Consuming Azure ML With F#

(This post is a continuation of this one)

So with a model that works well enough,  I selected only that model and saved it




Created a new experiment and used that model with the base data.  I then marked the project columns as the input and the score as the output (green and blue circle respectively)


After running it, I published it as a web service


And voila, an endpoint ready to go.  I then took the auto generated script and opened up a new Visual Studio F# project to use it.  The problem was that this is the data structure that the model needs

FeatureVector = new Dictionary<string, string>() { { "Precinct", "0" }, { "VRN", "0" }, { "VRstatus", "0" }, { "VRlastname", "0" }, { "VRfirstname", "0" }, { "VRmiddlename", "0" }, { "VRnamesufx", "0" }, { "VRstreetnum", "0" }, { "VRstreethalfcode", "0" }, { "VRstreetdir", "0" }, { "VRstreetname", "0" }, { "VRstreettype", "0" }, { "VRstreetsuff", "0" }, { "VRstreetunit", "0" }, { "VRrescity", "0" }, { "VRstate", "0" }, { "Zip Code", "0" }, { "VRfullresstreet", "0" }, { "VRrescsz", "0" }, { "VRmail1", "0" }, { "VRmail2", "0" }, { "VRmail3", "0" }, { "VRmail4", "0" }, { "VRmailcsz", "0" }, { "Race", "0" }, { "Party", "0" }, { "Gender", "0" }, { "Age", "0" }, { "VRregdate", "0" }, { "VRmuni", "0" }, { "VRmunidistrict", "0" }, { "VRcongressional", "0" }, { "VRsuperiorct", "0" }, { "VRjudicialdistrict", "0" }, { "VRncsenate", "0" }, { "VRnchouse", "0" }, { "VRcountycomm", "0" }, { "VRschooldistrict", "0" }, { "11/6/2012", "0" }, { "Voted Ind", "0" }, }, GlobalParameters = new Dictionary<string, string>() { } };

And since I am only using 6 of the columns, it made sense to reload the Wake County Voter Data with just the needed columns.  I went back to the original CSV and did that.  Interestingly, I could not set the original dataset as the publish input so I added a project column module that does nothing


With that in place, I republished the service and opened Visual Studio.  I decided to start with a script.  I was struggling though the async when Tomas P helped me on Stack Overflow here.  I’ll say it again, the F# community is tops.  In any event, here is the initial script:

#r @"C:\Program Files (x86)\Reference Assemblies\Microsoft\Framework\.NETFramework\v4.5\System.Net.Http.dll" #r @"..\packages\Microsoft.AspNet.WebApi.Client.5.2.2\lib\net45\System.Net.Http.Formatting.dll" open System open System.Net.Http open System.Net.Http.Headers open System.Net.Http.Formatting open System.Collections.Generic type scoreData = {FeatureVector:Dictionary<string,string>;GlobalParameters:Dictionary<string,string>} type scoreRequest = {Id:string; Instance:scoreData} let invokeService () = async { let apiKey = "" let uri = "" use client = new HttpClient() client.DefaultRequestHeaders.Authorization <- new AuthenticationHeaderValue("Bearer",apiKey) client.BaseAddress <- new Uri(uri) let input = new Dictionary<string,string>() input.Add("Zip Code","27519") input.Add("Race","W") input.Add("Party","UNA") input.Add("Gender","M") input.Add("Age","45") input.Add("Voted Ind","1") let instance = {FeatureVector=input; GlobalParameters=new Dictionary<string,string>()} let scoreRequest = {Id="score00001";Instance=instance} let! response = client.PostAsJsonAsync("",scoreRequest) |> Async.AwaitTask let! result = response.Content.ReadAsStringAsync() |> Async.AwaitTask if response.IsSuccessStatusCode then printfn "%s" result else printfn "FAILED: %s" result response |> ignore } invokeService() |> Async.RunSynchronously


Unfortunately, when I run it, it fails.  Below is the Fiddler trace:



So it looks like the Json Serializer is postpending the “@” symbol.  I changed the records to types and voila:


You can see the final script here.

So then throwing in some different numbers. 

  • A millennial: ["27519","W","D","F","25","1","1","0.62500011920929"]
  • A senior citizen: ["27519","W","D","F","75","1","1","0.879632294178009"]

I wonder why social security never gets cut?

In any event, just to check the model:

  • A 15 year old: ["27519","W","D","F","15","1","0","0.00147285079583526"]

Azure ML and Wake County Election Data

I have been spending the last couple of weeks using Azure ML and I think it is one of the most exciting technologies for business developers and analysts since ODBC and FSharp type providers.   If you remember, when ODBC came out, every relational database in the world became accessible and therefore usable/analyzable.   When type providers came out, programming, exploring, and analyzing data sources became much easier and it expanded from RDBMS to all formats (notably Json).  So getting data was no longer a problem, but analyzing it still was.

Enter Azure ML. 

I downloaded the Wake County Voter History data from here.  I took the Excel spreadsheet and converted it to a .csv locally.  I then logged into Azure ML and imported the data


I then created an experiment and added the dataset to the canvas



And looked at the basic statistics of the data set


(Note that I find that using the FSharp REPL  a better way to explore the data as I can just dot each element I am interested in and view the results).

In any event, the first question I want to answer is

“given a person’s ZipCode, Race, Party,Gender, and Age, can I predict if they will vote in November”

To that end, I first narrowed down the columns using a Column Projection and picked only the columns I care about.  I picked “11/6/2012” and the X variable because that was the last  national election and that is what we are going to have in November.  I prob should have done 2010 b/c that is a national without a President, but that can be analyzed at a later date.



I then ran my experiment so the data would be available in the Project Column step.



I then renamed the columns to make them a bit readable by using a series Metadata Editors (it does not look like you can do all renames in 1 step.  Equally as annoying is that you have to add each module, run it, then add the next.)


(one example)



I then added a Missing Values scrubber for the voted column.  So instead of a null field, people who didn’t vote get a “N”


The problem is that it doesn’t work –> looks like we can’t change the values per column.


I asked the question on the forum but in the interest of time, I decided to change the voted column from a categorical column to an indicator. That way I can do binary analysis.  That also failed.  I went back to the original spreadsheet and added a Indicator column and then also renamed the column headers so I am not cluttering up my canvas with those meta data transforms.  Finally, I realized I want only active voters but there does not seems to be a filtering ability (remove rows only works for missing) so I removed those also from the original dataset.  I think the ability to scrub and munge data is an area for improvement, but since this is release 1, I understand.

After re-importing the data, I changed my experiment like so


I then split the dataset into Training/Validation/And Testing using a 60/20/20 split


So the left point on the second split is 60% of the original dataset, the right point on the second split is 20% of the original dataset (or 75%/25% of the 80% of the first split)

I then added a SVM with a train and score module.  Note that I am training with 60% of the original dataset and I am validating with 20%



After it runs, there are 2 new columns in the dataset –> Scored labels and probabilities so each row now has a score.



With the model in place, I can then evaluate it using an evaluation model


And we can see an AUC of .666, which immediately made me think of this


In any event, I added a Logisitc Regression and a Boosted Decision Tree to the canvas and hooked them up to the training and validation sets


And this is what we have

image image


SVM: .666 AUC

Regression: .689 AUC

Boosted Decision Tree: .713 AUC

So with Boosted Decision Tree ahead, I added a Sweep Parameter module to see if I can tune it more.  I am using AUC as the performance metric



So the best AUC I am going to get is .7134 with the highlighted parameters.  I then added 1 more Model that uses those parameters against the entire training dataset (80% of the total) and then evaluates it against the remaining 20%.


With the final answer of


With that in hand, I can create a new experiment that will be the bases of a real time voting app.

Sql Saturday and MVP Monday

Thanks to everyone who came to my session on F# Type Providers.  The code is found here.

Also, my article on the Eject-A-Bed was selected for MVP Mondays.  You can see a link here.


Fun with Statistics and Charts

I am preparing my Raleigh Code Camp submission ‘Nerd Dinner With Brains” this weekend.  If you are not familiar, Nerd Dinner is the canonical example of a MVC application and is very familiar to Web Devs who want to learn MVC the Microsoft way.  You can see the walkthrough here.   For everything that Nerd Dinner is, it is not … smart.  There is no business rules outside of some basic input validation, which is pretty representative of many “Boring Line Of Business Applications (BLOBAs according to Scott Waschlan).  Not coincidently, the lack of business logic is the biggest  reason many BLOBAs don’t have many unit tests –> if all you are doing is wire framing a database, what business logic needs to be tested? 

The talk is going to take the Nerd Diner wireframe and inject some analytics to the application.  To that end, I first considered the person who is attending the dinner.  All we know about them is their name and possibly their location.  So what can a name tell you?  Turns out, plenty.

As I showed in this post, there is a great source of the number of names given by gender, yearOfBrith, and stateOfBirth from the US census.  Picking up where that post left off, I loaded in the entire data set into memory.

My first question was, “given a name, can I tell what gender the person is?”  This is very straight forward to calculate.

1 let genderSearch name = 2 let nameFilter = usaData 3 |> Seq.filter(fun r -> r.Mary = name) 4 |> Seq.groupBy(fun r -> r.F) 5 |> (n,a) -> n,a |> Seq.sumBy(fun (r) -> r.``14``)) 6 7 let nameSum = nameFilter |> Seq.sumBy(fun (n,c) -> c) 8 nameFilter 9 |> (n,c) -> n, c, float c/float nameSum) 10 |> Seq.toArray 11 12 genderSearch "James" 13

And the REPL shows me that is is very likely that “James” is a male:


I can then set up in the web.config file a confidence point where there name is a male/female, I am thinking 75%.  Once we have that, the app can respond differently.  Perhaps we have a product-placement advertisement that becomes a male-focused if we are reasonably certain that the user is a male.  Perhaps we can be more subtle and change the theme of the site, or the page navigation, to induce the person to do additional things on the site.

In any event, I then wanted to tackle age.  I spun up some code to isolate a person’s age

1 let ageSearch name = 2 let nameFilter = usaData 3 |> Seq.filter(fun r -> r.Mary = name) 4 |> Seq.groupBy(fun r -> r.``1910``) 5 |> (n,a) -> n,a |> Seq.sumBy(fun (r) -> r.``14``)) 6 |> Seq.toArray 7 let nameSum = nameFilter |> Seq.sumBy(fun (n,c) -> c) 8 nameFilter 9 |> (n,c) -> n, c, float c/float nameSum) 10 |> Seq.toArray

I had no idea if names have a certain age connotation so I decided to do some basic charting.  Isaac Abraham pointed me to FSharp.Chart which is a great way to do some basic charting for discovery.

1 let chartData = ageSearch "James" 2 |> (y,c,p) -> y, c) 3 |> Seq.sortBy(fun (y,c) -> y) 4 5 Chart.Line(chartData).ShowChart()

And sure enough, the name “James” has a real ebb and flow for its popularity.


so if the user has a name of “James”, you can make a reasonable assumption they are male and probably born before 1975.  Cue up the Van Halen!

And yes, because I had to:

1 let chartData = ageSearch "Britney" 2 |> (y,c,p) -> y, c) 3 |> Seq.sortBy(fun (y,c) -> y)


Kinda does match her career, no?

Anyway, back to the task at hand.  In terms of analytics, I want to be a bit more precise then eyeballing a chart.  I started with the following code:

1 ageSearch "James" 2 |> (y,c,p) -> float c) 3 |> Seq.average 4 5 ageSearch "James" 6 |> (y,c,p) -> float c) 7 |> Seq.min 8 9 ageSearch "James" 10 |> (y,c,p) -> float c) 11 |> Seq.max 12


With these basic statistics out of the way, I then wanted to look at when the name was no longer popular.  I decided to use 1 standard deviation away from the average to determine an outlier.  First the standard deviation:

1 let variance (source:float seq) = 2 let mean = Seq.average source 3 let deltas = x -> pown(x-mean) 2) source 4 Seq.average deltas 5 6 let standardDeviation(values:float seq) = 7 sqrt(variance(values)) 8 9 ageSearch "James" 10 |> (y,c,p) -> float c) 11 |> standardDeviation 12 13 let standardDeviation' = ageSearch "James" 14 |> (y,c,p) -> float c) 15 |> standardDeviation 16 17 let average = ageSearch "James" 18 |> (y,c,p) -> float c) 19 |> Seq.average 20 21 let attachmentPoint = average+standardDeviation'


And then I can get the last year that the name was within 1 standard deviation above the average (greater than 71,180 names given):

1 2 let popularYears = ageSearch "James" 3 |> (y,c,p) -> y, float c) 4 |> Seq.filter(fun (y,c) -> c > attachmentPoint) 5 |> Seq.sortBy(fun (y,c) -> y) 6 |> Seq.last


So “James” is very likely a male and likely born before 1964.  Cue up the Pink Floyd!

The last piece was the state of birth –> can I guess the state of birth for a user?  I first looked at the states on a plot

1 let chartData' = stateSearch "James" 2 |> (s,c,p) -> s,c) 3 4 Chart.Column(chartData').ShowChart() 5


Nothing really stands out at me –> states with the most births have the most names.  I could do an academic exercise of seeing what states favor certain names, but that does not help me with Nerd Dinner in guessing the state of birth when given a name.

I pressed on to look at the top 10 states:

1 let topTenStates = stateSearch "James" 2 |> Seq.sortBy(fun (s,c,p) -> -c-1) 3 |> Seq.take 10 4 5 let topTenTotal = topTenStates 6 |> Seq.sumBy(fun (s,c,p) -> c) 7 let total = stateSearch "James" 8 |> Seq.sumBy(fun (s,c,p) -> c) 9 10 float topTenTotal/float total


So 50% of “James” were born in 10 states.  Again, I am not sure there is any actionable information here.  For example, if a majority of “James” were born in MI, I might have something (cue up the Bob Seger). 

Interestingly, there are certain number of names where the state of birth does matter.  For example, consider “Jose”:


Unsurprisingly, the two states are CA and TX.  Just using James and Jose as an example:

  • James is a male born before 1964
  • Jose is a male born before 2008 in either TX or CA

As an academic exercise, we could construct a random forest to find the names with the greatest state affinity.  However, that won’t help us on Nerd Dinner so I am leaving that out for another day.

This analysis does not account for a host of factors (person not born in the USA, nicknames, etc..), but it is still better than the nothing that Nerd Dinner currently has.  This analysis is not particular sophisticated but I often find that even the most basic statistics can be very powerful if used correctly.  That will be the next part of the talk…






Hacking the Dream Cheeky Thunder

A couple of weeks ago, Atmel tweeted about some people that hacked the Dream Cheeky Thunder Missile Launcher by soldering on a Ardunino to the circuit board.


A quick Google search shows there are lots of people who done something similar, including this post.  Since I was not interested in messing around wit the circuit board, I decided to go the software hack route.  When the missile launcher arrived, I downloaded software and installed it on my Windows 7 machine.  I then used Telerik’s JustDecompile software to look at the source code.


Fortunately, the main executable is a .NET 2.0 Windows Form application.  Unfortunately, the code is a mess and it relies on user controls.  Specifically, the library that the .NET .exe consumes is called USBLib.dll which is a 23-bit Com component that creates a windows control that the main .exe uses.

When I took the code from JustDecompile and stuck it into Visual Studio (no F# option so I went with C#), it took about 2-3 hours to get all of the references set up, the resources set up, and the embedded code put into the right location, but I did manage to get a working Visual Studio solution


I then decided to build a brand new solution that controls the missile launcher without the graphical components that are baked into the app.  I added a reference to the USBLib.dll and then tried to make method calls.  No luck, it looks like the application uses Windows Event hooks to call and respond:

protected override void WndProc(ref Message m) { this.USB.ParseMessages(ref m); if (m.Msg == SingleProgramInstance.WakeupMessage) { if (base.WindowState == FormWindowState.Minimized) { base.Visible = true; base.WindowState = FormWindowState.Normal; } base.Activate(); } base.WndProc(ref m); }

Yuck!  I then did a quick search on Google to find a .NETUsbDriver that I could use because I already have the byte array values that the missile launcher is expected:

I found this but the suggestions did not compile and/or did not work.  I then found this site which in the right direction.  I added the code into my project like so:


I then wired up the main form like this:


With the code behind like this:

MissileLauncher _launcher = new MissileLauncher(); public Form1() { InitializeComponent(); _launcher.command_reset(); _launcher.command_switchLED(true); } private void upButton_Click(object sender, EventArgs e) { _launcher.command_Up(2000); } private void fireButton_Click(object sender, EventArgs e) { _launcher.command_Fire(); } private void downButton_Click(object sender, EventArgs e) { _launcher.command_Down(1000); } private void rightButton_Click(object sender, EventArgs e) { _launcher.command_Right(3000); } private void leftButton_Click(object sender, EventArgs e) { _launcher.command_Left(3000); }

Then, when I run it, it works like a champ.  I now just need to translate the values passed into the Thread.Sleep() and have it correspond to the angles.  The author of the code was on the right track because s/he named the parameter “degree”. 

In the meantime, I ported the code to FSharp.  You can see it here and you can see the missle launcher in action here.  The major difference is that the C# code had 182 lines and the F# code has 83.

Consuming and Analyzing Census Data Using F#

As part of my Nerd Dinner refactoring, I wanted to add the ability to guess a person’s age and gender based on their name.  I did a quick search on the internet and the only place that I found that has an API is here and it doesn’t have everything I am looking for.  Fortunately, the US Census website has some flat files with the kind of data I am looking for here.

I grabbed the data and  pumped it into Azure Blob Storage here.  You can swap out the state code to get each dataset.  I then loaded in a list of State Codes found here that match to the file names.

I then fired up Visual Studio and created a new FSharp project.  I added FSharp.Data to use a Type Provider to access the data.  I don’t need to install the Azure Storage .dlls b/c the blobs are public and I just have to read the file


Once Nuget was done with its magic, I opened up the script file, pointed to the newly-installed FSharp.Data, and added a reference to the datasets on blob storage:

#r "../packages/FSharp.Data.2.0.9/lib/portable-net40+sl5+wp8+win8/FSharp.Data.dll" open FSharp.Data type censusDataContext = CsvProvider<""> type stateCodeContext = CsvProvider<"">

(Note that I am going add FSharp as a language to my Live Writer code snippet add-in at a later date)

In any event, I then printed out all of the codes to see what it looks like:

let stateCodes = stateCodeContext.Load(""); stateCodes.Rows |> Seq.iter(fun r -> printfn "%A" r)


And by changing the lambda slightly like so,

stateCodes.Rows |> Seq.iter(fun r -> printfn "%A" r.Abbreviation)

I get all of the state codes


I then tested the census data with code and results are expected

let arkansasData = censusDataContext.Load(""); arkansasData.Rows |> Seq.iter(fun r -> printfn "%A" r)


So then I created a method to load all of the state census data and giving me the length of the total:

let stateCodes = stateCodeContext.Load(""); let usaData = stateCodes.Rows |> Seq.collect(fun r -> censusDataContext.Load(System.String.Format("{0}.TXT",r.Abbreviation)).Rows) |> Seq.length


Since this is a I/O bound operation, it made sense to load the data asynchronously, which speeded things up considerably.  You can see my question over on Stack Overflow here and the resulting code takes about 50% of the time on a my dual-processor machine:

stopwatch.Start() let fetchStateDataAsync(stateCode:string)= async{ let uri = System.String.Format("{0}.TXT",stateCode) let! stateData = censusDataContext.AsyncLoad(uri) return stateData.Rows } let usaData' = stateCodes.Rows |> r -> fetchStateDataAsync(r.Abbreviation)) |> Async.Parallel |> Async.RunSynchronously |> Seq.collect id |> Seq.length stopwatch.Stop() printfn "Parallel: %A" stopwatch.Elapsed.Seconds


With the data in hand, it was time to analyze the data to see if there is anything we can do.   Since 23 seconds is a bit too long to wait for a page load (Smile), I will need to put the 5.5 million records into a format that can be easily searched.  Thinking what we want is:

Given a name, what is the gender?

Given a name, what is the age?

Given a name, what is their state of birth?

Also, since we have their current location, we can also input the name and location and answer those questions.  If we make the assumption that their location is the same as their birth state, we can narrow down the list even further.

In any event, I first added a GroupBy to the name:

let nameSum = usaData' |> Seq.groupBy(fun r -> r.Mary) |> Seq.toArray


And then I summed up the counts of the names

let nameSum = usaData' |> Seq.groupBy(fun r -> r.Mary) |> (n,a) -> n,a |> Seq.sumBy(fun (r) -> r.``14``)) |> Seq.toArray


And then the total in the set:

let totalNames = nameSum |> Seq.sumBy(fun (n,c) -> c)


And then applied a simple average and sorted it descending

let nameAverage = nameSum |> (n,c) -> n,c,float c/ float totalNames) |> Seq.sortBy(fun (n,c,a) -> -a - 1.) |> Seq.toArray


So I feel really special that my parents gave me the most popular name in the US ever…

And focusing back to the task on hand, I want to determine the probability that a person is male or female based on their name:

let nameSearch = usaData' |> Seq.filter(fun r -> r.Mary = "James") |> Seq.groupBy(fun r -> r.F) |> (n,a) -> n,a |> Seq.sumBy(fun (r) -> r.``14``)) |> Seq.toArray


So 18196 parents thought is would be a good idea to name their daughter ‘James’.  I created a quick function like so:

let nameSearch' name = let nameFilter = usaData' |> Seq.filter(fun r -> r.Mary = name) |> Seq.groupBy(fun r -> r.F) |> (n,a) -> n,a |> Seq.sumBy(fun (r) -> r.``14``)) let nameSum = nameFilter |> Seq.sumBy(fun (n,c) -> c) nameFilter |> (n,c) -> n, c, float c/float nameSum) |> Seq.toArray nameSearch' "James"


So if I see the name “James”, there is a 99% chance it is a male.  This can lead to a whole host of questions like variance of names, names that are closest to gender neutral, etc….  Leaving those questions to another day, I now have something I can put into Nerd Dinner.  Now, if there was only a way to handle nicknames and friendly names….

You can see the full code here.







Controlling Servos Using Netdunio and Phidgets

As part of the Terminator program I am creating, I need a way of controlling servos to point the laser (and then gun) and different targets.  I decided to create a POC project and evaluate two different ways of controlling the servos.  As step one, I purchased a pan and tilt chassis from here


After playing with the servos from the kit, I decided to use my old stand-by servos that had a much higher quality and whose PWM signals I already know how to use.  With the chassis done, I needed a laser pointer so I figured why not get a shark with fricken laser?

I found one here.


So with the servos and laser ready to go, it was time to code.  I started with Netduninos:

public class Program { private const uint TILT_SERVO_STRAIGHT = 1500; private const uint TILT_SERVO_MAX_UP = 2000; private const uint TILT_SERVO_MAX_DOWN = 1000; private const uint PAN_SERVO_STRAIGHT = 1500; private const uint PAN_SERVO_MAX_LEFT = 1000; private const uint PAN_SERVO_MAX_RIGHT = 2000; private static PWM _tiltServo = null; private static PWM _panServo = null; private static uint _tiltServoCurrentPosition = 0; private static uint _panServoCurrentPosition = 0; public static void Main() { SetUpServos(); InputPort button = new InputPort(Pins.ONBOARD_BTN, false, Port.ResistorMode.Disabled); while (true) { if (button.Read()) { MoveServo(); } } } private static void SetUpServos() { uint period = 20000; _tiltServoCurrentPosition = TILT_SERVO_STRAIGHT; _panServoCurrentPosition = PAN_SERVO_STRAIGHT; _tiltServo = new PWM(PWMChannels.PWM_PIN_D3, period, _tiltServoCurrentPosition, PWM.ScaleFactor.Microseconds, false); _tiltServo.Start(); _panServo = new PWM(PWMChannels.PWM_PIN_D5, period, _panServoCurrentPosition, PWM.ScaleFactor.Microseconds, false); _panServo.Start(); } private static void MoveServo() { _panServo.Duration = PAN_SERVO_MAX_LEFT; Thread.Sleep(2000); _panServo.Duration = PAN_SERVO_MAX_RIGHT; Thread.Sleep(2000); _panServo.Duration = PAN_SERVO_STRAIGHT; Thread.Sleep(2000); _tiltServo.Duration = TILT_SERVO_MAX_UP; Thread.Sleep(2000); _tiltServo.Duration = TILT_SERVO_MAX_DOWN; Thread.Sleep(2000); _tiltServo.Duration = TILT_SERVO_STRAIGHT; } }

And sure enough the servos are behaving as expected

I then implemented a similar app using Phidgets.  Because the code is being executed on the PC, I could use F# to code (It does not look like the Netdunino/Microframework supports F#?)

open System open Phidgets let _servoController = new AdvancedServo() let mutable _isServoControllerReady = false let servoController_Attached(args:Events.AttachEventArgs) = let servoController = args.Device :?> AdvancedServo servoController.servos.[0].Engaged <- true servoController.servos.[7].Engaged <- true _isServoControllerReady <- true [<EntryPoint>] let main argv = _servoController.Attach.Add(servoController_Attached) _servoController.``open``() while true do if _isServoControllerReady = true then _servoController.servos.[0].Position<- 100. _servoController.servos.[7].Position<- 100. Console.ReadKey() |> ignore printfn "%A" argv 0


The choice then becomes using the Netduino or the Phidgets with my Kinect program.  I decided to defer the decision and use an interface for now.

type IWeaponsSystem = abstract member Activate: unit -> unit abstract member AquireTarget : float*float -> bool abstract member Fire: int -> bool

My decision about using Phidgets or Netduino is a series of trade-offs.  I can code Phidgets in C# or F# but I have to code Netduino in C#.  I would prefer to do this in F# so that makes me learn towards Phidgets.  I can put the Netduino anywhere and have it communicate via an Ethernet signal but I have to have the Phidgets wired to the PC.  Since the targeting system needs to be near the Kinect and the Kinect has to be tethered to the PC also, there is no real advantage of using the mobile Netduino.  Finally, the Phidgets API handles all communication to the servo control board for me, with the Netduino I would have to hook up a router to the Netduino and write the Ethernet communication code.  So I am leaning towards Phidgets, but since I am not sure, the interface allows me to swap in the Netduino at a later point without changing any code.  Love me some O in SOLID…

Up next, integrating the targeting system into the Terminator program.




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