diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 000000000..8762e87df --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,182 @@ +# This file is autogenerated by maturin v1.7.4 +# To update, run +# +# maturin generate-ci github +# + +name: CI + +on: + push: + branches: + - main + - master + tags: + - '*' + pull_request: + workflow_dispatch: + +permissions: + contents: read + +jobs: + linux: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: ubuntu-latest + target: x86_64 + - runner: ubuntu-latest + target: x86 + - runner: ubuntu-latest + target: aarch64 + - runner: ubuntu-latest + target: armv7 + - runner: ubuntu-latest + target: s390x + - runner: ubuntu-latest + target: ppc64le + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: 'true' + manylinux: auto + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-linux-${{ matrix.platform.target }} + path: dist + + musllinux: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: ubuntu-latest + target: x86_64 + - runner: ubuntu-latest + target: x86 + - runner: ubuntu-latest + target: aarch64 + - runner: ubuntu-latest + target: armv7 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: 'true' + manylinux: musllinux_1_2 + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-musllinux-${{ matrix.platform.target }} + path: dist + + windows: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: windows-latest + target: x64 + - runner: windows-latest + target: x86 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + architecture: ${{ matrix.platform.target }} + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: 'true' + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-windows-${{ matrix.platform.target }} + path: dist + + macos: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: macos-12 + target: x86_64 + - runner: macos-14 + target: aarch64 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: 'true' + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-macos-${{ matrix.platform.target }} + path: dist + + sdist: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Build sdist + uses: PyO3/maturin-action@v1 + with: + command: sdist + args: --out dist + - name: Upload sdist + uses: actions/upload-artifact@v4 + with: + name: wheels-sdist + path: dist + + release: + name: Release + runs-on: ubuntu-latest + if: ${{ startsWith(github.ref, 'refs/tags/') || github.event_name == 'workflow_dispatch' }} + needs: [linux, musllinux, windows, macos, sdist] + permissions: + # Use to sign the release artifacts + id-token: write + # Used to upload release artifacts + contents: write + # Used to generate artifact attestation + attestations: write + steps: + - uses: actions/download-artifact@v4 + - name: Generate artifact attestation + uses: actions/attest-build-provenance@v1 + with: + subject-path: 'wheels-*/*' + - name: Publish to PyPI + if: "startsWith(github.ref, 'refs/tags/')" + uses: PyO3/maturin-action@v1 + env: + MATURIN_PYPI_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + with: + command: upload + args: --non-interactive --skip-existing wheels-*/* \ No newline at end of file diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 3baf212c5..9fc89e407 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -18,6 +18,12 @@ jobs: curl -sSL https://install.python-poetry.org | python3 - echo "$HOME/.local/bin" >> $GITHUB_PATH poetry self add "poetry-dynamic-versioning[plugin]" + - name: Install the package + run: | + poetry install --with dev + - name: Build wheels + run: | + poetry run maturin build --release - name: Build a binary wheel and a source tarball run: >- poetry build diff --git a/.github/workflows/test-rs.yml b/.github/workflows/test-rs.yml new file mode 100644 index 000000000..823906d80 --- /dev/null +++ b/.github/workflows/test-rs.yml @@ -0,0 +1,23 @@ +name: Rust Crate CI + +on: + release: + types: [published] + pull_request: + types: [opened, synchronize, reopened] + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v3 + + - name: Install Rust + uses: actions-rs/toolchain@v1 + with: + toolchain: stable + + - name: Run tests + run: cargo test \ No newline at end of file diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 2d7df66cb..526f50ed2 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -37,18 +37,29 @@ jobs: # Step 4: Install System Dependencies - name: Install Graphviz run: sudo apt-get install -y graphviz + + # Step 5: Install Rust + - name: Install Rust + uses: actions-rs/toolchain@v1 + with: + toolchain: stable + profile: minimal - # Step 5: Install Python Dependencies using Poetry + # Step 6: Install Python Dependencies using Poetry - name: Install dependencies run: | poetry install --with dev + + # Step 7: Install the Rust package + - name: Build and Install the Package + run: poetry run maturin develop - # 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pyhgf.updates.posterior.exponential - -.. autosummary:: - :toctree: generated/pyhgf.updates.posterior.exponential - - posterior_update_exponential_family - Prediction steps ================ @@ -146,6 +136,16 @@ Dirichlet state nodes likely_cluster_proposal clusters_likelihood +Exponential family +^^^^^^^^^^^^^^^^^^ + +.. currentmodule:: pyhgf.updates.prediction_error.exponential + +.. autosummary:: + :toctree: generated/pyhgf.updates.prediction_error.exponential + + prediction_error_update_exponential_family + Distribution ************ diff --git a/docs/source/notebooks/0.3-Generalised_filtering.ipynb b/docs/source/notebooks/0.3-Generalised_filtering.ipynb index fd3a19c5f..257839b20 100644 --- a/docs/source/notebooks/0.3-Generalised_filtering.ipynb +++ b/docs/source/notebooks/0.3-Generalised_filtering.ipynb @@ -320,7 +320,7 @@ "\n", "### Using a fixed $\\nu$\n", "\n", - "This operation can be achieved using a continuous state node that implements the exponential family updates on the values that are passed by the value child nodes. Such nodes are referred to as `ef-` nodes, with the type of distribution (here a simple one-dimensional Gaussian distribution, therefore the kind is set to `\"ef-normal\"`). The input node is set to generic, which means that this input simply passes the observed value to the value parents without any additional computation. We can define such a model as follows:" + "This operation can be achieved using a continuous state node that implements the exponential family updates on the values that are passed by the value child nodes. Such nodes are referred to as `exponential-state` nodes, with the type of distribution (here a simple one-dimensional Gaussian distribution). The input node is set to generic, which means that this input simply passes the observed value to the value parents without any additional computation. We can define such a model as follows:" ] }, { @@ -340,7 +340,7 @@ "generalised_filter = (\n", " Network()\n", " .add_nodes(kind=\"generic-state\")\n", - " .add_nodes(kind=\"ef-normal\", value_children=0, xis=np.array([0, 1 / 8]))\n", + " .add_nodes(kind=\"exponential-state\", value_children=0, xis=np.array([0, 1 / 8]))\n", ")" ] }, diff --git a/poetry.lock b/poetry.lock index a24410a45..953345830 100644 --- a/poetry.lock +++ b/poetry.lock @@ -528,73 +528,73 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist" [[package]] name = "coverage" -version = "7.6.3" +version = "7.6.4" description = "Code coverage measurement for Python" optional = false python-versions = ">=3.9" files = [ - {file = "coverage-7.6.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6da42bbcec130b188169107ecb6ee7bd7b4c849d24c9370a0c884cf728d8e976"}, - {file = "coverage-7.6.3-cp310-cp310-macosx_11_0_arm64.whl", hash = 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files = [ - {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"}, - {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"}, + {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"}, + {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"}, ] [package.dependencies] @@ -3693,13 +3726,13 @@ files = [ [[package]] name = "xarray" -version = "2024.9.0" +version = "2024.10.0" description = "N-D labeled arrays and datasets in Python" optional = false python-versions = ">=3.10" files = [ - {file = "xarray-2024.9.0-py3-none-any.whl", hash = "sha256:4fd534abdf12d5fa75dd566c56483d5081f77864462cf3d6ad53e13f9db48222"}, - {file = "xarray-2024.9.0.tar.gz", hash = "sha256:e796a6b3eaec11da24f33e4bb14af41897011660a0516fa4037d3ae4bbd1d378"}, + {file = 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"pydap", "scipy", "zarr"] parallel = ["dask[complete]"] -viz = ["matplotlib", "nc-time-axis", "seaborn"] +viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"] [[package]] name = "xarray-einstats" @@ -3770,4 +3804,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "57f2c5fa5800793a25d89b3e07908b0b594841c110dacda450fda9aac66a5fd7" +content-hash = "8ad3b512644f5552c6f4c5577959a9fd770b111f55e41ecd54f876f311869d67" diff --git a/src/pyhgf/__init__.py b/pyhgf/__init__.py similarity index 100% rename from src/pyhgf/__init__.py rename to pyhgf/__init__.py diff --git a/src/pyhgf/data/binary_input.txt b/pyhgf/data/binary_input.txt similarity index 100% rename from src/pyhgf/data/binary_input.txt rename to pyhgf/data/binary_input.txt diff --git a/src/pyhgf/data/binary_response.txt b/pyhgf/data/binary_response.txt similarity index 100% rename from src/pyhgf/data/binary_response.txt rename to pyhgf/data/binary_response.txt diff --git a/src/pyhgf/data/usdchf.txt b/pyhgf/data/usdchf.txt similarity index 100% rename from src/pyhgf/data/usdchf.txt rename to pyhgf/data/usdchf.txt diff --git a/src/pyhgf/distribution.py b/pyhgf/distribution.py similarity index 100% rename from src/pyhgf/distribution.py rename to pyhgf/distribution.py diff --git a/src/pyhgf/math.py b/pyhgf/math.py similarity index 100% rename from src/pyhgf/math.py rename to pyhgf/math.py diff --git a/src/pyhgf/model/__init__.py b/pyhgf/model/__init__.py similarity index 100% rename from src/pyhgf/model/__init__.py rename to pyhgf/model/__init__.py diff --git a/src/pyhgf/model/hgf.py b/pyhgf/model/hgf.py similarity index 100% rename from src/pyhgf/model/hgf.py rename to pyhgf/model/hgf.py diff --git a/src/pyhgf/model/network.py b/pyhgf/model/network.py similarity index 95% rename from src/pyhgf/model/network.py rename to pyhgf/model/network.py index f2b9b1e9c..a6041341f 100644 --- a/src/pyhgf/model/network.py +++ b/pyhgf/model/network.py @@ -329,29 +329,14 @@ def add_nodes( be a regular state node that can have value and/or volatility parents/children. If `"binary-state"`, the node should be the value parent of a binary input. State nodes filtering distribution from the - exponential family can be created using the `"ef-"` prefix (e.g. - `"ef-normal"` for a univariate normal distribution). Note that only a few - distributions are implemented at the moment. - - In addition to state nodes, four types of input nodes are supported: - - `generic-input`: receive a value or an array and pass it to the parent - nodes. - - `continuous-input`: receive a continuous observation as input. - - `binary-input` receives a single boolean as observation. The parameters - provided to the binary input node contain: 1. `binary_precision`, the binary - input precision, which defaults to `jnp.inf`. 2. `eta0`, the lower bound of - the binary process, which defaults to `0.0`. 3. `eta1`, the higher bound of - the binary process, which defaults to `1.0`. - - `categorical-input` receives a boolean array as observation. The - parameters provided to the categorical input node contain: 1. - `n_categories`, the number of categories implied by the categorical state. + exponential family can be created using `"exponential-state"`. .. note:: When using a categorical state node, the `binary_parameters` can be used to parametrize the implied collection of binary HGFs. .. note: - When using `categorical-input`, the implied `n` binary HGFs are + When using `categorical-state`, the implied `n` binary HGFs are automatically created with a shared volatility parent at the third level, resulting in a network with `3n + 2` nodes in total. @@ -396,7 +381,7 @@ def add_nodes( """ if kind not in [ "DP-state", - "ef-normal", + "exponential-state", "categorical-state", "continuous-state", "binary-state", @@ -405,8 +390,8 @@ def add_nodes( raise ValueError( ( "Invalid node type. Should be one of the following: " - "'DP-state', 'continuous-state', 'binary-state', 'ef-normal'." - "'generic-state' or 'categorical-state'" + "'DP-state', 'continuous-state', 'binary-state', " + "'exponential-state', 'generic-state' or 'categorical-state'" ) ) @@ -483,12 +468,12 @@ def add_nodes( "mean": 0.0, "observed": 1, } - elif "ef-normal" in kind: + elif "exponential-state" in kind: default_parameters = { "nus": 3.0, "xis": jnp.array([0.0, 1.0]), "mean": 0.0, - "observed": 1.0, + "observed": 1, } elif kind == "categorical-state": if "n_categories" in node_parameters: @@ -577,7 +562,7 @@ def add_nodes( node_type = 1 elif kind == "continuous-state": node_type = 2 - elif kind == "ef-normal": + elif kind == "exponential-state": node_type = 3 elif kind == "DP-state": node_type = 4 diff --git a/src/pyhgf/plots.py b/pyhgf/plots.py similarity index 100% rename from src/pyhgf/plots.py rename to pyhgf/plots.py diff --git a/src/pyhgf/response.py b/pyhgf/response.py similarity index 100% rename from src/pyhgf/response.py rename to pyhgf/response.py diff --git a/src/pyhgf/typing.py b/pyhgf/typing.py similarity index 100% rename from src/pyhgf/typing.py rename to pyhgf/typing.py diff --git a/src/pyhgf/updates/__init__.py b/pyhgf/updates/__init__.py similarity index 100% rename from src/pyhgf/updates/__init__.py rename to pyhgf/updates/__init__.py diff --git a/src/pyhgf/updates/observation.py b/pyhgf/updates/observation.py similarity index 100% rename from src/pyhgf/updates/observation.py rename to pyhgf/updates/observation.py diff --git a/src/pyhgf/updates/posterior/__init__.py b/pyhgf/updates/posterior/__init__.py similarity index 100% rename from src/pyhgf/updates/posterior/__init__.py rename to pyhgf/updates/posterior/__init__.py diff --git a/src/pyhgf/updates/posterior/categorical.py b/pyhgf/updates/posterior/categorical.py similarity index 100% rename from src/pyhgf/updates/posterior/categorical.py rename to pyhgf/updates/posterior/categorical.py diff --git a/src/pyhgf/updates/posterior/continuous.py b/pyhgf/updates/posterior/continuous.py similarity index 100% rename from src/pyhgf/updates/posterior/continuous.py rename to pyhgf/updates/posterior/continuous.py diff --git a/src/pyhgf/updates/prediction/__init__.py b/pyhgf/updates/prediction/__init__.py similarity index 100% rename from src/pyhgf/updates/prediction/__init__.py rename to pyhgf/updates/prediction/__init__.py diff --git a/src/pyhgf/updates/prediction/binary.py b/pyhgf/updates/prediction/binary.py similarity index 100% rename from src/pyhgf/updates/prediction/binary.py rename to pyhgf/updates/prediction/binary.py diff --git a/src/pyhgf/updates/prediction/continuous.py b/pyhgf/updates/prediction/continuous.py similarity index 100% rename from src/pyhgf/updates/prediction/continuous.py rename to pyhgf/updates/prediction/continuous.py diff --git a/src/pyhgf/updates/prediction/dirichlet.py b/pyhgf/updates/prediction/dirichlet.py similarity index 94% rename from src/pyhgf/updates/prediction/dirichlet.py rename to pyhgf/updates/prediction/dirichlet.py index 03fb86e25..298fbd2b9 100644 --- a/src/pyhgf/updates/prediction/dirichlet.py +++ b/pyhgf/updates/prediction/dirichlet.py @@ -39,7 +39,7 @@ def dirichlet_node_prediction( Static parameters of the Dirichlet process node. """ - # get the parameter (mean and variance) from the EF-normal parent nodes + # get the parameter (mean and variance) from the exponential state parent nodes value_parent_idxs = edges[node_idx].value_parents if value_parent_idxs is not None: parameters = jnp.array( diff --git a/src/pyhgf/updates/prediction_error/__init__.py b/pyhgf/updates/prediction_error/__init__.py similarity index 100% rename from src/pyhgf/updates/prediction_error/__init__.py rename to pyhgf/updates/prediction_error/__init__.py diff --git a/src/pyhgf/updates/prediction_error/binary.py b/pyhgf/updates/prediction_error/binary.py similarity index 100% rename from src/pyhgf/updates/prediction_error/binary.py rename to pyhgf/updates/prediction_error/binary.py diff --git a/src/pyhgf/updates/prediction_error/categorical.py b/pyhgf/updates/prediction_error/categorical.py similarity index 100% rename from src/pyhgf/updates/prediction_error/categorical.py rename to pyhgf/updates/prediction_error/categorical.py diff --git a/src/pyhgf/updates/prediction_error/continuous.py b/pyhgf/updates/prediction_error/continuous.py similarity index 100% rename from src/pyhgf/updates/prediction_error/continuous.py rename to pyhgf/updates/prediction_error/continuous.py diff --git a/src/pyhgf/updates/prediction_error/dirichlet.py b/pyhgf/updates/prediction_error/dirichlet.py similarity index 100% rename from src/pyhgf/updates/prediction_error/dirichlet.py rename to pyhgf/updates/prediction_error/dirichlet.py diff --git a/src/pyhgf/updates/posterior/exponential.py b/pyhgf/updates/prediction_error/exponential.py similarity index 97% rename from src/pyhgf/updates/posterior/exponential.py rename to pyhgf/updates/prediction_error/exponential.py index f4b9ed519..a84b31b53 100644 --- a/src/pyhgf/updates/posterior/exponential.py +++ b/pyhgf/updates/prediction_error/exponential.py @@ -10,7 +10,7 @@ @partial(jit, static_argnames=("edges", "node_idx", "sufficient_stats_fn")) -def posterior_update_exponential_family( +def prediction_error_update_exponential_family( attributes: Dict, edges: Edges, node_idx: int, sufficient_stats_fn: Callable, **args ) -> Attributes: r"""Update the parameters of an exponential family distribution. diff --git a/src/pyhgf/updates/prediction_error/generic.py b/pyhgf/updates/prediction_error/generic.py similarity index 100% rename from src/pyhgf/updates/prediction_error/generic.py rename to pyhgf/updates/prediction_error/generic.py diff --git a/src/pyhgf/utils.py b/pyhgf/utils.py similarity index 98% rename from src/pyhgf/utils.py rename to pyhgf/utils.py index 227baf7cd..7a713cffa 100644 --- a/src/pyhgf/utils.py +++ b/pyhgf/utils.py @@ -18,7 +18,6 @@ continuous_node_posterior_update, continuous_node_posterior_update_ehgf, ) -from pyhgf.updates.posterior.exponential import posterior_update_exponential_family from pyhgf.updates.prediction.binary import binary_state_node_prediction from pyhgf.updates.prediction.continuous import continuous_node_prediction from pyhgf.updates.prediction.dirichlet import dirichlet_node_prediction @@ -28,6 +27,9 @@ ) from pyhgf.updates.prediction_error.continuous import continuous_node_prediction_error from pyhgf.updates.prediction_error.dirichlet import dirichlet_node_prediction_error +from pyhgf.updates.prediction_error.exponential import ( + prediction_error_update_exponential_family, +) from pyhgf.updates.prediction_error.generic import generic_state_prediction_error if TYPE_CHECKING: @@ -374,16 +376,6 @@ def get_update_sequence( elif update_type == "standard": update_fn = continuous_node_posterior_update - elif network.edges[idx].node_type == 3: - - # create the sufficient statistic function - # for the exponential family node - ef_update = Partial( - posterior_update_exponential_family, - sufficient_stats_fn=Normal().sufficient_statistics, - ) - update_fn = ef_update - elif network.edges[idx].node_type == 4: update_fn = None @@ -407,8 +399,21 @@ def get_update_sequence( ] # if this node has no parent, no need to compute prediction errors + # unless this is an exponential family state node if len(all_parents) == 0: - nodes_without_prediction_error.remove(idx) + if network.edges[idx].node_type == 3: + # create the sufficient statistic function + # for the exponential family node + ef_update = Partial( + prediction_error_update_exponential_family, + sufficient_stats_fn=Normal().sufficient_statistics, + ) + update_fn = ef_update + no_update = False + update_sequence.append((idx, update_fn)) + nodes_without_prediction_error.remove(idx) + else: + nodes_without_prediction_error.remove(idx) else: # if this node has been updated if idx not in nodes_without_posterior_update: @@ -419,8 +424,6 @@ def get_update_sequence( update_fn = binary_state_node_prediction_error elif network.edges[idx].node_type == 2: update_fn = continuous_node_prediction_error - elif network.edges[idx].node_type == 3: - update_fn = None elif network.edges[idx].node_type == 4: update_fn = dirichlet_node_prediction_error elif network.edges[idx].node_type == 5: diff --git a/pyproject.toml b/pyproject.toml index 7f622b736..678d407fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,5 @@ [tool.poetry] name = "pyhgf" -version = "0.0.0" description = "Dynamic neural networks for predictive coding" authors = ["Nicolas Legrand "] license = "GPL-3.0" @@ -13,6 +12,12 @@ include = [ "rc/pyhgf/data/binary_input.txt", "src/pyhgf/data/binary_response.txt", ] +classifiers = [ + "Programming Language :: Rust", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", +] +version = "0.0.0" [tool.poetry-dynamic-versioning] # Enable dynamic versioning @@ -49,7 +54,12 @@ ipykernel = "^6.29.5 " coverage = "^7.6.3" pytest = "^8.3.3" pytest-cov = "^5.0.0" +maturin = "^1.7.4" [build-system] -requires = ["poetry-core", "poetry-dynamic-versioning>=1.0.0,<2.0.0"] -build-backend = "poetry_dynamic_versioning.backend" +requires = ["poetry-core", "poetry-dynamic-versioning>=1.0.0,<2.0.0", "maturin>=1.4,<2.0"] +build-backend = "maturin" + +[tool.maturin] +# "extension-module" tells pyo3 we want to build an extension module (skips linking against libpython.so) +features = ["pyo3/extension-module"] \ No newline at end of file diff --git a/src/lib.rs b/src/lib.rs new file mode 100644 index 000000000..55ad98cd0 --- /dev/null +++ b/src/lib.rs @@ -0,0 +1,4 @@ +pub mod model; +pub mod utils; +pub mod math; +pub mod updates; \ No newline at end of file diff --git a/src/math.rs b/src/math.rs new file mode 100644 index 000000000..80cd8d4dd --- /dev/null +++ b/src/math.rs @@ -0,0 +1,3 @@ +pub fn sufficient_statistics(x: &f64) -> Vec { + vec![*x, x.powf(2.0)] +} \ No newline at end of file diff --git a/src/model.rs b/src/model.rs new file mode 100644 index 000000000..223458da3 --- /dev/null +++ b/src/model.rs @@ -0,0 +1,346 @@ +use std::collections::HashMap; +use crate::{updates::observations::observation_update, utils::function_pointer::FnType}; +use crate::utils::set_sequence::set_update_sequence; +use crate::utils::function_pointer::get_func_map; +use pyo3::types::PyTuple; +use pyo3::{prelude::*, types::{PyList, PyDict}}; +use numpy::{PyArray1, PyArray}; + +#[derive(Debug)] +#[pyclass] +pub struct AdjacencyLists{ + #[pyo3(get, set)] + pub node_type: String, + #[pyo3(get, set)] + pub value_parents: Option>, + #[pyo3(get, set)] + pub value_children: Option>, + #[pyo3(get, set)] + pub volatility_parents: Option>, + #[pyo3(get, set)] + pub volatility_children: Option>, +} + +#[derive(Debug)] +pub struct UpdateSequence { + pub predictions: Vec<(usize, FnType)>, + pub updates: Vec<(usize, FnType)>, +} + +#[derive(Debug)] +pub struct Attributes { + pub floats: HashMap>, + pub vectors: HashMap>>, +} + +#[derive(Debug)] +pub struct NodeTrajectories { + pub floats: HashMap>>, + pub vectors: HashMap>>>, +} + +#[derive(Debug)] +#[pyclass] +pub struct Network{ + pub attributes: Attributes, + pub edges: HashMap, + pub inputs: Vec, + pub update_sequence: UpdateSequence, + pub node_trajectories: NodeTrajectories, +} + +#[pymethods] +impl Network { + + // Create a new graph + #[new] // Define the constructor accessible from Python + pub fn new() -> Self { + Network { + attributes: Attributes {floats: HashMap::new(), vectors: HashMap::new()}, + edges: HashMap::new(), + inputs: Vec::new(), + update_sequence: UpdateSequence {predictions: Vec::new(), updates: Vec::new()}, + node_trajectories: NodeTrajectories {floats: HashMap::new(), vectors: HashMap::new()} + } + } + + /// Add nodes to the network. + /// + /// # Arguments + /// * `kind` - The type of node that should be added. + /// * `value_parents` - The indexes of the node's value parents. + /// * `value_children` - The indexes of the node's value children. + /// * `volatility_children` - The indexes of the node's volatility children. + /// * `volatility_parents` - The indexes of the node's volatility parents. + #[pyo3(signature = (kind="continuous-state", value_parents=None, value_children=None, volatility_parents=None, volatility_children=None,))] + pub fn add_nodes(&mut self, kind: &str, value_parents: Option>, + value_children: Option>, + volatility_parents: Option>, volatility_children: Option>, ) { + + // the node ID is equal to the number of nodes already in the network + let node_id: usize = self.edges.len(); + + // if this node has no children, this is an input node + if (value_children == None) & (volatility_children == None) { + self.inputs.push(node_id); + } + + let edges = AdjacencyLists{ + node_type: String::from(kind), + value_parents: value_parents, + value_children: value_children, + volatility_parents: volatility_parents, + volatility_children: volatility_children, + }; + + // add edges and attributes + if kind == "continuous-state" { + + let attributes = [ + (String::from("mean"), 0.0), + (String::from("expected_mean"), 0.0), + (String::from("precision"), 1.0), + (String::from("expected_precision"), 1.0), + (String::from("tonic_volatility"), -4.0), + (String::from("tonic_drift"), 0.0), + (String::from("autoconnection_strength"), 1.0)].into_iter().collect(); + + self.attributes.floats.insert(node_id, attributes); + self.edges.insert(node_id, edges); + + } else if kind == "exponential-state" { + + let floats_attributes = [ + (String::from("mean"), 0.0), + (String::from("nus"), 3.0)].into_iter().collect(); + let vector_attributes = [ + (String::from("xis"), vec![0.0, 1.0])].into_iter().collect(); + + self.attributes.floats.insert(node_id, floats_attributes); + self.attributes.vectors.insert(node_id, vector_attributes); + self.edges.insert(node_id, edges); + + } else { + println!("Invalid type of node provided ({}).", kind); + } + } + + pub fn set_update_sequence(&mut self) { + self.update_sequence = set_update_sequence(self); + } + + /// Single time slice belief propagation. + /// + /// # Arguments + /// * `observations` - A vector of values, each value is one new observation associated + /// with one node. + pub fn belief_propagation(&mut self, observations_set: Vec) { + + let predictions = self.update_sequence.predictions.clone(); + let updates = self.update_sequence.updates.clone(); + + // 1. prediction steps + for (idx, step) in predictions.iter() { + step(self, *idx); + } + + // 2. observation steps + for (i, observations) in observations_set.iter().enumerate() { + let idx = self.inputs[i]; + observation_update(self, idx, *observations); + } + + // 3. update steps + for (idx, step) in updates.iter() { + step(self, *idx); + } + } + + /// Add a sequence of observations. + /// + /// # Arguments + /// * `input_data` - A vector of vectors. Each vector is a time series of observations + /// associated with one node. + pub fn input_data(&mut self, input_data: Vec) { + + // initialize the belief trajectories result struture + let mut node_trajectories = NodeTrajectories {floats: HashMap::new(), vectors: HashMap::new()}; + + // add empty vectors in the floats hashmap + for (node_idx, node) in &self.attributes.floats { + let new_map: HashMap> = HashMap::new(); + node_trajectories.floats.insert(*node_idx, new_map); + if let Some(attr) = node_trajectories.floats.get_mut(node_idx) { + for key in node.keys() { + attr.insert(key.clone(), Vec::new()); + } + } + } + // add empty vectors in the vectors hashmap + for (node_idx, node) in &self.attributes.vectors { + let new_map: HashMap>> = HashMap::new(); + node_trajectories.vectors.insert(*node_idx, new_map); + if let Some(attr) = node_trajectories.vectors.get_mut(node_idx) { + for key in node.keys() { + attr.insert(key.clone(), Vec::new()); + } + } + } + + // iterate over the observations + for observation in input_data { + + // 1. belief propagation for one time slice + self.belief_propagation(vec![observation]); + + // 2. append the new beliefs in the trajectories structure + // iterate over the float hashmap + for (new_node_idx, new_node) in &self.attributes.floats { + for (new_key, new_value) in new_node { + // If the key exists in map1, append the vector from map2 + if let Some(old_node) = node_trajectories.floats.get_mut(&new_node_idx) { + if let Some(old_value) = old_node.get_mut(new_key) { + old_value.push(*new_value); + } + } + } + } + // iterate over the vector hashmap + for (new_node_idx, new_node) in &self.attributes.vectors { + for (new_key, new_value) in new_node { + // If the key exists in map1, append the vector from map2 + if let Some(old_node) = node_trajectories.vectors.get_mut(&new_node_idx) { + if let Some(old_value) = old_node.get_mut(new_key) { + old_value.push(new_value.clone()); + } + } + } + } + } + + self.node_trajectories = node_trajectories; + } + + #[getter] + pub fn get_node_trajectories<'py>(&self, py: Python<'py>) -> PyResult<&'py PyList> { + let py_list = PyList::empty(py); + + + // Iterate over the float hashmap and insert key-value pairs into the list as PyDict + for (node_idx, node) in &self.node_trajectories.floats { + let py_dict = PyDict::new(py); + for (key, value) in node { + // Create a new Python dictionary + py_dict.set_item(key, PyArray1::from_vec(py, value.clone()).to_owned()).expect("Failed to set item in PyDict"); + } + + // Iterate over the vector hashmap if any and insert key-value pairs into the list as PyDict + if let Some(vector_node) = self.node_trajectories.vectors.get(node_idx) { + for (vector_key, vector_value) in vector_node { + // Create a new Python dictionary + py_dict.set_item(vector_key, PyArray::from_vec2_bound(py, &vector_value).unwrap()).expect("Failed to set item in PyDict"); + } + } + py_list.append(py_dict)?; + } + + // Create a PyList from Vec + Ok(py_list) + } + + #[getter] + pub fn get_inputs<'py>(&self, py: Python<'py>) -> PyResult<&'py PyList> { + let py_list = PyList::new(py, &self.inputs); // Create a PyList from Vec + Ok(py_list) + } + + #[getter] + pub fn get_edges<'py>(&self, py: Python<'py>) -> PyResult<&'py PyList> { + // Create a new Python list + let py_list = PyList::empty(py); + + // Convert each struct in the Vec to a Python object and add to PyList + for i in 0..self.edges.len() { + // Create a new Python dictionary for each MyStruct + let py_dict = PyDict::new(py); + py_dict.set_item("value_parents", &self.edges[&i].value_parents)?; + py_dict.set_item("value_children", &self.edges[&i].value_children)?; + py_dict.set_item("volatility_parents", &self.edges[&i].volatility_parents)?; + py_dict.set_item("volatility_children", &self.edges[&i].volatility_children)?; + + // Add the dictionary to the list + py_list.append(py_dict)?; + } + Ok(py_list) + } + + #[getter] + pub fn get_update_sequence<'py>(&self, py: Python<'py>) -> PyResult<&'py PyList> { + + let func_map = get_func_map(); + let py_list = PyList::empty(py); + // Iterate over the Rust vector and convert each tuple + for &(num, func) in self.update_sequence.predictions.iter() { + // Retrieve the function name from the map + let func_name = func_map.get(&func).unwrap_or(&"unknown"); + + // Convert the Rust tuple to a Python tuple with the function name as a string + let py_tuple = PyTuple::new(py, &[num.into_py(py), (*func_name).into_py(py)]); + + // Append the Python tuple to the Python list + py_list.append(py_tuple)?; + } + for &(num, func) in self.update_sequence.updates.iter() { + // Retrieve the function name from the map + let func_name = func_map.get(&func).unwrap_or(&"unknown"); + + // Convert the Rust tuple to a Python tuple with the function name as a string + let py_tuple = PyTuple::new(py, &[num.into_py(py), (*func_name).into_py(py)]); + + // Append the Python tuple to the Python list + py_list.append(py_tuple)?; + } + Ok(py_list) + } +} + +// Create a module to expose the class to Python +#[pymodule] +fn rshgf(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + Ok(()) +} + +// Tests module for unit tests +#[cfg(test)] // Only compile and include this module when running tests +mod tests { + use super::*; // Import the parent module's items to test them + + + #[test] + fn test_exponential_family_gaussian() { + + // initialize network + let mut network = Network::new(); + + // create a network with two exponential family state nodes + network.add_nodes( + "exponential-state", + None, + None, + None, + None + ); + + // println!("Graph before belief propagation: {:?}", network); + + // belief propagation + let input_data = vec![1.0, 1.3, 1.5, 1.7]; + network.set_update_sequence(); + network.input_data(input_data); + + println!("Update sequence: {:?}", network.update_sequence); + println!("Node trajectories: {:?}", network.node_trajectories); + + } +} diff --git a/src/tests/exponential_family.rs b/src/tests/exponential_family.rs new file mode 100644 index 000000000..e69de29bb diff --git a/src/updates/mod.rs b/src/updates/mod.rs new file mode 100644 index 000000000..ad43beacb --- /dev/null +++ b/src/updates/mod.rs @@ -0,0 +1,4 @@ +pub mod posterior; +pub mod prediction; +pub mod prediction_error; +pub mod observations; diff --git a/src/updates/observations.rs b/src/updates/observations.rs new file mode 100644 index 000000000..1037b83b2 --- /dev/null +++ b/src/updates/observations.rs @@ -0,0 +1,20 @@ +use crate::model::Network; + + +/// Inject new observations into an input node +/// +/// # Arguments +/// * `network` - The main network containing the node. +/// * `node_idx` - The input node index. +/// * `observations` - The new observations. +/// +/// # Returns +/// * `network` - The network after message passing. +pub fn observation_update(network: &mut Network, node_idx: usize, observations: f64) { + + if let Some(node) = network.attributes.floats.get_mut(&node_idx) { + if let Some(mean) = node.get_mut("mean") { + *mean = observations; + } + } +} \ No newline at end of file diff --git a/src/updates/posterior/continuous.rs b/src/updates/posterior/continuous.rs new file mode 100644 index 000000000..b1941915d --- /dev/null +++ b/src/updates/posterior/continuous.rs @@ -0,0 +1,13 @@ +use crate::model::Network; + +/// Posterior update from a continuous state node +/// +/// # Arguments +/// * `network` - The main network containing the node. +/// * `node_idx` - The node index. +/// +/// # Returns +/// * `network` - The network after message passing. +pub fn posterior_update_continuous_state_node(network: &mut Network, node_idx: usize) { + let a = 1; +} \ No newline at end of file diff --git a/src/updates/posterior/mod.rs b/src/updates/posterior/mod.rs new file mode 100644 index 000000000..6817d49e7 --- /dev/null +++ b/src/updates/posterior/mod.rs @@ -0,0 +1 @@ +pub mod continuous; \ No newline at end of file diff --git a/src/updates/prediction/continuous.rs b/src/updates/prediction/continuous.rs new file mode 100644 index 000000000..f40d2c35e --- /dev/null +++ b/src/updates/prediction/continuous.rs @@ -0,0 +1,13 @@ +use crate::model::Network; + +/// Prediction from a continuous state node +/// +/// # Arguments +/// * `network` - The main network containing the node. +/// * `node_idx` - The node index. +/// +/// # Returns +/// * `network` - The network after message passing. +pub fn prediction_continuous_state_node(network: &mut Network, node_idx: usize) { + let a = 1; +} \ No newline at end of file diff --git a/src/updates/prediction/mod.rs b/src/updates/prediction/mod.rs new file mode 100644 index 000000000..6817d49e7 --- /dev/null +++ b/src/updates/prediction/mod.rs @@ -0,0 +1 @@ +pub mod continuous; \ No newline at end of file diff --git a/src/updates/prediction_error/continuous.rs b/src/updates/prediction_error/continuous.rs new file mode 100644 index 000000000..96e8990ca --- /dev/null +++ b/src/updates/prediction_error/continuous.rs @@ -0,0 +1,13 @@ +use crate::model::Network; + +/// Prediction error from a continuous state node +/// +/// # Arguments +/// * `network` - The main network containing the node. +/// * `node_idx` - The node index. +/// +/// # Returns +/// * `network` - The network after message passing. +pub fn prediction_error_continuous_state_node(network: &mut Network, node_idx: usize) { + let a = 1; +} \ No newline at end of file diff --git a/src/updates/prediction_error/exponential.rs b/src/updates/prediction_error/exponential.rs new file mode 100644 index 000000000..e2cebca05 --- /dev/null +++ b/src/updates/prediction_error/exponential.rs @@ -0,0 +1,35 @@ +use crate::model::Network; +use crate::math::sufficient_statistics; + +/// Updating an exponential family state node +/// +/// # Arguments +/// * `network` - The main network containing the node. +/// * `node_idx` - The node index. +/// +/// # Returns +/// * `network` - The network after message passing. +pub fn prediction_error_exponential_state_node(network: &mut Network, node_idx: usize) { + + if let Some(floats_attributes) = network.attributes.floats.get_mut(&node_idx) { + if let Some(vectors_attributes) = network.attributes.vectors.get_mut(&node_idx) { + let mean = floats_attributes.get("mean"); + let nus = floats_attributes.get("nus"); + let xis = vectors_attributes.get("xis"); + let new_xis = match (mean, nus, xis) { + (Some(mean), Some(nus), Some(xis)) => { + let suf_stats = sufficient_statistics(mean); + let mut new_xis = xis.clone(); + for i in 0..suf_stats.len() { + new_xis[i] = new_xis[i] + (1.0 / (1.0 + nus)) * (suf_stats[i] - xis[i]); + } + new_xis + } + _ => Vec::new(), + }; + if let Some(xis) = vectors_attributes.get_mut("xis") { + *xis = new_xis; // Modify the value directly + } + } + } + } \ No newline at end of file diff --git a/src/updates/prediction_error/mod.rs b/src/updates/prediction_error/mod.rs new file mode 100644 index 000000000..810699ed6 --- /dev/null +++ b/src/updates/prediction_error/mod.rs @@ -0,0 +1,2 @@ +pub mod continuous; +pub mod exponential; diff --git a/src/utils/function_pointer.rs b/src/utils/function_pointer.rs new file mode 100644 index 000000000..163c12d11 --- /dev/null +++ b/src/utils/function_pointer.rs @@ -0,0 +1,18 @@ +use std::collections::HashMap; + +use crate::{model::Network, updates::{posterior::continuous::posterior_update_continuous_state_node, prediction::continuous::prediction_continuous_state_node, prediction_error::{continuous::prediction_error_continuous_state_node, exponential::prediction_error_exponential_state_node}}}; + +// Create a default signature for update functions +pub type FnType = for<'a> fn(&'a mut Network, usize); + +pub fn get_func_map() -> HashMap { + let function_map: HashMap = [ + (posterior_update_continuous_state_node as FnType, "posterior_update_continuous_state_node"), + (prediction_continuous_state_node as FnType, "prediction_continuous_state_node"), + (prediction_error_continuous_state_node as FnType, "prediction_error_continuous_state_node"), + (prediction_error_exponential_state_node as FnType, "prediction_error_exponential_state_node"), + ] + .into_iter() + .collect(); + function_map +} \ No newline at end of file diff --git a/src/utils/mod.rs b/src/utils/mod.rs new file mode 100644 index 000000000..1918ac547 --- /dev/null +++ b/src/utils/mod.rs @@ -0,0 +1,2 @@ +pub mod set_sequence; +pub mod function_pointer; \ No newline at end of file diff --git a/src/utils/set_sequence.rs b/src/utils/set_sequence.rs new file mode 100644 index 000000000..191d734ab --- /dev/null +++ b/src/utils/set_sequence.rs @@ -0,0 +1,272 @@ +use crate::{model::{AdjacencyLists, Network, UpdateSequence}, updates::{posterior::continuous::posterior_update_continuous_state_node, prediction::continuous::prediction_continuous_state_node, prediction_error::{continuous::prediction_error_continuous_state_node, exponential::prediction_error_exponential_state_node}}}; +use crate::utils::function_pointer::FnType; + +pub fn set_update_sequence(network: &Network) -> UpdateSequence { + let predictions = get_predictions_sequence(network); + let updates = get_updates_sequence(network); + + // return the update sequence + let update_sequence = UpdateSequence {predictions: predictions, updates: updates}; + update_sequence +} + + +pub fn get_predictions_sequence(network: &Network) -> Vec<(usize, FnType)> { + + let mut predictions : Vec<(usize, FnType)> = Vec::new(); + + // 1. get prediction sequence ------------------------------------------------------ + + // list all nodes availables in the network + let mut nodes_idxs: Vec = network.edges.keys().cloned().collect(); + + // iterate over all nodes and add the prediction step if all criteria are met + let mut n_remaining = nodes_idxs.len(); + + while n_remaining > 0 { + + // were we able to add an update step in the list on that iteration? + let mut has_update = false; + + // loop over all the remaining nodes + for i in 0..nodes_idxs.len() { + + let idx = nodes_idxs[i]; + + // list the node's parents + let value_parents_idxs = &network.edges[&idx].value_parents; + let volatility_parents_idxs = &network.edges[&idx].volatility_parents; + + let parents_idxs = match (value_parents_idxs, volatility_parents_idxs) { + // If both are Some, merge the vectors + (Some(ref vec1), Some(ref vec2)) => { + // Create a new vector by merging the two + let vec: Vec = vec1.iter().chain(vec2.iter()).cloned().collect(); + Some(vec) // Return the merged vector wrapped in Some + } + // If one is Some and the other is None, return the one that's Some + (Some(vec), None) | (None, Some(vec)) => Some(vec.clone()), + // If both are None, return None + (None, None) => None, + }; + + + // check if there is any parent node that is still found in the to-be-updated list + let contains_common = match parents_idxs { + Some(vec) => vec.iter().any(|item| nodes_idxs.contains(item)), + None => false + }; + + // if all parents have processed their prediction, this one can be added + if !(contains_common) { + + // add the node in the update list + match network.edges.get(&idx) { + Some(AdjacencyLists {node_type, ..}) if node_type == "continuous-state" => { + predictions.push((idx, prediction_continuous_state_node)); + } + _ => () + + } + + // remove the node from the to-be-updated list + nodes_idxs.retain(|&x| x != idx); + n_remaining -= 1; + has_update = true; + break; + } + } + // 2. get update sequence ------------------------------------------------------ + + if !(has_update) { + break; + } + } + predictions + +} + +pub fn get_updates_sequence(network: &Network) -> Vec<(usize, FnType)> { + + let mut updates : Vec<(usize, FnType)> = Vec::new(); + + // 1. get update sequence ---------------------------------------------------------- + + // list all nodes availables in the network + let mut pe_nodes_idxs: Vec = network.edges.keys().cloned().collect(); + let mut po_nodes_idxs: Vec = network.edges.keys().cloned().collect(); + + // remove the input nodes from the to-be-visited nodes for posterior updates + po_nodes_idxs.retain(|x| !network.inputs.contains(x)); + + // iterate over all nodes and add the prediction step if all criteria are met + let mut n_remaining = po_nodes_idxs.len() + pe_nodes_idxs.len(); // posterior updates + prediction errors + + while n_remaining > 0 { + + // were we able to add an update step in the list on that iteration? + let mut has_update = false; + + // loop over all the remaining nodes for prediction errors --------------------- + // ----------------------------------------------------------------------------- + for i in 0..pe_nodes_idxs.len() { + + let idx = pe_nodes_idxs[i]; + + // to send a prediction error, this node should have been updated first + if !(po_nodes_idxs.contains(&idx)) { + + // only send a prediction error if this node has any parent + let value_parents_idxs = &network.edges[&idx].value_parents; + let volatility_parents_idxs = &network.edges[&idx].volatility_parents; + + let has_parents = match (value_parents_idxs, volatility_parents_idxs) { + // If both are None, return false + (None, None) => false, + _ => true, + }; + + // add the node in the update list + match (network.edges.get(&idx), has_parents) { + (Some(AdjacencyLists {node_type, ..}), true) if node_type == "continuous-state" => { + updates.push((idx, prediction_error_continuous_state_node)); + // remove the node from the to-be-updated list + pe_nodes_idxs.retain(|&x| x != idx); + n_remaining -= 1; + has_update = true; + break; + } + (Some(AdjacencyLists {node_type, ..}), _) if node_type == "exponential-state" => { + updates.push((idx, prediction_error_exponential_state_node)); + // remove the node from the to-be-updated list + pe_nodes_idxs.retain(|&x| x != idx); + n_remaining -= 1; + has_update = true; + break; + } + _ => () + + } + } + } + + // loop over all the remaining nodes for posterior updates --------------------- + // ----------------------------------------------------------------------------- + for i in 0..po_nodes_idxs.len() { + + let idx = po_nodes_idxs[i]; + + // to start a posterior update, all children should have sent prediction errors + // 1. get a list of all children + let value_children_idxs = &network.edges[&idx].value_children; + let volatility_children_idxs = &network.edges[&idx].volatility_children; + + let children_idxs = match (value_children_idxs, volatility_children_idxs) { + // If both are Some, merge the vectors + (Some(ref vec1), Some(ref vec2)) => { + // Create a new vector by merging the two + let merged_vec: Vec = vec1.iter().chain(vec2.iter()).cloned().collect(); + Some(merged_vec) // Return the merged vector wrapped in Some + } + // If one is Some and the other is None, return the one that's Some + (Some(vec), None) | (None, Some(vec)) => Some(vec.clone()), + // If both are None, return None + (None, None) => None, + }; + + // 2. check if any of the children is still on the to-be-visited list for prediction errors + // check if there is any parent node that is still found in the to-be-updated list + let missing_pe = match children_idxs { + Some(vec) => vec.iter().any(|item| pe_nodes_idxs.contains(item)), + None => false + }; + + // 3. if false, add the posterior update to the list + if !missing_pe { + + // add the node in the update list + match network.edges.get(&idx) { + Some(AdjacencyLists {node_type, ..}) if node_type == "continuous-state" => { + updates.push((idx, posterior_update_continuous_state_node)); + } + _ => () + + } + + // remove the node from the to-be-updated list + po_nodes_idxs.retain(|&x| x != idx); + n_remaining -= 1; + has_update = true; + break; + } + } + if !(has_update) { + break; + } + } + updates + +} + +// Tests module for unit tests +#[cfg(test)] // Only compile and include this module when running tests +mod tests { + use crate::utils::function_pointer::get_func_map; + + use super::*; // Import the parent module's items to test them + + #[test] + fn test_get_update_order() { + + let func_map = get_func_map(); + + // initialize network + let mut hgf_network = Network::new(); + + // create a network + hgf_network.add_nodes( + "continuous-state", + Some(vec![1]), + None, + Some(vec![2]), + None, + ); + hgf_network.add_nodes( + "continuous-state", + None, + Some(vec![0]), + None, + None, + ); + hgf_network.add_nodes( + "continuous-state", + None, + None, + None, + Some(vec![0]), + ); + hgf_network.set_update_sequence(); + + println!("Prediction sequence ----------"); + println!("Node: {} - Function name: {}", &hgf_network.update_sequence.predictions[0].0, func_map.get(&hgf_network.update_sequence.predictions[0].1).unwrap_or(&"unknown")); + println!("Node: {} - Function name: {}", &hgf_network.update_sequence.predictions[1].0, func_map.get(&hgf_network.update_sequence.predictions[1].1).unwrap_or(&"unknown")); + println!("Node: {} - Function name: {}", &hgf_network.update_sequence.predictions[2].0, func_map.get(&hgf_network.update_sequence.predictions[2].1).unwrap_or(&"unknown")); + println!("Update sequence ----------"); + println!("Node: {} - Function name: {}", &hgf_network.update_sequence.updates[0].0, func_map.get(&hgf_network.update_sequence.updates[0].1).unwrap_or(&"unknown")); + println!("Node: {} - Function name: {}", &hgf_network.update_sequence.updates[1].0, func_map.get(&hgf_network.update_sequence.updates[1].1).unwrap_or(&"unknown")); + println!("Node: {} - Function name: {}", &hgf_network.update_sequence.updates[2].0, func_map.get(&hgf_network.update_sequence.updates[2].1).unwrap_or(&"unknown")); + + // initialize network + let mut exp_network = Network::new(); + exp_network.add_nodes( + "exponential-state", + None, + None, + None, + None, + ); + exp_network.set_update_sequence(); + println!("Node: {} - Function name: {}", &exp_network.update_sequence.updates[0].0, func_map.get(&exp_network.update_sequence.updates[0].1).unwrap_or(&"unknown")); + + } +} diff --git a/tests/test_exponential_family.py b/tests/test_exponential_family.py new file mode 100644 index 000000000..1e5f834aa --- /dev/null +++ b/tests/test_exponential_family.py @@ -0,0 +1,33 @@ +# Author: Nicolas Legrand + +import numpy as np +from rshgf import Network as RsNetwork + +from pyhgf import load_data +from pyhgf.model import Network as PyNetwork + + +def test_1d_gaussain(): + + timeseries = load_data("continuous") + + # Rust ----------------------------------------------------------------------------- + rs_network = RsNetwork() + rs_network.add_nodes(kind="exponential-state") + rs_network.set_update_sequence() + rs_network.input_data(timeseries) + + # Python --------------------------------------------------------------------------- + py_network = PyNetwork().add_nodes(kind="exponential-state") + py_network.input_data(timeseries) + + # Ensure identical results + assert np.isclose( + py_network.node_trajectories[0]["xis"], rs_network.node_trajectories[0]["xis"] + ).all() + assert np.isclose( + py_network.node_trajectories[0]["mean"], rs_network.node_trajectories[0]["mean"] + ).all() + assert np.isclose( + py_network.node_trajectories[0]["nus"], rs_network.node_trajectories[0]["nus"] + ).all() diff --git a/tests/test_updates/prediction_errors/test_dirichlet.py b/tests/test_updates/prediction_errors/test_dirichlet.py index e6e12c3bd..d044ca80d 100644 --- a/tests/test_updates/prediction_errors/test_dirichlet.py +++ b/tests/test_updates/prediction_errors/test_dirichlet.py @@ -29,7 +29,7 @@ def test_dirichlet_node_prediction_error(): .add_nodes(kind="generic-state") .add_nodes(kind="DP-state", value_children=0, batch_size=2) .add_nodes( - kind="ef-normal", + kind="exponential-state", n_nodes=2, value_children=1, xis=jnp.array([0.0, 1 / 8]), diff --git a/tests/test_utils.py b/tests/test_utils.py index f3763ef4a..1dde30aec 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -74,7 +74,7 @@ def test_set_update_sequence(): network3 = ( Network() .add_nodes(kind="generic-state") - .add_nodes(kind="ef-normal", value_children=0) + .add_nodes(kind="exponential-state", value_children=0) .create_belief_propagation_fn() ) predictions, updates = network3.update_sequence @@ -87,7 +87,7 @@ def test_set_update_sequence(): .add_nodes(kind="generic-state") .add_nodes(kind="DP-state", value_children=0, alpha=0.1, batch_size=2) .add_nodes( - kind="ef-normal", + kind="exponential-state", n_nodes=2, value_children=1, xis=jnp.array([0.0, 1 / 8]),