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06e4b6f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | //! `python::som` — extraído do monólito na v0.10.0
//! (divisão SRP, crítica Qwen3.8 lote 3 nº 1; r-35 §2).
use super::*;
/// Treina um 4D-SOM e devolve telemetria completa (EQ/TE/massa/cobertura).
#[pyfunction]
pub(crate) fn treinar_som4d(py: Python, dados: Vec<Vec<f64>>, cfg: Option<String>) -> PyResult<PyObject> {
let dev = candle_core::Device::Cpu;
let t = tensor_de_2d(dados, &dev).map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let mut c = som4d::CfgSom4D::default();
if let Some(js) = &cfg {
if let Ok(v) = serde_json::from_str::<serde_json::Value>(js) {
if let Some(o) = v.as_object() {
for (k, val) in o {
match k.as_str() {
"nx" => c.nx = val.as_u64().unwrap_or(4) as usize,
"ny" => c.ny = val.as_u64().unwrap_or(4) as usize,
"nz" => c.nz = val.as_u64().unwrap_or(4) as usize,
"nt" => c.nt = val.as_u64().unwrap_or(4) as usize,
"epocas" => c.epocas = val.as_u64().unwrap_or(50) as usize,
"sigma0" => c.sigma0 = val.as_f64().unwrap_or(2.0),
"sigma1" => c.sigma1 = val.as_f64().unwrap_or(0.5),
"eta0" => c.eta0 = val.as_f64().unwrap_or(0.5),
"eta1" => c.eta1 = val.as_f64().unwrap_or(0.05),
"semente" => c.semente = val.as_u64().unwrap_or(42),
"espalhada" => {
c.inicializacao = if val.as_bool().unwrap_or(false) {
som4d::Inicializacao4D::Espalhada
} else {
som4d::Inicializacao4D::Dados
}
}
_ => {}
}
}
}
}
}
let som = som4d::treinar_som4d(&t, &c, &dev)
.map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let d = serde_json::json!({
"versao": som4d::versao(),
"celulas": som.n_celulas(),
"grade": [som.nx, som.ny, som.nz, som.nt],
"eq_inicial": som.eq_inicial,
"eq_final": som.eq_final,
"te_final": som.te_final,
"historico_eq_te": som.historia,
"massa_soma": som.massa.iter().sum::<f64>(),
});
Ok(pythonize::pythonize(py, &d)?.into())
}
/// Treina um GNG e devolve telemetria (vértices, arestas, componentes, EQ).
#[pyfunction]
pub(crate) fn treinar_gng(py: Python, dados: Vec<Vec<f64>>, cfg: Option<String>) -> PyResult<PyObject> {
let dev = candle_core::Device::Cpu;
let t = tensor_de_2d(dados, &dev).map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let mut c = som4d::CfgGng::default();
if let Some(js) = &cfg {
if let Ok(v) = serde_json::from_str::<serde_json::Value>(js) {
if let Some(o) = v.as_object() {
for (k, val) in o {
match k.as_str() {
"passos" => c.passos = val.as_u64().unwrap_or(4000) as usize,
"max_vertices" => c.max_vertices = val.as_u64().unwrap_or(60) as usize,
"lambda" => c.lambda = val.as_u64().unwrap_or(50) as usize,
"eps_b" => c.eps_b = val.as_f64().unwrap_or(0.05),
"eps_n" => c.eps_n = val.as_f64().unwrap_or(0.006),
"idade_max" => c.idade_max = val.as_u64().unwrap_or(50) as u32,
"semente" => c.semente = val.as_u64().unwrap_or(42),
_ => {}
}
}
}
}
}
let g = som4d::treinar_gng(&t, &c)
.map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let d = serde_json::json!({
"versao": som4d::versao(),
"vertices": g.vertices.len(),
"arestas": g.arestas.len(),
"componentes_conexas": g.n_componentes_conexas(),
"insercoes": g.insercoes,
"eq_medio": g.eq_medio,
});
Ok(pythonize::pythonize(py, &d)?.into())
}
/// Treina um H-SOM e devolve a decomposição hierárquica (Teo H-1).
#[pyfunction]
pub(crate) fn treinar_hsom(py: Python, dados: Vec<Vec<f64>>, cfg: Option<String>) -> PyResult<PyObject> {
let dev = candle_core::Device::Cpu;
let t = tensor_de_2d(dados, &dev).map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let mut c = som4d::CfgHSom::default();
if let Some(js) = &cfg {
if let Ok(v) = serde_json::from_str::<serde_json::Value>(js) {
if let Some(o) = v.as_object() {
if let Some(x) = o.get("celulas_finas") {
c.celulas_finas = x.as_u64().unwrap_or(9) as usize;
}
if let Some(x) = o.get("epocas_finas") {
c.epocas_finas = x.as_u64().unwrap_or(20) as usize;
}
}
}
}
let h = som4d::treinar_hsom(&t, &c, &dev)
.map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let cota = h.eq_grosso + 2.0 * h.r_fino + 2.0 * (h.eq_grosso * h.r_fino).sqrt();
let d = serde_json::json!({
"versao": som4d::versao(),
"eq_hierarquico": h.eq_hierarquico,
"eq_grosso": h.eq_grosso,
"r_fino": h.r_fino,
"cota_teo_h1": cota,
"cota_respeitada": h.eq_hierarquico <= cota + 1e-9,
});
Ok(pythonize::pythonize(py, &d)?.into())
}
/// Demo T-SOM: streaming com deriva controlada — rastreio (Teo T-1).
#[pyfunction]
pub(crate) fn demo_tsom(py: Python, dados_iniciais: Vec<Vec<f64>>, janela: Vec<Vec<f64>>, passos: usize, gamma: f64) -> PyResult<PyObject> {
let dev = candle_core::Device::Cpu;
let t0 = tensor_de_2d(dados_iniciais, &dev).map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let cfg = som4d::CfgTSom {
cfg_grade: som4d::CfgSom4D { nx: 3, ny: 3, nz: 2, nt: 2, epocas: 15, ..Default::default() },
gamma,
janela: janela.len(),
};
let mut ts = som4d::TSom4D::novo(&t0, &cfg, &dev)
.map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?;
let mut historico = Vec::new();
for _ in 0..passos {
let eq = ts.passo(&janela);
historico.push(eq);
}
let d = serde_json::json!({
"versao": som4d::versao(),
"gamma": gamma,
"eq_temporal_historico": historico,
"t_passos": ts.t,
});
Ok(pythonize::pythonize(py, &d)?.into())
}
/// Demo/gate do auto-ajuste SOM com tetos (S-AB/S-SL).
#[pyfunction]
pub(crate) fn demo_autoajuste_som(py: Python) -> PyResult<PyObject> {
let mut a = controlador::AutoAjusteSom::novo(0.5, 1.0);
let (al_qe, _) = a.passo(1.0, 0.0, 0.0);
let (al_cross, sg_cross) = a.passo(0.0, 0.0, 1.0);
let (al_max, sg_max) = a.passo(1.0, 1.0, 1.0);
let mut parou_5chunks = false;
for _ in 0..5 {
parou_5chunks = a.avaliar_parada(0.95, 0.1, 0.1, false, true, false, false);
}
let d_ = serde_json::json!({
"versao": "som-autoadjust-0.7.0",
"alfa_qe_alta": al_qe, "alfa_crossing": al_cross, "sigma_crossing": sg_cross,
"alfa_max": al_max, "sigma_max": sg_max,
"parou_5chunks": parou_5chunks,
"motivo": a.motivo_parada,
"gates": {
"tetos_ok": al_max <= 1.0 + 1e-9 && sg_max <= 2.0 + 1e-9,
"piso_ok": al_cross >= 0.5 - 1e-9,
"qe_sobe": al_qe > 0.5,
"stop_dispara": parou_5chunks,
},
});
Ok(pythonize::pythonize(py, &d_)?.into())
}
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