cosyne 0.3.4

CoSyNE - neuro evolution through cooperatively coevolved synapses
Documentation 
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use rand::{thread_rng, Rng};

use na::DMatrix as Matrix;

use crate::Activation;

#[derive(Debug, Clone)]
pub struct Layer {
    pub(crate) input_len: usize,
    pub(crate) output_len: usize,
    pub(crate) gene_len: usize,
    pub(crate) activation: Activation,
    weights: Matrix<f64>,
    biases: Matrix<f64>,
    act: Matrix<f64>, // activations
    act_func: fn(f64) -> f64,
    net: Matrix<f64>,
}

impl Layer {
    /// Create a new Layer with given input and output length and random weight and biases
    pub fn new(input_len: usize, output_len: usize, activation: Activation) -> Self {
        let weights = Matrix::from_vec(
            output_len,
            input_len,
            rand_vec_uniform(input_len * output_len),
        );
        let biases = Matrix::from_vec(output_len, 1, rand_vec_uniform(output_len));
        let act_func = activation.get_func();
        Self {
            input_len,
            output_len,
            activation,
            gene_len: output_len * input_len + output_len,
            weights,
            biases,
            act: Matrix::from_vec(input_len, 1, vec![0.0; input_len]),
            act_func,
            net: Matrix::from_vec(output_len, 1, vec![0.0; output_len]),
        }
    }

    /// map the weight and biases in Matrices to flat vector
    pub fn genes(&self) -> Vec<f64> {
        let mut out: Vec<f64> = Vec::new();
        out.append(&mut self.weights.as_slice().into());
        out.append(&mut self.biases.as_slice().into());
        out
    }

    /// Return the number of genes in this layer
    pub fn num_genes(&self) -> usize {
        self.weights.len() + self.biases.len()
    }

    /// Forward values through one layer
    pub(crate) fn forward(&mut self, m: &Matrix<f64>) -> Matrix<f64> {
        let net = &self.weights * m + &self.biases;
        net.apply_into(&self.act_func)
    }

    /// Set the weights for the layer
    pub(crate) fn set_weights(&mut self, w: Matrix<f64>) {
        assert_eq!(self.weights.nrows(), w.nrows());
        assert_eq!(self.weights.ncols(), w.ncols());
        self.weights = w;
    }

    /// Set the biases for the layer
    pub(crate) fn set_biases(&mut self, b: Matrix<f64>) {
        assert_eq!(self.biases.nrows(), b.nrows());
        assert_eq!(self.biases.ncols(), b.ncols());
        self.biases = b;
    }

    // set weights and biases of layer to the supplied genes
    // panics if genes.len() is wrong
    pub fn set_genes(&mut self, genes: &Vec<f64>) {
        let w_end = self.output_len * self.input_len;
        let weights: Matrix<f64> =
            Matrix::from_vec(self.output_len, self.input_len, genes[..w_end].to_vec());
        self.set_weights(weights);
        let biases: Matrix<f64> = Matrix::from_vec(self.output_len, 1, genes[w_end..].to_vec());
        self.set_biases(biases);
    }
}

/// Generate a r/andom vector of given length using a uniform distribution
/// values in range [-1.0, 1.0]
fn rand_vec_uniform(length: usize) -> Vec<f64> {
    let mut rng = thread_rng();
    (0..length).map(|_| rng.gen::<f64>() * 2.0 - 1.0).collect()
    // let mut out: Vec<f64> = vec![0.0; length];
    // for i in 0..length {
    //     out[i] = ;
    // }
    // out
}

#[cfg(test)]
mod tests {
    extern crate round;

    use super::*;
    use round::*;

    #[test]
    fn layer_forward1() {
        let mut l = Layer::new(3, 1, Activation::Relu);
        let w = Matrix::from_vec(1, 3, vec![0.2, 0.4, 0.8]);
        l.set_weights(w);
        let b = Matrix::from_vec(1, 1, vec![0.0]);
        l.set_biases(b);

        let input = Matrix::from_vec(3, 1, vec![0.2, 0.4, 0.8]);
        let output = l.forward(&input);

        assert_eq!(output.ncols(), 1);
        assert_eq!(output.nrows(), 1);
        assert_eq!(round(output.row(0)[0], 2), 0.84);
    }

    #[test]
    fn layer_forward2() {
        let mut l = Layer::new(3, 3, Activation::Relu);
        let w = Matrix::from_vec(3, 3, vec![1.0; 9]);
        l.set_weights(w);
        let b = Matrix::from_vec(3, 1, vec![0.0; 3]);
        l.set_biases(b);

        let input = Matrix::from_vec(3, 1, vec![1.0; 3]);
        let output = l.forward(&input);

        println!("\n--output: {:?}", output);
        assert_eq!(output, Matrix::from_vec(3, 1, vec![3.0, 3.0, 3.0]));
    }

    #[test]
    fn layer_set_weights1() {
        let mut l = Layer::new(3, 1, Activation::Relu);

        let w: Matrix<f64> = Matrix::from_vec(1, 3, vec![1.0; 3]);
        l.set_weights(w);
    }

    #[test]
    #[should_panic]
    fn layer_set_weights2() {
        let mut l = Layer::new(3, 1, Activation::Relu);

        let w: Matrix<f64> = Matrix::from_vec(3, 4, vec![1.0; 3]);
        l.set_weights(w);
    }

    #[test]
    fn layer_set_biases() {
        let mut l = Layer::new(3, 1, Activation::Relu);

        let b: Matrix<f64> = Matrix::from_vec(1, 1, vec![0.0]);
        l.set_biases(b);
    }

    #[test]
    fn layer_enc_fit() {
        // TODO: test layer_enc_fit
        // let mut l = Layer::new(3, 1, Activation::Relu);
        //
        // // set weights and biases of layer
        // let w: Matrix<f64> = Matrix::new(1, 3, vec![1.0; 3]);
        // l.set_weights(w);
        // let b: Matrix<f64> = Matrix::new(1, 1, vec![1.0]);
        // l.set_biases(b);
        //
        // // do forward pass so that derived results are set
        // let inputs: Matrix<f64> = Matrix::new(3, 1, vec![1.0;3]);
        // l.forward(&inputs);
        //
        // let fit = 0.1;
        // let enc_fit = l.enc_fit(fit);
        //
        // assert_eq!(enc_fit, vec![0.025])
    }

    #[test]
    fn layer_genes() {
        let l = Layer::new(3, 1, Activation::Relu);

        let genes = l.genes();
        assert_eq!(genes.len(), 4);
    }

    #[test]
    fn layer_gene_len() {
        let l = Layer::new(3, 1, Activation::Relu);

        assert_eq!(l.gene_len, 4);

        let l = Layer::new(3, 3, Activation::Relu);

        assert_eq!(l.gene_len, 12);
    }

    #[test]
    fn layer_set_genes() {
        let mut l = Layer::new(3, 1, Activation::Relu);
        let genes: Vec<f64> = vec![1.0; 4];
        l.set_genes(&genes);
        assert_eq!(l.genes(), genes);

        let mut l = Layer::new(3, 3, Activation::Relu);
        let genes: Vec<f64> = vec![1.0; 12];
        l.set_genes(&genes);
        assert_eq!(l.genes(), genes);
    }
}