22830269200

Committed 08 Mar 2026 09:28PM UTC coverage: 90.452% (+3.3%) from 87.11%

Build # 22830269200

Build Type

Pull #10

github

Committed by

web-flow

Commit Message

Merge 3abad3f15 into 5f8ad5902

Pull Request Pull Request #10: Generalise the data boundary

Coverage Stats

457 of 502 new or added lines in 11 files covered. (91.04%)

1 existing line in 1 file now uncovered.

1601 of 1770 relevant lines covered (90.45%)

15.35 hits per line

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92.68

/src/training/cpu/singlethreaded.rs

//! Training orchestration for predictive coding models.

use crate::{
  data_handling::data_handler,
  error::Result,
  model_structure::{
    model::PredictiveCodingModel,
    model_utils::set_rand_input_and_output
  },
  training::{
    train_handler::TrainingHandler,
    configuration::TrainConfig,
  }
};

use std::sync::Arc;
use tracing::info;


pub struct SingleThreadTrainHandler {
  config: TrainConfig,
  model: PredictiveCodingModel,
  data: Arc<dyn data_handler::TrainingDataset>,
  file_output_prefix: String
}

impl SingleThreadTrainHandler {
  pub fn new(
    config: TrainConfig,
    model: PredictiveCodingModel,
    data: Arc<dyn data_handler::TrainingDataset>,
    file_output_prefix: String
  ) -> Self {
    SingleThreadTrainHandler {
      config,
      model,
      data,
      file_output_prefix
    }
  }
}

impl TrainingHandler for SingleThreadTrainHandler {
  fn get_config(&self) -> &TrainConfig {
    &self.config
  }
  fn get_model(&mut self) -> &mut PredictiveCodingModel {
    &mut self.model
  }
  fn get_data(&self) -> &dyn data_handler::TrainingDataset {
    self.data.as_ref()
  }
  fn get_file_output_prefix(&self) -> &String {
    &self.file_output_prefix
  }

  fn pre_training_hook(&mut self) -> Result<()> {
    info!("Starting training with single-threaded strategy");
    Ok(())
  }

  fn train_step(&mut self, _step: u32) -> Result<()> {
    set_rand_input_and_output(&mut self.model, self.data.as_ref());
    self.model.reinitialise_latents();

    // Train on this example until convergence.
    self.model.converge_values();
    self.model.update_weights();

    Ok(())
  }

  fn report_hook(&mut self, step: u32) -> Result<()> {
    let energy: f32 = self.model.read_total_energy();
    info!("Step {}: Current model state: energy = {:.2}", step, energy);
    Ok(())
  }
}

1	//! Training orchestration for predictive coding models.
2
3	use crate::{
4	data_handling::data_handler,
5	error::Result,
6	model_structure::{
7	model::PredictiveCodingModel,
8	model_utils::set_rand_input_and_output
9	},
10	training::{
11	train_handler::TrainingHandler,
12	configuration::TrainConfig,
13	}
14	};
15
16	use std::sync::Arc;
17	use tracing::info;
18
19
20	pub struct SingleThreadTrainHandler {
21	config: TrainConfig,
22	model: PredictiveCodingModel,
23	data: Arc<dyn data_handler::TrainingDataset>,
24	file_output_prefix: String
25	}
26
27	impl SingleThreadTrainHandler {
28	pub fn new(	4✔
29	config: TrainConfig,	4✔
30	model: PredictiveCodingModel,	4✔
31	data: Arc<dyn data_handler::TrainingDataset>,	4✔
32	file_output_prefix: String	4✔
33	) -> Self {	4✔
34	SingleThreadTrainHandler {	4✔
35	config,	4✔
36	model,	4✔
37	data,	4✔
38	file_output_prefix	4✔
39	}	4✔
40	}	4✔
41	}
42
43	impl TrainingHandler for SingleThreadTrainHandler {
44	fn get_config(&self) -> &TrainConfig {	6✔
45	&self.config	6✔
46	}	6✔
47	fn get_model(&mut self) -> &mut PredictiveCodingModel {	11✔
48	&mut self.model	11✔
49	}	11✔
NEW 50	fn get_data(&self) -> &dyn data_handler::TrainingDataset {	×
NEW 51	self.data.as_ref()	×
NEW 52	}	×
53	fn get_file_output_prefix(&self) -> &String {	11✔
54	&self.file_output_prefix	11✔
55	}	11✔
56
57	fn pre_training_hook(&mut self) -> Result<()> {	3✔
58	info!("Starting training with single-threaded strategy");	3✔
59	Ok(())	3✔
60	}	3✔
61
62	fn train_step(&mut self, _step: u32) -> Result<()> {	7✔
63	set_rand_input_and_output(&mut self.model, self.data.as_ref());	7✔
64	self.model.reinitialise_latents();	7✔
65
66	// Train on this example until convergence.
67	self.model.converge_values();	7✔
68	self.model.update_weights();	7✔
69
70	Ok(())	7✔
71	}	7✔
72
73	fn report_hook(&mut self, step: u32) -> Result<()> {	2✔
74	let energy: f32 = self.model.read_total_energy();	2✔
75	info!("Step {}: Current model state: energy = {:.2}", step, energy);	2✔
76	Ok(())	2✔
77	}	2✔
78	}

wildjames / predictive_coding_rs / 22830269200

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