/* -*- c++ -*- */ /* * Copyright 2004,2010 Free Software Foundation, Inc. * * This file is part of GNU Radio * * GNU Radio is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * GNU Radio is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with GNU Radio; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifdef HAVE_CONFIG_H #include "config.h" #endif #include <trellis_siso_combined_f.h> #include <gr_io_signature.h> #include <stdexcept> #include <assert.h> #include <iostream> static const float INF = 1.0e9; trellis_siso_combined_f_sptr trellis_make_siso_combined_f ( const fsm &FSM, int K, int S0, int SK, bool POSTI, bool POSTO, trellis_siso_type_t SISO_TYPE, int D, const std::vector<float> &TABLE, trellis_metric_type_t TYPE) { return gnuradio::get_initial_sptr(new trellis_siso_combined_f (FSM,K,S0,SK,POSTI,POSTO,SISO_TYPE,D,TABLE,TYPE)); } trellis_siso_combined_f::trellis_siso_combined_f ( const fsm &FSM, int K, int S0, int SK, bool POSTI, bool POSTO, trellis_siso_type_t SISO_TYPE, int D, const std::vector<float> &TABLE, trellis_metric_type_t TYPE) : gr_block ("siso_combined_f", gr_make_io_signature (1, -1, sizeof (float)), gr_make_io_signature (1, -1, sizeof (float))), d_FSM (FSM), d_K (K), d_S0 (S0), d_SK (SK), d_POSTI (POSTI), d_POSTO (POSTO), d_SISO_TYPE (SISO_TYPE), d_D (D), d_TABLE (TABLE), d_TYPE (TYPE)//, //d_alpha(FSM.S()*(K+1)), //d_beta(FSM.S()*(K+1)) { int multiple; if (d_POSTI && d_POSTO) multiple = d_FSM.I()+d_FSM.O(); else if(d_POSTI) multiple = d_FSM.I(); else if(d_POSTO) multiple = d_FSM.O(); else throw std::runtime_error ("Not both POSTI and POSTO can be false."); //printf("constructor: Multiple = %d\n",multiple); set_output_multiple (d_K*multiple); //what is the meaning of relative rate for a block with 2 inputs? //set_relative_rate ( multiple / ((double) d_FSM.I()) ); // it turns out that the above gives problems in the scheduler, so // let's try (assumption O>I) //set_relative_rate ( multiple / ((double) d_FSM.O()) ); // I am tempted to automate like this if(d_FSM.I() <= d_D) set_relative_rate ( multiple / ((double) d_D) ); else set_relative_rate ( multiple / ((double) d_FSM.I()) ); } void trellis_siso_combined_f::forecast (int noutput_items, gr_vector_int &ninput_items_required) { int multiple; if (d_POSTI && d_POSTO) multiple = d_FSM.I()+d_FSM.O(); else if(d_POSTI) multiple = d_FSM.I(); else if(d_POSTO) multiple = d_FSM.O(); else throw std::runtime_error ("Not both POSTI and POSTO can be false."); //printf("forecast: Multiple = %d\n",multiple); assert (noutput_items % (d_K*multiple) == 0); int input_required1 = d_FSM.I() * (noutput_items/multiple) ; int input_required2 = d_D * (noutput_items/multiple) ; //printf("forecast: Output requirements: %d\n",noutput_items); //printf("forecast: Input requirements: %d %d\n",input_required1,input_required2); unsigned ninputs = ninput_items_required.size(); assert(ninputs % 2 == 0); for (unsigned int i = 0; i < ninputs/2; i++) { ninput_items_required[2*i] = input_required1; ninput_items_required[2*i+1] = input_required2; } } inline float min(float a, float b) { return a <= b ? a : b; } inline float min_star(float a, float b) { return (a <= b ? a : b)-log(1+exp(a <= b ? a-b : b-a)); } void siso_algorithm_combined(int I, int S, int O, const std::vector<int> &NS, const std::vector<int> &OS, const std::vector< std::vector<int> > &PS, const std::vector< std::vector<int> > &PI, int K, int S0,int SK, bool POSTI, bool POSTO, float (*p2mymin)(float,float), int D, const std::vector<float> &TABLE, trellis_metric_type_t TYPE, const float *priori, const float *observations, float *post//, //std::vector<float> &alpha, //std::vector<float> &beta ) { float norm,mm,minm; std::vector<float> alpha(S*(K+1)); std::vector<float> beta(S*(K+1)); float *prioro = new float[O*K]; if(S0<0) { // initial state not specified for(int i=0;i<S;i++) alpha[0*S+i]=0; } else { for(int i=0;i<S;i++) alpha[0*S+i]=INF; alpha[0*S+S0]=0.0; } for(int k=0;k<K;k++) { // forward recursion calc_metric(O, D, TABLE, &(observations[k*D]), &(prioro[k*O]),TYPE); // calc metrics norm=INF; for(int j=0;j<S;j++) { minm=INF; for(unsigned int i=0;i<PS[j].size();i++) { //int i0 = j*I+i; mm=alpha[k*S+PS[j][i]]+priori[k*I+PI[j][i]]+prioro[k*O+OS[PS[j][i]*I+PI[j][i]]]; minm=(*p2mymin)(minm,mm); } alpha[(k+1)*S+j]=minm; if(minm<norm) norm=minm; } for(int j=0;j<S;j++) alpha[(k+1)*S+j]-=norm; // normalize total metrics so they do not explode } if(SK<0) { // final state not specified for(int i=0;i<S;i++) beta[K*S+i]=0; } else { for(int i=0;i<S;i++) beta[K*S+i]=INF; beta[K*S+SK]=0.0; } for(int k=K-1;k>=0;k--) { // backward recursion norm=INF; for(int j=0;j<S;j++) { minm=INF; for(int i=0;i<I;i++) { int i0 = j*I+i; mm=beta[(k+1)*S+NS[i0]]+priori[k*I+i]+prioro[k*O+OS[i0]]; minm=(*p2mymin)(minm,mm); } beta[k*S+j]=minm; if(minm<norm) norm=minm; } for(int j=0;j<S;j++) beta[k*S+j]-=norm; // normalize total metrics so they do not explode } if (POSTI && POSTO) { for(int k=0;k<K;k++) { // input combining norm=INF; for(int i=0;i<I;i++) { minm=INF; for(int j=0;j<S;j++) { mm=alpha[k*S+j]+prioro[k*O+OS[j*I+i]]+beta[(k+1)*S+NS[j*I+i]]; minm=(*p2mymin)(minm,mm); } post[k*(I+O)+i]=minm; if(minm<norm) norm=minm; } for(int i=0;i<I;i++) post[k*(I+O)+i]-=norm; // normalize metrics } for(int k=0;k<K;k++) { // output combining norm=INF; for(int n=0;n<O;n++) { minm=INF; for(int j=0;j<S;j++) { for(int i=0;i<I;i++) { mm= (n==OS[j*I+i] ? alpha[k*S+j]+priori[k*I+i]+beta[(k+1)*S+NS[j*I+i]] : INF); minm=(*p2mymin)(minm,mm); } } post[k*(I+O)+I+n]=minm; if(minm<norm) norm=minm; } for(int n=0;n<O;n++) post[k*(I+O)+I+n]-=norm; // normalize metrics } } else if(POSTI) { for(int k=0;k<K;k++) { // input combining norm=INF; for(int i=0;i<I;i++) { minm=INF; for(int j=0;j<S;j++) { mm=alpha[k*S+j]+prioro[k*O+OS[j*I+i]]+beta[(k+1)*S+NS[j*I+i]]; minm=(*p2mymin)(minm,mm); } post[k*I+i]=minm; if(minm<norm) norm=minm; } for(int i=0;i<I;i++) post[k*I+i]-=norm; // normalize metrics } } else if(POSTO) { for(int k=0;k<K;k++) { // output combining norm=INF; for(int n=0;n<O;n++) { minm=INF; for(int j=0;j<S;j++) { for(int i=0;i<I;i++) { mm= (n==OS[j*I+i] ? alpha[k*S+j]+priori[k*I+i]+beta[(k+1)*S+NS[j*I+i]] : INF); minm=(*p2mymin)(minm,mm); } } post[k*O+n]=minm; if(minm<norm) norm=minm; } for(int n=0;n<O;n++) post[k*O+n]-=norm; // normalize metrics } } else throw std::runtime_error ("Not both POSTI and POSTO can be false."); delete [] prioro; } int trellis_siso_combined_f::general_work (int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items) { assert (input_items.size() == 2*output_items.size()); int nstreams = output_items.size(); //printf("general_work:Streams: %d\n",nstreams); int multiple; if (d_POSTI && d_POSTO) multiple = d_FSM.I()+d_FSM.O(); else if(d_POSTI) multiple = d_FSM.I(); else if(d_POSTO) multiple = d_FSM.O(); else throw std::runtime_error ("Not both POSTI and POSTO can be false."); assert (noutput_items % (d_K*multiple) == 0); int nblocks = noutput_items / (d_K*multiple); //printf("general_work:Blocks: %d\n",nblocks); //for(int i=0;i<ninput_items.size();i++) //printf("general_work:Input items available: %d\n",ninput_items[i]); float (*p2min)(float, float) = NULL; if(d_SISO_TYPE == TRELLIS_MIN_SUM) p2min = &min; else if(d_SISO_TYPE == TRELLIS_SUM_PRODUCT) p2min = &min_star; for (int m=0;m<nstreams;m++) { const float *in1 = (const float *) input_items[2*m]; const float *in2 = (const float *) input_items[2*m+1]; float *out = (float *) output_items[m]; for (int n=0;n<nblocks;n++) { siso_algorithm_combined(d_FSM.I(),d_FSM.S(),d_FSM.O(), d_FSM.NS(),d_FSM.OS(),d_FSM.PS(),d_FSM.PI(), d_K,d_S0,d_SK, d_POSTI,d_POSTO, p2min, d_D,d_TABLE,d_TYPE, &(in1[n*d_K*d_FSM.I()]),&(in2[n*d_K*d_D]), &(out[n*d_K*multiple])//, //d_alpha,d_beta ); } } for (unsigned int i = 0; i < input_items.size()/2; i++) { consume(2*i,d_FSM.I() * noutput_items / multiple ); consume(2*i+1,d_D * noutput_items / multiple ); } return noutput_items; }