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use ndarray::{Array, Array2};
use pyo3::prelude::*;
use pyo3::types::PyType;
use serde_derive::{Deserialize, Serialize};
use serde_json;
use serde_yaml;
use crate::costs::CostFunc;
use crate::layers::{Activation, Dense, Layer};
use crate::network::Sequential;
#[pyclass]
#[derive(Serialize, Deserialize)]
pub struct PyrusSequential {
network: Sequential,
}
#[pymethods]
impl PyrusSequential {
#[new]
fn __new__(
obj: &PyRawObject,
lr: f32,
n_epoch: usize,
batch_size: usize,
cost_func: String,
) -> PyResult<()> {
obj.init(|_| {
let cost_func = CostFunc::from(cost_func);
let network = Sequential::new(lr, n_epoch, batch_size, cost_func);
PyrusSequential { network }
})
}
fn to_yaml(&self) -> PyResult<String> {
Ok(serde_yaml::to_string(&self).unwrap())
}
#[classmethod]
fn from_yaml(_cls: &PyType, conf: String) -> PyResult<PyrusSequential> {
Ok(serde_yaml::from_str(&conf).unwrap())
}
fn to_json(&self) -> PyResult<String> {
Ok(serde_json::to_string(&self).unwrap())
}
#[classmethod]
fn from_json(_cls: &PyType, conf: String) -> PyResult<PyrusSequential> {
Ok(serde_json::from_str(&conf).unwrap())
}
fn add_dense(&mut self, n_input: usize, n_output: usize, activation: String) -> PyResult<()> {
self.network
.add(Dense::new(n_input, n_output, Activation::from(activation)))
.unwrap();
Ok(())
}
fn fit(&mut self, x: Vec<Vec<f32>>, y: Vec<Vec<f32>>) -> PyResult<()> {
let x: Array2<f32> = vec2d_into_array2d(x);
let y: Array2<f32> = vec2d_into_array2d(y);
self.network.fit(x.view(), y.view());
Ok(())
}
fn predict(&mut self, x: Vec<Vec<f32>>) -> PyResult<Vec<Vec<f32>>> {
let x: Array2<f32> = vec2d_into_array2d(x);
let out = self
.network
.forward(x.view())
.outer_iter()
.map(|v| v.to_vec())
.collect::<Vec<Vec<f32>>>();
Ok(out)
}
}
fn vec2d_into_array2d(vec: Vec<Vec<f32>>) -> Array2<f32> {
let shape = (vec.len(), vec[0].len());
Array::from_iter(vec.into_iter().flat_map(|v| v))
.into_shape(shape)
.unwrap()
}
#[pymodinit]
fn pyrus_nn(_py: Python, m: &PyModule) -> PyResult<()> {
m.add_class::<PyrusSequential>()?;
Ok(())
}