[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_26":27,"_28":29,"_30":12,"_31":12,"_32":12,"_33":12,"_34":12,"_35":12,"_36":37},"routes/canvas.shared.$sharedTextdocId",{"_9":10,"_11":12},"sharedTextdoc",{"_13":14,"_15":-7,"_16":17,"_18":19,"_20":21,"_22":23,"_24":12,"_25":12},"isAuthenticated",false,"sharedTextdocId","68f7c30a956481918b2d563d8cfb0587","versionInt","title","K-order-entropy-tool","type","code/react","content","import React, { useState } from 'react';\n\nexport default function SimpleEntropyTool() {\n const [text, setText] = useState('');\n const [results, setResults] = useState([]);\n\n // Pre-saved strings as buttons\n const savedStrings = [\n { label: 'Sample 1', value: 'Hello world! This is a test string.' },\n { label: 'Sample 2', value: 'Entropy computation example text.' },\n { label: 'Sample 3', value: 'Another pre-saved text for quick analysis.' },\n ];\n\n function computeEntropy() {\n const s = text.trim();\n if (!s) {\n setResults([]);\n return;\n }\n const maxOrder = 5;\n const out = [];\n\n const unigramCounts = {};\n for (let i = 0; i < s.length; i++) {\n const g = s[i];\n unigramCounts[g] = (unigramCounts[g] || 0) + 1;\n }\n const totalUni = Object.values(unigramCounts).reduce((a, b) => a + b, 0);\n let H0 = 0;\n for (const g in unigramCounts) {\n const p = unigramCounts[g] / totalUni;\n H0 += -p * Math.log2(p);\n }\n out.push({ order: 0, gramLength: 1, entropyBitsPerGram: Number(H0.toFixed(3)), entropyBitsPerSymbol: Number(H0.toFixed(3)) });\n\n for (let k = 1; k <= maxOrder; k++) {\n const L = k + 1;\n const counts = {};\n for (let i = 0; i + L <= s.length; i++) {\n const g = s.substring(i, i + L);\n counts[g] = (counts[g] || 0) + 1;\n }\n const total = Object.values(counts).reduce((a, b) => a + b, 0);\n let H_joint = 0;\n for (const g in counts) {\n const p = counts[g] / total;\n H_joint += -p * Math.log2(p);\n }\n const H_conditional = H_joint - out[k - 1].entropyBitsPerGram;\n out.push({\n order: k,\n gramLength: L,\n entropyBitsPerGram: Number(H_joint.toFixed(3)),\n entropyBitsPerSymbol: Number(H_conditional.toFixed(3)),\n });\n }\n\n setResults(out);\n }\n\n return (\n

K‑Order Conditional Entropy Calculator (Orders 0–5)

\n\n

\n {savedStrings.map((item, idx) => (\n setText(item.value)}\n className=\"mr-2 mb-2 px-3 py-1 bg-gray-200 rounded hover:bg-gray-300 text-sm\"\n >\n {item.label}\n \n ))}\n

\n\n setText(e.target.value)}\n rows={8}\n className=\"w-full p-3 border rounded-md font-mono text-sm mb-3\"\n placeholder=\"Paste text here or select a sample...\"\n />\n\n \n Compute\n \n\n {results.length > 0 && (\n

\n \n \n \n \n \n \n \n \n \n \n {results.map((r) => (\n \n \n \n \n \n \n ))}\n \n

Order (k)	Gram length (k+1)	Joint Entropy H(X₁..Xₖ) (bits/gram)	Conditional Entropy H(Xₖ\|X₁..Xₖ₋₁) (bits/symbol)
{r.order}	{r.gramLength}	{r.entropyBitsPerGram}	{r.entropyBitsPerSymbol}

\n )}\n\n {results.length === 0 && (\n

No data yet. Paste some text or select a sample and press Compute.

\n )}\n\n

Notes:

Order 0 shows standard Shannon entropy for unigrams.
For higher orders, conditional entropy = H(order k) − H(order k−1).
This value represents additional uncertainty given the preceding context.

\n );\n}\n","access","public","isModerationBlocked","isAnonifyApiKeyDetected","dd",{"_38":39,"_40":41},"disablePrefetch",true,"shouldPrefetchAccount","shouldPrefetchUser","shouldPrefetchSystemHints","promoteCss","disableSSR","stripModulepreloadImports","statsigGateEvaluationsPromise",["P",37],"traceId","13964625823577656635","traceTime",1783545373239]