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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 enums;

/// 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.
pub fn linear(x: &[f64], xstride: usize, y: &[f64], ystride: usize, n: usize, c0: &mut f64, c1: &mut f64, cov00: &mut f64,
    cov01: &mut f64, cov11: &mut f64, sumsq: f64) -> enums::Value {
    unsafe { ::ffi::gsl_fit_linear(x.as_ptr(), xstride, y.as_ptr(), ystride, n, 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.
pub fn wlinear(x: &[f64], xstride: usize, w: &[f64], wstride: usize, y: &[f64], ystride: usize, n: usize, c0: &mut f64, c1: &mut f64,
    cov00: &mut f64, cov01: &mut f64, cov11: &mut f64, chisq: &mut f64) -> enums::Value {
    unsafe { ::ffi::gsl_fit_wlinear(x.as_ptr(), xstride, w.as_ptr(), wstride, y.as_ptr(), ystride, n, 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.
pub fn linear_est(x: f64, c0: f64, c1: f64, cov00: f64, cov01: f64, cov11: f64, y: &mut f64, y_err: &mut f64) -> enums::Value {
    unsafe { ::ffi::gsl_fit_linear_est(x, c0, c1, cov00, cov01, cov11, 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.
pub fn mul(x: &[f64], xstride: usize, y: &[f64], ystride: usize, n: usize, c1: &mut f64, cov11: &mut f64, sumsq: &mut f64) -> enums::Value {
    unsafe { ::ffi::gsl_fit_mul(x.as_ptr(), xstride, y.as_ptr(), ystride, n, c1, cov11, sumsq) }
}

/// This function computes the best-fit linear regression coefficient c1 of the model Y = c_1 X for the weighted datasets (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 variance of the parameter c1 is computed using the weights and returned via the parameter cov11.
/// The weighted sum of squares of the residuals from the best-fit line, \chi^2, is returned in chisq.
pub fn wmul(x: &[f64], xstride: usize, w: &[f64], wstride: usize, y: &[f64], ystride: usize, n: usize, c1: &mut f64, cov11: &mut f64,
	sumsq: &mut f64) -> enums::Value {
    unsafe { ::ffi::gsl_fit_wmul(x.as_ptr(), xstride, w.as_ptr(), wstride, y.as_ptr(), ystride, n, 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.
pub fn mul_est(x: f64, c1: f64, cov11: f64, y: &mut f64, y_err: &mut f64) -> enums::Value {
    unsafe { ::ffi::gsl_fit_mul_est(x, c1, cov11, y, y_err) }
}