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HTMD

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  • Installation
  • Tutorials
  • Documentation
  • API
  • Acellera
  • Twitter
  • GitHub
  • LinkedIn
  • Youtube
  • Medium

Section Navigation

  • Building
    • Solvating
    • CHARMM builder
    • AMBER builder
  • MD Simulations
    • Adaptive sampling
      • Adaptive sampling
  • Simulation List
  • Projections
    • MetricData - Storage for projected data
    • Metric - Helper class for combining Metrics for projection
  • Dimensionality Reduction
    • TICA - Time independent component analysis
    • KMeansTri - kmeans triangle inequality
    • GWPCA Principal Component Analysis
  • Clustering
    • KCenters clustering method
    • RegCluster regular sized clustering
  • Markov state models
  • Kinetics
  • Documentation
  • Dimensionality Reduction

Dimensionality Reduction#

On top of projection methods it is also highly recommended to use dimensionality reduction methods to further reduce the space. HTMD provides, for instance, time independent component analysis (TICA) and Kmeans with triangle inequality. These can be used on MetricData objects. TICA is recommended for Markov Model construction.

Contents:

  • TICA - Time independent component analysis
  • KMeansTri - kmeans triangle inequality
  • GWPCA Principal Component Analysis

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htmd.projections.metric module

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htmd.projections.tica module

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