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//use crate::common::validate_inputs;
use crate::indicators::linreg::{
min_data, output_length, IndicatorState, State, INPUTS_WIDTH, OPTIONS_WIDTH,
};
use crate::indicators::simd_indicators::linreg_simd::{calc_simd, SimdState};
use crate::indicators::simd_indicators::road_train::{Asset, Driver, PrimeMover};
use crate::types::IndicatorError;
use crate::{common::validate_options, common_simd::assets::validate_inputs};
use std::simd::Simd;
/// SIMD driver that advances the Linear Regression (LINREG) across `N` asset lanes
/// per scheduling epoch.
struct LinregDriver {
want_optional_outputs: (bool, bool, bool),
period: usize,
}
impl Driver<State> for LinregDriver {
/// Processes one epoch of bars for `N` assets simultaneously using SIMD.
///
/// Reads from `inputs[asset][0]` (real), writes the LINREG to `outputs[asset][0]`,
/// optional slope to `outputs[asset][1]`, optional intercept to `outputs[asset][2]`,
/// and updates `states[asset]` in place.
fn next_run<const N: usize>(
&mut self,
inputs: Vec<Vec<&[f64]>>,
mut outputs: Vec<Vec<&mut [f64]>>,
mut states: Vec<&mut State>,
_options: Vec<Option<&()>>,
) {
let mut state = SimdState::<N>::new_mut_ref(&states);
let len = inputs[0][0].len();
let simd_period = Simd::splat(self.period as f64);
let (has_optional, want_slope, want_intercept) = self.want_optional_outputs;
// Optimization 1: Direct array construction instead of collect+try_into
//collect outputs
let (linreg_line_ptr, slope_line_ptr, intercept_line_ptr) = crate::extract_output_ptrs!(
outputs,
N,
linreg_line_ptr,
slope_line_ptr,
intercept_line_ptr
);
// Optimization 2: Pre-compute all input and output pointers
let real_ptrs = crate::extract_input_ptrs!(inputs, N, real_ptrs);
// Optimization 3: Simplified main loop with pre-computed offsets
for (j, i) in (self.period..len).enumerate() {
// Get inputs arrays for stocks
let (real, prev_real) = crate::extract_simd_at_indices!(N, real_ptrs,
real @ i,
prev_real @ j+1//i + 1 - self.period
);
let (linreg, slope, intercept) = calc_simd(&mut state, prev_real, real, simd_period);
// Store results using pre-computed pointers
crate::write_simd_at_indices!(N, j,
linreg_line_ptr => linreg
);
if has_optional {
crate::store_simd_optional_outputs!(j, N,
want_slope, slope_line_ptr => slope,
want_intercept, intercept_line_ptr => intercept
);
}
}
// Update states efficiently
state.write_states(&mut states);
}
}
/// Calculates the Linear Regression (LINREG) for `N` assets simultaneously using SIMD
/// parallelism.
///
/// Uses the [`PrimeMover`] scheduler to batch assets into SIMD-width groups.
///
/// # Arguments
/// * `inputs` - An array of `N` asset input sets; `inputs[i]` is `[&[f64]; INPUTS_WIDTH]`
/// containing `[real]` for asset `i`.
/// * `options` - Shared options slice; `options[0]` is the period.
/// * `optional_outputs` - Optional slice selecting extra outputs: index `0` = `linregslope`,
/// index `1` = `linregintercept`.
///
/// # Returns
/// `Ok((outputs, states))` where `outputs[i][0]` is the LINREG line for asset `i`,
/// `outputs[i][1]` is the optional slope, `outputs[i][2]` is the optional intercept,
/// and `states[i]` is the final [`IndicatorState`] for asset `i`.
/// Returns `Err(IndicatorError)` if any input slice is too short or options are invalid.
pub fn indicator_by_assets<const N: usize>(
inputs: &[&[&[f64]; INPUTS_WIDTH]; N], //stock[ fields [ field [f64] ] ]
options: &[f64; OPTIONS_WIDTH],
optional_outputs: Option<&[bool]>,
) -> Result<(Vec<Vec<Vec<f64>>>, Vec<IndicatorState>), IndicatorError> {
validate_inputs::<INPUTS_WIDTH>(inputs, min_data(options))?;
validate_options(options)?;
let period = options[0] as usize;
let mut road_train = PrimeMover::<N, State>::new();
let mut want_optional_outputs = (false, false, false);
let mut output_buffers = Vec::with_capacity(N);
for i in 0..N {
let asset_inputs = vec![
inputs[i][0], // real
];
let (linreg_line, slope_line, intercept_line);
{
let capacity = output_length(inputs[i][0].len(), options);
(slope_line, intercept_line) = crate::init_optional_outputs_eff!(
optional_outputs, &[false, false],
slope_line: capacity,
intercept_line: capacity
);
linreg_line = crate::uninit_vec!(f64, capacity);
}
let state = State::init_state(&inputs[i][0][1..period], period);
if i == 0 {
want_optional_outputs = crate::calc_want_flags!(slope_line, intercept_line);
}
let mut output_buffer = vec![linreg_line, slope_line, intercept_line];
//let adosc_len = output_buffer[0].len();
let mut asset_outputs = Vec::with_capacity(output_buffer.len());
for j in 0..output_buffer.len() {
unsafe {
//let slice_len = output_buffer.len() - starts[j];
// Get a mutable reference to the output buffer for this asset
let output_buffer = &mut output_buffer[j];
asset_outputs.push(std::slice::from_raw_parts_mut(
output_buffer.as_mut_ptr(), //slice from
output_buffer.len(), // slice to
));
}
}
road_train.add_asset(Asset::new(
asset_inputs,
asset_outputs,
i,
period,
period,
state,
None,
));
output_buffers.push(output_buffer);
}
let mut driver = LinregDriver {
period,
want_optional_outputs,
};
let states_vec = road_train.drive(&mut driver);
let mut states = Vec::with_capacity(N);
for (i, state) in states_vec.into_iter().enumerate() {
states.push(IndicatorState::new(
state,
unsafe { inputs.get_unchecked(i).get_unchecked(0) },
period,
));
}
Ok((output_buffers, states))
}