Models

Models

This module contains small supervised learning models built on SicLib tensors. The current API covers k-nearest neighbors and a linear model. I use these implementations to test the tensor layer and study fit and prediction interfaces, not for production workloads.

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Documentation

class KNearestNeighbors:
    def __init__(self) -> None: ...
    def fit_model(self, x_vals: _pysiclib.linalg.Tensor, y_vals: _pysiclib.linalg.Tensor, num_neighbors: int, metric: int = ...) -> None: ...
    def predict(self, arg0: _pysiclib.linalg.Tensor) -> _pysiclib.linalg.Tensor: ...

class LinearModel:
    def __init__(self) -> None: ...
    def fit_model(self, x_vals: _pysiclib.linalg.Tensor, y_vals: _pysiclib.linalg.Tensor, fit_procedure: int = ...) -> None: ...
    def predict(self, arg0: _pysiclib.linalg.Tensor) -> _pysiclib.linalg.Tensor: ...