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#! /bin/bash | ||
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cp -R dynaml-core/target/scala-2.11/api/* docs/api_docs/v1.4.3/dynaml-core/ | ||
cp -R dynaml-pipes/target/scala-2.11/api/* docs/api_docs/v1.4.3/dynaml-pipes/ | ||
cp -R dynaml-examples/target/scala-2.11/api/* docs/api_docs/v1.4.3/dynaml-examples/ | ||
cp -R target/scala-2.11/api/* docs/api_docs/v1.4.3/dynaml-repl/ | ||
sbt stage | ||
cp -R dynaml-core/target/scala-2.11/api/* ../transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-core/ | ||
cp -R dynaml-pipes/target/scala-2.11/api/* ../transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-pipes/ | ||
cp -R dynaml-examples/target/scala-2.11/api/* ../transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-examples/ | ||
cp -R target/scala-2.11/api/* ../transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-repl/ |
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!!! summary "" | ||
Version 1.4.3 of DynaML, released June 13, 2017. Updates, improvements and new features. | ||
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## DynaML REPL | ||
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### Additions | ||
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**Module** `scripts` | ||
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- Added `gp_mcmc_santafe.sc` worksheet to try new MCMC feature on GP models; applied on the Santa Fe laser data set. | ||
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**General** | ||
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- Updated Ammonite version to `0.9.9` | ||
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## Pipes API | ||
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### Additions | ||
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**Package** `dynaml.pipes` | ||
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- Added `._1` and `._2` members in `#!scala ParallelPipe` | ||
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## Core API | ||
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### Additions | ||
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**Package** `dynaml.models.neuralnets` | ||
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- Added [SELU](https://transcendent-ai-labs.github.io/api_docs/DynaML/recent/dynaml-core/#io.github.mandar2812.dynaml.models.neuralnets.VectorSELU) activation function proposed by [Hochreiter et. al](https://arxiv.org/abs/1706.02515) | ||
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**Package** `dynaml.models.bayes` | ||
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- Added `*` method to `#!scala CoRegGPPrior` which scales it with to a `#!scala ParallelPipe` | ||
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**Package** `dynaml.probability.mcmc` | ||
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- Added `#!scala HyperParameterMCMC` for performing MCMC sampling for models extending `#!scala GloballyOptimizable`. | ||
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**Package** `dynaml.utils` | ||
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- Added trait `#!scala HyperParameters` outlining methods that must be implemented by entities having hyper-parameters | ||
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- Added `#!scala MeanScaler`, `#!scala PCAScaler` to perform mean centering and PCA on data sets. Also added to `#!scala DynaMLPipe` pipe library. | ||
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- Added tail recursive computation of the [Chebyshev](https://en.wikipedia.org/wiki/Chebyshev_polynomials) polynomials of the first and second kind in [`#!scala utils.chebyshev`](https://transcendent-ai-labs.github.io/api_docs/DynaML/recent/dynaml-core/#io.github.mandar2812.dynaml.utils.package). | ||
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### Improvements | ||
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**Package** `dynaml.models.bayes` | ||
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- Added `#!scala trendParamsEncoder` which converts the trend/mean parameters into a scala `#!scala Map[String, Double]` making them | ||
consistent with covariance parameters. Added to `#!scala GaussianProcessPrior` and `#!scala ESGPPrior` families. | ||
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<meta http-equiv="REFRESH" content="0;url=https://transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-core/"></HEAD> |
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<meta http-equiv="REFRESH" content="0;url=https://transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-examples/"></HEAD> |
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<meta http-equiv="REFRESH" content="0;url=https://transcendent-ai-labs.github.io/api_docs/DynaML/v1.4.3/dynaml-pipes/"></HEAD> |
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