X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fmath%2Fcovariance.c;h=79fce25cef28362dbe7b404377d436e92727111c;hb=101250dc9f516758bd9d13f869f1a770d6dd1ca3;hp=5dc1782638aff4f294253fd20d87c4e6b2e4c426;hpb=a3dd22c0ed3bcf0d3cf5cf3e009c215eeb35b567;p=pspp diff --git a/src/math/covariance.c b/src/math/covariance.c index 5dc1782638..79fce25cef 100644 --- a/src/math/covariance.c +++ b/src/math/covariance.c @@ -25,6 +25,7 @@ #include "libpspp/assertion.h" #include "libpspp/misc.h" #include "math/categoricals.h" +#include "math/interaction.h" #include "math/moments.h" #include "gl/xalloc.h" @@ -61,7 +62,7 @@ resize_matrix (gsl_matrix *in, size_t new_size) gsl_matrix_set (out, i, j, x); } } - + gsl_matrix_free (in); return out; @@ -69,6 +70,9 @@ resize_matrix (gsl_matrix *in, size_t new_size) 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 *const *vars; @@ -96,7 +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; @@ -104,14 +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; }; @@ -123,7 +129,7 @@ struct covariance be identical. If missing values are involved, then element (i,j) is the moment of the i th variable, when paired with the j th variable. */ -const gsl_matrix * +gsl_matrix * covariance_moments (const struct covariance *cov, int m) { return cov->moments[m]; @@ -135,15 +141,17 @@ covariance_moments (const struct covariance *cov, int m) */ struct covariance * covariance_1pass_create (size_t n_vars, const struct variable *const *vars, - const struct variable *weight, enum mv_class exclude) + const struct variable *weight, enum mv_class exclude, + bool centered) { size_t i; - struct covariance *cov = xzalloc (sizeof *cov); + struct covariance *cov = XZALLOC (struct covariance); + cov->centered = centered; cov->passes = 1; cov->state = 0; cov->pass_one_first_case_seen = cov->pass_two_first_case_seen = false; - + cov->vars = vars; cov->wv = weight; @@ -151,16 +159,16 @@ covariance_1pass_create (size_t n_vars, const struct variable *const *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 = (n_vars * (n_vars - 1) ) / 2; + cov->n_cm = (n_vars * (n_vars - 1)) / 2; + - if (cov->n_cm > 0) - cov->cm = xcalloc (sizeof *cov->cm, cov->n_cm); + cov->cm = xcalloc (cov->n_cm, sizeof *cov->cm); cov->categoricals = NULL; return cov; @@ -175,15 +183,17 @@ covariance_1pass_create (size_t n_vars, const struct variable *const *vars, struct covariance * covariance_2pass_create (size_t n_vars, const struct variable *const *vars, struct categoricals *cats, - const struct variable *wv, enum mv_class exclude) + const struct variable *wv, enum mv_class exclude, + bool centered) { size_t i; struct covariance *cov = xmalloc (sizeof *cov); + cov->centered = centered; cov->passes = 2; cov->state = 0; cov->pass_one_first_case_seen = cov->pass_two_first_case_seen = false; - + cov->vars = vars; cov->wv = wv; @@ -191,7 +201,7 @@ covariance_2pass_create (size_t n_vars, const struct variable *const *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); @@ -201,11 +211,12 @@ covariance_2pass_create (size_t n_vars, const struct variable *const *vars, 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 @@ -217,21 +228,21 @@ cm_idx (const struct covariance *cov, int i, int j) int as; const int n2j = cov->dim - 2 - j; const int nj = cov->dim - 2 ; - + assert (i >= 0); assert (j < cov->dim); - if ( i == 0) + if (i == 0) return -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; @@ -239,26 +250,26 @@ cm_idx (const struct covariance *cov, int i, int j) /* - Returns true iff the variable corresponding to the Ith element of the covariance matrix + 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_variable_by_subscript (cov->categoricals, 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); + return (var_is_value_missing (var, val) & cov->exclude) != 0; } static double get_val (const struct covariance *cov, int i, const struct ccase *c) { - if ( i < cov->n_vars) + if (i < cov->n_vars) { const struct variable *var = cov->vars[i]; @@ -267,7 +278,7 @@ get_val (const struct covariance *cov, int i, const struct ccase *c) return val->f; } - return categoricals_get_binary_by_subscript (cov->categoricals, i - cov->n_vars, c); + return categoricals_get_effects_code_for_case (cov->categoricals, i - cov->n_vars, c); } #if 0 @@ -290,7 +301,7 @@ void covariance_accumulate_pass1 (struct covariance *cov, const struct ccase *c) { size_t i, j, m; - const double weight = cov->wv ? case_data (c, cov->wv)->f : 1.0; + const double weight = cov->wv ? case_num (c, cov->wv) : 1.0; assert (cov->passes == 2); if (!cov->pass_one_first_case_seen) @@ -306,14 +317,14 @@ covariance_accumulate_pass1 (struct covariance *cov, const struct ccase *c) { double v1 = get_val (cov, i, c); - if ( is_missing (cov, i, c)) + if (is_missing (cov, i, c)) continue; for (j = 0 ; j < cov->dim; ++j) { double pwr = 1.0; - if ( is_missing (cov, j, c)) + if (is_missing (cov, j, c)) continue; for (m = 0 ; m <= MOMENT_MEAN; ++m) @@ -335,7 +346,7 @@ void covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) { size_t i, j; - const double weight = cov->wv ? case_data (c, cov->wv)->f : 1.0; + const double weight = cov->wv ? case_num (c, cov->wv) : 1.0; assert (cov->passes == 2); assert (cov->state >= 1); @@ -346,14 +357,16 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) 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_total (cov->categoricals) - - categoricals_get_n_variables (cov->categoricals); + cov->dim += categoricals_df_total (cov->categoricals); - cov->n_cm = (cov->dim * (cov->dim - 1) ) / 2; - cov->cm = xcalloc (sizeof *cov->cm, cov->n_cm); + 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 */ @@ -362,9 +375,6 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) cov->moments[i] = resize_matrix (cov->moments[i], cov->dim); } - if (cov->categoricals) - categoricals_done (cov->categoricals); - /* Populate the moments matrices with the categorical value elements */ for (i = cov->n_vars; i < cov->dim; ++i) { @@ -409,7 +419,7 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) { double v1 = get_val (cov, i, c); - if ( is_missing (cov, i, c)) + if (is_missing (cov, i, c)) continue; for (j = 0 ; j < cov->dim; ++j) @@ -420,7 +430,7 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) const double s = pow2 (v1 - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) * weight; - if ( is_missing (cov, j, c)) + if (is_missing (cov, j, c)) continue; { @@ -428,9 +438,9 @@ covariance_accumulate_pass2 (struct covariance *cov, const struct ccase *c) *x += s; } - ss = + ss = (v1 - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) - * + * (v2 - gsl_matrix_get (cov->moments[MOMENT_MEAN], i, j)) * weight ; @@ -454,13 +464,13 @@ void covariance_accumulate (struct covariance *cov, const struct ccase *c) { size_t i, j, m; - const double weight = cov->wv ? case_data (c, cov->wv)->f : 1.0; + const double weight = cov->wv ? case_num (c, cov->wv) : 1.0; assert (cov->passes == 1); - if ( !cov->pass_one_first_case_seen) + if (!cov->pass_one_first_case_seen) { - assert ( cov->state == 0); + assert (cov->state == 0); cov->state = 1; } @@ -468,7 +478,7 @@ covariance_accumulate (struct covariance *cov, const struct ccase *c) { const union value *val1 = case_data (c, cov->vars[i]); - if ( is_missing (cov, i, c)) + if (is_missing (cov, i, c)) continue; for (j = 0 ; j < cov->dim; ++j) @@ -477,7 +487,7 @@ covariance_accumulate (struct covariance *cov, const struct ccase *c) int idx; const union value *val2 = case_data (c, cov->vars[j]); - if ( is_missing (cov, j, c)) + if (is_missing (cov, j, c)) continue; idx = cm_idx (cov, i, j); @@ -500,7 +510,7 @@ covariance_accumulate (struct covariance *cov, const struct ccase *c) } -/* +/* Allocate and return a gsl_matrix containing the covariances of the data. */ @@ -511,7 +521,7 @@ cm_to_gsl (struct covariance *cov) gsl_matrix *m = gsl_matrix_calloc (cov->dim, cov->dim); /* Copy the non-diagonal elements from cov->cm */ - for ( j = 0 ; j < cov->dim - 1; ++j) + for (j = 0 ; j < cov->dim - 1; ++j) { for (i = j+1 ; i < cov->dim; ++i) { @@ -545,7 +555,7 @@ covariance_calculate_double_pass (struct covariance *cov) *x /= gsl_matrix_get (cov->moments[MOMENT_NONE], i, j); idx = cm_idx (cov, i, j); - if ( idx >= 0) + if (idx >= 0) { x = &cov->cm [idx]; *x /= gsl_matrix_get (cov->moments[MOMENT_NONE], i, j); @@ -565,7 +575,7 @@ covariance_calculate_single_pass (struct covariance *cov) for (m = 0; m < n_MOMENTS; ++m) { /* Divide the moments by the number of samples */ - if ( m > 0) + if (m > 0) { for (i = 0 ; i < cov->dim; ++i) { @@ -574,26 +584,29 @@ covariance_calculate_single_pass (struct covariance *cov) double *x = gsl_matrix_ptr (cov->moments[m], i, j); *x /= gsl_matrix_get (cov->moments[0], i, j); - if ( m == MOMENT_VARIANCE) + if (m == MOMENT_VARIANCE) *x -= pow2 (gsl_matrix_get (cov->moments[1], i, j)); } } } } - /* Centre the moments */ - for ( j = 0 ; j < cov->dim - 1; ++j) + if (cov->centered) { - for (i = j + 1 ; i < cov->dim; ++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); + } } } @@ -609,16 +622,16 @@ covariance_calculate_single_pass (struct covariance *cov) gsl_matrix * covariance_calculate (struct covariance *cov) { - if ( 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 (); @@ -631,22 +644,6 @@ covariance_calculate (struct covariance *cov) static gsl_matrix * covariance_calculate_double_pass_unnormalized (struct covariance *cov) { - size_t i, j; - for (i = 0 ; i < cov->dim; ++i) - { - for (j = 0 ; j < cov->dim; ++j) - { - int idx; - double *x = gsl_matrix_ptr (cov->moments[MOMENT_VARIANCE], i, j); - - idx = cm_idx (cov, i, j); - if ( idx >= 0) - { - x = &cov->cm [idx]; - } - } - } - return cm_to_gsl (cov); } @@ -655,26 +652,30 @@ covariance_calculate_single_pass_unnormalized (struct covariance *cov) { size_t i, j; - for (i = 0 ; i < cov->dim; ++i) + if (cov->centered) { - for (j = 0 ; j < cov->dim; ++j) + for (i = 0 ; i < cov->dim; ++i) { - 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; ++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) + + for (j = 0 ; j < cov->dim - 1; ++j) { - 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); + 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); + } } } @@ -683,27 +684,31 @@ covariance_calculate_single_pass_unnormalized (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. + returned matrix is owned by the structure, and must not be freed. Call this function only after all data have been accumulated. */ -gsl_matrix * +const gsl_matrix * covariance_calculate_unnormalized (struct covariance *cov) { - if ( cov->state <= 0 ) + if (cov->state <= 0) return NULL; + if (cov->unnormalised != NULL) + return cov->unnormalised; + switch (cov->passes) { case 1: - return covariance_calculate_single_pass_unnormalized (cov); + cov->unnormalised = covariance_calculate_single_pass_unnormalized (cov); break; case 2: - return covariance_calculate_double_pass_unnormalized (cov); + cov->unnormalised = covariance_calculate_double_pass_unnormalized (cov); break; default: NOT_REACHED (); } + + return cov->unnormalised; } /* Function to access the categoricals used by COV @@ -727,6 +732,7 @@ covariance_destroy (struct covariance *cov) 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); @@ -738,4 +744,72 @@ 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/pivot-table.h" +#include "data/format.h" + + +/* Create a table which can be populated with the encodings for + the covariance matrix COV */ +struct pivot_table * +covariance_dump_enc_header (const struct covariance *cov) +{ + struct pivot_table *table = pivot_table_create ("Covariance Encoding"); + + struct pivot_dimension *factors = pivot_dimension_create ( + table, PIVOT_AXIS_COLUMN, "Factor"); + for (size_t i = 0 ; i < cov->n_vars; ++i) + pivot_category_create_leaf (factors->root, + pivot_value_new_variable (cov->vars[i])); + for (size_t i = 0, n = 0; i < cov->dim - cov->n_vars; n++) + { + const struct interaction *iact = + categoricals_get_interaction_by_subscript (cov->categoricals, i); + + struct string str = DS_EMPTY_INITIALIZER; + interaction_to_string (iact, &str); + struct pivot_category *group = pivot_category_create_group__ ( + factors->root, + pivot_value_new_user_text_nocopy (ds_steal_cstr (&str))); + + int df = categoricals_df (cov->categoricals, n); + for (int j = 0; j < df; j++) + pivot_category_create_leaf_rc (group, pivot_value_new_integer (j), + PIVOT_RC_INTEGER); + + i += df; + } + + struct pivot_dimension *matrix = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, "Matrix", "Matrix"); + matrix->hide_all_labels = true; + + return table; +} + + +/* + 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 pivot_table *table) +{ + int row = pivot_category_create_leaf ( + table->dimensions[1]->root, + pivot_value_new_integer (table->dimensions[1]->n_leaves)); + for (int i = 0 ; i < cov->dim; ++i) + pivot_table_put2 ( + table, i, row, pivot_value_new_number (get_val (cov, i, c))); +}