reservoir_weights.py

# -*- coding: utf-8 -*-
"""
Created on Tue Jun 14 12:04:41 2016

@author: fox
"""
import mdp
import scipy
import numpy as np
from scipy.sparse.linalg import eigs


'''
This script contains the code to genrate sparse input and reservoir weight matrices. Here we used the the sparse matrices
for generating the weights.

Note: To compensate this change we modified Oger reservoir_nodes.py to all dot product of sparse matrics with dense
         matrics'''
 
def generate_sparse_w(output_size, specrad, fan_in_res=10, seed=1):
    converged = False
    mdp.numx.random.seed(seed)

    # Initialize reservoir weight matrix from a normal distribution
    nrentries = mdp.numx.int32(output_size * fan_in_res)
    # Keep generating random matrices until convergence
    while not converged:
        try:
            #$%ij = mdp.numx.zeros((2,nrentries))
            ij = mdp.numx.random.randint(0,output_size,(2,nrentries))
            datavec =  mdp.numx.random.randn(nrentries)
            w = scipy.sparse.csc_matrix((datavec, ij),dtype=np.float32, shape=(output_size, output_size))
            we = eigs(w,return_eigenvectors=False,k=3)
            converged = True
            w *= (specrad / mdp.numx.amax(mdp.numx.absolute(we)))
        except:
            pass
    return w.toarray()
    #return w

def generate_sparse_w_in(output_size, input_size, scaling, fan_in_i=2,seed=1):
    import scipy.sparse

    mdp.numx.random.seed(seed)
    # Initialize reservoir weight matrix from a normal distribution
    nrentries = mdp.numx.int32(output_size * fan_in_i)
    # Keep generating random matrices until convergence
    ij = mdp.numx.zeros((2,nrentries))
    ij[0,:] = mdp.numx.random.randint(0,output_size,(1,nrentries))
    ij[1,:] = mdp.numx.random.randint(0,input_size,(1,nrentries))
    datavec =  mdp.numx.random.randn(nrentries)
    w = scaling * scipy.sparse.csc_matrix((datavec, ij),dtype=np.float32, shape=(output_size, input_size))
    return w.toarray()
    #return w