blob: e2c01547a0377bbd5879e9980e418505df93e9c7 [file] [log] [blame]
<!DOCTYPE html>
<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1" />
<script src="lib/simpleRequire.js"></script>
<script src="lib/config.js"></script>
<script src="../dist/echarts.js"></script>
<script src="lib/jquery.min.js"></script>
<script src="lib/facePrint.js"></script>
<script src="lib/testHelper.js"></script>
<link rel="stylesheet" href="lib/reset.css" />
</head>
<body>
<style>
</style>
<div id="main-cluster-step"></div>
<script>
require(['echarts', 'ecStat'], function (echarts, ecStat) {
var originalData = [[3.275154, 2.957587], [-3.344465, 2.603513], [0.355083, -3.376585], [1.852435, 3.547351], [-2.078973, 2.552013], [-0.993756, -0.884433], [2.682252, 4.007573], [-3.087776, 2.878713], [-1.565978, -1.256985], [2.441611, 0.444826], [-0.659487, 3.111284], [-0.459601, -2.618005], [2.17768, 2.387793], [-2.920969, 2.917485], [-0.028814, -4.168078], [3.625746, 2.119041], [-3.912363, 1.325108], [-0.551694, -2.814223], [2.855808, 3.483301], [-3.594448, 2.856651], [0.421993, -2.372646], [1.650821, 3.407572], [-2.082902, 3.384412], [-0.718809, -2.492514], [4.513623, 3.841029], [-4.822011, 4.607049], [-0.656297, -1.449872], [1.919901, 4.439368], [-3.287749, 3.918836], [-1.576936, -2.977622], [3.598143, 1.97597], [-3.977329, 4.900932], [-1.79108, -2.184517], [3.914654, 3.559303], [-1.910108, 4.166946], [-1.226597, -3.317889], [1.148946, 3.345138], [-2.113864, 3.548172], [0.845762, -3.589788], [2.629062, 3.535831], [-1.640717, 2.990517], [-1.881012, -2.485405], [4.606999, 3.510312], [-4.366462, 4.023316], [0.765015, -3.00127], [3.121904, 2.173988], [-4.025139, 4.65231], [-0.559558, -3.840539], [4.376754, 4.863579], [-1.874308, 4.032237], [-0.089337, -3.026809], [3.997787, 2.518662], [-3.082978, 2.884822], [0.845235, -3.454465], [1.327224, 3.358778], [-2.889949, 3.596178], [-0.966018, -2.839827], [2.960769, 3.079555], [-3.275518, 1.577068], [0.639276, -3.41284]];
var DIM_CLUSTER_INDEX = 2;
var DATA_DIM_IDX = [0, 1];
var CENTER_DIM_IDX = [3, 4];
var step = ecStat.clustering.hierarchicalKMeans(originalData, {
clusterCount: 6,
outputType: 'single',
outputClusterIndexDimension: DIM_CLUSTER_INDEX,
outputCentroidDimensions: CENTER_DIM_IDX,
stepByStep: true
});
var colorAll = [
'#bbb', '#37A2DA', '#e06343', '#37a354', '#b55dba', '#b5bd48', '#8378EA', '#96BFFF'
];
var ANIMATION_DURATION_UPDATE = 1500;
function renderItemPoint(params, api) {
var coord = api.coord([api.value(0), api.value(1)]);
var clusterIdx = api.value(2);
if (clusterIdx == null || isNaN(clusterIdx)) {
clusterIdx = 0;
}
var isNewCluster = clusterIdx === api.value(3);
var extra = {
transition: []
};
var contentColor = colorAll[clusterIdx];
// addColorTransition(extra, contentColor, 'content');
return {
type: 'circle',
x: coord[0],
y: coord[1],
shape: {
cx: 0,
cy: 0,
r: 10
},
extra: extra,
style: {
fill: contentColor,
stroke: '#333',
lineWidth: 1,
shadowColor: contentColor,
shadowBlur: isNewCluster ? 12 : 0,
transition: ['shadowBlur', 'fill']
}
};
}
function renderBoundary(params, api) {
var xVal = api.value(0);
var yVal = api.value(1);
var maxDist = api.value(2);
var center = api.coord([xVal, yVal]);
var size = api.size([maxDist, maxDist]);
var renderProgressStart;
return {
type: 'ellipse',
shape: {
cx: isNaN(center[0]) ? 0 : center[0],
cy: isNaN(center[1]) ? 0 : center[1],
rx: isNaN(size[0]) ? 0 : size[0] + 15,
ry: isNaN(size[1]) ? 0 : size[1] + 15
},
extra: {
renderProgress: ++targetRenderProgress,
enterFrom: {
renderProgress: 0
},
transition: 'renderProgress'
},
style: {
fill: null,
stroke: 'rgba(0,0,0,0.2)',
lineDash: [4, 4],
lineWidth: 4
},
during: function (apiDuring) {
var currProgress = apiDuring.getExtra('renderProgress');
renderProgressStart == null && (renderProgressStart = currProgress);
var opacity = (currProgress - renderProgressStart)
/ (targetRenderProgress - renderProgressStart);
apiDuring.setStyle('opacity', opacity);
}
};
}
function makeStepOption(option, data, centroids) {
var newCluIdx = centroids ? centroids.length - 1 : -1;
var maxDist = 0;
for (var i = 0; i < data.length; i++) {
var line = data[i];
if (line[DIM_CLUSTER_INDEX] === newCluIdx) {
var dist0 = Math.pow(line[DATA_DIM_IDX[0]] - line[CENTER_DIM_IDX[0]], 2);
var dist1 = Math.pow(line[DATA_DIM_IDX[1]] - line[CENTER_DIM_IDX[1]], 2);
maxDist = Math.max(maxDist, dist0 + dist1);
}
}
var boundaryData = centroids
? [
[
centroids[newCluIdx][0],
centroids[newCluIdx][1],
Math.sqrt(maxDist)
]
]
: [];
option.options.push({
series: [{
type: 'custom',
encode: {
tooltip: [0, 1]
},
renderItem: renderItemPoint,
data: data
}, {
type: 'custom',
renderItem: renderBoundary,
animationDuration: 3000,
silent: true,
data: boundaryData
}]
});
}
var targetRenderProgress = 0;
var option = {
timeline: {
top: 'center',
right: 50,
height: 300,
width: 10,
inverse: true,
autoPlay: false,
playInterval: 2500,
symbol: 'none',
orient: 'vertical',
axisType: 'category',
label: {
formatter: 'step {value}',
position: 10
},
checkpointStyle: {
color: '#555',
borderWidth: 0,
animationDuration: ANIMATION_DURATION_UPDATE
},
data: []
},
baseOption: {
animationDurationUpdate: ANIMATION_DURATION_UPDATE,
tooltip: {
},
xAxis: {
type: 'value'
},
yAxis: {
type: 'value'
},
series: [{
type: 'scatter'
}]
},
options: []
};
makeStepOption(option, originalData);
option.timeline.data.push('0');
for (var i = 1, stepResult; !(stepResult = step.next()).isEnd; i++) {
makeStepOption(
option,
echarts.util.clone(stepResult.data),
echarts.util.clone(stepResult.centroids)
);
option.timeline.data.push(i + '');
}
var chart = testHelper.create(echarts, 'main-cluster-step', {
title: [
'Cluster algorithm visualization'
],
height: 600,
option: option
});
});
</script>
</body>
</html>