<%@include file="includes/setup.md.rsp"%>

<%@string colname="colWeightedMeans"%>
<%@string rowname="rowWeightedMeans"%>
<%@string fcnname="colRowWeightedMeans_subset"%>
<%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%>
<%@meta author="Dongcan Jiang"%>
<%@meta date="2015-06-07"%>

<%@include file="${header}"%>


# <%@meta name="title"%>

This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation.


## Data
```r
<%=withCapture({
<%@include file="R/random-matrices.R"%>
data <- rmatrices(mode = "double")
})%>
```

## Results

<% for (dataLabel in names(data)) { %>
<% message(dataLabel) %>
### <%=dataLabel%> matrix


```r
<%=withCapture({
X <- data[[.dataLabel.]]
rows <- sample.int(nrow(X), size = nrow(X)*0.7)
cols <- sample.int(ncol(X), size = ncol(X)*0.7)
X_S <- X[rows, cols]
w <- runif(nrow(X))
w_S <- w[rows]
gc()

colStats <- microbenchmark(
 "colWeightedMeans_X_w_S"                 = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
 "colWeightedMeans(X, w, rows, cols)"        = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
 "colWeightedMeans(X[rows, cols], w[rows])" = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
 unit = "ms"
)

X <- t(X)
X_S <- t(X_S)
gc()

rowStats <- microbenchmark(
 "rowWeightedMeans_X_w_S"                 = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
 "rowWeightedMeans(X, w, cols, rows)"        = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
 "rowWeightedMeans(X[cols, rows], w[rows])" = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
 unit = "ms"
)
})%>
```

<% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %>

<% } # for (dataLabel ...) %>


<%@include file="${footer}"%>


<%---------------------------------------------------------------------------
HISTORY:
2015-06-07
o Created.
---------------------------------------------------------------------------%>