// Gaussian Quantile // // gaussian_quantile // // Gaussian quantile function (inverse CDF) // // Q(p) = Φ^(-1)(p) // // Inputs: 1 // Outputs: 1 // Constants: 0 #include #include "ts.h" ts_parameter_t const ts_gaussian_quantile_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 0, .params_size = 0 }; uint_t ts_gaussian_quantile_offset( ts_constant_t const *c ) { ( void )c; return ( 0 ); } int ts_gaussian_quantile( 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_gaussian_quantile_p ); (void)c; /* Peter Acklam's approximation for inverse normal CDF */ /* Coefficients for rational approximation */ fp_t const a[] = { -3.969683028665376e+01, 2.209460984245205e+02, -2.759285104469687e+02, 1.383577518672690e+02, -3.066479806614716e+01, 2.506628277459239e+00 }; fp_t const b[] = { -5.447609879822406e+01, 1.615858368580409e+02, -1.556989798598866e+02, 6.680131188771972e+01, -1.328068155288572e+01 }; fp_t const cc[] = { -7.784894002430993e-03, 3.223964580411365e-01, -2.400758277161838e+00, -2.549732539343734e+00, 4.374664141464968e+00, 2.938163982698783e+00 }; fp_t const d[] = { 7.784695709041462e-03, 3.224671290700398e-01, 2.445134137142996e+00, 3.754408661907416e+00 }; fp_t const p_low = 0.02425; fp_t const p_high = 1.0 - p_low; for (uint_t i = 0; i < n; ++i) { fp_t p = x[0][i]; if (p <= 0.0) { y[0][i] = -INFINITY; continue; } if (p >= 1.0) { y[0][i] = INFINITY; continue; } fp_t q, r; if (p < p_low) { q = sqrt(-2.0 * log(p)); y[0][i] = -(((((cc[0]*q + cc[1])*q + cc[2])*q + cc[3])*q + cc[4])*q + cc[5]) / ((((d[0]*q + d[1])*q + d[2])*q + d[3])*q + 1.0); } else if (p <= p_high) { q = p - 0.5; r = q * q; y[0][i] = (((((a[0]*r + a[1])*r + a[2])*r + a[3])*r + a[4])*r + a[5])*q / (((((b[0]*r + b[1])*r + b[2])*r + b[3])*r + b[4])*r + 1.0); } else { q = sqrt(-2.0 * log(1.0 - p)); y[0][i] = (((((cc[0]*q + cc[1])*q + cc[2])*q + cc[3])*q + cc[4])*q + cc[5]) / ((((d[0]*q + d[1])*q + d[2])*q + d[3])*q + 1.0); } } return 0; }