gsl_matrix_set (out, i, j, x);
}
}
-
+
gsl_matrix_free (in);
return out;
double *cm;
int n_cm;
- /* 1 for single pass algorithm;
+ /* 1 for single pass algorithm;
2 for double pass algorithm
*/
short passes;
/*
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;
};
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;
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->passes = 2;
cov->state = 0;
cov->pass_one_first_case_seen = cov->pass_two_first_case_seen = false;
-
+
cov->vars = vars;
cov->wv = wv;
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->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
int as;
const int n2j = cov->dim - 2 - j;
const int nj = cov->dim - 2 ;
-
+
assert (i >= 0);
assert (j < cov->dim);
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;
/*
- 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] :
+ 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 val->f;
}
- return categoricals_get_code_for_case (cov->categoricals, i - cov->n_vars, c);
+ return categoricals_get_effects_code_for_case (cov->categoricals, i - cov->n_vars, c);
}
#if 0
categoricals_done (cov->categoricals);
cov->dim = cov->n_vars;
-
+
if (cov->categoricals)
cov->dim += categoricals_df_total (cov->categoricals);
*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
;
}
-/*
+/*
Allocate and return a gsl_matrix containing the covariances of the
data.
*/
for (i = j + 1 ; i < cov->dim; ++i)
{
double *x = &cov->cm [cm_idx (cov, i, j)];
-
+
*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], i, j)
*
- gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i);
+ gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i);
}
}
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 ();
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], i, j)
*
- gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i)
+ gsl_matrix_get (cov->moments[MOMENT_MEAN], j, i)
/ gsl_matrix_get (cov->moments[MOMENT_NONE], i, j);
}
}
/* 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 )
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
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);
tab_title (t, "Covariance Encoding");
- tab_box (t,
+ tab_box (t,
TAL_2, TAL_2, 0, 0,
0, 0, tab_nc (t) - 1, tab_nr (t) - 1);
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);
+ tab_double (t, i, row, 0, v, i < cov->n_vars ? NULL : &F_8_0, RC_OTHER);
}
}