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//
// A rust binding for the GSL library by Guillaume Gomez (guillaume1.gomez@gmail.com)
//
/*!
Linear Regression
The functions described in this section can be used to perform least-squares fits to a straight line model, Y(c,x) = c_0 + c_1 x.
!*/
use crate::Value;
/// This function computes the best-fit linear regression coefficients (c0,c1) of the model
/// Y = c_0 + c_1 X for the dataset (x, y), two vectors of length n with strides xstride and
/// ystride.
///
/// The errors on y are assumed unknown so the variance-covariance matrix for the parameters
/// (c0, c1) is estimated from the scatter of the points around the best-fit line and returned via
/// the parameters (cov00, cov01, cov11).
///
/// The sum of squares of the residuals from the best-fit line is returned in sumsq. Note: the
/// correlation coefficient of the data can be computed using gsl_stats_correlation (see
/// [`Correlation`](http://www.gnu.org/software/gsl/manual/html_node/Correlation.html#Correlation)),
/// it does not depend on the fit.
///
/// Returns `(c0, c1, cov00, cov01, cov11, sumsq)`.
#[doc(alias = "gsl_fit_linear")]
pub fn linear(
x: &[f64],
xstride: usize,
y: &[f64],
ystride: usize,
n: usize,
) -> Result<(f64, f64, f64, f64, f64, f64), Value> {
let mut c0 = 0.;
let mut c1 = 0.;
let mut cov00 = 0.;
let mut cov01 = 0.;
let mut cov11 = 0.;
let mut sumsq = 0.;
let ret = unsafe {
::sys::gsl_fit_linear(
x.as_ptr(),
xstride,
y.as_ptr(),
ystride,
n,
&mut c0,
&mut c1,
&mut cov00,
&mut cov01,
&mut cov11,
&mut sumsq,
)
};
result_handler!(ret, (c0, c1, cov00, cov01, cov11, sumsq))
}
/// This function computes the best-fit linear regression coefficients (c0,c1) of the model
/// Y = c_0 + c_1 X for the weighted dataset (x, y), two vectors of length n with strides xstride
/// and ystride.
///
/// The vector w, of length n and stride wstride, specifies the weight of each datapoint.
///
/// The weight is the reciprocal of the variance for each datapoint in y.
///
/// The covariance matrix for the parameters (c0, c1) is computed using the weights and returned via
/// the parameters (cov00, cov01, cov11).
/// The weighted sum of squares of the residuals from the best-fit line, \chi^2, is returned in chisq.
///
/// Returns `(c0, c1, cov00, cov01, cov11, chisq)`.
#[doc(alias = "gsl_fit_wlinear")]
pub fn wlinear(
x: &[f64],
xstride: usize,
w: &[f64],
wstride: usize,
y: &[f64],
ystride: usize,
n: usize,
) -> Result<(f64, f64, f64, f64, f64, f64), Value> {
let mut c0 = 0.;
let mut c1 = 0.;
let mut cov00 = 0.;
let mut cov01 = 0.;
let mut cov11 = 0.;
let mut chisq = 0.;
let ret = unsafe {
::sys::gsl_fit_wlinear(
x.as_ptr(),
xstride,
w.as_ptr(),
wstride,
y.as_ptr(),
ystride,
n,
&mut c0,
&mut c1,
&mut cov00,
&mut cov01,
&mut cov11,
&mut chisq,
)
};
result_handler!(ret, (c0, c1, cov00, cov01, cov11, chisq))
}
/// This function uses the best-fit linear regression coefficients c0, c1 and their covariance
/// cov00, cov01, cov11 to compute the fitted function y and its standard deviation y_err for the
/// model Y = c_0 + c_1 X at the point x.
///
/// Returns `(y, y_err)`.
#[doc(alias = "gsl_fit_linear_est")]
pub fn linear_est(
x: f64,
c0: f64,
c1: f64,
cov00: f64,
cov01: f64,
cov11: f64,
) -> Result<(f64, f64), Value> {
let mut y = 0.;
let mut y_err = 0.;
let ret =
unsafe { ::sys::gsl_fit_linear_est(x, c0, c1, cov00, cov01, cov11, &mut y, &mut y_err) };
result_handler!(ret, (y, y_err))
}
/// This function computes the best-fit linear regression coefficient c1 of the model Y = c_1 X for
/// the datasets (x, y), two vectors of length n with strides xstride and ystride.
/// The errors on y are assumed unknown so the variance of the parameter c1 is estimated from the
/// scatter of the points around the best-fit line and returned via the parameter cov11.
/// The sum of squares of the residuals from the best-fit line is returned in sumsq.
///
/// Returns `(c1, cov11, sumsq)`.
#[doc(alias = "gsl_fit_mul")]
pub fn mul(
x: &[f64],
xstride: usize,
y: &[f64],
ystride: usize,
n: usize,
) -> Result<(f64, f64, f64), Value> {
let mut c1 = 0.;
let mut cov11 = 0.;
let mut sumsq = 0.;
let ret = unsafe {
crate::sys::gsl_fit_mul(
x.as_ptr(),
xstride,
y.as_ptr(),
ystride,
n,
&mut c1,
&mut cov11,
&mut sumsq,
)
};
result_handler!(ret, (c1, cov11, sumsq))
}
/// Returns `(c1, cov11, sumsq)`.
#[doc(alias = "gsl_fit_wmul")]
pub fn wmul(
x: &[f64],
xstride: usize,
w: &[f64],
wstride: usize,
y: &[f64],
ystride: usize,
n: usize,
) -> Result<(f64, f64, f64), Value> {
let mut c1 = 0.;
let mut cov11 = 0.;
let mut sumsq = 0.;
let ret = unsafe {
crate::sys::gsl_fit_wmul(
x.as_ptr(),
xstride,
w.as_ptr(),
wstride,
y.as_ptr(),
ystride,
n,
&mut c1,
&mut cov11,
&mut sumsq,
)
};
result_handler!(ret, (c1, cov11, sumsq))
}
/// This function uses the best-fit linear regression coefficient c1 and its covariance cov11 to
/// compute the fitted function y and its standard deviation y_err for the model Y = c_1 X at the
/// point x.
///
/// Returns `(y, y_err)`.
#[doc(alias = "gsl_fit_mul_est")]
pub fn mul_est(x: f64, c1: f64, cov11: f64) -> Result<(f64, f64), Value> {
let mut y = 0.;
let mut y_err = 0.;
let ret = unsafe { crate::sys::gsl_fit_mul_est(x, c1, cov11, &mut y, &mut y_err) };
result_handler!(ret, (y, y_err))
}