Visualisation of the Kmean generated cluster

Hello,
This is the code for the visulalisation of the workshop using rust and vega_lite_3.

Cargo.toml

[dependencies]
vega_lite_3 = {version = "0.1", features = ["show_vega"]}
serde = {version = "1.0", features = ["derive"]}
serde_json = "1.0.39"
derive_builder = "0.9.0"
showata =  "0.1.0"
failure = "0.1.5"
csv = "1.1.1"

main.rs

use csv;
use failure;
use serde::{Deserialize, Serialize};
use showata::Showable;
use std::path::Path;
use vega_lite_3::*;

#[derive(Serialize, Deserialize, Debug)]
pub struct Item {
    pub x: f64,
    pub y: f64,
    pub idx: usize,
}

macro_rules! build {
    ($s:expr ) => {
        $s.build().map_err(|s| failure::format_err!("{}", s))?
    };
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Read in the data using the csv crate
    // The data are organised this way:
    //
    // Point_ID ,Point_x,Point_y, Cluster_idx
    // 0, 9.887634 , 10.700742, 3
    // 1, 10.895053 , 9.602507, 3
    // ...
    // 1475, 1.959400, 13.254207, 1
    let mut rdr = csv::Reader::from_path(Path::new("clustered_points.csv"))?;
    let values = rdr
        .deserialize()
        .into_iter()
        .collect::<Result<Vec<Item>, csv::Error>>()?;
    // Create the plot
    let chart = build!(VegaliteBuilder::default()
        .title("Dataset Points")
        .width(800.0)
        .height(800.0)
        .description("Plot of the clustered dataset generated in by ndarray workshop.")
        .data(&values)
        .mark(Mark::Point)
        .encoding(build!(EncodingBuilder::default()
            .x(build!(XClassBuilder::default()
                .field("x")
                .def_type(StandardType::Quantitative)))
            .y(build!(YClassBuilder::default()
                .field("y")
                .def_type(StandardType::Quantitative)))
            .color(ValueDefWithConditionMarkPropFieldDefStringNull {
                aggregate: None,
                bin: None,
                condition: None,
                field: Some(Field::String("idx".to_string())),
                legend: None,
                scale: None,
                sort: None,
                time_unit: None,
                title: None,
                value: None,
                value_def_with_condition_mark_prop_field_def_string_null_type: None
            }))));
    chart.show()?;
    Ok(())
}

检查 Cargo.toml 和 main.rs 中的 Rust 示例，包括 clustered_points.csv 输入和 Vega-Lite 图表构建。使用提供的聚类数据运行该示例，并确定预期的可视化效果；当生成的聚类点正确显示时，工作即告完成。