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SOLR-12701: Improve Monte Carlo example

This commit is contained in:
Joel Bernstein 2018-08-27 20:31:13 -04:00
parent d833b4c9d3
commit 04a50b6a2e
1 changed files with 9 additions and 6 deletions
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15
solr/solr-ref-guide/src/simulations.adoc
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@ -46,11 +46,11 @@ The Monte Carlo simulation below performs the following steps:
* A normal distribution with a mean of 2.2 and a standard deviation of .0195 is created to model the length of componentA. * A normal distribution with a mean of 2.2 and a standard deviation of .0195 is created to model the length of componentA.
* A normal distribution with a mean of 2.71 and a standard deviation of .0198 is created to model the length of componentB. * A normal distribution with a mean of 2.71 and a standard deviation of .0198 is created to model the length of componentB.
* The `monteCarlo` function is used to simulate component pairs. The `monteCarlo` function * The `monteCarlo` function samples from the componentA and componentB distributions and sets the values to variables sampleA and sampleB. It then
calls the *add(sample(componentA), sample(componentB))* function 100000 times and collects the results in an array. Each calls the *add(sampleA, sampleB)* function to find the combined lengths of the samples. The `monteCarlo` function runs a set number of times, 100000 in the example below, and collects the results in an array. Each
time the function is called a random sample is drawn from the componentA time the function is called new samples are drawn from the componentA
and componentB length distributions. The `add` function adds the two samples to calculate the combined length. and componentB distributions. On each run, the `add` function adds the two samples to calculate the combined length.
The result of each function run is collected in an array and assigned to the *simresults* variable. The result of each run is collected in an array and assigned to the *simresults* variable.
* An `empiricalDistribution` function is then created from the *simresults* array to model the distribution of the * An `empiricalDistribution` function is then created from the *simresults* array to model the distribution of the
simulation results. simulation results.
* Finally, the `cumulativeProbability` function is called on the *simmodel* to determine the cumulative probability * Finally, the `cumulativeProbability` function is called on the *simmodel* to determine the cumulative probability
@ -62,7 +62,10 @@ be 5 or less.
---- ----
let(componentA=normalDistribution(2.2, .0195), let(componentA=normalDistribution(2.2, .0195),
componentB=normalDistribution(2.71, .0198), componentB=normalDistribution(2.71, .0198),
simresults=monteCarlo(add(sample(componentA), sample(componentB)), 100000), simresults=monteCarlo(sampleA=sample(componentA),
sampleB=sample(componentB),
add(sampleA, sampleB),
100000),
simmodel=empiricalDistribution(simresults), simmodel=empiricalDistribution(simresults),
prob=cumulativeProbability(simmodel, 5)) prob=cumulativeProbability(simmodel, 5))
---- ----