tx · 93J7ZQZ4A2gErEDM76v3ufnNkgYrkUokkebJzjty2jH1

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.07 21:13 [3007974] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "93J7ZQZ4A2gErEDM76v3ufnNkgYrkUokkebJzjty2jH1",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1709835257654,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "5wBPiFUHF7GgawEopvhWp42mA3z5rd4iJrmpDXjADvD2hJR4Z7cBPrwusSAWdtwCu2o7SLGK2nEQQ8J3Fev1ALPK"
    ],
    "script": "base64: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",
    "height": 3007974,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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{-# STDLIB_VERSION 5 #-}
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{-# SCRIPT_TYPE ACCOUNT #-}
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{-# CONTENT_TYPE DAPP #-}
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let layer1Weights = [[4051769, 4062273], [-5948515, -6010085]]
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let layer1Biases = [-6307843, 2229872]
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let layer2Weights = [[-8372358, -8139317]]
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let layer2Biases = [4083679]
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func sigmoid (z) = {
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    let e = 2718281
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    let base = 1000000
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    let negativeZ = (-1 * z)
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    let expPart = fraction(e, negativeZ, base)
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    fraction(base, 1000000, (base + expPart))
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    }
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func dotProduct (a,b) = {
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    let product0 = fraction(a[0], b[0], 1000000)
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    let product1 = fraction(a[1], b[1], 1000000)
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    (product0 + product1)
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    }
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func forwardPass (input,weights,biases) = {
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    let sum0 = (dotProduct(input, weights[0]) + biases[0])
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    let sum1 = (dotProduct(input, weights[1]) + biases[1])
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    let sig0 = sigmoid(sum0)
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    let sig1 = sigmoid(sum1)
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[sig0, sig1]
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    }
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func xorNeuralNetwork (input1,input2) = {
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    let input = [input1, input2]
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    let hiddenLayerOutput = forwardPass(input, layer1Weights, layer1Biases)
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    let outputLayerSum = (dotProduct(hiddenLayerOutput, layer2Weights[0]) + layer2Biases[0])
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    let output = sigmoid(outputLayerSum)
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    output
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    }
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@Callable(i)
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func predict (inputData) = {
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    let input1 = if (if ((inputData == 0))
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        then true
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        else (inputData == 1))
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        then 0
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        else 1000000
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    let input2 = if (if ((inputData == 0))
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        then true
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        else (inputData == 2))
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        then 0
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        else 1000000
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    let result = xorNeuralNetwork(input1, input2)
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[IntegerEntry("result", result)]
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    }
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github/deemru/w8io/6500d08 
19.21 ms ◑