Bounded predictive-gain optimizer / results.json

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  1{
  2  "seed": 2037,
  3  "checks": [
  4    {
  5      "name": "sign and magnitude of consecutive-direction product",
  6      "prediction": {
  7        "delta_a": "eta*r*g^2/(1+rho)",
  8        "slope": 0.08450000000000002
  9      },
 10      "observed": {
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 21          1.1102230246251565e-16,
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 23          0.08450000000000013
 24        ],
 25        "slope": 0.08450000000000005,
 26        "max_abs_error": 1.1102230246251565e-16
 27      },
 28      "pass": true
 29    },
 30    {
 31      "name": "Bregman persistence contraction when predictive product is zero",
 32      "prediction": {
 33        "error_ratio": 0.6666666666666666
 34      },
 35      "observed": {
 36        "errors": [
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 39          0.5777777777777777,
 40          0.3851851851851853,
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 43        ],
 44        "ratios": [
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 48          0.6666666666666664,
 49          0.6666666666666666
 50        ],
 51        "max_abs_ratio_error": 3.3306690738754696e-16
 52      },
 53      "pass": true
 54    },
 55    {
 56      "name": "quadratic first sign reversal and stability boundary",
 57      "prediction": {
 58        "first_reversal_threshold": 1.0,
 59        "stability_interval": [
 60          0.0,
 61          2.0
 62        ]
 63      },
 64      "observed": {
 65        "first_grid_reversal": 1.2,
 66        "first_grid_point_above_threshold": 1.2,
 67        "stable_boundary_test_mismatches": 0,
 68        "grid": [
 69          0.2,
 70          0.4,
 71          0.6,
 72          0.8,
 73          1.0,
 74          1.2,
 75          1.4,
 76          1.5999999999999999,
 77          1.7999999999999998,
 78          1.9999999999999998,
 79          2.1999999999999997,
 80          2.4
 81        ],
 82        "reversal": [
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 84          false,
 85          false,
 86          false,
 87          false,
 88          true,
 89          true,
 90          true,
 91          true,
 92          true,
 93          true,
 94          true
 95        ]
 96      },
 97      "pass": true
 98    }
 99  ],
100  "mini_experiment": {
101    "baseline": {
102      "final_loss": 0.13887640326417106,
103      "best_loss": 0.05718613847298501,
104      "final_accuracy": 0.93375,
105      "loss_at_40": 0.16235985468717123,
106      "gain_trajectories": [
107        0.8,
108        0.8
109      ],
110      "clip_events": 0,
111      "reversal_events": 0
112    },
113    "bounded_predictive_gain": {
114      "final_loss": 0.1388562616475848,
115      "best_loss": 0.05718930868369484,
116      "final_accuracy": 0.93375,
117      "loss_at_40": 0.1624317609217938,
118      "gain_trajectories": [
119        0.799784109800687,
120        0.7996852175830025
121      ],
122      "clip_events": 0,
123      "reversal_events": 185
124    }
125  }
126}