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| Join the creator of FeynCalc, Vladyslav Shtabovenko, tomorrow, April 24th at 10 AM CST! He will be discussing new features of FeynCalc 10 and how to use the Mathematica package. |
| I think you should use a generator function, for example: enc = NetEncoder[{"Function", Flatten[IntegerDigits[#, 2, 8]] &, {192}}] b = ByteArray[Table[RandomInteger[{0, 255}], 10 * 24]] genTrain = Function[ArrayReshape[Normal[b[[1 ;;... |
| Nikolay -- thanks so much for your response. The private function you mentioned: MachineLearning`file23DecisionTree` PackagePrivate` toTree@p[[1]]["Model"]["Tree"] Did indeed produce a visual of a tree. Unfortunately, the information in... |
| No offense but I think that Wolfram should have released new NN backend with 14.0 already. Moving too slowly in this key field. And ExternalEvaluate support for Python and Julia etc. is about the same as crippling. |
| Thank you! I modified your function to support the 24 8-bit integers expanding to 192 integers as follows: NetEncoder[{"Function", Flatten[IntegerDigits[#, 2, 8]] &, {192}}] and the following commands indeed produce the same output: ... |
| Thank you for clarification. Could you please provide few references? I am not aware of this biased estimator for the variance. |
| Hi Gianluca and thanks for sharing this! One question: Why do you use shared Weight of NetArray in LinearLayer? |
| Is this what you looking for? > Plot[Sin[x], {x, 0, 6 Pi}, AxesOrigin -> {6 Pi, 0} , ScalingFunctions -> {Identity, "Reverse"} , Frame -> Automatic , FrameTicks -> {{False, True}, {False, True}} ] ![enter image description here][1] ... |
| We have it in several places in the documentation. You can cite it from here for example: https://reference.wolfram.com/language/ref/FindClusters.html See the Cite As at the bottom of the page |