# MixtureMapping
[](https://vk.github.io/mixturemapping-doc)
[](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
```
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"description": "# MixtureMapping\n\n[](https://vk.github.io/mixturemapping-doc)\n[](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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