gistfile1.jl

push!(LOAD_PATH, "/Users/czhang/Desktop/Projects/dw_/julia/")
import DimmWitted
DimmWitted.set_libpath("/Users/czhang/Desktop/Projects/dw_/libdw_julia")

######################################
# The following function creates a
# synthetic data set:
#    - Data type is Cdouble
#    - Modle type is Array{Cdouble}
#
nexp = 100000
nfeat = 1024
examples = Array(Cdouble, nexp, nfeat+1)
for row = 1:nexp
	for col = 1:nfeat
		examples[row, col] = 1
	end
	if rand() > 0.8
		examples[row, nfeat+1] = 0
	else
		examples[row, nfeat+1] = 1
	end
end
model = Cdouble[0 for i = 1:nfeat]


######################################
# Define the loss function and gradient
# function for logistic regression
#
function loss(row::Array{Cdouble,1}, model::Array{Cdouble,1})
	@inbounds begin
		const label = row[length(row)]
		const nfeat = length(model)
		d = 0.0
		for i = 1:nfeat
			d = row[i]*model[i]
		end
		e = log(exp(d)+1.0)
	end
	return (-label * d + log(exp(d) + 1.0))
end

function grad(row::Array{Cdouble,1}, model::Array{Cdouble,1})
	@inbounds begin
		const label = row[length(row)]
		const nfeat = length(model)
		d = 0.0
		for i = 1:nfeat
			d = row[i]*model[i]
		end
		d = exp(-d)
  		Z = 0.00001 * (-label + 1.0/(1.0+d))
	  	for i = 1:nfeat
	  		model[i] = model[i] - row[i] * Z
	  	end
  	end
	return 1.0
end


######################################
# Create a DimmWitted object using data
# and model. You do not need to specify
# the type, they are infer'ed by the 
# open() function, which is parametric.
#
DimmWitted.open(examples, model)

######################################
# Register functions.
#
handle_loss = DimmWitted.register_row(loss)
handle_grad = DimmWitted.register_row(grad)

######################################
# Run 10 epoches.
#
for iepoch = 1:10
	rs = DimmWitted.exec(handle_loss)
	println("LOSS: ", rs/nexp)
	rs = DimmWitted.exec(handle_grad)
end