#!/bin/sh

train=../mf-train

predict=../mf-predict

##########################################################################

# Build package if no binary found and this script is exectuted via the

# following command.

# libmf/demo > sh demo.sh

##########################################################################

if [ ! -s $train ] || [ ! -s $predict ]

then

(cd .. && make)

fi

##########################################################################

# Real-valued matrix factorization (RVMF)

##########################################################################

echo "--------------------------------"

echo "Real-valued matrix factorization"

echo "--------------------------------"

# In-memory training with holdout valudation

$train -f 0 -l2 0.05 -k 100 -t 10 -p real_matrix.te.txt real_matrix.tr.txt rvmf_model.txt

# Do prediction and show MAE

$predict -e 1 real_matrix.te.txt rvmf_model.txt rvmf_output.txt

##########################################################################

# Binary matrix factorization (BMF)

##########################################################################

echo "---------------------------"

echo "binary matrix factorization"

echo "---------------------------"

# In-memory training with holdout valudation

$train -f 5 -l2 0.01 -k 64 -p binary_matrix.te.txt binary_matrix.tr.txt bmf_model.txt

# Do prediction and show accuracy

$predict -e 6 binary_matrix.te.txt bmf_model.txt bmf_output.txt

##########################################################################

# One-class matrix factorization (OCMF)

##########################################################################

echo "-----------------------------------------------------------------"

echo "one-class matrix factorization using a stochastic gradient method"

echo "-----------------------------------------------------------------"

# In-memory training with holdout validation

$train -f 10 -l2 0.01 -k 32 -p all_one_matrix.te.txt all_one_matrix.tr.txt ocmf_model.txt

# Do prediction and show row-oriented MPR

$predict -e 10 all_one_matrix.te.txt ocmf_model.txt ocmf_output.txt

# Do prediction and show row-oriented AUC

$predict -e 12 all_one_matrix.te.txt ocmf_model.txt ocmf_output.txt

echo "----------------------------------------------------------------"

echo "one-class matrix factorization using a coordinate descent method"

echo "----------------------------------------------------------------"

# In-memory training with holdout validation

$train -f 12 -l2 0.01 -k 32 -a 0.001 -c 0.0001 -p all_one_matrix.te.txt all_one_matrix.tr.txt ocmf_model.txt

# Do prediction and show row-oriented MPR

$predict -e 10 all_one_matrix.te.txt ocmf_model.txt ocmf_output.txt

# Do prediction and show row-oriented AUC

$predict -e 12 all_one_matrix.te.txt ocmf_model.txt ocmf_output.txt