LogisticRegressionPlain#
- class LogisticRegressionPlain#
A plaintext representation of a LogisticRegression model.
- get_activation(self: pyhelayers.LogisticRegressionPlain) pyhelayers.LRActivation#
Gets the activation to be used in the LogisticRegressionPlain prediction.
- get_bias(self: pyhelayers.LogisticRegressionPlain) numpy.ndarray[numpy.float64]#
Returns the model bias.
- get_weights(self: pyhelayers.LogisticRegressionPlain) numpy.ndarray[numpy.float64]#
Returns the model weights.
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class LogisticRegressionPlain : public helayers::PlainModel#
A plaintext representation of a Logistic Regression model.
Public Functions
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LogisticRegressionPlain()#
Construct an empty LogisticRegressionPlain object.
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void initFromTensor(const PlainModelHyperParams &hyperParams, const DoubleTensor &w, double b)#
Initializes the model and its weights from a tensor object.
- Parameters:
hyperParams – hyperparameters for this LogisticRegressionPlain
w – a tensor object with the weights
b – the bias value
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virtual std::shared_ptr<HeModel> getEmptyHeModel(const HeContext &he) const override#
Returns an empty HE LogisticRegression object.
- Parameters:
he – the context
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inline LRActivationType getActivation() const#
Returns the activation function.
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inline DoubleTensor getWeights() const#
Returns the coefficient (trained weights) of the features in the decision function.
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inline DoubleTensor getBias() const#
Intercept (a.k.a. bias) added to the decision function.
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inline virtual std::string getClassName() const override#
Retunrs the name of this class.
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virtual void debugPrint(const std::string &title = "", Verbosity verbosity = VERBOSITY_REGULAR, std::ostream &out = std::cout) const override#
Prints the content of this object.
- Parameters:
title – Text to add to the print
verbosity – Verbosity level
out – Output stream
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LogisticRegressionPlain()#