Deep Learning Pattern Recognition Tool

Deep Learning Pattern Recognition Tool (Simulated)

This tool simulates an AI's ability to recognize patterns in numerical data. It uses heuristic algorithms to detect trends, cycles, anomalies, and specific shapes. A true deep learning system would involve complex neural networks and vast datasets, beyond the scope of a client-side web tool.

Analyzed Data & Detected Patterns

Data Series with Detected Patterns

AI-Generated Pattern Analysis

  • No significant patterns detected based on current parameters.

Indices: ${pattern.indices.join(', ')}

${pattern.description}

`; patternListUl.appendChild(listItem); }); } } // Function to reset the tool window.resetTool = function() { dataSeriesInput.value = ''; trendWindowInput.value = '5'; anomalyThresholdInput.value = '2.0'; clearError(); resultsSection.style.display = 'none'; patternChartSvg.innerHTML = ''; // Clear chart patternListUl.innerHTML = '
  • No significant patterns detected based on current parameters.
  • '; }; // PDF Download Functionality window.downloadPdf = function() { const { jsPDF } = window.jspdf; const doc = new jsPDF(); doc.setFontSize(22); doc.text("Deep Learning Pattern Recognition Report", 105, 20, null, null, "center"); doc.setFontSize(12); doc.text(`Report Date: ${new Date().toLocaleDateString()}`, 14, 35); doc.text(`Trend Analysis Window: ${trendWindowInput.value} periods`, 14, 42); doc.text(`Anomaly Threshold: ${anomalyThresholdInput.value} Std Devs`, 14, 49); let yOffset = 60; // Input Data Section doc.setFontSize(16); doc.text("Input Data Series", 14, yOffset); yOffset += 10; doc.setFontSize(10); doc.text(`Data: ${dataSeriesInput.value.replace(/,/g, ', ')}`, 14, yOffset, { maxWidth: 180 }); yOffset += doc.getTextDimensions(`Data: ${dataSeriesInput.value}`, { maxWidth: 180 }).h + 20; // Chart Placeholder doc.setFontSize(16); doc.text("Data Series with Detected Patterns (Visualized On-Screen)", 14, yOffset); yOffset += 10; doc.setFontSize(10); doc.text("The interactive chart on the tool visually highlights the detected patterns.", 14, yOffset); doc.text("Please refer to the online tool for the full visual representation.", 14, yOffset + 5); yOffset += 20; // AI-Generated Pattern Analysis doc.setFontSize(16); doc.text("AI-Generated Pattern Analysis", 14, yOffset); yOffset += 10; const patterns = []; const patternItems = patternListUl.querySelectorAll('.pattern-item'); if (patternItems.length === 0 || patternListUl.querySelector('.no-patterns-detected')) { patterns.push([{ content: "No significant patterns detected based on current parameters.", styles: { fontStyle: 'italic', textColor: [102, 102, 102] } }]); } else { patternItems.forEach(item => { const type = item.querySelector('.type').textContent; const strength = item.querySelector('strong').textContent; const indices = item.querySelector('p:nth-child(2)').textContent.replace('Indices: ', ''); const description = item.querySelector('p:nth-child(3)').textContent; patterns.push([{ content: `${type} (Strength: ${strength})`, styles: { fontStyle: 'bold', fillColor: [240, 248, 255], textColor: [0, 123, 255] } }]); patterns.push([`Indices: ${indices}`]); patterns.push([description]); patterns.push(['']); // Empty row for spacing }); } doc.autoTable({ startY: yOffset, body: patterns, theme: 'plain', styles: { font: 'helvetica', fontSize: 10, cellPadding: 3, valign: 'top', lineColor: [230, 230, 230], lineWidth: 0.1 }, didParseCell: function(data) { if (data.cell.raw && data.cell.raw.content && data.cell.raw.content.includes("No significant patterns")) { data.cell.styles.halign = 'center'; } } }); doc.save(`Pattern_Recognition_Report.pdf`); }; // Initial example data and analysis on load dataSeriesInput.value = '10, 12, 11, 15, 14, 18, 17, 20, 22, 21, 15, 8, 5, 12, 10, 15, 20, 25, 22, 28, 30, 29, 27, 24, 20, 18, 15, 10, 8, 5'; trendWindowInput.value = '7'; anomalyThresholdInput.value = '2.5'; recognizePatterns(); // Run analysis on page load with example data });
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