rbm_predict.dml

#-------------------------------------------------------------
#
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# to you under the Apache License, Version 2.0 (the
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#-------------------------------------------------------------

# THIS SCRIPT USES A TRAINED RESTRICTED BOLTZMANN MACHINES TO PROCESS A SET OF OBSERVATIONS
#
# The RBM training is handled by the rbm_minibatch.dml script.
#
# INPUT PARAMETERS:
# --------------------------------------------------------------------------------------------
# NAME       TYPE   DEFAULT  MEANING
# --------------------------------------------------------------------------------------------
# X          String  --- Location (on HDFS) to read the matrix of observations X
# W 	     String  --- Location (on HDFS) to read the RBM's weight vector
# A 	     String  --- Location (on HDFS) to read the RBM's visible units bias vector
# B 	     String  --- Location (on HDFS) to read the RBM's hidden units bias vector
# O 	     String  --- Location to store the processed observations
# fmt 	     String  csv Matrix output format for O, usually "text" or "csv"
# --------------------------------------------------------------------------------------------
# OUTPUT: A matrix containing a sample of P(h=1|v) for each observation in X
#
# 	OUTPUT SIZE:   OUTPUT CONTENTS:
# O	N x ncol(W)    RBM outputs for each observation in X
#

# HOW TO INVOKE THIS SCRIPT - EXAMPLE:
# hadoop jar SystemDS.jar -f rbm_run.dml -nvargs X=$INPUT_DIR/X W=$INPUT_DIR/w A=$INPUT_DIR/a B=$INPUT_DIR/b O=$OUTPUT_DIR/O fmt="csv"


# -------------------------------------------- START OF RBM_MINIBATCH.DML --------------------------------------------

# Default values
output_format	      = ifdef($fmt, "csv")

print("BEGIN RBM SCRIPT")

print("Reading model data...")
#Read data set
X = read($X)

# Get the total number of observations
N = nrow(X)

# Read model coefficients
w = read($W)
a = read($A)
b = read($B)

hidden_units_count = ncol(w)

# Rescale to interval [0,1]
minX = min(X)
maxX = max(X)
if (minX < 0.0 | maxX > 1.0) {
  print("X not in [0,1]. Rescaling...")
  X = (X-minX)/(maxX-minX)
}

print("Running RBM...")

# Compute the probabilities P(h=1|v)
p_h1_given_v = 1.0 / (1 + exp(-(X %*% w + b)))

# Sample from P(h=1|v)
h1 = p_h1_given_v > rand(rows = N, cols = hidden_units_count)

print("Saving hidden layer sample...")
write(h1, $O, format=output_format)

print("END RBM SCRIPT")

# -------------------------------------------- END OF RBM_RUN.DML --------------------------------------------