add: Added ensamble CVE (ECVE) capability.

CVE/R/CVE.R

@@ -199,6 +199,8 @@ cve <- function(formula, data, method = "simple", max.dim = 10L, ...) {
 #'          below.
 #'      \item "weighted" variation with addaptive weighting of slices.
 #' }
+#' @param func_list a list of functions applied to \code{Y} to form the ensamble
+#'      CVE for central sub-space estimation.
 #' @param k Dimension of lower dimensional projection, if \code{k} is given
 #'      only the specified dimension \code{B} matrix is estimated.
 #' @param min.dim lower bounds for \code{k}, (ignored if \code{k} is supplied).
@@ -251,6 +253,7 @@ cve <- function(formula, data, method = "simple", max.dim = 10L, ...) {
 #' coef(cve.obj.simple2, k = 1)
 #' @export
 cve.call <- function(X, Y, method = "simple",
+                     func_list = list(function (x) x),
                      nObs = sqrt(nrow(X)), h = NULL,
                      min.dim = 1L, max.dim = 10L, k = NULL,
                      momentum = 0.0, tau = 1.0, tol = 1e-3,
@@ -375,8 +378,11 @@ cve.call <- function(X, Y, method = "simple",
         }
     }
 
+    # Evaluate each function given `Y` and build a `n x |func_list|` matrix.
+    Fy <- vapply(func_list, do.call, Y, list(Y))
+
     # Convert numerical values to "double".
-    storage.mode(X) <- storage.mode(Y) <- "double"
+    storage.mode(X) <- storage.mode(Fy) <- "double"
 
     if (is.function(logger)) {
         loggerEnv <- environment(logger)
@@ -396,7 +402,7 @@ cve.call <- function(X, Y, method = "simple",
         }
 
         dr.k <- .Call('cve_export', PACKAGE = 'CVE',
-                      X, Y, k, h,
+                      X, Fy, k, h,
                       method_nr,
                       V.init,
                       momentum, tau, tol,
@@ -415,6 +421,7 @@ cve.call <- function(X, Y, method = "simple",
     # augment result information
     dr$X <- X
     dr$Y <- Y
+    dr$Fy <- Fy
     dr$method <- method
     dr$call <- call
     class(dr) <- "cve"

CVE/man/cve.call.Rd

@@ -4,10 +4,26 @@
 \alias{cve.call}
 \title{Conditional Variance Estimator (CVE).}
 \usage{
-cve.call(X, Y, method = "simple", nObs = sqrt(nrow(X)), h = NULL,
+cve.call(
-  min.dim = 1L, max.dim = 10L, k = NULL, momentum = 0, tau = 1,
+  X,
-  tol = 0.001, slack = 0, gamma = 0.5, V.init = NULL,
+  Y,
-  max.iter = 50L, attempts = 10L, logger = NULL)
+  method = "simple",
+  func_list = list(function(x) x),
+  nObs = sqrt(nrow(X)),
+  h = NULL,
+  min.dim = 1L,
+  max.dim = 10L,
+  k = NULL,
+  momentum = 0,
+  tau = 1,
+  tol = 0.001,
+  slack = 0,
+  gamma = 0.5,
+  V.init = NULL,
+  max.iter = 50L,
+  attempts = 10L,
+  logger = NULL
+)
 }
 \arguments{
 \item{X}{Design predictor matrix.}
@@ -21,6 +37,9 @@ cve.call(X, Y, method = "simple", nObs = sqrt(nrow(X)), h = NULL,
      \item "weighted" variation with addaptive weighting of slices.
 }}
 
+\item{func_list}{a list of functions applied to `Y` to form the ensamble
+CVE for central sub-space estimation.}
+
 \item{nObs}{parameter for choosing bandwidth \code{h} using
 \code{\link{estimate.bandwidth}} (ignored if \code{h} is supplied).}
 

CVE/man/directions.cve.Rd

@@ -20,8 +20,8 @@ the \eqn{n\times k}{n x k} dimensional matrix \eqn{X B} where \eqn{B}
  is the  cve-estimate for dimension \eqn{k}.
 }
 \description{
-Returns \eqn{B'X}. That is the dimensional design matrix \eqn{X} on the
+Returns \eqn{B'X}. That is, it computes the projection of the \eqn{n x p}
-columnspace of the cve-estimate for given dimension \eqn{k}.
+design matrix \eqn{X} on the column space of \eqn{B} of dimension \eqn{k}.
 }
 \examples{
 # create B for simulation (k = 1)

CVE/man/estimate.bandwidth.Rd

@@ -49,6 +49,5 @@ y <- x \%*\% B + 0.25 * rnorm(100)
 # calculate cve with method 'simple' for k = 1
 set.seed(21)
 cve.obj.simple <- cve(y ~ x, k = k)
-print(cve.obj.simple$res$'1'$h)
 print(estimate.bandwidth(x, k = k))
 }

CVE/man/plot.cve.Rd

@@ -16,9 +16,9 @@
 \description{
 Boxplots of the output \code{L} from \code{\link{cve}} over \code{k} from
 \code{min.dim} to \code{max.dim}. For given \code{k}, \code{L} corresponds
-to \eqn{L_n(V, X_i)} where \eqn{V} is a stiefel manifold element as 
+to \eqn{L_n(V, X_i)} where \eqn{V} is the minimizer of \eqn{L_n(V)} where
-minimizer of
+\eqn{V} is an element of a Stiefel manifold (see
-\eqn{L_n(V)}, for further details see Fertl, L. and Bura, E. (2019).
+Fertl, L. and Bura, E. (2019)).
 }
 \examples{
 # create B for simulation

CVE/man/predict.cve.Rd

@@ -17,11 +17,11 @@
 \item{...}{further arguments passed to \code{\link{mars}}.}
 }
 \value{
-prediced response at \code{newdata}.
+prediced respone(s) for \code{newdata}.
 }
 \description{
-Predict response using projected data \eqn{B'C} by fitting
+Predict response using projected data. The forward model \eqn{g(B' X)} is
-\eqn{g(B'C) + \epsilon} using \code{\link{mars}}.
+estimated with \code{\link{mars}} in the \code{\pkg{mda}} package.
 }
 \examples{
 # create B for simulation

CVE/man/predict_dim.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/predict_dim.R
 \name{predict_dim}
 \alias{predict_dim}
-\title{Estimate Dimension of Reduction Space.}
+\title{Estimate Dimension of the Sufficient Reduction.}
 \usage{
 predict_dim(object, ..., method = "CV")
 }
@@ -12,29 +12,30 @@ predict_dim(object, ..., method = "CV")
 
 \item{...}{ignored.}
 
-\item{method}{This parameter specify which method will be used in dimension
+\item{method}{This parameter specifies which method is used in dimension
-estimation. It provides three methods \code{'CV'} (default), \code{'elbow'},
+ estimation. It provides three options: \code{'CV'} (default),
-and \code{'wilcoxon'} to estimate the dimension of the SDR.}
+\code{'elbow'} and \code{'wilcoxon'}.}
 }
 \value{
-list with
+A \code{list} with
 \describe{
-   \item{}{cretirion of method for \code{k = min.dim, ..., max.dim}.}
+   \item{}{criterion for method and \code{k = min.dim, ..., max.dim}.}
-   \item{k}{estimated dimension as argmin over \eqn{k} of criterion.}
+   \item{k}{estimated dimension is the minimizer of the criterion.}
 }
 }
 \description{
-This function estimates the dimension of the mean dimension reduction space,
+This function estimates the dimension, i.e. the rank of \eqn{B}. The default
-i.e. number of columns of \eqn{B} matrix. The default method \code{'CV'}
+method \code{'CV'} performs leave-one-out (LOO) cross-validation using
-performs l.o.o cross-validation using \code{mars}. Given
+\code{mars} as follows for \code{k = min.dim, ..., max.dim} a
-\code{k = min.dim, ..., max.dim} a cross-validation via \code{mars} is
+cross-validation via \code{mars} is
 performed on the dataset \eqn{(Y_i, B_k' X_i)_{i = 1, ..., n}} where
 \eqn{B_k} is the \eqn{p \times k}{p x k} dimensional CVE estimate. The
 estimated SDR dimension is the \eqn{k} where the
 cross-validation mean squared error is minimal. The method \code{'elbow'}
 estimates the dimension via \eqn{k = argmin_k L_n(V_{p - k})} where
-\eqn{V_{p - k}} is space that is orthogonal to the columns-space of the CVE estimate of \eqn{B_k}. Method \code{'wilcoxon'} is similar to \code{'elbow'}
+\eqn{V_{p - k}} is the space that is orthogonal to the column space of the 
-but finds the minimum using the wilcoxon-test.
+CVE estimate of \eqn{B_k}. Method \code{'wilcoxon'}  finds the minimum using
+the Wilcoxon test.
 }
 \examples{
 # create B for simulation

CVE/man/rStiefel.Rd

@@ -2,8 +2,7 @@
 % Please edit documentation in R/util.R
 \name{rStiefel}
 \alias{rStiefel}
-\title{Draws a sample from the invariant measure on the Stiefel manifold
+\title{Random sample from Stiefel manifold.}
-\eqn{S(p, q)}.}
 \usage{
 rStiefel(p, q)
 }
@@ -13,12 +12,12 @@ rStiefel(p, q)
 \item{q}{col dimension}
 }
 \value{
-\eqn{p \times q}{p x q} semi-orthogonal matrix.
+A \eqn{p \times q}{p x q} semi-orthogonal matrix.
 }
 \description{
-Draws a sample from the invariant measure on the Stiefel manifold
+Draws a random sample from the invariant measure on the Stiefel manifold
 \eqn{S(p, q)}.
 }
 \examples{
- V <- rStiefel(6, 4)
+V <- rStiefel(6, 4)
 }

CVE/man/summary.cve.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/summary.R
 \name{summary.cve}
 \alias{summary.cve}
-\title{Prints a summary of a \code{cve} result.}
+\title{Prints summary statistics of the \eqn{L} \code{cve} component.}
 \usage{
 \method{summary}{cve}(object, ...)
 }
@@ -13,8 +13,7 @@
 \item{...}{ignored.}
 }
 \description{
-Prints a summary statistics of output \code{L} from \code{cve} for
+Prints a summary statistics of the \code{L} component of a \code{cve} object #' for \code{k = min.dim, ..., max.dim}.
-\code{k = min.dim, ..., max.dim}.
 }
 \examples{
 # create B for simulation

CVE/src/callLogger.c

@@ -32,11 +32,11 @@ void callLogger(SEXP logger, SEXP env,
     SEXP r_iter = PROTECT(ScalarInteger(iter + 1));
 
     /* Create R representations of L, V and G */
-    SEXP r_L = PROTECT(allocVector(REALSXP, L->nrow));
+    SEXP r_L = PROTECT(allocMatrix(REALSXP, L->nrow, L->ncol));
     SEXP r_V = PROTECT(allocMatrix(REALSXP, V->nrow, V->ncol));
     SEXP r_G = PROTECT(allocMatrix(REALSXP, G->nrow, G->ncol));
     /* Copy data to R objects */
-    memcpy(REAL(r_L), L->elem, L->nrow * sizeof(double));
+    memcpy(REAL(r_L), L->elem, L->nrow * L->ncol * sizeof(double));
     memcpy(REAL(r_V), V->elem, V->nrow * V->ncol * sizeof(double));
     memcpy(REAL(r_G), G->elem, G->nrow * G->ncol * sizeof(double));
 

CVE/src/cve.c

@@ -2,7 +2,7 @@
 
 #include "cve.h"
 
-void cve(const mat *X, const mat *Y, const double h,
+void cve(const mat *X, const mat *Fy, const double h,
          const unsigned int method,
          const double momentum,
          const double tau_init, const double tol_init,
@@ -24,13 +24,14 @@ void cve(const mat *X, const mat *Y, const double h,
 
     /* Create further intermediate or internal variables. */
     mat *lvecD_e  = (void*)0;
-    mat *Ysquared = (void*)0;
+    mat *Fy_sq    = (void*)0;
     mat *XV       = (void*)0;
     mat *lvecD    = (void*)0; // TODO: combine. aka in-place lvecToSym
     mat *D        = (void*)0; // TODO: combine. aka in-place lvecToSym
     mat *lvecK    = (void*)0; // TODO: combine. aka in-place lvecToSym
     mat *K        = (void*)0; // TODO: combine. aka in-place lvecToSym
     mat *colSumsK = (void*)0;
+    mat *rowSumsL = (void*)0;
     mat *W        = (void*)0;
     mat *y1       = (void*)0;
     mat *y2       = (void*)0;
@@ -51,7 +52,7 @@ void cve(const mat *X, const mat *Y, const double h,
     double *workMem = (double*)R_alloc(workLen, sizeof(double));
 
     lvecD_e = rowDiffSquareSums(X, lvecD_e);
-    Ysquared = hadamard(1.0, Y, Y, 0.0, Ysquared);
+    Fy_sq = hadamard(1.0, Fy, Fy, 0.0, Fy_sq);
 
     do {
         /* (Re)set learning rate. */
@@ -77,22 +78,26 @@ void cve(const mat *X, const mat *Y, const double h,
         /* Normalize K columns to obtain weight matrix W */
         W = colApply(K, '/', colSumsK, W);
         /* first and second order weighted responces */
-        y1 = matrixprod(1.0, W, Y,        0.0, y1);
+        y1 = matrixprod(1.0, W, Fy,    0.0, y1);
-        y2 = matrixprod(1.0, W, Ysquared, 0.0, y2);
+        y2 = matrixprod(1.0, W, Fy_sq, 0.0, y2);
         /* Compute losses */
         L = hadamard(-1.0, y1, y1, 1.0, copy(y2, L));
         /* Compute initial loss */
         if (method == weighted) {
             colSumsK = elemApply(colSumsK, '-', 1.0, colSumsK);
             sumK = sum(colSumsK);
-            loss_last = dot(L, '*', colSumsK) / sumK;
+            if (L->ncol == 1) {
+                loss_last = dot(L, '*', colSumsK) / sumK;
+            } else {
+                loss_last = dot(rowSums(L, rowSumsL), '*', colSumsK) / sumK;
+            }
             c = agility / sumK;
             /* Calculate the scaling matrix S */
-            S = laplace(adjacence(L, Y, y1, D, K, gauss, S), workMem);
+            S = laplace(adjacence(L, Fy, y1, D, K, gauss, S), workMem);
         } else { /* simple */
             loss_last = mean(L);
             /* Calculate the scaling matrix S */
-            S = laplace(adjacence(L, Y, y1, D, W, gauss, S), workMem);
+            S = laplace(adjacence(L, Fy, y1, D, W, gauss, S), workMem);
         }
         /* Gradient */
         tmp1 = matrixprod(1.0, S, X, 0.0, tmp1);
@@ -132,15 +137,19 @@ void cve(const mat *X, const mat *Y, const double h,
             /* Normalize K columns to obtain weight matrix W */
             W = colApply(K, '/', colSumsK, W);
             /* first and second order weighted responces */
-            y1 = matrixprod(1.0, W, Y,        0.0, y1);
+            y1 = matrixprod(1.0, W, Fy,    0.0, y1);
-            y2 = matrixprod(1.0, W, Ysquared, 0.0, y2);
+            y2 = matrixprod(1.0, W, Fy_sq, 0.0, y2);
             /* Compute losses */
             L = hadamard(-1.0, y1, y1, 1.0, copy(y2, L));
             /* Compute loss */
             if (method == weighted) {
                 colSumsK = elemApply(colSumsK, '-', 1.0, colSumsK);
                 sumK = sum(colSumsK);
-                loss = dot(L, '*', colSumsK) / sumK;
+                if (L->ncol == 1) {
+                    loss = dot(L, '*', colSumsK) / sumK;
+                } else {
+                    loss = dot(rowSums(L, rowSumsL), '*', colSumsK) / sumK;
+                }
             } else { /* simple */
                 loss = mean(L);
             }
@@ -158,9 +167,6 @@ void cve(const mat *X, const mat *Y, const double h,
             /* Compute error, use workMem. */
             err = dist(V, V_tau);
 
-            // Rprintf("%2d - iter: %2d, loss: %1.3f, err: %1.3f, tau: %1.3f, norm(G) = %1.3f\n",
-            //         attempt, iter, loss, err, tau, sqrt(squareSum(G)));
-
             /* Shift next step to current step and store loss to last. */
             V = copy(V_tau, V);
             loss_last = loss;
@@ -177,11 +183,11 @@ void cve(const mat *X, const mat *Y, const double h,
 
             if (method == weighted) {
                 /* Calculate the scaling matrix S */
-                S = laplace(adjacence(L, Y, y1, D, K, gauss, S), workMem);
+                S = laplace(adjacence(L, Fy, y1, D, K, gauss, S), workMem);
                 c = agility / sumK; // n removed previousely
             } else { /* simple */
                 /* Calculate the scaling matrix S */
-                S = laplace(adjacence(L, Y, y1, D, W, gauss, S), workMem);
+                S = laplace(adjacence(L, Fy, y1, D, W, gauss, S), workMem);
             }
 
             /* Gradient */
@@ -194,8 +200,6 @@ void cve(const mat *X, const mat *Y, const double h,
             A = skew(tau, G, V, 0.0, A);
         }
 
-        // Rprintf("\n");
-
         /* Check if current attempt improved previous ones */
         if (attempt == 0 || loss < loss_best) {
             loss_best = loss;

CVE/src/cve_subroutines.c

@@ -106,57 +106,69 @@ mat* applyKernel(const mat* A, const double h, kernel kernel, mat* B) {
  *                          n +--------------------+
  */
 // TODO: fix: cache misses in Y?!
-mat* adjacence(const mat *vec_L, const mat *vec_Y, const mat *vec_y1,
+mat* adjacence(const mat *mat_L, const mat *mat_Fy, const mat *mat_y1,
                const mat *mat_D, const mat *mat_W, kernel kernel,
                mat *mat_S) {
-    int i, j, k, n = vec_L->nrow;
+    int i, j, k, l, n = mat_L->nrow, m = mat_L->ncol;
     int block_size, block_batch_size;
     int max_size = 64 < n ? 64 : n; // Block Size set to 64
 
-    double Y_j, tmp0, tmp1, tmp2, tmp3;
+    double Y_j, t0, t1, t2, t3; // internal temp. values.
-    double *Y  = vec_Y->elem;
+    double *Y, *L, *y1;
-    double *L  = vec_L->elem;
-    double *y1 = vec_y1->elem;
     double *D, *W, *S;
 
     // TODO: Check dims.
 
     if (!mat_S) {
-        mat_S = matrix(n, n);
+        mat_S = zero(n, n);
+    } else {
+        memset(mat_S->elem, 0, n * n * sizeof(double));
     }
 
-    for (i = 0; i < n; i += block_size) {
+    for (l = 0; l < m; ++l) {
-        /* Set blocks (left upper corner) */
+        Y  = mat_Fy->elem + l * n;
-        S = mat_S->elem + i;
+        L  = mat_L->elem + l * n;
-        D = mat_D->elem + i;
+        y1 = mat_y1->elem + l * n;
-        W = mat_W->elem + i;
+        for (i = 0; i < n; i += block_size) {
-        /* determine block size */
+            /* Set blocks (left upper corner) */
-        block_size = n - i;
+            S = mat_S->elem + i;
-        if (block_size > max_size) {
+            D = mat_D->elem + i;
-            block_size = max_size;
+            W = mat_W->elem + i;
-        }
+            /* determine block size */
-        block_batch_size = 4 * (block_size / 4);
+            block_size = n - i;
-        /* for each column in the block */
+            if (block_size > max_size) {
-        for (j = 0; j < n; ++j, S += n, D += n, W += n) {
+                block_size = max_size;
-            Y_j = Y[j];
-            /* iterate over block rows */
-            for (k = 0; k < block_batch_size; k += 4) {
-                tmp0 = Y_j - y1[k];
-                tmp1 = Y_j - y1[k + 1];
-                tmp2 = Y_j - y1[k + 2];
-                tmp3 = Y_j - y1[k + 3];
-                S[k]     = (L[k]     - (tmp0 * tmp0)) * D[k]     * W[k];
-                S[k + 1] = (L[k + 1] - (tmp1 * tmp1)) * D[k + 1] * W[k + 1];
-                S[k + 2] = (L[k + 2] - (tmp2 * tmp2)) * D[k + 2] * W[k + 2];
-                S[k + 3] = (L[k + 3] - (tmp3 * tmp3)) * D[k + 3] * W[k + 3];
             }
-            for (; k < block_size; ++k) {
+            block_batch_size = 4 * (block_size / 4);
-                tmp0 = Y_j - y1[k];
+            /* for each column in the block */
-                S[k] = (L[k] - (tmp0 * tmp0)) * D[k] * W[k];
+            for (j = 0; j < n; ++j, S += n, D += n, W += n) {
+                Y_j = Y[j];
+                /* iterate over block rows */
+                for (k = 0; k < block_batch_size; k += 4) {
+                    t0 = Y_j - y1[k];
+                    t1 = Y_j - y1[k + 1];
+                    t2 = Y_j - y1[k + 2];
+                    t3 = Y_j - y1[k + 3];
+                    S[k]     += (L[k]     - (t0 * t0)) * D[k]     * W[k];
+                    S[k + 1] += (L[k + 1] - (t1 * t1)) * D[k + 1] * W[k + 1];
+                    S[k + 2] += (L[k + 2] - (t2 * t2)) * D[k + 2] * W[k + 2];
+                    S[k + 3] += (L[k + 3] - (t3 * t3)) * D[k + 3] * W[k + 3];
+                }
+                for (; k < block_size; ++k) {
+                    t0 = Y_j - y1[k];
+                    S[k] += (L[k] - (t0 * t0)) * D[k] * W[k];
+                }
             }
+            L  += block_size;
+            y1 += block_size;
+        }
+    }
+
+    if (m > 1) {
+        S = mat_S->elem;
+        for (i = 0; i < n * n; ++i) {
+            S[i] /= m;
         }
-        L  += block_size;
-        y1 += block_size;
     }
 
     return mat_S;

CVE/src/export.c

@@ -25,7 +25,7 @@ static mat* asMat(SEXP S) {
     return M;
 }
 
-SEXP cve_export(SEXP X, SEXP Y, SEXP k, SEXP h,
+SEXP cve_export(SEXP X, SEXP Fy, SEXP k, SEXP h,
                 SEXP method,
                 SEXP V, // initial
                 SEXP momentum, SEXP tau, SEXP tol,
@@ -47,7 +47,7 @@ SEXP cve_export(SEXP X, SEXP Y, SEXP k, SEXP h,
 
     /* Create output list. */
     SEXP Vout = PROTECT(allocMatrix(REALSXP, p, q));
-    SEXP Lout = PROTECT(allocVector(REALSXP, n));
+    SEXP Lout = PROTECT(allocMatrix(REALSXP, n, ncols(Fy)));
 
     /* Check `attempts`, if not positive use passed values of `V` as
      * optimization start value without further attempts.
@@ -58,7 +58,7 @@ SEXP cve_export(SEXP X, SEXP Y, SEXP k, SEXP h,
     }
 
     /* Call CVE */
-    cve(asMat(X), asMat(Y), asReal(h),
+    cve(asMat(X), asMat(Fy), asReal(h),
         asInteger(method),
         asReal(momentum), asReal(tau), asReal(tol),
         asReal(slack), asReal(gamma),

CVE/src/init.c

@@ -4,7 +4,7 @@
 #include <R_ext/Rdynload.h>
 
 /* .Call calls */
-extern SEXP cve_export(SEXP X, SEXP Y, SEXP k, SEXP h,
+extern SEXP cve_export(SEXP X, SEXP Fy, SEXP k, SEXP h,
                        SEXP method,
                        SEXP V, // initial
                        SEXP momentum, SEXP tau, SEXP tol,