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Prague 2010-2013
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Luk\u00e1\u0161 Kauck\u00fd","d1":[[2010.75,-10.469527236752,"\u010cSSD","#F18811"],[2011.25,-8.9652833212851,"\u010cSSD","#F18811"],[2011.75,-8.0721175462709,"\u010cSSD","#F18811"],[2012.25,-8.8728198581633,"\u010cSSD","#F18811"],[2012.75,-9.4208209886455,"\u010cSSD","#F18811"],[2013.25,-9.4945072960941,"\u010cSSD","#F18811"]],"d2":[[2010.75,9.9660051621814,"\u010cSSD","#F18811"],[2011.25,5.7043167976758,"\u010cSSD","#F18811"],[2011.75,-5.8638461930295,"\u010cSSD","#F18811"],[2012.25,-8.2853263428274,"\u010cSSD","#F18811"],[2012.75,-9.0972172220586,"\u010cSSD","#F18811"],[2013.25,-8.7160567884736,"\u010cSSD","#F18811"]],"color":[[2010.75,"#F18811"],[2011.25,"#F18811"],[2011.75,"#F18811"],[2012.25,"#F18811"],[2012.75,"#F18811"],[2013.25,"#F18811"]]},{"name":"Ji\u0159\u00ed Li\u0161ka","d1":[[2010.75,11.679330917994,"TOP 09","#673B6C"],[2011.25,10.443929735385,"TOP 09","#673B6C"],[2011.75,7.8346793676776,"TOP 09","#673B6C"],[2012.25,3.8350090506357,"TOP 09","#673B6C"],[2012.75,5.3363145483824,"TOP 09","#673B6C"],[2013.25,4.2733849336834,"TOP 09","#673B6C"]],"d2":[[2010.75,-11.040276173021,"TOP 09","#673B6C"],[2011.25,-5.6898506466846,"TOP 09","#673B6C"],[2011.75,2.1469903943048,"TOP 09","#673B6C"],[2012.25,3.3951636802664,"TOP 09","#673B6C"],[2012.75,3.3402390559788,"TOP 09","#673B6C"],[2013.25,3.2714380762372,"TOP 09","#673B6C"]],"color":[[2010.75,"#673B6C"],[2011.25,"#673B6C"],[2011.75,"#673B6C"],[2012.25,"#673B6C"],[2012.75,"#673B6C"],[2013.25,"#673B6C"]]},{"name":"Mgr. Luk\u00e1\u0161 Manhart","d1":[[2010.75,11.817952830132,"TOP 09","#673B6C"],[2011.25,9.9462687831692,"TOP 09","#673B6C"],[2011.75,8.0134945287428,"TOP 09","#673B6C"],[2012.25,3.6121310977567,"TOP 09","#673B6C"],[2012.75,6.2797271449972,"TOP 09","#673B6C"],[2013.25,5.0989297075978,"TOP 09","#673B6C"]],"d2":[[2010.75,-11.056719021387,"TOP 09","#673B6C"],[2011.25,-6.2736423477671,"TOP 09","#673B6C"],[2011.75,1.0436562351589,"TOP 09","#673B6C"],[2012.25,3.3546301185096,"TOP 09","#673B6C"],[2012.75,4.8810695001982,"TOP 09","#673B6C"],[2013.25,4.2682914518838,"TOP 09","#673B6C"]],"color":[[2010.75,"#673B6C"],[2011.25,"#673B6C"],[2011.75,"#673B6C"],[2012.25,"#673B6C"],[2012.75,"#673B6C"],[2013.25,"#673B6C"]]},{"name":"Ing. Ji\u0159\u00ed Nouza","d1":[[2010.75,10.15812240111,"TOP 09","#673B6C"],[2011.25,10.69060247402,"TOP 09","#673B6C"],[2011.75,8.7213777452098,"TOP 09","#673B6C"],[2012.25,3.7159913017694,"TOP 09","#673B6C"],[2012.75,4.5381420163578,"TOP 09","#673B6C"],[2013.25,3.8409411385317,"TOP 09","#673B6C"]],"d2":[[2010.75,-9.9032248352077,"TOP 09","#673B6C"],[2011.25,-6.0296736665784,"TOP 09","#673B6C"],[2011.75,1.4545979194505,"TOP 09","#673B6C"],[2012.25,3.3510635480418,"TOP 09","#673B6C"],[2012.75,3.7582253783766,"TOP 09","#673B6C"],[2013.25,3.1137966671704,"TOP 09","#673B6C"]],"color":[[2010.75,"#673B6C"],[2011.25,"#673B6C"],[2011.75,"#673B6C"],[2012.25,"#673B6C"],[2012.75,"#673B6C"],[2013.25,"#673B6C"]]},{"name":"Ing. V\u00e1clav Novotn\u00fd","d1":[[2010.75,12.357160885029,"TOP 09","#673B6C"],[2011.25,11.291678793482,"TOP 09","#673B6C"],[2011.75,8.3115471041328,"TOP 09","#673B6C"],[2012.25,3.6431671481771,"TOP 09","#673B6C"],[2012.75,6.5183544127899,"TOP 09","#673B6C"],[2013.25,5.2349922681966,"TOP 09","#673B6C"]],"d2":[[2010.75,-11.353917044282,"TOP 09","#673B6C"],[2011.25,-6.5311510032636,"TOP 09","#673B6C"],[2011.75,0.80085830957671,"TOP 09","#673B6C"],[2012.25,3.5252752758419,"TOP 09","#673B6C"],[2012.75,4.5771315206121,"TOP 09","#673B6C"],[2013.25,4.448108706453,"TOP 09","#673B6C"]],"color":[[2010.75,"#673B6C"],[2011.25,"#673B6C"],[2011.75,"#673B6C"],[2012.25,"#673B6C"],[2012.75,"#673B6C"],[2013.25,"#673B6C"]]},{"name":"Mgr. Roman Vaculka","d1":[[2011.25,10.977596879799,"TOP 09","#673B6C"],[2011.75,8.7906991059544,"TOP 09","#673B6C"],[2012.25,3.5279175592334,"TOP 09","#673B6C"],[2012.75,5.0644465909157,"TOP 09","#673B6C"],[2013.25,5.0533830679688,"TOP 09","#673B6C"]],"d2":[[2011.25,-6.0734138568599,"TOP 09","#673B6C"],[2011.75,1.5410063035593,"TOP 09","#673B6C"],[2012.25,2.9496593164016,"TOP 09","#673B6C"],[2012.75,4.3785767459635,"TOP 09","#673B6C"],[2013.25,4.240046725513,"TOP 09","#673B6C"]],"color":[[2011.25,"#673B6C"],[2011.75,"#673B6C"],[2012.25,"#673B6C"],[2012.75,"#673B6C"],[2013.25,"#673B6C"]]},{"name":"Mgr. Lucie V\u00e1lov\u00e1","d1":[[2012.75,-8.30087400858,"\u010cSSD","#F18811"],[2013.25,-9.3967297418356,"\u010cSSD","#F18811"]],"d2":[[2012.75,-8.9515965300698,"\u010cSSD","#F18811"],[2013.25,-8.708780972669,"\u010cSSD","#F18811"]],"color":[[2012.75,"#F18811"],[2013.25,"#F18811"]]}]
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Praha 2010-2013</title>
<link rel="stylesheet" href="http://code.jquery.com/mobile/1.3.1/jquery.mobile-1.3.1.min.css" />
<script src="http://code.jquery.com/jquery-1.8.2.min.js"></script>
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<style>
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height: 506px;
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font: 10px sans-serif;
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</head>
<body>
<div data-role="page" class="type-home">
<div data-role="content">
<header>
<aside>May 27, 2013, 70th birthday of my dad!</aside>
<a href="/michalskop/" rel="author">Michal Škop</a>
</header>
<h1>Praha 2010-2013</h1>
<form>
<label for="slider">Slider:</label>
<input name="slider" id="slider" min="2010.75" max="2013.25" value="2010.75" step=".01" type="range" />
</form>
<p>
<a href="#" id="play" data-role="button" data-inline="true" data-icon="refresh"><span id="playText">Play ></span></a>
</p>
<p id="chart"><svg><defs id="gradients"></defs></svg></p>
<!-- <p id="slide">XXX</p> -->
<p>Method: weighted PCA; Time intervals: 6 months (projection of 6-month blocks into all data)</p>
<p>There are missing data about Richter (ODS) and Šťastný (ODS) on the <a href="http://praha.eu">official website</a></p>
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svg.append("text")
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.attr("y", 6)
.attr("dy", ".75em")
.attr("transform", "rotate(-90)")
.text("Dimension 2");
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var label = svg.append("text")
.attr("class", "year label")
.attr("text-anchor", "end")
.attr("y", height - 24)
.attr("x", width)
.text("2010/9");
// Load the data.
d3.json("cz_praha_2010-2013_a1_1h.json", function(nations) {
// A bisector since many nation's data is sparsely-defined.
var bisect = d3.bisector(function(d) { return d[0]; });
// Add a dot per nation. Initialize the data at 1800, and set the colors.
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.attr("class", "dots")
.selectAll(".dot")
.data(interpolateData(2010.75))
.enter().append("circle")
.attr("class", "dot")
//.style("fill", function(d) { return d.group; })
//.style("fill", function (d) { return gradient(colorScale(color(d))) })
.call(position)
.sort(order)
.on("mouseover", animateFirstStep)
.on("mouseout", animateSecondStep)
.on("mousedown", animateFirstStep);
// Add a title.
dot.append("title")
.text(function(d) { return d.name});
var i=0;
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$("#play").click(function() {
if(playing === false) {
startPlaying();
} else {
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}
});
function startPlaying() {
playing = true;
$("#playText").html("Stop ||");
$('#slider').slider('disable');
// Start a transition that interpolates the data based on year.
svg.transition()
.duration(30000)
.ease("linear")
.tween("year", function() {return tweenYear($('#slider').val()) })
.each("end", stopPlaying);
}
function stopPlaying() {
playing = false;
$("#playText").html("Play >");
svg.transition().duration(0);
$('#slider').slider('enable');
}
// Positions the dots based on data.
function position(dot) {
dot .attr("cx", function(d) { return xScale(x(d)); })
.attr("cy", function(d) { return yScale(y(d)); })
.attr("r", function(d) { return radiusScale(radius(d)) })
//.style("fill", function(d) { return color(d); });
.style("fill", function (d) { return gradient(color(d)) })
//.attr("style", function (d) { return "display:" + display(d)+";";});
.attr("display", function (d) { return display(d);});
//alert(dot.data);
}
// Defines a sort order so that the smallest dots are drawn on top.
function order(a, b) {
return radius(b) - radius(a);
}
// Tweens the entire chart by first tweening the year, and then the data.
// For the interpolated data, the dots and label are redrawn.
function tweenYear(start) {
var year = d3.interpolateNumber(2010.75,2013.25);
return function(t) { displayYear(year(t)); };
}
// Updates the display to show the specified year.
function displayYear(year) {
dot.data(interpolateData(year), key).call(position).sort(order);
month = Math.ceil((year - Math.floor(year))*12);
label.text(Math.floor(year) + '/' + month);
i++;
if ((i%25) == 0) {
$("#slider").val(year);
$('#slider').slider('refresh');
}
}
// Interpolates the dataset for the given (fractional) year.
function interpolateData(year) {
return nations.map(function(d) {
return {
name: d.name,
//group: d.group,
d1: interpolateValues(d.d1, year),
d2: interpolateValues(d.d2, year),
//d3: interpolateValues(d.d3, year),
color: findColor(d.color, year),
display: isDisplayed(d.d1, year)
//category: d.group
//lifeExpectancy: interpolateValues(d.lifeExpectancy, year)
};
});
}
// Finds (and possibly interpolates) the value for the specified year.
function interpolateValues(values, year) {
var i = bisect.left(values, year, 0, values.length - 1),
a = values[i];
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var b = values[i - 1],
t = (year - a[0]) / (b[0] - a[0]);
return a[1] * (1 - t) + b[1] * t;
}
return a[1];
}
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var i = bisect.left(values, year, 0, values.length - 1);
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d3.select(this)
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.duration(1000)
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//see http://michalskop.tumblr.com/post/37352195911/strange-behaviour-of-jquery-change
$('#slider').ready(function() {
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//http://dexvis.wordpress.com/2012/12/25/motion-charts-revisited/
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return "#"+RR+GG+BB;
}
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{
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.attr("offset", "100%")
.attr("style", "stop-color:" + darkColor)
console.log("url(#" + gradientId + ")")
return "url(#" + gradientId + ")";
}
});
</script>
<script>
</script>
</div> <!-- /content -->
</div><!-- /page -->
</body>
</html>
#install.packages("reshape2")
library("reshape2")
#install.packages("sqldf")
library("sqldf")
path = "~/project/mpv_motion/dev/"
#dataname = "cz_psp_2006-2010_a1_1"
#analysisname = 'cz_psp_2006-2010_a1_1q'
dataname = "cz_praha_2010-2013_a1_1"
analysisname = 'cz_praha_2010-2013_a1_1h'
#raw data in 3 columns (values in [-1,1])
Graw = read.table(paste(path,dataname,"/",dataname,".csv",sep=""), header=FALSE, sep=",")
#data divisions x persons
G = acast(Graw,V2~V1,value.var='V3')
#scaled divisions x persons (mean=0 and sd=1 for each division)
H=t(scale(t(G),scale=TRUE))
#weights
#weights 1, based on number of persons in division
w1 = apply(abs(G)==1,1,sum,na.rm=TRUE)/max(apply(abs(G)==1,1,sum,na.rm=TRUE))
w1[is.na(w1)] = 0
#weights 2, "100:100" vs. "195:5"
w2 = 1 - abs(apply(G==1,1,sum,na.rm=TRUE) - apply(G==-1,1,sum,na.rm=TRUE))/apply(!is.na(G),1,sum)
w2[is.na(w2)] = 0
#analytics
plot(w1)
plot(w2)
plot(w1*w2)
#weighted scaled divisions x persons
Hw = H * w1 * w2
#index of missing data
#index of missing data divisions x persons
HI = H
HI[!is.na(H)]=1
HI[is.na(H)] = 0
#weighted scaled with NA->0 division x persons
Hw0 = Hw
Hw0[is.na(Hw)]=0
#eliminate persons with too few votes (weighted)
#lower limit to eliminate from calculations
lo_limit = .1
#weights for non missing data division x persons
HIw = HI*w1*w2
#sum of weights of divisions for each persons
tmp = apply(HIw,2,sum)
pw = tmp/max(tmp)
#index of persons in calculation
pI = pw > lo_limit
#weighted scaled with NA->0 and cutted persons with too few votes division x persons
Hw0c = Hw0[,pI]
#index of missing data cutted persons with too few votes divisions x persons
HIc = HI[,pI]
#"covariance" matrix adjusted according to missing data
Hcov=t(Hw0c)%*%Hw0c * 1/(t(HIc)%*%HIc) * (dim(Hw0c)[1])
#Hcov=t(Hw0)%*%Hw0 * 1/(t(HI)%*%HI-1) * (dim(H)[1]-1)
#substitution of missing data in "covariance" matrix
Hcov0 = Hcov
Hcov0[is.na(Hcov)] = 0 #*********
#eigendecomposition
He=eigen(Hcov0)
# V (rotation values of persons)
V = He$vectors
#projected divisions into dimensions
Hy=Hw0c%*%V
#analytics
plot(Hy[,1],Hy[,2])
plot(sqrt(He$values[1:10]))
#sigma matrix
sigma = diag(sqrt(He$values))
sigma[is.na(sigma)] = 0
#projection of persons into dimensions
Hproj = t(sigma%*%t(V))
#analytics
plot(Hproj[,1],Hproj[,2])
#sigma^-1 matrix
sigma_1 = diag(sqrt(1/He$values))
sigma_1[is.na(sigma_1)] = 0
# U (rotation values of divisions)
U = Hw0c%*%V%*%sigma_1
#U%*%sigma%*%t(V) != Hw0c ;because of adjusting of "covariance" matrix
# NEW MP (or partial)
#New persons / partial (like a projection of divisions from a smaller time interval into all divisions)
analdb = dbConnect(SQLite(), dbname=paste(path,dataname,"/",analysisname,"/",analysisname,".sqlite3",sep=""))
datadb = dbConnect(SQLite(), dbname=paste(path,dataname,"/",dataname,".sqlite3",sep=""))
rotation = strsplit(dbGetQuery(analdb,"SELECT orientation FROM analysis_info")[1,1],",")[[1]]
for (j in 2:length(rotation)) {
if (Hproj[rot_mp_rank,j-1] * as.double(rotation[j]) < 0) {
U[,j-1] = -1*U[,j-1]
}
}
aU = abs(U)
mp_names = dbGetQuery(datadb,"SELECT name FROM mp ORDER BY CAST(code as INTEGER)")
attr(G,'dimnames')[2][[1]] = mp_names$name
#attr(Hw0c,'dimnames')[2][[1]] = mp_names$name[pI]
interval_names = dbGetQuery(analdb,"SELECT distinct(interval_name) FROM analysis_division_in_interval ORDER BY interval_name")
#lower limit to eliminate from projections
lo_limitT = lo_limit
for (i in 1:dim(interval_names)[1]) {
TIf = dbGetQuery(analdb,paste("SELECT division_code, CASE interval_name='",interval_names[i,],"' WHEN 1 THEN 'TRUE' ELSE 'FALSE' END as in_interval FROM analysis_division_in_interval ORDER BY division_code",sep=""))
TIf$in_interval = as.logical(TIf$in_interval)
max_division_code = TIf$division_code[max(which(as.logical(TIf$in_interval)))]
min_division_code = TIf$division_code[min(which(as.logical(TIf$in_interval)))]
TI = TIf$in_interval
GTc = G[,pI]
GTc[!TI,] = NA
HTc = (GTc - attr(H,"scaled:center"))/attr(H,"scaled:scale")
HTIc = HTc
HTIc[!is.na(HTIc)] = 1
HTIc[is.na(HTIc)] = 0
HTw0c = HTc * w1 * w2
HTw0c[is.na(HTw0c)] = 0
#weights for non missing data division x persons
HTIcw = HTIc*w1*w2
#sum of weights of divisions for each persons
tmp = apply(HTIcw,2,sum)
pTw = tmp/max(tmp)
#index of persons in calculation
pTI = pTw > lo_limitT
#weighted scaled with NA->0 and cutted persons with too few votes division x persons
HTw0cc = HTw0c[,pTI]
#index of missing data cutted persons with too few votes divisions x persons
HTIcc = HTIc[,pTI]
dweights = t(t(aU)%*%HTIcc / apply(aU,2,sum)) #person x division
dweights[is.na(dweights)] = 0
HTw0ccproj = t(HTw0cc)%*%U / dweights
parties = as.matrix(dbGetQuery(datadb,paste('SELECT COALESCE(NULLIF(tmax.code,""),tmin.code) as code, COALESCE(NULLIF(tmax.color,""),tmin.color) as color FROM (SELECT mvf.mp_code, g.code, g.color FROM mp_vote_full as mvf LEFT JOIN "group" as g ON mvf.group_code=g.code WHERE division_code="',max_division_code,'") as tmax LEFT JOIN (SELECT mvf.mp_code, g.code, g.color FROM mp_vote_full as mvf LEFT JOIN "group" as g ON mvf.group_code=g.code WHERE division_code="',min_division_code,'") as tmin ON tmax.mp_code=tmin.mp_code ORDER BY CAST(tmax.mp_code as INTEGER)',sep="")))
partiescc = (parties[pI,])[pTI,]
write.csv(format(cbind(HTw0ccproj[,1:2],partiescc),digits=3),file=paste(path,dataname,"/",analysisname,"/",interval_names[i,],"_result.csv",sep=""))
#*************************
}
# CUTTING LINES
# based on overall model
#minimizing function
f_cutting_lines=function(beta0) -1*sum(apply(cbind(y*(x%*%beta+beta0),zeros),1,min))
#how many dimensions?
n_first_dimensions = 2
#preparing variables
normals = Hy[,1:n_first_dimensions]
loss_f = data.frame(matrix(0,nrow=dim(H)[1],ncol=4))
colnames(loss_f)=c("Parameter1","Loss1","Parameter_1","Loss_1")
parameters = data.frame(matrix(0,nrow=dim(H)[1],ncol=3))
colnames(parameters)=c("Parameter","Loss","Direction")
xfull = t(t(He$vectors[,1:n_first_dimensions]) * sqrt(He$values[1:n_first_dimensions]))
#calculating all cutting lines
i=1
for (i in as.numeric(1:dim(H)[1])) {
beta = Hy[i,1:n_first_dimensions]
y = t(as.matrix(H[i,]))[,pI]
x = xfull[which(!is.na(y)),]
y = y[which(!is.na(y))]
zeros = as.matrix(rep(0,length(y)))
#** place for improvement: "1000"
res1 = optim(c(1),f_cutting_lines,method="Brent",lower=-1000,upper=1000) #1000? arbitrary
#the sign is arbitrary, the real result may be -1*; we need to minimize the other way as well!!
y=-y
res2 = optim(c(1),f_cutting_lines,method="Brent",lower=-1000,upper=1000) #1000? arbitrary
#the real parameter is the one with lower loss function
#theoretically should be the same (either +1 or -1) for all divisions(rows), however, due to missing data, the calculation may lead to a few divisions with the other direction
loss_f[i,] = c(res1$par,res1$value,res2$par,res2$value)
if (res1$value<=res2$value) {
parameters[i,] = c(res1$par,res1$value,1)
} else {
parameters[i,] = c(res2$par,res2$value,-1)
}
}
CuttingLines = list(normals=normals,parameters=parameters,loss_function=loss_f,weights=cbind(w1,w2))
##
#cutting lines:
I = w1*w2 > .75
for (i in as.numeric(1:dim(H)[1])) {
if (I[i] && ((i %% 10) == 0)) {
beta = CuttingLines$normals[i,]
beta0 = CuttingLines$parameters$Parameter[i]
abline(-beta0/beta[2],-beta[1]/beta[2])
#break
}
}
# only second half
plot(Hproj[,1],Hproj[,2])
> for (j in as.numeric(1:round(dim(H)[1]/2))) {
i = round(dim(H)[1]/2) + j
if (I[i] && ((i %% 10) == 0)) {
beta = CuttingLines$normals[i,]
beta0 = CuttingLines$parameters$Parameter[i]
abline(-beta0/beta[2],-beta[1]/beta[2])
#break
}
}
# when y=0, x is ?
plot(CuttingLines$parameters$Parameter/CuttingLines$normals[,1],ylim=c(-100,100))
# slopes
plot(CuttingLines$parameters$Parameter/CuttingLines$normals[,2],ylim=c(-10,10))
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