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mlpack 3.3.0
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@rcurtin rcurtin released this 07 Apr 14:13
· 7289 commits to master since this release
3.3.0
5677c2b

Released April 7th, 2020.

  • Templated return type of Forward function of loss functions (#2339).

  • Added R2 Score regression metric (#2323).

  • Added mean squared logarithmic error loss function for neural networks (#2210).

  • Added mean bias loss function for neural networks (#2210).

  • The DecisionStump class has been marked deprecated; use the DecisionTree class with NoRecursion=true or use ID3DecisionStump instead (#2099).

  • Added probabilities_file parameter to get the probabilities matrix of AdaBoost classifier (#2050).

  • Fix STB header search paths (#2104).

  • Add DISABLE_DOWNLOADS CMake configuration option (#2104).

  • Add padding layer in TransposedConvolutionLayer (#2082).

  • Fix pkgconfig generation on non-Linux systems (#2101).

  • Use log-space to represent HMM initial state and transition probabilities (#2081).

  • Add functions to access parameters of Convolution and AtrousConvolution layers (#1985).

  • Add Compute Error function in lars regression and changing Train function to return computed error (#2139).

  • Add Julia bindings (#1949). Build settings can be controlled with the BUILD_JULIA_BINDINGS=(ON/OFF) and JULIA_EXECUTABLE=/path/to/julia CMake parameters.

  • CMake fix for finding STB include directory (#2145).

  • Add bindings for loading and saving images (#2019); mlpack_image_converter from the command-line, mlpack.image_converter() from Python.

  • Add normalization support for CF binding (#2136).

  • Add Mish activation function (#2158).

  • Update init_rules in AMF to allow users to merge two initialization rules (#2151).

  • Add GELU activation function (#2183).

  • Better error handling of eigendecompositions and Cholesky decompositions (#2088, #1840).

  • Add LiSHT activation function (#2182).

  • Add Valid and Same Padding for Transposed Convolution layer (#2163).

  • Add CELU activation function (#2191)

  • Add Log-Hyperbolic-Cosine Loss function (#2207)

  • Change neural network types to avoid unnecessary use of rvalue references (#2259).

  • Bump minimum Boost version to 1.58 (#2305).

  • Refactor STB support so HAS_STB macro is not needed when compiling against mlpack (#2312).

  • Add Hard Shrink Activation Function (#2186).

  • Add Soft Shrink Activation Function (#2174).

  • Add Hinge Embedding Loss Function (#2229).

  • Add Cosine Embedding Loss Function (#2209).

  • Add Margin Ranking Loss Function (#2264).

  • Bugfix for incorrect parameter vector sizes in logistic regression and softmax regression (#2359).

Assets 2