+++ /dev/null
-/* PSPP - a program for statistical analysis.
- Copyright (C) 2005 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/>. */
-
-/*
- Exported C code for a regression model. The EXPORT subcommand causes PSPP
- to save a model as a small C program. This file contains some of the code
- of that saved program.
- */
-#ifndef REG_EXPORT_COMMENTS_H
-#define REG_EXPORT_COMMENTS_H
-const char reg_header[] = "#ifndef REG_EXPORT_COMMENTS_H\n#define REG_EXPORT_COMMENTS_H\n"
-"double pspp_reg_estimate (const double *, const char *[]);\n\n"
-"double pspp_reg_variance (const double *var_vals, const char *[]);\n\n"
-"double pspp_reg_confidence_interval_U "
-"(const double *var_vals, const char *var_names[], double p);\n\n"
-"double pspp_reg_confidence_interval_L "
-"(const double *var_vals, const char *var_names[], double p);\n\n"
-"double pspp_reg_prediction_interval_U "
-"(const double *var_vals, const char *var_names[], double p);\n\n"
-"double pspp_reg_prediction_interval_L "
-"(const double *var_vals, const char *var_names[], double p);\n"
-"#endif\n";
-
-const char reg_preamble[] = "/*\n This program contains functions which return estimates\n"
-" and confidence intervals for a linear model. The EXPORT subcommand\n"
-" of the REGRESSION procedure of GNU PSPP generated this program.\n*/\n\n"
-"#include <string.h>\n#include <math.h>\n#define PSPP_REG_MAXLEN 1024\n\n";
-
-const char reg_mean_cmt[] = "/*\n Estimate the mean of Y, the dependent variable for\n"
-" the linear model of the form \n\n"
-" Y = b0 + b1 * X1 + b2 * X2 + ... + bk * Xk + error\n\n"
-" where X1, ..., Xk are the independent variables\n"
-" whose values are stored in var_vals and whose names, \n"
-" as known by PSPP, are stored in var_names. The estimated \n"
-" regression coefficients (i.e., the estimates of b0,...,bk) \n"
-" are stored in model_coeffs.\n*/\n";
-
-const char reg_getvar[] = "{\n\t\tj = pspp_reg_getvar (var_names[i]);\n"
-"\t\testimate += var_vals[j] * model_coeffs[j];\n"
-"\t}\n\t\n\treturn estimate;\n}\n\n"
-"/*\n Variance of an estimated mean of this form:\n\t"
-"Y = b0 + b1 * X1 + ... + bk * Xk\n where X1,...Xk are the dependent variables,"
-" stored in\n var_vals and b0,...,bk are the estimated regression coefficients.\n*/\n"
-"double\npspp_reg_variance (const double *var_vals, "
-"const char *var_names[])\n{\n\t";
-
-const char reg_export_t_quantiles_1[] = "/*\n Quantiles for the T distribution.\n*/\n"
-"static double\npspp_reg_t_quantile "
-"(double prob)\n{\n\n\tint i;\n\tdouble quantiles[] = {\n\t\t";
-
-const char reg_export_t_quantiles_2[] = "i = (int) 100.0 * prob;\n\treturn quantiles[i];\n}\n";
-
-const char reg_variance[] = "double result = 0.0;\n\n\tfor(i = 0; i < n_vars; i++)\n\t"
-"{\n\t\tj = pspp_reg_getvar (var_names[i]);\n\t\t"
-"unshuffled_vals[j] = var_vals[i];\n\t}\n\t"
-"for (i = 0; i < n_vars; i++)\n\t"
-"{\n\t\tresult += cov[i][i] * unshuffled_vals[i] * unshuffled_vals[i];\n\t\t"
-"for (j = i + 1; j < n_vars; j++)\n\t\t{\n\t\t\t"
-"result += 2.0 * cov[i][j] * unshuffled_vals[i] * unshuffled_vals[j];"
-"\n\t\t}\n\t}\n\treturn result;\n}\n";
-
-const char reg_export_confidence_interval[] = "/*\n Upper confidence limit for an "
-"estimated mean b0 + b1 * X1 + ... + bk * Xk.\n The confidence interval is a "
-"100 * p percent confidence interval.\n*/\n"
-"double pspp_reg_confidence_interval_U "
-"(const double *var_vals, const char *var_names[], double p)\n{\n\t"
-"double result;\n\t"
-"result = sqrt (pspp_reg_variance (var_vals, var_names));\n\t"
-"result *= pspp_reg_t_quantile ((1.0 + p) / 2.0);\n\t"
-"result += pspp_reg_estimate (var_vals, var_names);\n\treturn result;\n}\n"
-"/*\n Lower confidence limit for an "
-"estimated mean b0 + b1 * X1 + ... + bk * Xk.\n The confidence interval is a "
-"100 * p percent confidence interval.\n*/\n"
-"double pspp_reg_confidence_interval_L "
-"(const double *var_vals, const char *var_names[], double p)\n{\n\t"
-"double result;\n\t"
-"result = -sqrt (pspp_reg_variance (var_vals, var_names));\n\t"
-"result *= pspp_reg_t_quantile ((1.0 + p) / 2.0);\n\t"
-"result += pspp_reg_estimate (var_vals, var_names);\n\treturn result;\n}\n";
-
-const char reg_export_prediction_interval_1[] = "/*\n Upper prediction limit for a "
-"predicted value b0 + b1 * X1 + ... + bk * Xk.\n The prediction interval is a "
-"100 * p percent prediction interval.\n*/\n"
-"double pspp_reg_prediction_interval_U "
-"(const double *var_vals, const char *var_names[], double p)\n{\n\t"
-"double result;\n\tresult = sqrt (";
-
-const char reg_export_prediction_interval_2[] = " + pspp_reg_variance (var_vals, var_names));\n"
-"\tresult *= pspp_reg_t_quantile ((1.0 + p) / 2.0);\n\t"
-"result += pspp_reg_estimate (var_vals, var_names);\n\treturn result;\n}\n"
-"/*\n Lower prediction limit for a "
-"predicted value b0 + b1 * X1 + ... + bk * Xk.\n The prediction interval is a "
-"100 * p percent prediction interval.\n*/\n"
-"double pspp_reg_prediction_interval_L "
-"(const double *var_vals, const char *var_names[], double p)\n{\n\t"
-"double result;\n\t"
-"result = -sqrt (";
-
-const char reg_export_prediction_interval_3[] = " + pspp_reg_variance (var_vals, var_names));"
-"\n\tresult *= pspp_reg_t_quantile ((1.0 + p) / 2.0);\n\t"
-"result += pspp_reg_estimate (var_vals, var_names);\n\treturn result;\n}\n";
-
-/*
- Change categorical values to binary vectors. The routine will use
- an encoding in which a categorical variable with n values is mapped
- to a vector with n-1 entries. Value 0 is mapped to the zero vector,
- value 1 is mapped to a vector whose first entry is 1 and all others are
- 0, etc. For example, if a variable can have 'a', 'b' or 'c' as values,
- then the value 'a' will be encoded as (0,0), 'b' as (1,0) and 'c' as
- (0,1). If the design matrix used to create the model used a different
- encoding, then the function pspp_reg_categorical_encode () will return
- a vector which does not match its categorical value in the model.
- */
-const char reg_export_categorical_encode_1[] = "struct pspp_reg_categorical_variable\n"
-"{\n\tchar * name;\n\tsize_t n_vals;\n\tchar *values[1024];\n};\n\n"
-"/*\n This function returns the binary vector which corresponds to the value\n"
-" of the categorical variable stored in 'value'. The name of the variable is\n"
-" stored in the 'var' argument. Notice the values stored in the\n"
-" pspp_categorical_variable structures all end with a space character.\n"
-" That means the values of the categorical variables you pass to any function\n"
-" in this program should also end with a space character.\n*/\n"
-"static\ndouble * pspp_reg_get_value_vector (char *var, char *value)\n{\n\tdouble *result;\n\t"
-"int i;\n\t";
-
-const char reg_export_categorical_encode_2[] = "int v_index = 0;\n\t"
-"while (v_index < n_vars && strncmp (var, varlist[i]->name, PSPP_REG_MAXLEN) != 0)\n\t{\n\t\t"
-"v_index++;\n\t}\n\tresult = (double *) malloc (varlist[v_index]->n_vals * sizeof (*result));\n\t"
-"for (i = 0; i < varlist[v_index]->n_vals; i++)\n\t{\n\t\t"
-"if (strncmp ( (varlist[v_index]->values)[i], value, PSPP_REG_MAXLEN) == 0)\n\t\t{\n\t\t\t"
-"result[i] = 1.0;\n\t\t}\n\t\telse result[i] = 0.0;\n\t}\n\n\t"
-"return result;\n}\n\n";
-#endif
projects / pspp-builds.git / commitdiff