def main(): # define array receive_bit = np.zeros(const.BITN, dtype=int) transmit_bit = np.zeros(const.BITN, dtype=int) receive_signal = np.zeros((const.SYMBOLN + const.GI), dtype=complex) transmit_signal = np.zeros((const.SYMBOLN + const.GI), dtype=complex) try: f = open(const.FILENAME, 'w') except IOError as err: print("File error", str(err)) finally: print( "[%s] LOOPN=%d, symbol number=%d, SNR from %d~%d dB, channel=%s" % (time.asctime(time.localtime(time.time())), const.LOOPN, const.SYMBOLN, const.SNR_START, const.SNR_STOP, const.CHANNEL)) f.write( "[ %s ] LOOPN=%d, symbol number=%d, SNR from %d~%d dB, channel=%s" % (time.asctime(time.localtime(time.time())), const.LOOPN, const.SYMBOLN, const.SNR_START, const.SNR_STOP, const.CHANNEL)) CNR = 0.0 for Eb_N0 in range(const.SNR_START, const.SNR_STOP + 1): CNR = float(Eb_N0) + 3.0 for loop in range(const.LOOPN): # transmission transmitter.ofdm_transmitter(transmit_bit, transmit_signal) if const.CHANNEL == "AWGN": channel.AWGN(transmit_signal, receive_signal, CNR) elif const.CHANNEL == "RAYLEIGH": channel.Rayleigh(transmit_signal, receive_signal, CNR) receiver.ofdm_receiver(receive_signal, receive_bit) ber.ber(loop, transmit_bit, receive_bit, f, CNR) f.close()
def __init__(self, ref_data, output_dim): input_dim = ref_data.shape[1] ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data') rng = RandomStreams() ae_bricks = [] ae_input = ref_data_sh ae_costs = [] for i, (idim, odim) in enumerate(zip([input_dim] + ae_dims[:-1], ae_dims)): ae_mlp = MLP(activations=[ae_activations[i]], dims=[idim, odim], name='enc%i'%i) enc = ae_mlp.apply(ae_input) enc_n = ae_mlp.apply(ae_input + rng.normal(size=ae_input.shape, std=ae_f_noise_std)) ae_mlp_dec = MLP(activations=[ae_activations[i]], dims=[odim, idim], name='dec%i'%i) dec = ae_mlp_dec.apply(enc_n) cost = tensor.sqrt(((ae_input - dec) ** 2).sum(axis=1)).mean() + \ ae_l1_pen * abs(enc).sum(axis=1).mean() ae_costs.append(cost) ae_input = enc ae_bricks = ae_bricks + [ae_mlp, ae_mlp_dec] self.ae_costs = ae_costs ref_data_enc = ae_input # Construct the model j = tensor.lvector('j') r = ref_data_enc[j, :] x = tensor.fmatrix('x') y = tensor.ivector('y') # input_dim must be nr mlp = MLP(activations=activation_functions, dims=[ae_dims[-1]] + hidden_dims + [n_inter], name='inter_gen') mlp2 = MLP(activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name='end_mlp') inter_weights = mlp.apply(r) if inter_bias == None: ibias = Bias(n_inter) ibias.biases_init = Constant(0) ibias.initialize() inter = ibias.apply(tensor.dot(x, inter_weights)) else: inter = tensor.dot(x, inter_weights) - inter_bias inter = inter_act_fun.apply(inter) final = mlp2.apply(inter) cost = Softmax().categorical_cross_entropy(y, final) confidence = Softmax().apply(final) pred = final.argmax(axis=1) # error_rate = tensor.neq(y, pred).mean() ber = balanced_error_rate.ber(y, pred) # Initialize parameters for brick in ae_bricks + [mlp, mlp2]: brick.weights_init = IsotropicGaussian(0.01) brick.biases_init = Constant(0.001) brick.initialize() # apply regularization cg = ComputationGraph([cost, ber]) if r_dropout != 0: # - dropout on input vector r : r_dropout cg = apply_dropout(cg, [r], r_dropout) if x_dropout != 0: cg = apply_dropout(cg, [x], x_dropout) if s_dropout != 0: # - dropout on intermediate layers of first mlp : s_dropout s_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([inter_weights]))) - set([inter_weights])) cg = apply_dropout(cg, s_dropout_vars, s_dropout) if i_dropout != 0: # - dropout on input to second mlp : i_dropout cg = apply_dropout(cg, [inter], i_dropout) if a_dropout != 0: # - dropout on hidden layers of second mlp : a_dropout a_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([final]))) - set([inter_weights]) - set(s_dropout_vars)) cg = apply_dropout(cg, a_dropout_vars, a_dropout) if r_noise_std != 0: cg = apply_noise(cg, [r], r_noise_std) if w_noise_std != 0: # - apply noise on weight variables weight_vars = VariableFilter(roles=[WEIGHT])(cg) cg = apply_noise(cg, weight_vars, w_noise_std) [cost_reg, ber_reg] = cg.outputs if s_l1pen != 0: s_weights = VariableFilter(bricks=mlp.linear_transformations, roles=[WEIGHT])(cg) cost_reg = cost_reg + s_l1pen * sum(abs(w).sum() for w in s_weights) if i_l1pen != 0: cost_reg = cost_reg + i_l1pen * abs(inter).sum() if a_l1pen != 0: a_weights = VariableFilter(bricks=mlp2.linear_transformations, roles=[WEIGHT])(cg) cost_reg = cost_reg + a_l1pen * sum(abs(w).sum() for w in a_weights) self.cost = cost self.cost_reg = cost_reg self.ber = ber self.ber_reg = ber_reg self.pred = pred self.confidence = confidence
def __init__(self, ref_data, output_dim): input_dim = ref_data.shape[1] ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data') rng = RandomStreams() ae_bricks = [] ae_input = ref_data_sh ae_costs = [] for i, (idim, odim) in enumerate(zip([input_dim] + ae_dims[:-1], ae_dims)): ae_mlp = MLP(activations=[ae_activations[i]], dims=[idim, odim], name='enc%i' % i) enc = ae_mlp.apply(ae_input) enc_n = ae_mlp.apply( ae_input + rng.normal(size=ae_input.shape, std=ae_f_noise_std)) ae_mlp_dec = MLP(activations=[ae_activations[i]], dims=[odim, idim], name='dec%i' % i) dec = ae_mlp_dec.apply(enc_n) cost = tensor.sqrt(((ae_input - dec) ** 2).sum(axis=1)).mean() + \ ae_l1_pen * abs(enc).sum(axis=1).mean() ae_costs.append(cost) ae_input = enc ae_bricks = ae_bricks + [ae_mlp, ae_mlp_dec] self.ae_costs = ae_costs ref_data_enc = ae_input # Construct the model j = tensor.lvector('j') r = ref_data_enc[j, :] x = tensor.fmatrix('x') y = tensor.ivector('y') # input_dim must be nr mlp = MLP(activations=activation_functions, dims=[ae_dims[-1]] + hidden_dims + [n_inter], name='inter_gen') mlp2 = MLP(activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name='end_mlp') inter_weights = mlp.apply(r) if inter_bias == None: ibias = Bias(n_inter) ibias.biases_init = Constant(0) ibias.initialize() inter = ibias.apply(tensor.dot(x, inter_weights)) else: inter = tensor.dot(x, inter_weights) - inter_bias inter = inter_act_fun.apply(inter) final = mlp2.apply(inter) cost = Softmax().categorical_cross_entropy(y, final) confidence = Softmax().apply(final) pred = final.argmax(axis=1) # error_rate = tensor.neq(y, pred).mean() ber = balanced_error_rate.ber(y, pred) # Initialize parameters for brick in ae_bricks + [mlp, mlp2]: brick.weights_init = IsotropicGaussian(0.01) brick.biases_init = Constant(0.001) brick.initialize() # apply regularization cg = ComputationGraph([cost, ber]) if r_dropout != 0: # - dropout on input vector r : r_dropout cg = apply_dropout(cg, [r], r_dropout) if x_dropout != 0: cg = apply_dropout(cg, [x], x_dropout) if s_dropout != 0: # - dropout on intermediate layers of first mlp : s_dropout s_dropout_vars = list( set( VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([inter_weights]))) - set([inter_weights])) cg = apply_dropout(cg, s_dropout_vars, s_dropout) if i_dropout != 0: # - dropout on input to second mlp : i_dropout cg = apply_dropout(cg, [inter], i_dropout) if a_dropout != 0: # - dropout on hidden layers of second mlp : a_dropout a_dropout_vars = list( set( VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([final]))) - set([inter_weights]) - set(s_dropout_vars)) cg = apply_dropout(cg, a_dropout_vars, a_dropout) if r_noise_std != 0: cg = apply_noise(cg, [r], r_noise_std) if w_noise_std != 0: # - apply noise on weight variables weight_vars = VariableFilter(roles=[WEIGHT])(cg) cg = apply_noise(cg, weight_vars, w_noise_std) [cost_reg, ber_reg] = cg.outputs if s_l1pen != 0: s_weights = VariableFilter(bricks=mlp.linear_transformations, roles=[WEIGHT])(cg) cost_reg = cost_reg + s_l1pen * sum( abs(w).sum() for w in s_weights) if i_l1pen != 0: cost_reg = cost_reg + i_l1pen * abs(inter).sum() if a_l1pen != 0: a_weights = VariableFilter(bricks=mlp2.linear_transformations, roles=[WEIGHT])(cg) cost_reg = cost_reg + a_l1pen * sum( abs(w).sum() for w in a_weights) self.cost = cost self.cost_reg = cost_reg self.ber = ber self.ber_reg = ber_reg self.pred = pred self.confidence = confidence
def __init__(self, ref_data, output_dim): ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data') # Construct the model j = tensor.lvector('j') x = tensor.fmatrix('x') y = tensor.ivector('y') last_outputs = [] s_dropout_vars = [] r_dropout_vars = [] i_dropout_vars = [] for i in range(nparts): fs = numpy.random.binomial(1, part_r_proba, size=(ref_data.shape[1], )) input_dim = int(fs.sum()) fs_sh = theano.shared(fs) r = ref_data_sh[j, :][:, fs_sh.nonzero()[0]] mlp = MLP(activations=activation_functions, dims=[input_dim] + hidden_dims + [n_inter], name='inter_gen_%d' % i) mlp2 = MLP(activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name='end_mlp_%d' % i) inter_weights = mlp.apply(r) ibias = Bias(n_inter, name='inter_bias_%d' % i) inter = ibias.apply(tensor.dot(x, inter_weights)) inter = inter_act_fun.apply(inter) out = mlp2.apply(inter) last_outputs.append(out) r_dropout_vars.append(r) s_dropout_vars = s_dropout_vars + (VariableFilter( bricks=[Tanh], name='output')(ComputationGraph([inter_weights ]))) i_dropout_vars.append(inter) # Initialize parameters for brick in [mlp, mlp2, ibias]: brick.weights_init = IsotropicGaussian(0.01) brick.biases_init = Constant(0.001) brick.initialize() final = tensor.concatenate([o[:, :, None] for o in last_outputs], axis=2).mean(axis=2) cost = Softmax().categorical_cross_entropy(y, final) confidence = Softmax().apply(final) pred = final.argmax(axis=1) # error_rate = tensor.neq(y, pred).mean() ber = balanced_error_rate.ber(y, pred) # apply regularization cg = ComputationGraph([cost, ber]) if r_noise_std != 0: cg = apply_noise(cg, r_dropout_vars, r_noise_std) if w_noise_std != 0: # - apply noise on weight variables weight_vars = VariableFilter(roles=[WEIGHT])(cg) cg = apply_noise(cg, weight_vars, w_noise_std) if s_dropout != 0: cg = apply_dropout(cg, s_dropout_vars, s_dropout) if x_dropout != 0: cg = apply_dropout(cg, [x], x_dropout) if r_dropout != 0: cg = apply_dropout(cg, r_dropout_vars, r_dropout) if i_dropout != 0: cg = apply_dropout(cg, i_dropout_vars, i_dropout) [cost_reg, ber_reg] = cg.outputs self.cost = cost self.cost_reg = cost_reg self.ber = ber self.ber_reg = ber_reg self.pred = pred self.confidence = confidence
def __init__(self, ref_data, output_dim): ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data') # Construct the model j = tensor.lvector('j') x = tensor.fmatrix('x') y = tensor.ivector('y') last_outputs = [] s_dropout_vars = [] r_dropout_vars = [] i_dropout_vars = [] for i in range(nparts): fs = numpy.random.binomial(1, part_r_proba, size=(ref_data.shape[1],)) input_dim = int(fs.sum()) fs_sh = theano.shared(fs) r = ref_data_sh[j, :][:, fs_sh.nonzero()[0]] mlp = MLP(activations=activation_functions, dims=[input_dim] + hidden_dims + [n_inter], name='inter_gen_%d'%i) mlp2 = MLP(activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name='end_mlp_%d'%i) inter_weights = mlp.apply(r) ibias = Bias(n_inter, name='inter_bias_%d'%i) inter = ibias.apply(tensor.dot(x, inter_weights)) inter = inter_act_fun.apply(inter) out = mlp2.apply(inter) last_outputs.append(out) r_dropout_vars.append(r) s_dropout_vars = s_dropout_vars + ( VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([inter_weights])) ) i_dropout_vars.append(inter) # Initialize parameters for brick in [mlp, mlp2, ibias]: brick.weights_init = IsotropicGaussian(0.01) brick.biases_init = Constant(0.001) brick.initialize() final = tensor.concatenate([o[:, :, None] for o in last_outputs], axis=2).mean(axis=2) cost = Softmax().categorical_cross_entropy(y, final) confidence = Softmax().apply(final) pred = final.argmax(axis=1) # error_rate = tensor.neq(y, pred).mean() ber = balanced_error_rate.ber(y, pred) # apply regularization cg = ComputationGraph([cost, ber]) if r_noise_std != 0: cg = apply_noise(cg, r_dropout_vars, r_noise_std) if w_noise_std != 0: # - apply noise on weight variables weight_vars = VariableFilter(roles=[WEIGHT])(cg) cg = apply_noise(cg, weight_vars, w_noise_std) if s_dropout != 0: cg = apply_dropout(cg, s_dropout_vars, s_dropout) if x_dropout != 0: cg = apply_dropout(cg, [x], x_dropout) if r_dropout != 0: cg = apply_dropout(cg, r_dropout_vars, r_dropout) if i_dropout != 0: cg = apply_dropout(cg, i_dropout_vars, i_dropout) [cost_reg, ber_reg] = cg.outputs self.cost = cost self.cost_reg = cost_reg self.ber = ber self.ber_reg = ber_reg self.pred = pred self.confidence = confidence
def __init__(self, ref_data, output_dim): if pca_dims is not None: covmat = numpy.dot(ref_data.T, ref_data) ev, evec = numpy.linalg.eig(covmat) best_i = ev.argsort()[-pca_dims:] best_evecs = evec[:, best_i] best_evecs = best_evecs / numpy.sqrt( (best_evecs**2).sum(axis=0)) #normalize ref_data = numpy.dot(ref_data, best_evecs) input_dim = ref_data.shape[1] ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data') # Construct the model j = tensor.lvector('j') r = ref_data_sh[j, :] x = tensor.fmatrix('x') y = tensor.ivector('y') # input_dim must be nr mlp = MLP(activations=activation_functions, dims=[input_dim] + hidden_dims + [n_inter], name='inter_gen') mlp2 = MLP(activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name='end_mlp') inter_weights = mlp.apply(r) if inter_bias == None: ibias = Bias(n_inter) ibias.biases_init = Constant(0) ibias.initialize() inter = ibias.apply(tensor.dot(x, inter_weights)) else: inter = tensor.dot(x, inter_weights) - inter_bias inter = inter_act_fun.apply(inter) final = mlp2.apply(inter) cost = Softmax().categorical_cross_entropy(y, final) confidence = Softmax().apply(final) pred = final.argmax(axis=1) # error_rate = tensor.neq(y, pred).mean() ber = balanced_error_rate.ber(y, pred) # Initialize parameters for brick in [mlp, mlp2]: brick.weights_init = IsotropicGaussian(0.01) brick.biases_init = Constant(0.001) brick.initialize() # apply regularization cg = ComputationGraph([cost, ber]) if r_dropout != 0: # - dropout on input vector r : r_dropout cg = apply_dropout(cg, [r], r_dropout) if x_dropout != 0: cg = apply_dropout(cg, [x], x_dropout) if s_dropout != 0: # - dropout on intermediate layers of first mlp : s_dropout s_dropout_vars = list( set( VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([inter_weights]))) - set([inter_weights])) cg = apply_dropout(cg, s_dropout_vars, s_dropout) if i_dropout != 0: # - dropout on input to second mlp : i_dropout cg = apply_dropout(cg, [inter], i_dropout) if a_dropout != 0: # - dropout on hidden layers of second mlp : a_dropout a_dropout_vars = list( set( VariableFilter(bricks=[Tanh], name='output') (ComputationGraph([final]))) - set([inter_weights]) - set(s_dropout_vars)) cg = apply_dropout(cg, a_dropout_vars, a_dropout) if r_noise_std != 0: cg = apply_noise(cg, [r], r_noise_std) if w_noise_std != 0: # - apply noise on weight variables weight_vars = VariableFilter(roles=[WEIGHT])(cg) cg = apply_noise(cg, weight_vars, w_noise_std) [cost_reg, ber_reg] = cg.outputs if s_l1pen != 0: s_weights = VariableFilter(bricks=mlp.linear_transformations, roles=[WEIGHT])(cg) cost_reg = cost_reg + s_l1pen * sum( abs(w).sum() for w in s_weights) if i_l1pen != 0: cost_reg = cost_reg + i_l1pen * abs(inter).sum() if a_l1pen != 0: a_weights = VariableFilter(bricks=mlp2.linear_transformations, roles=[WEIGHT])(cg) cost_reg = cost_reg + a_l1pen * sum( abs(w).sum() for w in a_weights) self.cost = cost self.cost_reg = cost_reg self.ber = ber self.ber_reg = ber_reg self.pred = pred self.confidence = confidence
def main(): rrcos = rrcosfilter(NBAUDS * UPSAMPLE, ROLL_OFF, 1. / BAUD_RATE, SAMPLE_RATE)[1] rrcos = rrcos / np.sqrt(UPSAMPLE) rrcos_fixed = arrayFixedInt(COEF_NBITS, COEF_FBITS, rrcos) prbs_r = prbs(SEED_R) tx_r = tx(rrcos_fixed, UPSAMPLE, COEF_NBITS, COEF_FBITS, TX_NBITS, TX_FBITS) rx_r = rx(rrcos_fixed, UPSAMPLE, COEF_NBITS, COEF_FBITS, TX_NBITS, TX_FBITS) ber_r = ber(SEQ_LEN) rrcos_float = [i.fValue for i in rrcos_fixed] prbs_r_v = [] tx_r_v = [] rx_r_v = [] rx_full_v = [] prbs_r.reset() tx_r.reset() rx_r.reset() ber_r.reset() phase = DX_SWITCH_SEL prbs_r_s = prbs_r.prbs_out tx_r_s = tx_r.tx_out rx_r_s = rx_r.rx_out enable_prbs = 0 enable_tx = 1 enable_rx = 1 enable_ber = 0 counter = 0 for i in range(NCLK): prbs_r_s = prbs_r.prbs_out tx_r_s = tx_r.tx_out rx_r_s = rx_r.rx_out rx_full_out = rx_r.rx_full_out prbs_r_v.append(prbs_r_s) tx_r_v.append(tx_r_s.fValue) rx_r_v.append(rx_r_s) rx_full_v.append(rx_full_out.fValue) prbs_r.run(enable_prbs) ber_r.run(prbs_r_s, rx_r_s, enable_ber) rx_r.run(tx_r_s, phase, enable_rx) tx_r.run(prbs_r_s, enable_tx) if counter == 0: enable_prbs = 1 enable_ber = 1 else: enable_prbs = 0 enable_ber = 0 counter = (counter + 1) % 4 vector = zip(range(NCLK), prbs_r_v, tx_r_v, rx_full_v, rx_r_v) """ for i in vector[0:20]: print i exit() """ plt.figure() plt.grid() plt.plot(tx_r_v[:200]) plt.figure() plt.grid() plt.plot(rx_full_v[:200]) rx_a = arrayFixedInt(8, 7, rx_full_v[:200]) rx_a = [i.fValue for i in rx_a] plt.figure() plt.grid() plt.plot(rx_a) """ eyediagram(rx_full_v[12:], 4, 1, UPSAMPLE) rrcos_float = [i.fValue for i in rrcos_fixed] H,A,F = resp_freq(rrcos_float, 1./BAUD_RATE, 512) plt.figure() plt.grid() plt.semilogx(F, 20*np.log(H)) plt.figure() plt.grid() plt.plot([i.fValue for i in rrcos_fixed]) """ plt.show()