module onnxrt.ops_cpu.op_svm_regressor
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Short summary#
module mlprodict.onnxrt.ops_cpu.op_svm_regressor
Runtime operator.
Classes#
class |
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SVMRegressor (ai.onnx.ml) ========================= Support Vector Machine regression prediction and one-class SVM anomaly … |
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SVMRegressorDouble (mlprodict) ============================== Version Onnx name: SVMRegressorDouble … |
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Defines a schema for operators added in this package such as |
Properties#
property |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of modified parameters. |
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Returns the list of modified parameters. |
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Returns the list of modified parameters. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns all parameters in a dictionary. |
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Returns all parameters in a dictionary. |
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Returns all parameters in a dictionary. |
Methods#
method |
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Finds a custom operator defined by this runtime. |
Finds a custom operator defined by this runtime. |
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Finds a custom operator defined by this runtime. |
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This is a C++ implementation coming from onnxruntime. svm_regressor.cc. … |
This is a C++ implementation coming from onnxruntime. svm_regressor.cc. … |
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This is a C++ implementation coming from onnxruntime. svm_regressor.cc. … |
Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_svm_regressor.SVMRegressor(ai.onnx.ml)#
Bases:
SVMRegressorCommon
Support Vector Machine regression prediction and one-class SVM anomaly detection.
Attributes
coefficients: Support vector coefficients. default value cannot be automatically retrieved (FLOATS)
kernel_params: List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. default value cannot be automatically retrieved (FLOATS)
kernel_type: The kernel type, one of ‘LINEAR,’ ‘POLY,’ ‘RBF,’ ‘SIGMOID’. Default value is
namekerneltypesLINEARtypeSTRING
(STRING)n_supports: The number of support vectors. Default value is
namensupportsi0typeINT
(INT)one_class: Flag indicating whether the regression is a one-class SVM or not. Default value is
nameoneclassi0typeINT
(INT)post_transform: Indicates the transform to apply to the score. One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT.’ Default value is
nameposttransformsNONEtypeSTRING
(STRING)rho: default value cannot be automatically retrieved (FLOATS)
support_vectors: Chosen support vectors default value cannot be automatically retrieved (FLOATS)
Inputs
X (heterogeneous)T: Data to be regressed.
Outputs
Y (heterogeneous)tensor(float): Regression outputs (one score per target per example).
Type Constraints
T tensor(float), tensor(double), tensor(int64), tensor(int32): The input type must be a tensor of a numeric type, either [C] or [N,C].
Version
Onnx name: SVMRegressor
This version of the operator has been available since version 1 of domain ai.onnx.ml.
Runtime implementation:
SVMRegressor
- __init__(onnx_node, desc=None, **options)#
- class mlprodict.onnxrt.ops_cpu.op_svm_regressor.SVMRegressorCommon(dtype, onnx_node, desc=None, expected_attributes=None, **options)#
Bases:
OpRunUnaryNum
- __init__(dtype, onnx_node, desc=None, expected_attributes=None, **options)#
- _find_custom_operator_schema(op_name)#
Finds a custom operator defined by this runtime.
- _get_typed_attributes(k)#
- _init(dtype)#
- _run(x, attributes=None, verbose=0, fLOG=None)#
This is a C++ implementation coming from onnxruntime. svm_regressor.cc. See class
RuntimeSVMRegressor
.
- class mlprodict.onnxrt.ops_cpu.op_svm_regressor.SVMRegressorDouble(mlprodict)#
Bases:
SVMRegressorCommon
Version
Onnx name: SVMRegressorDouble
This version of the operator has been available since version of domain mlprodict.
Runtime implementation:
SVMRegressorDouble
- __init__(onnx_node, desc=None, **options)#
- class mlprodict.onnxrt.ops_cpu.op_svm_regressor.SVMRegressorDoubleSchema#
Bases:
OperatorSchema
Defines a schema for operators added in this package such as
SVMRegressorDouble
.- __init__()#