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# generates random data to test multiclass SVM

# 100K dataset
# hadoop jar SystemDS.jar -f genRandData4MultiClassSVM.dml -args 100000 500 5 5 itau/svm/w_100k itau/svm/X_100k_500 itau/svm/y_100k 0 0 0.01

# 5M dataset
# hadoop jar SystemDS.jar -f genRandData4MultiClassSVM.dml -args 5000000 5000 5 5 itau/svm/w_5m itau/svm/X_5m_5k itau/svm/y_5m 0 0 0.001

# $1 is number of samples
# $2 is number of features (independent variables)
# $3 is maximum feature value (absolute value)
# $4 is maximum weight (absolute value)
# $5 is location to store generated weights
# $6 is location to store generated data
# $7 is location to store generated labels
# $8 addNoise. if 0 then no noise is added, to add noise set this to 1
# $9 is b, 0 disables intercept
# $10 controls sparsity in the generated data

numSamples = $1
numFeatures = $2
maxFeatureValue = $3
maxWeight = $4
addNoise = $8
b = $9

X = Rand(rows=numSamples, cols=numFeatures, min=-1, max=1, pdf="uniform", seed=0, sparsity=$10)
X = X * maxFeatureValue 

w = Rand(rows=numFeatures, cols=1, min=-1, max=1, pdf="uniform", seed=0)
w = w * maxWeight

ot = X%*%w
if(b!=0) {
	b_mat = Rand(rows=1, cols=1, min=b, max=b, pdf="uniform")
	w =  t(cbind(t(w), b_mat))
	ot = ot + b
}

prob = 1/(1+exp(-ot))
if(addNoise == 1){
	r = Rand(rows=numSamples, cols=1, min=0, max=1, pdf="uniform", seed=0)
}else{
	print("this data generator generates the same dataset for both noise=0 and noise=1")
	r = Rand(rows=numSamples, cols=1, min=0, max=1, pdf="uniform", seed=0)
	#r = Rand(rows=numSamples, cols=1, min=0.5, max=0.5, pdf="uniform")
}
Y = 1 - 2*(prob < r)
Y = (Y+3)/2

write(w, $5, format="binary")
write(X, $6, format="binary")
write(Y, $7, format="binary")
