X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fmath%2Fcovariance.c;h=66b44c12c10859cd5a882db66e7c22407ce47d84;hb=edd5c738dfef01c90d02e06a33b93fc9d38320b8;hp=1548187b3c0b1c7c19d8481c2e74e9b267e809a8;hpb=3bbe45fcc70f8d059b4bf6715d629209b50fa48e;p=pspp diff --git a/src/math/covariance.c b/src/math/covariance.c index 1548187b3c..66b44c12c1 100644 --- a/src/math/covariance.c +++ b/src/math/covariance.c @@ -1,5 +1,5 @@ /* PSPP - a program for statistical analysis. - Copyright (C) 2009 Free Software Foundation, Inc. + Copyright (C) 2009, 2010, 2011 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 @@ -16,27 +16,69 @@ #include -#include -#include "covariance.h" -#include -#include "moments.h" +#include "math/covariance.h" + #include -#include -#include -#include + +#include "data/case.h" +#include "data/variable.h" +#include "libpspp/assertion.h" +#include "libpspp/misc.h" +#include "math/categoricals.h" +#include "math/interaction.h" +#include "math/moments.h" + +#include "gl/xalloc.h" #define n_MOMENTS (MOMENT_VARIANCE + 1) +/* Create a new matrix of NEW_SIZE x NEW_SIZE and copy the elements of + matrix IN into it. IN must be a square matrix, and in normal usage + it will be smaller than NEW_SIZE. + IN is destroyed by this function. The return value must be destroyed + when no longer required. +*/ +static gsl_matrix * +resize_matrix (gsl_matrix *in, size_t new_size) +{ + size_t i, j; + + gsl_matrix *out = NULL; + + assert (in->size1 == in->size2); + + if (new_size <= in->size1) + return in; + + out = gsl_matrix_calloc (new_size, new_size); + + for (i = 0; i < in->size1; ++i) + { + for (j = 0; j < in->size2; ++j) + { + double x = gsl_matrix_get (in, i, j); + + gsl_matrix_set (out, i, j, x); + } + } + + gsl_matrix_free (in); + + return out; +} + struct covariance { + /* True if the covariances are centerered. (ie Real covariances) */ + bool centered; + /* The variables for which the covariance matrix is to be calculated. */ size_t n_vars; - const struct variable **vars; + const struct variable *const *vars; /* Categorical variables. */ - size_t n_catvars; - const struct variable **catvars; + struct categoricals *categoricals; /* Array containing number of categories per categorical variable. */ size_t *n_categories; @@ -58,7 +100,7 @@ struct covariance double *cm; int n_cm; - /* 1 for single pass algorithm; + /* 1 for single pass algorithm; 2 for double pass algorithm */ short passes; @@ -66,14 +108,16 @@ struct covariance /* 0 : No pass has been made 1 : First pass has been started - 2 : Second pass has been - + 2 : Second pass has been + IE: How many passes have been (partially) made. */ short state; /* Flags indicating that the first case has been seen */ bool pass_one_first_case_seen; bool pass_two_first_case_seen; + + gsl_matrix *unnormalised; }; @@ -96,28 +140,26 @@ covariance_moments (const struct covariance *cov, int m) /* Create a covariance struct. */ struct covariance * -covariance_create (size_t n_vars, const struct variable **vars, - const struct variable *weight, enum mv_class exclude, - short passes) +covariance_1pass_create (size_t n_vars, const struct variable *const *vars, + const struct variable *weight, enum mv_class exclude, + bool centered) { size_t i; - struct covariance *cov = xmalloc (sizeof *cov); - assert (passes == 1 || passes == 2); - cov->passes = passes; + struct covariance *cov = xzalloc (sizeof *cov); + + cov->centered = centered; + cov->passes = 1; cov->state = 0; cov->pass_one_first_case_seen = cov->pass_two_first_case_seen = false; - - cov->vars = xmalloc (sizeof *cov->vars * n_vars); + + cov->vars = vars; cov->wv = weight; cov->n_vars = n_vars; cov->dim = n_vars; - for (i = 0; i < n_vars; ++i) - cov->vars[i] = vars[i]; - cov->moments = xmalloc (sizeof *cov->moments * n_MOMENTS); - + for (i = 0; i < n_MOMENTS; ++i) cov->moments[i] = gsl_matrix_calloc (n_vars, n_vars); @@ -125,7 +167,9 @@ covariance_create (size_t n_vars, const struct variable **vars, cov->n_cm = (n_vars * (n_vars - 1) ) / 2; - cov->cm = xcalloc (sizeof *cov->cm, cov->n_cm); + + cov->cm = xcalloc (cov->n_cm, sizeof *cov->cm); + cov->categoricals = NULL; return cov; } @@ -137,37 +181,42 @@ covariance_create (size_t n_vars, const struct variable **vars, until then. */ struct covariance * -covariance_2pass_create (size_t n_vars, const struct variable **vars, - size_t n_catvars, const struct variable **catvars, - const struct variable *weight, enum mv_class exclude) +covariance_2pass_create (size_t n_vars, const struct variable *const *vars, + struct categoricals *cats, + const struct variable *wv, enum mv_class exclude, + bool centered) { size_t i; struct covariance *cov = xmalloc (sizeof *cov); - cov->vars = xmalloc (sizeof *cov->vars * n_vars); - cov->catvars = xnmalloc (n_catvars, sizeof (*cov->catvars)); - cov->n_categories = xnmalloc (n_catvars, sizeof (cov->n_categories)); - cov->wv = weight; - cov->n_vars = n_vars; - cov->n_catvars = n_catvars; + cov->centered = centered; + cov->passes = 2; + cov->state = 0; + cov->pass_one_first_case_seen = cov->pass_two_first_case_seen = false; - for (i = 0; i < n_vars; ++i) - cov->vars[i] = vars[i]; + cov->vars = vars; - for (i = 0; i < n_catvars; i++) - { - cov->catvars[i] = catvars[i]; - cov->n_categories[i] = 0; - } + cov->wv = wv; + cov->n_vars = n_vars; + cov->dim = n_vars; cov->moments = xmalloc (sizeof *cov->moments * n_MOMENTS); - + + for (i = 0; i < n_MOMENTS; ++i) + cov->moments[i] = gsl_matrix_calloc (n_vars, n_vars); + cov->exclude = exclude; + cov->n_cm = -1; + cov->cm = NULL; + + cov->categoricals = cats; + cov->unnormalised = NULL; + return cov; } -/* Return an integer, which can be used to index +/* Return an integer, which can be used to index into COV->cm, to obtain the I, J th element of the covariance matrix. If COV->cm does not contain that element, then a negative value @@ -177,29 +226,63 @@ static int cm_idx (const struct covariance *cov, int i, int j) { int as; - const int n2j = cov->n_vars - 2 - j; - const int nj = cov->n_vars - 2 ; - + const int n2j = cov->dim - 2 - j; + const int nj = cov->dim - 2 ; + assert (i >= 0); - assert (j < cov->n_vars); + assert (j < cov->dim); if ( i == 0) return -1; - if (j >= cov->n_vars - 1) + if (j >= cov->dim - 1) return -1; - if ( i <= j) + if ( i <= j) return -1 ; as = nj * (nj + 1) ; - as -= n2j * (n2j + 1) ; + as -= n2j * (n2j + 1) ; as /= 2; return i - 1 + as; } -static void + +/* + Returns true iff the variable corresponding to the Ith element of the covariance matrix + has a missing value for case C +*/ +static bool +is_missing (const struct covariance *cov, int i, const struct ccase *c) +{ + const struct variable *var = i < cov->n_vars ? + cov->vars[i] : + categoricals_get_interaction_by_subscript (cov->categoricals, i - cov->n_vars)->vars[0]; + + const union value *val = case_data (c, var); + + return var_is_value_missing (var, val, cov->exclude); +} + + +static double +get_val (const struct covariance *cov, int i, const struct ccase *c) +{ + if ( i < cov->n_vars) + { + const struct variable *var = cov->vars[i]; + + const union value *val = case_data (c, var); + + return val->f; + } + + return categoricals_get_effects_code_for_case (cov->categoricals, i - cov->n_vars, c); +} + +#if 0 +void dump_matrix (const gsl_matrix *m) { size_t i, j; @@ -211,6 +294,7 @@ dump_matrix (const gsl_matrix *m) printf ("\n"); } } +#endif /* Call this function for every case in the data set */ void @@ -226,19 +310,21 @@ covariance_accumulate_pass1 (struct covariance *cov, const struct ccase *c) cov->state = 1; } - for (i = 0 ; i < cov->n_vars; ++i) + if (cov->categoricals) + categoricals_update (cov->categoricals, c); + + for (i = 0 ; i < cov->dim; ++i) { - const union value *val1 = case_data (c, cov->vars[i]); + double v1 = get_val (cov, i, c); - if ( var_is_value_missing (cov->vars[i], val1, cov->exclude)) + if ( is_missing (cov, i, c)) continue; - for (j = 0 ; j < cov->n_vars; ++j) + for (j = 0 ; j < cov->dim; ++j) { double pwr = 1.0; - const union value *val2 = case_data (c, cov->vars[j]); - if ( var_is_value_missing (cov->vars[j], val2, cov->exclude)) + if ( is_missing (cov, j, c)) continue; for (m = 0 ; m <= MOMENT_MEAN; ++m) @@ -246,7 +332,7 @@ covariance_accumulate_pass1 (struct covariance *cov, const struct ccase *c) double *x = gsl_matrix_ptr (cov->moments[m], i, j); *x += pwr * weight; - pwr *= val1->f; + pwr *= v1; } } } @@ -267,36 +353,84 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) if (! cov->pass_two_first_case_seen) { + size_t m; assert (cov->state == 1); cov->state = 2; + if (cov->categoricals) + categoricals_done (cov->categoricals); + + cov->dim = cov->n_vars; + + if (cov->categoricals) + cov->dim += categoricals_df_total (cov->categoricals); + + cov->n_cm = (cov->dim * (cov->dim - 1) ) / 2; + cov->cm = xcalloc (cov->n_cm, sizeof *cov->cm); + + /* Grow the moment matrices so that they're large enough to accommodate the + categorical elements */ + for (i = 0; i < n_MOMENTS; ++i) + { + cov->moments[i] = resize_matrix (cov->moments[i], cov->dim); + } + + /* Populate the moments matrices with the categorical value elements */ + for (i = cov->n_vars; i < cov->dim; ++i) + { + for (j = 0 ; j < cov->dim ; ++j) /* FIXME: This is WRONG !!! */ + { + double w = categoricals_get_weight_by_subscript (cov->categoricals, i - cov->n_vars); + + gsl_matrix_set (cov->moments[MOMENT_NONE], i, j, w); + + w = categoricals_get_sum_by_subscript (cov->categoricals, i - cov->n_vars); + + gsl_matrix_set (cov->moments[MOMENT_MEAN], i, j, w); + } + } + + /* FIXME: This is WRONG!! It must be fixed to properly handle missing values. For + now it assumes there are none */ + for (m = 0 ; m < n_MOMENTS; ++m) + { + for (i = 0 ; i < cov->dim ; ++i) + { + double x = gsl_matrix_get (cov->moments[m], i, cov->n_vars -1); + for (j = cov->n_vars; j < cov->dim; ++j) + { + gsl_matrix_set (cov->moments[m], i, j, x); + } + } + } + /* Divide the means by the number of samples */ - for (i = 0; i < cov->n_vars; ++i) + for (i = 0; i < cov->dim; ++i) { - for (j = 0; j < cov->n_vars; ++j) + for (j = 0; j < cov->dim; ++j) { double *x = gsl_matrix_ptr (cov->moments[MOMENT_MEAN], i, j); *x /= gsl_matrix_get (cov->moments[MOMENT_NONE], i, j); - } + } } } - for (i = 0 ; i < cov->n_vars; ++i) + for (i = 0 ; i < cov->dim; ++i) { - const union value *val1 = case_data (c, cov->vars[i]); + double v1 = get_val (cov, i, c); - if ( var_is_value_missing (cov->vars[i], val1, cov->exclude)) + if ( is_missing (cov, i, c)) continue; - for (j = 0 ; j < cov->n_vars; ++j) + for (j = 0 ; j < cov->dim; ++j) { int idx; double ss ; - const union value *val2 = case_data (c, cov->vars[j]); + double v2 = get_val (cov, j, c); - const double s = pow2 (val1->f - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) * weight; + const double s = pow2 (v1 - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) * weight; - if ( var_is_value_missing (cov->vars[j], val2, cov->exclude)) + if ( is_missing (cov, j, c)) continue; { @@ -304,10 +438,10 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) *x += s; } - ss = - (val1->f - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) - * - (val2->f - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) + ss = + (v1 - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) + * + (v2 - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) * weight ; @@ -316,7 +450,6 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) { cov->cm [idx] += ss; } - } } @@ -341,20 +474,20 @@ covariance_accumulate (struct covariance *cov, const struct ccase *c) cov->state = 1; } - for (i = 0 ; i < cov->n_vars; ++i) + for (i = 0 ; i < cov->dim; ++i) { const union value *val1 = case_data (c, cov->vars[i]); - if ( var_is_value_missing (cov->vars[i], val1, cov->exclude)) + if ( is_missing (cov, i, c)) continue; - for (j = 0 ; j < cov->n_vars; ++j) + for (j = 0 ; j < cov->dim; ++j) { double pwr = 1.0; int idx; const union value *val2 = case_data (c, cov->vars[j]); - if ( var_is_value_missing (cov->vars[j], val2, cov->exclude)) + if ( is_missing (cov, j, c)) continue; idx = cm_idx (cov, i, j); @@ -377,7 +510,7 @@ covariance_accumulate (struct covariance *cov, const struct ccase *c) } -/* +/* Allocate and return a gsl_matrix containing the covariances of the data. */ @@ -385,12 +518,12 @@ static gsl_matrix * cm_to_gsl (struct covariance *cov) { int i, j; - gsl_matrix *m = gsl_matrix_calloc (cov->n_vars, cov->n_vars); + gsl_matrix *m = gsl_matrix_calloc (cov->dim, cov->dim); /* Copy the non-diagonal elements from cov->cm */ - for ( j = 0 ; j < cov->n_vars - 1; ++j) + for ( j = 0 ; j < cov->dim - 1; ++j) { - for (i = j+1 ; i < cov->n_vars; ++i) + for (i = j+1 ; i < cov->dim; ++i) { double x = cov->cm [cm_idx (cov, i, j)]; gsl_matrix_set (m, i, j, x); @@ -399,7 +532,7 @@ cm_to_gsl (struct covariance *cov) } /* Copy the diagonal elements from cov->moments[2] */ - for (j = 0 ; j < cov->n_vars ; ++j) + for (j = 0 ; j < cov->dim ; ++j) { double sigma = gsl_matrix_get (cov->moments[2], j, j); gsl_matrix_set (m, j, j, sigma); @@ -409,13 +542,13 @@ cm_to_gsl (struct covariance *cov) } -static const gsl_matrix * +static gsl_matrix * covariance_calculate_double_pass (struct covariance *cov) { size_t i, j; - for (i = 0 ; i < cov->n_vars; ++i) + for (i = 0 ; i < cov->dim; ++i) { - for (j = 0 ; j < cov->n_vars; ++j) + for (j = 0 ; j < cov->dim; ++j) { int idx; double *x = gsl_matrix_ptr (cov->moments[MOMENT_VARIANCE], i, j); @@ -433,7 +566,7 @@ covariance_calculate_double_pass (struct covariance *cov) return cm_to_gsl (cov); } -static const gsl_matrix * +static gsl_matrix * covariance_calculate_single_pass (struct covariance *cov) { size_t i, j; @@ -444,9 +577,9 @@ covariance_calculate_single_pass (struct covariance *cov) /* Divide the moments by the number of samples */ if ( m > 0) { - for (i = 0 ; i < cov->n_vars; ++i) + for (i = 0 ; i < cov->dim; ++i) { - for (j = 0 ; j < cov->n_vars; ++j) + for (j = 0 ; j < cov->dim; ++j) { double *x = gsl_matrix_ptr (cov->moments[m], i, j); *x /= gsl_matrix_get (cov->moments[0], i, j); @@ -458,19 +591,22 @@ covariance_calculate_single_pass (struct covariance *cov) } } - /* Centre the moments */ - for ( j = 0 ; j < cov->n_vars - 1; ++j) + if (cov->centered) { - for (i = j + 1 ; i < cov->n_vars; ++i) + /* Centre the moments */ + for ( j = 0 ; j < cov->dim - 1; ++j) { - double *x = &cov->cm [cm_idx (cov, i, j)]; - - *x /= gsl_matrix_get (cov->moments[0], i, j); + for (i = j + 1 ; i < cov->dim; ++i) + { + double *x = &cov->cm [cm_idx (cov, i, j)]; - *x -= - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j) - * - gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i); + *x /= gsl_matrix_get (cov->moments[0], i, j); + + *x -= + gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j) + * + gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i); + } } } @@ -478,31 +614,111 @@ covariance_calculate_single_pass (struct covariance *cov) } +/* Return a pointer to gsl_matrix containing the pairwise covariances. The + caller owns the returned matrix and must free it when it is no longer + needed. -/* - Return a pointer to gsl_matrix containing the pairwise covariances. - The matrix remains owned by the COV object, and must not be freed. - Call this function only after all data have been accumulated. -*/ -const gsl_matrix * + Call this function only after all data have been accumulated. */ +gsl_matrix * covariance_calculate (struct covariance *cov) { - assert ( cov->state > 0 ); + if ( cov->state <= 0 ) + return NULL; switch (cov->passes) { case 1: - return covariance_calculate_single_pass (cov); + return covariance_calculate_single_pass (cov); break; case 2: - return covariance_calculate_double_pass (cov); + return covariance_calculate_double_pass (cov); break; default: NOT_REACHED (); } } +/* + Covariance computed without dividing by the sample size. + */ +static gsl_matrix * +covariance_calculate_double_pass_unnormalized (struct covariance *cov) +{ + return cm_to_gsl (cov); +} + +static gsl_matrix * +covariance_calculate_single_pass_unnormalized (struct covariance *cov) +{ + size_t i, j; + + if (cov->centered) + { + for (i = 0 ; i < cov->dim; ++i) + { + for (j = 0 ; j < cov->dim; ++j) + { + double *x = gsl_matrix_ptr (cov->moments[MOMENT_VARIANCE], i, j); + *x -= pow2 (gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) + / gsl_matrix_get (cov->moments[MOMENT_NONE], i, j); + } + } + + for ( j = 0 ; j < cov->dim - 1; ++j) + { + for (i = j + 1 ; i < cov->dim; ++i) + { + double *x = &cov->cm [cm_idx (cov, i, j)]; + + *x -= + gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j) + * + gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i) + / gsl_matrix_get (cov->moments[MOMENT_NONE], i, j); + } + } + } + + return cm_to_gsl (cov); +} + + +/* Return a pointer to gsl_matrix containing the pairwise covariances. The + returned matrix is owned by the structure, and must not be freed. + + Call this function only after all data have been accumulated. */ +const gsl_matrix * +covariance_calculate_unnormalized (struct covariance *cov) +{ + if ( cov->state <= 0 ) + return NULL; + + if (cov->unnormalised != NULL) + return cov->unnormalised; + + switch (cov->passes) + { + case 1: + cov->unnormalised = covariance_calculate_single_pass_unnormalized (cov); + break; + case 2: + cov->unnormalised = covariance_calculate_double_pass_unnormalized (cov); + break; + default: + NOT_REACHED (); + } + + return cov->unnormalised; +} +/* Function to access the categoricals used by COV + The return value is owned by the COV +*/ +const struct categoricals * +covariance_get_categoricals (const struct covariance *cov) +{ + return cov->categoricals; +} /* Destroy the COV object */ @@ -510,12 +726,107 @@ void covariance_destroy (struct covariance *cov) { size_t i; - free (cov->vars); + + categoricals_destroy (cov->categoricals); for (i = 0; i < n_MOMENTS; ++i) gsl_matrix_free (cov->moments[i]); + gsl_matrix_free (cov->unnormalised); free (cov->moments); free (cov->cm); free (cov); } + +size_t +covariance_dim (const struct covariance * cov) +{ + return (cov->dim); +} + + + +/* + Routines to assist debugging. + The following are not thoroughly tested and in certain respects + unreliable. They should only be + used for aids to development. Not as user accessible code. +*/ + +#include "libpspp/str.h" +#include "output/tab.h" +#include "data/format.h" + + +/* Create a table which can be populated with the encodings for + the covariance matrix COV */ +struct tab_table * +covariance_dump_enc_header (const struct covariance *cov, int length) +{ + struct tab_table *t = tab_create (cov->dim, length); + int n; + int i; + + tab_title (t, "Covariance Encoding"); + + tab_box (t, + TAL_2, TAL_2, 0, 0, + 0, 0, tab_nc (t) - 1, tab_nr (t) - 1); + + tab_hline (t, TAL_2, 0, tab_nc (t) - 1, 1); + + + for (i = 0 ; i < cov->n_vars; ++i) + { + tab_text (t, i, 0, TAT_TITLE, var_get_name (cov->vars[i])); + tab_vline (t, TAL_1, i + 1, 0, tab_nr (t) - 1); + } + + n = 0; + while (i < cov->dim) + { + struct string str; + int idx = i - cov->n_vars; + const struct interaction *iact = + categoricals_get_interaction_by_subscript (cov->categoricals, idx); + int df; + + ds_init_empty (&str); + interaction_to_string (iact, &str); + + df = categoricals_df (cov->categoricals, n); + + tab_joint_text (t, + i, 0, + i + df - 1, 0, + TAT_TITLE, ds_cstr (&str)); + + if (i + df < tab_nr (t) - 1) + tab_vline (t, TAL_1, i + df, 0, tab_nr (t) - 1); + + i += df; + n++; + ds_destroy (&str); + } + + return t; +} + + +/* + Append table T, which should have been returned by covariance_dump_enc_header + with an entry corresponding to case C for the covariance matrix COV + */ +void +covariance_dump_enc (const struct covariance *cov, const struct ccase *c, + struct tab_table *t) +{ + static int row = 0; + int i; + ++row; + for (i = 0 ; i < cov->dim; ++i) + { + double v = get_val (cov, i, c); + tab_double (t, i, row, 0, v, i < cov->n_vars ? NULL : &F_8_0, RC_OTHER); + } +}