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// Copyright (c) 2017, Apple Inc. All rights reserved.
//
// Use of this source code is governed by a BSD-3-clause license that can be
// found in LICENSE.txt or at https://opensource.org/licenses/BSD-3-Clause
syntax = "proto3";
option optimize_for = LITE_RUNTIME;
import public "DataStructures.proto";
package CoreML.Specification;
// Kernel Definitions
// ------------------
/*
* A linear kernel.
*
* This function has the following formula:
*
* .. math::
* K(\boldsymbol{x}, \boldsymbol{x'}) = \boldsymbol{x}^T \boldsymbol{x'}
*/
message LinearKernel {}
/*
* A Gaussian radial basis function (RBF) kernel.
*
* This function has the following formula:
*
* .. math::
* K(\boldsymbol{x}, \boldsymbol{x'}) = \
* \exp(-\gamma || \boldsymbol{x} - \boldsymbol{x'} ||^2 )
*
*/
message RBFKernel {
double gamma = 1;
}
/*
* A polynomial kernel.
*
* This function has the following formula:
*
* .. math::
* K(\boldsymbol{x}, \boldsymbol{x'}) = \
* (\gamma \boldsymbol{x}^T \boldsymbol{x'} + c)^{degree}
*/
message PolyKernel {
int32 degree = 1;
double c = 2;
double gamma = 3;
}
/*
* A sigmoid kernel.
*
* This function has the following formula:
*
* .. math::
* K(\boldsymbol{x}, \boldsymbol{x'}) = \
* \tanh(\gamma \boldsymbol{x}^T \boldsymbol{x'} + c)
*/
message SigmoidKernel {
double gamma = 1;
double c = 2;
}
/*
* A kernel.
*/
message Kernel {
oneof kernel {
LinearKernel linearKernel = 1;
RBFKernel rbfKernel = 2;
PolyKernel polyKernel = 3;
SigmoidKernel sigmoidKernel = 4;
}
}
// Support Vector Definitions
// --------------------------
/*
* A sparse node.
*/
message SparseNode {
int32 index = 1; // 1-based indexes, like libsvm
double value = 2;
}
/*
* A sparse vector.
*/
message SparseVector {
repeated SparseNode nodes = 1;
}
/*
* One or more sparse support vectors.
*/
message SparseSupportVectors {
repeated SparseVector vectors = 1;
}
/*
* A dense vector.
*/
message DenseVector {
repeated double values = 1;
}
/*
* One or more dense support vectors.
*/
message DenseSupportVectors {
repeated DenseVector vectors = 1;
}
/*
* One or more coefficients.
*/
message Coefficients {
repeated double alpha = 1;
}
/*
* A support vector regressor.
*/
message SupportVectorRegressor {
Kernel kernel = 1;
// Support vectors, either sparse or dense format
oneof supportVectors {
SparseSupportVectors sparseSupportVectors = 2;
DenseSupportVectors denseSupportVectors = 3;
}
// Coefficients, one for each support vector
Coefficients coefficients = 4;
double rho = 5;
}
/*
* A support vector classifier
*/
message SupportVectorClassifier {
Kernel kernel = 1;
/*
* The number of support vectors for each class.
*/
repeated int32 numberOfSupportVectorsPerClass = 2;
/*
* The support vectors, in either sparse or dense format.
*/
oneof supportVectors {
SparseSupportVectors sparseSupportVectors = 3;
DenseSupportVectors denseSupportVectors = 4;
}
/*
* The coefficients, essentially a two dimensional array of
* size: (numberOfClasses-1) by (total number of support vectors)
*/
repeated Coefficients coefficients = 5;
/*
* Constants for decision function,
* with K*(K-1) / 2 elements,
* where K is the number of classes.
*/
repeated double rho = 6;
/*
* Pairwise probability information for A vs B classifier.
* Total of K*(K-1)/2 elements where K is the number of classes.
* These fields are optional,
* and only required if you want probabilities or multi class predictions.
*/
repeated double probA = 7;
repeated double probB = 8;
/*
* Class label mapping.
*/
oneof ClassLabels {
StringVector stringClassLabels = 100;
Int64Vector int64ClassLabels = 101;
}
}
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