Struct PyLinearRegression 

pub struct PyLinearRegression { /* private fields */ }

Ordinary least squares Linear Regression.

LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

Parameters

fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

copy_X : bool, default=True If True, X will be copied; else, it may be overwritten.

n_jobs : int, default=None The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly n_targets > 1 and secondly X is sparse or if positive is set to True. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors.

positive : bool, default=False When set to True, forces the coefficients to be positive. This option is only supported for dense arrays.

Attributes

coef_ : array of shape (n_features,) or (n_targets, n_features) Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.

intercept_ : float or array of shape (n_targets,) Independent term in the linear model. Set to 0.0 if fit_intercept = False.

n_features_in_ : int Number of features seen during :term:fit.

Examples

import numpy as np from sklears_python import LinearRegression X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])

y = 1 * x_0 + 2 * x_1 + 3

y = np.dot(X, [1, 2]) + 3 reg = LinearRegression().fit(X, y) reg.score(X, y) 1.0 reg.coef_ array([1., 2.]) reg.intercept_ 3.0… reg.predict(np.array([[3, 5]])) array([16.])

Notes

From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares (scipy.optimize.nnls) wrapped as a predictor object.

Trait Implementations

impl<'py> IntoPyObject<'py> for PyLinearRegression

type Target = PyLinearRegression

The Python output type

type Output = Bound<'py, <PyLinearRegression as IntoPyObject<'py>>::Target>

The smart pointer type to use.

type Error = PyErr

The type returned in the event of a conversion error.

fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.

impl PyClass for PyLinearRegression

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for PyLinearRegression

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]

const RAW_DOC: &'static CStr = /// Ordinary least squares Linear Regression. /// /// LinearRegression fits a linear model with coefficients w = (w1, ..., wp) /// to minimize the residual sum of squares between the observed targets in /// the dataset, and the targets predicted by the linear approximation. /// /// Parameters /// ---------- /// fit_intercept : bool, default=True /// Whether to calculate the intercept for this model. If set /// to False, no intercept will be used in calculations /// (i.e. data is expected to be centered). /// /// copy_X : bool, default=True /// If True, X will be copied; else, it may be overwritten. /// /// n_jobs : int, default=None /// The number of jobs to use for the computation. This will only provide /// speedup in case of sufficiently large problems, that is if firstly /// `n_targets > 1` and secondly `X` is sparse or if `positive` is set /// to `True`. ``None`` means 1 unless in a /// :obj:`joblib.parallel_backend` context. ``-1`` means using all /// processors. /// /// positive : bool, default=False /// When set to ``True``, forces the coefficients to be positive. This /// option is only supported for dense arrays. /// /// Attributes /// ---------- /// coef_ : array of shape (n_features,) or (n_targets, n_features) /// Estimated coefficients for the linear regression problem. /// If multiple targets are passed during the fit (y 2D), this /// is a 2D array of shape (n_targets, n_features), while if only /// one target is passed, this is a 1D array of length n_features. /// /// intercept_ : float or array of shape (n_targets,) /// Independent term in the linear model. Set to 0.0 if /// `fit_intercept = False`. /// /// n_features_in_ : int /// Number of features seen during :term:`fit`. /// /// Examples /// -------- /// >>> import numpy as np /// >>> from sklears_python import LinearRegression /// >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) /// >>> # y = 1 * x_0 + 2 * x_1 + 3 /// >>> y = np.dot(X, [1, 2]) + 3 /// >>> reg = LinearRegression().fit(X, y) /// >>> reg.score(X, y) /// 1.0 /// >>> reg.coef_ /// array([1., 2.]) /// >>> reg.intercept_ /// 3.0... /// >>> reg.predict(np.array([[3, 5]])) /// array([16.]) /// /// Notes /// ----- /// From the implementation point of view, this is just plain Ordinary /// Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares /// (scipy.optimize.nnls) wrapped as a predictor object.

Docstring for the class provided on the struct or enum.

const DOC: &'static CStr

Fully rendered class doc, including the text_signature if a constructor is defined.

type BaseType = PyAny

Base class

type ThreadChecker = SendablePyClass<PyLinearRegression>

This handles following two situations:

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<isize>

fn weaklist_offset() -> Option<isize>

impl PyClassNewTextSignature for PyLinearRegression

const TEXT_SIGNATURE: &'static str = "(fit_intercept=True, copy_x=True, n_jobs=None, positive=False)"

impl<'a, 'holder, 'py> PyFunctionArgument<'a, 'holder, 'py, false> for &'holder PyLinearRegression

type Holder = Option<PyClassGuard<'a, PyLinearRegression>>

fn extract( obj: &'a Bound<'py, PyAny>, holder: &'holder mut Self::Holder, ) -> PyResult<Self>

impl<'a, 'holder, 'py> PyFunctionArgument<'a, 'holder, 'py, false> for &'holder mut PyLinearRegression

type Holder = Option<PyClassGuardMut<'a, PyLinearRegression>>

fn extract( obj: &'a Bound<'py, PyAny>, holder: &'holder mut Self::Holder, ) -> PyResult<Self>

impl PyMethods<PyLinearRegression> for PyClassImplCollector<PyLinearRegression>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for PyLinearRegression

const NAME: &'static str = "LinearRegression"

Class name.

const MODULE: Option<&'static str> = ::core::option::Option::None

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.

impl DerefToPyAny for PyLinearRegression