func TrainRBM() {
train_file := "./data_0.dat"
validation_file := "./data_1.dat"
class_sizes := []int{4, 2, 5, 1}
hidden_layer_size := 2
train_data_accessor := rbm.NewInstanceLoader(train_file, len(class_sizes))
defer train_data_accessor.Close()
validation_data_accessor := rbm.NewInstanceLoader(validation_file, len(class_sizes))
defer validation_data_accessor.Close()
class_biases, y_bias := rbm.GetBiases(class_sizes, train_data_accessor)
var rbm_m rbm.SparseClassRBM
(&rbm_m).Initialize(class_sizes, class_biases, hidden_layer_size, y_bias)
var trainer rbm.RBMTrainer
learning_rate := rbm.WeightT(0.001)
regularization := rbm.WeightT(0.0)
momentum := rbm.WeightT(0.0)
gen_learning_imp := rbm.WeightT(0.0)
gibs_chain_len := 1
trainer.Initialize(&rbm_m, train_data_accessor, validation_data_accessor,
learning_rate, regularization, momentum, gen_learning_imp, gibs_chain_len)
trainer.Train()
//Evaluate RBM
// test_file := "./data_2.dat"
}
func GenTrainData() {
rand.Seed(time.Now().Unix())
model := SimpleModel{
[][]rbm.WeightT{
{
0.1, 0.2, 0.3, 0.4,
}, {
0.5, 0.5,
}, {
0.7, 0.01, 0.09, 0.1, 0.1,
}, {
1.0,
},
},
[][]rbm.WeightT{
{
0.2, -0.3, 1.2, 3.5,
}, {
0.2, 0.3,
}, {
-0.01, 0.39, 0.2, -1.0, 0.09,
}, {
1.0,
},
},
0.8,
0.0,
}
instance_cnt := []int{100000, 20000, 40000}
prefix := "./data"
for i, c := range instance_cnt {
out_fd, err := os.Create(fmt.Sprintf("%s_%d.dat", prefix, i))
if err != nil {
fmt.Printf("Failed to create file: %s, %s.\n", out_fd, err)
return
}
defer out_fd.Close()
for j := 0; j < c; j++ {
x, y := model.GenerateInstance()
out_fd.WriteString(InstanceToString(x, y) + "\n")
}
}
validation_file := "./data_1.dat"
validation_data_accessor := rbm.NewInstanceLoader(validation_file, len(model.feature_distribution))
defer validation_data_accessor.Close()
validation_auc := rbm.ROCAuc(model, validation_data_accessor)
log_likelihood := rbm.LogLikelihood(model, validation_data_accessor)
rmse := rbm.RMSE(model, validation_data_accessor)
fmt.Printf("Max Validation AUC: %f\n", validation_auc)
fmt.Printf("Max Validation LogLikelihood: %f\n", log_likelihood)
fmt.Printf("max RMSE: %f\n", rmse)
}