├── .gitignore
├── Cargo.lock
├── Cargo.toml
├── LICENSE
├── README.md
├── names.txt
├── notes.txt
└── src
    ├── main.rs
    ├── nb001.rs
    ├── nb002.rs
    ├── nb003.rs
    ├── nb004.rs
    ├── nb005.rs
    ├── numr.rs
    ├── plot.rs
    ├── rust_mllib.rs
    └── utils.rs


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1 | /target
2 | *.dot
3 | *.png
4 | 


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4900 |  "byteorder",
4901 |  "bzip2",
4902 |  "constant_time_eq",
4903 |  "crc32fast",
4904 |  "crossbeam-utils",
4905 |  "flate2",
4906 |  "hmac",
4907 |  "pbkdf2",
4908 |  "sha1",
4909 |  "time",
4910 |  "zstd",
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4921 |  "crossbeam-utils",
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4923 |  "indexmap",
4924 |  "num_enum",
4925 |  "thiserror",
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--------------------------------------------------------------------------------
/Cargo.toml:
--------------------------------------------------------------------------------
 1 | [package]
 2 | name = "makemore_rs"
 3 | version = "0.1.0"
 4 | edition = "2021"
 5 | 
 6 | [dependencies]
 7 | burn = { version = "0.15.0", features = ["ndarray", "autodiff"] }
 8 | burn-ndarray = "0.16.0"
 9 | ndarray = "0.16.1"
10 | num-traits = "0.2.19"
11 | plotters = "0.3.7"
12 | rand = "0.8.5"
13 | rand_distr = "0.4.3"
14 | 
15 | [dependencies.uuid]
16 | version = "1.11.0"
17 | features = [
18 |     "v4",    
19 | ]
20 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | The MIT License (MIT) Copyright (c) 2025 Arjun Mukherjee, Shoestring Software Private Limited
2 | 
3 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
4 | 
5 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
6 | 
7 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # makemore-rs
  2 | 
  3 | ### Objective
  4 | 
  5 | This is a rewrite of Andrej Karpathy's character level language model which he codes in the following video:
  6 | 
  7 | <https://www.youtube.com/watch?v=PaCmpygFfXo>
  8 | 
  9 | [Rust](https://www.rust-lang.org/) is the only requirement.
 10 | 
 11 | ### Prerequisites
 12 | 
 13 | You must have Rust installed. Follow instructions here:
 14 | <https://www.rust-lang.org/tools/install>
 15 | 
 16 | ### Installation
 17 | 
 18 | Clone this repo in any folder.
 19 | 
 20 | Inside the folder, just do:
 21 | 
 22 | ```bash
 23 | cargo run
 24 | ```
 25 | 
 26 | ### Code layout
 27 | 
 28 | The code has been written as a single binary crate with modules for libs as well as *notebook* explorations.
 29 | 
 30 | It can be refactored into a library crate with multiple binaries for each of the notebook later.
 31 | 
 32 | The *notebook* files are prefixed with `nb`. For e.g. `nb001`, `nb002`, etc.
 33 | 
 34 | Each of the *cell* inside a *notebook* file is written as a Rust function prefixed with `ex`.
 35 | For e.g. `ex1`, `ex2`, etc.
 36 | 
 37 | ### Usage
 38 | 
 39 | Whenever you want to execute cells in a particular notebook, do the following:
 40 | - go to `main.rs`.
 41 | - In the `main` function, select the notebook (for e.g. `nb005`) by:
 42 | ```rust
 43 | nb005::main();
 44 | ```
 45 | - Each notebook file has its own `main` function.
 46 | - Inside the `nb005.rs` file, execute any cell (for e.g. `ex11` by executing the `ex11()` function in `nb005` `main` function.
 47 | 
 48 | ### Highlights
 49 | 
 50 | Andrej was using matplotlib and PyTorch. Many functions like plotting the heat map, bigram matrix, multinomial sampling, PyTorch one hot encoding. etc. were written easily using other Rust crates.
 51 | 
 52 | We used [ndarray crate](https://crates.io/crates/ndarray) for majority of the notebook examples till we came to the point where gradients had to be calculated. We switched to [burn crate](https://crates.io/crates/burn) thereafter.
 53 | 
 54 | ### Code examples
 55 | 
 56 | The *notebooks* of importance are everything except `nb004`. Just ignore that.
 57 | 
 58 | Andrej this time did not write a library like when he did for [microgradr](https://github.com/shoestringinc/microgradr). Everyhting was done in a notebook.
 59 | 
 60 | The entire `makemore` language model was coded in a Python notebook and we likewise coded in `nb001` to `nb005`.
 61 | 
 62 | We kept on adding functions for notebook users in the following files:
 63 | - `rust_mllib` which has functions that use `Burn lib`. This is our `PyTorch` equivalent.
 64 | - `numr` which has functions that usse `ndarray` crate. This is our `numpy` equivalent.
 65 | - `plot` has all the plotting functions. This is our `matplotlib` equivalent.
 66 | 
 67 | The library files are little messy as we got ambitious creating super convenient interface for notebook users. But we wanted to finish the project so some structs, etc. are not used and not complete.
 68 | 
 69 | But nevertheless, the lib modules have tons of tips for using various Rust AI crates. You will find lots of useful nuggets.
 70 | 
 71 | For the finale, read the cell `ex11` in `nb005` notebook.
 72 | It trains the neural net and then generates the words using multinomial distribution from the probabilities calculated from the final weights.
 73 | 
 74 | 
 75 | ### Important imports for notebooks
 76 | 
 77 | ```rust
 78 | use crate::numr::*;
 79 | use crate::plot::*;
 80 | use crate::rust_mllib::*;
 81 | use crate::utils::*;
 82 | ```
 83 | 
 84 | There are others too which are just exposing the lower level crates. Maybe in future we will refactor them to use only the above crates.
 85 | 
 86 | ### Plotting
 87 | 
 88 | Andrej uses matplotlib for plotting.
 89 | 
 90 | Check the `plot` module.
 91 | 
 92 | The main functions are:
 93 | - `plot_to_file` (for plotting points. We can rename it :-)
 94 | - `plot_heat_map`
 95 | - `plot_data` (used for plotting bigram matrix counts)
 96 | 
 97 | Open the files in any rendering app. We use `Preview` on `osx`. `Preview` automatically re-renders as the graph changes.
 98 | 
 99 | 
100 | ### Final notes
101 | 
102 | As usual, lots of reorgnization and refactoring can be done including separate test folder, lib and binaries etc.
103 | 
104 | ### License
105 | 
106 | MIT
107 | 
108 | See the LICENSE file for more info.
109 | 


--------------------------------------------------------------------------------
/notes.txt:
--------------------------------------------------------------------------------
1 | 
2 | 


--------------------------------------------------------------------------------
/src/main.rs:
--------------------------------------------------------------------------------
 1 | #![allow(unused)]
 2 | 
 3 | mod nb001;
 4 | mod nb002;
 5 | mod nb003;
 6 | mod nb004;
 7 | mod nb005;
 8 | 
 9 | mod utils;
10 | mod rust_mllib;
11 | mod numr;
12 | mod plot;
13 | 
14 | fn main() {
15 |     nb005::main();
16 |     // nb001::main();
17 |     // numr::main();
18 |     // rust_mllib::main();
19 | }
20 | 


--------------------------------------------------------------------------------
/src/nb001.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | use std::collections::HashSet;
  4 | use std::ops::Range;
  5 | use std::{collections::HashMap, fs};
  6 | 
  7 | use crate::numr::*;
  8 | use crate::plot::*;
  9 | use crate::rust_mllib::*;
 10 | use crate::utils::{create_char_matrix_ndarr, ctoi, get_names};
 11 | 
 12 | fn ex1() {
 13 |     let names = fs::read_to_string("names.txt").unwrap();
 14 |     let names: Vec<_> = names.lines().collect();
 15 |     // NOTE: we have a get_names() function in utils module to save some typing in cells.
 16 |     // println!("{:#?}", &names);
 17 | 
 18 |     let xs = &names[0..10]; // first 10 names
 19 |                             // dbg!(&xs);
 20 | 
 21 |     println!("Total names: {}", names.len());
 22 | 
 23 |     // shortest word
 24 |     let shortest = names.iter().map(|e| e.len()).min().unwrap();
 25 |     println!("shortest length: {}", shortest);
 26 | 
 27 |     // longest word
 28 |     let longest = names.iter().map(|e| e.len()).max().unwrap();
 29 |     println!("longest length: {}", longest);
 30 | }
 31 | 
 32 | fn ex2() {
 33 |     let names = get_names();
 34 |     // println!("{:#?}", &names);
 35 | 
 36 |     for w in &names[0..3] {
 37 |         println!("{w}");
 38 |         for (ch1, ch2) in w.chars().zip(w.chars().skip(1)) {
 39 |             println!("{ch1} {ch2}");
 40 |         }
 41 |         println!("===");
 42 |     }
 43 | }
 44 | 
 45 | fn ex3() {
 46 |     let names = get_names();
 47 | 
 48 |     let mut b: HashMap<(char, char), usize> = HashMap::new();
 49 |     // for w in &names[0..3] {
 50 |     for w in &names {
 51 |         let w = format!("<{}>", w);
 52 |         for (ch1, ch2) in w.chars().zip(w.chars().skip(1)) {
 53 |             let bigram = (ch1, ch2);
 54 |             b.entry(bigram).and_modify(|e| *e += 1).or_insert(1);
 55 |             // println!("{ch1} {ch2}");
 56 |         }
 57 |     }
 58 | 
 59 |     // println!("{:#?}", &b);
 60 | 
 61 |     let mut sorted_entries: Vec<_> = b.iter().collect();
 62 |     sorted_entries.sort_by(|a, b| b.1.cmp(a.1));
 63 |     sorted_entries
 64 |         .iter()
 65 |         .take(10)
 66 |         .for_each(|e| println!("{:?}", e));
 67 | }
 68 | 
 69 | fn ex4() {
 70 |     // Rough work: Testing Rust stuff
 71 |     let a = 1;
 72 |     let b = 2;
 73 |     println!("{:?}", a.cmp(&b));
 74 |     println!("{:?}", b.cmp(&a));
 75 |     let mut xs = vec![a, b];
 76 |     xs.sort_by(|x, y| x.cmp(y));
 77 |     println!("{:?}", xs);
 78 |     let mut xs = vec![a, b];
 79 |     xs.sort_by(|x, y| y.cmp(x));
 80 |     println!("{:?}", xs);
 81 | }
 82 | 
 83 | fn ex5() {
 84 |     let mut a = zeros_int(3, 5);
 85 |     dbg!(&a);
 86 |     a[[1, 3]] = 1;
 87 |     dbg!(&a);
 88 |     a[[1, 3]] += 1;
 89 |     dbg!(&a);
 90 |     a[[0, 0]] = 5;
 91 |     dbg!(&a);
 92 | }
 93 | 
 94 | fn ex6() {
 95 |     let names = get_names();
 96 |     let mut chs: HashSet<char> = names.join("").chars().collect();
 97 |     // println!("{chs:?}");
 98 |     // println!("{}", chs.len());
 99 |     let mut chs = Vec::from_iter(chs);
100 |     chs.sort();
101 |     chs.push('<');
102 |     chs.push('>');
103 |     println!("{chs:?}");
104 |     let xs: HashMap<char, usize> = (0..).zip(chs).map(|e| (e.1, e.0)).collect();
105 |     dbg!(&xs);
106 | 
107 |     // we have refactored this into a function calle ctoi in utils
108 | 
109 |     // let a = zeros_int(28, 28);
110 | }
111 | 
112 | fn ex7() {
113 |     let ctoi = ctoi();
114 |     let mut n = zeros_int(28, 28);
115 |     let names = get_names();
116 |     for w in &names {
117 |         let w = format!("<{}>", w);
118 |         for (ch1, ch2) in w.chars().zip(w.chars().skip(1)) {
119 |             let ix1 = ctoi[&ch1];
120 |             let ix2 = ctoi[&ch2];
121 |             n[[ix1, ix2]] += 1;
122 |         }
123 |     }
124 |     // dbg!(&n);
125 |     plot_heat_map(&n, "heat.png");
126 | }
127 | 
128 | fn ex8() {
129 |     let ctoi = ctoi();
130 |     let itoc: HashMap<usize, char> = ctoi.iter().map(|(&k, &v)| (v, k)).collect();
131 |     println!("{:?}", itoc);
132 | }
133 | 
134 | fn ex9() {
135 |     let data = create_char_matrix_ndarr();
136 |     plot_heat_map(&data, "heat7_1.png");
137 | }
138 | 
139 | fn ex10() {
140 |     let data = create_char_matrix_ndarr();
141 |     plot_data(&data, "data.png");
142 | }
143 | 
144 | fn ex11() {
145 |     let data = create_char_matrix_ndarr();
146 |     let (v, min, max) = ndarray_to_image_data(&data);
147 |     dbg!(&v);
148 | }
149 | 
150 | pub fn main() {
151 |     println!("Notebook 001");
152 |     ex9();
153 | }
154 | 


--------------------------------------------------------------------------------
/src/nb002.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | use std::collections::HashSet;
  4 | use std::ops::Range;
  5 | use std::{collections::HashMap, fs};
  6 | 
  7 | use ndarray::Array1;
  8 | use rand::thread_rng;
  9 | 
 10 | use crate::utils::{create_char_matrix_ndarr, ctoi, get_names, itoc};
 11 | use crate::rust_mllib::*;
 12 | use crate::numr::*;
 13 | use crate::plot::*;
 14 | 
 15 | 
 16 | fn ex1() {
 17 |     // Andrej switches to dot replacing <S> and <E> we used chars '<', '>'
 18 |     let ctoi = ctoi();
 19 |     dbg!(&ctoi);
 20 | }
 21 | 
 22 | fn ex2() {
 23 |     let data = create_char_matrix_ndarr();
 24 |     plot_data(&data, "data3.png");
 25 | 
 26 |     // first row will be all the starting characters - ".a", ".b", etc.
 27 |     // first column are all the ending characters - "a.", "b."
 28 | }
 29 | 
 30 | fn ex3() {
 31 |     // let us begin with starting chars i.e. row 0
 32 |     let data = create_char_matrix_ndarr();
 33 |     
 34 |     let row0 = data.row(0);
 35 |     // println!("{:?}", row0);
 36 |     
 37 |     // convert to floats
 38 |     let row0f = row0.mapv(|e|e as f64);
 39 |     // println!("{:?}", row0f);
 40 | 
 41 |     // probability distribution
 42 |     let total = row0f.sum();
 43 |     let p = row0f.mapv(|e| e/ total);
 44 |     // println!("{:?}", p);
 45 | 
 46 |     // let samples = multinomial_distrib(100, &p);
 47 |     // dbg!(&samples);
 48 |     // println!("{}", samples.len());
 49 | 
 50 |     // let samples = multinomial_distrib(1, &p);
 51 |     // let ctoi = ctoi();
 52 |     // let itoc = itoc(&ctoi);
 53 |     // // find the first index having 1
 54 |     // let x = (0..).zip(samples.iter()).find(|(idx, &value)|value == 1).unwrap().0;
 55 |     // println!("{:?}", samples);
 56 |     // println!("{}", x);
 57 |     // println!("{}", itoc[&x]);
 58 | 
 59 | }
 60 | 
 61 | fn ex4() {
 62 |     let data = create_char_matrix_ndarr();
 63 |     let ctoi = ctoi();
 64 |     let itoc = itoc(&ctoi);
 65 | 
 66 |     let probs = probability_distrib(&data, 0);
 67 |     let mut rng = thread_rng();
 68 |     let res = multinomial_distrib(1, &probs, &mut rng);
 69 |     dbg!(&res);
 70 |     let idx = res[0];
 71 |     println!("{}", itoc[&idx]);
 72 | 
 73 |     // let idx = multinomial_char_index(&probs);
 74 |     // println!("char - {}", itoc[&idx]);
 75 | }
 76 | 
 77 | fn ex5() {
 78 |     let data = create_char_matrix_ndarr();
 79 |     let ctoi = ctoi();
 80 |     let itoc = itoc(&ctoi);
 81 |     let mut rng = thread_rng();
 82 | 
 83 |     let mut row_num = 0_usize;
 84 |     let mut chs = Vec::new();
 85 |     loop {
 86 |         let probs = probability_distrib(&data, row_num);
 87 |         row_num = multinomial_distrib(1, &probs, &mut rng)[0];// remember we get Array so extract
 88 |         let ch = itoc[&row_num];
 89 |         if row_num == 0 {
 90 |             break;
 91 |         }
 92 |         chs.push(ch);
 93 |     }
 94 | 
 95 |     let name: String = chs.iter().collect();
 96 | 
 97 |     println!("{}", name);
 98 | 
 99 | }
100 | 
101 | fn ex6() {
102 |     // Bigram generation was not that good but better than uniform distribution shown here
103 | 
104 |     let data = create_char_matrix_ndarr();
105 |     let ctoi = ctoi();
106 |     let itoc = itoc(&ctoi);
107 |     let mut rng = thread_rng();
108 | 
109 |     let mut row_num = 0_usize;
110 |     let mut chs = Vec::new();
111 |     let probs = ones_distrib(27);
112 |     loop {
113 |         row_num = multinomial_distrib(1, &probs, &mut rng)[0];// remember we get Array so extract
114 |         let ch = itoc[&row_num];
115 |         if row_num == 0 {
116 |             break;
117 |         }
118 |         chs.push(ch);
119 |     }
120 | 
121 |     let name: String = chs.iter().collect();
122 | 
123 |     println!("{}", name);
124 | 
125 | }
126 | 
127 | fn ex7() {
128 |     let data = create_char_matrix_ndarr();
129 |     let pd = probability_distrib_matrix(&data);
130 |     dbg!(&pd[0]);
131 | }
132 | 
133 | fn ex8() {
134 |     let data = create_char_matrix_ndarr();
135 |     let pd = probability_distrib_matrix(&data);
136 |     let ctoi = ctoi();
137 |     let itoc = itoc(&ctoi);
138 |     let mut rng = thread_rng();
139 | 
140 |     let mut row_num = 0_usize;
141 |     let mut chs = Vec::new();
142 |     loop {
143 |         row_num = multinomial_distrib(1, &pd[row_num], &mut rng)[0];// remember we get Array so extract
144 |         let ch = itoc[&row_num];
145 |         if row_num == 0 {
146 |             break;
147 |         }
148 |         chs.push(ch);
149 |     }
150 | 
151 |     let name: String = chs.iter().collect();
152 | 
153 |     println!("{}", name);
154 | 
155 | 
156 | }
157 | 
158 | fn ex9() {
159 |     let data = create_char_matrix_ndarr();
160 |     let pd = probability_distrib_matrix(&data);
161 |     println!("{}", &pd[1].sum());
162 |     println!("{:?}", &pd[0]);
163 | 
164 | }
165 | 
166 | 
167 | pub fn main() {
168 |     println!("Notebook 002");
169 |     ex9();
170 | }
171 | 
172 | // 1:06:26


--------------------------------------------------------------------------------
/src/nb003.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | use std::collections::HashSet;
  4 | use std::f64::consts::E;
  5 | use std::ops::Range;
  6 | use std::{collections::HashMap, fs};
  7 | 
  8 | use ndarray::{Array1, Array2};
  9 | use rand::thread_rng;
 10 | 
 11 | use std::sync::OnceLock;
 12 | 
 13 | use crate::utils::{create_char_matrix_ndarr, ctoi, get_names, itoc};
 14 | use crate::rust_mllib::*;
 15 | use crate::numr::*;
 16 | use crate::plot::*;
 17 | 
 18 | static WORDS: OnceLock<Vec<String>> = OnceLock::new();
 19 | static DATA: OnceLock<Array2<i64>> = OnceLock::new();
 20 | static PD: OnceLock<Vec<Array1<f64>>> = OnceLock::new();
 21 | static CTOI: OnceLock<HashMap<char, usize>> = OnceLock::new();
 22 | static ITOC: OnceLock<HashMap<usize, char>> = OnceLock::new();
 23 | 
 24 | 
 25 | fn init_core_data() {
 26 |     WORDS.get_or_init(|| get_names());
 27 |     DATA.get_or_init(|| create_char_matrix_ndarr());
 28 |     let data = DATA.get().unwrap();
 29 |     PD.get_or_init(|| probability_distrib_matrix(data));
 30 |     CTOI.get_or_init(||ctoi());
 31 |     let ctoi = CTOI.get().unwrap();
 32 |     ITOC.get_or_init(||itoc(ctoi));
 33 | }
 34 | 
 35 | fn ex1() {
 36 |     let words = WORDS.get().unwrap();
 37 |     let data = DATA.get().unwrap();
 38 |     let pd = PD.get().unwrap();
 39 |     let ctoi = CTOI.get().unwrap();
 40 |     let itoc = ITOC.get().unwrap();
 41 | }
 42 | 
 43 | fn ex2() {
 44 |     let words = WORDS.get().unwrap();
 45 |     let data = DATA.get().unwrap();
 46 |     let pd = PD.get().unwrap();
 47 |     let ctoi = CTOI.get().unwrap();
 48 |     let itoc = ITOC.get().unwrap();
 49 | 
 50 |     let mut log_likelihood = 0.0;
 51 |     let mut n = 0_usize;
 52 | 
 53 |     for w in &words[..3] {
 54 |         let chs = format!(".{}.", w);
 55 |         for (ch1, ch2) in chs.chars().zip(chs.chars().skip(1)) {
 56 |             let ix1 = ctoi[&ch1];
 57 |             let ix2 = ctoi[&ch2];
 58 |             let prob = &pd[ix1][ix2];
 59 |             let logprob = prob.log(E); // natural log - base `e`
 60 |             log_likelihood += logprob;
 61 |             n += 1;
 62 |             // println!("{}{}: {:.4} {:.4}", ch1, ch2, prob, logprob);
 63 |         }
 64 |     }
 65 |     // maximize the probs which are model params
 66 |     // we maximize the log_likelihood (log is monotonic - lease negative)
 67 |     // equivalent to minimizing the nll
 68 |     // equivalent to minimizing the avg nll - lowest 0
 69 |     println!("{}", log_likelihood); // log likelihood is 0 when prob 1. Otherwise grows to negative inf as prob decreases.
 70 |     let nll = -log_likelihood; // higher it is bad. near 0 best
 71 |     println!("{nll}");
 72 |     let avg_nll = nll / (n as f64); // lower is better
 73 |     println!("{}", avg_nll);
 74 | }
 75 | 
 76 | fn ex3() {
 77 |     let words = WORDS.get().unwrap();
 78 |     let data = DATA.get().unwrap();
 79 |     let pd = PD.get().unwrap();
 80 |     let ctoi = CTOI.get().unwrap();
 81 |     let itoc = ITOC.get().unwrap();
 82 | 
 83 |     let mut log_likelihood = 0.0;
 84 |     let mut n = 0_usize;
 85 | 
 86 |     // just adding q - make the loss function `inf`
 87 |     for w in ["andrejq"] {
 88 |         let chs = format!(".{}.", w);
 89 |         for (ch1, ch2) in chs.chars().zip(chs.chars().skip(1)) {
 90 |             let ix1 = ctoi[&ch1];
 91 |             let ix2 = ctoi[&ch2];
 92 |             let prob = &pd[ix1][ix2];
 93 |             let logprob = prob.log(E); // natural log - base `e`
 94 |             log_likelihood += logprob;
 95 |             n += 1;
 96 |             println!("{}{}: {:.4} {:.4}", ch1, ch2, prob, logprob);
 97 |         }
 98 |     }
 99 |     println!("{}", log_likelihood); // log likelihood is 0 when prob 1. Otherwise grows to negative inf as prob decreases.
100 |     let nll = -log_likelihood; // higher it is bad. near 0 best
101 |     println!("{nll}");
102 |     let avg_nll = nll / (n as f64); // lower is better
103 |     println!("{}", avg_nll);
104 | 
105 | }
106 | 
107 | fn ex3_1() {
108 |     let data = DATA.get().unwrap();
109 |     plot_data(data, "data_ndarr.png");
110 | 
111 | }
112 | 
113 | fn ex4() {
114 |     let words = WORDS.get().unwrap();
115 |     let data = DATA.get().unwrap();
116 |     let pd = PD.get().unwrap();
117 |     let ctoi = CTOI.get().unwrap();
118 |     let itoc = ITOC.get().unwrap();
119 | 
120 |     // println!("{:?}", data[[0,1]]);
121 |     // println!("{:?}", data.row(0));
122 | 
123 |     // model smoothing
124 |     // so that in our avg_nll we don't get infinity
125 |     // respectable bigram character level langaueg model
126 |     let data = data.mapv(|e| e+ 1); 
127 |     let pd = probability_distrib_matrix(&data);
128 | 
129 |     let mut log_likelihood = 0.0;
130 |     let mut n = 0_usize;
131 | 
132 |     // just adding q - make the loss function `inf`
133 |     for w in ["andrejq"] {
134 |         let chs = format!(".{}.", w);
135 |         for (ch1, ch2) in chs.chars().zip(chs.chars().skip(1)) {
136 |             let ix1 = ctoi[&ch1];
137 |             let ix2 = ctoi[&ch2];
138 |             let prob = &pd[ix1][ix2];
139 |             let logprob = prob.log(E); // natural log - base `e`
140 |             log_likelihood += logprob;
141 |             n += 1;
142 |             println!("{}{}: {:.4} {:.4}", ch1, ch2, prob, logprob);
143 |         }
144 |     }
145 |     println!("{}", log_likelihood); // log likelihood is 0 when prob 1. Otherwise grows to negative inf as prob decreases.
146 |     let nll = -log_likelihood; // higher it is bad. near 0 best
147 |     println!("{nll}");
148 |     let avg_nll = nll / (n as f64); // lower is better
149 |     println!("{}", avg_nll);
150 | 
151 | 
152 | }
153 | 
154 | pub fn main() {
155 |     println!("Notebook 002");
156 |     init_core_data();
157 |     ex3_1();
158 | }
159 | 
160 | // 56:17


--------------------------------------------------------------------------------
/src/nb004.rs:
--------------------------------------------------------------------------------
 1 | #![allow(unused)]
 2 | 
 3 | use std::collections::HashSet;
 4 | use std::f64::consts::E;
 5 | use std::ops::Range;
 6 | use std::{collections::HashMap, fs};
 7 | 
 8 | use ndarray::{Array1, Array2};
 9 | use rand::thread_rng;
10 | 
11 | use std::sync::OnceLock;
12 | 
13 | use crate::utils::{create_char_matrix_ndarr, create_char_matrix_tensor, ctoi, get_names, itoc};
14 | use crate::rust_mllib::*;
15 | use crate::numr::*;
16 | use crate::plot::*;
17 | 
18 | // Ignore this notebook
19 | // We were planning to rewrite all the cells using Burn Tensor rather than ndarray crate
20 | // But for fast rewrite we will continue till probably we reach auto diff position
21 | // Go to notebook 5
22 | 
23 | fn ex1() {
24 |     let words = get_names();
25 |     let ctoi = ctoi();
26 |     let itoc = itoc(&ctoi);
27 | 
28 |     let mut xs: Vec<f64> = vec![]; // input char
29 |     let mut ys: Vec<f64> = vec![]; // desired char
30 | 
31 |     for w in &words[..1] {
32 |         let chs = format!(".{}.", w);
33 |         for (ch1, ch2) in chs.chars().zip(chs.chars().skip(1)) {
34 |             let ix1 = ctoi[&ch1];
35 |             let ix2 = ctoi[&ch2];
36 |             println!("{} {}", ch1, ch2);
37 |             xs.push(ix1 as f64);
38 |             ys.push(ix2 as f64);
39 |         }
40 |     }
41 | 
42 |     let xs = tensor1df(&xs);
43 |     let ys = tensor1df(&ys);
44 |     println!("{}", &xs);
45 |     let l = xs.dims();
46 |     println!("{}", l[0]);
47 | 
48 |     println!("length of xs: {}", len1df(&xs));
49 | 
50 |     let onehot = one_hot_tensor(&xs, 27);
51 |     println!("{}", onehot);
52 | }
53 | 
54 | fn ex2() {
55 |     // Use the struct TensorInt and TensorFloat rather than the functions - much easier
56 |     let matrix = create_char_matrix_tensor();
57 |     // println!("{}", matrix);
58 |     let tensor = matrix.tensor();
59 |     let t1 = tensor.clone().slice([0..1, 0..27]);
60 |     // println!("{}", t1);
61 |     let t2 = tensor.clone().slice([0..1, 1..2]);
62 |     println!("{}", t2);
63 | }
64 | 
65 | pub fn main() {
66 |     println!("Notebook 004");
67 |     ex2();
68 | 
69 | }
70 | 
71 | // 53:38


--------------------------------------------------------------------------------
/src/nb005.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | use burn::backend::autodiff::grads::Gradients;
  4 | use ndarray::{arr2, Array1, Array2};
  5 | use ndarray::{stack, Axis};
  6 | use rand::thread_rng;
  7 | use std::f64::consts::E;
  8 | 
  9 | use crate::numr::*;
 10 | use crate::plot::*;
 11 | use crate::rust_mllib::*;
 12 | use crate::utils::*;
 13 | // use crate::utils::{
 14 | //     create_char_matrix_ndarr, create_char_matrix_tensor, ctoi, get_names, itoc,
 15 | //     rand_normal_distrib, rand_uniform_nums,
 16 | // };
 17 | 
 18 | fn input_output_pairs(num_words: usize) -> (Vec<usize>, Vec<usize>) {
 19 |     let words = get_names();
 20 |     let ctoi = ctoi();
 21 |     let itoc = itoc(&ctoi);
 22 | 
 23 |     let mut xs: Vec<usize> = vec![]; // input char
 24 |     let mut ys: Vec<usize> = vec![]; // desired char
 25 | 
 26 |     for w in &words[..num_words] {
 27 |         let chs = format!(".{}.", w);
 28 |         for (ch1, ch2) in chs.chars().zip(chs.chars().skip(1)) {
 29 |             let ix1 = ctoi[&ch1];
 30 |             let ix2 = ctoi[&ch2];
 31 |             xs.push(ix1);
 32 |             ys.push(ix2);
 33 |         }
 34 |     }
 35 | 
 36 |     (xs, ys)
 37 | }
 38 | 
 39 | fn ex1() {
 40 |     let words = get_names();
 41 |     let ctoi = ctoi();
 42 |     let itoc = itoc(&ctoi);
 43 | 
 44 |     let mut xs: Vec<usize> = vec![]; // input char
 45 |     let mut ys: Vec<usize> = vec![]; // desired char
 46 | 
 47 |     for w in &words[..1] {
 48 |         let chs = format!(".{}.", w);
 49 |         for (ch1, ch2) in chs.chars().zip(chs.chars().skip(1)) {
 50 |             let ix1 = ctoi[&ch1];
 51 |             let ix2 = ctoi[&ch2];
 52 |             // println!("{} {}", ch1, ch2);
 53 |             xs.push(ix1);
 54 |             ys.push(ix2);
 55 |         }
 56 |     }
 57 | 
 58 |     // single word i.e. first will have 5 examples for the neural net
 59 |     // dbg!(&xs);
 60 |     // dbg!(&ys);
 61 | 
 62 |     let xenc = one_hot_vec(&xs, 27);
 63 |     // dbg!(&xenc);
 64 | 
 65 |     // Because plot_heat_map takes ndarray Array2
 66 |     let t1 = vec_to_ndarr2(&xenc, xenc.len(), xenc[0].len());
 67 | 
 68 |     // dbg!(&t1);
 69 |     plot_heat_map(&t1, "one_hot2.png");
 70 | }
 71 | 
 72 | fn ex2() {
 73 |     let (xs, ys) = input_output_pairs(1);
 74 |     let xenc = one_hot(&xs, 27);
 75 | 
 76 |     // Weights
 77 |     let weights = randn(27, 1);
 78 |     dbg!(&weights);
 79 | 
 80 |     // 5 x 1
 81 |     // Basically for each input what value did the neuron give!
 82 |     // Here we had only one neuron
 83 |     let xs_x_ws = xenc.dot(&weights);
 84 |     dbg!(&xs_x_ws);
 85 | }
 86 | 
 87 | fn ex3() {
 88 |     // Dot product check
 89 |     // 2 x 3
 90 |     let inputs = vec![1, 2, 3, 4, 5, 6];
 91 |     let t1 = Array2::from_shape_vec((2, 3), inputs).unwrap();
 92 |     dbg!(&t1);
 93 |     // 3 x 1
 94 |     let weights = vec![10, 20, 30];
 95 |     let t2 = Array2::from_shape_vec((3, 1), weights).unwrap();
 96 |     dbg!(&t2);
 97 |     // 2 x 1
 98 |     let t3 = t1.dot(&t2);
 99 |     dbg!(&t3);
100 | }
101 | 
102 | fn ex4() {
103 |     let (xs, ys) = input_output_pairs(1);
104 |     let inputs = one_hot(&xs, 27);
105 |     // weights for 27 neurons
106 |     let weights = randn(27, 27);
107 |     // dbg!(&weights);
108 | 
109 |     // for each input what is the value of each of the 27 neurons
110 |     // 5 x 27
111 |     let res = inputs.dot(&weights);
112 |     // println!("{}", res);
113 | 
114 |     // First input and the second neuron
115 |     let x = res[[0, 1]];
116 |     // println!("{x}");
117 | 
118 |     // 3rd input on the 13th neuron
119 |     // println!("13th neuron firing on 3rd input {}", res[[2, 12]]); // 2.8925935942128285
120 | 
121 |     /*
122 |     [[-2.6896996588544386, -1.5949525349934195, -0.36076418044950387, 0.4091364954293706, 2.6120071232583846, 1.2314528278303034, -2.838831036125167, 0.33008849484841907, 3.0133161273678764, -0.35825709187816734, -0.6420595146743118, 1.5875684977386153, 1.8350184501541702, -0.6456139093298119, -1.9579950436862585, 2.648992573329092, 1.3222773043017235, -1.3252280993555992, -2.001235221408179, -2.9887649454088847, -1.1458983501403546, 2.257846216908319, 1.7772263189951798, 0.1501913083161388, -0.9796481956789109, -2.677147109388177, 0.8744016396511647],
123 |      [1.14150296134278, 1.1684028926131984, -1.9888881302044372, -2.4273821098120187, -2.6507106299391148, 0.16882557016191546, -1.1275460307685656, -2.9991717770551842, -2.0690872682826056, 1.1784448747673388, 0.42905521502608757, -1.1296264777749223, -1.7768623223259352, 0.8230961606019918, -2.9185420465990592, -2.0427854171285342, -1.7164251125736245, 0.820068555661086, 1.3812426363022312, 0.7198595207154219, 1.3916527284260618, 0.7719923798457673, -1.9528237901573842, -1.6580223728891172, 1.381601839069209, -3.0598736900607317, 2.70972898766277],
124 |      [2.7364450625162093, -1.4804293758899, 2.3449644944575945, -1.8241268282369218, 2.295022887019592, 2.634656372868091, -2.357621156287847, -2.3759311533525147, -2.8726209958495392, -0.030764101098648133, 1.7477583825770275, 0.6661904676565276, 2.8925935942128285, 0.5016242514520788, -0.8393768333410856, 2.8318586096005025, 0.9476680056897604, -2.0984665285721937, -0.6364859649935615, -2.4372038136858114, 0.41510535351636557, -1.819521334684037, 2.9592667579412413, 2.089531878287825, -1.2108739131442412, 0.35087157482443354, -0.6282696840285347],
125 |      [2.7364450625162093, -1.4804293758899, 2.3449644944575945, -1.8241268282369218, 2.295022887019592, 2.634656372868091, -2.357621156287847, -2.3759311533525147, -2.8726209958495392, -0.030764101098648133, 1.7477583825770275, 0.6661904676565276, 2.8925935942128285, 0.5016242514520788, -0.8393768333410856, 2.8318586096005025, 0.9476680056897604, -2.0984665285721937, -0.6364859649935615, -2.4372038136858114, 0.41510535351636557, -1.819521334684037, 2.9592667579412413, 2.089531878287825, -1.2108739131442412, 0.35087157482443354, -0.6282696840285347],
126 |      [-0.4796817763134116, 2.550442308247694, -1.3461200878793176, 1.4538877412171343, -2.3848771152149033, -3.037387056610661, 2.8421633473301573, 2.4433997195850634, -2.436300151093138, 1.018084680537577, -1.542138822300317, 0.6312516894379772, 1.3019996289959805, -2.6872518711520126, -3.0214084136512707, 1.4270891982515468, -3.086351429775178, -1.4526508069631214, -0.5338361782536127, -1.6680061995542634, 1.2349638869443553, 1.96173543854704, 1.194760487087747, 1.2998929911940684, 0.35068435171745493, 2.705259270207009, -1.7281041665883825]]
127 | 
128 |     */
129 |     let third_input = inputs.row(2);
130 |     let thirteenth_neuron = weights.column(12);
131 |     // println!("Third input: \n{}", third_input);
132 |     // println!("Thirteenth neuron: \n{}", thirteenth_neuron);
133 |     let neuron_val = third_input.dot(&thirteenth_neuron);
134 |     println!("13th neuron firing on 3rd input {}", res[[2, 12]]);
135 |     println!("Neuron value verified by dot product: {}", neuron_val);
136 | }
137 | 
138 | fn ex5() {
139 |     let (xs, ys) = input_output_pairs(1);
140 | 
141 |     // Set of inputs to be fed into the neural net.
142 |     let inputs = one_hot(&xs, 27);
143 | 
144 |     // weights for each of the 27 neurons.
145 |     let weights = randn(27, 27);
146 | 
147 |     // for each input what is the value of each of the 27 neurons
148 |     // 5 x 27
149 |     // Interpret: X dot W, as log counts or logits.
150 |     let logits = inputs.dot(&weights);
151 |     // println!("{}", res)
152 | 
153 |     // If we exponentiate then we can interpret them as counts
154 |     // So this matrix equivalent to our 2d char matrix that we create using create_char_matrix
155 |     let counts = logits.exp();
156 |     // dbg!(&counts);
157 | 
158 |     // Now to get the probabilities like in our char matrix
159 |     let prob = probabiity_row_wise(&counts);
160 |     // dbg!(&prob);
161 |     println!("{}", prob.row(0).sum());
162 | }
163 | 
164 | fn ex6() {
165 |     // ndarray api explorations
166 | 
167 |     // 2 x 3 matrix
168 |     let a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
169 |     let mut xs = Vec::new();
170 |     for row in a.rows() {
171 |         let sum = row.sum();
172 |         let new_row = row.mapv(|e| e / sum);
173 |         xs.push(new_row);
174 |     }
175 |     let xs: Vec<_> = xs.iter().map(|r| r.view()).collect();
176 |     let a1 = stack(Axis(0), &xs).unwrap();
177 |     dbg!(&a1);
178 | }
179 | 
180 | fn ex7() {
181 |     // Rough calc
182 |     let xs = [
183 |         9.489309669402608,
184 |         0.13125712870354542,
185 |         2.925142047537846,
186 |         1.0059390165304172,
187 |         9.278091884400373,
188 |         0.0754846633095178,
189 |         1.296102016519838,
190 |         4.786387470159512,
191 |         2.577201356724872,
192 |         1.382037567031469,
193 |         0.2654712204372306,
194 |         0.3615618334072086,
195 |         0.2199932013834451,
196 |         5.502815071258198,
197 |         9.44850250687406,
198 |         0.2690937339965065,
199 |         0.13325850812268905,
200 |         0.200961135799917,
201 |         4.517666126512665,
202 |         0.10964306268307805,
203 |         7.0953902334609635,
204 |         3.8108544650483362,
205 |         0.15610612772999363,
206 |         5.614646996336443,
207 |         0.11316566217358716,
208 |         0.11959632979313718,
209 |         0.9425025089400019,
210 |     ];
211 |     let sum: f64 = xs.iter().sum();
212 |     println!("{}", xs[0] / sum);
213 |     println!("{}", xs[1] / sum);
214 | }
215 | 
216 | fn ex8() {
217 |     let (xs, ys) = input_output_pairs(1);
218 | 
219 |     let num_classes = 27_usize;
220 | 
221 |     let inputs = one_hot(&xs, num_classes);
222 |     let weights = randn(num_classes, num_classes);
223 | 
224 |     // forward pass of neural net starts here
225 |     // Interpret: X dot W, as log counts or logits.
226 |     let logits = inputs.dot(&weights);
227 |     let counts = logits.exp();
228 | 
229 |     let probs = probabiity_row_wise(&counts);
230 |     // forward pass - everything is differentiable so we can do backpropagation!
231 | 
232 |     // println!("{:?}", probs.shape());
233 | 
234 |     // Because we are starting on back propagation we will use Burn Tensors now!
235 |     let inputs_t = ndarr2_to_tensor(&inputs);
236 |     let weights_t = ndarr2_to_tensor(&weights);
237 | 
238 |     // WARNING: Do not set gradient on computed i.e. non-leaf tensor
239 |     let logits_t = inputs_t.matmul(weights_t);
240 |     let counts_t = logits_t.exp();
241 | 
242 |     // Probability calculation Tensor style (now thatis Gangname style)
243 |     // Burn supports auto broadcasting!
244 |     let row_sums = counts_t.clone().sum_dim(1);
245 |     let probs_t = counts_t.clone() / row_sums;
246 | }
247 | 
248 | fn ex9() {
249 |     let ctoi = ctoi();
250 |     let itoc = itoc(&ctoi);
251 | 
252 |     let (xs, ys) = input_output_pairs(1);
253 | 
254 |     // We have already written this using ndarray, so we will convert these to tensors
255 |     let inputs = one_hot(&xs, 27);
256 |     let weights = randn(27, 27);
257 | 
258 |     // Now we have the tensors for each of the above
259 |     let inputs_t = ndarr2_to_tensor(&inputs);
260 |     let weights_t = ndarr2_to_tensor(&weights);
261 | 
262 |     let logits_t = inputs_t.matmul(weights_t);
263 |     let counts_t = logits_t.exp();
264 | 
265 |     let row_sums = counts_t.clone().sum_dim(1);
266 |     let probs_t = counts_t.clone() / row_sums;
267 | 
268 |     let mut nlls = create_empty_2d_tensor(xs.len(), 1);
269 | 
270 |     let num_cols = probs_t.dims()[1];
271 | 
272 |     // dbg!(&probs_t);
273 |     for i in 0..5 {
274 |         // ith-bigram
275 |         let x = xs[i]; // input character index
276 |         let y = ys[i]; // label character index
277 |         println!("-------------");
278 |         println!(
279 |             "bigram example {}: {}{} (indexes {},{})",
280 |             i + 1,
281 |             itoc[&x],
282 |             itoc[&y],
283 |             x,
284 |             y
285 |         );
286 |         println!("input to the neural net: {}", x);
287 |         let prob_i = get_row(&probs_t, i);
288 |         println!("output probabilities from the neural net: {:?}", &prob_i);
289 |         println!("index (actual next character): {}", y);
290 |         let p = get_elem(&prob_i, y);
291 |         println!(
292 |             "probability assigned by the neural net to the correct character: {}",
293 |             p.clone().into_scalar()
294 |         );
295 |         // Remember log likelihood goes negative if probability goes towards zero
296 |         let logp = p.log();
297 |         println!("log likelihood: {}", logp.clone().into_scalar());
298 |         let nll = logp.neg();
299 |         println!("negative log likelihood: {}", nll.clone().into_scalar());
300 |         nlls = nlls.slice_assign([i..i + 1, 0..1], nll);
301 |     }
302 |     println!("{}", nlls.clone());
303 |     println!("==========");
304 |     let avg_nll = nlls.mean();
305 |     println!("average negative log likelihood, i.e. loss = {}", avg_nll);
306 | }
307 | 
308 | fn ex10() {
309 |     let ctoi = ctoi();
310 |     let itoc = itoc(&ctoi);
311 | 
312 |     let (xs, ys) = input_output_pairs(1);
313 | 
314 |     let inputs = one_hot(&xs, 27);
315 |     let weights = randn(27, 27);
316 | 
317 |     let inputs_t = ndarr2_to_tensor(&inputs);
318 |     let mut weights_t = ndarr2_to_tensor(&weights);
319 | 
320 |     // let null_grad = weights_t.zeros_like();
321 | 
322 |     let logits_t = inputs_t.matmul(weights_t.clone());
323 |     let counts_t = logits_t.exp();
324 | 
325 |     let row_sums = counts_t.clone().sum_dim(1);
326 |     let probs_t = counts_t.clone() / row_sums;
327 | 
328 |     let mut tgt_probs = create_empty_2d_tensor(xs.len(), 1);
329 | 
330 |     for i in 0..(xs.len()) {
331 |         let row = get_row(&probs_t, i);
332 |         let tgt_index = ys[i];
333 |         let p = get_elem(&row, tgt_index);
334 |         tgt_probs = tgt_probs.slice_assign([i..i + 1, 0..1], p);
335 |     }
336 | 
337 |     // println!("{}", tgt_probs);
338 |     let loss = tgt_probs.log().neg().mean();
339 |     println!("Loss: {}", loss);
340 | 
341 |     // let node_ref = loss
342 | 
343 |     // backward pass
344 |     // let mut grads = Gradients::new(root_node, root_tensor)
345 |     // weights_t = weights_t.set_require_grad(true);
346 |     let mut grads = loss.backward();
347 |     let g = weights_t.grad(&grads).unwrap();
348 |     dbg!(&g);
349 |     let ng = g.zeros_like();
350 |     weights_t.grad_replace(&mut grads, ng);
351 |     let g = weights_t.grad(&grads).unwrap();
352 |     dbg!(&g);
353 | }
354 | 
355 | fn ex11() {
356 |     // Showtime
357 | 
358 |     let names = get_names();
359 |     let total_words = names.len();
360 |     println!("total words: {}", total_words);
361 | 
362 |     // Prepare data
363 |     // Putting total words gives a huge number of input pairs 2lakh plus
364 |     // and my machine crawls. Time for update.
365 |     // So spiffy speed we put 20
366 |     // NOte, I am not using GPU backend in Burn.
367 |     // Plain NdArray
368 |     let (xs, ys) = input_output_pairs(20);
369 |     // let total_inputs = xs.len();
370 |     // println!("total inputs: {}", total_inputs);
371 | 
372 |     let inputs = one_hot(&xs, 27);
373 |     let weights = randn(27, 27);
374 | 
375 |     let inputs_t = ndarr2_to_tensor(&inputs);
376 | 
377 |     // this is going to change
378 |     let mut weights_t = ndarr2_to_tensor(&weights);
379 | 
380 |     for k in 0..100 {
381 |         let inputs_t = inputs_t.clone();
382 |         let logits_t = inputs_t.matmul(weights_t.clone());
383 |         let counts_t = logits_t.exp();
384 | 
385 |         let row_sums = counts_t.clone().sum_dim(1);
386 |         let probs_t = counts_t.clone() / row_sums;
387 | 
388 |         let mut tgt_probs = create_empty_2d_tensor(xs.len(), 1);
389 | 
390 |         // We have to find the probability assigned to bigram pairs.
391 |         // Probability has to be high or nll should be least.
392 |         // Actually we will look at mean nll
393 |         for i in 0..(xs.len()) {
394 |             let row = get_row(&probs_t, i);
395 |             let tgt_index = ys[i];
396 |             let p = get_elem(&row, tgt_index);
397 |             tgt_probs = tgt_probs.slice_assign([i..i + 1, 0..1], p);
398 |         }
399 | 
400 |         // println!("{}", tgt_probs);
401 |         let loss = tgt_probs.log().neg().mean();
402 |         println!("loss: {}", loss.clone().into_scalar());
403 |         let mut grads = loss.backward();
404 |         let wg = weights_t.grad(&grads);
405 |         if let Some(wg) = wg {
406 |             let nudge = (wg.clone() * -10.0);
407 |             let nudge = convert_nd_to_autodiff(&nudge);
408 |             weights_t = weights_t.clone() + nudge;
409 |             let ng = wg.zeros_like();
410 |             weights_t.grad_replace(&mut grads, ng);
411 |         } else {
412 |             // println!("no gradient. Weights remain same");
413 |             weights_t = weights_t.clone();
414 |         }
415 |     }
416 | 
417 |     // WORD GENERATION (based on our trained weights)
418 | 
419 |     // Now we will take the weight_t as it comes from the training
420 |     // We used bigrams from just 20 words and did 100 passes to get the weights
421 |     // But we will now use those weights for probability distribution of all the bigrams! Fidgety :-)
422 |     // If you have a powerful machine then you can use all the bigram pairs to train and
423 |     // use even more passes to get lowest loss!
424 | 
425 |     let (xs, _) = input_output_pairs(total_words);
426 |     // println!("Total bigram pairs: {}", xs.len());
427 | 
428 |     let inputs = one_hot(&xs, 27);
429 |     let inputs_t = ndarr2_to_tensor(&inputs);
430 | 
431 |     let logits_t = inputs_t.matmul(weights_t.clone());
432 |     let counts_t = logits_t.exp();
433 | 
434 |     let row_sums = counts_t.clone().sum_dim(1);
435 | 
436 |     let probs_t = counts_t.clone() / row_sums;
437 |     // println!("Trained probability dim: {:?}", probs_t.dims());
438 | 
439 |     // I have already written a multinomial in numr module using NdArray
440 |     // so I am going to reuse it. Later we will create a tensor version.
441 |     // which is really not required.
442 |     // Once you have trained the neural net and have the calculation then
443 |     // all we need is the trained probabilities in any structure.
444 |     let probs_nd = tensor2d_to_ndarr2(&probs_t);
445 |     let pd = probability_distrib_matrix(&probs_nd);
446 | 
447 |     let ctoi = ctoi();
448 |     let itoc = itoc(&ctoi);
449 |     let mut rng = thread_rng();
450 | 
451 |     // The moment we were waiting for!
452 |     for i in 0..5 {
453 |         let mut row_num = 0_usize;
454 |         let mut chs = Vec::new();
455 | 
456 |         loop {
457 |             row_num = multinomial_distrib(1, &pd[row_num], &mut rng)[0]; // remember we get Array so extract
458 |             let ch = itoc[&row_num];
459 |             if row_num == 0 {
460 |                 break;
461 |             }
462 |             chs.push(ch);
463 |         }
464 | 
465 |         let name: String = chs.iter().collect();
466 | 
467 |         println!("{}", name);
468 |     }
469 | }
470 | 
471 | fn rough_calc() {}
472 | 
473 | pub fn main() {
474 |     println!("Notebook 005");
475 |     ex11();
476 |     // rough_calc()
477 | }
478 | 
479 | // 31:20
480 | 


--------------------------------------------------------------------------------
/src/numr.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | // This is like Numpy equivalent - just wrappers using ndarray crate.
  4 | 
  5 | use std::cell::RefCell;
  6 | use std::collections::HashMap;
  7 | use std::rc::Rc;
  8 | 
  9 | use ndarray::prelude::*;
 10 | use ndarray::{stack, Array, Array1, Array2, Axis};
 11 | 
 12 | use rand::distributions::WeightedIndex;
 13 | use rand::prelude::*;
 14 | 
 15 | use num_traits::{Bounded, Num, ToPrimitive};
 16 | 
 17 | use crate::utils::rand_normal_distrib;
 18 | 
 19 | pub fn arange(start: f64, stop: f64, step: f64) -> Array1<f64> {
 20 |     Array::range(start, stop, step)
 21 | }
 22 | 
 23 | pub fn max_array1(xs: &Array1<f64>) -> f64 {
 24 |     xs.iter().cloned().fold(f64::MIN, |a, b| a.max(b))
 25 | }
 26 | 
 27 | pub fn min_array1(xs: &Array1<f64>) -> f64 {
 28 |     xs.iter().cloned().fold(f64::MAX, |a, b| a.min(b))
 29 | }
 30 | 
 31 | pub fn zeros_float(num_rows: usize, num_cols: usize) -> Array2<f64> {
 32 |     Array2::zeros((num_rows, num_cols))
 33 | }
 34 | 
 35 | pub fn zeros_int(num_rows: usize, num_cols: usize) -> Array2<i64> {
 36 |     Array2::zeros((num_rows, num_cols))
 37 | }
 38 | 
 39 | // pub fn multinomial_distrib(num_samples: usize, input: &ArrayView1<f64>, rng: &mut ThreadRng) -> Array1<usize> {
 40 | pub fn multinomial_distrib(
 41 |     num_samples: usize,
 42 |     input: &Array1<f64>,
 43 |     rng: &mut ThreadRng,
 44 | ) -> Array1<usize> {
 45 |     // let input = Array1::from_vec(vec![0.2, 0.5, 0.3]);
 46 |     // let num_samples = 10;
 47 | 
 48 |     // pass the weights
 49 |     let dist = WeightedIndex::new(input.to_vec()).unwrap();
 50 | 
 51 |     let mut samples = vec![0_usize; num_samples];
 52 |     for i in 0..num_samples {
 53 |         samples[i] = dist.sample(rng);
 54 |     }
 55 | 
 56 |     Array1::from(samples)
 57 | 
 58 |     // let mut rng = thread_rng();
 59 | 
 60 |     // Sample from the multinomial distribution
 61 |     // let mut counts = vec![0usize; input.len() ];
 62 |     // for _ in 0..num_samples {
 63 |     //     let sample = dist.sample(rng);
 64 |     //     counts[sample] += 1;
 65 |     // }
 66 | 
 67 |     // println!("{:?}", counts);
 68 | 
 69 |     // let xs = Array1::from(counts);
 70 |     // xs
 71 | }
 72 | 
 73 | // pub fn multinomial_char_index(input: &Array1<f64>) -> usize {
 74 | //     let samples = multinomial_distrib(1, input);
 75 | //     let idx = (0usize..).zip(samples.iter()).find(|(idx, &value)|value == 1).unwrap().0;
 76 | //     idx
 77 | // }
 78 | 
 79 | pub fn probability_distrib<T>(data: &Array2<T>, row_num: usize) -> Array1<f64>
 80 | where
 81 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
 82 | {
 83 |     let row_n = data.row(row_num);
 84 |     let row_n_f = row_n.mapv(|e| (e.to_f64()).unwrap());
 85 |     let total = row_n_f.sum();
 86 | 
 87 |     let probabilities = row_n_f.mapv(|e| e / total);
 88 |     probabilities
 89 | }
 90 | 
 91 | pub fn probability_distrib_matrix<T>(data: &Array2<T>) -> Vec<Array1<f64>>
 92 | where
 93 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
 94 | {
 95 |     let data = data.mapv(|e| (e.to_f64()).unwrap());
 96 | 
 97 |     let mut pd_vec = Vec::new();
 98 | 
 99 |     for row in data.rows() {
100 |         let total = row.sum();
101 |         let probs = row.mapv(|e| e / total);
102 |         pd_vec.push(probs);
103 |     }
104 | 
105 |     // let xs = Array2::from_shape_vec(
106 |     //     (data.nrows(), data.ncols()),
107 |     //     pd_vec.into_iter().flatten().collect(),
108 |     // ).unwrap();
109 | 
110 |     // let mut res = Vec::new();
111 | 
112 |     pd_vec
113 | }
114 | 
115 | pub fn ones_distrib(num_cols: usize) -> Array1<f64> {
116 |     // let x: Array1<i32> = Array1::<i32>::ones(27);
117 |     // let ones = Array::<f64, _>::ones((1, num_cols)); // dim is 2
118 |     // let ones = ones.row(0);
119 |     let ones: Array1<f64> = Array1::<f64>::ones(num_cols);
120 |     let d = num_cols as f64;
121 |     let p = ones.mapv(|n| n / d);
122 |     p
123 | }
124 | 
125 | // Return type could have been u8 too!
126 | pub fn one_hot_vec(xs: &[usize], num_classes: usize) -> Vec<Vec<f64>> {
127 |     let mut res = Vec::new();
128 |     for &x in xs {
129 |         let mut v = vec![0.0_f64; num_classes];
130 |         v[x] = 1.0;
131 |         res.push(v);
132 |     }
133 |     res
134 | }
135 | 
136 | pub fn one_hot(xs: &[usize], num_classes: usize) -> Array2<f64> {
137 |     let v = one_hot_vec(xs, num_classes);
138 |     vec_to_ndarr2(&v, v.len(), v[0].len())
139 | }
140 | 
141 | pub fn vec_to_ndarr2<T>(xs: &Vec<Vec<T>>, num_rows: usize, num_cols: usize) -> Array2<T>
142 | where
143 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
144 | {
145 |     let v: Vec<T> = xs.iter().cloned().flatten().collect();
146 |     // let v1: Vec<i64> = v.iter().map(|e| (e.to_i64()).unwrap()).collect();
147 |     Array2::from_shape_vec((num_rows, num_cols), v).unwrap()
148 | }
149 | 
150 | pub fn randn(num_rows: usize, num_cols: usize) -> Array2<f64> {
151 |     let total_elems = num_rows * num_cols;
152 |     let xs = rand_normal_distrib(total_elems);
153 |     Array2::from_shape_vec((num_rows, num_cols), xs).unwrap()
154 | }
155 | 
156 | pub fn probabiity_row_wise(a: &Array2<f64>) -> Array2<f64> {
157 |     let mut xs = Vec::new();
158 |     for row in a.rows() {
159 |         let sum = row.sum();
160 |         let new_row = row.mapv(|e| e / sum);
161 |         xs.push(new_row);
162 |     }
163 |     let xs: Vec<_> = xs.iter().map(|r| r.view()).collect();
164 |     let a1 = stack(Axis(0), &xs).unwrap();
165 |     a1
166 | }
167 | 
168 | fn explore1() {
169 |     let mut rng = thread_rng();
170 |     let input = Array1::from_vec(vec![0.2, 0.5, 0.3]);
171 |     let res = multinomial_distrib(100, &input, &mut rng);
172 |     dbg!(&res);
173 | }
174 | 
175 | fn explore2() {
176 |     let array = Array2::from_shape_vec((2, 3), vec![1, 2, 3, 4, 5, 6]).unwrap();
177 | 
178 |     let new_array = Array2::from_shape_vec(
179 |         (2, 3),
180 |         array
181 |             .axis_iter(Axis(0))
182 |             .flat_map(|row| row.mapv(|x| x * 2))
183 |             .collect(),
184 |     )
185 |     .unwrap();
186 | 
187 |     dbg!(new_array);
188 | }
189 | 
190 | fn explore3() {
191 |     let array = Array2::from_shape_vec((2, 3), vec![1, 2, 3, 4, 5, 6]).unwrap();
192 |     let shape = array.shape();
193 |     let (num_rows, num_cols) = (shape[0], shape[1]);
194 |     println!("row: {}, cols: {}", num_rows, num_cols);
195 | }
196 | 
197 | pub fn main() {
198 |     println!("NumRust explore");
199 |     explore3();
200 | }
201 | 


--------------------------------------------------------------------------------
/src/plot.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | // This is for plotting graphs like matplotlib.
  4 | // We use plotters crate for actual rendering.
  5 | 
  6 | use std::fmt::Debug;
  7 | use std::fmt::Display;
  8 | use std::i64;
  9 | use std::rc::Rc;
 10 | 
 11 | use crate::numr::*;
 12 | use full_palette::DEEPPURPLE;
 13 | use full_palette::GREY;
 14 | use ndarray::{Array, Array1, Array2, Axis};
 15 | use plotters::coord::types::RangedCoordusize;
 16 | use plotters::prelude::*;
 17 | use plotters::style::colors::colormaps;
 18 | use plotters::style::text_anchor::HPos;
 19 | use plotters::style::text_anchor::Pos;
 20 | use plotters::style::text_anchor::VPos;
 21 | 
 22 | // use ndarray::prelude::*;
 23 | // use ndarray::Array;
 24 | use crate::utils::*;
 25 | use num_traits::{Bounded, Num, ToPrimitive};
 26 | use std::collections::HashMap;
 27 | 
 28 | // plotting points
 29 | pub fn plot_to_file(xs: &Array1<f64>, ys: &Array1<f64>, file_name: &str) {
 30 |     let xmin = min_array1(xs);
 31 |     let xmax = max_array1(xs) + 0.1;
 32 |     let xrange = xmin..xmax;
 33 | 
 34 |     let ymin = min_array1(ys);
 35 |     let ymax = max_array1(ys) + 0.1;
 36 |     let yrange = ymin..ymax;
 37 | 
 38 |     let root = BitMapBackend::new(file_name, (640, 480));
 39 |     let draw = root.into_drawing_area();
 40 |     draw.fill(&WHITE);
 41 | 
 42 |     let mut chart = ChartBuilder::on(&draw)
 43 |         .caption("Simple Plot", ("sans-serif", 40).into_font())
 44 |         .margin(20)
 45 |         .x_label_area_size(20)
 46 |         .y_label_area_size(20)
 47 |         .build_cartesian_2d(xrange, yrange)
 48 |         .unwrap();
 49 | 
 50 |     chart.configure_mesh().draw().unwrap();
 51 | 
 52 |     chart
 53 |         .draw_series(LineSeries::new(
 54 |             xs.iter().zip(ys.iter()).map(|(&x, &y)| (x, y)),
 55 |             &RED,
 56 |         ))
 57 |         .unwrap();
 58 | }
 59 | 
 60 | // fn ndarray_to_normalized_image_data<T>(data: &Array2<T>) -> (Vec<Vec<f64>>, f64, f64)
 61 | // where
 62 | //     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
 63 | // {
 64 | //     let shape = data.shape();
 65 | //     let (num_rows, num_cols) = (shape[0], shape[1]);
 66 | //     let mut image_data = Vec::new();
 67 | //     let mut min_value = T::max_value();
 68 | //     let mut max_value = T::min_value();
 69 | //     for i in 0..num_rows {
 70 | //         let mut row_data = Vec::new();
 71 | //         for j in 0..num_cols {
 72 | //             let v = data[[i,j]];
 73 | //             if v > max_value {
 74 | //                 max_value = v;
 75 | //             }
 76 | //             if v < min_value {
 77 | //                 min_value = v;
 78 | //             }
 79 | //             let v = v.to_f64();
 80 | //             row_data.push(v.unwrap()); // [row, col]
 81 | //         }
 82 | //         image_data.push(row_data);
 83 | //     }
 84 | 
 85 | //     let min = min_value.to_f64().unwrap();
 86 | //     let max = max_value.to_f64().unwrap();
 87 | //     let range = max - min;
 88 | 
 89 | //     // Normalize the data
 90 | //     let image_data_normalized: Vec<Vec<f64>> = image_data.iter()
 91 | //         .map(|row|{
 92 | //             row.iter()
 93 | //                 .map(|&value|{
 94 | //                     (value - min) / range
 95 | //                 }).collect()
 96 | //         }).collect();
 97 | 
 98 | //     (image_data_normalized, min, max)
 99 | 
100 | // }
101 | 
102 | fn normalize_image_data<T>(image_data: &Vec<Vec<T>>, min: T, max: T) -> Vec<Vec<f64>>
103 | where
104 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
105 | {
106 |     let min = min.to_f64().unwrap();
107 |     let max = max.to_f64().unwrap();
108 |     let range = max - min;
109 |     let xs: Vec<Vec<f64>> = image_data
110 |         .iter()
111 |         .map(|row| {
112 |             row.iter()
113 |                 .map(|value| {
114 |                     let v = value.to_f64().unwrap();
115 |                     (v - min) / range
116 |                 })
117 |                 .collect()
118 |         })
119 |         .collect();
120 |     xs
121 | }
122 | 
123 | pub fn ndarray_to_image_data<T>(data: &Array2<T>) -> (Vec<Vec<T>>, T, T)
124 | where
125 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
126 | {
127 |     let shape = data.shape();
128 |     let (num_rows, num_cols) = (shape[0], shape[1]);
129 |     let mut image_data: Vec<Vec<T>> = Vec::new();
130 | 
131 |     let mut min_value = T::max_value();
132 |     let mut max_value = T::min_value();
133 | 
134 |     for i in 0..num_rows {
135 |         let mut row_data = Vec::new();
136 |         for j in 0..num_cols {
137 |             let v = data[[i, j]]; // [row, col]
138 |             if v > max_value {
139 |                 max_value = v;
140 |             }
141 |             if v < min_value {
142 |                 min_value = v;
143 |             }
144 |             row_data.push(v);
145 |         }
146 |         image_data.push(row_data);
147 |     }
148 |     (image_data, min_value, max_value)
149 | }
150 | 
151 | enum ChartType {
152 |     HeatMap,
153 |     Text,
154 | }
155 | 
156 | type RenderContext<'a> =
157 |     ChartContext<'a, BitMapBackend<'a>, Cartesian2d<RangedCoordusize, RangedCoordusize>>;
158 | 
159 | fn prepare_chart_data<'a, 'b: 'a, T>(
160 |     image_data: &Vec<Vec<T>>,
161 |     file_name: &'b str,
162 |     chart_type: ChartType,
163 | ) -> RenderContext<'a>
164 | where
165 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
166 | {
167 |     let ifactor: usize = match chart_type {
168 |         ChartType::HeatMap => 10,
169 |         ChartType::Text => 40,
170 |     };
171 | 
172 |     let width = (image_data[0].len() * ifactor) as u32;
173 |     let height = (image_data.len() * ifactor) as u32;
174 | 
175 |     let root = BitMapBackend::new(file_name, (width, height)).into_drawing_area();
176 |     root.fill(&WHITE);
177 | 
178 |     let caption: &str = match chart_type {
179 |         ChartType::HeatMap => "Heat Map",
180 |         ChartType::Text => "Data Plot",
181 |     };
182 | 
183 |     let mut chart = ChartBuilder::on(&root)
184 |         .caption(caption, ("sans-serif", 20).into_font())
185 |         .margin(20)
186 |         .build_cartesian_2d(0..image_data[0].len(), (image_data.len()..0))
187 |         .unwrap();
188 | 
189 |     chart.configure_mesh().draw().unwrap();
190 |     chart
191 | }
192 | 
193 | pub fn plot_heat_map<T>(data: &Array2<T>, file_name: &str)
194 | where
195 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
196 | {
197 |     let (image_data, min, max) = ndarray_to_image_data(&data);
198 |     let mut chart = prepare_chart_data(&image_data, file_name, ChartType::HeatMap);
199 | 
200 |     // Since this is heat map we have to normalize the value.
201 |     let image_data = normalize_image_data(&image_data, min, max);
202 | 
203 |     chart
204 |         .draw_series(image_data.iter().enumerate().flat_map(|(y, row)| {
205 |             row.iter().enumerate().map(move |(x, &value)| {
206 |                 let color = colormaps::ViridisRGB::get_color(value);
207 |                 Rectangle::new([(x, y), (x + 1, y + 1)], color.filled())
208 |             })
209 |         }))
210 |         .unwrap();
211 | }
212 | 
213 | fn blue_colormap<T>(value: T, max: T) -> RGBColor
214 | where
215 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd + Debug + Display,
216 | {
217 |     let value = value.to_f64().unwrap();
218 |     let max = max.to_f64().unwrap();
219 | 
220 |     // clamping
221 |     let t = value / max;
222 | 
223 |     // from light blue to dark blue
224 |     let r = (173.0 * (1.0 - t)) as u8;
225 |     let g = (216.0 * (1.0 - t)) as u8;
226 |     let b = (230.0 * (1.0 - t) + 139.0 * t) as u8;
227 | 
228 |     // let blue = (normal_value * 255.0) as u8;
229 |     RGBColor(r, g, b)
230 | }
231 | 
232 | pub fn plot_data<T>(data: &Array2<T>, file_name: &str)
233 | where
234 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd + Debug + Display,
235 | {
236 |     let (image_data, min, max) = ndarray_to_image_data(&data);
237 |     let mut chart = prepare_chart_data(&image_data, file_name, ChartType::Text);
238 | 
239 |     // dbg!(&image_data);
240 | 
241 |     let ctoi = ctoi();
242 |     let itoc: HashMap<usize, char> = ctoi.iter().map(|(&k, &v)| (v, k)).collect();
243 | 
244 |     // Not required - much better option which works.
245 |     // let pos_label = Pos::new(HPos::Center, VPos::Top);
246 |     // let pos_val = Pos::new(HPos::Center, VPos::Center);
247 | 
248 |     let root = chart.plotting_area();
249 |     let (width_px, height_px) = root.dim_in_pixel();
250 |     let rect_width = (width_px as f64) / (image_data[0].len() as f64);
251 |     let rect_height = (height_px as f64) / (image_data.len() as f64);
252 | 
253 |     let x_loc = ((rect_width / 2.0) - 10.0) as i32;
254 |     let y_val = ((rect_height / 2.0) - 10.0) as i32;
255 |     let y_lbl = ((rect_height / 2.0) - 4.0) as i32;
256 | 
257 |     let text_el_val = |v: T| {
258 |         Text::new(
259 |             format!("{}", v),
260 |             (x_loc, y_val), // 15, 15 when we hard coded for testing
261 |             ("sans-serif", 10).into_font().color(&BLACK), // .pos(pos_val)
262 |         )
263 |     };
264 | 
265 |     let text_el_label = |v: String| {
266 |         Text::new(
267 |             format!("{}", v),
268 |             (x_loc, y_lbl),                                    // 15, 25
269 |             ("sans-serif", 15).into_font().color(&DEEPPURPLE), // .pos(pos_val)
270 |         )
271 |     };
272 | 
273 |     // Draw the background
274 |     for (y, row) in image_data.iter().enumerate() {
275 |         for (x, &value) in row.iter().enumerate() {
276 |             let color = blue_colormap(value, max);
277 |             chart
278 |                 .draw_series(std::iter::once(Rectangle::new(
279 |                     [(x, y), (x + 1, y + 1)],
280 |                     color.filled(),
281 |                 )))
282 |                 .unwrap();
283 |         }
284 |     }
285 | 
286 |     // Overlay the text
287 |     for (y, row) in image_data.iter().enumerate() {
288 |         for (x, &value) in row.iter().enumerate() {
289 |             let chstr = format!("{}{}", itoc[&y], itoc[&x]);
290 |             let e1 = EmptyElement::at((x, y)) + text_el_val(value);
291 |             let e2 = EmptyElement::at((x, y)) + text_el_label(chstr);
292 |             chart.draw_series(vec![e1, e2]).unwrap();
293 |         }
294 |     }
295 | }
296 | 
297 | // pub fn plot_data_3<T>(data: &Array2<T>, file_name: &str)
298 | // where
299 | //     T: Copy + Num + Bounded + ToPrimitive + PartialOrd + Debug + Display,
300 | // {
301 | //     let (image_data, min, max) = ndarray_to_image_data(&data);
302 | //     let mut chart = prepare_chart_data(&image_data, file_name, ChartType::Text);
303 | 
304 | //     // dbg!(&image_data);
305 | 
306 | //     // I know we use little bit of advanced Rust
307 | //     // that is because we are using th maps inside closure
308 | //     // with move semantics and we don't want expensive cloning!
309 | //     let ctoi = Rc::new(ctoi());
310 | //     let itoc: HashMap<usize, char> = ctoi.iter().map(|(&k, &v)| (v, k)).collect();
311 | //     let itoc = Rc::new(itoc);
312 | 
313 | //     let pos_label = Pos::new(HPos::Center, VPos::Top);
314 | //     let pos_val = Pos::new(HPos::Center, VPos::Bottom);
315 | 
316 | //     let max_row = image_data.len() - 1;
317 | 
318 | //     chart.draw_series(
319 | //         image_data.iter().enumerate().flat_map(|(y, row)| {
320 | //             let y = max_row - y;
321 | //             println!("{y}");
322 | //             let ctoi = ctoi.clone();
323 | //             let itoc = itoc.clone();
324 | //             row.iter().enumerate().flat_map(move |(x, &value)| {
325 | //                 let val_str = value.to_string();
326 | //                 let chstr = format!("{}{}", itoc[&y], itoc[&x]);
327 | 
328 | //                 let tcell1 = Text::new(chstr, (x * TEXT_SCALE_FACTOR, y * TEXT_SCALE_FACTOR),
329 | //                     ("sans-serif", 15).into_font()
330 | //                         .color(&GREY)
331 | //                         .pos(pos_label)
332 | //                 );
333 | //                 let tcell2 = Text::new(val_str,
334 | //                     (x * TEXT_SCALE_FACTOR, y * TEXT_SCALE_FACTOR),
335 | //                     ("sans-serif", 10).into_font()
336 | //                     .color(&BLACK)
337 | //                     .pos(pos_val)
338 | //                 );
339 | //                 vec![tcell1, tcell2]
340 | 
341 | //                 // let color = colormaps::ViridisRGB::get_color(value);
342 | //                 // Rectangle::new([(x, y), (x+1, y+1)], color.filled())
343 | 
344 | //             })
345 | //         }),
346 | //     ).unwrap();
347 | // }
348 | 
349 | // fn plot_data_old<T>(data: &Array2<T>, file_name: &str)
350 | // where
351 | //     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
352 | // {
353 | //     let (image_data, min, max) = ndarray_to_normalized_image_data(data);
354 | 
355 | //     let width = (image_data[0].len() * 10) as u32;
356 | //     let height = (image_data.len() * 10) as u32;
357 | 
358 | //     let root =
359 | //         BitMapBackend::new(file_name, (width, height)).into_drawing_area();
360 | //     root.fill(&WHITE);
361 | 
362 | //     let mut chart = ChartBuilder::on(&root)
363 | //         .caption("Heat Map", ("sans-serif", 20).into_font())
364 | //         .margin(20)
365 | //         .build_cartesian_2d(0..image_data[0].len(), 0..image_data.len()).unwrap();
366 | //     // TODO: Think how to make the cartesian x & y range to be more flexible.
367 | 
368 | //     chart.configure_mesh().draw().unwrap();
369 | 
370 | //     chart.draw_series(
371 | //         image_data.iter().rev().enumerate().flat_map(|(y, row)| {
372 | //             row.iter().enumerate().map(move|(x, &value)| {
373 | //                 let color = colormaps::ViridisRGB::get_color(value);
374 | //                 Rectangle::new([(x, y), (x+1, y+1)], color.filled())
375 | //             })
376 | //         }),
377 | //     ).unwrap();
378 | 
379 | // }
380 | 
381 | // fn plot_heat_map_old(data: &Array2<i64>, file_name: &str) {
382 | //     let shape = data.shape();
383 | //     let (num_rows, num_cols) = (shape[0], shape[1]);
384 | //     let mut image_data = Vec::new();
385 | //     let mut min_value = i64::MAX;
386 | //     let mut max_value = i64::MIN;
387 | //     for i in 0..num_rows {
388 | //         let mut row_data = Vec::new();
389 | //         for j in 0..num_cols {
390 | //             let v = data[[i,j]];
391 | //             if v > max_value {
392 | //                 max_value = v;
393 | //             }
394 | //             if v < min_value {
395 | //                 min_value = v;
396 | //             }
397 | //             row_data.push(v as f64); // [row, col]
398 | //         }
399 | //         image_data.push(row_data);
400 | //     }
401 | //     // dbg!(&image_data);
402 | //     println!("min {}", min_value);
403 | //     println!("max {}", max_value);
404 | 
405 | //     let min = min_value as f64;
406 | //     let max = max_value as f64;
407 | //     let range = max - min;
408 | 
409 | //     // Normalize the data
410 | //     let image_data_normalized: Vec<Vec<f64>> = image_data.iter()
411 | //         .map(|row|{
412 | //             row.iter()
413 | //                 .map(|&value|{
414 | //                     (value - min) / range
415 | //                 }).collect()
416 | //         }).collect();
417 | 
418 | //     let width = (image_data[0].len() * 10) as u32;
419 | //     let height = (image_data.len() * 10) as u32;
420 | //     println!("{}, {}", width, height);
421 | 
422 | //     let root =
423 | //         BitMapBackend::new(file_name, (width, height)).into_drawing_area();
424 | //     root.fill(&WHITE);
425 | 
426 | //     let mut chart = ChartBuilder::on(&root)
427 | //         .caption("Heat Map", ("sans-serif", 20).into_font())
428 | //         .margin(20)
429 | //         .build_cartesian_2d(0..image_data[0].len(), 0..image_data.len()).unwrap();
430 | 
431 | //     chart.configure_mesh().draw().unwrap();
432 | 
433 | //     // we start from the last row upwards ! Exact heatmap as Andrej did using
434 | //     // matplotlib.pyplot.imshow()
435 | //     chart.draw_series(
436 | //         image_data_normalized.iter().rev().enumerate().flat_map(|(y, row)| {
437 | //             row.iter().enumerate().map(move|(x, &value)| {
438 | //                 let color = colormaps::ViridisRGB::get_color(value);
439 | //                 Rectangle::new([(x, y), (x+1, y+1)], color.filled())
440 | //             })
441 | //         }),
442 | //     ).unwrap();
443 | 
444 | // }
445 | 


--------------------------------------------------------------------------------
/src/rust_mllib.rs:
--------------------------------------------------------------------------------
  1 | #![allow(unused)]
  2 | 
  3 | // These are wrappers over Rust Burn library to look more like PyTorch
  4 | // that Andrej used in his examples.
  5 | 
  6 | // use burn::backend::ndarray::NdArrayDevice;
  7 | // use burn::tensor::{Shape, Tensor, TensorData};
  8 | 
  9 | use std::cell::RefCell;
 10 | use std::rc::Rc;
 11 | 
 12 | use burn::backend::autodiff::grads::Gradients;
 13 | use burn::backend::{ndarray::NdArrayDevice, Autodiff, NdArray};
 14 | use burn::prelude::*;
 15 | use burn::tensor::backend::Backend;
 16 | 
 17 | use ndarray::Array2;
 18 | 
 19 | use num_traits::{Bounded, Num, ToPrimitive};
 20 | 
 21 | use crate::utils::create_char_matrix_ndarr;
 22 | 
 23 | const DEVICE: NdArrayDevice = NdArrayDevice::Cpu;
 24 | 
 25 | type BckAutoDiff<T> = Autodiff<NdArray<T>>; // this is for f32 and f64 only
 26 |                                             // we choose f64
 27 | type BckAutoDiffFloat = BckAutoDiff<f64>;
 28 | 
 29 | pub type FloatTensor2d = Tensor<BckAutoDiffFloat, 2>;
 30 | pub type FloatTensor1d = Tensor<BckAutoDiffFloat, 1>;
 31 | 
 32 | pub type IntTensor2d = Tensor<NdArray, 2, Int>;
 33 | pub type IntTensor1d = Tensor<NdArray, 1, Int>;
 34 | 
 35 | pub fn tensor1df(data: &[f64]) -> FloatTensor1d {
 36 |     Tensor::<BckAutoDiffFloat, 1>::from_data(data, &DEVICE)
 37 | }
 38 | 
 39 | pub fn tensor2df(data: &[f64], num_rows: usize, num_cols: usize) -> FloatTensor2d {
 40 |     let shape = &[num_rows, num_cols];
 41 |     let tensor_data = TensorData::new(data.to_vec(), shape); // unfortunately vec is reqd.
 42 |     Tensor::<BckAutoDiffFloat, 2>::from_data(tensor_data, &DEVICE)
 43 | }
 44 | 
 45 | pub fn ndarr2_to_tensor(arr: &Array2<f64>) -> FloatTensor2d {
 46 |     let shape = arr.shape();
 47 |     let raw_vec: Vec<f64> = arr.iter().cloned().collect();
 48 |     // let raw_vec: Vec<f64> = arr.into_raw_vec_and_offset();
 49 |     let tensor_data = TensorData::new(raw_vec, shape);
 50 |     // This is assuming a leaf tensor!
 51 |     Tensor::<BckAutoDiffFloat, 2>::from_data(tensor_data, &DEVICE).require_grad()
 52 | }
 53 | 
 54 | pub fn len1df(tensor: &FloatTensor1d) -> usize {
 55 |     tensor.dims()[0]
 56 | }
 57 | 
 58 | pub fn zeros2d(num_rows: usize, num_cols: usize) -> FloatTensor2d {
 59 |     let shape = Shape::new([num_rows, num_cols]);
 60 |     let mut tensor = Tensor::<BckAutoDiffFloat, 2>::zeros(shape, &DEVICE);
 61 |     tensor
 62 | }
 63 | 
 64 | pub fn tensor2d_to_nested_vec(tensor: &FloatTensor2d) -> Vec<Vec<f64>> {
 65 |     let shape = tensor.dims();
 66 |     let num_cols = shape[1];
 67 |     let data = tensor.to_data();
 68 |     let xs: Vec<f64> = data.to_vec().unwrap();
 69 |     let ys: Vec<Vec<f64>> = xs.chunks(num_cols).map(|row| row.to_vec()).collect();
 70 |     ys
 71 | }
 72 | 
 73 | pub fn zeros1d(num_elems: usize) -> FloatTensor1d {
 74 |     let shape = Shape::new([num_elems]);
 75 |     Tensor::<BckAutoDiffFloat, 1>::zeros(shape, &DEVICE)
 76 | }
 77 | 
 78 | pub fn ndarray_matrix_to_tensor<T>(matrix: &Array2<T>) -> FloatTensor2d
 79 | where
 80 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
 81 | {
 82 |     let v: Vec<f64> = matrix.iter().map(|&e| (e.to_f64()).unwrap()).collect();
 83 |     let shape = &[matrix.nrows(), matrix.ncols()];
 84 |     let tensor_data = TensorData::new(v, shape);
 85 |     Tensor::<BckAutoDiffFloat, 2>::from_data(tensor_data, &DEVICE)
 86 | }
 87 | 
 88 | pub fn one_hot_tensor(indices: &FloatTensor1d, num_classes: usize) -> FloatTensor2d {
 89 |     let depth = len1df(indices);
 90 |     // Tensor2d dim = depth x num_classes
 91 |     let mut zero_tensor = zeros2d(depth, num_classes);
 92 | 
 93 |     let indices: Vec<f64> = indices.clone().into_data().to_vec().unwrap();
 94 | 
 95 |     let xs: [usize; 2] = zero_tensor.shape().dims();
 96 |     println!("DIMS: {:?}", &xs);
 97 | 
 98 |     // zero_tensor.iter_dim(1).for_each(|e|);
 99 |     // zero_tensor.select_assign(dim, indices, values)
100 | 
101 |     zero_tensor
102 | }
103 | 
104 | pub fn get_row(tensor: &FloatTensor2d, i: usize) -> FloatTensor2d {
105 |     let num_cols = tensor.dims()[1];
106 |     get_tensor(tensor, i, i + 1, 0, num_cols)
107 | }
108 | 
109 | pub fn get_elem(tensor: &FloatTensor2d, i: usize) -> FloatTensor2d {
110 |     // Assume there is only one row. We are just using 2d tensors instead of 1d tensor.
111 |     get_tensor(tensor, 0, 1, i, i + 1)
112 | }
113 | 
114 | fn get_tensor(tensor: &FloatTensor2d, a1: usize, a2: usize, b1: usize, b2: usize) -> FloatTensor2d {
115 |     tensor.clone().slice([a1..a2, b1..b2])
116 | }
117 | 
118 | pub fn create_empty_2d_tensor(num_rows: usize, num_cols: usize) -> FloatTensor2d {
119 |     let shape = Shape::new([num_rows, 1]);
120 |     Tensor::<BckAutoDiffFloat, 2>::empty(shape, &DEVICE)
121 | }
122 | 
123 | pub fn null_grad(num_rows: usize, num_cols: usize) -> Tensor<NdArray<f64>, 2> {
124 |     let shape = Shape::new([num_rows, num_cols]);
125 |     let t = Tensor::<NdArray<f64>, 2>::zeros(shape, &DEVICE);
126 |     t
127 | }
128 | 
129 | pub fn vec_to_tensor1d<T>(xs: &Vec<T>) -> FloatTensor1d
130 | where
131 |     T: Copy + Num + Bounded + ToPrimitive + PartialOrd,
132 | {
133 |     let shape = Shape::new([xs.len()]);
134 |     let xs: Vec<f64> = xs.iter().map(|&e| (e.to_f64()).unwrap()).collect();
135 |     let tensor_data = TensorData::new(xs, shape);
136 |     Tensor::<BckAutoDiffFloat, 1>::from_data(tensor_data, &DEVICE)
137 | }
138 | 
139 | pub fn convert_nd_to_autodiff(
140 |     tensor: &Tensor<NdArray<f64>, 2>,
141 | ) -> Tensor<Autodiff<NdArray<f64>>, 2> {
142 |     let tensor_data = tensor.to_data();
143 |     Tensor::<BckAutoDiffFloat, 2>::from_data(tensor_data, &DEVICE)
144 | }
145 | 
146 | pub fn tensor2d_to_ndarr2(tensor: &FloatTensor2d) -> Array2<f64> {
147 |     let dims = tensor.dims();
148 |     let data: Vec<f64> = tensor.to_data().to_vec().unwrap();
149 |     Array2::from_shape_vec((dims[0], dims[1]), data).unwrap()
150 | }
151 | 
152 | fn ex1() {
153 |     let data = create_char_matrix_ndarr();
154 |     let v: Vec<i64> = data.iter().cloned().collect();
155 |     v.iter().take(10).for_each(|e| println!("{e}"));
156 |     let shape = data.shape();
157 |     dbg!(&shape);
158 |     println!("rows: {}", data.nrows());
159 |     println!("cols: {}", data.ncols());
160 | }
161 | 
162 | fn ex2() {
163 |     // Since tensors of Burn libs are all floats we convert our word matrix too into floats
164 |     let data = create_char_matrix_ndarr();
165 |     let v: Vec<f64> = data.iter().map(|&e| e as f64).collect();
166 | 
167 |     let t1 = tensor2df(&v, data.nrows(), data.ncols());
168 | }
169 | 
170 | fn ex3() {
171 |     let narr = Array2::from_shape_vec((2, 3), vec![1, 2, 3, 4, 5, 6]).unwrap();
172 | 
173 |     let tensor = ndarray_matrix_to_tensor(&narr);
174 |     let t2 = tensor.clone() + 1.;
175 |     dbg!(&t2);
176 | }
177 | 
178 | fn ex4() {
179 |     let indices = tensor1df(&[1.0, 2.0, 3.0]);
180 |     let num_classes = 27;
181 |     let depth = len1df(&indices);
182 |     let mut zero_tensor = zeros2d(depth, num_classes);
183 | 
184 |     let indices: Vec<f64> = indices.clone().into_data().to_vec().unwrap();
185 | 
186 |     let xs: [usize; 2] = zero_tensor.shape().dims();
187 |     println!("DIMS: {:?}", &xs);
188 | 
189 |     // zero_tensor.select_assign(1, , values)
190 | 
191 |     // Tensor<B, 1, Int>
192 | }
193 | 
194 | trait MLTensor {}
195 | 
196 | #[derive(Debug)]
197 | pub struct TensorInt<const D: usize> {
198 |     tensor: Tensor<NdArray, D, Int>,
199 | }
200 | 
201 | impl<const D: usize> TensorInt<D> {
202 |     pub fn new(xs: &[i64], shape: &[usize]) -> Self {
203 |         if D == 1 {
204 |             Self::create1d(xs)
205 |         } else {
206 |             assert!(shape.len() == 2);
207 |             let num_rows = shape[0];
208 |             let num_cols = shape[1];
209 |             let total_elems = num_rows * num_cols;
210 |             assert_eq!(xs.len(), total_elems);
211 |             Self::create2d(xs, num_rows, num_cols)
212 |         }
213 |     }
214 |     fn create1d(xs: &[i64]) -> Self {
215 |         let xs = xs.to_vec();
216 |         let shape = Shape::new([xs.len()]);
217 |         let tensor_data = TensorData::new(xs, shape);
218 |         let tensor = Tensor::<NdArray, D, Int>::from_data(tensor_data, &DEVICE);
219 |         Self { tensor }
220 |     }
221 | 
222 |     fn create2d(xs: &[i64], num_rows: usize, num_cols: usize) -> Self {
223 |         let xs = xs.to_vec();
224 |         let shape = Shape::new([num_rows, num_cols]);
225 |         let tensor_data = TensorData::new(xs, shape);
226 |         let tensor = Tensor::<NdArray, D, Int>::from_data(tensor_data, &DEVICE);
227 |         Self { tensor }
228 |     }
229 | 
230 |     pub fn tensor(&self) -> &Tensor<NdArray, D, Int> {
231 |         &self.tensor
232 |     }
233 | }
234 | 
235 | impl<const D: usize> MLTensor for TensorInt<D> {}
236 | 
237 | impl<const D: usize> std::fmt::Display for TensorInt<D> {
238 |     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
239 |         self.tensor.fmt(f)
240 |     }
241 | }
242 | 
243 | // This is an autograd tensor.
244 | // For time and simplicity purpose from user perspective there is some duplication.
245 | pub struct TensorFloat<const D: usize> {
246 |     tensor: Tensor<Autodiff<NdArray<f64>>, D>,
247 | }
248 | 
249 | impl<const D: usize> TensorFloat<D> {
250 |     pub fn new(xs: &[f64], shape: &[usize]) -> Self {
251 |         if D == 1 {
252 |             Self::create1d(xs)
253 |         } else {
254 |             assert!(shape.len() == 2);
255 |             let num_rows = shape[0];
256 |             let num_cols = shape[1];
257 |             let total_elems = num_rows * num_cols;
258 |             assert_eq!(xs.len(), total_elems);
259 |             Self::create2d(xs, num_rows, num_cols)
260 |         }
261 |     }
262 | 
263 |     fn create1d(xs: &[f64]) -> Self {
264 |         let xs = xs.to_vec();
265 |         let shape = Shape::new([xs.len()]);
266 |         let tensor_data = TensorData::new(xs, shape);
267 |         let tensor = Tensor::<Autodiff<NdArray<f64>>, D>::from_data(tensor_data, &DEVICE);
268 |         Self { tensor }
269 |     }
270 | 
271 |     fn create2d(xs: &[f64], num_rows: usize, num_cols: usize) -> Self {
272 |         let xs = xs.to_vec();
273 |         let shape = Shape::new([num_rows, num_cols]);
274 |         let tensor_data = TensorData::new(xs, shape);
275 |         let tensor = Tensor::<Autodiff<NdArray<f64>>, D>::from_data(tensor_data, &DEVICE);
276 |         Self { tensor }
277 |     }
278 | 
279 |     pub fn tensor(&self) -> &Tensor<Autodiff<NdArray<f64>>, D> {
280 |         &self.tensor
281 |     }
282 | }
283 | 
284 | impl<const D: usize> std::fmt::Display for TensorFloat<D> {
285 |     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
286 |         self.tensor.fmt(f)
287 |     }
288 | }
289 | 
290 | impl<const D: usize> MLTensor for TensorFloat<D> {}
291 | 
292 | // // Trying to wrap both the types of tensor into a common one
293 | // // so that we can call the methods without duplication.
294 | // enum BackendDType {
295 | //     IntType,
296 | //     FloatType
297 | // }
298 | 
299 | // pub struct RTensor<const D: usize> {
300 | //     tensor: Rc<RefCell<dyn MLTensor>>,
301 | // }
302 | 
303 | // impl<const D: usize> RTensor<D> {
304 | //     pub fn new(tensor_type: BackendDType) -> Self {
305 | //         todo!()
306 | //     }
307 | // }
308 | 
309 | fn ex5() {
310 |     // This backend has support for Int
311 |     let l1: Tensor<NdArray, 1, Int> = Tensor::from_ints([1, 2, 3], &DEVICE);
312 |     let l2: Tensor<NdArray, 1, Float> = Tensor::from_floats([1.0, 2.0], &DEVICE);
313 |     let l2: Tensor<NdArray<f64>, 1> = Tensor::from_floats([1.0, 2.0], &DEVICE);
314 | 
315 |     // i64 is supported!
316 |     let xs: Vec<u64> = vec![1, 2, 3]; // usize is not supported for trait bound Element
317 |     let shape = Shape::new([xs.len()]);
318 |     let tdata = TensorData::new(xs, shape);
319 | 
320 |     let l3 = Tensor::<NdArray, 1, Int>::from_data(tdata, &DEVICE);
321 |     println!("{}", l3);
322 | 
323 |     let ys: Vec<f64> = vec![1., 2., 3.];
324 |     let shape = Shape::new([ys.len()]);
325 |     let tdata = TensorData::new(ys, shape);
326 |     // dtype still will be f32. For f64 see below
327 |     let l4 = Tensor::<NdArray, 1, Float>::from_data(tdata, &DEVICE);
328 |     println!("{}", l4);
329 | 
330 |     let ys: Vec<f64> = vec![1., 2., 3.];
331 |     let shape = Shape::new([ys.len()]);
332 |     let tdata = TensorData::new(ys, shape);
333 |     let l4 = Tensor::<NdArray<f64>, 1>::from_data(tdata, &DEVICE);
334 |     println!("{}", l4);
335 | }
336 | 
337 | fn ex6() {
338 |     let t = TensorInt::<1>::new(&[1, 2, 3], &[]);
339 |     println!("{}", t);
340 |     let v = vec![1_i64, 2, 3, 4, 5, 6];
341 |     let t1 = TensorInt::<2>::new(&v, &[2, 3]);
342 |     println!("{}", t1);
343 | }
344 | 
345 | fn ex7() {
346 |     // let t: Tensor<dyn Backend, 1, Int> = Tensor::<NdArray, 1, Int>::from_ints([1,2,3], &DEVICE);
347 | }
348 | 
349 | pub fn main() {
350 |     println!("Exploring Burn API");
351 |     ex6();
352 | }
353 | 


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/src/utils.rs:
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  1 | #![allow(unused)]
  2 | 
  3 | use ndarray::Array2;
  4 | use std::collections::{HashMap, HashSet};
  5 | use std::fs;
  6 | 
  7 | use rand::distributions::Uniform;
  8 | use rand::Rng;
  9 | 
 10 | use crate::numr::*;
 11 | use crate::rust_mllib::{IntTensor2d, TensorInt};
 12 | 
 13 | pub fn get_names() -> Vec<String> {
 14 |     let names = fs::read_to_string("names.txt").unwrap();
 15 |     let names: Vec<_> = names.lines().map(|e| e.to_string()).collect();
 16 |     names
 17 | }
 18 | 
 19 | fn old_ctoi() -> HashMap<char, usize> {
 20 |     let names = get_names();
 21 |     let mut chs: HashSet<char> = names.join("").chars().collect();
 22 |     let mut chs = Vec::from_iter(chs);
 23 |     chs.sort();
 24 |     chs.push('<');
 25 |     chs.push('>');
 26 |     let xs: HashMap<char, usize> = (0..).zip(chs).map(|e| (e.1, e.0)).collect();
 27 |     xs
 28 | }
 29 | 
 30 | pub fn ctoi() -> HashMap<char, usize> {
 31 |     let names = get_names();
 32 |     let mut chs: HashSet<char> = names.join("").chars().collect();
 33 |     let mut chs = Vec::from_iter(chs);
 34 |     chs.sort();
 35 |     let mut chs1 = vec!['.']; // we need this to be at index 0
 36 |     chs1.extend_from_slice(&chs);
 37 |     let xs: HashMap<char, usize> = (0..).zip(chs1).map(|e| (e.1, e.0)).collect();
 38 |     xs
 39 | }
 40 | 
 41 | pub fn itoc(ctoi: &HashMap<char, usize>) -> HashMap<usize, char> {
 42 |     let h = ctoi.iter().map(|(&k, &v)| (v, k)).collect();
 43 |     h
 44 | }
 45 | 
 46 | // Returns an NdArray object not Burn Tensor
 47 | // pub fn create_char_matrix_ndarr() -> Array2<i64> {
 48 | //     let ctoi = ctoi();
 49 | //     let num_chars = ctoi.len();
 50 | //     let mut n = zeros_int(num_chars, num_chars);
 51 | //     let names = get_names();
 52 | //     for w in &names {
 53 | //         let w = format!(".{}.", w);
 54 | //         for (ch1, ch2) in w.chars().zip(w.chars().skip(1)) {
 55 | //             let ix1 = ctoi[&ch1];
 56 | //             let ix2 = ctoi[&ch2];
 57 | //             n[[ix1, ix2]] += 1;
 58 | //         }
 59 | //     }
 60 | //     n
 61 | // }
 62 | 
 63 | pub fn create_char_matrix_ndarr() -> Array2<i64> {
 64 |     let matrix_vec = create_char_matrix_vec();
 65 |     let num_rows = matrix_vec.len();
 66 |     let num_cols = matrix_vec[0].len();
 67 |     let raw_data: Vec<i64> = matrix_vec.into_iter().flatten().collect();
 68 |     Array2::from_shape_vec((num_rows, num_cols), raw_data).unwrap()
 69 | }
 70 | 
 71 | pub fn create_char_matrix_tensor() -> TensorInt<2> {
 72 |     let matrix_vec = create_char_matrix_vec();
 73 |     let num_rows = matrix_vec.len();
 74 |     let num_cols = matrix_vec[0].len();
 75 |     let raw_data: Vec<i64> = matrix_vec.into_iter().flatten().collect();
 76 |     let t = TensorInt::<2>::new(&raw_data, &[num_rows, num_cols]);
 77 |     t
 78 | }
 79 | 
 80 | pub fn create_char_matrix_vec() -> Vec<Vec<i64>> {
 81 |     let ctoi = ctoi();
 82 |     let num_chars = ctoi.len();
 83 |     let words = get_names();
 84 | 
 85 |     // create z zeroed 2d vector
 86 |     let mut matrix: Vec<Vec<i64>> = vec![vec![0; num_chars]; num_chars];
 87 | 
 88 |     for w in &words {
 89 |         let w = format!(".{}.", w);
 90 |         for (ch1, ch2) in w.chars().zip(w.chars().skip(1)) {
 91 |             let ix1 = ctoi[&ch1];
 92 |             let ix2 = ctoi[&ch2];
 93 |             matrix[ix1][ix2] += 1;
 94 |         }
 95 |     }
 96 | 
 97 |     matrix
 98 | }
 99 | 
100 | pub fn rand_uniform_nums(n: usize, low: f64, high: f64) -> Vec<f64> {
101 |     let mut rng = rand::thread_rng();
102 | 
103 |     let uniform_range = Uniform::new(low, high);
104 | 
105 |     // 0..n is fine too :-). Just that I like this more. Sounds natural!
106 |     (1..=n).map(|_| rng.sample(uniform_range)).collect()
107 | }
108 | 
109 | pub fn rand_normal_distrib(n: usize) -> Vec<f64> {
110 |     rand_uniform_nums(n, -3.1, 3.1)
111 | }
112 | 


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