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# generates data to test linear regression

# $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 is 0/1. 0 suppresses noise, 1 will add noise to Y
# $9 is b, 0 disables intercept
# $10 controls sparsity in the generated data
# $11 output format

numSamples = $1
numFeatures = $2
maxFeatureValue = $3
maxWeight = $4
addNoise = $8
b = $9
fmt = $11

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

if( b != 0 ) {
	b_mat = Rand(rows=1, cols=1, min=b, max=b, pdf="uniform")
	w =  rbind(w, t(b_mat))
	Y = Y + b
}

noise = Rand(rows=numSamples, cols=1, pdf="normal", seed=0)
Y = Y + addNoise*noise

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