// Chi-Squared PDF
//
// chi_squared_pdf - χ² probability density
//
// f(x; k) = x^(k/2-1) * e^(-x/2) / (2^(k/2) * Γ(k/2)), for x > 0
//
// Inputs:    1
// Outputs:   1 (PDF value)
// Constants: 1 (degrees of freedom)

#include <math.h>
#include "ts.h"

typedef struct {
    fp_t k;
} ts_chi_squared_pdf_params_t;

ts_parameter_t const ts_chi_squared_pdf_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_chi_squared_pdf_params_t) };

uint_t ts_chi_squared_pdf_offset( ts_constant_t const *c )
{
	( void )c;
	return ( 0 );
}

int ts_chi_squared_pdf(
	fp_t * const *y, uint_t y_n,
	fp_t const * const *x, uint_t x_n,
	uint_t n, ts_constant_t const *c
)
{
	TS_PROLOGUE( ts_chi_squared_pdf_p );
	if ( !c ) return -1;
	fp_t k = ((ts_chi_squared_pdf_params_t const *)c)->k;
	if ( k <= 0 )
	{
		for( uint_t i = 0; i < n; ++i ) y[0][i] = 0;
	return -1;
	}
	for( uint_t i = 0; i < n; ++i )
	{
		fp_t xi = x[0][i];
		y[0][i] = xi > 0 ? pow( xi, k / 2.0 - 1.0 ) * exp( -xi / 2.0 ) / ( pow( 2.0, k / 2.0 ) * tgamma( k / 2.0 ) ) : 0.0;
	}
	return 0;
}