mixturemapping-binning


Namemixturemapping-binning JSON
Version 0.5.5 PyPI version JSON
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home_pagehttps://vk.github.io/mixturemapping-doc/
SummaryNone
upload_time2024-05-21 13:07:42
maintainerNone
docs_urlNone
authorNone
requires_python>=3.7
licenseNone
keywords binning mixturemapping
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            # MixtureMapping

[![Documentation](https://github.com/VK/dash-express-components/workflows/Publish%20release/badge.svg)](https://vk.github.io/mixturemapping-doc)
[![PyPI version](https://badge.fury.io/py/mixturemapping.svg)](https://badge.fury.io/py/mixturemapping)

Train Gaussian Mixture Mappings


## Provides:
  1. Layers to build tensorflow models to map Gaussian mixtures
  2. Tools to compute yield values of Gaussian mixtures in complex binning schemes

## Example
  
    import mixturemapping as mm  
    import tensorflow as tf

    inMeans = tf.keras.Input(shape=(mixN, inputMixM), name="Means", dtype=dataType)
    inStdDevs = tf.keras.Input(shape=(mixN, inputMixM), name="StdDevs", dtype=dataType)
    inWeight = tf.keras.Input(shape=(mixN), name="Weights", dtype=dataType)

    mapModel = tf.keras.Sequential()
    mapModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
    mapModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
    mapModel.add( tf.keras.layers.Dense(outputMixM))
    y = mapModel(inMeans)

    deltaModel = tf.keras.Sequential()
    deltaModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
    deltaModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
    deltaModel.add( tf.keras.layers.Dense(outputMixM))
    yDelta = deltaModel(inMeans)

    covALayer = mm.layers.TrainableCovMatrix(outputMixM, name="CovA")
    covA = covALayer(inMeans)

    mapLayer = mm.layers.GeneralMapping(outputMixM, name="Mapping", dtype=dataType)
    newDist = mapLayer({'means': inMeans, 'y':y, 'yDelta':yDelta, 'stdDevs': inStdDevs, 'weights': inWeight, 'covA': covA})

    distLayer = mm.layers.Distribution(dtype=dataType, regularize_cov_epsilon=0.95)
    dist = distLayer(newDist)

### Developement
```
$ py -m venv env
$ .\env\Scripts\activate
$ pip install -r requirements.txt
```

Raw data

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    "description": "# MixtureMapping\n\n[![Documentation](https://github.com/VK/dash-express-components/workflows/Publish%20release/badge.svg)](https://vk.github.io/mixturemapping-doc)\n[![PyPI version](https://badge.fury.io/py/mixturemapping.svg)](https://badge.fury.io/py/mixturemapping)\n\nTrain Gaussian Mixture Mappings\n\n\n## Provides:\n  1. Layers to build tensorflow models to map Gaussian mixtures\n  2. Tools to compute yield values of Gaussian mixtures in complex binning schemes\n\n## Example\n  \n    import mixturemapping as mm  \n    import tensorflow as tf\n\n    inMeans = tf.keras.Input(shape=(mixN, inputMixM), name=\"Means\", dtype=dataType)\n    inStdDevs = tf.keras.Input(shape=(mixN, inputMixM), name=\"StdDevs\", dtype=dataType)\n    inWeight = tf.keras.Input(shape=(mixN), name=\"Weights\", dtype=dataType)\n\n    mapModel = tf.keras.Sequential()\n    mapModel.add( tf.keras.layers.Dense(40, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)) )\n    mapModel.add( tf.keras.layers.Dense(40, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)) )\n    mapModel.add( tf.keras.layers.Dense(outputMixM))\n    y = mapModel(inMeans)\n\n    deltaModel = tf.keras.Sequential()\n    deltaModel.add( tf.keras.layers.Dense(40, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)) )\n    deltaModel.add( tf.keras.layers.Dense(40, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)) )\n    deltaModel.add( tf.keras.layers.Dense(outputMixM))\n    yDelta = deltaModel(inMeans)\n\n    covALayer = mm.layers.TrainableCovMatrix(outputMixM, name=\"CovA\")\n    covA = covALayer(inMeans)\n\n    mapLayer = mm.layers.GeneralMapping(outputMixM, name=\"Mapping\", dtype=dataType)\n    newDist = mapLayer({'means': inMeans, 'y':y, 'yDelta':yDelta, 'stdDevs': inStdDevs, 'weights': inWeight, 'covA': covA})\n\n    distLayer = mm.layers.Distribution(dtype=dataType, regularize_cov_epsilon=0.95)\n    dist = distLayer(newDist)\n\n### Developement\n```\n$ py -m venv env\n$ .\\env\\Scripts\\activate\n$ pip install -r requirements.txt\n```\n\n",
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