, 0.843414180834243 },
new double[] { -2.14221021749314, -2.83117892529093, 2.61354519154994 }
};
double[][] expectedLogL =
{
new double[] { -0.145606614365135, -2.66874434442222, -2.41528841111469 },
new double[] { -0.146125659911391, -2.12163759796483, -2.3883043096263 },
new double[] { -0.211716960454159, -2.11453945718522, -2.37154474995633 },
new double[] { -0.145606614365135, -2.66874434442222, -2.41528841111469 },
new double[] { -0.146125659911391, -2.12163759796483, -2.3883043096263 },
new double[] { -2.4943161092787, -0.542383360363463, -1.26452689970624 },
new double[] { -2.24328358118314, -0.151678833375872, -2.3883043096263 },
new double[] { -2.24328358118314, -0.151678833375872, -2.3883043096263 },
new double[] { -2.25918730624753, -2.12163759796483, -0.234447327588685 },
new double[] { -2.24153091066541, -2.11453945718522, -0.2358711195715 },
new double[] { -1.67856232802554, -2.0950136294762, -0.357841632335707 },
new double[] { -2.25321037906455, -2.88845047104229, -0.0707140798850236 }
};
double[][] expectedProbs =
{
new double[] { 0.844913862516144, 0.0677684640174953, 0.0873176734663607 },
new double[] { 0.803266328757473, 0.111405242674824, 0.0853284285677024 },
new double[] { 0.790831391595502, 0.117950175028754, 0.0912184333757438 },
new double[] { 0.844913862516144, 0.0677684640174953, 0.0873176734663607 },
new double[] { 0.803266328757473, 0.111405242674824, 0.0853284285677024 },
new double[] { 0.0872387667998771, 0.614360294206236, 0.298400938993887 },
new double[] { 0.100372339295793, 0.812805149315815, 0.0868225113883914 },
new double[] { 0.100372339295793, 0.812805149315815, 0.0868225113883914 },
new double[] { 0.102863726210119, 0.11803188195247, 0.779104391837411 },
new double[] { 0.104532503226998, 0.118686968710368, 0.776780528062634 },
new double[] { 0.184996665350572, 0.121983586443407, 0.693019748206021 },
new double[] { 0.0961702585148881, 0.0509517983210315, 0.85287794316408 }
};
int[] actual = predicted.ArgMax(dimension: 1);
Assert.IsTrue(actual.IsEqual(outputs));
// Must be exactly the same as test above
Assert.AreEqual(0, error);
Assert.AreEqual(0.5, ((Gaussian)machine[0].Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[1].Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[2].Kernel).Gamma);
Assert.AreEqual(2.9395943260892361, loss);
Assert.IsTrue(expectedScores.IsEqual(scores, 1e-10));
Assert.IsTrue(expectedLogL.IsEqual(logl, 1e-10));
Assert.IsTrue(expectedProbs.IsEqual(prob, 1e-10));
double[] probabilities = CorrectProbabilities(machine, inputs[0]);
double[] actualProb = machine.Probabilities(inputs[0]);
Assert.IsTrue(probabilities.IsEqual(actualProb, 1e-8));
}
public void multilabel_calibration()
{
#region doc_learn_calibration
// 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 MultilabelSupportVectorLearning <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
}
};
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
// Create the multi-class learning algorithm for the machine
var calibration = new MultilabelSupportVectorLearning <Gaussian>()
{
Model = machine, // We will start with an existing machine
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new ProbabilisticOutputCalibration <Gaussian>()
{
Model = param.Model // Start with an existing machine
}
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(inputs, outputs);
// Obtain class predictions for each sample
bool[][] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[][] scores = machine.Scores(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 using mean accuracy (mAcc)
double error = new HammingLoss(outputs).Loss(predicted);
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
#endregion
string a = scores.ToCSharp();
string b = logl.ToCSharp();
string c = prob.ToCSharp();
double[][] expectedScores =
{
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { 1.44477953581274, -1.98592298465108, -2.27356092239125 },
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { -2.40815576360914, 0.328362962196791, -0.932721757919691 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.14888646926108, -1.99399145231447, 1.33101148524982 },
new double[] { -2.12915064678299, -1.98592298465108, 1.3242171079396 },
new double[] { -1.47197826667149, -1.96368715704762, 0.843414180834243 },
new double[] { -2.14221021749314, -2.83117892529093, 2.61354519154994 }
};
double[][] expectedLogL =
{
new double[] { -0.145606614365135, -2.66874434442222, -2.41528841111469 },
new double[] { -0.146125659911391, -2.12163759796483, -2.3883043096263 },
new double[] { -0.211716960454159, -2.11453945718522, -2.37154474995633 },
new double[] { -0.145606614365135, -2.66874434442222, -2.41528841111469 },
new double[] { -0.146125659911391, -2.12163759796483, -2.3883043096263 },
new double[] { -2.4943161092787, -0.542383360363463, -1.26452689970624 },
new double[] { -2.24328358118314, -0.151678833375872, -2.3883043096263 },
new double[] { -2.24328358118314, -0.151678833375872, -2.3883043096263 },
new double[] { -2.25918730624753, -2.12163759796483, -0.234447327588685 },
new double[] { -2.24153091066541, -2.11453945718522, -0.2358711195715 },
new double[] { -1.67856232802554, -2.0950136294762, -0.357841632335707 },
new double[] { -2.25321037906455, -2.88845047104229, -0.0707140798850236 }
};
double[][] expectedProbs =
{
new double[] { 0.844913862516144, 0.0677684640174953, 0.0873176734663607 },
new double[] { 0.803266328757473, 0.111405242674824, 0.0853284285677024 },
new double[] { 0.790831391595502, 0.117950175028754, 0.0912184333757438 },
new double[] { 0.844913862516144, 0.0677684640174953, 0.0873176734663607 },
new double[] { 0.803266328757473, 0.111405242674824, 0.0853284285677024 },
new double[] { 0.0872387667998771, 0.614360294206236, 0.298400938993887 },
new double[] { 0.100372339295793, 0.812805149315815, 0.0868225113883914 },
new double[] { 0.100372339295793, 0.812805149315815, 0.0868225113883914 },
new double[] { 0.102863726210119, 0.11803188195247, 0.779104391837411 },
new double[] { 0.104532503226998, 0.118686968710368, 0.776780528062634 },
new double[] { 0.184996665350572, 0.121983586443407, 0.693019748206021 },
new double[] { 0.0961702585148881, 0.0509517983210315, 0.85287794316408 }
};
int[] actual = predicted.ArgMax(dimension: 1);
Assert.IsTrue(actual.IsEqual(outputs));
Assert.AreEqual(0, error);
Assert.AreEqual(3, machine.Count);
Assert.AreEqual(0.5, machine[0].Kernel.Gamma);
Assert.AreEqual(0.5, machine[1].Kernel.Gamma);
Assert.AreEqual(0.5, machine[2].Kernel.Gamma);
Assert.AreEqual(2.9395943260892361, loss);
Assert.IsTrue(expectedScores.IsEqual(scores, 1e-10));
Assert.IsTrue(expectedLogL.IsEqual(logl, 1e-10));
Assert.IsTrue(expectedProbs.IsEqual(prob, 1e-10));
double[] rowSums = expectedProbs.Sum(1);
Assert.IsTrue(rowSums.IsEqual(Vector.Ones(expectedProbs.Length), 1e-10));
{
bool[][] predicted2 = null;
double[][] scores2 = machine.Scores(inputs, ref predicted2);
Assert.IsTrue(scores2.IsEqual(scores));
Assert.IsTrue(predicted2.IsEqual(predicted));
double[][] logl2 = machine.LogLikelihoods(inputs, ref predicted2);
Assert.IsTrue(logl2.IsEqual(logl));
Assert.IsTrue(predicted2.IsEqual(predicted));
double[][] prob2 = machine.Probabilities(inputs, ref predicted2);
Assert.IsTrue(prob2.IsEqual(prob));
Assert.IsTrue(predicted2.IsEqual(predicted));
bool[][] predicted3 = new bool[predicted2.Length][];
double[][] scores3 = inputs.ApplyWithIndex((x, i) => machine.Scores(x, ref predicted3[i]));
Assert.IsTrue(scores3.IsEqual(scores));
Assert.IsTrue(predicted3.IsEqual(predicted));
double[][] logl3 = inputs.ApplyWithIndex((x, i) => machine.LogLikelihoods(x, ref predicted3[i]));
Assert.IsTrue(logl3.IsEqual(logl));
Assert.IsTrue(predicted3.IsEqual(predicted));
double[][] prob3 = inputs.ApplyWithIndex((x, i) => machine.Probabilities(x, ref predicted3[i]));
Assert.IsTrue(prob3.IsEqual(prob));
Assert.IsTrue(predicted3.IsEqual(predicted));
}
{
double[] ed = new double[scores.Length];
double[] es = new double[scores.Length];
double[] el = new double[scores.Length];
double[] ep = new double[scores.Length];
for (int i = 0; i < expectedScores.Length; i++)
{
int j = scores[i].ArgMax();
ed[i] = j;
es[i] = scores[i][j];
el[i] = logl[i][j];
ep[i] = prob[i][j];
}
int[] predicted2 = null;
double[] scores2 = machine.ToMulticlass().Score(inputs, ref predicted2);
Assert.IsTrue(scores2.IsEqual(es));
Assert.IsTrue(predicted2.IsEqual(ed));
double[] logl2 = machine.ToMulticlass().LogLikelihood(inputs, ref predicted2);
Assert.IsTrue(logl2.IsEqual(el));
Assert.IsTrue(predicted2.IsEqual(ed));
double[] prob2 = machine.ToMulticlass().Probability(inputs, ref predicted2);
Assert.IsTrue(prob2.IsEqual(ep));
Assert.IsTrue(predicted2.IsEqual(ed));
int[] predicted3 = new int[predicted2.Length];
double[] scores3 = inputs.ApplyWithIndex((x, i) => machine.ToMulticlass().Score(x, out predicted3[i]));
Assert.IsTrue(scores3.IsEqual(es));
Assert.IsTrue(predicted3.IsEqual(ed));
double[] logl3 = inputs.ApplyWithIndex((x, i) => machine.ToMulticlass().LogLikelihood(x, out predicted3[i]));
Assert.IsTrue(logl3.IsEqual(el));
Assert.IsTrue(predicted3.IsEqual(ed));
double[] prob3 = inputs.ApplyWithIndex((x, i) => machine.ToMulticlass().Probability(x, out predicted3[i]));
Assert.IsTrue(prob3.IsEqual(ep));
Assert.IsTrue(predicted3.IsEqual(ed));
}
}
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_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 multilabel_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 MultilabelSupportVectorLearning <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 MultilabelSupportVectorLearning <IKernel>(machine)
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (p) => new ProbabilisticOutputCalibration <IKernel>(p.Model)
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(inputs, outputs);
// Obtain class predictions for each sample
bool[][] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[][] scores = machine.Scores(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 using mean accuracy (mAcc)
double error = new HammingLoss(outputs).Loss(predicted);
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
string a = scores.ToCSharp();
string b = logl.ToCSharp();
string c = prob.ToCSharp();
double[][] expectedScores =
{
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { 1.44477953581274, -1.98592298465108, -2.27356092239125 },
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { -2.40815576360914, 0.328362962196791, -0.932721757919691 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.14888646926108, -1.99399145231447, 1.33101148524982 },
new double[] { -2.12915064678299, -1.98592298465108, 1.3242171079396 },
new double[] { -1.47197826667149, -1.96368715704762, 0.843414180834243 },
new double[] { -2.14221021749314, -2.83117892529093, 2.61354519154994 }
};
double[][] expectedLogL =
{
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { 1.44477953581274, -1.98592298465108, -2.27356092239125 },
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { -2.40815576360914, 0.328362962196791, -0.932721757919691 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.14888646926108, -1.99399145231447, 1.33101148524982 },
new double[] { -2.12915064678299, -1.98592298465108, 1.3242171079396 },
new double[] { -1.47197826667149, -1.96368715704762, 0.843414180834243 },
new double[] { -2.14221021749314, -2.83117892529093, 2.61354519154994 }
};
double[][] expectedProbs =
{
new double[] { 6.37994947365835, 0.0745053832890827, 0.0981065622139132 },
new double[] { 6.35559784678136, 0.136150899620619, 0.101061104020747 },
new double[] { 4.24091706941419, 0.137253872418087, 0.102944947658882 },
new double[] { 6.37994947365835, 0.0745053832890827, 0.0981065622139132 },
new double[] { 6.35559784678136, 0.136150899620619, 0.101061104020747 },
new double[] { 0.0899810880411361, 1.38869292386051, 0.393481290780948 },
new double[] { 0.118705270957796, 6.10551277113228, 0.101061104020747 },
new double[] { 0.118705270957796, 6.10551277113228, 0.101061104020747 },
new double[] { 0.116613938707895, 0.136150899620619, 3.78486979203385 },
new double[] { 0.118938271567046, 0.137253872418087, 3.75924112261421 },
new double[] { 0.229471080877097, 0.140340010119971, 2.3242889884131 },
new double[] { 0.11739508739354, 0.0589433229176013, 13.6473476521179 }
};
int[] actual = predicted.ArgMax(dimension: 1);
Assert.IsTrue(actual.IsEqual(outputs));
// Must be exactly the same as test above
Assert.AreEqual(0, error);
Assert.AreEqual(0.5, ((Gaussian)machine[0].Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[1].Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[2].Kernel).Gamma);
Assert.AreEqual(-18.908706961799737, loss);
Assert.IsTrue(expectedScores.IsEqual(scores, 1e-10));
Assert.IsTrue(expectedLogL.IsEqual(logl, 1e-10));
Assert.IsTrue(expectedProbs.IsEqual(prob, 1e-10));
}
public void multilabel_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 MultilabelSupportVectorLearning<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 MultilabelSupportVectorLearning<IKernel>(machine)
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (p) => new ProbabilisticOutputCalibration<IKernel>(p.Model)
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(inputs, outputs);
// Obtain class predictions for each sample
bool[][] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[][] scores = machine.Scores(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 using mean accuracy (mAcc)
double error = new HammingLoss(outputs).Loss(predicted);
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
string a = scores.ToCSharp();
string b = logl.ToCSharp();
string c = prob.ToCSharp();
double[][] expectedScores =
{
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { 1.44477953581274, -1.98592298465108, -2.27356092239125 },
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { -2.40815576360914, 0.328362962196791, -0.932721757919691 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.14888646926108, -1.99399145231447, 1.33101148524982 },
new double[] { -2.12915064678299, -1.98592298465108, 1.3242171079396 },
new double[] { -1.47197826667149, -1.96368715704762, 0.843414180834243 },
new double[] { -2.14221021749314, -2.83117892529093, 2.61354519154994 }
};
double[][] expectedLogL =
{
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { 1.44477953581274, -1.98592298465108, -2.27356092239125 },
new double[] { 1.85316017783605, -2.59688389729331, -2.32170102153988 },
new double[] { 1.84933597524124, -1.99399145231446, -2.2920299547693 },
new double[] { -2.40815576360914, 0.328362962196791, -0.932721757919691 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.13111157264226, 1.809192096031, -2.2920299547693 },
new double[] { -2.14888646926108, -1.99399145231447, 1.33101148524982 },
new double[] { -2.12915064678299, -1.98592298465108, 1.3242171079396 },
new double[] { -1.47197826667149, -1.96368715704762, 0.843414180834243 },
new double[] { -2.14221021749314, -2.83117892529093, 2.61354519154994 }
};
double[][] expectedProbs =
{
new double[] { 6.37994947365835, 0.0745053832890827, 0.0981065622139132 },
new double[] { 6.35559784678136, 0.136150899620619, 0.101061104020747 },
new double[] { 4.24091706941419, 0.137253872418087, 0.102944947658882 },
new double[] { 6.37994947365835, 0.0745053832890827, 0.0981065622139132 },
new double[] { 6.35559784678136, 0.136150899620619, 0.101061104020747 },
new double[] { 0.0899810880411361, 1.38869292386051, 0.393481290780948 },
new double[] { 0.118705270957796, 6.10551277113228, 0.101061104020747 },
new double[] { 0.118705270957796, 6.10551277113228, 0.101061104020747 },
new double[] { 0.116613938707895, 0.136150899620619, 3.78486979203385 },
new double[] { 0.118938271567046, 0.137253872418087, 3.75924112261421 },
new double[] { 0.229471080877097, 0.140340010119971, 2.3242889884131 },
new double[] { 0.11739508739354, 0.0589433229176013, 13.6473476521179 } };
int[] actual = predicted.ArgMax(dimension: 1);
Assert.IsTrue(actual.IsEqual(outputs));
// Must be exactly the same as test above
Assert.AreEqual(0, error);
Assert.AreEqual(0.5, ((Gaussian)machine[0].Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[1].Kernel).Gamma);
Assert.AreEqual(0.5, ((Gaussian)machine[2].Kernel).Gamma);
Assert.AreEqual(-18.908706961799737, loss);
Assert.IsTrue(expectedScores.IsEqual(scores, 1e-10));
Assert.IsTrue(expectedLogL.IsEqual(logl, 1e-10));
Assert.IsTrue(expectedProbs.IsEqual(prob, 1e-10));
}
