all performance section

Chapter09/Cargo.toml

@@ -6,6 +6,7 @@ version = "1.0.0"
 flame = "0.2"
 rand = "0.4.2"
 requests = "0.0.30"
+rusty-machine = "0.5.4"
 
 [[bin]]
 name = "performance_release_mode"
@@ -58,3 +59,7 @@ path = "performance_polynomial3.rs"
 [[bin]]
 name = "performance_polynomial4"
 path = "performance_polynomial4.rs"
+
+[[bin]]
+name = "performance_exponential"
+path = "performance_exponential.rs"

Chapter09/performance_exponential.rs

@@ -0,0 +1,11 @@
+fn bomb(n: u64) -> u64 {
+   if n > 0 {
+      bomb(n-1);
+      bomb(n-1);
+   }
+   n
+}
+
+fn main() {
+   bomb(1000);
+}

Chapter09/performance_polynomial4.rs

@@ -1,2 +1,19 @@
+extern crate rusty_machine;
+use rusty_machine::linalg::{Matrix,Vector};
+use rusty_machine::learning::gp::{GaussianProcess,ConstMean};
+use rusty_machine::learning::toolkit::kernel;
+use rusty_machine::learning::SupModel;
+
 fn main() {
+    let inputs = Matrix::new(3,3,vec![1.1,1.2,1.3,2.1,2.2,2.3,3.1,3.2,3.3]);
+    let targets = Vector::new(vec![0.1,0.8,0.3]);
+
+    let test_inputs = Matrix::new(2,3, vec![1.2,1.3,1.4,2.2,2.3,2.4]);
+
+    let ker = kernel::SquaredExp::new(2., 1.);
+    let zero_mean = ConstMean::default();
+    let mut gp = GaussianProcess::new(ker, zero_mean, 0.5);
+
+    gp.train(&inputs, &targets).unwrap();
+    let _ = gp.predict(&test_inputs).unwrap();
 }