Design Patterns MCP Server

{ "id": "difference-based-hypothesis-generation", "name": "Difference-Based Hypothesis Generation", "category": "Big Data Analysis", "description": "Generates hypotheses by analyzing differences between datasets, time periods, or conceptual spaces to identify patterns and anomalies", "when_to_use": "Time series analysis\nSpatial data comparison\nAnomaly detection\nTrend identification\nBefore-after analysis", "benefits": "Reveals hidden patterns\nQuantifies changes\nSupports evidence-based decisions\nApplicable to various data types", "drawbacks": "Requires comparable datasets\nMay miss gradual changes\nNoise sensitivity\nComputational complexity", "use_cases": "Market trend analysis\nMedical diagnosis comparison\nEnvironmental monitoring\nQuality control\nPolicy impact assessment", "complexity": "Medium", "tags": [ "big-data", "hypothesis-generation", "difference-analysis", "pattern-discovery", "temporal-analysis" ], "examples": { "python": { "language": "python", "code": "import pandas as pd\nimport numpy as np\n\ndef difference_based_analysis(data1, data2, threshold=0.1):\n \"\"\"\n Basic difference analysis between two datasets\n \"\"\"\n diff = data2 - data1\n significant_changes = diff.abs() > threshold\n \n return {\n 'differences': diff,\n 'significant_changes': significant_changes,\n 'change_magnitude': diff.abs().mean()\n }\n\n# Time series difference\ndef time_difference_analysis(time_series, window=7):\n \"\"\"\n Analyze differences over time windows\n \"\"\"\n diff = time_series.diff(window)\n anomalies = diff.abs() > diff.std() * 3\n \n return {\n 'time_differences': diff,\n 'anomalies': anomalies\n }\n\n# Usage example\ndata_before = pd.Series([100, 105, 102, 108, 110])\ndata_after = pd.Series([95, 98, 105, 115, 120])\n\nresult = difference_based_analysis(data_before, data_after)\nprint(f\"Significant changes: {result['significant_changes'].sum()}\")\nprint(f\"Average change magnitude: {result['change_magnitude']:.2f}\")" } } }