//
// PSPP - a program for statistical analysis.
// Copyright (C) 2005, 2006, 2009, 2010, 2011, 2012, 2015, 2016 Free Software Foundation, Inc.
-//
+//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
-//
+//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
-//
+//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
function SIN (x) = sin (x);
function SQRT (x >= 0) = sqrt (x);
function TAN (x) = check_errno (tan (x));
-function TRUNC (x) = x >= 0. ? floor (x) : -floor (-x);
+function TRUNC (x) = round_zero (x, 1, 0);
+function TRUNC (x, mult != 0) = round_zero (x, mult, 0);
+function TRUNC (x, mult != 0, fuzzbits >= 0) = round_zero (x, mult, fuzzbits);
absorb_miss function MOD (n, d)
{
absorb_miss function NMISS (a[n])
{
- size_t i;
- size_t missing_cnt = 0;
-
- for (i = 0; i < n; i++)
- missing_cnt += a[i] == SYSMIS;
- return missing_cnt;
+ size_t n_missings = 0;
+ for (size_t i = 0; i < n; i++)
+ n_missings += a[i] == SYSMIS;
+ return n_missings;
}
absorb_miss function NVALID (a[n])
{
- size_t i;
- size_t valid_cnt = 0;
-
- for (i = 0; i < n; i++)
- valid_cnt += a[i] != SYSMIS;
- return valid_cnt;
+ size_t n_valids = 0;
+ for (size_t i = 0; i < n; i++)
+ n_valids += a[i] != SYSMIS;
+ return n_valids;
}
absorb_miss boolean function RANGE (x != SYSMIS, a[n*2])
string function LTRIM (string s)
{
- while (s.length > 0 && s.string[0] == ' ')
+ while (s.length > 0 && s.string[0] == ' ')
{
s.length--;
s.string++;
{
if (c.length == 1)
{
- while (s.length > 0 && s.string[0] == c.string[0])
+ while (s.length > 0 && s.string[0] == c.string[0])
{
s.length--;
s.string++;
if (s.length > f->w)
s.length = f->w;
- error = data_in (s, C_ENCODING, f->type, &out, 0, NULL);
+ error = data_in (s, C_ENCODING, f->type, settings_get_fmt_settings (),
+ &out, 0, NULL);
if (error == NULL)
- data_in_imply_decimals (s, C_ENCODING, f->type, f->d, &out);
+ data_in_imply_decimals (s, C_ENCODING, f->type, f->d,
+ settings_get_fmt_settings (), &out);
else
{
msg (SE, "Cannot parse `%.*s' as format %s: %s",
v.f = x;
assert (!fmt_is_string (f->type));
- s = data_out (&v, C_ENCODING, f);
+ s = data_out (&v, C_ENCODING, f, settings_get_fmt_settings ());
dst = alloc_string (e, strlen (s));
strcpy (dst.string, s);
free (s);
function IDF.GAMMA (P >= 0 && P <= 1, a > 0, b > 0)
= gsl_cdf_gamma_Pinv (P, a, 1. / b);
function PDF.GAMMA (x >= 0, a > 0, b > 0) = gsl_ran_gamma_pdf (x, a, 1. / b);
-no_opt function RV.GAMMA (a > 0, b > 0)
+no_opt function RV.GAMMA (a > 0, b > 0)
= gsl_ran_gamma (get_rng (), a, 1. / b);
// Half-normal distribution.
function IDF.LAPLACE (P > 0 && P < 1, a, b > 0)
= a + b * gsl_cdf_laplace_Pinv (P, 1);
function PDF.LAPLACE (x, a, b > 0) = gsl_ran_laplace_pdf ((x - a) / b, 1) / b;
-no_opt function RV.LAPLACE (a, b > 0)
+no_opt function RV.LAPLACE (a, b > 0)
= a + b * gsl_ran_laplace (get_rng (), 1);
// Levy alpha-stable distribution.
-no_opt extension function RV.LEVY (c, alpha > 0 && alpha <= 2)
+no_opt extension function RV.LEVY (c, alpha > 0 && alpha <= 2)
= gsl_ran_levy (get_rng (), c, alpha);
// Levy skew alpha-stable distribution.
no_opt extension function RV.LVSKEW (c, alpha > 0 && alpha <= 2,
- beta >= -1 && beta <= 1)
+ beta >= -1 && beta <= 1)
= gsl_ran_levy_skew (get_rng (), c, alpha, beta);
// Logistic distribution.
= a + b * gsl_cdf_logistic_Pinv (P, 1);
function PDF.LOGISTIC (x, a, b > 0)
= gsl_ran_logistic_pdf ((x - a) / b, 1) / b;
-no_opt function RV.LOGISTIC (a, b > 0)
+no_opt function RV.LOGISTIC (a, b > 0)
= a + b * gsl_ran_logistic (get_rng (), 1);
// Lognormal distribution.
= gsl_cdf_lognormal_Pinv (P, log (m), s);
function PDF.LNORMAL (x >= 0, m > 0, s > 0)
= gsl_ran_lognormal_pdf (x, log (m), s);
-no_opt function RV.LNORMAL (m > 0, s > 0)
+no_opt function RV.LNORMAL (m > 0, s > 0)
= gsl_ran_lognormal (get_rng (), log (m), s);
// Normal distribution.
// Normal tail distribution.
function PDF.NTAIL (x, a > 0, sigma > 0)
= gsl_ran_gaussian_tail_pdf (x, a, sigma);
-no_opt function RV.NTAIL (a > 0, sigma > 0)
+no_opt function RV.NTAIL (a > 0, sigma > 0)
= gsl_ran_gaussian_tail (get_rng (), a, sigma);
// Pareto distribution.
= gsl_cdf_rayleigh_Pinv (P, sigma);
extension function PDF.RAYLEIGH (x, sigma > 0)
= gsl_ran_rayleigh_pdf (x, sigma);
-no_opt extension function RV.RAYLEIGH (sigma > 0)
+no_opt extension function RV.RAYLEIGH (sigma > 0)
= gsl_ran_rayleigh (get_rng (), sigma);
// Rayleigh tail distribution.
extension function PDF.RTAIL (x, a, sigma)
= gsl_ran_rayleigh_tail_pdf (x, a, sigma);
-no_opt extension function RV.RTAIL (a, sigma)
+no_opt extension function RV.RTAIL (a, sigma)
= gsl_ran_rayleigh_tail (get_rng (), a, sigma);
// Studentized maximum modulus distribution.
// Type-1 Gumbel distribution.
extension function CDF.T1G (x, a, b) = gsl_cdf_gumbel1_P (x, a, b);
extension function IDF.T1G (P >= 0 && P <= 1, a, b)
- = gsl_cdf_gumbel1_P (P, a, b);
+ = gsl_cdf_gumbel1_Pinv (P, a, b);
extension function PDF.T1G (x, a, b) = gsl_ran_gumbel1_pdf (x, a, b);
no_opt extension function RV.T1G (a, b) = gsl_ran_gumbel1 (get_rng (), a, b);
// Type-2 Gumbel distribution.
extension function CDF.T2G (x, a, b) = gsl_cdf_gumbel2_P (x, a, b);
extension function IDF.T2G (P >= 0 && P <= 1, a, b)
- = gsl_cdf_gumbel2_P (P, a, b);
+ = gsl_cdf_gumbel2_Pinv (P, a, b);
extension function PDF.T2G (x, a, b) = gsl_ran_gumbel2_pdf (x, a, b);
no_opt extension function RV.T2G (a, b) = gsl_ran_gumbel2 (get_rng (), a, b);
no_opt function RV.WEIBULL (a > 0, b > 0) = gsl_ran_weibull (get_rng (), a, b);
// Bernoulli distribution.
-function CDF.BERNOULLI (k == 0 || k == 1, p >= 0 && p <= 1)
+function CDF.BERNOULLI (k == 0 || k == 1, p >= 0 && p <= 1)
= k ? 1 : 1 - p;
function PDF.BERNOULLI (k == 0 || k == 1, p >= 0 && p <= 1)
= gsl_ran_bernoulli_pdf (k, p);
-no_opt function RV.BERNOULLI (p >= 0 && p <= 1)
+no_opt function RV.BERNOULLI (p >= 0 && p <= 1)
= gsl_ran_bernoulli (get_rng (), p);
// Binomial distribution.
n > 0 && n == floor (n),
p >= 0 && p <= 1)
= gsl_ran_binomial_pdf (k, p, n);
-no_opt function RV.BINOM (p > 0 && p == floor (p), n >= 0 && n <= 1)
+no_opt function RV.BINOM (p > 0 && p == floor (p), n >= 0 && n <= 1)
= gsl_ran_binomial (get_rng (), p, n);
// Geometric distribution.
// Logarithmic distribution.
extension function PDF.LOG (k >= 1, p > 0 && p <= 1)
= gsl_ran_logarithmic_pdf (k, p);
-no_opt extension function RV.LOG (p > 0 && p <= 1)
+no_opt extension function RV.LOG (p > 0 && p <= 1)
= gsl_ran_logarithmic (get_rng (), p);
// Negative binomial distribution.
= gsl_cdf_negative_binomial_P (k, p, n);
function PDF.NEGBIN (k >= 1, n == floor (n), p > 0 && p <= 1)
= gsl_ran_negative_binomial_pdf (k, p, n);
-no_opt function RV.NEGBIN (n == floor (n), p > 0 && p <= 1)
+no_opt function RV.NEGBIN (n == floor (n), p > 0 && p <= 1)
= gsl_ran_negative_binomial (get_rng (), p, n);
// Poisson distribution.
no_opt function RV.POISSON (mu > 0) = gsl_ran_poisson (get_rng (), mu);
// Weirdness.
-absorb_miss boolean function MISSING (x) = x == SYSMIS || !finite (x);
-absorb_miss boolean function SYSMIS (x) = x == SYSMIS || !finite (x);
+absorb_miss boolean function MISSING (x) = x == SYSMIS || !isfinite (x);
+absorb_miss boolean function SYSMIS (x) = x == SYSMIS || !isfinite (x);
no_opt boolean function SYSMIS (num_var v)
case c;
{
vector v;
case c;
{
- if (idx >= 1 && idx <= vector_get_var_cnt (v))
+ if (idx >= 1 && idx <= vector_get_n_vars (v))
{
const struct variable *var = vector_get_var (v, (size_t) idx - 1);
double value = case_num (c, var);
- return !var_is_num_missing (var, value, MV_USER) ? value : SYSMIS;
+ return !var_is_num_missing (var, value, MV_USER) ? value : SYSMIS;
}
else
{
vector v;
case c;
{
- if (idx >= 1 && idx <= vector_get_var_cnt (v))
+ if (idx >= 1 && idx <= vector_get_n_vars (v))
{
struct variable *var = vector_get_var (v, (size_t) idx - 1);
return copy_string (e, CHAR_CAST_BUG (char *, case_str (c, var)),
projects / pspp / blobdiff