feat: add ml/rusty-mathine

README.md

@@ -57,6 +57,8 @@ Project or files:
 │   └── unixsocket
 ├── __log
 │   └── log4rs
+├── __machinelearning
+│   └── rusty-machine
 ├── __misc
 │   ├── better_panic
 │   ├── fancy-regex
@@ -145,7 +147,7 @@ Project or files:
     ├── vec.rs
     └── while.rs
 
-117 directories, 26 files
+119 directories, 26 files
 
 ```
 

__machinelearning/rusty-machine/Cargo.lock

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__machinelearning/rusty-machine/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "rusty-machine"
+version = "0.1.0"
+authors = ["Hatter Jiang <jht5945@gmail.com>"]
+edition = "2018"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+rusty-machine = "0.5.4"
+

__machinelearning/rusty-machine/README.md

@@ -0,0 +1,5 @@
+
+
+Rusty-Machine 机器学习库
+https://mp.weixin.qq.com/s/fWNMnp846Yqy4AGENB1JWw
+

__machinelearning/rusty-machine/src/main.rs

@@ -0,0 +1,37 @@
+use rusty_machine::linalg::{Matrix, Vector};
+use rusty_machine::learning::gp::{GaussianProcess, ConstMean};
+use rusty_machine::learning::toolkit::kernel::SquaredExp;
+use rusty_machine::learning::SupModel;
+
+fn main() {
+    // 首先获取一些数据。
+
+    // 一些示例训练数据。
+    let inputs = Matrix::new(3,3,vec![1.,1.,1.,2.,2.,2.,3.,3.,3.]);
+    let targets = Vector::new(vec![0.,1.,0.]);
+
+    // 一些示例测试数据。
+    let test_inputs = Matrix::new(2,3, vec![1.5,1.5,1.5,2.5,2.5,2.5]);
+
+    // 现在设置好我们的模组
+    // 这几乎是rusty-machine 中最复杂的模组了！
+
+    // 设置平方指数核函数，长度参数 2，宽度参数 1。
+    let ker = SquaredExp::new(2., 1.);
+
+    // 零函数
+    let zero_mean = ConstMean::default();
+
+    // 用核函数，平均值, 噪声0.5来构建一个高斯过程。
+    let mut gp = GaussianProcess::new(ker, zero_mean, 0.5);
+
+    // 现在我们可以训练并且用这个模组进行预测了。
+
+    // 训练模组！
+    gp.train(&inputs, &targets).unwrap();
+
+    // 使用测试数据来测试预测。
+    let outputs = gp.predict(&test_inputs).unwrap();
+
+    println!("{:?}", outputs);
+}