concrete.ml.sklearn.svm.md
Last updated
Last updated
concrete.ml.sklearn.svm
Implement Support Vector Machine.
LinearSVR
A Regression Support Vector Machine (SVM).
Parameters:
n_bits
(int, Dict[str, int]): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op_inputs" and "op_weights" as keys with corresponding number of quantization bits so that: - op_inputs : number of bits to quantize the input values - op_weights: number of bits to quantize the learned parameters Default to 8.
For more details on LinearSVR please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html
__init__
property fhe_circuit
Get the FHE circuit.
The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: https://docs.zama.ai/concrete/developer/terminology_and_structure#terminology Is None if the model is not fitted.
Returns:
Circuit
: The FHE circuit.
property is_compiled
Indicate if the model is compiled.
Returns:
bool
: If the model is compiled.
property is_fitted
Indicate if the model is fitted.
Returns:
bool
: If the model is fitted.
property onnx_model
Get the ONNX model.
Is None if the model is not fitted.
Returns:
onnx.ModelProto
: The ONNX model.
dump_dict
load_dict
LinearSVC
A Classification Support Vector Machine (SVM).
Parameters:
n_bits
(int, Dict[str, int]): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op_inputs" and "op_weights" as keys with corresponding number of quantization bits so that: - op_inputs : number of bits to quantize the input values - op_weights: number of bits to quantize the learned parameters Default to 8.
For more details on LinearSVC please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html
__init__
property fhe_circuit
Get the FHE circuit.
The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: https://docs.zama.ai/concrete/developer/terminology_and_structure#terminology Is None if the model is not fitted.
Returns:
Circuit
: The FHE circuit.
property is_compiled
Indicate if the model is compiled.
Returns:
bool
: If the model is compiled.
property is_fitted
Indicate if the model is fitted.
Returns:
bool
: If the model is fitted.
property onnx_model
Get the ONNX model.
Is None if the model is not fitted.
Returns:
onnx.ModelProto
: The ONNX model.
dump_dict
load_dict