library(devtools)
load_all(".")
# convert iris to matrix
x = as.matrix(iris[,1:4])
y = as.vector(as.numeric(iris[,5]))
# make sure its binary
y = replace(y, y == 2, 0)
y = replace(y, y == 3, 0)
# SVM: budgeted SGD
cat ("\n# Budgeted SGD example \n")
model = SharkBSGDTrain (x, y, C = 0.0001, budget = 5, gamma = 1, epochs = 1, strategy = "Merge")
results = SharkBSGDPredict (x, model)
cat ("BSGD training error is ", sum(abs(y - results$predictions))/length(y), "\n")
# four-layer (deep) neural network with pretraining
cat("\n# Deep network example.\n")
model = DeepNetworkTrain (x, y, nHidden1 = 32, nHidden2 = 32)
results = DeepNetworkPredict (x, model)
# prediction is not argmaxed yet
networkPrediction = apply (results$prediction, 1, which.max) - 1
errors = sum(abs(y - networkPrediction))/length(y)
cat("Network produced ", errors, "errors.\n")
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