rename .util namespace to .ut

Author: psaris

adaboost.q

@@ -7,12 +7,12 @@
 -1"discrete adaboost requires the target feature to have values -1 and 1";
 t:update -1 1 "M"=diagnosis from 11#/:wdbc.t
 -1"we can then split into train and test partitions";
-d:.util.part[`train`test!3 1;0N?] t
+d:.ut.part[`train`test!3 1;0N?] t
 -1 "building a full tree is perfect on the training data";
 tr:.ml.ct[();::] d.train
-.util.assert[1f] .util.rnd[.01] avg d.train.diagnosis=.ml.pdt[tr] d.train
+.ut.assert[1f] .ut.rnd[.01] avg d.train.diagnosis=.ml.pdt[tr] d.train
 -1 "but not as good on the test data";
-.util.assert[.9] .util.rnd[.01] avg d.test.diagnosis=.ml.pdt[tr] d.test
+.ut.assert[.9] .ut.rnd[.01] avg d.test.diagnosis=.ml.pdt[tr] d.test
 -1 "how many leaves did we create?";
 count .ml.leaves tr
 -1 "adaboost creates an ensemble of weak learners to produce a strong learning";
@@ -26,24 +26,24 @@ k:50
 -1 "let's run ",string[k]," rounds of adaboost";
 m:.ml.fab[k;stump;.ml.pdt] d.train
 p:.ml.pab[k;.ml.pdt;m] d.train
-.util.assert[.98] .util.rnd[.01] avg d.train.diagnosis=p
+.ut.assert[.98] .ut.rnd[.01] avg d.train.diagnosis=p
 -1 "plot the improvement to accuracy on the training set as we increase the ensemble size";
 P:.ml.pab[1+til k;.ml.pdt;m] d.train
-show .util.plt avg d.train.diagnosis = P
+show .ut.plt avg d.train.diagnosis = P
 
 -1 "but how does each extra stump help in predicting the test set?";
 pt:.ml.pab[k;.ml.pdt;m] d.test
-.util.assert[.97] .util.rnd[.01] avg d.test.diagnosis=pt
+.ut.assert[.97] .ut.rnd[.01] avg d.test.diagnosis=pt
 -1 "we can also plot the improvement to accuracy on the test set as we increase the ensemble size";
 Pt:.ml.pab[1+til k;.ml.pdt;m] d.test
-show .util.plt avg d.test.diagnosis = Pt
+show .ut.plt avg d.test.diagnosis = Pt
 
 -1 "the number of elements in our ensemble should be decided by cross validation";
 ks:1+til 20
 
 n:10
 -1"cross validate with ", string[n], " buckets";
-ts:.util.part[n#1;0N?] t
+ts:.ut.part[n#1;0N?] t
 ff:.ml.fab[;stump;.ml.pdt]
 pf:.ml.pab[;.ml.pdt]
 e:ts[;`diagnosis]=P:.ml.xv[ff ks;pf ks;ts] peach til n

berkstan.q

@@ -1,5 +1,5 @@
 berkstan.f:"web-BerkStan.txt"
 berkstan.b:"http://snap.stanford.edu/data/"
 -1"[down]loading berkstan network graph";
-.util.download[berkstan.b;;".gz";.util.gunzip] berkstan.f;
+.ut.download[berkstan.b;;".gz";.ut.gunzip] berkstan.f;
 berkstan.l:("II";"\t") 0:  4_read0 `$berkstan.f

bible.q

@@ -2,6 +2,6 @@
 bible.f:"10.txt"
 bible.b:"http://www.gutenberg.org/files/10/"
 -1"[down]loading bible text";
-.util.download[bible.b;;"";""] bible.f;
+.ut.download[bible.b;;"";""] bible.f;
 bible.txt:read0 `$bible.f
 bible.s:1_"\n1:1 " vs "\n" sv 39_-373_ bible.txt

binary.q

@@ -1,6 +1,6 @@
 binary.f:"binary.csv"
 binary.b:"https://www.ats.ucla.edu/stat/data/"
 -1"[down]loading binary data set";
-.util.download[binary.b;;"";""] binary.f;
+.ut.download[binary.b;;"";""] binary.f;
 binary.t:("BIFI";1#",") 0: `$binary.f
 binary[`Y`X]: 0 1 cut "f"$value flip binary.t

cifar.q

@@ -2,7 +2,7 @@
 cifar.f:"cifar-10-binary"
 cifar.b:"http://www.cs.toronto.edu/~kriz/"
 -1"[down]loading CFAR-10 data set";
-.util.download[cifar.b;;".tar.gz";.util.untar] cifar.f;
+.ut.download[cifar.b;;".tar.gz";.ut.untar] cifar.f;
 cifar.d:"cifar-10-batches-bin/"
 -1"reading labels";
 cifar.labels:`$10#read0`$cifar.d,"batches.meta.txt"

citibike.q

@@ -1,25 +1,25 @@
-\l util.q
+\l ut.q
 .z.zd:17 2 6
 sd:2017.01m                     / start date
 ed:2017.12m                     / end date
 
 -1"[down]loading citibike data";
 b:"http://s3.amazonaws.com/tripdata/"
-m1:.util.sseq[1] . 2014.09 2016.12m
+m1:.ut.sseq[1] . 2014.09 2016.12m
 m1:m1 where m1 within (sd;ed)
 f1:,[;"-citibike-tripdata"] each string[m1] except\: "."
-.util.download[b;;".zip";.util.unzip] f1;
+.ut.download[b;;".zip";.ut.unzip] f1;
 
-m2:.util.sseq[1] . 2017.01 2017.12m
+m2:.ut.sseq[1] . 2017.01 2017.12m
 m2:m2 where m2 within (sd;ed)
 f2:,[;"-citibike-tripdata"] each string[m2] except\: "."
-.util.download[b;;".csv.zip";.util.unzip] f2;
+.ut.download[b;;".csv.zip";.ut.unzip] f2;
 
 / data since 2018 has an extra column
-/ m3:.util.sseq[1] . 2018.01m,-1+"m"$.z.D
+/ m3:.ut.sseq[1] . 2018.01m,-1+"m"$.z.D
 / f3:,[;"_citibikenyc_tripdata"] each string[m3] except\: "."
 / -1"[down]loading citibike data";
-/ .util.download[b;;".csv.zip";.util.unzip] f3;
+/ .ut.download[b;;".csv.zip";.ut.unzip] f3;
 
 process:{[month;f]
  -1"parsing ", string f;

cloud9.q

@@ -1,5 +1,5 @@
 cloud9.f:("sample-small.txt";"sample-medium.txt";"sample-large.txt") 2
 cloud9.b:"http://lintool.github.io/Cloud9/docs/exercises/"
 -1"[down]loading cloud9 network graph";
-.util.download[cloud9.b;;"";""] cloud9.f;
+.ut.download[cloud9.b;;"";""] cloud9.f;
 cloud9.l:flip raze {x[0],/:1_ x} each "J"$"\t" vs/: read0 `$cloud9.f

cossim.q

@@ -6,4 +6,4 @@
 X:.ml.normalize iris.X
 flip C:.ml.skmeans[X] over .ml.forgy[3] X / spherical k-means
 show m:.ml.mode each iris.y I:.ml.cgroup[.ml.cosdist;X;C] / classify
-avg iris.y=.util.ugrp m!I / accuracy
+avg iris.y=.ut.ugrp m!I / accuracy

decisiontree.q

@@ -24,7 +24,7 @@ show t:weather.t
 avg t.Play=p:.ml.pdt[tr] t / accuracy
 -1"since the test and training data are the same, it is no surprise we have 100% accuracy";
 -1".ml.pdt does not fail on missing features. it digs deeper into the tree";
-.util.assert[.71428571428571431] avg t.Play=p:.ml.pdt[.ml.id3[();::] (1#`Outlook) _ t] t
+.ut.assert[.71428571428571431] avg t.Play=p:.ml.pdt[.ml.id3[();::] (1#`Outlook) _ t] t
 -1"id3 only handles discrete features.  c4.5 handles continues features";
 -1".ml.q45 implements many of the features of c4.5 including:";
 -1"* information gain normalized by split info";
@@ -41,32 +41,32 @@ show s:@[t;`Humidity;:;85 90 78 96 80 70 65 95 70 80 70 90 75 80f]
 
 z:@[{.qml.nicdf x};.0125;2.241403];
 -1 .ml.ptree[0] tr:.ml.prune[.ml.perr[z]] .ml.q45[();::] s;
-.util.assert[1f] avg s.Play=p:.ml.pdt[tr] s / accuracy
+.ut.assert[1f] avg s.Play=p:.ml.pdt[tr] s / accuracy
 -1"we can still handle null values by using the remaining features";
-.util.assert[`Yes] .ml.pdt[tr] d:`Outlook`Temperature`Humidity`Wind!(`Rain;`Hot;85f;`)
+.ut.assert[`Yes] .ml.pdt[tr] d:`Outlook`Temperature`Humidity`Wind!(`Rain;`Hot;85f;`)
 -1"we can even can handle nulls in the training data by propagating them down the tree";
 s:update Temperature:` from s where Humidity=70f
 -1 .ml.ptree[0] tr:.ml.q45[();::] s;
-.util.assert[`No] .ml.pdt[tr] d
+.ut.assert[`No] .ml.pdt[tr] d
 -1 "we can also use the Gini impurity instead of entropy (faster with similar behavior)";
 -1 .ml.ptree[0] tr:.ml.dt[.ml.gr;.ml.ogr;.ml.wgini;();::] t;
 d:`Outlook`Temperature`Humidity`Wind!(`Rain;`Hot;`High;`) / remove null
-.util.assert[`No] .ml.pdt[tr] d
+.ut.assert[`No] .ml.pdt[tr] d
 -1 "we can also create an aid tree when the target is numeric";
 -1 .ml.ptree[0] tr:.ml.aid[(1#`minsl)!1#3;::] update "e"$`Yes=Play from t; / regression tree
-.util.assert[.2] .ml.pdt[tr] d
+.ut.assert[.2] .ml.pdt[tr] d
 -1 "we can also create a thaid tree for classification";
 -1 .ml.ptree[0] tr:.ml.thaid[(1#`minsl)!1#3;::] t; / classification tree
-.util.assert[`Yes] .ml.pdt[tr] d
+.ut.assert[`Yes] .ml.pdt[tr] d
 
 -1 "we can now split the iris data into training and test batches (w/ stratification)";
 w:`train`test!3 1
-show d:.util.part[w;iris.t.species] iris.t
+show d:.ut.part[w;iris.t.species] iris.t
 -1 "note that stratification can work on any type of list or table";
-.util.part[w;;iris.t] count[iris.t]?5;
-.util.part[w;select species from iris.t] iris.t;
+.ut.part[w;;iris.t] count[iris.t]?5;
+.ut.part[w;select species from iris.t] iris.t;
 -1 "next we confirm relative frequencies of species are the same";
-.util.assert[1b] .ml.identical value count each group d.train.species
+.ut.assert[1b] .ml.identical value count each group d.train.species
 -1 "then create a classification tree";
 -1 .ml.ptree[0] tr:.ml.ct[();::] `species xcols d`train;
 -1 "testing the tree on the test set produces an accuracy of:";
@@ -78,9 +78,9 @@ avg d.test.species=p:.ml.pdt[tr] d`test
 
 -1 "we can predict iris petal lengths with a regression tree";
 -1 "first we need to one-hot encode the species";
-t:"f"$.util.onehot iris.t
+t:"f"$.ut.onehot iris.t
 -1 "then split the data into training and test batches"
-show d:.util.part[w;0N?] t
+show d:.ut.part[w;0N?] t
 -1 "and generate a regression tree";
 -1 .ml.ptree[0] tr:.ml.rt[();::]  `plength xcols d`train;
 -1 "we now compute the root mean square error (rmse)";
@@ -95,38 +95,38 @@ t:([]z:`b`b`b`b`w`w`w`w`w`w`b`b`w`w`b`b)
 t:t,'([]x:1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4)
 t:t,'([]y:1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 )
 -1 .ml.ptree[0] tr:dtf t;
-.util.assert[0 0.125 0.125 0.25] first atr:flip .ml.dtmina[ef] scan (0f;tr)
+.ut.assert[0 0.125 0.125 0.25] first atr:flip .ml.dtmina[ef] scan (0f;tr)
 
 -1 "we then pick the alpha (and therefore subtree) with cross validation";
 b:sqrt (1_a,0w)*a:atr 0 / geometric mean
-ts:.util.part[(k:10)#1;0N?] t
+ts:.ut.part[(k:10)#1;0N?] t
 show e:avg each ts[;`z]=p:.ml.dtxv[dtf;ef;b;ts] peach til k
 -1 .ml.ptree[0] atr[1] 0N!.ml.imax 0N!avg e;
 
 -1 "returning to the iris data, we can grow and prune that too";
 -1 .ml.ptree[0] tr:dtf iris.t;
-.util.assert[0 .01 .02 .02 .04 .88 1f] 3*first atr:flip .ml.dtmina[ef] scan (0f;tr)
+.ut.assert[0 .01 .02 .02 .04 .88 1f] 3*first atr:flip .ml.dtmina[ef] scan (0f;tr)
 b:sqrt (1_a,0w)*a:atr 0 / geometric mean
-ts:.util.part[(k:10)#1;0N?]iris.t
+ts:.ut.part[(k:10)#1;0N?]iris.t
 show e:avg each ts[;`species]=p:.ml.dtxv[dtf;ef;b;ts] peach til k
 -1 .ml.ptree[0] atr[1] 0N!.ml.imax 0N!avg e;
 
 -1 "or even grow and prune a regression tree with wine quality data";
-d:.util.part[`train`test!1 1;0N?] winequality.red.t
+d:.ut.part[`train`test!1 1;0N?] winequality.red.t
 dtf:.ml.rt[();::]
 ef:.ml.wmse
 -1 "the fully grown tree has more than 200 leaves!";
-.util.assert[1b] 200<0N!count .ml.leaves tr:dtf d`train
+.ut.assert[1b] 200<0N!count .ml.leaves tr:dtf d`train
 -1 "we can improve this by performing k-fold cross validation";
 -1 "first we find the list of critical alphas";
 atr:flip .ml.dtmina[ef] scan (0f;tr)
 b:sqrt (1_a,0w)*a:atr 0 / geometric mean
-ts:.util.part[(k:5)#1;0N?]d`train
+ts:.ut.part[(k:5)#1;0N?]d`train
 -1 "then we compute the accuracy of each of these alphas with kfxv";
 show e:avg each e*e:ts[;`quality]-p:(.ml.dtxv[dtf;ef;b;ts]0N!) peach til k
 -1 "finally, we pick the tree whose alpha had the min error";
 -1 .ml.ptree[0] btr:atr[1] 0N!.ml.imin 0N!avg e;
 -1 "the pruned tree has less than 25 leaves";
-.util.assert[1b] 25>0N!count .ml.leaves btr
+.ut.assert[1b] 25>0N!count .ml.leaves btr
 -1 "and an rms less than .73";
-.util.assert[1b] .73>0N!.ml.rms d.test.quality - .ml.pdt[btr] d`test
+.ut.assert[1b] .73>0N!.ml.rms d.test.quality - .ml.pdt[btr] d`test

dji.q

@@ -2,5 +2,5 @@ dji.f:"dow_jones_index"
 dji.b:"http://archive.ics.uci.edu/ml/machine-learning-databases/"
 dji.b,:"00312/"
 -1"[down]loading dji data set";
-.util.download[dji.b;;".zip";.util.unzip] dji.f;
+.ut.download[dji.b;;".zip";.ut.unzip] dji.f;
 dji.t:("HSDEEEEJFFJEEFHF";1#",")0: ssr[;"$";""] each read0 `$dji.f,".data"

em.q

@@ -18,8 +18,8 @@ phi:2#1f%2f                  / coins are picked with equal probability
 / which flips came from which THETA? pick maximum log likelihood
 
 pT:(.ml.em[1b;lf;mf;x]//) pT
-.util.assert[1 0 0 1 0] .ml.imax .ml.likelihood[0b;lf;x] . pT
-.util.assert[1 0 0 1 0] .ml.imax .ml.likelihood[1b;.ml.binll[n];x] . pT
+.ut.assert[1 0 0 1 0] .ml.imax .ml.likelihood[0b;lf;x] . pT
+.ut.assert[1 0 0 1 0] .ml.imax .ml.likelihood[1b;.ml.binll[n];x] . pT
 
 
 / multinomial example
@@ -36,7 +36,7 @@ p:.ml.imax .ml.likelihood[1b;.ml.mmmll;X] . pT
 show m:.ml.mode each y group p
 avg y=m p
 -1"what does the confusion matrix look like?";
-show .util.totals[`TOTAL] .ml.cm[y;m p]
+show .ut.totals[`TOTAL] .ml.cm[y;m p]
 
 
 / Gaussian mixtures
@@ -46,7 +46,7 @@ mu0:10 20 30                    / distribution's mu
 s20:s0*s0:1 3 2                 / distribution's variance
 m0:100 200 150                  / number of points per distribution
 X:raze X0:mu0+s0*(.ml.bm ?[;1f]::) each m0 / build dataset
-show .util.plt raze each (X0;0f*X0),'(X0;.ml.gaussl'[mu0;s20;X0]) / plot 1d data and gaussian curves
+show .ut.plt raze each (X0;0f*X0),'(X0;.ml.gaussl'[mu0;s20;X0]) / plot 1d data and gaussian curves
 k:count mu0
 phi:k#1f%k;      / guess that distributions occur with equal frequency
 mu:neg[k]?X;     / pick k random points as centers
@@ -71,8 +71,8 @@ p:.ml.imax .ml.likelihood[1b;.ml.gaussmvll;X] . pT
 show m:.ml.mode each y group p
 avg y=m p
 -1"what does the confusion matrix look like?";
-show .util.totals[`TOTAL] .ml.cm[y;m p]
--1 value .util.plt .ml.append[0;X 0 2],'.ml.append[1] flip[pT[1;;0]] 0 2;
+show .ut.totals[`TOTAL] .ml.cm[y;m p]
+-1 value .ut.plt .ml.append[0;X 0 2],'.ml.append[1] flip[pT[1;;0]] 0 2;
 
 -1"let's cluster hand written numbers into groups";
 -1"assuming each pixel of a black/white image is a Bernoulli distribution,";
@@ -82,7 +82,7 @@ show .util.totals[`TOTAL] .ml.cm[y;m p]
 X:1000#'X;y:1000#y;
 -1"convert the grayscale image into black/white";
 X>:128
-plt:value .util.plot[28;14;.util.c10;avg] .util.hmap flip 28 cut
+plt:value .ut.plot[28;14;.ut.c10;avg] .ut.hmap flip 28 cut
 k:10
 -1"let's use ",string[k]," clusters";
 -1"we first initialize phi to be equal weight across all clusters";
@@ -108,4 +108,4 @@ p:.ml.imax .ml.likelihood[1b;.ml.bmmll[1];X] . pT
 show m:.ml.mode each y group p
 avg y=m p
 -1"what does the confusion matrix look like?";
-show .util.totals[`TOTAL] .ml.cm[y;m p]
+show .ut.totals[`TOTAL] .ml.cm[y;m p]

emma.q

@@ -2,7 +2,7 @@
 emma.f:"158.txt"
 emma.b:"http://www.gutenberg.org/files/158/"
 -1"[down]loading emma text";
-.util.download[emma.b;;"";""] emma.f;
+.ut.download[emma.b;;"";""] emma.f;
 emma.txt:{x where not x like "VOLUME*"} read0 `$emma.f
 emma.chapters:1_"CHAPTER" vs "\n" sv 39_-373_emma.txt
 emma.s:{(3+first x ss"\n\n\n")_x} each emma.chapters

etl9b.q

@@ -1,9 +1,9 @@
 etl9b.f:"ETL9B"
 etl9b.b:"http://etlcdb.db.aist.go.jp/etlcdb/data/"
 -1"[down]loading handwritten-kanji data set";
-.util.download[etl9b.b;;".zip";.util.unzip] etl9b.f;
+.ut.download[etl9b.b;;".zip";.ut.unzip] etl9b.f;
 -1"loading etl9b ('binalized' dataset)";
-etl9b.x:.util.etl9b read1 `:ETL9B/ETL9B_1
+etl9b.x:.ut.etl9b read1 `:ETL9B/ETL9B_1
 -1"extracting the X matrix and y vector";
 etl9b.h:0x24,/:"x"$0x21+0x01*til 83 / hiragana
 / etl9b.h:0x25,/:"x"$0x21+0x01*til 83 / katakana (missing)

funq.q

@@ -1,8 +1,8 @@
-\l util.q
+\l ut.q
 \l ml.q
 \l fmincg.q
 \l porter.q
 
 / attempt to load c libraries
-(.util.loadf ` sv hsym[`$getenv`QHOME],) each`qml.q`svm.q`linear.q;
+(.ut.loadf ` sv hsym[`$getenv`QHOME],) each`qml.q`svm.q`linear.q;
 if[`qml in key `;system "l qmlmm.q"] / use qml matrix operators

hac.q

@@ -14,19 +14,19 @@ L:.ml.link[`.ml.lw.ward] D
 -1"generate cluster indices";
 I:.ml.clust[L] 1+til 10
 -1"plot elbow curve (k vs ssw)";
-show .util.plt .ml.ssw[X] peach I
+show .ut.plt .ml.ssw[X] peach I
 -1"plot elbow curve (k vs % of variance explained)";
-show .util.plt (.ml.ssb[X] peach I)%.ml.sse[X]
+show .ut.plt (.ml.ssb[X] peach I)%.ml.sse[X]
 -1"link into 3 clusters";
 I:.ml.clust[L] 3
 -1"confirm accuracy";
 g:(.ml.mode each seeds.y I)!I
-.util.assert[0.9] .util.rnd[.01] avg seeds.y=.util.ugrp g
+.ut.assert[0.9] .ut.rnd[.01] avg seeds.y=.ut.ugrp g
 
 -1"we can also check for maximum silhouette";
 -1"plot silhouette curve (k vs silhouette)";
 I:.ml.clust[L] 1+til 10
-show .util.plt (avg raze .ml.silhouette[.ml.edist;X]::) peach I
+show .ut.plt (avg raze .ml.silhouette[.ml.edist;X]::) peach I
 
 
 -1"normalize iris data set features";
@@ -38,35 +38,35 @@ L:.ml.link[`.ml.lw.median] D
 -1"generate cluster indices";
 I:.ml.clust[L] 1+til 10
 -1"plot elbow curve (k vs ssw)";
-show .util.plt .ml.ssw[X] peach I
+show .ut.plt .ml.ssw[X] peach I
 -1"plot elbow curve (k vs % of variance explained)";
-show .util.plt (.ml.ssb[X] peach I)%.ml.sse[X]
+show .ut.plt (.ml.ssb[X] peach I)%.ml.sse[X]
 
 -1"link into 3 clusters";
 I:.ml.clust[L] 3
 -1"confirm accuracy";
 g:(.ml.mode each iris.y I)!I
-.util.assert[.97] .util.rnd[.01] avg iris.y=.util.ugrp g
+.ut.assert[.97] .ut.rnd[.01] avg iris.y=.ut.ugrp g
 -1"generate clusters indices";
 I:.ml.clust[L] 1+til 10
 -1"plot silhouette curve (k vs silhouette)";
-show .util.plt (avg raze .ml.silhouette[.ml.edist;X]::) peach I
+show .ut.plt (avg raze .ml.silhouette[.ml.edist;X]::) peach I
 
 -1"let's apply the analysis to one of the uef reference cluster datasets";
 X:uef.d32
-show .util.plot[39;20;.util.c10;sum] X
+show .ut.plot[39;20;.ut.c10;sum] X
 -1"using pedist2 makes calculating the dissimilarity matrix much faster";
 D:sqrt .ml.pedist2[X;X]
 -1"generate hierarchical clustering linkage stats with ward metric";
 L:.ml.link[`.ml.lw.ward] D
 -1"generate cluster indices";
 I:.ml.clust[L] ks:1+til 19
 -1"plot elbow curve (k vs ssw)";
-show .util.plt .ml.ssw[X] peach I
+show .ut.plt .ml.ssw[X] peach I
 -1"plot elbow curve (k vs % of variance explained)";
-show .util.plt (.ml.ssb[X] peach I)%.ml.sse[X]
+show .ut.plt (.ml.ssb[X] peach I)%.ml.sse[X]
 -1"plot silhouette curve (k vs silhouette)";
-show .util.plt s:(avg raze .ml.silhouette[.ml.edist;X]::) peach I
-.util.assert[16] ks i:.ml.imax s
+show .ut.plt s:(avg raze .ml.silhouette[.ml.edist;X]::) peach I
+.ut.assert[16] ks i:.ml.imax s
 -1"plot the clustered data";
-show .util.plot[39;20;.util.c68;.ml.mode] X[0 1],enlist .util.ugrp I i
+show .ut.plot[39;20;.ut.c68;.ml.mode] X[0 1],enlist .ut.ugrp I i