devtools::rebuild_readme()

Author: t-kalinowski

README.md

@@ -140,9 +140,9 @@ timings
 #> # A tibble: 3 × 6
 #>   expression      min   median `itr/sec` mem_alloc `gc/sec`
 #>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
-#> 1 r             493ms 496.46ms      2.01     782KB     2.01
-#> 2 quickr        934µs   1.07ms    930.       782KB    19.5 
-#> 3 c             956µs   1.08ms    924.       782KB    19.6
+#> 1 r             493ms  494.1ms      2.02     782KB     3.04
+#> 2 quickr        994µs   1.09ms    912.       782KB    18.1 
+#> 3 c             952µs   1.09ms    908.       782KB    18.1
 plot(timings) + bench::scale_x_bench_time(base = NULL)
 ```
 
@@ -182,11 +182,11 @@ In the case of `convolve()`, `quick()` returns a function approximately
     #>  [71] log          log10        logical      matrix       max         
     #>  [76] min          ncol         next         nrow         numeric     
     #>  [81] outer        print        prod         qr.solve     raw         
-    #>  [86] rbind        repeat       rev          runif        seq         
-    #>  [91] seq_along    seq_len      sin          solve        sqrt        
-    #>  [96] stop         sum          svd          t            tan         
-    #> [101] tanh         tcrossprod   trunc        which.max    which.min   
-    #> [106] while
+    #>  [86] rbind        rep.int      repeat       rev          runif       
+    #>  [91] seq          seq_along    seq_len      sin          solve       
+    #>  [96] sqrt         stop         sum          svd          t           
+    #> [101] tan          tanh         tcrossprod   trunc        which.max   
+    #> [106] which.min    while
 
 Many of these restrictions are expected to be relaxed as the project
 matures. However, quickr is intended primarily for high-performance
@@ -303,8 +303,8 @@ timings
 #> # A tibble: 2 × 6
 #>   expression         min   median `itr/sec` mem_alloc `gc/sec`
 #>   <bch:expr>    <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
-#> 1 slow_viterbi   62.81µs   75.6µs    12852.    1.59KB     28.2
-#> 2 quick_viterbi   5.45µs   6.19µs   158835.        0B      0
+#> 1 slow_viterbi   63.18µs  76.42µs    12554.    1.59KB     30.2
+#> 2 quick_viterbi   4.96µs   5.74µs   169250.        0B      0
 plot(timings)
 ```
 
@@ -395,8 +395,8 @@ summary(timings, relative = TRUE)
 #> # A tibble: 2 × 6
 #>   expression           min median `itr/sec` mem_alloc `gc/sec`
 #>   <bch:expr>         <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
-#> 1 diffuse_heat        6.84   11.6       1       4893.     28.8
-#> 2 quick_diffuse_heat  1       1        10.2        1       1
+#> 1 diffuse_heat        9.17   8.46      1        4893.      Inf
+#> 2 quick_diffuse_heat  1      1         7.75        1       NaN
 plot(timings)
 ```
 
@@ -442,9 +442,9 @@ timings
 #> # A tibble: 3 × 6
 #>   expression      min   median `itr/sec` mem_alloc `gc/sec`
 #>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
-#> 1 r           78.19ms  85.82ms      9.02  124.24MB    23.5 
-#> 2 rcpp         6.21ms    6.3ms    156.      4.46MB     0   
-#> 3 quickr       2.37ms   2.49ms    394.    781.35KB     3.98
+#> 1 r           82.63ms  94.61ms      7.76  124.24MB    25.2 
+#> 2 rcpp         6.24ms   6.58ms    149.      4.46MB     1.98
+#> 3 quickr       4.22ms   4.61ms    216.    781.35KB     1.98
 
 timings$expression <- factor(names(timings$expression), rev(names(timings$expression)))
 plot(timings) + bench::scale_x_bench_time(base = NULL)
@@ -558,10 +558,10 @@ timings
 #> # A tibble: 4 × 6
 #>   expression         min   median `itr/sec` mem_alloc `gc/sec`
 #>   <bch:expr>    <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
-#> 1 reference      12.26ms  12.85ms      78.2    29.6MB     73.8
-#> 2 RcppArmadillo   1.66ms   2.04ms     476.         0B      0  
-#> 3 plain R         1.59ms    1.9ms     516.     13.2MB    133. 
-#> 4 quickr        974.32µs   1.09ms     849.         0B      0
+#> 1 reference       14.5ms  15.28ms      65.3    29.6MB     61.0
+#> 2 RcppArmadillo   2.85ms   3.27ms     304.         0B      0  
+#> 3 plain R         3.65ms   4.07ms     242.     13.2MB     62.3
+#> 4 quickr          2.97ms   3.22ms     307.         0B      0
 plot(timings) + ggplot2::scale_y_discrete(limits = rev(c( 
   "reference", "RcppArmadillo", "plain R", "quickr"
 )))