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Comment: link to submission requirements
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{{{#!wiki important | {{{#!wiki comment |
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$r$ equal parts; each contributing to the production of $r$ new (distinct) items in generation $g+1$. This cycle continues (each item in generation $g+1$ has probability $p$ of continuing on to generation $g+2$ as $r$ new distinct items. The initial generation will be $g=0$, so any item in the use-recycle-use chain at generation $i = 1,2,3,4\ldots$ will have $\frac{1}{r^i}$ of the original ($g=0$) item within it. | $r$ equal parts; each contributing to the production of $r$ new (distinct) items in generation $g+1$. This cycle continues (each item in generation $g+1$ has probability $p$ of continuing on to generation $g+2$ as $r$ new distinct items. The initial generation will be $g=0$, so any item in the use-recycle-use chain at generation $g = 1,2,3,4\ldots$ will have $\frac{1}{r^g}$ of the original ($g=0$) item within it. |
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The parameters $p$ and $r$ will be provided to your `SIM` via command line parameters, and that wraps it up for this projects specification model. | The parameters $p$ and $r$ will be provided to your `SIM` via command line parameters. The confidence intervals will be calculated to the half width $\pm0.1$. |
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|| `GENS` || The number of generations to simulate || | |
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|| `RANDOM.DAT` || As usual, a source of random values $Uniform(0,1)$.|| | |
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You will report the following values from `SIM` (in this order, and according to the course [[Assignments/Requirements|submission requirements]]): | You will `OUTPUT` the following values from `SIM` (in this order, and according to the course [[Assignments/Requirements|submission requirements]]): |
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1. ... 1. ... |
1. the number of simulations run in order to achieve the confidence interval required 1. the lower bound of the confidence interval, '''use eight digit precision and scientific notation output''' 1. the upper bound of the confidence interval, '''use eight digit precision and scientific notation output''' |
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1. ... 1. ... |
1. While a particular call to $Generate()$ in your `SIM` will return an integer value for $g$, the average and confidence interval bounds will of course be $\Re$ valued numbers. 1. Beware of tracking each individual item at each generation with an object - with $p=0.70$, $r=10$ you will have about $75\times10^6$ individual items in a data structure by the ninth generation. Yikes :o It is better to maintain the following '''two pieces of information''' as a "generation" loop plods forward, breaking when $f$ becomes less than $F$: the ''number of items'' in the current generation, and of course the ''current generation'' $g$. In this case, the remaining fraction of the original item is \[ f=\frac{\mbox{items}}{r^g} \] {{{#!wiki warning Even with this more efficient approach, we must beware of numerical issues! Depending on the random numbers (of course), you can still overflow a 32-bit integer tracking the number of items currently in the recycling chain. '''Use a 64-bit integer, or a language with arbitrary precision integers (Python, for instance) for your `items` variable.''' }}} Ideally, we would use a $Binomial(\mbox{items},p)$ random variate to determine how many progress to the next generation. But this would require a numerical inversion of the incomplete beta function (Appendix D), which is beyond the scope of this course. Your `SIM` can simply "flip" a $Bernoulli(p)$ coin for each item. |
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First, download <<DjHR(XXX-student.tar.bz2,this tarball)>> to your Mines Linux account ("alamode" machines!) and unroll it in a temporary directory. | First, download <<DjHR(alrecycling-student.tar.bz2,this tarball)>> to your Mines Linux account ("alamode" machines!) and unroll it in a temporary directory. |
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XXX-student.tar.bz2 | alrecycling-student.tar.bz2 |
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$ tar xjf ../XXX-student.tar.bz2 | $ tar xjf ../alrecycling-student.tar.bz2 |
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Now go to the directory holding your !XxXx `SIM` and execute the `grader.sh` script from the `XXX-grader.tar.bz2` resource. | Now go to the directory holding your !AlRecycling `SIM` and execute the `grader.sh` script from the `alrecycling-grader.tar.bz2` resource. |
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$ cd ~/sim/cwalksim | $ cd ~/sim/alrsim |
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$ ~/tmp/Xyz/grader.sh | $ ~/tmp/AlRecycling/grader.sh |
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The latter test generates NNN PDF files for your inspection ($OutputVar1$, $OutputVar2$ curves, and $OutputVar3$ confidence intervals). For plots generating CDF comparison curves: the <<SpanText(red line,color=red)>> is the result of '''your `SIM`''' with $N=GRADER_RUNS$ data points; the <<SpanText(blue dots,color=blue)>> are the expected results from an $N=GOLD_RUNS$ trial, and the <<SpanText(blue lines,color=blue)>> are the results of three separate trials with $N=EXAMPLE_RUNS$ for comparison. Your `SIM`'s red line should be a better approximation of the blue dots than the blue lines. |
The latter test generates 3 PDF files for your inspection (one $n$ curve comparison, and two confidence interval comparisons). For plots generating CDF comparison curves: the <<SpanText(red line,color=red)>> is the result of '''your `SIM`''' with $N=200$ data points; the <<SpanText(blue dots,color=blue)>> are the expected results from an $N=500$ trial, and the <<SpanText(blue lines,color=blue)>> are the results of three separate trials with $N=100$ for comparison. Your `SIM`'s red line should be a better approximation of the blue dots than the blue lines. |
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For plots generating overlaped confidence intervals: the <<SpanText(red lines,color=red)>> are the $GRADER_RUNS$ intervals generated by '''your `SIM`'''; the <<SpanText(green lines,color=green)>> are $GOLD_RUNS$ intervals from a good implementation, and the <<SpanText(blue lines,color=blue)>> are the results of three separate trials with $N=EXAMPLE_RUNS$ intervals. Your `SIM`'s <SpanText(red intervals,color=red) should not be any more varied from the <SpanText(green intervals)> than the <SpanText(blue lines,color=blue) are. | For plots generating overlapped confidence intervals: the <<SpanText(red lines,color=red)>> are the $200$ intervals generated by '''your `SIM`'''; the <<SpanText(green lines,color=green)>> are $500$ intervals from a good implementation, and the <<SpanText(blue lines,color=blue)>> are the results of three separate trials with $N=100$ intervals. Your `SIM`'s <SpanText(red intervals,color=red) should not be any more varied from the <SpanText(green intervals)> than the <SpanText(blue lines,color=blue) are. |
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{{attachment:_s_metric1-xxx1.svg|xxx1-alternate-title|width="60%"}} {{attachment:_s_metric2-xxx2.svg|xxx2-alternate-title|width="60%"}} |
{{attachment:_s_n-2,70,8.svg|ns for 2 gens, 0.70%, 8 parts|width="60%"}} {{attachment:_s_cis-2,70,8.svg|CIs for 2 gens, 0.70%, 8 parts|width="60%"}} |
In the Fall of 2019, I heard the following radio story: Eliminating Single Use Plastic.... In it we learn that a single use aluminum container is recycled $70\%$ of the time, to which the program host says (surprisingly):1
Host:
"There's probably a diet-rite that I was drinking in the early 90s whose aluminum is still in the system, is what you're saying."
Guest:
"That's correct."
Really?
Conceptual Model
We want to know the fraction remaining of a single use recyclable aluminum container after a certain number of "generations". The probability that a single use item in generation $g$ will be recycled and "live on" to generation $g+1$ is $p$. When an item is recycled, we envision it being divided into $r$ equal parts; each contributing to the production of $r$ new (distinct) items in generation $g+1$. This cycle continues (each item in generation $g+1$ has probability $p$ of continuing on to generation $g+2$ as $r$ new distinct items. The initial generation will be $g=0$, so any item in the use-recycle-use chain at generation $g = 1,2,3,4\ldots$ will have $\frac{1}{r^g}$ of the original ($g=0$) item within it.
Specification Model
The parameters $p$ and $r$ will be provided to your SIM via command line parameters. The confidence intervals will be calculated to the half width $\pm0.1$.
Project Requirements
Your SIM will calculate a confidence interval for the average number of generations it takes for an item's remaining fraction $f$ to fall below some threshold $F$. Your SIM will use Algorithm 8.1.2 for the calculation.
Input
The command line parameters provided to your SIM will be:
Argument |
Value |
GENS |
The number of generations to simulate |
$t$ |
idfNormal() value to use for confidence interval construction, called $t^{*}_\infty$ the text |
$p$ |
The probability that an item in generation $g$ is recycled and reused in generation $g+1$ |
$r$ |
The (equally sized) number of parts an item is broken into during the recycling process, each goes into a new distinct item. |
RANDOM.DAT |
As usual, a source of random values $Uniform(0,1)$. |
Output
You will OUTPUT the following values from SIM (in this order, and according to the course submission requirements):
- the number of simulations run in order to achieve the confidence interval required
the lower bound of the confidence interval, use eight digit precision and scientific notation output
the upper bound of the confidence interval, use eight digit precision and scientific notation output
Hints and SIM Testing
While a particular call to $Generate()$ in your SIM will return an integer value for $g$, the average and confidence interval bounds will of course be $\Re$ valued numbers.
Beware of tracking each individual item at each generation with an object - with $p=0.70$, $r=10$ you will have about $75\times10^6$ individual items in a data structure by the ninth generation. Yikes
It is better to maintain the following two pieces of information as a "generation" loop plods forward, breaking when $f$ becomes less than $F$: the number of items in the current generation, and of course the current generation $g$. In this case, the remaining fraction of the original item is \[ f=\frac{\mbox{items}}{r^g} \]
Even with this more efficient approach, we must beware of numerical issues! Depending on the random numbers (of course), you can still overflow a 32-bit integer tracking the number of items currently in the recycling chain. Use a 64-bit integer, or a language with arbitrary precision integers (Python, for instance) for your items variable.
Ideally, we would use a $Binomial(\mbox{items},p)$ random variate to determine how many progress to the next generation. But this would require a numerical inversion of the incomplete beta function (Appendix D), which is beyond the scope of this course. Your SIM can simply "flip" a $Bernoulli(p)$ coin for each item.
grader.sh
I am providing to students the same tarball the grader will use for testing your SIM. Here is how to use it:
First, download this tarball to your Mines Linux account ("alamode" machines!) and unroll it in a temporary directory.
$ ls XXX-student.* alrecycling-student.tar.bz2 $ mkdir tmp $ cd tmp $ tar xjf ../alrecycling-student.tar.bz2
Second, set the SIMGRADING environmental variable with:
$ source ~khellman/SIMGRADING/setup.sh ~khellman/SIMGRADING
Now go to the directory holding your AlRecycling SIM and execute the grader.sh script from the alrecycling-grader.tar.bz2 resource.
$ cd ~/sim/alrsim $ ls SIM SIM $ ~/tmp/AlRecycling/grader.sh : : :
You will need to read any messages from the script carefully, and hit ENTER several times throughout its course. This script checks for:
- missing tracefiles
- truncated tracefiles
and the difference between SIM results and expected results
The latter test generates 3 PDF files for your inspection (one $n$ curve comparison, and two confidence interval comparisons). For plots generating CDF comparison curves: the red line is the result of your SIM with $N=200$ data points; the blue dots are the expected results from an $N=500$ trial, and the blue lines are the results of three separate trials with $N=100$ for comparison. Your SIM's red line should be a better approximation of the blue dots than the blue lines.
For plots generating overlapped confidence intervals: the red lines are the $200$ intervals generated by your SIM; the green lines are $500$ intervals from a good implementation, and the blue lines are the results of three separate trials with $N=100$ intervals. Your SIM's <SpanText(red intervals,color=red) should not be any more varied from the <SpanText(green intervals)> than the <SpanText(blue lines,color=blue) are.
Here is an example of one of the plots generated by grader.sh:
Submit Your Work
Rubric
This work is worth ?? points.
Requirements |
Points |
Notes |
10 |
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ABCDEFG |
1000 |
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At about 3:00 minutes into the interview (1)