825f157749
- Bump minimum Python version from 3.11 to 3.13 across all apps - Add Python 3.11-3.13 test matrix to CLI workflow - Document Python 3.11+ requirement in .env.example - Fix Starlette Broadcast removal with in-process fallback implementation - Add _InProcessBroadcast class for tests when Starlette Broadcast is unavailable - Refactor API key validators to read live settings instead of cached values - Update database models with explicit
49 lines
1.5 KiB
Plaintext
49 lines
1.5 KiB
Plaintext
pragma circom 2.0.0;
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include "node_modules/circomlib/circuits/poseidon.circom";
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/*
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* Simplified ML Training Verification Circuit
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*
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* Basic proof of gradient descent training without complex hashing
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*/
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template SimpleTrainingVerification(PARAM_COUNT, EPOCHS) {
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signal input initial_parameters[PARAM_COUNT];
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signal input learning_rate;
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signal output final_parameters[PARAM_COUNT];
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signal output training_complete;
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// Input validation constraints
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// Learning rate should be positive and reasonable (0 < lr < 1)
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learning_rate * (1 - learning_rate) === learning_rate; // Ensures 0 < lr < 1
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// Simulate simple training epochs
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signal current_parameters[EPOCHS + 1][PARAM_COUNT];
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// Initialize with initial parameters
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for (var i = 0; i < PARAM_COUNT; i++) {
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current_parameters[0][i] <== initial_parameters[i];
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}
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// Simple training: gradient descent simulation
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for (var e = 0; e < EPOCHS; e++) {
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for (var i = 0; i < PARAM_COUNT; i++) {
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// Simplified gradient descent: param = param - learning_rate * gradient_constant
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// Using constant gradient of 0.1 for demonstration
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current_parameters[e + 1][i] <== current_parameters[e][i] - learning_rate * 1;
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}
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}
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// Output final parameters
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for (var i = 0; i < PARAM_COUNT; i++) {
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final_parameters[i] <== current_parameters[EPOCHS][i];
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}
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// Training completion constraint
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training_complete <== 1;
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}
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component main = SimpleTrainingVerification(4, 3);
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