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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"]]}] |
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<!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> | |
<script src="http://code.jquery.com/mobile/1.3.1/jquery.mobile-1.3.1.min.js"></script> | |
<style> | |
#chart { | |
margin-left: -40px; | |
height: 506px; | |
} | |
text { | |
font: 10px sans-serif; | |
} | |
.dot { | |
stroke: #000; | |
opacity: .5; | |
stroke-opacity: .75; | |
} | |
.axis path, .axis line { | |
fill: none; | |
stroke: #000; | |
shape-rendering: crispEdges; | |
} | |
.label { | |
fill: #777; | |
} | |
.year.label { | |
font: 500 100px "Helvetica Neue"; | |
fill: #ddd; | |
} | |
.year.label.active { | |
fill: #aaa; | |
} | |
.overlay { | |
fill: none; | |
pointer-events: all; | |
cursor: ew-resize; | |
} | |
</style> | |
</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> | |
<script src="http://d3js.org/d3.v2.js?2.8.1"></script> | |
<script> | |
// Various accessors that specify the four dimensions of data to visualize. | |
function x(d) { return d.d1; } | |
function y(d) { return d.d2; } | |
function z(d) { return d.d3; } | |
function radius(d) { return 1; } | |
function color(d) { return d.color; } | |
//function color(d) { return d.group; } | |
function key(d) { return d.name; } | |
function display(d) {return d.display;} | |
// Chart dimensions. | |
var margin = {top: 19.5, right: 19.5, bottom: 19.5, left: 39.5}, | |
width = 600 - margin.right, | |
height = 400 - margin.top - margin.bottom; | |
// Various scales. These domains make assumptions of data, naturally. | |
var xScale = d3.scale.linear().domain([-15, 15]).range([0, width]), | |
yScale = d3.scale.linear().domain([-15, 15]).range([height, 0]), | |
radiusScale = d3.scale.sqrt().domain([0, 1]).range([0, 10]); | |
//colorScale = d3.scale.category10(); | |
var colorScale = d3.scale.category20c(); | |
// The x & y axes. | |
var xAxis = d3.svg.axis().orient("bottom").scale(xScale).ticks(12, d3.format(",d")), | |
yAxis = d3.svg.axis().scale(yScale).orient("left"); | |
// Create the SVG container and set the origin. | |
var svg = d3.select("#chart svg") //d3.select("#chart").append("svg") | |
.attr("width", width + margin.left + margin.right) | |
.attr("height", height + margin.top + margin.bottom) | |
.append("g") | |
.attr("transform", "translate(" + margin.left + "," + margin.top + ")"); | |
// Add the x-axis. | |
svg.append("g") | |
.attr("class", "x axis") | |
.attr("transform", "translate(0," + height + ")") | |
.call(xAxis); | |
// Add the y-axis. | |
svg.append("g") | |
.attr("class", "y axis") | |
.call(yAxis); | |
// Add an x-axis label. | |
svg.append("text") | |
.attr("class", "x label") | |
.attr("text-anchor", "end") | |
.attr("x", width) | |
.attr("y", height - 6) | |
.text("Dimension 1"); | |
// Add a y-axis label. | |
svg.append("text") | |
.attr("class", "y label") | |
.attr("text-anchor", "end") | |
.attr("y", 6) | |
.attr("dy", ".75em") | |
.attr("transform", "rotate(-90)") | |
.text("Dimension 2"); | |
// Add the year label; the value is set on transition. | |
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. | |
var dot = svg.append("g") | |
.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; | |
var playing = false; | |
$("#play").click(function() { | |
if(playing === false) { | |
startPlaying(); | |
} else { | |
stopPlaying(); | |
} | |
}); | |
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]; | |
if (i > 0) { | |
var b = values[i - 1], | |
t = (year - a[0]) / (b[0] - a[0]); | |
return a[1] * (1 - t) + b[1] * t; | |
} | |
return a[1]; | |
} | |
function findColor(values, year) { | |
var i = bisect.left(values, year, 0, values.length - 1); | |
return values[i][1]; | |
} | |
function isDisplayed(values, year) { | |
if ( (year < values[0][0]) || (year > values[values.length - 1][0])) | |
return 'none'; | |
else | |
return 'inherit'; | |
} | |
function animateFirstStep(d){ | |
d3.select(this) | |
.transition() | |
.delay(0) | |
.duration(1000) | |
.attr("r", 10*2) | |
//.attr("cx", 300*d.x+150+50) | |
;//.each("end", animateSecondStep); | |
} | |
function animateSecondStep(d){ | |
d3.select(this) | |
.transition() | |
.duration(1000) | |
.attr("r", 10); | |
//.attr("cx", 300*d.x+150); | |
} | |
//slider | |
//see http://michalskop.tumblr.com/post/37352195911/strange-behaviour-of-jquery-change | |
$('#slider').ready(function() { | |
$('#slider').change(function(){ | |
displayYear($(this).val()); | |
}); | |
}); | |
//color gradients | |
//http://dexvis.wordpress.com/2012/12/25/motion-charts-revisited/ | |
function shadeColor(color, percent) { | |
var R = parseInt(color.substring(1,3),16) | |
var G = parseInt(color.substring(3,5),16) | |
var B = parseInt(color.substring(5,7),16); | |
R = parseInt(R * (100 + percent) / 100); | |
G = parseInt(G * (100 + percent) / 100); | |
B = parseInt(B * (100 + percent) / 100); | |
R = (R<255)?R:255; | |
G = (G<255)?G:255; | |
B = (B<255)?B:255; | |
var RR = ((R.toString(16).length==1)?"0"+R.toString(16):R.toString(16)); | |
var GG = ((G.toString(16).length==1)?"0"+G.toString(16):G.toString(16)); | |
var BB = ((B.toString(16).length==1)?"0"+B.toString(16):B.toString(16)); | |
return "#"+RR+GG+BB; | |
} | |
function gradient(baseColor) | |
{ | |
var gradientId = "gradient" + baseColor.substring(1) | |
console.log("COLOR: " + gradientId); | |
//var lightColor = shadeColor(baseColor, -10) | |
var darkColor = shadeColor(baseColor, -20) | |
var grad = d3.select("#gradients").selectAll("#" + gradientId) | |
.data([ gradientId ]) | |
.enter() | |
.append("radialGradient") | |
.attr("id", gradientId) | |
.attr("gradientUnits", "objectBoundingBox") | |
.attr("fx", "30%") | |
.attr("fy", "30%") | |
grad.append("stop") | |
.attr("offset", "0%") | |
.attr("style", "stop-color:#FFFFFF") | |
// Middle | |
grad.append("stop") | |
.attr("offset", "40%") | |
.attr("style", "stop-color:" + baseColor) | |
// Outer Edges | |
grad.append("stop") | |
.attr("offset", "100%") | |
.attr("style", "stop-color:" + darkColor) | |
console.log("url(#" + gradientId + ")") | |
return "url(#" + gradientId + ")"; | |
} | |
}); | |
</script> | |
<script> | |
</script> | |
</div> <!-- /content --> | |
</div><!-- /page --> | |
</body> | |
</html> |
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#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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