generating.py

import torch
import torch.utils.data as Data
from scipy.linalg import eigvalsh
import useful_utils
import numpy as np
import math
import params


def gen_x_Amatrix_y(n,m, N, cplx_flag, DEVICE):
    """create x, y, and Amatrix"""
    x_1 = torch.randn((N,n), dtype=torch.double,device=DEVICE)
    x_2 = torch.randn((N,n), dtype=torch.double,device=DEVICE)*1j*cplx_flag
    x= x_1 + x_2
    x_1_val = torch.randn((N,n), dtype=torch.double,device=DEVICE)
    x_2_val = torch.randn((N,n), dtype=torch.double,device=DEVICE)*1j*cplx_flag
    x_val = x_1_val + x_2_val
    Amatrix_1 = torch.randn((m,n), dtype=torch.double,device=DEVICE)
    Amatrix_2 = torch.randn((m,n), dtype=torch.double,device=DEVICE)*1j*cplx_flag
    scale = math.sqrt(2)**cplx_flag
    Amatrix = (Amatrix_1 + Amatrix_2)/scale
    y_trans = torch.abs(torch.linalg.matmul(Amatrix,x.T),device=DEVICE)  # y_i=|a_i x|
    y_trans_val = torch.abs(torch.linalg.matmul(Amatrix,x_val.T),device=DEVICE)  # y_i=|a_i x|
    return x, x_val, Amatrix, y_trans.T, y_trans_val.T
    # torch.save(x, 'x.pt')
    # torch.save(x_val, 'x_val.pt')
    # torch.save(Amatrix, 'Amatrix.pt')
    # torch.save(y_trans.T, 'y.pt')
    # torch.save(y_trans_val.T, 'y_val.pt')

# gen_x_Amatrix_y(params.n,params.m,params.N,params.cplx_flag)