added mean and cov of a gaussian

Author: Jelena Markovic

selectiveInference/R/RcppExports.R

@@ -0,0 +1,35 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+update1_ <- function(Q2, w, m, k) {
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+}
+
+downdate1_ <- function(Q1, R, j0, m, n) {
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+}
+
+log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+

selectiveInference/R/funs.randomized.R

@@ -239,7 +239,6 @@ importance_weight = function(noise_scale,
         A = apply(A, 2, function(x) {return(x + target_transform$offset_term + opt_transform$offset_term)})
         log_num = -apply(A^2, 2, sum) / noise_scale^2
     } else {
-
         log_num = log_density_gaussian_(noise_scale,
                                         target_transform$linear_term,
                                         as.matrix(target_sample),
@@ -262,6 +261,15 @@ importance_weight = function(noise_scale,
     W = W - max(W)
     return(exp(W))
 }
+
+get_mean_cov = function(noise_scale, linear_term, offset_term){
+    temp = solve(t(linear_term) %*% linear_term)
+    cov = noise_scale^2*temp
+    mean = temp %*% linear_term %*% offset_term
+    return(list(mean=mean, cov=cov))
+}
+
+
 
 conditional_density = function(noise_scale, lasso_soln) {