svm_regressor.predict

   fn predict(ref self: SVMRegressor<T>, X: Tensor<T>) -> Tensor<T>;

Support Vector Machine regression prediction and one-class SVM anomaly detection.

Args

• self: SVMRegressor - A SVMRegressor object.

• X: Input 2D tensor.

Returns

• Tensor containing the Support Vector Machine regression prediction and one-class SVM anomaly detection of the input X.

Type Constraints

SVMRegressor and X must be fixed points

Examples

use orion::numbers::FP16x16;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
use orion::operators::tensor::FP16x16TensorPartialEq;

use orion::operators::ml::svm::svm_regressor::{SVMRegressorTrait, POST_TRANSFORM, SVMRegressor};
use orion::operators::ml::svm::core::{KERNEL_TYPE};

fn example_svm_regressor_linear() -> Tensor<FP16x16> {
    let coefficients: Span<FP16x16> = array![
        FP16x16 { mag: 65536, sign: false },
        FP16x16 { mag: 65536, sign: true },
        FP16x16 { mag: 54959, sign: false },
        FP16x16 { mag: 54959, sign: true },
        FP16x16 { mag: 29299, sign: false },
        FP16x16 { mag: 65536, sign: true },
        FP16x16 { mag: 36236, sign: false }
    ]
        .span();
    let n_supports: usize = 7;
    let one_class: usize = 0;
    let rho: Span<FP16x16> = array![FP16x16 { mag: 35788, sign: false }].span();
    let support_vectors: Span<FP16x16> = array![
        FP16x16 { mag: 8421, sign: true },
        FP16x16 { mag: 5842, sign: false },
        FP16x16 { mag: 4510, sign: false },
        FP16x16 { mag: 5202, sign: true },
        FP16x16 { mag: 14783, sign: true },
        FP16x16 { mag: 17380, sign: true },
        FP16x16 { mag: 60595, sign: false },
        FP16x16 { mag: 1674, sign: true },
        FP16x16 { mag: 38669, sign: true },
        FP16x16 { mag: 63803, sign: false },
        FP16x16 { mag: 87720, sign: true },
        FP16x16 { mag: 22236, sign: false },
        FP16x16 { mag: 61816, sign: false },
        FP16x16 { mag: 34267, sign: true },
        FP16x16 { mag: 36418, sign: false },
        FP16x16 { mag: 27471, sign: false },
        FP16x16 { mag: 28421, sign: false },
        FP16x16 { mag: 69270, sign: true },
        FP16x16 { mag: 152819, sign: false },
        FP16x16 { mag: 4065, sign: false },
        FP16x16 { mag: 62274, sign: true }
    ]
        .span();
    let post_transform = POST_TRANSFORM::NONE;
    let kernel_params: Span<FP16x16> = array![
        FP16x16 { mag: 27812, sign: false },
        FP16x16 { mag: 0, sign: false },
        FP16x16 { mag: 196608, sign: false }
    ]
        .span();
    let kernel_type = KERNEL_TYPE::LINEAR;

    let mut regressor: SVMRegressor<FP16x16> = SVMRegressor {
        coefficients,
        kernel_params,
        kernel_type,
        n_supports,
        one_class,
        post_transform,
        rho,
        support_vectors,
    };

    let mut X: Tensor<FP16x16> = TensorTrait::new(
        array![3, 3].span(),
        array![
            FP16x16 { mag: 32768, sign: true },
            FP16x16 { mag: 26214, sign: true },
            FP16x16 { mag: 19660, sign: true },
            FP16x16 { mag: 13107, sign: true },
            FP16x16 { mag: 6553, sign: true },
            FP16x16 { mag: 0, sign: false },
            FP16x16 { mag: 6553, sign: false },
            FP16x16 { mag: 13107, sign: false },
            FP16x16 { mag: 19660, sign: false },
        ]
            .span()
    );

    return SVMRegressorTrait::predict(ref regressor, X);
}

>>> [[-0.468206], [0.227487], [0.92318]]