Initial values in WinBUGS - winbugs

I gave all values for stochastic nodes, but WinBUGS still gives me the message that a chain contains uninitialized variables. When I try to generate it, BUGS gives me an error undefined real result. What node I am missing here?
model {
# Population Model start on the 1 of October 2014
# Look up initial numbers in each age class
F1.2014 <- 4 # no. first-year females October 2014
F2.2014 <- 2 # no. older females October 2014
F.2014 <- F1.2014+F2.2014 # total no. of females in October 2014
F.trans <- 15 # no. of trans. juv females in March 2015
F2[1] ~ dbin(phi.af.annual, F.2014) # sample number older females alive October 2015 (12 mo)
mutot.2014 <- mu.1*F1.2014+mu.2*F2.2014 # expected number of fledglings 2014/15
J.2014 ~ dpois(mutot.2014) # sample actual number of fledglings
JF.2014 ~ dbin(0.5,J.2014) # sample no. juv females in January 2015
phi.trans<-pow((phi.jf.mo),6) # probability trans juvs survive from March to October
F1.trans ~ dbin(phi.trans, F.trans) # sample no. translocated female juven in October 2015
F1.juvs ~ dbin(phi.jf,JF.2014) # sample no. female juven in October 2015
F1[1]<-F1.juvs+F1.trans # total number of first year birds in October 2015
F[1] <- F1[1]+F2[1]
PE[1] <- step(-F[1]) # prob extinction by October 2015
# run simulations for 20 years
for (i in 1:20) {
mutot[i] <- mu.1*F1[i]*mu.2*F2[i] # expected number of total fledglings by females
J[i] ~ dpois(mutot[i]) # sample total fledglings
JF[i] ~ dbin(0.5,J[i]) # sample number female fledglings
F1[i+1] ~ dbin(phi.jf,JF[i]) # sample number female recruits next year
F2[i+1] ~ dbin(phi.af.annual,F[i]) # sample number adult females that survive to next year
F[i+1] <- F1[i+1]+F2[i+1] # total number of females next year
PE[i+1] <- step(-F[i+1]) # prob that population extinct next year
}
}
# Data
list(phi.af.annual=0.6, mu.1=2.5, mu.2=3.1, phi.jf.mo=0.8, phi.jf=0.87)
# Inits
list(F1=c(NA, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5),
F2=c(NA, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3),
F1.trans=10, F1.juvs=6, J.2014=20, JF.2014=10,
J=c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10),
JF=c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5)
)

Related

How should you test the significance of 2 classification accuracy scores: paired permutation test

I have a single trained classifier tested on 2 related multiclass classification tasks. As each trial of the classification tasks are related, the 2 sets of predictions constitute paired data. I would like to run a paired permutation test to find out if the difference in classification accuracy between the 2 prediction sets is significant.
So my data consists of 2 lists of predicted classes, where each prediction is related to the prediction in the other test set at the same index.
Example:
actual_classes = [1, 3, 6, 1, 22, 1, 11, 12, 9, 2]
predictions1 = [1, 3, 6, 1, 22, 1, 11, 12, 9 10] # 90% acc.
predictions2 = [1, 3, 7, 10, 22, 1, 7, 12, 2, 10] # 50% acc.
H0: There is no significant difference in classification accuracy.
How do I go about running a paired permutation test to test significance of the difference in classification accuracy?
I have been thinking about this and I'm going to post a proposed solution and see if someone approves or explains why I'm wrong.
actual_classes = [1, 3, 6, 1, 22, 1, 11, 12, 9, 2]
predictions1 = [1, 3, 6, 1, 22, 1, 11, 12, 9 10] # 90% acc.
predictions2 = [1, 3, 7, 10, 22, 1, 7, 12, 2, 10] # 50% acc.
paired_predictions = [[1,1], [3,3], [6,7], [1,10], [22,22], [1,1], [11,7], [12,12], [9,2], [10,10]]
actual_test_statistic = predictions1 - predictions2 # 90%-50%=40 # 0.9-0.5=0.4
all_simulations = [] # empty list
for number_of_iterations:
shuffle(paired_predictions) # only shuffle between pairs, not within
simulated_predictions1 = paired_predictions[first prediction of each pair]
simulated_predictions2 = paired_predictions[second prediction of each pair]
simulated_accuracy1 = proportion of times simulated_predictions1 equals actual_classes
simulated_accuracy2 = proportion of times simulated_predictions2 equals actual_classes
all_simulations.append(simulated_accuracy1 - simulated_accuracy2) # Put the simulated difference in the list
p = count(absolute(all_simulations) > absolute(actual_test_statistic ))/number_of_iterations
If you have any thoughts, let me know in the comments. Or better still, provide your own corrected version in your own answer. Thank you!

Read data set line by line and convert each space seperated line to comma seperated in Scala

I have a dasaset of length 200 and length of each row of data is also 200. This dataset is space separated. Here is sample dataset (first row).
-0.1100208269729097 0.1248460463105589 -0.01559138588255286 -0.01625839428292603 -0.05323888667281371 0.06722185430549973 -0.0490877148079949 -0.05039368886946847 0.0897270838973875 0.00754589058726465 -0.06693447805463611 -0.1193740974362337 -0.02214573804045866 0.02930806967704801 -0.009567144727872222 -0.02288991169653539 0.04256313697292451 -0.08190168271952417 0.008274133732539695 -0.02299227162395361 0.0111923018567119 -0.009872522389769637 0.06866110814693088 0.04622954799009332 0.05498202029091768 -0.06672541846259043 -0.05130079655965012 0.1107659505844031 0.07912810279475517 0.02246390669165305 -0.06997067603392053 -0.02069109953229961 -0.05191987832821615 -0.01971016519416264 -0.008691704006401698 -0.02963829527404451 0.02332929010677706 -0.1035585589634834 0.03801924036385142 -0.07035181096148016 -0.02460761051792025 0.05545479574143786 0.06632500394350074 -0.01693623441811409 -0.0202000922412099 0.0387732166529701 -0.06835009268170482 -0.06684471565316714 0.09737868086728406 -0.03776102176325794 -0.03087980353481784 -0.04630278791951752 -0.1129739647985331 0.09622849675187727 0.05975310144103099 -0.08083650075114446 0.05258346559791484 0.05583993856089118 -0.03916345795047688 -0.2981097687887527 0.04087798461219992 0.07153463501552468 0.07113045074135986 0.01717619972420815 -0.01893649865573213 -0.007503347735166889 0.06551854299072507 -0.005153581328393866 -0.08659840104899437 0.04864888731854276 0.08965801176651583 -0.004562179660153576 -0.1252787635844004 0.06896990208188783 -0.003925090827015415 -0.05755687748680104 -0.02544736897698906 0.02530385776038159 -0.125784848738536 0.07433650535349738 0.02153916317259382 0.04738213124034089 -0.03299623626264642 0.02073383160046674 -0.008966711746564809 0.04983292315200202 0.01974696673478601 -0.04419678420395467 -0.02442715323795661 -0.0694663145847256 0.1101497271416977 0.04200639135007367 -0.06082113335723243 -0.01473508072467703 0.01142600017146485 -0.03532257289246362 -0.02260329422449697 0.05396810070565884 0.1581078158241939 -0.05426153505070038 -0.01534772560258162 -0.04461245038675606 -0.05082561044342486 0.003953621713155758 -0.09395992245069541 0.02029879424655968 0.09397373054431565 -0.01540603811173099 -0.00188325436669238 0.07341578917873427 -0.07930228379622654 -0.01519407550785842 0.01388266474816023 0.09152064522133056 0.0106446218365201 -0.2157572256227169 0.04804075039482639 0.01970079327929429 -0.04738197196862703 0.06770927522186629 0.1006260778362594 -0.06299061441376895 0.02961951153113571 0.01572783315493193 0.1349089347411493 -0.0242042239418958 -0.07337276266118564 -0.09620055007994345 0.04754719051788902 -0.04777964847293222 0.01477148963357754 0.06678924792453055 0.05579081171364433 -0.03405429131223387 0.03615588517175376 -0.1554971840439641 -0.04581567263300179 -0.07873107398807083 0.05966093431149457 -0.128446162280915 -0.05912532817875745 0.1194692701951161 0.1103496401807509 0.0153127716173752 0.01607453121383664 -0.07114032721360454 0.03276185612322021 0.1169776569257143 0.07706242373764424 0.04889932405415184 0.0008715101384050066 0.006894007893755344 0.04519320187367908 -0.001306669064508431 0.0291067296150834 -0.02697983215093226 -0.07374490898814057 -0.04408652590757124 0.118965444980577 0.08668199929217432 0.02704832616237655 0.01473294258443707 0.02049896556673346 -0.0569226246137925 -0.0120183686689177 -0.1007080842912528 0.03517628230997978 -0.2003177929062758 0.01491215547976228 0.04590546935765301 0.1670139443078561 -0.05992676476987346 0.07038240324837636 -0.003567431692839979 0.08197255057946093 -0.01384071718153512 0.01443837418022523 -0.0393556604031245 0.003264844777785919 -0.190455395258628 -0.09122702488367737 -0.007113243408323287 0.1221344569965773 -0.06583221256210335 0.002275841418885295 -0.02418590378253777 -0.02462843336523757 -0.1054326841702153 -0.009075125286585313 0.05233463322601897 -0.09944517224527978 0.08201627957443283 0.1144830692826725 -0.1488155291532296 0.001711351371442085 0.06463339531524601 0.02089587578959802 -0.05699940762150812 0.01798950350182588 -0.01642350646709232
I tried in following way to convert it into comma seperated data. Here is my code
val bufferedSource1 = Source.fromFile(Path1 + name)
val lines1 : Iterator[String] = bufferedSource1.getLines()
val lines2 = lines1.toArray
println( lines2(0).toList )
Result of last line of code is
List(-, 0, ., 1, 1, 0, 0, 2, 0, 8, 2, 6, 9, 7, 2, 9, 0, 9, 7, , 0, ., 1, 2, 4, 8, 4, 6, 0, 4, 6, 3, 1, 0, 5, 5, 8, 9, , -, 0, ., 0, 1, 5, 5, 9, 1, 3, 8, 5, 8, 8, 2, 5, 5, 2, 8, 6, , -, 0, ., 0, 1, 6, 2, 5, 8, 3, 9, 4, 2, 8, 2, 9, 2, 6, 0, 3, , -, 0, ., 0, 5, 3, 2,.........
This is returning me single character but I want complete row that will be space separated. How can I fix This issue?
here is remaining code for conversion
val data1 : Array[Array[Double]] = lines2.flatMap{xz : String =>
Seq (xz.replaceAll(" ", ",").split(",").map(_.toDouble) )
}.toArray
import spark.implicits._
val ds = List("-0.1100208269729097 0.1248460463105589 -0.01559138588255286 -0.01625839428292603 -0.05323888667281371 0.06722185430549973 -0.0490877148079949 -0.05039368886946847 0.0897270838973875 0.00754589058726465 -0.06693447805463611 -0.1193740974362337 -0.02214573804045866 0.02930806967704801 -0.009567144727872222 -0.02288991169653539 0.04256313697292451 -0.08190168271952417 0.008274133732539695 -0.02299227162395361 0.0111923018567119 -0.009872522389769637 0.06866110814693088 0.04622954799009332 0.05498202029091768 -0.06672541846259043 -0.05130079655965012 0.1107659505844031 0.07912810279475517 0.02246390669165305 -0.06997067603392053 -0.02069109953229961 -0.05191987832821615 -0.01971016519416264 ","-0.1100208269729097 0.1248460463105589 -0.01559138588255286 -0.01625839428292603 -0.05323888667281371 0.06722185430549973 -0.0490877148079949 -0.05039368886946847 0.0897270838973875 0.00754589058726465 -0.06693447805463611 -0.1193740974362337 -0.02214573804045866 0.02930806967704801 -0.009567144727872222 -0.02288991169653539 0.04256313697292451 -0.08190168271952417 0.008274133732539695 -0.02299227162395361 0.0111923018567119 -0.009872522389769637 0.06866110814693088 0.04622954799009332 0.05498202029091768 -0.06672541846259043 -0.05130079655965012 0.1107659505844031 0.07912810279475517 0.02246390669165305 -0.06997067603392053 -0.02069109953229961 -0.05191987832821615 -0.01971016519416264 ").toDS()
ds.map(i=> i.split(" ").mkString(",")).show(false)
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|-0.1100208269729097,0.1248460463105589,-0.01559138588255286,-0.01625839428292603,-0.05323888667281371,0.06722185430549973,-0.0490877148079949,-0.05039368886946847,0.0897270838973875,0.00754589058726465,-0.06693447805463611,-0.1193740974362337,-0.02214573804045866,0.02930806967704801,-0.009567144727872222,-0.02288991169653539,0.04256313697292451,-0.08190168271952417,0.008274133732539695,-0.02299227162395361,0.0111923018567119,-0.009872522389769637,0.06866110814693088,0.04622954799009332,0.05498202029091768,-0.06672541846259043,-0.05130079655965012,0.1107659505844031,0.07912810279475517,0.02246390669165305,-0.06997067603392053,-0.02069109953229961,-0.05191987832821615,-0.01971016519416264|
|-0.1100208269729097,0.1248460463105589,-0.01559138588255286,-0.01625839428292603,-0.05323888667281371,0.06722185430549973,-0.0490877148079949,-0.05039368886946847,0.0897270838973875,0.00754589058726465,-0.06693447805463611,-0.1193740974362337,-0.02214573804045866,0.02930806967704801,-0.009567144727872222,-0.02288991169653539,0.04256313697292451,-0.08190168271952417,0.008274133732539695,-0.02299227162395361,0.0111923018567119,-0.009872522389769637,0.06866110814693088,0.04622954799009332,0.05498202029091768,-0.06672541846259043,-0.05130079655965012,0.1107659505844031,0.07912810279475517,0.02246390669165305,-0.06997067603392053,-0.02069109953229961,-0.05191987832821615,-0.01971016519416264|
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

rrule dates are offset by a week

I'm getting an unexpected result using the python-dateutil rrule module and I'm wondering if this is WAI.
I'm dynamically creating the rrule using:
dtstart = datetime.date(2019, 1, 7)
until = datetime.date(2029, 11, 29)
freq = MONTHLY
byweekday=MO(2)
interval = 4
This results in the following rrule
DTSTART:20190107T000000
RRULE:FREQ=MONTHLY;INTERVAL=4;UNTIL=20291129T000000;BYDAY=+2MO
However, when generating the dates (looping on the rrule for this python module), I get the following dates:
[datetime.datetime(2019, 1, 14, 0, 0),
datetime.datetime(2019, 5, 13, 0, 0),
datetime.datetime(2019, 9, 9, 0, 0),
datetime.datetime(2020, 1, 13, 0, 0),
datetime.datetime(2020, 5, 11, 0, 0),
datetime.datetime(2020, 9, 14, 0, 0),
datetime.datetime(2021, 1, 11, 0, 0),
datetime.datetime(2021, 5, 10, 0, 0),
datetime.datetime(2021, 9, 13, 0, 0),
datetime.datetime(2022, 1, 10, 0, 0),
datetime.datetime(2022, 5, 9, 0, 0),
datetime.datetime(2022, 9, 12, 0, 0),
datetime.datetime(2023, 1, 9, 0, 0),
datetime.datetime(2023, 5, 8, 0, 0),
datetime.datetime(2023, 9, 11, 0, 0),
datetime.datetime(2024, 1, 8, 0, 0),
datetime.datetime(2024, 5, 13, 0, 0),
datetime.datetime(2024, 9, 9, 0, 0),
datetime.datetime(2025, 1, 13, 0, 0),
datetime.datetime(2025, 5, 12, 0, 0),
datetime.datetime(2025, 9, 8, 0, 0),
datetime.datetime(2026, 1, 12, 0, 0),
datetime.datetime(2026, 5, 11, 0, 0),
datetime.datetime(2026, 9, 14, 0, 0),
datetime.datetime(2027, 1, 11, 0, 0),
datetime.datetime(2027, 5, 10, 0, 0),
datetime.datetime(2027, 9, 13, 0, 0),
datetime.datetime(2028, 1, 10, 0, 0),
datetime.datetime(2028, 5, 8, 0, 0),
datetime.datetime(2028, 9, 11, 0, 0),
datetime.datetime(2029, 1, 8, 0, 0),
datetime.datetime(2029, 5, 14, 0, 0),
datetime.datetime(2029, 9, 10, 0, 0)]
Notice that the first date is offset by a week! Why is this the case? And is this a bug in the library?
Thanks,
David
It's not a bug in the library. 2019-01-14 is the first date which matches your rule (it's the 2nd Monday of January 2019). Apparently python-dateutil has chosen to not include the start date you provide, which is completely legit.
RRULE is specified in RFC 5545, which states in Section 3.8.5.3 (under "Description"):
The recurrence set generated with a "DTSTART" property
value not synchronized with the recurrence rule is undefined.
Which essentially means there is no right or wrong interpretation because the input data is "broken" if the start date doesn't match the rule.
Note, many other implementations would probably return both, your start date 2019-01-07 and the result 2019-01-14. I don't think any implementation would omit 2019-01-14, simply because it is the first date which matches the rule. It's debatable whether the start date 2019-01-07 should be in the results or not, but 2019-01-14 should definitely be in there.
In Python by_weekly code could be implemented like this.
from calendar import isleap
from datetime import datetime
from dateutil.rrule import rrule, DAILY, WEEKLY, MONTHLY
def bi_weekly(start_date=datetime.now(),count=53,interval=2):
"""
dateTImeSart = bi_weekly(datetime.strptime('2021-01-01', '%Y-%m-%d'),53)
print(dateTImeSart[0].strftime("%Y-%m-%d"))
print(dateTImeSart[1].strftime("%Y-%m-%d"))
print(dateTImeSart[50].strftime("%Y-%m-%d"))
print(dateTImeSart[51].strftime("%Y-%m-%d"))
print(dateTImeSart[52].strftime("%Y-%m-%d"))
2021-01-01
2021-01-15
2022-12-02
2022-12-16
2022-12-30
"""
# returns the datetime for an year and calculates them for 1 By weekly
return list(rrule(WEEKLY, count=count,interval=interval, dtstart=start_date))

Swap first and last columns in matrix

For example, we have a matrix.
1, 2, 3, 4, 5
6, 7, 8, 9, 10
11, 12, 13, 14, 15
16, 17, 18, 19, 20
21, 22, 23, 24, 25
Perhaps the simplest way to resolve the problem sounded in the title of the topic in Perl6 looks like
my #matrix = [1..5], [6..10], [11..15], [16..20], [21..25];
#matrix.map:{.[0,*-1] = .[*-1,0]};
Result
5, 2, 3, 4, 1
10, 7, 8, 9, 6
15, 12, 13, 14, 11
20, 17, 18, 19, 16
25, 22, 23, 24, 21
How to do the same is also beautiful in PowerShell?
Your code snippet translated to PowerShell would look like this:
$matrix = (1..5), (6..10), (11..15), (16..20), (21..25)
$matrix | ForEach-Object { $_[0], $_[-1] = $_[-1], $_[0] }

ZXing library Reed Solomon example

I want to try the ReedSolomonDecoder from the ZXing library on the example given on page 10 of this paper
Basically, it encodes the message
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
using the generator polynomial
x^4 + 15x^3 + 3x^2 + x + 12
which results in
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 3, 3, 12, 12
I want to decode this in the following manner:
int[] data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 3, 3, 12, 12};
GenericGF field = new GenericGF(?, 16, 1); // what integer should I use for primitive here?
ReedSolomonDecoder decoder = new ReedSolomonDecoder(field);
decoder.decode(data, 4);
I don't know how to create a GenericGF object from the given generator polynomial. I know that it expects a binary integer representation of the polynomial, but to do that, I would need the polynomial to be in an irreducible form, i.e. all the coefficients to be either 0 or 1. How can I achieve that from this given generator polynomial?
I'm pretty new to this as well but I think you would want to use
public static GenericGF AZTEC_PARAM = new GenericGF(0x13, 16, 1);