sand15

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7 years, 182 days

MaplePrimes Activity


These are questions asked by sand15

I have just come across this curious but really boring problem.
In the code snippet below, expr1 and expr2 are identical.

restart:
data := [a=1, b=2]:
convert(
  piecewise(And(x(t) > a, x(t) < b), 1, 0),
  Heaviside
):
expr1 := eval(eval(%, data),  x(t)=z):
plot(expr1, z=0..3);


convert(
  piecewise(And(z > a, z< b), 1, 0),
  Heaviside
):
expr2 := eval(%, data),:
plot(expr2, z=0..3);


But if I change the parameterization of the problem, expr2 is still correct but expr1 is not

restart:
data := [d=1.5, a=0.5]:
convert(
  piecewise(And(x(t) > d-a, x(t) < d+a), 1, 0),
  Heaviside
):
expr1 := eval(eval(%, data),  x(t)=z):
plot(expr1, z=0..3);


convert(
  piecewise(And(z > d-a, z< d+a), 1, 0),
  Heaviside
):
expr2 := eval(%, data),:
plot(expr2, z=0..3);

Where does this come from?

PS: I'm sorry not to be able loading the mw file

restart:

alias(f=f(t)):
alias(g=g(t)):

diff([f, g], t):  #ok

a := [alias()]:
diff(a, t);
      [0, 0]

More generally, how can we differentiate a list of aliases without naming them explicitely?

Thanks in advance

I'm adjusting a Maple 2015 code for it works correctly in Maple 2020.
A being some matrix, this command executed in Maple 2015 returns a plot with the desired color.

matrixplot(A, heights=histogram, color="X11 Thistle1")

When executed in Maple 2020 the color of the bars is desperatly black.
Note that syntaxes like color=red or color=ColorTools:-Color([1, 0, 0]), despite what seems to be said in the matrixplot help page (wherein the reference to plot:-color help page) keep returning a black plot.
The only thing I'm able to do to turn the plot to red is this

F := (x, y) -> 1:
matrixplot(A, heights=histogram, color=F)


How can I obtain a plot with the color I want?

PS: maybe I'm not very astute, but it looks like the help pages are not very explicit on this point.

Hi, 

I try using the DeepLearning package.
I use the function Classify and, even in the simplest test case presented in the its help page (please look at it), I regularly get connection errors to the mpython server as soon as I execute classifier := Classify(...) or classifier(...) more than once.
Errors are one of these twos

Error, (in Train) unable to communicate with mpython server
or
Error (in Python:-EvalFunction) unable to communicate with mpython server

I work with Windows 7 Enterprise, on an 8 proc PC and 64 GB of memory. The worst situation happened when Maple didn't even return these errors and that I saw inflating the consumed memory in 2 minutes, forcing me to manually shut down my PC because the task manager wasn't no longer  operational.

Is it a known problem?
Could it be an installation problem?


Even if it's not the point here, I would like to say that trying to use the DeepLearning package is really challenging considering the poverty of the help pages.

Hi, 

A year ago I submitted a problem about the sampling of a Gaussian Random Variable (GRV).
A serious problem with Statistics:-Sample()
In short, the default method (Ziggurat method) used in the Stratistics package to sample a GRV overestimates the weights of the tails of the distribution.


Forcing the method to "envelope" is a way to obtain a correct sample

Statistics:-Sample(Normal(0, 1), N, method=envelope)

(another one is to use for instance the Box-Muller sampling algorithm ; look to the reference above for the fast implementation acer proposed).

I recently observed that the envelope method generates an error ("too many inflexion points...") when the standard deviation of the GRV is not one.
I tried to avoid this error by adding the suboption "range" :

restart:
f := (sigma, k, N) => Statistics:-Sample(Normal, 0, sigma), N,  method=[envelope, range=-k*sigma..k*sigma]):
# this works
f(1, 3, 10):
# these do not work
f(0.1, 3, 10):
f(10, 3, 10):

Here, k is a positive real value (which could depend on N but can be imagined to be around 5 or 6 to fix the ideas
Even with this suboption I keep receivind the same error.

If there is no way to parameterize correctly the envelope method, this means that Maple is unable to sample correctly a GRV.

Of course, if X is a GRV of mean mu and standard deviation sigma on could do this to generate a sample of X:

Xstd := RandomVariable(Normal(0, 1)):
Sstd := Sample(Xstd, 10^6, method=envelope):
S := mu +~ sigma *~ Sstd

But this should not be a permanent solution.

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