#!/usr/bin/bash
  
  # DEPRECATED.  See local/rnnlm/run_tdnn.sh.
  
  # version that makes use of sparse features.
  # this will eventually be totally refactored and moved into steps/.
  
  dir=exp/rnnlm_data_prep
  vocab=data/vocab/words.txt
  embedding_dim=600
  feat_dim=$(tail -n 1 $dir/features.txt | awk '{print $1 + 1;}')
  # work out the number of splits.
  ns=$(rnnlm/get_num_splits.sh 1000000 data/text $dir/data_weights.txt)
  vocab_size=$(tail -n 1 $vocab |awk '{print $NF + 1}')
  
  # split the data into pieces that individual jobs will train on.
  # rnnlm/split_data.sh data/text $ns
  
  
  rnnlm/prepare_split_data.py --vocab-file=$vocab --data-weights-file=$dir/data_weights.txt \
                              --num-splits=$ns data/text  $dir/text
  
  . ./path.sh
  
  # cat >$dir/config <<EOF
  # input-node name=input dim=$embedding_dim
  # component name=affine1 type=NaturalGradientAffineComponent input-dim=$embedding_dim output-dim=$embedding_dim
  # component-node input=input name=affine1 component=affine1
  # output-node input=affine1 name=output
  # EOF
  
  mkdir -p $dir/configs
  cat >$dir/configs/network.xconfig <<EOF
  input dim=$embedding_dim name=input
  relu-renorm-layer name=tdnn1 dim=512 input=Append(0, IfDefined(-1))
  relu-renorm-layer name=tdnn2 dim=512 input=Append(0, IfDefined(-2))
  relu-renorm-layer name=tdnn3 dim=512 input=Append(0, IfDefined(-2))
  output-layer name=output include-log-softmax=false dim=$embedding_dim learning-rate-factor=0.125 max-change=0.125
  EOF
  
  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  nnet3-init $dir/configs/final.config - | nnet3-copy --learning-rate=0.0001 - $dir/0.rnnlm
  
  # we may later initialize this to identity for the first block.
  rnnlm/initialize_matrix.pl --first-element 1.0 --stddev 0.001 $feat_dim $embedding_dim > $dir/embedding.0.mat
  
  # alternative path:
  rnnlm/initialize_matrix.pl --first-column 1.0 $vocab_size $embedding_dim > $dir/word_embedding.0.mat
  
  
  rnnlm-train --use-gpu=no --read-rnnlm=$dir/0.rnnlm --write-rnnlm=$dir/1.rnnlm --read-embedding=$dir/embedding.0.mat \
              --read-sparse-word-features=$dir/word_feats.txt \
              --write-embedding=/$dir/embedding.1.mat "ark:rnnlm-get-egs --vocab-size=$vocab_size $dir/text/1.txt ark,t:- |"
  
  # or with GPU:
  rnnlm-train --rnnlm.max-param-change=0.5 --embedding.max-param-change=0.5 \
              --read-sparse-word-features=$dir/word_feats.txt \
               --use-gpu=yes --read-rnnlm=$dir/0.rnnlm --write-rnnlm=$dir/1.rnnlm --read-embedding=$dir/embedding.0.mat \
               --write-embedding=$dir/embedding.1.mat 'ark:for n in 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; do cat exp/rnnlm_data_prep/text/*.txt; done | rnnlm-get-egs --vocab-size=10003 - ark:- |'
  
  # and evaluate on dev ata:
  rnnlm-get-word-embedding $dir/word_feats.txt $dir/embedding.1.mat $dir/word_embedding.1.mat
  
  # get the dev-data proability
  rnnlm-get-egs --vocab-size=10003 $dir/text/dev.txt ark:- | \
   rnnlm-compute-prob --use-gpu=yes $dir/1.rnnlm $dir/word_embedding.1.mat ark:-
  
  # with GPU, no sparse features.
  rnnlm-train --rnnlm.max-param-change=0.5 --embedding.max-param-change=0.5 \
               --use-gpu=yes --read-rnnlm=$dir/0.rnnlm --write-rnnlm=$dir/1.rnnlm --read-embedding=$dir/word_embedding.0.mat \
              --write-embedding=$dir/embedding.1.mat 'ark:for n in 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; do cat exp/rnnlm_data_prep/text/*.txt; done | rnnlm-get-egs --vocab-size=10003 - ark:- |'
  
  
  # just a note on the unigram entropy of PTB training set:
  # awk '{for (n=1;n<=NF;n++) { count[$n]++; } count["</s>"]++; } END{ tot_count=0; tot_entropy=0.0; for(k in count) tot_count += count[k];  for (k in count) { p = count[k]*1.0/tot_count; tot_entropy += p*log(p); }  print "entropy is " -tot_entropy; }' <data/text/ptb.txt
  # 6.52933
  
  # .. and entropy of bigrams:
  # awk '{hist="<s>"; for (n=1;n<=NF;n++) { count[hist,$n]++; hist=$n; } count[hist,"</s>"]++; } END{ tot_count=0; tot_entropy=0.0; for(k in count) tot_count += count[k];  for (k in count) { p = count[k]*1.0/tot_count; tot_entropy += p*log(p); }  print "entropy is " -tot_entropy; }' <data/text/ptb.txt
  # 10.7482
  # in information theory, H(X) = H(Y) = 6.52, H(X,Y) = 10.7482, so H(Y | X) = 10.7482 - 6.52 = ***4.2282***, which
  # is the entropy of the next symbol given the preceding symbol.  this gives a limit on the expected training
  # objective given just a single word of context.