Example: Data Preprocessing Libraries

1. DataNormalization Library

Ensures data used in AI models is clean, formatted correctly, and ready for analysis.

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

library DataNormalization {
    
    // Function to normalize data within a specific range
    function normalize(uint256[] memory data, uint256 minRange, uint256 maxRange) internal pure returns (uint256[] memory) {
        uint256[] memory normalizedData = new uint256[](data.length);
        uint256 min = data[0];
        uint256 max = data[0];
        
        // Find the min and max values in the data array
        for (uint i = 1; i < data.length; i++) {
            if (data[i] < min) {
                min = data[i];
            }
            if (data[i] > max) {
                max = data[i];
            }
        }
        
        // Normalize the data
        for (uint i = 0; i < data.length; i++) {
            normalizedData[i] = ((data[i] - min) * (maxRange - minRange)) / (max - min) + minRange;
        }
        
        return normalizedData;
    }
}

2. DataTransformation Library

Handles data transformation tasks like encoding categorical data and standardizing datasets.

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

library DataTransformation {
    
    // Function to encode categorical data to numerical values
    function encodeCategorical(string[] memory categories) internal pure returns (uint256[] memory) {
        uint256[] memory encodedData = new uint256[](categories.length);
        for (uint i = 0; i < categories.length; i++) {
            encodedData[i] = uint256(keccak256(abi.encodePacked(categories[i])));
        }
        return encodedData;
    }

    // Function to standardize data to have zero mean and unit variance
    function standardize(uint256[] memory data) internal pure returns (int256[] memory) {
        int256[] memory standardizedData = new int256[](data.length);
        int256 mean = 0;
        int256 variance = 0;
        
        // Calculate the mean
        for (uint i = 0; i < data.length; i++) {
            mean += int256(data[i]);
        }
        mean /= int256(data.length);
        
        // Calculate the variance
        for (uint i = 0; i < data.length; i++) {
            variance += (int256(data[i]) - mean) * (int256(data[i]) - mean);
        }
        variance /= int256(data.length);
        
        // Standardize the data
        for (uint i = 0; i < data.length; i++) {
            standardizedData[i] = (int256(data[i]) - mean) * 1e18 / sqrt(variance); // Multiplying by 1e18 to retain precision
        }
        
        return standardizedData;
    }
    
    // Helper function to calculate square root (Babylonian method)
    function sqrt(int256 x) internal pure returns (int256 y) {
        int256 z = (x + 1) / 2;
        y = x;
        while (z < y) {
            y = z;
            z = (x / z + z) / 2;
        }
    }
}

Example of usage

Here's a smart contract example that integrates the DataNormalization and DataTransformation libraries within a use case that manages tokenized assets, incorporating data normalization and transformation for AI model predictions in asset management.

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

import "./DataNormalization.sol";
import "./DataTransformation.sol";

contract AssetManagement {
    using DataNormalization for uint256[];
    using DataTransformation for string[];
    using DataTransformation for uint256[];

    struct Asset {
        uint256 id;
        string name;
        uint256 value;
        uint256[] historicalData;
    }

    mapping(uint256 => Asset) public assets;
    uint256 public nextAssetId;
    address public owner;

    event AssetAdded(uint256 assetId, string name, uint256 value);
    event DataNormalized(uint256 assetId, uint256[] normalizedData);
    event DataEncoded(uint256 assetId, uint256[] encodedData);
    event DataStandardized(uint256 assetId, int256[] standardizedData);

    modifier onlyOwner() {
        require(msg.sender == owner, "Only owner can perform this action");
        _;
    }

    constructor() {
        owner = msg.sender;
    }

    function addAsset(string memory name, uint256 value, uint256[] memory historicalData) public onlyOwner {
        assets[nextAssetId] = Asset(nextAssetId, name, value, historicalData);
        emit AssetAdded(nextAssetId, name, value);
        nextAssetId++;
    }

    function normalizeAssetData(uint256 assetId, uint256 minRange, uint256 maxRange) public onlyOwner {
        Asset storage asset = assets[assetId];
        uint256[] memory normalizedData = asset.historicalData.normalize(minRange, maxRange);
        emit DataNormalized(assetId, normalizedData);
    }

    function encodeAssetName(uint256 assetId) public onlyOwner {
        Asset storage asset = assets[assetId];
        string[] memory names = new string[](1);
        names[0] = asset.name;
        uint256[] memory encodedData = names.encodeCategorical();
        emit DataEncoded(assetId, encodedData);
    }

    function standardizeAssetData(uint256 assetId) public onlyOwner {
        Asset storage asset = assets[assetId];
        int256[] memory standardizedData = asset.historicalData.standardize();
        emit DataStandardized(assetId, standardizedData);
    }
}

Explanation

1. The DataNormalization and DataTransformation libraries are imported and used in the contract.

2. The contract allows the owner to add assets with a name, value, and historical data.

3. The normalizeAssetData function normalizes historical data within a specified range.

4. The encodeAssetName function encodes the asset name using categorical encoding.

5. The standardizeAssetData function standardizes the historical data to have zero mean and unit variance.

6. Events are emitted to log the actions performed on assets for transparency and traceability.

This contract demonstrates how to integrate data processing functionalities into a smart contract managing tokenized assets, enabling advanced data operations essential for AI-driven asset management and analysis.