X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fregression.q;h=28289622aaded41975b27df35eda4830b4d8c2bf;hb=93600e1ad40fd910a21a5a6528c1782b73000844;hp=e69761e3d39a21176728868ec951d93317f811e2;hpb=4aa81a07a03b3322e3bcf2ce141fe119c02f87f7;p=pspp diff --git a/src/regression.q b/src/regression.q index e69761e3d3..28289622aa 100644 --- a/src/regression.q +++ b/src/regression.q @@ -26,19 +26,23 @@ #include "case.h" #include "casefile.h" #include "cat.h" +#include "cat-routines.h" #include "command.h" +#include "design-matrix.h" #include "dictionary.h" #include "error.h" #include "file-handle.h" #include "gettext.h" #include "lexer.h" #include +#include "missing-values.h" #include "tab.h" #include "var.h" #include "vfm.h" -/* (headers) */ +#define REG_LARGE_DATA 1000 +/* (headers) */ /* (specification) "REGRESSION" (regression_): @@ -60,6 +64,7 @@ f, defaults, all; + export=custom; ^dependent=varlist; ^method=enter. */ @@ -72,6 +77,12 @@ static struct cmd_regression cmd; */ size_t *indep_vars; +/* + File where the model will be saved if the EXPORT subcommand + is given. + */ +struct file_handle *model_file; + /* Return value for the procedure. */ @@ -460,6 +471,76 @@ subcommand_statistics (int *keywords, pspp_linreg_cache * c) statistics_keyword_output (reg_stats_tol, keywords[tol], c); statistics_keyword_output (reg_stats_selection, keywords[selection], c); } +static void +subcommand_export (int export, pspp_linreg_cache *c) +{ + FILE *fp; + size_t i; + struct pspp_linreg_coeff coeff; + + if (export) + { + assert (c != NULL); + assert (model_file != NULL); + assert (fp != NULL); + fp = fopen (handle_get_filename (model_file), "w"); + fprintf (fp, "#include \n\n"); + fprintf (fp, "/*\n Estimate the mean of Y, the dependent variable for\n"); + fprintf (fp, " the linear model of the form \n\n"); + fprintf (fp, " Y = b0 + b1 * X1 + b2 * X2 + ... + bk * X2 + error\n\n"); + fprintf (fp, " where X1, ..., Xk are the independent variables\n"); + fprintf (fp, " whose values are stored in var_vals and whose names, \n"); + fprintf (fp, " as known by PSPP, are stored in var_names. The estimated \n"); + fprintf (fp, " regression coefficients (i.e., the estimates of b0,...,bk) \n"); + fprintf (fp, " are stored in model_coeffs.\n*/\n"); + fprintf (fp, "double\npspp_reg_estimate (const double *var_vals, const char *var_names[])\n{\n\tchar *model_depvars[%d] = {", c->n_indeps); + for (i = 1; i < c->n_indeps; i++) + { + coeff = c->coeff[i]; + fprintf (fp, "\"%s\",\n\t\t", coeff.v->name); + } + coeff = c->coeff[i]; + fprintf (fp, "\"%s\"};\n\t", coeff.v->name); + fprintf (fp, "double model_coeffs[%d] = {", c->n_indeps); + for (i = 1; i < c->n_indeps; i++) + { + coeff = c->coeff[i]; + fprintf (fp, "%.15e,\n\t\t", coeff.estimate); + } + coeff = c->coeff[i]; + fprintf (fp, "%.15e};\n\t", coeff.estimate); + coeff = c->coeff[0]; + fprintf (fp, "double estimate = %.15e;\n\t", coeff.estimate); + fprintf (fp, "int i;\n\tint j;\n\n\t"); + fprintf (fp, "for (i = 0; i < %d; i++)\n\t", c->n_indeps); + fprintf (fp, "{\n\t\tfor (j = 0; j < %d; j++)\n\t\t", c->n_indeps); + fprintf (fp, "{\n\t\t\tif (strcmp (var_names[i], model_depvars[j]) == 0)\n"); + fprintf (fp, "\t\t\t{\n\t\t\t\testimate += var_vals[i] * model_coeffs[j];\n"); + fprintf (fp, "\t\t\t}\n\t\t}\n\t}\n\treturn estimate;\n}\n"); + fclose (fp); + } +} +static int +regression_custom_export (struct cmd_regression *cmd) +{ + /* 0 on failure, 1 on success, 2 on failure that should result in syntax error */ + if (!lex_force_match ('(')) + return 0; + + if (lex_match ('*')) + model_file = NULL; + else + { + model_file = fh_parse (); + if (model_file == NULL) + return 0; + } + + if (!lex_force_match (')')) + return 0; + + return 1; +} int cmd_regression (void) @@ -496,104 +577,136 @@ static void run_regression (const struct casefile *cf, void *cmd_ UNUSED) { size_t i; - size_t k; size_t n_data = 0; size_t row; + size_t case_num; int n_indep; int j = 0; + /* + Keep track of the missing cases. + */ + int *is_missing_case; const union value *val; struct casereader *r; struct casereader *r2; struct ccase c; - const struct variable *v; - struct recoded_categorical_array *ca; - struct recoded_categorical *rc; + struct variable *v; + struct variable **indep_vars; struct design_matrix *X; gsl_vector *Y; pspp_linreg_cache *lcache; pspp_linreg_opts lopts; n_data = casefile_get_case_cnt (cf); + + is_missing_case = xnmalloc (n_data, sizeof (*is_missing_case)); + for (i = 0; i < n_data; i++) + is_missing_case[i] = 0; + n_indep = cmd.n_variables - cmd.n_dependent; indep_vars = xnmalloc (n_indep, sizeof *indep_vars); - Y = gsl_vector_alloc (n_data); lopts.get_depvar_mean_std = 1; lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int)); - lcache = pspp_linreg_cache_alloc (n_data, n_indep); - lcache->indep_means = gsl_vector_alloc (n_indep); - lcache->indep_std = gsl_vector_alloc (n_indep); /* Read from the active file. The first pass encodes categorical - variables. + variables and drops cases with missing values. */ - ca = cr_recoded_cat_ar_create (cmd.n_variables, cmd.v_variables); - for (r = casefile_get_reader (cf); - casereader_read (r, &c); case_destroy (&c)) + j = 0; + for (i = 0; i < cmd.n_variables; i++) { - for (i = 0; i < ca->n_vars; i++) + if (!is_depvar (i)) { - v = (*(ca->a + i))->v; - val = case_data (&c, v->fv); - cr_value_update (*(ca->a + i), val); + v = cmd.v_variables[i]; + indep_vars[j] = v; + j++; + if (v->type == ALPHA) + { + /* Make a place to hold the binary vectors + corresponding to this variable's values. */ + cat_stored_values_create (v); + } + for (r = casefile_get_reader (cf); + casereader_read (r, &c); case_destroy (&c)) + { + row = casereader_cnum (r) - 1; + + val = case_data (&c, v->fv); + cat_value_update (v, val); + if (mv_is_value_missing (&v->miss, val)) + { + if (!is_missing_case[row]) + { + /* Now it is missing. */ + n_data--; + is_missing_case[row] = 1; + } + } + } } } - cr_create_value_matrices (ca); + + Y = gsl_vector_alloc (n_data); X = - design_matrix_create (n_indep, (const struct variable **) cmd.v_variables, - ca, n_data); + design_matrix_create (n_indep, (const struct variable **) indep_vars, + n_data); + lcache = pspp_linreg_cache_alloc (X->m->size1, X->m->size2); + lcache->indep_means = gsl_vector_alloc (X->m->size2); + lcache->indep_std = gsl_vector_alloc (X->m->size2); /* The second pass creates the design matrix. */ + row = 0; for (r2 = casefile_get_reader (cf); casereader_read (r2, &c); case_destroy (&c)) /* Iterate over the cases. */ { - k = 0; - row = casereader_cnum (r2) - 1; - for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables + case_num = casereader_cnum (r2) - 1; + if (!is_missing_case[case_num]) + { + for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables for the current case. */ - { - v = cmd.v_variables[i]; - val = case_data (&c, v->fv); - /* - Independent/dependent variable separation. The - 'variables' subcommand specifies a varlist which contains - both dependent and independent variables. The dependent - variables are specified with the 'dependent' - subcommand. We need to separate the two. - */ - if (is_depvar (i)) - { - if (v->type != NUMERIC) - { - msg (SE, gettext ("Dependent variable must be numeric.")); - pspp_reg_rc = CMD_FAILURE; - return; - } - lcache->depvar = (const struct var *) v; - gsl_vector_set (Y, row, val->f); - } - else { - if (v->type == ALPHA) + v = cmd.v_variables[i]; + val = case_data (&c, v->fv); + /* + Independent/dependent variable separation. The + 'variables' subcommand specifies a varlist which contains + both dependent and independent variables. The dependent + variables are specified with the 'dependent' + subcommand. We need to separate the two. + */ + if (is_depvar (i)) { - rc = cr_var_to_recoded_categorical (v, ca); - design_matrix_set_categorical (X, row, v, val, rc); + if (v->type != NUMERIC) + { + msg (SE, + gettext ("Dependent variable must be numeric.")); + pspp_reg_rc = CMD_FAILURE; + return; + } + lcache->depvar = (const struct variable *) v; + gsl_vector_set (Y, row, val->f); } - else if (v->type == NUMERIC) + else { - design_matrix_set_numeric (X, row, v, val); + if (v->type == ALPHA) + { + design_matrix_set_categorical (X, row, v, val); + } + else if (v->type == NUMERIC) + { + design_matrix_set_numeric (X, row, v, val); + } + + lopts.get_indep_mean_std[i] = 1; } - - indep_vars[k] = i; - k++; - lopts.get_indep_mean_std[i] = 1; } + row++; } } /* @@ -609,16 +722,25 @@ run_regression (const struct casefile *cf, void *cmd_ UNUSED) (const struct variable *) design_matrix_col_to_var (X, i); assert (lcache->coeff[j].v != NULL); } + /* + For large data sets, use QR decomposition. + */ + if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA) + { + lcache->method = PSPP_LINREG_SVD; + } /* Find the least-squares estimates and other statistics. */ pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, lcache); subcommand_statistics (cmd.a_statistics, lcache); + subcommand_export (cmd.sbc_export, lcache); gsl_vector_free (Y); design_matrix_destroy (X); pspp_linreg_cache_free (lcache); free (lopts.get_indep_mean_std); free (indep_vars); + free (is_missing_case); casereader_destroy (r); return; }