gg402 · Normalize Data
Normalize numerical data to consistent scales with reversible parameters.
What it does
Normalize numerical data to consistent scales with reversible parameters.
- Standardize ML training datasets
- Normalize financial time series
- Prepare data for comparison across sources
Ideal buyer
Data scientists and ML engineers preprocessing datasets for model training.
Run this through your governed agent wallet.
- 01Bootstrap AXON once with
npx @axon402/init. - 02Use the AXON runtime MCP tools to
search_x402_servicesorinspect_x402_offerfor this service. - 03Quote, test-buy, then run the governed paid fetch through AXON.
Send this
Prompt for your agent
A natural-language instruction for your LLM agent — with this endpoint exposed as a tool — to call this resource. Not sent to the endpoint; the endpoint consumes the JSON body below.
Pasting this prompt into a raw ChatGPT or unconfigured agent will notexecute the paid endpoint flow. Run it through an agent with the AXON runtime / MCP tools exposed (see “Use with AXON” above) so the 402 challenge, quote, and governed fetch are handled for you.
“Normalize this array of prices to 0-1 range: [100, 250, 500, 750, 1000]”
Endpoint request body
The JSON payload your agent sends to the endpoint.
{
"data": [
100,
250,
500,
750,
1000
],
"method": "min-max"
}Advanced HTTP details
For integrators who need the raw protocol surface. Most agents should use AXON above instead of calling these directly.
Endpoint URL
curl fallback
curl https://gg402.vercel.app/data_normalization \ -H "Content-Type: application/json" \ -H "X-PAYMENT: [signed_payment_envelope]" \ -d '{"data":[100,250,500,750,1000],"method":"min-max"}'
Payment & settlement details
Raw on-chain settlement parameters. AXON above handles these automatically through quote / test-buy / governed fetch.
Price & network
Trust & risk
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