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Lex Vorona
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Release/v1.0.5 (#950)
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CHANGELOG.md

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## v0.1.5
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- Significant RAM usage improvements throughout the codebase
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- Add Multilayer Perceptron (MLP), a type of neural network to pymoose predictors
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- `PrimDeriveSeed` operator renamed to `DeriveSeed`
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- `PrimPrfKeyGen` operator renamed to `PrfKeyGen`
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- Added `Host` prefix to `Seed`, `PrfKey` and `Unit`
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- Unified `MeanOp` and fuse `RepFixedpointMeanOp` with `RingFixedpointMeanOp`
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- Using BitVec as the data storage for HostBitTensor
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- Replace sodium oxide with thread_rng and blake3
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- Fix binary LinearClassifier logic
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### Other changes
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- Uniform textual format for rendezvous and sync keys
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## v0.1.4
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elk/Cargo.toml

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[package]
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name = "elk"
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version = "0.1.5-beta.1"
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version = "0.1.5"
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license = "MIT"
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edition = "2018"
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authors = []

macros/Cargo.toml

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[package]
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name = "macros"
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version = "0.1.5-beta.1"
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version = "0.1.5"
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license = "MIT"
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edition = "2018"
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modules/Cargo.toml

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[package]
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name = "moose-modules"
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version = "0.1.5-beta.1"
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version = "0.1.5"
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license = "MIT"
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edition = "2018"
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authors = []

moose/Cargo.toml

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[package]
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name = "moose"
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version = "0.1.5-beta.1"
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version = "0.1.5"
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license = "MIT"
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authors = [""]
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description = ""

moose/src/computation.rs

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// The names below are deprecated aliases, maintained for a long period of time for compatibility
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"PrimDeriveSeed" => DeriveSeedOp::from_textual, // pre v0.1.5
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"PrimPrfKeyGen" => PrfKeyGenOp::from_textual, // pre v0.1.5
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"HostMean" => MeanOp::from_textual, // pre v0.1.5
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"FixedpointMeanOp" => MeanOp::from_textual, // pre v0.1.5
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"FloatingpointMeanOp" => MeanOp::from_textual, // pre v0.1.5
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"RepFixedpointMean" => MeanOp::from_textual, // pre v0.1.5
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_ => parse_operator_error,
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}
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}

pymoose/Cargo.toml

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[package]
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name = "pymoose"
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version = "0.1.5-beta.1"
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version = "0.1.5"
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license = "MIT/Apache-2.0"
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authors = [""]
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description = ""
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x = Constant{value=HostFloat32Tensor([[1.0, 2.0], [3.0, 4.0]])}: () -> HostFloat32Tensor @Host(alice)
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mean = HostMean{}: (HostFloat32Tensor) -> HostFloat32Tensor (x) @Host(alice)
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x = Constant{value=HostFloat32Tensor([[1.0, 2.0], [3.0, 4.0]])}: () -> HostFloat32Tensor @Host(alice)
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mean = Mean{}: (HostFloat32Tensor) -> HostFloat32Tensor (x) @Host(alice)

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