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Implement the 2 empty function bodies in specs/ml/optimizer/adagrad.t27 #4067

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Merged
gHashTag merged 1 commit into from
Sep 17, 2026
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Implement the 2 empty function bodies in specs/ml/optimizer/adagrad.t27 #4067

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42 changes: 27 additions & 15 deletions specs/ml/optimizer/adagrad.t27
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Original file line number Diff line number Diff line change
Expand Up @@ -32,29 +32,41 @@ module Adagrad;
// 3. Core Functions
// ═══════════════════════════════════════════════════════════

// init_state(num_params: u32) → void
fn init_state(num_params: u32) -> void {
// TODO: Implement from .tri spec
// init_state(num_params: u32) → AdagradState
fn init_state(num_params: u32) -> AdagradState {
let state = AdagradState { sum_squared_grad: [] };
return state;
}

// update(params: []f32) → void
fn update(params: []f32) -> void {
// TODO: Implement from .tri spec
// update(params: []f32, grads: []f32, state: AdagradState, config: AdagradConfig) → AdagradState
fn update(params: []f32, grads: []f32, state: AdagradState, config: AdagradConfig) -> AdagradState {
let mut new_state = state;
for i in range(0, len(params)) {
new_state.sum_squared_grad[i] += grads[i] * grads[i];
let adaptive_lr = config.learning_rate / (sqrt(new_state.sum_squared_grad[i]) + config.epsilon);
params[i] -= adaptive_lr * grads[i];
}
return new_state;
}

// ═══════════════════════════════════════════════════════════
// TDD: Tests (from .tri behaviors)
// ═══════════════════════════════════════════════════════════

test init_state_basic_case
given input = default_input()
when result = init_state(input)
then result != undefined

test update_basic_case
given input = default_input()
when result = update(input)
then result != undefined
test init_state_returns_correct_size
given num_params = 5u32
when state = init_state(num_params)
then len(state.sum_squared_grad) == num_params

test update_accumulates_gradients_properly
given params = [1.0f32, 2.0f32, 3.0f32]
grads = [0.1f32, 0.2f32, 0.3f32]
state = init_state(3u32)
config = AdagradConfig { learning_rate: 0.01, epsilon: 1e-8, weight_decay: 0.0 }
when new_state = update(params, grads, state, config)
then new_state.sum_squared_grad[0] == 0.01f32
and new_state.sum_squared_grad[1] == 0.04f32
and new_state.sum_squared_grad[2] == 0.09f32

// ═══════════════════════════════════════════════════════════
// TDD: Invariants (from .tri constraints)
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