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default expected behavior for arrays (i.e., array-args to [ return a vector of entries in the array) does not carry over to tf-vectors:
library(tf)
x<- tf_rgp(5, arg=1:5)
# to extract function values at specific args for each function, we need:
as.matrix(x)[cbind(1:5, 1:5)]
#> [1] -0.3721783 0.6235588 1.3563847 -1.4085395 1.1629906# .. and more complicated code to match arg-values to array-column-indices in general...# default behavior for arrays does not carry over to tf-vectors:x[cbind(1:5, 1:5)] # treats array arg as a vector :(#> tfd[10] on (1,5) based on 5 evaluations each#> interpolation by tf_approx_linear #> [1]: (1, -0.4);(2, -1.0);(3, 0.8); ...#> [2]: (1, -0.8);(2, 0.6);(3, 0.3); ...#> [3]: (1, 1.9);(2, 0.6);(3, 1.4); ...#> [4]: (1, -2.5);(2, -1.0);(3, -0.6); ...#> [5]: (1,-0.07);(2, 0.91);(3,-0.63); ...#> [6]: (1, -0.4);(2, -1.0);(3, 0.8); ...#> [7]: (1, -0.8);(2, 0.6);(3, 0.3); ...#> [8]: (1, 1.9);(2, 0.6);(3, 1.4); ...#> [9]: (1, -2.5);(2, -1.0);(3, -0.6); ...#> [10]: (1,-0.07);(2, 0.91);(3,-0.63); ...x<- tf_rgp(5)
x[cbind(1:5, runif(5))] # fails because i-arg does not check for array-type :(#> Error in `[.tf`(x, cbind(1:5, runif(5))): Assertion on 'i' failed: Must be of type 'integerish', but element 6 is not close to an integer.
The text was updated successfully, but these errors were encountered:
x[1:3, , matrix = FALSE] # or TRUE
tfd[3] on (0,1) based on 51 evaluations each
interpolation by tf_approx_linear
1: (0.00,-0.30);(0.02,-0.25);(0.04,-0.14); ...
should be the same as x[1:3, tf_arg(x), matrix = ?] but it's not....
default expected behavior for arrays (i.e., array-args to [ return a vector of entries in the array) does not carry over to tf-vectors:
The text was updated successfully, but these errors were encountered: