Skip to main content

list manipulation - Generalization to AllTrue, AnyTrue and NoneTrue


I am wondering if there is a natural Mathematica way to generalize those functions. To be specific, All three functions AllTrue, AnyTrue and NoneTrue return a Boolean value when a list of length L contains certain number of elements satisfying a predicate.


For example, AllTrue returns true when the number of elements satisfying the predicate is L while NoneTrue returns true when the number is 0. On the contrary, AnyTrue returns true when the number is at least 1.


I am considering the generalization SomeTrue[ list, pred, n] that it returns true when the number is at least n.


Another generalization might be SomeTrue[ list, pred, {n}] that it returns true when the number is exactly n.


One natural way I can think of is just using Count function to calculate the number and compare the criterion but it does not allow the early exit(i.e., every element should be checked even for the case where the result is determined in the early stage).


For people like me migrating from procedural language, the algorithm might be obvious using For loop with counters and early return statements. It would be nice if you can show some examples with truly Mathematica way to attack such problems.




Answer



Select is fairly close to this already, notably including early exit behavior, so perhaps:


someTrue[list_, pred_, {m_} | n_] :=
n + m == Length @ Select[list, pred, 1 n + m]

Test:


someTrue[Range@10, PrimeQ, 3]
someTrue[Range@10, PrimeQ, 5]
someTrue[Range@10, PrimeQ, {4}]
someTrue[Range@10, PrimeQ, {2}]



True

False

True

False




The code above is me trying to be clever with vanishing patterns. The longer but more legible form:


someTrue[list_, pred_, n_]   := n == Length @ Select[list, pred, n]
someTrue[list_, pred_, {n_}] := n == Length @ Select[list, pred, n + 1]

Comments

Popular posts from this blog

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

plotting - Filling between two spheres in SphericalPlot3D

Manipulate[ SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, Mesh -> None, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], {n, 0, 1}] I cant' seem to be able to make a filling between two spheres. I've already tried the obvious Filling -> {1 -> {2}} but Mathematica doesn't seem to like that option. Is there any easy way around this or ... Answer There is no built-in filling in SphericalPlot3D . One option is to use ParametricPlot3D to draw the surfaces between the two shells: Manipulate[ Show[SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], ParametricPlot3D[{ r {Sin[t] Cos[1.5 Pi], Sin[t] Sin[1.5 Pi], Cos[t]}, r {Sin[t] Cos[0 Pi], Sin[t] Sin[0 Pi], Cos[t]}}, {r, 1, 2 - n}, {t, 0, Pi}, PlotStyle -> Yellow, Mesh -> {2, 15}]], {n, 0, 1}]

plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....