add julia version

julia/main.jl

@@ -0,0 +1,59 @@
+using JSON
+
+data = JSON.parsefile("../pytorch_model.json")
+
+means = data["means"]
+stds = data["stds"]
+hidden_weight = data["hidden.weight"]
+hidden_bias = data["hidden.bias"]
+output_weight = data["output.weight"]
+output_bias = data["output.bias"]
+
+relu(x) = max(0, x)
+
+sigmoid(x) = 1 / (1 + exp(-x))
+
+function scale_input(input)
+    return (input .- means) ./ stds
+end
+
+function calculate_layer(layer_size, parent_layer, weight, bias)
+    new_layer = []
+    for hl = 1:layer_size
+        node = 0
+        for parent = 1:length(parent_layer)
+            node += parent_layer[parent] * weight[hl][parent]
+        end
+        node += bias[hl]
+        push!(new_layer, node)
+    end
+    return new_layer
+end
+
+function evaluate(input)
+    scaled_input = scale_input(input)
+    hidden_layer = calculate_layer(
+        length(hidden_bias),
+        scaled_input,
+        hidden_weight,
+        hidden_bias,
+    )
+    hidden_layer = relu.(hidden_layer)
+    output_layer = calculate_layer(
+        length(output_bias),
+        hidden_layer,
+        output_weight,
+        output_bias,
+    )
+    output_layer = sigmoid.(output_layer)
+    return output_layer
+end
+
+ang = 30
+m = 1e24
+v = 2
+gamma = 0.6
+wp = wt = 1e-4
+println(evaluate([ang, v, m, gamma, wt, wp]))
+
+@timev evaluate([ang, v, m, gamma, wt, wp])