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#-------------------------------------------------------------

# THIS SCRIPT IMPLEMENTS A TRAINING ALGORITHM FOR RESTRICTED BOLTZMANN MACHINES
#
# The training algorithm uses one-step Contrastive Divergence as suggested in [Bengio, Y., Learning Deep 
# Architectures for AI, Foundations and Trends in Machine Learning, Volume 2 Issue 1, January 2009, p. 1–127]. 
# This implemention is slightly modified and uses mini-batches to speed up the learning process.
#
# INPUT PARAMETERS:
# --------------------------------------------------------------------------------------------
# NAME       TYPE   DEFAULT  MEANING
# --------------------------------------------------------------------------------------------
# X          String  ---- Location (on HDFS) to read the matrix X of feature vectors
# W 	     String  ---- Location to store the estimated RBM weights 
# A 	     String  ---- Location to store the visible units bias vector
# B 	     String  ---- Location to store the hidden units bias vector
# batchsize  Int     100  Number of observations in a single batch    
# vis        Int     ---- Number of units in the visible layer. If not specified this will be set 
#			  to the number of features in X
# hid        Int     2    Number of units in the hidden layer
# epochs     Int     10   Number of training epochs 
# alpha      Double  0.1  Learning rate
# fmt 	     String  text Matrix output format for W,A, and B, usually "text" or "csv"
# --------------------------------------------------------------------------------------------
# OUTPUT: Matrices containing weights and biases for the trained network
#
# 	OUTPUT SIZE:   OUTPUT CONTENTS:
# W	vis x hid      RBM weights
# A	1   x vis      Visible units biases
# B	1   x hid      Hidden units biases
#
# To run an observation through the trained network use the rbm_run.dml script. 
# Alternatively, you can simply compute P(h=1|v) using 1.0 / (1 + exp(-(X %*% W + B)) and 
# get the values for the hidden layer by sampling from P(h=1|v).
#

# HOW TO INVOKE THIS SCRIPT - EXAMPLE:
# hadoop jar SystemML.jar -f rbm.dml -nvargs X=$INPUT_DIR/X W=$OUTPUT_DIR/w A=$OUTPUT_DIR/a B=$OUTPUT_DIR/b batchsize=100 vis=10 hid=2 epochs=10 alpha=0.1


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

# Default values
learning_rate	    = ifdef($alpha, 0.1)
hidden_units_count  = ifdef($hid, 2)
epoch_count	    = ifdef($epochs, 10)
batch_size 	    = ifdef($batchsize, 100)
output_format	    = ifdef($fmt, "text")

print("BEGIN RBM MINIBATCH TRAINING SCRIPT")

print("Reading X...")
X = read($X)

# If number of visible units is not set, 
# set it to the number of features in X
visible_units_count = ifdef ($vis, ncol(X))

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

# 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)
}

# Initialise a weight matrix (visible_units_count x hidden_units_count)
w = 0.1 * rand(rows = visible_units_count, cols = hidden_units_count, pdf = "uniform")

# Setup biases and initialise with 0
a = matrix(0, rows = 1, cols = visible_units_count)    # Visible units bias
b = matrix(0, rows = 1, cols = hidden_units_count)     # Hidden units bias

# Train the RBM
print("Training starts...")

for (epoch in 1:epoch_count) {

  # Cumulative error for this epoch
  epoch_err = 0

  # Loop over each batch
  for (batch_start in seq(1, N, batch_size)) {

    # Compute the end of the batch for indexing
    batch_end = batch_start + batch_size - 1

    # Is this the last batch? 
    if (batch_end > N) {
      batch_end = N
    }

    # Present the current minibatch to the visible layer
    v_1 = X[batch_start:batch_end,]

    # Get the number of observations in v_1
    batch_N = nrow(v_1)

    # POSITIVE PHASE

    # Compute the probabilities P(h=1|v)
    p_h1_given_v = 1.0 / (1 + exp(-(v_1 %*% w + b)))   

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

    # NEGATIVE PHASE         
        
    # Compute the probabilities P(v2=1|h1)
    p_v2_given_h1  = 1.0 / (1 + exp(-(h1 %*% t(w) + a)))

    # Compute the probabilities P(h2=1|v2)          
    p_h2_given_v2 = 1.0 / (1 + exp(-(p_v2_given_h1 %*% w + b)))         

    # UPDATE WEIGHTS AND BIASES
    w = w + learning_rate * ((t(v_1) %*% p_h1_given_v - t(p_v2_given_h1) %*% p_h2_given_v2) / batch_N)        
    a = a + (learning_rate / batch_N) * (colSums(v_1) - colSums(p_v2_given_h1))
    b = b + (learning_rate / batch_N) * (colSums(p_h1_given_v) - colSums(p_h2_given_v2))          

    # COMPUTE ERROR    
    minibatch_err = sum((v_1 - p_v2_given_h1) ^ 2) / batch_N
    epoch_err = epoch_err + minibatch_err
  }
  
  print("Epoch " + epoch + " completed. Cummulative error is " + epoch_err)

}

# Save the RBM model
print("Saving RBM weights...")
write(w, $W, format=output_format)

print("Saving RBM biases...")
write(a, $A, format=output_format)
write(b, $B, format=output_format)

print("END OF RBM MINIBATCH TRAINING SCRIPT")

# -------------------------------------------- END OF RBM_MINIBATCH.DML --------------------------------------------