labeled_weights
weights
updated_weights
// Distance metric, computes normalized high-dimensional distance between 2 rows in the data table.
// r1, r2 = references to data table rows.
function Distance(r1, r2) {
return d3.sum(
data.attrs.map((attr) => Math.abs(r1[attr] - r2[attr]) * data.weights[attr])
); // weighted L1
//Math.abs(r1[attr] - r2[attr]) / data.stdevs[attr] * data.weights[attr] )); // weighted L1 normalized
//return d3.sum(data.attrs.map(attr =>
// Math.abs(r1[attr] - r2[attr]) / data.stdevs[attr] )); // L1
//return Math.sqrt(d3.sum(data.attrs.map(attr =>
// Math.pow((r1[attr] - r2[attr])/data.stdevs[attr], 2)))); // L2
}
d3.mean(graph.edges, (e) => e.mydistance)
{ // update the MDS based on slider changes
data.weights = w;
graph.edges.forEach( (e, i) => {
e.mydistance = Distance(e.source.row, e.target.row)} );
simulation.force("link")
.distance(e => e.mydistance * scale * distanceScale);
simulation.alpha(1).restart();
}
// MDS force simulator
simulation = d3
.forceSimulation(graph.vertices)
.force(
"link",
d3
.forceLink(graph.edges)
.distance((e) => e.mydistance * distanceScale)
//.strength(e => 10.0 / e.distance)
.strength(0.2)
)
//.force("charge", d3.forceManyBody())
.force("center", d3.forceCenter(height / 2.0, height / 2.0))
width
scale_to_pix.domain()
scale_to_pix.invert(431)
//manhattan_distance([2,2], [1,8])
//euclidean_distance([1,2], [2,5])
//testing_data = [[24.9, 8],[25.1, 6],[24.8, 3.39]]
//pairwise_distance(testing_data)
//x = [[0, 25, 31.88], [25, 0, 16.28], [31.88, 16.28, 0]]
//y = [[0,2.3,5], [2.31,0,2.76], [5.04, 2.76, 0]]
//stress(x, y)
//testing_data = [[1,2], [3,4]]
//testing_weights = [1, 0.5]
attrs.length
FileAttachment("Animal_Data_Andromeda2.csv").csv()
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
