// Beta PDF
//
// beta_pdf - beta probability density
//
// f(x; α, β) = x^(α-1) * (1-x)^(β-1) / B(α, β), for 0 < x < 1
//
// Inputs:    1
// Outputs:   1 (PDF value)
// Constants: 2 (alpha, beta)

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

typedef struct {
    fp_t alpha;
    fp_t beta;
} ts_beta_pdf_params_t;

ts_parameter_t const ts_beta_pdf_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 2, .params_size = sizeof(ts_beta_pdf_params_t) };

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

int ts_beta_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_beta_pdf_p );
	if ( !c ) return -1;
	fp_t alpha = ((ts_beta_pdf_params_t *)c)->alpha;
	fp_t beta = ((ts_beta_pdf_params_t *)c)->beta;
	if (isnan(alpha) || isnan(beta) || alpha <= 0 || beta <= 0)
	{
		for( uint_t i = 0; i < n; ++i ) y[0][i] = 0;
	return -1;
	}
	fp_t beta_fn = exp( lgamma( alpha ) + lgamma( beta ) - lgamma( alpha + beta ) );
	for( uint_t i = 0; i < n; ++i )
	{
		fp_t xi = x[0][i];
		y[0][i] = ( xi > 0 && xi < 1 ) ? pow( xi, alpha - 1.0 ) * pow( 1.0 - xi, beta - 1.0 ) / beta_fn : 0.0;
	}
	return 0;
}