utils.py

__author__ = 'yihanjiang'
import torch
import numpy as np
import math

def errors_ber(y_true, y_pred, positions = 'default'):
    y_true = y_true.view(y_true.shape[0], -1, 1)
    y_pred = y_pred.view(y_pred.shape[0], -1, 1)

    myOtherTensor = torch.ne(torch.round(y_true), torch.round(y_pred)).float()
    if positions == 'default':
        res = sum(sum(myOtherTensor))/(myOtherTensor.shape[0]*myOtherTensor.shape[1])
    else:
        res = torch.mean(myOtherTensor, dim=0).type(torch.FloatTensor)
        for pos in positions:
            res[pos] = 0.0
        res = torch.mean(res)
    return res

def errors_ber_list(y_true, y_pred):
    block_len = y_true.shape[1]
    y_true = y_true.view(y_true.shape[0], -1)
    y_pred = y_pred.view(y_pred.shape[0], -1)

    myOtherTensor = torch.ne(torch.round(y_true), torch.round(y_pred))
    res_list_tensor = torch.sum(myOtherTensor, dim = 1).type(torch.FloatTensor)/block_len

    return res_list_tensor


def errors_ber_pos(y_true, y_pred, discard_pos = []):
    y_true = y_true.view(y_true.shape[0], -1, 1)
    y_pred = y_pred.view(y_pred.shape[0], -1, 1)

    myOtherTensor = torch.ne(torch.round(y_true), torch.round(y_pred)).float()

    tmp =  myOtherTensor.sum(0)/myOtherTensor.shape[0]
    res = tmp.squeeze(1)
    return res

def code_power(the_codes):
    the_codes = the_codes.cpu().numpy()
    the_codes = np.abs(the_codes)**2
    the_codes = the_codes.sum(2)/the_codes.shape[2]
    tmp =  the_codes.sum(0)/the_codes.shape[0]
    res = tmp
    return res

def errors_bler(y_true, y_pred, positions = 'default'):

    y_true = y_true.view(y_true.shape[0], -1, 1)
    y_pred = y_pred.view(y_pred.shape[0], -1, 1)

    decoded_bits = torch.round(y_pred)
    X_test       = torch.round(y_true)
    tp0 = (abs(decoded_bits-X_test)).view([X_test.shape[0],X_test.shape[1]])
    tp0 = tp0.cpu().numpy()

    if positions == 'default':
        bler_err_rate = sum(np.sum(tp0,axis=1)>0)*1.0/(X_test.shape[0])
    else:
        for pos in positions:
            tp0[:, pos] = 0.0
        bler_err_rate = sum(np.sum(tp0,axis=1)>0)*1.0/(X_test.shape[0])

    return bler_err_rate

# note there are a few definitions of SNR. In our result, we stick to the following SNR setup.
def snr_db2sigma(train_snr):
    return 10**(-train_snr*1.0/20)

def snr_sigma2db(train_sigma):
    try:
        return -20.0 * math.log(train_sigma, 10)
    except:
        return -20.0 * torch.log10(train_sigma)