Iris data (#29)

Cargo.lock

@@ -32,6 +32,27 @@ version = "1.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
 
+[[package]]
+name = "csv"
+version = "1.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "626ae34994d3d8d668f4269922248239db4ae42d538b14c398b74a52208e8086"
+dependencies = [
+ "csv-core",
+ "itoa",
+ "ryu",
+ "serde",
+]
+
+[[package]]
+name = "csv-core"
+version = "0.1.10"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2b2466559f260f48ad25fe6317b3c8dac77b5bdb5763ac7d9d6103530663bc90"
+dependencies = [
+ "memchr",
+]
+
 [[package]]
 name = "funty"
 version = "2.0.0"
@@ -60,6 +81,12 @@ dependencies = [
  "packed_struct_codegen",
 ]
 
+[[package]]
+name = "itoa"
+version = "1.0.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "453ad9f582a441959e5f0d088b02ce04cfe8d51a8eaf077f12ac6d3e94164ca6"
+
 [[package]]
 name = "libc"
 version = "0.2.142"
@@ -79,12 +106,19 @@ dependencies = [
 name = "little_learner_app"
 version = "0.1.0"
 dependencies = [
+ "csv",
  "immutable-chunkmap",
  "little_learner",
  "ordered-float",
  "rand",
 ]
 
+[[package]]
+name = "memchr"
+version = "2.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2dffe52ecf27772e601905b7522cb4ef790d2cc203488bbd0e2fe85fcb74566d"
+
 [[package]]
 name = "num-traits"
 version = "0.2.15"
@@ -184,6 +218,18 @@ dependencies = [
  "getrandom",
 ]
 
+[[package]]
+name = "ryu"
+version = "1.0.13"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f91339c0467de62360649f8d3e185ca8de4224ff281f66000de5eb2a77a79041"
+
+[[package]]
+name = "serde"
+version = "1.0.164"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9e8c8cf938e98f769bc164923b06dce91cea1751522f46f8466461af04c9027d"
+
 [[package]]
 name = "syn"
 version = "1.0.109"

little_learner/src/block.rs

@@ -0,0 +1,50 @@
+use crate::auto_diff::{Differentiable, RankedDifferentiableTagged};
+use crate::ext::relu;
+use crate::traits::NumLike;
+
+pub struct Block<F, const N: usize> {
+    f: F,
+    ranks: [usize; N],
+}
+
+pub fn compose<'a, A, T, B, C, F, G, const N: usize, const M: usize>(
+    b1: Block<F, N>,
+    b2: Block<G, M>,
+    j: usize,
+) -> Block<impl FnOnce(A, &'a [T]) -> C, { N + M }>
+where
+    F: FnOnce(A, &'a [T]) -> B,
+    G: FnOnce(B, &'a [T]) -> C,
+    T: 'a,
+{
+    let mut ranks = [0usize; N + M];
+    ranks.copy_from_slice(&b1.ranks[..N]);
+    ranks[N..(M + N)].copy_from_slice(&b2.ranks[..M]);
+    Block {
+        f: move |t, theta| (b2.f)((b1.f)(t, theta), &theta[j..]),
+        ranks,
+    }
+}
+
+pub fn dense<'a, 'b, A, Tag>(
+    input_len: usize,
+    neuron_count: usize,
+) -> Block<
+    impl FnOnce(&'a RankedDifferentiableTagged<A, Tag, 1>, &'b [Differentiable<A>]) -> Differentiable<A>,
+    2,
+>
+where
+    Tag: Clone,
+    A: NumLike + PartialOrd + Default,
+{
+    Block {
+        f: |t, theta: &'b [Differentiable<A>]| -> Differentiable<A> {
+            relu(
+                t,
+                &(theta[0].clone().attach_rank().unwrap()),
+                &(theta[1].clone().attach_rank().unwrap()),
+            )
+        },
+        ranks: [input_len, neuron_count],
+    }
+}

little_learner/src/ext.rs

@@ -430,7 +430,7 @@ mod tests {
                 crate::decider::relu(
                     &t,
                     &RankedDifferentiable::of_slice(weights),
-                    Scalar::make(bias.clone()),
+                    Scalar::make(*bias),
                 )
                 .clone_real_part()
                 .into_inner(),

little_learner/src/gradient_descent.rs

@@ -404,8 +404,11 @@ mod tests {
             .map(|x| x.into_inner())
             .collect::<Vec<_>>();
         let fitted_theta1 = theta1.to_scalar().real_part().into_inner();
-        assert_eq!(fitted_theta0, [3.9746454441720851, 1.9714549220774951]);
-        assert_eq!(fitted_theta1, 6.1645790482740361);
+        assert_eq!(
+            fitted_theta0,
+            [3.974_645_444_172_085, 1.971_454_922_077_495]
+        );
+        assert_eq!(fitted_theta1, 6.164_579_048_274_036);
     }
 
     #[test]
@@ -452,7 +455,7 @@ mod tests {
         let fitted_theta1 = theta1.to_scalar().real_part().into_inner();
         assert_eq!(
             fitted_theta0,
-            [3.980_262_420_345_729_5, 1.977_071_898_301_443_9]
+            [3.980_262_420_345_729_5, 1.977_071_898_301_444]
         );
         assert_eq!(fitted_theta1, 6.170_196_024_282_712_5);
     }

little_learner/src/lib.rs

@@ -3,6 +3,7 @@
 #![feature(array_methods)]
 
 pub mod auto_diff;
+pub mod block;
 pub mod decider;
 pub mod ext;
 pub mod gradient_descent;

little_learner/src/smooth.rs

@@ -106,7 +106,7 @@ mod test_smooth {
         ]
         .map(|x| hydrate(&x));
 
-        let mut current = hydrate(&vec![0.8, 3.1, 2.2]);
+        let mut current = hydrate(&[0.8, 3.1, 2.2]);
         let mut output = Vec::with_capacity(inputs.len());
         for input in inputs {
             current = smooth(decay.clone(), &current, &input);

little_learner_app/Cargo.toml

@@ -10,3 +10,4 @@ immutable-chunkmap = "1.0.5"
 ordered-float = "3.6.0"
 little_learner = { path = "../little_learner" }
 rand = "0.8.5"
+csv = "1.2.2"

little_learner_app/src/iris.csv

@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+

little_learner_app/src/iris.rs

@@ -0,0 +1,77 @@
+use csv::ReaderBuilder;
+use std::io::Cursor;
+use std::str::FromStr;
+
+const IRIS_DATA: &str = include_str!("iris.csv");
+
+#[derive(Eq, PartialEq, Debug)]
+pub enum IrisType {
+    Setosa,
+    Versicolor,
+    Virginica,
+}
+
+impl FromStr for IrisType {
+    type Err = String;
+
+    fn from_str(s: &str) -> Result<Self, Self::Err> {
+        match s {
+            "Iris-virginica" => Ok(IrisType::Virginica),
+            "Iris-versicolor" => Ok(IrisType::Versicolor),
+            "Iris-setosa" => Ok(IrisType::Setosa),
+            _ => Err(String::from(s)),
+        }
+    }
+}
+
+#[derive(PartialEq, Debug)]
+pub struct Iris {
+    pub class: IrisType,
+    pub petal_length: f32,
+    pub petal_width: f32,
+    pub sepal_length: f32,
+    pub sepal_width: f32,
+}
+
+pub fn import() -> Vec<Iris> {
+    let mut reader = ReaderBuilder::new()
+        .has_headers(false)
+        .from_reader(Cursor::new(IRIS_DATA));
+    let mut output = Vec::new();
+    for record in reader.records() {
+        let record = record.unwrap();
+        let petal_length = f32::from_str(&record[0]).unwrap();
+        let petal_width = f32::from_str(&record[1]).unwrap();
+        let sepal_length = f32::from_str(&record[2]).unwrap();
+        let sepal_width = f32::from_str(&record[3]).unwrap();
+        let class = IrisType::from_str(&record[4]).unwrap();
+        output.push(Iris {
+            class,
+            petal_length,
+            petal_width,
+            sepal_length,
+            sepal_width,
+        });
+    }
+
+    output
+}
+
+pub(crate) const EXPECTED_FIRST: Iris = Iris {
+    class: IrisType::Setosa,
+    petal_length: 5.1,
+    petal_width: 3.5,
+    sepal_length: 1.4,
+    sepal_width: 0.2,
+};
+
+#[cfg(test)]
+mod test {
+    use crate::iris::import;
+
+    #[test]
+    fn first_element() {
+        let irises = import();
+        assert_eq!(irises[0], crate::iris::EXPECTED_FIRST);
+    }
+}

little_learner_app/src/main.rs

@@ -1,71 +1,14 @@
 #![allow(incomplete_features)]
 #![feature(generic_const_exprs)]
 
-use little_learner::auto_diff::{Differentiable, RankedDifferentiable, RankedDifferentiableTagged};
+use crate::rms_example::rms_example;
 
-use little_learner::gradient_descent::gradient_descent;
-use little_learner::hyper;
-use little_learner::loss::predict_plane;
-use little_learner::not_nan::{to_not_nan_1, to_not_nan_2};
-use little_learner::predictor;
-use little_learner::scalar::Scalar;
-use little_learner::traits::Zero;
-use ordered_float::NotNan;
-
-const PLANE_XS: [[f64; 2]; 6] = [
-    [1.0, 2.05],
-    [1.0, 3.0],
-    [2.0, 2.0],
-    [2.0, 3.91],
-    [3.0, 6.13],
-    [4.0, 8.09],
-];
-const PLANE_YS: [f64; 6] = [13.99, 15.99, 18.0, 22.4, 30.2, 37.94];
+mod iris;
+mod rms_example;
 
 fn main() {
-    let beta = NotNan::new(0.9).expect("not nan");
-    let stabilizer = NotNan::new(0.000_000_01).expect("not nan");
-    let hyper = hyper::RmsGradientDescent::default(NotNan::new(0.01).expect("not nan"), 3000)
-        .with_stabilizer(stabilizer)
-        .with_beta(beta);
+    rms_example();
 
-    let iterated = {
-        let xs = to_not_nan_2(PLANE_XS);
-        let ys = to_not_nan_1(PLANE_YS);
-        let zero_params = [
-            RankedDifferentiable::of_slice(&[NotNan::<f64>::zero(), NotNan::<f64>::zero()])
-                .to_unranked(),
-            Differentiable::of_scalar(Scalar::zero()),
-        ];
-
-        gradient_descent(
-            hyper,
-            &xs,
-            RankedDifferentiableTagged::of_slice_2::<_, 2>,
-            &ys,
-            zero_params,
-            predictor::rms(predict_plane),
-            hyper::RmsGradientDescent::to_immutable,
-        )
-    };
-
-    let [theta0, theta1] = iterated;
-
-    let theta0 = theta0.attach_rank::<1>().expect("rank 1 tensor");
-    let theta1 = theta1.attach_rank::<0>().expect("rank 0 tensor");
-
-    let fitted_theta0 = theta0
-        .collect()
-        .iter()
-        .map(|x| x.into_inner())
-        .collect::<Vec<_>>();
-    let fitted_theta1 = theta1.to_scalar().real_part().into_inner();
-    assert_eq!(
-        fitted_theta0,
-        [3.974_645_444_172_085, 1.971_454_922_077_495]
-    );
-    assert_eq!(fitted_theta1, 6.164_579_048_274_036);
+    let irises = iris::import();
+    assert_eq!(irises[0], crate::iris::EXPECTED_FIRST);
 }
-
-#[cfg(test)]
-mod tests {}

little_learner_app/src/rms_example.rs

@@ -0,0 +1,65 @@
+use little_learner::auto_diff::{Differentiable, RankedDifferentiable, RankedDifferentiableTagged};
+
+use little_learner::gradient_descent::gradient_descent;
+use little_learner::hyper;
+use little_learner::loss::predict_plane;
+use little_learner::not_nan::{to_not_nan_1, to_not_nan_2};
+use little_learner::predictor;
+use little_learner::scalar::Scalar;
+use little_learner::traits::Zero;
+use ordered_float::NotNan;
+
+const PLANE_XS: [[f64; 2]; 6] = [
+    [1.0, 2.05],
+    [1.0, 3.0],
+    [2.0, 2.0],
+    [2.0, 3.91],
+    [3.0, 6.13],
+    [4.0, 8.09],
+];
+const PLANE_YS: [f64; 6] = [13.99, 15.99, 18.0, 22.4, 30.2, 37.94];
+
+pub(crate) fn rms_example() {
+    let beta = NotNan::new(0.9).expect("not nan");
+    let stabilizer = NotNan::new(0.000_000_01).expect("not nan");
+    let hyper = hyper::RmsGradientDescent::default(NotNan::new(0.01).expect("not nan"), 3000)
+        .with_stabilizer(stabilizer)
+        .with_beta(beta);
+
+    let iterated = {
+        let xs = to_not_nan_2(PLANE_XS);
+        let ys = to_not_nan_1(PLANE_YS);
+        let zero_params = [
+            RankedDifferentiable::of_slice(&[NotNan::<f64>::zero(), NotNan::<f64>::zero()])
+                .to_unranked(),
+            Differentiable::of_scalar(Scalar::zero()),
+        ];
+
+        gradient_descent(
+            hyper,
+            &xs,
+            RankedDifferentiableTagged::of_slice_2::<_, 2>,
+            &ys,
+            zero_params,
+            predictor::rms(predict_plane),
+            hyper::RmsGradientDescent::to_immutable,
+        )
+    };
+
+    let [theta0, theta1] = iterated;
+
+    let theta0 = theta0.attach_rank::<1>().expect("rank 1 tensor");
+    let theta1 = theta1.attach_rank::<0>().expect("rank 0 tensor");
+
+    let fitted_theta0 = theta0
+        .collect()
+        .iter()
+        .map(|x| x.into_inner())
+        .collect::<Vec<_>>();
+    let fitted_theta1 = theta1.to_scalar().real_part().into_inner();
+    assert_eq!(
+        fitted_theta0,
+        [3.974_645_444_172_085, 1.971_454_922_077_495]
+    );
+    assert_eq!(fitted_theta1, 6.164_579_048_274_036);
+}