From: Jason Stover Date: Sat, 15 Jul 2006 20:24:17 +0000 (+0000) Subject: fixed computation of sample covariance X-Git-Tag: v0.6.0~759 X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=f70f1b22e925d55c246372376de1c6ffaacf8a4b;p=pspp-builds.git fixed computation of sample covariance --- diff --git a/src/math/ts/ChangeLog b/src/math/ts/ChangeLog index 0fbeb23e..d40070a0 100644 --- a/src/math/ts/ChangeLog +++ b/src/math/ts/ChangeLog @@ -1,3 +1,11 @@ +2006-07-15 Jason Stover + + * innovations.c (get_covariance): Fixed computation of + covariance. Made COV[i] the lag i covariance. + (update_cov): New function. + (get_covariance): Use gsl_vector_view's to get rows of correct + lag. + 2006-07-14 Jason Stover * innovations.c (innovations_struct_init): Fix initialization of diff --git a/src/math/ts/innovations.c b/src/math/ts/innovations.c index 75b88d6c..089665ac 100644 --- a/src/math/ts/innovations.c +++ b/src/math/ts/innovations.c @@ -38,8 +38,8 @@ #include static void -get_mean_variance (const gsl_matrix *data, - struct innovations_estimate **est) +get_mean (const gsl_matrix *data, + struct innovations_estimate **est) { size_t n; @@ -49,9 +49,8 @@ get_mean_variance (const gsl_matrix *data, for (n = 0; n < data->size2; n++) { - est[n]->n_obs = 2.0; + est[n]->n_obs = 0.0; est[n]->mean = 0.0; - est[n]->variance = 0.0; } for (i = 0; i < data->size1; i++) { @@ -60,20 +59,36 @@ get_mean_variance (const gsl_matrix *data, tmp = gsl_matrix_get (data, i, n); if (!gsl_isnan (tmp)) { + est[n]->n_obs += 1.0; d = (tmp - est[n]->mean) / est[n]->n_obs; est[n]->mean += d; - est[n]->variance += est[n]->n_obs * est[n]->n_obs * d * d; - est[n]->n_obs += 1.0; } } } - for (n = 0; n < data->size2; n++) +} +static void +update_cov (struct innovations_estimate **est, gsl_vector_const_view x, + gsl_vector_const_view y, size_t lag) +{ + size_t j; + double xj; + double yj; + + for (j = 0; j < x.vector.size; j++) { - /* Maximum likelihood estimate of the variance. */ - est[n]->variance /= est[n]->n_obs; + xj = gsl_vector_get (&x.vector, j); + yj = gsl_vector_get (&y.vector, j); + if (!gsl_isnan (xj)) + { + if (!gsl_isnan (yj)) + { + xj -= est[j]->mean; + yj -= est[j]->mean; + *(est[j]->cov + lag) += xj * yj; + } + } } } - static int get_covariance (const gsl_matrix *data, struct innovations_estimate **est, size_t max_lag) @@ -81,42 +96,41 @@ get_covariance (const gsl_matrix *data, size_t lag; size_t j; size_t i; - double x; - double y; int rc = 1; assert (data != NULL); assert (est != NULL); - + + for (j = 0; j < data->size2; j++) + { + for (lag = 0; lag <= max_lag; lag++) + { + *(est[j]->cov + lag) = 0.0; + } + } + /* + The rows are in the outer loop because a gsl_matrix is stored in + row-major order. + */ for (i = 0; i < data->size1; i++) { - for (j = 0; j < data->size2; j++) + for (lag = 0; lag < max_lag && lag < data->size1 - i; lag++) { - x = gsl_matrix_get (data, i, j); - - if (!gsl_isnan (x)) - { - x -= est[j]->mean; - for (lag = 1; lag <= max_lag && lag < (data->size1 - i); lag++) - { - y = gsl_matrix_get (data, i + lag, j); - if (!gsl_isnan (y)) - { - y -= est[j]->mean; - *(est[j]->cov + lag - 1) += y * x; - est[j]->n_obs += 1.0; - } - } - } + update_cov (est, gsl_matrix_const_row (data, i), + gsl_matrix_const_row (data, i + lag), lag); } } for (j = 0; j < data->size2; j++) { - *(est[j]->cov + lag - 1) /= est[j]->n_obs; + for (lag = 0; lag <= max_lag; lag++) + { + *(est[j]->cov + lag) /= est[j]->n_obs; + } } return rc; } + static double innovations_convolve (double **theta, struct innovations_estimate *est, int i, int j) @@ -140,7 +154,7 @@ innovations_update_scale (struct innovations_estimate *est, double *theta, if (i < (size_t) est->max_lag) { - result = est->variance; + result = est->cov[0]; for (j = 0; j < i; j++) { k = i - j - 1; @@ -236,22 +250,31 @@ get_coef (const gsl_matrix *data, } static void -innovations_struct_init (struct innovations_estimate *est, size_t lag) +innovations_struct_init (struct innovations_estimate *est, + const struct design_matrix *dm, + size_t lag) { size_t j; est->mean = 0.0; - est->variance = 0.0; - est->cov = xnmalloc (lag, sizeof (*est->cov)); + /* COV[0] stores the lag 0 covariance (i.e., the variance), COV[1] + holds the lag-1 covariance, etc. + */ + est->cov = xnmalloc (lag + 1, sizeof (*est->cov)); est->scale = xnmalloc (lag + 1, sizeof (*est->scale)); - est->coeff = xnmalloc (lag, sizeof (*est->coeff)); - est->max_lag = (double) lag; - /* COV does not the variance (i.e., the lag 0 covariance). So COV[0] - holds the lag 1 covariance, COV[i] holds the lag i+1 covariance. */ + est->coeff = xnmalloc (lag, sizeof (*est->coeff)); /* No intercept. */ + + /* + The loop below is an unusual use of PSPP_COEFF_INIT(). In a + typical model, one column of a DESIGN_MATRIX has one + coefficient. But in a time-series model, one column has many + coefficients. + */ for (j = 0; j < lag; j++) { - est->coeff[j] = xmalloc (sizeof (*(est->coeff[j]))); + pspp_coeff_init (est->coeff + j, dm); } + est->max_lag = (double) lag; } struct innovations_estimate ** @@ -265,10 +288,10 @@ pspp_innovations (const struct design_matrix *dm, size_t lag) { est[i] = xmalloc (sizeof *est[i]); /* est[i]->variable = vars[i]; */ - innovations_struct_init (est[i], lag); + innovations_struct_init (est[i], dm, lag); } - get_mean_variance (dm->m, est); + get_mean (dm->m, est); get_covariance (dm->m, est, lag); get_coef (dm->m, est, lag);