std_dev::regression::gradient_descent

Struct SimultaneousOptions

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pub struct SimultaneousOptions {
    pub learn_rate: f64,
    pub factor_decrease: f64,
    pub factor_increase: f64,
    pub target_accuracy: f64,
}
Available on crate feature regression only.

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§learn_rate: f64§factor_decrease: f64§factor_increase: f64§target_accuracy: f64

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impl SimultaneousOptions

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pub fn new(target_accuracy: f64) -> Self

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impl SimultaneousOptions

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pub fn polynomial_optimization( &self, n: usize, fitness_function: impl Fn(&[f64]) -> f64, ) -> Vec<f64>

Trait Implementations§

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impl LinearEstimator for SimultaneousOptions

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fn model_linear( &self, predictors: &[f64], outcomes: &[f64], ) -> LinearCoefficients

Model the LinearCoefficients from predictors and outcomes. Read more
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fn boxed_linear(self) -> Box<dyn LinearEstimator>
where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.
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impl PolynomialEstimator for SimultaneousOptions

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fn model_polynomial( &self, predictors: &[f64], outcomes: &[f64], degree: usize, ) -> PolynomialCoefficients

Model the PolynomialCoefficients from predictors and outcomes. Also takes a degree of the target polynomial. Some estimators may panic when degree is out of their range. Read more
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fn boxed_polynomial(self) -> Box<dyn PolynomialEstimator>
where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

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