Merge branch 'master' into kernel_distances

.bumpversion.cfg

@@ -1,10 +1,10 @@
 [bumpversion]
-current_version = 0.7.6
+current_version = 0.4.2
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
-serialize = {major}.{minor}.{patch}
+serialize = 
-message = build: bump version {current_version} → {new_version}
+	{major}.{minor}.{patch}
 
 [bumpversion:file:setup.py]
 

.codacy.yml

@@ -0,0 +1,15 @@
+# To validate the contents of your configuration file
+# run the following command in the folder where the configuration file is located:
+# codacy-analysis-cli validate-configuration --directory `pwd`
+# To analyse, run:
+# codacy-analysis-cli analyse --tool remark-lint --directory `pwd`
+---
+engines:
+  pylintpython3:
+    exclude_paths:
+      - config/engines.yml
+  remark-lint:
+    exclude_paths:
+      - config/engines.yml
+exclude_paths:
+  - 'tests/**'

.codecov.yml

@@ -0,0 +1,2 @@
+comment:
+  require_changes: yes

.github/ISSUE_TEMPLATE/bug_report.md

@@ -10,29 +10,22 @@ assignees: ''
 **Describe the bug**
 A clear and concise description of what the bug is.
 
-**Steps to reproduce the behavior**
+**To Reproduce**
-1. ...
+Steps to reproduce the behavior:
-2. Run script '...' or this snippet:
+1. Install Prototorch by running '...'
-```python
+2. Run script '...'
-import prototorch as pt
-
-...
-```
 3. See errors
 
 **Expected behavior**
 A clear and concise description of what you expected to happen.
 
-**Observed behavior**
-A clear and concise description of what actually happened.
-
 **Screenshots**
 If applicable, add screenshots to help explain your problem.
 
-**System and version information**
+**Desktop (please complete the following information):**
-- OS: [e.g. Ubuntu 20.10]
+ - OS: [e.g. Ubuntu 20.10]
-- ProtoTorch Version: [e.g. 0.4.0]
+ - Prototorch Version: [e.g. v0.4.0]
-- Python Version: [e.g. 3.9.5]
+ - Python Version: [e.g. 3.9.5]
 
 **Additional context**
-Add any other context about the problem here.
+Add any other context about the problem here.

.github/workflows/pythonapp.yml

@@ -5,71 +5,33 @@ name: tests
 
 on:
   push:
+    branches: [ master, dev ]
   pull_request:
-    branches: [master]
+    branches: [ master ]
 
 jobs:
-  style:
+  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.11
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.11"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[all]
-      - uses: pre-commit/action@v3.0.0
-  compatibility:
-    needs: style
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
-        os: [ubuntu-latest, windows-latest]
-        exclude:
-          - os: windows-latest
-            python-version: "3.8"
-          - os: windows-latest
-            python-version: "3.9"
-          - os: windows-latest
-            python-version: "3.10"
 
-    runs-on: ${{ matrix.os }}
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[all]
-      - name: Test with pytest
-        run: |
-          pytest
-  publish_pypi:
-    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
-    needs: compatibility
     runs-on: ubuntu-latest
+
     steps:
-      - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
-      - name: Set up Python 3.10
+    - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+      uses: actions/setup-python@v1
-        with:
+      with:
-          python-version: "3.11"
+        python-version: 3.8
-      - name: Install dependencies
+    - name: Install dependencies
-        run: |
+      run: |
-          python -m pip install --upgrade pip
+        python -m pip install --upgrade pip
-          pip install .[all]
+        pip install .[all]
-          pip install wheel
+    - name: Lint with flake8
-      - name: Build package
+      run: |
-        run: python setup.py sdist bdist_wheel
+        pip install flake8
-      - name: Publish a Python distribution to PyPI
+        # stop the build if there are Python syntax errors or undefined names
-        uses: pypa/gh-action-pypi-publish@release/v1
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        with:
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          user: __token__
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-          password: ${{ secrets.PYPI_API_TOKEN }}
+    - name: Test with pytest
+      run: |
+        pip install pytest
+        pytest

.gitignore

@@ -129,6 +129,14 @@ dmypy.json
 
 # End of https://www.gitignore.io/api/python
 
+# ProtoFlow
+core
+checkpoint
+logs/
+saved_weights/
+scratch*
+
+
 # Created by https://www.gitignore.io/api/visualstudiocode
 # Edit at https://www.gitignore.io/?templates=visualstudiocode
 
@@ -146,13 +154,4 @@ dmypy.json
 # End of https://www.gitignore.io/api/visualstudiocode
 .vscode/
 
-# Vim
+reports
-*~
-*.swp
-*.swo
-
-# Artifacts created by ProtoTorch
-reports
-artifacts
-examples/_*.py
-examples/_*.ipynb

.pre-commit-config.yaml

@@ -1,53 +0,0 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
-
-repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
-  hooks:
-  - id: trailing-whitespace
-  - id: end-of-file-fixer
-  - id: check-yaml
-  - id: check-added-large-files
-  - id: check-ast
-  - id: check-case-conflict
-
-- repo: https://github.com/myint/autoflake
-  rev: v2.1.1
-  hooks:
-  - id: autoflake
-
-- repo: http://github.com/PyCQA/isort
-  rev: 5.12.0
-  hooks:
-  - id: isort
-
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v1.3.0
-  hooks:
-  - id: mypy
-    files: prototorch
-    additional_dependencies: [types-pkg_resources]
-
-- repo: https://github.com/pre-commit/mirrors-yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-
-- repo: https://github.com/pre-commit/pygrep-hooks
-  rev: v1.10.0
-  hooks:
-  - id: python-use-type-annotations
-  - id: python-no-log-warn
-  - id: python-check-blanket-noqa
-
-- repo: https://github.com/asottile/pyupgrade
-  rev: v3.7.0
-  hooks:
-  - id: pyupgrade
-
-- repo: https://github.com/si-cim/gitlint
-  rev: v0.15.2-unofficial
-  hooks:
-  - id: gitlint
-    args: [--contrib=CT1, --ignore=B6, --msg-filename]

.readthedocs.yml

@@ -19,7 +19,7 @@ formats: all
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.9
+  version: 3.8
   install:
     - method: pip
       path: .

.remarkrc

@@ -1,7 +0,0 @@
-{
-  "plugins": [
-    "remark-preset-lint-recommended",
-    ["remark-lint-list-item-indent", false],
-    ["no-emphasis-as-header", false]
-  ]
-}

.travis.yml

@@ -0,0 +1,34 @@
+dist: bionic
+sudo: false
+language: python
+python: 3.8
+cache:
+  directories:
+  - "./tests/artifacts"
+install:
+- pip install .[all] --progress-bar off
+
+# Generate code coverage report
+script:
+- coverage run -m pytest
+
+# Push the results to codecov
+after_success:
+- bash <(curl -s https://codecov.io/bash)
+
+# Publish on PyPI
+deploy:
+  provider: pypi
+  username: __token__
+  password:
+    secure: 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
+  on:
+    tags: true
+    skip_existing: true
+
+# The password is encrypted with:
+# `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`
+# See https://docs.travis-ci.com/user/deployment/pypi and
+# https://github.com/travis-ci/travis.rb#installation
+# for more details
+# Note: The encrypt command does not work well in ZSH.

LICENSE

@@ -1,7 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Saxon Institute for Computational Intelligence and Machine
+Copyright (c) 2020 si-cim
-Learning (SICIM)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,9 +2,13 @@
 
 ![ProtoTorch Logo](https://prototorch.readthedocs.io/en/latest/_static/horizontal-lockup.png)
 
+[![Build Status](https://travis-ci.org/si-cim/prototorch.svg?branch=master)](https://travis-ci.org/si-cim/prototorch)
 ![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg)
 [![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/si-cim/prototorch?color=yellow&label=version)](https://github.com/si-cim/prototorch/releases)
 [![PyPI](https://img.shields.io/pypi/v/prototorch)](https://pypi.org/project/prototorch/)
+[![codecov](https://codecov.io/gh/si-cim/prototorch/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/prototorch)
+[![Codacy Badge](https://api.codacy.com/project/badge/Grade/76273904bf9343f0a8b29cd8aca242e7)](https://www.codacy.com/gh/si-cim/prototorch?utm_source=github.com&utm_medium=referral&utm_content=si-cim/prototorch&utm_campaign=Badge_Grade)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/prototorch?color=blue)
 [![GitHub license](https://img.shields.io/github/license/si-cim/prototorch)](https://github.com/si-cim/prototorch/blob/master/LICENSE)
 
 *Tensorflow users, see:* [ProtoFlow](https://github.com/si-cim/protoflow)
@@ -44,23 +48,6 @@ pip install -e .[all]
 The documentation is available at <https://www.prototorch.ml/en/latest/>. Should
 that link not work try <https://prototorch.readthedocs.io/en/latest/>.
 
-## Contribution
-
-This repository contains definition for [git hooks](https://githooks.com).
-[Pre-commit](https://pre-commit.com) is automatically installed as development
-dependency with prototorch or you can install it manually with `pip install
-pre-commit`.
-
-Please install the hooks by running:
-```bash
-pre-commit install
-pre-commit install --hook-type commit-msg
-```
-before creating the first commit.
-
-The commit will fail if the commit message does not follow the specification
-provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
-
 ## Bibtex
 
 If you would like to cite the package, please use this:

RELEASE.md

@@ -1,16 +1,13 @@
 # ProtoTorch Releases
 
-## Release 0.5.0
-
-- Breaking: Removed deprecated `prototorch.modules.Prototypes1D`.
-  - Use `prototorch.components.LabeledComponents` instead.
-
 ## Release 0.2.0
 
+### Includes
 - Fixes in example scripts.
 
 ## Release 0.1.1-dev0
 
+### Includes
 - Minor bugfixes.
 - 100% line coverage.
 

docs/source/api.rst

@@ -1,24 +1,13 @@
-.. ProtoTorch API Reference
+.. ProtoFlow API Reference
 
-ProtoTorch API Reference
+ProtoFlow API Reference
 ======================================
 
 Datasets
 --------------------------------------
-
-Common Datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: prototorch.datasets
    :members:
-
+   :undoc-members:
-
-Abstract Datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Abstract Datasets are used to build your own datasets.
-
-.. autoclass:: prototorch.datasets.abstract.NumpyDataset
-   :members:
 
 Functions
 --------------------------------------

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.7.6"
+release = "0.4.2"
 
 # -- General configuration ---------------------------------------------------
 
@@ -46,7 +46,6 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinx_rtd_theme",
     "sphinxcontrib.katex",
-    'sphinx_autodoc_typehints',
 ]
 
 # katex_prerender = True
@@ -120,7 +119,7 @@ html_css_files = [
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = "prototorchdoc"
+htmlhelp_basename = "protoflowdoc"
 
 # -- Options for LaTeX output ---------------------------------------------
 
@@ -180,9 +179,6 @@ texinfo_documents = [
 intersphinx_mapping = {
     "python": ("https://docs.python.org/", None),
     "numpy": ("https://docs.scipy.org/doc/numpy/", None),
-    "torch": ('https://pytorch.org/docs/stable/', None),
-    "pytorch_lightning":
-    ("https://pytorch-lightning.readthedocs.io/en/stable/", None),
 }
 
 # -- Options for Epub output ----------------------------------------------

examples/cbc_iris.py

@@ -1,100 +0,0 @@
-"""ProtoTorch CBC example using 2D Iris data."""
-
-import logging
-
-import torch
-from matplotlib import pyplot as plt
-
-import prototorch as pt
-
-
-class CBC(torch.nn.Module):
-
-    def __init__(self, data, **kwargs):
-        super().__init__(**kwargs)
-        self.components_layer = pt.components.ReasoningComponents(
-            distribution=[2, 1, 2],
-            components_initializer=pt.initializers.SSCI(data, noise=0.1),
-            reasonings_initializer=pt.initializers.PPRI(components_first=True),
-        )
-
-    def forward(self, x):
-        components, reasonings = self.components_layer()
-        sims = pt.similarities.euclidean_similarity(x, components)
-        probs = pt.competitions.cbcc(sims, reasonings)
-        return probs
-
-
-class VisCBC2D():
-
-    def __init__(self, model, data):
-        self.model = model
-        self.x_train, self.y_train = pt.utils.parse_data_arg(data)
-        self.title = "Components Visualization"
-        self.fig = plt.figure(self.title)
-        self.border = 0.1
-        self.resolution = 100
-        self.cmap = "viridis"
-
-    def on_train_epoch_end(self):
-        x_train, y_train = self.x_train, self.y_train
-        _components = self.model.components_layer._components.detach()
-        ax = self.fig.gca()
-        ax.cla()
-        ax.set_title(self.title)
-        ax.axis("off")
-        ax.scatter(
-            x_train[:, 0],
-            x_train[:, 1],
-            c=y_train,
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="o",
-            s=30,
-        )
-        ax.scatter(
-            _components[:, 0],
-            _components[:, 1],
-            c="w",
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
-        x = torch.vstack((x_train, _components))
-        mesh_input, xx, yy = pt.utils.mesh2d(x, self.border, self.resolution)
-        with torch.no_grad():
-            y_pred = self.model(
-                torch.Tensor(mesh_input).type_as(_components)).argmax(1)
-        y_pred = y_pred.cpu().reshape(xx.shape)
-        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-        plt.pause(0.2)
-
-
-if __name__ == "__main__":
-    train_ds = pt.datasets.Iris(dims=[0, 2])
-
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
-
-    model = CBC(train_ds)
-
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
-    criterion = pt.losses.MarginLoss(margin=0.1)
-    vis = VisCBC2D(model, train_ds)
-
-    for epoch in range(200):
-        correct = 0.0
-        for x, y in train_loader:
-            y_oh = torch.eye(3)[y]
-            y_pred = model(x)
-            loss = criterion(y_pred, y_oh).mean(0)
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            correct += (y_pred.argmax(1) == y).float().sum(0)
-
-        acc = 100 * correct / len(train_ds)
-        logging.info(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
-        vis.on_train_epoch_end()

examples/glvq_iris.py

@@ -0,0 +1,120 @@
+"""ProtoTorch GLVQ example using 2D Iris data."""
+
+import numpy as np
+import torch
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torchinfo import summary
+
+from prototorch.functions.competitions import wtac
+from prototorch.functions.distances import euclidean_distance
+from prototorch.modules.losses import GLVQLoss
+from prototorch.modules.prototypes import Prototypes1D
+
+# Prepare and preprocess the data
+scaler = StandardScaler()
+x_train, y_train = load_iris(return_X_y=True)
+x_train = x_train[:, [0, 2]]
+scaler.fit(x_train)
+x_train = scaler.transform(x_train)
+
+
+# Define the GLVQ model
+class Model(torch.nn.Module):
+    def __init__(self):
+        """GLVQ model for training on 2D Iris data."""
+        super().__init__()
+        self.proto_layer = Prototypes1D(
+            input_dim=2,
+            prototypes_per_class=3,
+            nclasses=3,
+            prototype_initializer="stratified_random",
+            data=[x_train, y_train],
+        )
+
+    def forward(self, x):
+        protos = self.proto_layer.prototypes
+        plabels = self.proto_layer.prototype_labels
+        dis = euclidean_distance(x, protos)
+        return dis, plabels
+
+
+# Build the GLVQ model
+model = Model()
+
+# Print summary using torchinfo (might be buggy/incorrect)
+print(summary(model))
+
+# Optimize using SGD optimizer from `torch.optim`
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
+
+x_in = torch.Tensor(x_train)
+y_in = torch.Tensor(y_train)
+
+# Training loop
+title = "Prototype Visualization"
+fig = plt.figure(title)
+for epoch in range(70):
+    # Compute loss
+    dis, plabels = model(x_in)
+    loss = criterion([dis, plabels], y_in)
+    with torch.no_grad():
+        pred = wtac(dis, plabels)
+        correct = pred.eq(y_in.view_as(pred)).sum().item()
+    acc = 100.0 * correct / len(x_train)
+    print(
+        f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%"
+    )
+
+    # Take a gradient descent step
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    # Get the prototypes form the model
+    protos = model.proto_layer.prototypes.data.numpy()
+    if np.isnan(np.sum(protos)):
+        print("Stopping training because of `nan` in prototypes.")
+        break
+
+    # Visualize the data and the prototypes
+    ax = fig.gca()
+    ax.cla()
+    ax.set_title(title)
+    ax.set_xlabel("Data dimension 1")
+    ax.set_ylabel("Data dimension 2")
+    cmap = "viridis"
+    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
+    ax.scatter(
+        protos[:, 0],
+        protos[:, 1],
+        c=plabels,
+        cmap=cmap,
+        edgecolor="k",
+        marker="D",
+        s=50,
+    )
+
+    # Paint decision regions
+    x = np.vstack((x_train, protos))
+    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
+    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
+                         np.arange(y_min, y_max, 1 / 50))
+    mesh_input = np.c_[xx.ravel(), yy.ravel()]
+
+    torch_input = torch.Tensor(mesh_input)
+    d = model(torch_input)[0]
+    w_indices = torch.argmin(d, dim=1)
+    y_pred = torch.index_select(plabels, 0, w_indices)
+    y_pred = y_pred.reshape(xx.shape)
+
+    # Plot voronoi regions
+    ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
+
+    ax.set_xlim(left=x_min + 0, right=x_max - 0)
+    ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
+
+    plt.pause(0.1)

examples/gmlvq.py

@@ -1,76 +0,0 @@
-"""ProtoTorch GMLVQ example using Iris data."""
-
-import torch
-
-import prototorch as pt
-
-
-class GMLVQ(torch.nn.Module):
-    """
-    Implementation of Generalized Matrix Learning Vector Quantization.
-    """
-
-    def __init__(self, data, **kwargs):
-        super().__init__(**kwargs)
-
-        self.components_layer = pt.components.LabeledComponents(
-            distribution=[1, 1, 1],
-            components_initializer=pt.initializers.SMCI(data, noise=0.1),
-        )
-
-        self.backbone = pt.transforms.Omega(
-            len(data[0][0]),
-            len(data[0][0]),
-            pt.initializers.RandomLinearTransformInitializer(),
-        )
-
-    def forward(self, data):
-        """
-        Forward function that returns a tuple of dissimilarities and label information.
-        Feed into GLVQLoss to get a complete GMLVQ model.
-        """
-        components, label = self.components_layer()
-
-        latent_x = self.backbone(data)
-        latent_components = self.backbone(components)
-
-        distance = pt.distances.squared_euclidean_distance(
-            latent_x, latent_components)
-
-        return distance, label
-
-    def predict(self, data):
-        """
-        The GMLVQ has a modified prediction step, where a competition layer is applied.
-        """
-        components, label = self.components_layer()
-        distance = pt.distances.squared_euclidean_distance(data, components)
-        winning_label = pt.competitions.wtac(distance, label)
-        return winning_label
-
-
-if __name__ == "__main__":
-    train_ds = pt.datasets.Iris()
-
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
-
-    model = GMLVQ(train_ds)
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.05)
-    criterion = pt.losses.GLVQLoss()
-
-    for epoch in range(200):
-        correct = 0.0
-        for x, y in train_loader:
-            d, labels = model(x)
-            loss = criterion(d, y, labels).mean(0)
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            with torch.no_grad():
-                y_pred = model.predict(x)
-                correct += (y_pred == y).float().sum(0)
-
-        acc = 100 * correct / len(train_ds)
-        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")

examples/gmlvq_tecator.py

@@ -0,0 +1,104 @@
+"""ProtoTorch "siamese" GMLVQ example using Tecator."""
+
+import matplotlib.pyplot as plt
+import torch
+from torch.utils.data import DataLoader
+
+from prototorch.datasets.tecator import Tecator
+from prototorch.functions.distances import sed
+from prototorch.modules import Prototypes1D
+from prototorch.modules.losses import GLVQLoss
+from prototorch.utils.colors import get_legend_handles
+
+# Prepare the dataset and dataloader
+train_data = Tecator(root="./artifacts", train=True)
+train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
+
+
+class Model(torch.nn.Module):
+    def __init__(self, **kwargs):
+        """GMLVQ model as a siamese network."""
+        super().__init__()
+        x, y = train_data.data, train_data.targets
+        self.p1 = Prototypes1D(
+            input_dim=100,
+            prototypes_per_class=2,
+            nclasses=2,
+            prototype_initializer="stratified_random",
+            data=[x, y],
+        )
+        self.omega = torch.nn.Linear(in_features=100,
+                                     out_features=100,
+                                     bias=False)
+        torch.nn.init.eye_(self.omega.weight)
+
+    def forward(self, x):
+        protos = self.p1.prototypes
+        plabels = self.p1.prototype_labels
+
+        # Process `x` and `protos` through `omega`
+        x_map = self.omega(x)
+        protos_map = self.omega(protos)
+
+        # Compute distances and output
+        dis = sed(x_map, protos_map)
+        return dis, plabels
+
+
+# Build the GLVQ model
+model = Model()
+
+# Print a summary of the model
+print(model)
+
+# Optimize using Adam optimizer from `torch.optim`
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001_0)
+scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=75, gamma=0.1)
+criterion = GLVQLoss(squashing="identity", beta=10)
+
+# Training loop
+for epoch in range(150):
+    epoch_loss = 0.0  # zero-out epoch loss
+    optimizer.zero_grad()  # zero-out gradients
+    for xb, yb in train_loader:
+        # Compute loss
+        distances, plabels = model(xb)
+        loss = criterion([distances, plabels], yb)
+        epoch_loss += loss.item()
+        # Backprop
+        loss.backward()
+    # Take a gradient descent step
+    optimizer.step()
+    scheduler.step()
+
+    lr = optimizer.param_groups[0]["lr"]
+    print(f"Epoch: {epoch + 1:03d} Loss: {epoch_loss:06.02f} lr: {lr:07.06f}")
+
+# Get the omega matrix form the model
+omega = model.omega.weight.data.numpy().T
+
+# Visualize the lambda matrix
+title = "Lambda Matrix Visualization"
+fig = plt.figure(title)
+ax = fig.gca()
+ax.set_title(title)
+im = ax.imshow(omega.dot(omega.T), cmap="viridis")
+plt.show()
+
+# Get the prototypes form the model
+protos = model.p1.prototypes.data.numpy()
+plabels = model.p1.prototype_labels
+
+# Visualize the prototypes
+title = "Tecator Prototypes"
+fig = plt.figure(title)
+ax = fig.gca()
+ax.set_title(title)
+ax.set_xlabel("Spectral frequencies")
+ax.set_ylabel("Absorption")
+clabels = ["Class 0 - Low fat", "Class 1 - High fat"]
+handles, colors = get_legend_handles(clabels, marker="line", zero_indexed=True)
+for x, y in zip(protos, plabels):
+    ax.plot(x, c=colors[int(y)])
+ax.legend(handles, clabels)
+plt.show()

examples/gtlvq_mnist.py

@@ -0,0 +1,184 @@
+"""
+ProtoTorch GTLVQ example using MNIST data.
+The GTLVQ is placed as an classification model on
+top of a CNN, considered as featurer extractor.
+Initialization of subpsace and prototypes in
+Siamnese fashion
+For more info about GTLVQ see:
+DOI:10.1109/IJCNN.2016.7727534
+"""
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+from torchvision import transforms
+
+from prototorch.functions.helper import calculate_prototype_accuracy
+from prototorch.modules.losses import GLVQLoss
+from prototorch.modules.models import GTLVQ
+
+# Parameters and options
+n_epochs = 50
+batch_size_train = 64
+batch_size_test = 1000
+learning_rate = 0.1
+momentum = 0.5
+log_interval = 10
+cuda = "cuda:1"
+random_seed = 1
+device = torch.device(cuda if torch.cuda.is_available() else "cpu")
+
+# Configures reproducability
+torch.manual_seed(random_seed)
+np.random.seed(random_seed)
+
+# Prepare and preprocess the data
+train_loader = torch.utils.data.DataLoader(
+    torchvision.datasets.MNIST(
+        "./files/",
+        train=True,
+        download=True,
+        transform=torchvision.transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307, ), (0.3081, ))
+        ]),
+    ),
+    batch_size=batch_size_train,
+    shuffle=True,
+)
+
+test_loader = torch.utils.data.DataLoader(
+    torchvision.datasets.MNIST(
+        "./files/",
+        train=False,
+        download=True,
+        transform=torchvision.transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307, ), (0.3081, ))
+        ]),
+    ),
+    batch_size=batch_size_test,
+    shuffle=True,
+)
+
+
+# Define the GLVQ model plus appropriate feature extractor
+class CNNGTLVQ(torch.nn.Module):
+    def __init__(
+        self,
+        num_classes,
+        subspace_data,
+        prototype_data,
+        tangent_projection_type="local",
+        prototypes_per_class=2,
+        bottleneck_dim=128,
+    ):
+        super(CNNGTLVQ, self).__init__()
+
+        # Feature Extractor - Simple CNN
+        self.fe = nn.Sequential(
+            nn.Conv2d(1, 32, 3, 1),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 3, 1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Dropout(0.25),
+            nn.Flatten(),
+            nn.Linear(9216, bottleneck_dim),
+            nn.Dropout(0.5),
+            nn.LeakyReLU(),
+            nn.LayerNorm(bottleneck_dim),
+        )
+
+        # Forward pass of subspace and prototype initialization data through feature extractor
+        subspace_data = self.fe(subspace_data)
+        prototype_data[0] = self.fe(prototype_data[0])
+
+        # Initialization of GTLVQ
+        self.gtlvq = GTLVQ(
+            num_classes,
+            subspace_data,
+            prototype_data,
+            tangent_projection_type=tangent_projection_type,
+            feature_dim=bottleneck_dim,
+            prototypes_per_class=prototypes_per_class,
+        )
+
+    def forward(self, x):
+        # Feature Extraction
+        x = self.fe(x)
+
+        # GTLVQ Forward pass
+        dis = self.gtlvq(x)
+        return dis
+
+
+# Get init data
+subspace_data = torch.cat(
+    [next(iter(train_loader))[0],
+     next(iter(test_loader))[0]])
+prototype_data = next(iter(train_loader))
+
+# Build the CNN GTLVQ  model
+model = CNNGTLVQ(
+    10,
+    subspace_data,
+    prototype_data,
+    tangent_projection_type="local",
+    bottleneck_dim=128,
+).to(device)
+
+# Optimize using SGD optimizer from `torch.optim`
+optimizer = torch.optim.Adam(
+    [{
+        "params": model.fe.parameters()
+    }, {
+        "params": model.gtlvq.parameters()
+    }],
+    lr=learning_rate,
+)
+criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
+
+# Training loop
+for epoch in range(n_epochs):
+    for batch_idx, (x_train, y_train) in enumerate(train_loader):
+        model.train()
+        x_train, y_train = x_train.to(device), y_train.to(device)
+        optimizer.zero_grad()
+
+        distances = model(x_train)
+        plabels = model.gtlvq.cls.prototype_labels.to(device)
+
+        # Compute loss.
+        loss = criterion([distances, plabels], y_train)
+        loss.backward()
+        optimizer.step()
+
+        # GTLVQ uses projected SGD, which means to orthogonalize the subspaces after every gradient update.
+        model.gtlvq.orthogonalize_subspace()
+
+        if batch_idx % log_interval == 0:
+            acc = calculate_prototype_accuracy(distances, y_train, plabels)
+            print(
+                f"Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
+              Train Acc: {acc.item():02.02f}")
+
+    # Test
+    with torch.no_grad():
+        model.eval()
+        correct = 0
+        total = 0
+        for x_test, y_test in test_loader:
+            x_test, y_test = x_test.to(device), y_test.to(device)
+            test_distances = model(torch.tensor(x_test))
+            test_plabels = model.gtlvq.cls.prototype_labels.to(device)
+            i = torch.argmin(test_distances, 1)
+            correct += torch.sum(y_test == test_plabels[i])
+            total += y_test.size(0)
+        print("Accuracy of the network on the test images: %d %%" %
+              (torch.true_divide(correct, total) * 100))
+
+# Save the model
+PATH = "./glvq_mnist_model.pth"
+torch.save(model.state_dict(), PATH)

examples/lgmlvq_iris.py

@@ -0,0 +1,110 @@
+"""ProtoTorch LGMLVQ example using 2D Iris data."""
+
+import numpy as np
+import torch
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import accuracy_score
+
+from prototorch.functions.competitions import stratified_min
+from prototorch.functions.distances import lomega_distance
+from prototorch.functions.init import eye_
+from prototorch.modules.losses import GLVQLoss
+from prototorch.modules.prototypes import Prototypes1D
+
+# Prepare training data
+x_train, y_train = load_iris(True)
+x_train = x_train[:, [0, 2]]
+
+
+# Define the model
+class Model(torch.nn.Module):
+    def __init__(self):
+        """Local-GMLVQ model."""
+        super().__init__()
+        self.p1 = Prototypes1D(
+            input_dim=2,
+            prototype_distribution=[1, 2, 2],
+            prototype_initializer="stratified_random",
+            data=[x_train, y_train],
+        )
+        omegas = torch.zeros(5, 2, 2)
+        self.omegas = torch.nn.Parameter(omegas)
+        eye_(self.omegas)
+
+    def forward(self, x):
+        protos = self.p1.prototypes
+        plabels = self.p1.prototype_labels
+        omegas = self.omegas
+        dis = lomega_distance(x, protos, omegas)
+        return dis, plabels
+
+
+# Build the model
+model = Model()
+
+# Optimize using Adam optimizer from `torch.optim`
+optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
+
+x_in = torch.Tensor(x_train)
+y_in = torch.Tensor(y_train)
+
+# Training loop
+title = "Prototype Visualization"
+fig = plt.figure(title)
+for epoch in range(100):
+    # Compute loss
+    dis, plabels = model(x_in)
+    loss = criterion([dis, plabels], y_in)
+    y_pred = np.argmin(stratified_min(dis, plabels).detach().numpy(), axis=1)
+    acc = accuracy_score(y_train, y_pred)
+    log_string = f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} "
+    log_string += f"Acc: {acc * 100:05.02f}%"
+    print(log_string)
+
+    # Take a gradient descent step
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    # Get the prototypes form the model
+    protos = model.p1.prototypes.data.numpy()
+
+    # Visualize the data and the prototypes
+    ax = fig.gca()
+    ax.cla()
+    ax.set_title(title)
+    ax.set_xlabel("Data dimension 1")
+    ax.set_ylabel("Data dimension 2")
+    cmap = "viridis"
+    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
+    ax.scatter(
+        protos[:, 0],
+        protos[:, 1],
+        c=plabels,
+        cmap=cmap,
+        edgecolor="k",
+        marker="D",
+        s=50,
+    )
+
+    # Paint decision regions
+    x = np.vstack((x_train, protos))
+    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
+    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
+                         np.arange(y_min, y_max, 1 / 50))
+    mesh_input = np.c_[xx.ravel(), yy.ravel()]
+
+    d, plabels = model(torch.Tensor(mesh_input))
+    y_pred = np.argmin(stratified_min(d, plabels).detach().numpy(), axis=1)
+    y_pred = y_pred.reshape(xx.shape)
+
+    # Plot voronoi regions
+    ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
+
+    ax.set_xlim(left=x_min + 0, right=x_max - 0)
+    ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
+
+    plt.pause(0.1)

examples/new_components.py

@@ -1,35 +1,39 @@
 """This example script shows the usage of the new components architecture.
 
 Serialization/deserialization also works as expected.
-
 """
 
+# DATASET
 import torch
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
 
-import prototorch as pt
+scaler = StandardScaler()
+x_train, y_train = load_iris(return_X_y=True)
+x_train = x_train[:, [0, 2]]
+scaler.fit(x_train)
+x_train = scaler.transform(x_train)
 
-ds = pt.datasets.Iris()
+x_train = torch.Tensor(x_train)
+y_train = torch.Tensor(y_train)
+num_classes = len(torch.unique(y_train))
 
-unsupervised = pt.components.Components(
+# CREATE NEW COMPONENTS
-    6,
+from prototorch.components import *
-    initializer=pt.initializers.ZCI(2),
+from prototorch.components.initializers import *
-)
+
+unsupervised = Components(6, SelectionInitializer(x_train))
 print(unsupervised())
 
-prototypes = pt.components.LabeledComponents(
+prototypes = LabeledComponents(
-    (3, 2),
+    (3, 2), StratifiedSelectionInitializer(x_train, y_train))
-    components_initializer=pt.initializers.SSCI(ds),
-)
 print(prototypes())
 
-components = pt.components.ReasoningComponents(
+components = ReasoningComponents(
-    (3, 2),
+    (3, 6), StratifiedSelectionInitializer(x_train, y_train))
-    components_initializer=pt.initializers.SSCI(ds),
+print(components())
-    reasonings_initializer=pt.initializers.PPRI(),
-)
-print(prototypes())
 
-# Test Serialization
+# TEST SERIALIZATION
 import io
 
 save = io.BytesIO()
@@ -37,20 +41,25 @@ torch.save(unsupervised, save)
 save.seek(0)
 serialized_unsupervised = torch.load(save)
 
-assert torch.all(unsupervised.components == serialized_unsupervised.components)
+assert torch.all(unsupervised.components == serialized_unsupervised.components
+                 ), "Serialization of Components failed."
 
 save = io.BytesIO()
 torch.save(prototypes, save)
 save.seek(0)
 serialized_prototypes = torch.load(save)
 
-assert torch.all(prototypes.components == serialized_prototypes.components)
+assert torch.all(prototypes.components == serialized_prototypes.components
-assert torch.all(prototypes.labels == serialized_prototypes.labels)
+                 ), "Serialization of Components failed."
+assert torch.all(prototypes.component_labels == serialized_prototypes.
+                 component_labels), "Serialization of Components failed."
 
 save = io.BytesIO()
 torch.save(components, save)
 save.seek(0)
 serialized_components = torch.load(save)
 
-assert torch.all(components.components == serialized_components.components)
+assert torch.all(components.components == serialized_components.components
-assert torch.all(components.reasonings == serialized_components.reasonings)
+                 ), "Serialization of Components failed."
+assert torch.all(components.reasonings == serialized_components.reasonings
+                 ), "Serialization of Components failed."

prototorch/__init__.py

@@ -1,40 +1,21 @@
-"""ProtoTorch package"""
+"""ProtoTorch package."""
 
+# Core Setup
+__version__ = "0.4.2"
+
+__all_core__ = [
+    "datasets",
+    "functions",
+    "modules",
+]
+
+from .datasets import *
+
+# Plugin Loader
 import pkgutil
 
 import pkg_resources
 
-from . import datasets  # noqa: F401
-from . import nn  # noqa: F401
-from . import utils  # noqa: F401
-from .core import competitions  # noqa: F401
-from .core import components  # noqa: F401
-from .core import distances  # noqa: F401
-from .core import initializers  # noqa: F401
-from .core import losses  # noqa: F401
-from .core import pooling  # noqa: F401
-from .core import similarities  # noqa: F401
-from .core import transforms  # noqa: F401
-
-# Core Setup
-__version__ = "0.7.6"
-
-__all_core__ = [
-    "competitions",
-    "components",
-    "core",
-    "datasets",
-    "distances",
-    "initializers",
-    "losses",
-    "nn",
-    "pooling",
-    "similarities",
-    "transforms",
-    "utils",
-]
-
-# Plugin Loader
 __path__ = pkgutil.extend_path(__path__, __name__)
 
 

prototorch/components/__init__.py

@@ -0,0 +1,2 @@
+from prototorch.components.components import *
+from prototorch.components.initializers import *

prototorch/components/components.py

@@ -0,0 +1,151 @@
+"""ProtoTorch components modules."""
+
+import warnings
+from typing import Tuple
+
+import torch
+from prototorch.components.initializers import (ClassAwareInitializer,
+                                                ComponentsInitializer,
+                                                EqualLabelsInitializer,
+                                                UnequalLabelsInitializer,
+                                                ZeroReasoningsInitializer)
+from prototorch.functions.initializers import get_initializer
+from torch.nn.parameter import Parameter
+
+
+class Components(torch.nn.Module):
+    """Components is a set of learnable Tensors."""
+    def __init__(self,
+                 number_of_components=None,
+                 initializer=None,
+                 *,
+                 initialized_components=None,
+                 dtype=torch.float32):
+        super().__init__()
+
+        # Ignore all initialization settings if initialized_components is given.
+        if initialized_components is not None:
+            self._components = Parameter(initialized_components)
+            if number_of_components is not None or initializer is not None:
+                wmsg = "Arguments ignored while initializing Components"
+                warnings.warn(wmsg)
+        else:
+            self._initialize_components(number_of_components, initializer)
+
+    def _precheck_initializer(self, initializer):
+        if not isinstance(initializer, ComponentsInitializer):
+            emsg = f"`initializer` has to be some subtype of " \
+                f"{ComponentsInitializer}. " \
+                f"You have provided: {initializer=} instead."
+            raise TypeError(emsg)
+
+    def _initialize_components(self, number_of_components, initializer):
+        self._precheck_initializer(initializer)
+        self._components = Parameter(
+            initializer.generate(number_of_components))
+
+    @property
+    def components(self):
+        """Tensor containing the component tensors."""
+        return self._components.detach().cpu()
+
+    def forward(self):
+        return self._components
+
+    def extra_repr(self):
+        return f"components.shape: {tuple(self._components.shape)}"
+
+
+class LabeledComponents(Components):
+    """LabeledComponents generate a set of components and a set of labels.
+
+    Every Component has a label assigned.
+    """
+    def __init__(self,
+                 distribution=None,
+                 initializer=None,
+                 *,
+                 initialized_components=None):
+        if initialized_components is not None:
+            super().__init__(initialized_components=initialized_components[0])
+            self._labels = initialized_components[1]
+        else:
+            self._initialize_labels(distribution)
+            super().__init__(number_of_components=len(self._labels),
+                             initializer=initializer)
+
+    def _initialize_components(self, number_of_components, initializer):
+        if isinstance(initializer, ClassAwareInitializer):
+            self._precheck_initializer(initializer)
+            self._components = Parameter(
+                initializer.generate(number_of_components, self.distribution))
+        else:
+            super()._initialize_components(self, number_of_components,
+                                           initializer)
+
+    def _initialize_labels(self, distribution):
+        if type(distribution) == tuple:
+            num_classes, prototypes_per_class = distribution
+            labels = EqualLabelsInitializer(num_classes, prototypes_per_class)
+        elif type(distribution) == list:
+            labels = UnequalLabelsInitializer(distribution)
+
+        self.distribution = labels.distribution
+        self._labels = labels.generate()
+
+    @property
+    def component_labels(self):
+        """Tensor containing the component tensors."""
+        return self._labels.detach().cpu()
+
+    def forward(self):
+        return super().forward(), self._labels
+
+
+class ReasoningComponents(Components):
+    """ReasoningComponents generate a set of components and a set of reasoning matrices.
+
+    Every Component has a reasoning matrix assigned.
+
+    A reasoning matrix is a Nx2 matrix, where N is the number of Classes. The
+    first element is called positive reasoning :math:`p`, the second negative
+    reasoning :math:`n`. A components can reason in favour (positive) of a
+    class, against (negative) a class or not at all (neutral).
+
+    It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0
+    \leq n+p \leq 1`. Therefore :math:`n` and :math:`p` are two elements of a
+    three element probability distribution.
+
+    """
+    def __init__(self,
+                 reasonings=None,
+                 initializer=None,
+                 *,
+                 initialized_components=None):
+        if initialized_components is not None:
+            super().__init__(initialized_components=initialized_components[0])
+            self._reasonings = initialized_components[1]
+        else:
+            self._initialize_reasonings(reasonings)
+            super().__init__(number_of_components=len(self._reasonings),
+                             initializer=initializer)
+
+    def _initialize_reasonings(self, reasonings):
+        if type(reasonings) == tuple:
+            num_classes, number_of_components = reasonings
+            reasonings = ZeroReasoningsInitializer(num_classes,
+                                                   number_of_components)
+
+        self._reasonings = reasonings.generate()
+
+    @property
+    def reasonings(self):
+        """Returns Reasoning Matrix.
+
+        Dimension NxCx2
+
+        """
+        return self._reasonings.detach().cpu()
+
+    def forward(self):
+        return super().forward(), self._reasonings

prototorch/components/initializers.py

@@ -0,0 +1,197 @@
+"""ProtoTroch Initializers."""
+import warnings
+from collections.abc import Iterable
+from itertools import chain
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+
+def parse_init_arg(arg):
+    if isinstance(arg, Dataset):
+        data, labels = next(iter(DataLoader(arg, batch_size=len(arg))))
+        # data = data.view(len(arg), -1)  # flatten
+    else:
+        data, labels = arg
+        if not isinstance(data, torch.Tensor):
+            wmsg = f"Converting data to {torch.Tensor}."
+            warnings.warn(wmsg)
+            data = torch.Tensor(data)
+        if not isinstance(labels, torch.Tensor):
+            wmsg = f"Converting labels to {torch.Tensor}."
+            warnings.warn(wmsg)
+            labels = torch.Tensor(labels)
+    return data, labels
+
+
+# Components
+class ComponentsInitializer(object):
+    def generate(self, number_of_components):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class DimensionAwareInitializer(ComponentsInitializer):
+    def __init__(self, c_dims):
+        super().__init__()
+        if isinstance(c_dims, Iterable):
+            self.components_dims = tuple(c_dims)
+        else:
+            self.components_dims = (c_dims, )
+
+
+class OnesInitializer(DimensionAwareInitializer):
+    def generate(self, length):
+        gen_dims = (length, ) + self.components_dims
+        return torch.ones(gen_dims)
+
+
+class ZerosInitializer(DimensionAwareInitializer):
+    def generate(self, length):
+        gen_dims = (length, ) + self.components_dims
+        return torch.zeros(gen_dims)
+
+
+class UniformInitializer(DimensionAwareInitializer):
+    def __init__(self, c_dims, min=0.0, max=1.0):
+        super().__init__(c_dims)
+
+        self.min = min
+        self.max = max
+
+    def generate(self, length):
+        gen_dims = (length, ) + self.components_dims
+        return torch.ones(gen_dims).uniform_(self.min, self.max)
+
+
+class PositionAwareInitializer(ComponentsInitializer):
+    def __init__(self, positions):
+        super().__init__()
+        self.data = positions
+
+
+class SelectionInitializer(PositionAwareInitializer):
+    def generate(self, length):
+        indices = torch.LongTensor(length).random_(0, len(self.data))
+        return self.data[indices]
+
+
+class MeanInitializer(PositionAwareInitializer):
+    def generate(self, length):
+        mean = torch.mean(self.data, dim=0)
+        repeat_dim = [length] + [1] * len(mean.shape)
+        return mean.repeat(repeat_dim)
+
+
+class ClassAwareInitializer(ComponentsInitializer):
+    def __init__(self, arg):
+        super().__init__()
+        data, labels = parse_init_arg(arg)
+        self.data = data
+        self.labels = labels
+
+        self.clabels = torch.unique(self.labels)
+        self.num_classes = len(self.clabels)
+
+    def _get_samples_from_initializer(self, length, dist):
+        if not dist:
+            per_class = length // self.num_classes
+            dist = self.num_classes * [per_class]
+        samples_list = [
+            init.generate(n) for init, n in zip(self.initializers, dist)
+        ]
+        return torch.vstack(samples_list)
+
+
+class StratifiedMeanInitializer(ClassAwareInitializer):
+    def __init__(self, arg):
+        super().__init__(arg)
+
+        self.initializers = []
+        for clabel in self.clabels:
+            class_data = self.data[self.labels == clabel]
+            class_initializer = MeanInitializer(class_data)
+            self.initializers.append(class_initializer)
+
+    def generate(self, length, dist=[]):
+        samples = self._get_samples_from_initializer(length, dist)
+        return samples
+
+
+class StratifiedSelectionInitializer(ClassAwareInitializer):
+    def __init__(self, arg, *, noise=None):
+        super().__init__(arg)
+        self.noise = noise
+
+        self.initializers = []
+        for clabel in self.clabels:
+            class_data = self.data[self.labels == clabel]
+            class_initializer = SelectionInitializer(class_data)
+            self.initializers.append(class_initializer)
+
+    def add_noise(self, x):
+        """Shifts some dimensions of the data randomly."""
+        n1 = torch.rand_like(x)
+        n2 = torch.rand_like(x)
+        mask = torch.bernoulli(n1) - torch.bernoulli(n2)
+        return x + (self.noise * mask)
+
+    def generate(self, length, dist=[]):
+        samples = self._get_samples_from_initializer(length, dist)
+        if self.noise is not None:
+            # samples = self.add_noise(samples)
+            samples = samples + self.noise
+        return samples
+
+
+# Labels
+class LabelsInitializer:
+    def generate(self):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class UnequalLabelsInitializer(LabelsInitializer):
+    def __init__(self, dist):
+        self.dist = dist
+
+    @property
+    def distribution(self):
+        return self.dist
+
+    def generate(self):
+        clabels = range(len(self.dist))
+        labels = list(chain(*[[i] * n for i, n in zip(clabels, self.dist)]))
+        return torch.tensor(labels)
+
+
+class EqualLabelsInitializer(LabelsInitializer):
+    def __init__(self, classes, per_class):
+        self.classes = classes
+        self.per_class = per_class
+
+    @property
+    def distribution(self):
+        return self.classes * [self.per_class]
+
+    def generate(self):
+        return torch.arange(self.classes).repeat(self.per_class, 1).T.flatten()
+
+
+# Reasonings
+class ReasoningsInitializer:
+    def generate(self, length):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class ZeroReasoningsInitializer(ReasoningsInitializer):
+    def __init__(self, classes, length):
+        self.classes = classes
+        self.length = length
+
+    def generate(self):
+        return torch.zeros((self.length, self.classes, 2))
+
+
+# Aliases
+SSI = StratifiedSampleInitializer = StratifiedSelectionInitializer
+SMI = StratifiedMeanInitializer
+Random = RandomInitializer = UniformInitializer

prototorch/core/__init__.py

@@ -1,10 +0,0 @@
-"""ProtoTorch core"""
-
-from .competitions import *
-from .components import *
-from .distances import *
-from .initializers import *
-from .losses import *
-from .pooling import *
-from .similarities import *
-from .transforms import *

prototorch/core/competitions.py

@@ -1,93 +0,0 @@
-"""ProtoTorch competitions"""
-
-import torch
-
-
-def wtac(distances: torch.Tensor, labels: torch.LongTensor):
-    """Winner-Takes-All-Competition.
-
-    Returns the labels corresponding to the winners.
-
-    """
-    winning_indices = torch.min(distances, dim=1).indices
-    winning_labels = labels[winning_indices].squeeze()
-    return winning_labels
-
-
-def knnc(distances: torch.Tensor, labels: torch.LongTensor, k: int = 1):
-    """K-Nearest-Neighbors-Competition.
-
-    Returns the labels corresponding to the winners.
-
-    """
-    winning_indices = torch.topk(-distances, k=k, dim=1).indices
-    winning_labels = torch.mode(labels[winning_indices], dim=1).values
-    return winning_labels
-
-
-def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
-    """Classification-By-Components Competition.
-
-    Returns probability distributions over the classes.
-
-    `detections` must be of shape [batch_size, num_components].
-    `reasonings` must be of shape [num_components, num_classes, 2].
-
-    """
-    A, B = reasonings.permute(2, 1, 0).clamp(0, 1)
-    pk = A
-    nk = (1 - A) * B
-    numerator = (detections @ (pk - nk).T) + nk.sum(1)
-    probs = numerator / ((pk + nk).sum(1) + 1e-8)
-    return probs
-
-
-class WTAC(torch.nn.Module):
-    """Winner-Takes-All-Competition Layer.
-
-    Thin wrapper over the `wtac` function.
-
-    """
-
-    def forward(self, distances, labels):  # pylint: disable=no-self-use
-        return wtac(distances, labels)
-
-
-class LTAC(torch.nn.Module):
-    """Loser-Takes-All-Competition Layer.
-
-    Thin wrapper over the `wtac` function.
-
-    """
-
-    def forward(self, probs, labels):  # pylint: disable=no-self-use
-        return wtac(-1.0 * probs, labels)
-
-
-class KNNC(torch.nn.Module):
-    """K-Nearest-Neighbors-Competition.
-
-    Thin wrapper over the `knnc` function.
-
-    """
-
-    def __init__(self, k=1, **kwargs):
-        super().__init__(**kwargs)
-        self.k = k
-
-    def forward(self, distances, labels):
-        return knnc(distances, labels, k=self.k)
-
-    def extra_repr(self):
-        return f"k: {self.k}"
-
-
-class CBCC(torch.nn.Module):
-    """Classification-By-Components Competition.
-
-    Thin wrapper over the `cbcc` function.
-
-    """
-
-    def forward(self, detections, reasonings):  # pylint: disable=no-self-use
-        return cbcc(detections, reasonings)

prototorch/core/components.py

@@ -1,380 +0,0 @@
-"""ProtoTorch components"""
-
-import inspect
-from typing import Union
-
-import torch
-from torch.nn.parameter import Parameter
-
-from prototorch.utils import parse_distribution
-
-from .initializers import (
-    AbstractClassAwareCompInitializer,
-    AbstractComponentsInitializer,
-    AbstractLabelsInitializer,
-    AbstractReasoningsInitializer,
-    LabelsInitializer,
-    PurePositiveReasoningsInitializer,
-    RandomReasoningsInitializer,
-)
-
-
-def validate_initializer(initializer, instanceof):
-    """Check if the initializer is valid."""
-    if not isinstance(initializer, instanceof):
-        emsg = f"`initializer` has to be an instance " \
-            f"of some subtype of {instanceof}. " \
-            f"You have provided: {initializer} instead. "
-        helpmsg = ""
-        if inspect.isclass(initializer):
-            helpmsg = f"Perhaps you meant to say, {initializer.__name__}() " \
-                f"with the brackets instead of just {initializer.__name__}?"
-        raise TypeError(emsg + helpmsg)
-    return True
-
-
-def gencat(ins, attr, init, *iargs, **ikwargs):
-    """Generate new items and concatenate with existing items."""
-    new_items = init.generate(*iargs, **ikwargs)
-    if hasattr(ins, attr):
-        items = torch.cat([getattr(ins, attr), new_items])
-    else:
-        items = new_items
-    return items, new_items
-
-
-def removeind(ins, attr, indices):
-    """Remove items at specified indices."""
-    mask = torch.ones(len(ins), dtype=torch.bool)
-    mask[indices] = False
-    items = getattr(ins, attr)[mask]
-    return items, mask
-
-
-def get_cikwargs(init, distribution):
-    """Return appropriate key-word arguments for a component initializer."""
-    if isinstance(init, AbstractClassAwareCompInitializer):
-        cikwargs = dict(distribution=distribution)
-    else:
-        distribution = parse_distribution(distribution)
-        num_components = sum(distribution.values())
-        cikwargs = dict(num_components=num_components)
-    return cikwargs
-
-
-class AbstractComponents(torch.nn.Module):
-    """Abstract class for all components modules."""
-
-    @property
-    def num_components(self):
-        """Current number of components."""
-        return len(self._components)
-
-    @property
-    def components(self):
-        """Detached Tensor containing the components."""
-        return self._components.detach().cpu()
-
-    def _register_components(self, components):
-        self.register_parameter("_components", Parameter(components))
-
-    def extra_repr(self):
-        return f"components: (shape: {tuple(self._components.shape)})"
-
-    def __len__(self):
-        return self.num_components
-
-
-class Components(AbstractComponents):
-    """A set of adaptable Tensors."""
-
-    def __init__(self, num_components: int,
-                 initializer: AbstractComponentsInitializer):
-        super().__init__()
-        self.add_components(num_components, initializer)
-
-    def add_components(self, num_components: int,
-                       initializer: AbstractComponentsInitializer):
-        """Generate and add new components."""
-        assert validate_initializer(initializer, AbstractComponentsInitializer)
-        _components, new_components = gencat(self, "_components", initializer,
-                                             num_components)
-        self._register_components(_components)
-        return new_components
-
-    def remove_components(self, indices):
-        """Remove components at specified indices."""
-        _components, mask = removeind(self, "_components", indices)
-        self._register_components(_components)
-        return mask
-
-    def forward(self):
-        """Simply return the components parameter Tensor."""
-        return self._components
-
-
-class AbstractLabels(torch.nn.Module):
-    """Abstract class for all labels modules."""
-
-    @property
-    def labels(self):
-        return self._labels.cpu()
-
-    @property
-    def num_labels(self):
-        return len(self._labels)
-
-    @property
-    def unique_labels(self):
-        return torch.unique(self._labels)
-
-    @property
-    def num_unique(self):
-        return len(self.unique_labels)
-
-    @property
-    def distribution(self):
-        unique, counts = torch.unique(self._labels,
-                                      sorted=True,
-                                      return_counts=True)
-        return dict(zip(unique.tolist(), counts.tolist()))
-
-    def _register_labels(self, labels):
-        self.register_buffer("_labels", labels)
-
-    def extra_repr(self):
-        r = f"num_labels: {self.num_labels}, num_unique: {self.num_unique}"
-        if len(self.distribution) < 11:  # avoid lengthy representations
-            d = self.distribution
-            unique, counts = list(d.keys()), list(d.values())
-            r += f", unique: {unique}, counts: {counts}"
-        return r
-
-    def __len__(self):
-        return self.num_labels
-
-
-class Labels(AbstractLabels):
-    """A set of standalone labels."""
-
-    def __init__(self,
-                 distribution: Union[dict, list, tuple],
-                 initializer: AbstractLabelsInitializer = LabelsInitializer()):
-        super().__init__()
-        self.add_labels(distribution, initializer)
-
-    def add_labels(
-        self,
-        distribution: Union[dict, tuple, list],
-        initializer: AbstractLabelsInitializer = LabelsInitializer()):
-        """Generate and add new labels."""
-        assert validate_initializer(initializer, AbstractLabelsInitializer)
-        _labels, new_labels = gencat(self, "_labels", initializer,
-                                     distribution)
-        self._register_labels(_labels)
-        return new_labels
-
-    def remove_labels(self, indices):
-        """Remove labels at specified indices."""
-        _labels, mask = removeind(self, "_labels", indices)
-        self._register_labels(_labels)
-        return mask
-
-    def forward(self):
-        """Simply return the labels."""
-        return self._labels
-
-
-class LabeledComponents(AbstractComponents):
-    """A set of adaptable components and corresponding unadaptable labels."""
-
-    def __init__(
-        self,
-        distribution: Union[dict, list, tuple],
-        components_initializer: AbstractComponentsInitializer,
-        labels_initializer: AbstractLabelsInitializer = LabelsInitializer()):
-        super().__init__()
-        self.add_components(distribution, components_initializer,
-                            labels_initializer)
-
-    @property
-    def distribution(self):
-        unique, counts = torch.unique(self._labels,
-                                      sorted=True,
-                                      return_counts=True)
-        return dict(zip(unique.tolist(), counts.tolist()))
-
-    @property
-    def num_classes(self):
-        return len(self.distribution.keys())
-
-    @property
-    def labels(self):
-        """Tensor containing the component labels."""
-        return self._labels.cpu()
-
-    def _register_labels(self, labels):
-        self.register_buffer("_labels", labels)
-
-    def add_components(
-        self,
-        distribution,
-        components_initializer,
-        labels_initializer: AbstractLabelsInitializer = LabelsInitializer()):
-        """Generate and add new components and labels."""
-        assert validate_initializer(components_initializer,
-                                    AbstractComponentsInitializer)
-        assert validate_initializer(labels_initializer,
-                                    AbstractLabelsInitializer)
-        cikwargs = get_cikwargs(components_initializer, distribution)
-        _components, new_components = gencat(self, "_components",
-                                             components_initializer,
-                                             **cikwargs)
-        _labels, new_labels = gencat(self, "_labels", labels_initializer,
-                                     distribution)
-        self._register_components(_components)
-        self._register_labels(_labels)
-        return new_components, new_labels
-
-    def remove_components(self, indices):
-        """Remove components and labels at specified indices."""
-        _components, mask = removeind(self, "_components", indices)
-        _labels, mask = removeind(self, "_labels", indices)
-        self._register_components(_components)
-        self._register_labels(_labels)
-        return mask
-
-    def forward(self):
-        """Simply return the components parameter Tensor and labels."""
-        return self._components, self._labels
-
-
-class Reasonings(torch.nn.Module):
-    """A set of standalone reasoning matrices.
-
-    The `reasonings` tensor is of shape [num_components, num_classes, 2].
-
-    """
-
-    def __init__(
-        self,
-        distribution: Union[dict, list, tuple],
-        initializer:
-        AbstractReasoningsInitializer = RandomReasoningsInitializer(),
-    ):
-        super().__init__()
-        self.add_reasonings(distribution, initializer)
-
-    @property
-    def num_classes(self):
-        return self._reasonings.shape[1]
-
-    @property
-    def reasonings(self):
-        """Tensor containing the reasoning matrices."""
-        return self._reasonings.detach().cpu()
-
-    def _register_reasonings(self, reasonings):
-        self.register_buffer("_reasonings", reasonings)
-
-    def add_reasonings(
-        self,
-        distribution: Union[dict, list, tuple],
-        initializer:
-        AbstractReasoningsInitializer = RandomReasoningsInitializer()):
-        """Generate and add new reasonings."""
-        assert validate_initializer(initializer, AbstractReasoningsInitializer)
-        _reasonings, new_reasonings = gencat(self, "_reasonings", initializer,
-                                             distribution)
-        self._register_reasonings(_reasonings)
-        return new_reasonings
-
-    def remove_reasonings(self, indices):
-        """Remove reasonings at specified indices."""
-        _reasonings, mask = removeind(self, "_reasonings", indices)
-        self._register_reasonings(_reasonings)
-        return mask
-
-    def forward(self):
-        """Simply return the reasonings."""
-        return self._reasonings
-
-
-class ReasoningComponents(AbstractComponents):
-    r"""A set of components and a corresponding adapatable reasoning matrices.
-
-    Every component has its own reasoning matrix.
-
-    A reasoning matrix is an Nx2 matrix, where N is the number of classes. The
-    first element is called positive reasoning :math:`p`, the second negative
-    reasoning :math:`n`. A components can reason in favour (positive) of a
-    class, against (negative) a class or not at all (neutral).
-
-    It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0
-    \leq n+p \leq 1`. Therefore :math:`n` and :math:`p` are two elements of a
-    three element probability distribution.
-
-    """
-
-    def __init__(
-        self,
-        distribution: Union[dict, list, tuple],
-        components_initializer: AbstractComponentsInitializer,
-        reasonings_initializer:
-        AbstractReasoningsInitializer = PurePositiveReasoningsInitializer()):
-        super().__init__()
-        self.add_components(distribution, components_initializer,
-                            reasonings_initializer)
-
-    @property
-    def num_classes(self):
-        return self._reasonings.shape[1]
-
-    @property
-    def reasonings(self):
-        """Tensor containing the reasoning matrices."""
-        return self._reasonings.detach().cpu()
-
-    @property
-    def reasoning_matrices(self):
-        """Reasoning matrices for each class."""
-        with torch.no_grad():
-            A, B = self._reasonings.permute(2, 1, 0).clamp(0, 1)
-            pk = A
-            nk = (1 - pk) * B
-            ik = 1 - pk - nk
-            matrices = torch.stack([pk, nk, ik], dim=-1).permute(1, 2, 0)
-        return matrices.cpu()
-
-    def _register_reasonings(self, reasonings):
-        self.register_parameter("_reasonings", Parameter(reasonings))
-
-    def add_components(self, distribution, components_initializer,
-                       reasonings_initializer: AbstractReasoningsInitializer):
-        """Generate and add new components and reasonings."""
-        assert validate_initializer(components_initializer,
-                                    AbstractComponentsInitializer)
-        assert validate_initializer(reasonings_initializer,
-                                    AbstractReasoningsInitializer)
-        cikwargs = get_cikwargs(components_initializer, distribution)
-        _components, new_components = gencat(self, "_components",
-                                             components_initializer,
-                                             **cikwargs)
-        _reasonings, new_reasonings = gencat(self, "_reasonings",
-                                             reasonings_initializer,
-                                             distribution)
-        self._register_components(_components)
-        self._register_reasonings(_reasonings)
-        return new_components, new_reasonings
-
-    def remove_components(self, indices):
-        """Remove components and reasonings at specified indices."""
-        _components, mask = removeind(self, "_components", indices)
-        _reasonings, mask = removeind(self, "_reasonings", indices)
-        self._register_components(_components)
-        self._register_reasonings(_reasonings)
-        return mask
-
-    def forward(self):
-        """Simply return the components and reasonings."""
-        return self._components, self._reasonings

prototorch/core/distances.py

@@ -1,95 +0,0 @@
-"""ProtoTorch distances"""
-
-import torch
-
-
-def squared_euclidean_distance(x, y):
-    r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm y`.
-
-    Compute :math:`{\langle \bm x - \bm y \rangle}_2`
-
-    **Alias:**
-    ``prototorch.functions.distances.sed``
-    """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
-    expanded_x = x.unsqueeze(dim=1)
-    batchwise_difference = y - expanded_x
-    differences_raised = torch.pow(batchwise_difference, 2)
-    distances = torch.sum(differences_raised, axis=2)
-    return distances
-
-
-def euclidean_distance(x, y):
-    r"""Compute the Euclidean distance between :math:`x` and :math:`y`.
-
-    Compute :math:`\sqrt{{\langle \bm x - \bm y \rangle}_2}`
-
-    :returns: Distance Tensor of shape :math:`X \times Y`
-    :rtype: `torch.tensor`
-    """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
-    distances_raised = squared_euclidean_distance(x, y)
-    distances = torch.sqrt(distances_raised)
-    return distances
-
-
-def euclidean_distance_v2(x, y):
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
-    diff = y - x.unsqueeze(1)
-    pairwise_distances = (diff @ diff.permute((0, 2, 1))).sqrt()
-    # Passing `dim1=-2` and `dim2=-1` to `diagonal()` takes the
-    # batch diagonal. See:
-    # https://pytorch.org/docs/stable/generated/torch.diagonal.html
-    distances = torch.diagonal(pairwise_distances, dim1=-2, dim2=-1)
-    return distances
-
-
-def lpnorm_distance(x, y, p):
-    r"""Calculate the lp-norm between :math:`\bm x` and :math:`\bm y`.
-    Also known as Minkowski distance.
-
-    Compute :math:`{\| \bm x - \bm y \|}_p`.
-
-    Calls ``torch.cdist``
-
-    :param p: p parameter of the lp norm
-    """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
-    distances = torch.cdist(x, y, p=p)
-    return distances
-
-
-def omega_distance(x, y, omega):
-    r"""Omega distance.
-
-    Compute :math:`{\| \Omega \bm x - \Omega \bm y \|}_p`
-
-    :param `torch.tensor` omega: Two dimensional matrix
-    """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
-    projected_x = x @ omega
-    projected_y = y @ omega
-    distances = squared_euclidean_distance(projected_x, projected_y)
-    return distances
-
-
-def lomega_distance(x, y, omegas):
-    r"""Localized Omega distance.
-
-    Compute :math:`{\| \Omega_k \bm x - \Omega_k \bm y_k \|}_p`
-
-    :param `torch.tensor` omegas: Three dimensional matrix
-    """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
-    projected_x = x @ omegas
-    projected_y = torch.diagonal(y @ omegas).T
-    expanded_y = torch.unsqueeze(projected_y, dim=1)
-    batchwise_difference = expanded_y - projected_x
-    differences_squared = batchwise_difference**2
-    distances = torch.sum(differences_squared, dim=2)
-    distances = distances.permute(1, 0)
-    return distances
-
-
-# Aliases
-sed = squared_euclidean_distance

prototorch/core/initializers.py

@@ -1,555 +0,0 @@
-"""ProtoTorch code initializers"""
-
-import warnings
-from abc import ABC, abstractmethod
-from collections.abc import Iterable
-from typing import (
-    Callable,
-    Type,
-    Union,
-)
-
-import torch
-
-from prototorch.utils import parse_data_arg, parse_distribution
-
-
-# Components
-class AbstractComponentsInitializer(ABC):
-    """Abstract class for all components initializers."""
-    ...
-
-
-class LiteralCompInitializer(AbstractComponentsInitializer):
-    """'Generate' the provided components.
-
-    Use this to 'generate' pre-initialized components elsewhere.
-
-    """
-
-    def __init__(self, components):
-        self.components = components
-
-    def generate(self, num_components: int = 0):
-        """Ignore `num_components` and simply return `self.components`."""
-        provided_num_components = len(self.components)
-        if provided_num_components != num_components:
-            wmsg = f"The number of components ({provided_num_components}) " \
-                f"provided to {self.__class__.__name__} " \
-                f"does not match the expected number ({num_components})."
-            warnings.warn(wmsg)
-        if not isinstance(self.components, torch.Tensor):
-            wmsg = f"Converting components to {torch.Tensor}..."
-            warnings.warn(wmsg)
-            self.components = torch.Tensor(self.components)
-        return self.components
-
-
-class ShapeAwareCompInitializer(AbstractComponentsInitializer):
-    """Abstract class for all dimension-aware components initializers."""
-
-    def __init__(self, shape: Union[Iterable, int]):
-        if isinstance(shape, Iterable):
-            self.component_shape = tuple(shape)
-        else:
-            self.component_shape = (shape, )
-
-    @abstractmethod
-    def generate(self, num_components: int):
-        ...
-
-
-class ZerosCompInitializer(ShapeAwareCompInitializer):
-    """Generate zeros corresponding to the components shape."""
-
-    def generate(self, num_components: int):
-        components = torch.zeros((num_components, ) + self.component_shape)
-        return components
-
-
-class OnesCompInitializer(ShapeAwareCompInitializer):
-    """Generate ones corresponding to the components shape."""
-
-    def generate(self, num_components: int):
-        components = torch.ones((num_components, ) + self.component_shape)
-        return components
-
-
-class FillValueCompInitializer(OnesCompInitializer):
-    """Generate components with the provided `fill_value`."""
-
-    def __init__(self, shape, fill_value: float = 1.0):
-        super().__init__(shape)
-        self.fill_value = fill_value
-
-    def generate(self, num_components: int):
-        ones = super().generate(num_components)
-        components = ones.fill_(self.fill_value)
-        return components
-
-
-class UniformCompInitializer(OnesCompInitializer):
-    """Generate components by sampling from a continuous uniform distribution."""
-
-    def __init__(self, shape, minimum=0.0, maximum=1.0, scale=1.0):
-        super().__init__(shape)
-        self.minimum = minimum
-        self.maximum = maximum
-        self.scale = scale
-
-    def generate(self, num_components: int):
-        ones = super().generate(num_components)
-        components = self.scale * ones.uniform_(self.minimum, self.maximum)
-        return components
-
-
-class RandomNormalCompInitializer(OnesCompInitializer):
-    """Generate components by sampling from a standard normal distribution."""
-
-    def __init__(self, shape, shift=0.0, scale=1.0):
-        super().__init__(shape)
-        self.shift = shift
-        self.scale = scale
-
-    def generate(self, num_components: int):
-        ones = super().generate(num_components)
-        components = self.scale * (torch.randn_like(ones) + self.shift)
-        return components
-
-
-class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
-    """Abstract class for all data-aware components initializers.
-
-    Components generated by data-aware components initializers inherit the shape
-    of the provided data.
-
-    `data` has to be a torch tensor.
-
-    """
-
-    def __init__(self,
-                 data: torch.Tensor,
-                 noise: float = 0.0,
-                 transform: Callable = torch.nn.Identity()):
-        self.data = data
-        self.noise = noise
-        self.transform = transform
-
-    def generate_end_hook(self, samples):
-        drift = torch.rand_like(samples) * self.noise
-        components = self.transform(samples + drift)
-        return components
-
-    @abstractmethod
-    def generate(self, num_components: int):
-        ...
-        return self.generate_end_hook(...)
-
-    def __del__(self):
-        del self.data
-
-
-class DataAwareCompInitializer(AbstractDataAwareCompInitializer):
-    """'Generate' the components from the provided data."""
-
-    def generate(self, num_components: int = 0):
-        """Ignore `num_components` and simply return transformed `self.data`."""
-        components = self.generate_end_hook(self.data)
-        return components
-
-
-class SelectionCompInitializer(AbstractDataAwareCompInitializer):
-    """Generate components by uniformly sampling from the provided data."""
-
-    def generate(self, num_components: int):
-        indices = torch.LongTensor(num_components).random_(0, len(self.data))
-        samples = self.data[indices]
-        components = self.generate_end_hook(samples)
-        return components
-
-
-class MeanCompInitializer(AbstractDataAwareCompInitializer):
-    """Generate components by computing the mean of the provided data."""
-
-    def generate(self, num_components: int):
-        mean = self.data.mean(dim=0)
-        repeat_dim = [num_components] + [1] * len(mean.shape)
-        samples = mean.repeat(repeat_dim)
-        components = self.generate_end_hook(samples)
-        return components
-
-
-class AbstractClassAwareCompInitializer(AbstractComponentsInitializer):
-    """Abstract class for all class-aware components initializers.
-
-    Components generated by class-aware components initializers inherit the shape
-    of the provided data.
-
-    `data` could be a torch Dataset or DataLoader or a list/tuple of data and
-    target tensors.
-
-    """
-
-    def __init__(self,
-                 data,
-                 noise: float = 0.0,
-                 transform: Callable = torch.nn.Identity()):
-        self.data, self.targets = parse_data_arg(data)
-        self.noise = noise
-        self.transform = transform
-        self.clabels = torch.unique(self.targets).int().tolist()
-        self.num_classes = len(self.clabels)
-
-    def generate_end_hook(self, samples):
-        drift = torch.rand_like(samples) * self.noise
-        components = self.transform(samples + drift)
-        return components
-
-    @abstractmethod
-    def generate(self, distribution: Union[dict, list, tuple]):
-        ...
-        return self.generate_end_hook(...)
-
-    def __del__(self):
-        del self.data
-        del self.targets
-
-
-class ClassAwareCompInitializer(AbstractClassAwareCompInitializer):
-    """'Generate' components from provided data and requested distribution."""
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        """Ignore `distribution` and simply return transformed `self.data`."""
-        components = self.generate_end_hook(self.data)
-        return components
-
-
-class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
-    """Abstract class for all stratified components initializers."""
-
-    @property
-    @abstractmethod
-    def subinit_type(self) -> Type[AbstractDataAwareCompInitializer]:
-        ...
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        distribution = parse_distribution(distribution)
-        components = torch.tensor([])
-        for k, v in distribution.items():
-            stratified_data = self.data[self.targets == k]
-            if len(stratified_data) == 0:
-                raise ValueError(f"No data available for class {k}.")
-            initializer = self.subinit_type(
-                stratified_data,
-                noise=self.noise,
-                transform=self.transform,
-            )
-            samples = initializer.generate(num_components=v)
-            components = torch.cat([components, samples])
-        return components
-
-
-class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
-    """Generate components using stratified sampling from the provided data."""
-
-    @property
-    def subinit_type(self):
-        return SelectionCompInitializer
-
-
-class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
-    """Generate components at stratified means of the provided data."""
-
-    @property
-    def subinit_type(self):
-        return MeanCompInitializer
-
-
-# Labels
-class AbstractLabelsInitializer(ABC):
-    """Abstract class for all labels initializers."""
-
-    @abstractmethod
-    def generate(self, distribution: Union[dict, list, tuple]):
-        ...
-
-
-class LiteralLabelsInitializer(AbstractLabelsInitializer):
-    """'Generate' the provided labels.
-
-    Use this to 'generate' pre-initialized labels elsewhere.
-
-    """
-
-    def __init__(self, labels):
-        self.labels = labels
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        """Ignore `distribution` and simply return `self.labels`.
-
-        Convert to long tensor, if necessary.
-        """
-        labels = self.labels
-        if not isinstance(labels, torch.LongTensor):
-            wmsg = f"Converting labels to {torch.LongTensor}..."
-            warnings.warn(wmsg)
-            labels = torch.LongTensor(labels)
-        return labels
-
-
-class DataAwareLabelsInitializer(AbstractLabelsInitializer):
-    """'Generate' the labels from a torch Dataset."""
-
-    def __init__(self, data):
-        self.data, self.targets = parse_data_arg(data)
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        """Ignore `num_components` and simply return `self.targets`."""
-        return self.targets
-
-
-class LabelsInitializer(AbstractLabelsInitializer):
-    """Generate labels from `distribution`."""
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        distribution = parse_distribution(distribution)
-        labels_list = []
-        for k, v in distribution.items():
-            labels_list.extend([k] * v)
-        labels = torch.LongTensor(labels_list)
-        return labels
-
-
-class OneHotLabelsInitializer(LabelsInitializer):
-    """Generate one-hot-encoded labels from `distribution`."""
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        distribution = parse_distribution(distribution)
-        num_classes = len(distribution.keys())
-        # this breaks if class labels are not [0,...,nclasses]
-        labels = torch.eye(num_classes)[super().generate(distribution)]
-        return labels
-
-
-# Reasonings
-def compute_distribution_shape(distribution):
-    distribution = parse_distribution(distribution)
-    num_components = sum(distribution.values())
-    num_classes = len(distribution.keys())
-    return (num_components, num_classes, 2)
-
-
-class AbstractReasoningsInitializer(ABC):
-    """Abstract class for all reasonings initializers."""
-
-    def __init__(self, components_first: bool = True):
-        self.components_first = components_first
-
-    def generate_end_hook(self, reasonings):
-        if not self.components_first:
-            reasonings = reasonings.permute(2, 1, 0)
-        return reasonings
-
-    @abstractmethod
-    def generate(self, distribution: Union[dict, list, tuple]):
-        ...
-        return self.generate_end_hook(...)
-
-
-class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
-    """'Generate' the provided reasonings.
-
-    Use this to 'generate' pre-initialized reasonings elsewhere.
-
-    """
-
-    def __init__(self, reasonings, **kwargs):
-        super().__init__(**kwargs)
-        self.reasonings = reasonings
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        """Ignore `distributuion` and simply return self.reasonings."""
-        reasonings = self.reasonings
-        if not isinstance(reasonings, torch.Tensor):
-            wmsg = f"Converting reasonings to {torch.Tensor}..."
-            warnings.warn(wmsg)
-            reasonings = torch.Tensor(reasonings)
-        reasonings = self.generate_end_hook(reasonings)
-        return reasonings
-
-
-class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
-    """Reasonings are all initialized with zeros."""
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        shape = compute_distribution_shape(distribution)
-        reasonings = torch.zeros(*shape)
-        reasonings = self.generate_end_hook(reasonings)
-        return reasonings
-
-
-class OnesReasoningsInitializer(AbstractReasoningsInitializer):
-    """Reasonings are all initialized with ones."""
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        shape = compute_distribution_shape(distribution)
-        reasonings = torch.ones(*shape)
-        reasonings = self.generate_end_hook(reasonings)
-        return reasonings
-
-
-class RandomReasoningsInitializer(AbstractReasoningsInitializer):
-    """Reasonings are randomly initialized."""
-
-    def __init__(self, minimum=0.4, maximum=0.6, **kwargs):
-        super().__init__(**kwargs)
-        self.minimum = minimum
-        self.maximum = maximum
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        shape = compute_distribution_shape(distribution)
-        reasonings = torch.ones(*shape).uniform_(self.minimum, self.maximum)
-        reasonings = self.generate_end_hook(reasonings)
-        return reasonings
-
-
-class PurePositiveReasoningsInitializer(AbstractReasoningsInitializer):
-    """Each component reasons positively for exactly one class."""
-
-    def generate(self, distribution: Union[dict, list, tuple]):
-        num_components, num_classes, _ = compute_distribution_shape(
-            distribution)
-        A = OneHotLabelsInitializer().generate(distribution)
-        B = torch.zeros(num_components, num_classes)
-        reasonings = torch.stack([A, B], dim=-1)
-        reasonings = self.generate_end_hook(reasonings)
-        return reasonings
-
-
-# Transforms
-class AbstractTransformInitializer(ABC):
-    """Abstract class for all transform initializers."""
-    ...
-
-
-class AbstractLinearTransformInitializer(AbstractTransformInitializer):
-    """Abstract class for all linear transform initializers."""
-
-    def __init__(self, out_dim_first: bool = False):
-        self.out_dim_first = out_dim_first
-
-    def generate_end_hook(self, weights):
-        if self.out_dim_first:
-            weights = weights.permute(1, 0)
-        return weights
-
-    @abstractmethod
-    def generate(self, in_dim: int, out_dim: int):
-        ...
-        return self.generate_end_hook(...)
-
-
-class ZerosLinearTransformInitializer(AbstractLinearTransformInitializer):
-    """Initialize a matrix with zeros."""
-
-    def generate(self, in_dim: int, out_dim: int):
-        weights = torch.zeros(in_dim, out_dim)
-        return self.generate_end_hook(weights)
-
-
-class OnesLinearTransformInitializer(AbstractLinearTransformInitializer):
-    """Initialize a matrix with ones."""
-
-    def generate(self, in_dim: int, out_dim: int):
-        weights = torch.ones(in_dim, out_dim)
-        return self.generate_end_hook(weights)
-
-
-class RandomLinearTransformInitializer(AbstractLinearTransformInitializer):
-    """Initialize a matrix with random values."""
-
-    def generate(self, in_dim: int, out_dim: int):
-        weights = torch.rand(in_dim, out_dim)
-        return self.generate_end_hook(weights)
-
-
-class EyeLinearTransformInitializer(AbstractLinearTransformInitializer):
-    """Initialize a matrix with the largest possible identity matrix."""
-
-    def generate(self, in_dim: int, out_dim: int):
-        weights = torch.zeros(in_dim, out_dim)
-        I = torch.eye(min(in_dim, out_dim))
-        weights[:I.shape[0], :I.shape[1]] = I
-        return self.generate_end_hook(weights)
-
-
-class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
-    """Abstract class for all data-aware linear transform initializers."""
-
-    def __init__(self,
-                 data: torch.Tensor,
-                 noise: float = 0.0,
-                 transform: Callable = torch.nn.Identity(),
-                 out_dim_first: bool = False):
-        super().__init__(out_dim_first)
-        self.data = data
-        self.noise = noise
-        self.transform = transform
-
-    def generate_end_hook(self, weights: torch.Tensor):
-        drift = torch.rand_like(weights) * self.noise
-        weights = self.transform(weights + drift)
-        if self.out_dim_first:
-            weights = weights.permute(1, 0)
-        return weights
-
-
-class PCALinearTransformInitializer(AbstractDataAwareLTInitializer):
-    """Initialize a matrix with Eigenvectors from the data."""
-
-    def generate(self, in_dim: int, out_dim: int):
-        _, _, weights = torch.pca_lowrank(self.data, q=out_dim)
-        return self.generate_end_hook(weights)
-
-
-class LiteralLinearTransformInitializer(AbstractDataAwareLTInitializer):
-    """'Generate' the provided weights."""
-
-    def generate(self, in_dim: int, out_dim: int):
-        return self.generate_end_hook(self.data)
-
-
-# Aliases - Components
-CACI = ClassAwareCompInitializer
-DACI = DataAwareCompInitializer
-FVCI = FillValueCompInitializer
-LCI = LiteralCompInitializer
-MCI = MeanCompInitializer
-OCI = OnesCompInitializer
-RNCI = RandomNormalCompInitializer
-SCI = SelectionCompInitializer
-SMCI = StratifiedMeanCompInitializer
-SSCI = StratifiedSelectionCompInitializer
-UCI = UniformCompInitializer
-ZCI = ZerosCompInitializer
-
-# Aliases - Labels
-DLI = DataAwareLabelsInitializer
-LI = LabelsInitializer
-LLI = LiteralLabelsInitializer
-OHLI = OneHotLabelsInitializer
-
-# Aliases - Reasonings
-LRI = LiteralReasoningsInitializer
-ORI = OnesReasoningsInitializer
-PPRI = PurePositiveReasoningsInitializer
-RRI = RandomReasoningsInitializer
-ZRI = ZerosReasoningsInitializer
-
-# Aliases - Transforms
-ELTI = Eye = EyeLinearTransformInitializer
-OLTI = OnesLinearTransformInitializer
-RLTI = RandomLinearTransformInitializer
-ZLTI = ZerosLinearTransformInitializer
-PCALTI = PCALinearTransformInitializer
-LLTI = LiteralLinearTransformInitializer

prototorch/core/losses.py

@@ -1,184 +0,0 @@
-"""ProtoTorch losses"""
-
-import torch
-
-from prototorch.nn.activations import get_activation
-
-
-# Helpers
-def _get_matcher(targets, labels):
-    """Returns a boolean tensor."""
-    matcher = torch.eq(targets.unsqueeze(dim=1), labels)
-    if labels.ndim == 2:
-        # if the labels are one-hot vectors
-        num_classes = targets.size()[1]
-        matcher = torch.eq(torch.sum(matcher, dim=-1), num_classes)
-    return matcher
-
-
-def _get_dp_dm(distances, targets, plabels, with_indices=False):
-    """Returns the d+ and d- values for a batch of distances."""
-    matcher = _get_matcher(targets, plabels)
-    not_matcher = torch.bitwise_not(matcher)
-
-    inf = torch.full_like(distances, fill_value=float("inf"))
-    d_matching = torch.where(matcher, distances, inf)
-    d_unmatching = torch.where(not_matcher, distances, inf)
-    dp = torch.min(d_matching, dim=-1, keepdim=True)
-    dm = torch.min(d_unmatching, dim=-1, keepdim=True)
-    if with_indices:
-        return dp, dm
-    return dp.values, dm.values
-
-
-# GLVQ
-def glvq_loss(distances, target_labels, prototype_labels):
-    """GLVQ loss function with support for one-hot labels."""
-    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
-    mu = (dp - dm) / (dp + dm)
-    return mu
-
-
-def lvq1_loss(distances, target_labels, prototype_labels):
-    """LVQ1 loss function with support for one-hot labels.
-
-    See Section 4 [Sado&Yamada]
-    https://papers.nips.cc/paper/1995/file/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf
-    """
-    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
-    mu = dp
-    mu[dp > dm] = -dm[dp > dm]
-    return mu
-
-
-def lvq21_loss(distances, target_labels, prototype_labels):
-    """LVQ2.1 loss function with support for one-hot labels.
-
-    See Section 4 [Sado&Yamada]
-    https://papers.nips.cc/paper/1995/file/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf
-    """
-    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
-    mu = dp - dm
-
-    return mu
-
-
-# Probabilistic
-def _get_class_probabilities(probabilities, targets, prototype_labels):
-    # Create Label Mapping
-    uniques = prototype_labels.unique(sorted=True).tolist()
-    key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
-
-    target_indices = torch.LongTensor(list(map(key_val.get, targets.tolist())))
-
-    whole = probabilities.sum(dim=1)
-    correct = probabilities[torch.arange(len(probabilities)), target_indices]
-    wrong = whole - correct
-
-    return whole, correct, wrong
-
-
-def nllr_loss(probabilities, targets, prototype_labels):
-    """Compute the Negative Log-Likelihood Ratio loss."""
-    _, correct, wrong = _get_class_probabilities(probabilities, targets,
-                                                 prototype_labels)
-
-    likelihood = correct / wrong
-    log_likelihood = torch.log(likelihood)
-    return -1.0 * log_likelihood
-
-
-def rslvq_loss(probabilities, targets, prototype_labels):
-    """Compute the Robust Soft Learning Vector Quantization (RSLVQ) loss."""
-    whole, correct, _ = _get_class_probabilities(probabilities, targets,
-                                                 prototype_labels)
-
-    likelihood = correct / whole
-    log_likelihood = torch.log(likelihood)
-    return -1.0 * log_likelihood
-
-
-def margin_loss(y_pred, y_true, margin=0.3):
-    """Compute the margin loss."""
-    dp = torch.sum(y_true * y_pred, dim=-1)
-    dm = torch.max(y_pred - y_true, dim=-1).values
-    return torch.nn.functional.relu(dm - dp + margin)
-
-
-class GLVQLoss(torch.nn.Module):
-
-    def __init__(self,
-                 margin=0.0,
-                 transfer_fn="identity",
-                 beta=10,
-                 add_dp=False,
-                 **kwargs):
-        super().__init__(**kwargs)
-        self.margin = margin
-        self.transfer_fn = get_activation(transfer_fn)
-        self.beta = torch.tensor(beta)
-        self.add_dp = add_dp
-
-    def forward(self, outputs, targets, plabels):
-        # mu = glvq_loss(outputs, targets, plabels)
-        dp, dm = _get_dp_dm(outputs, targets, plabels)
-        mu = (dp - dm) / (dp + dm)
-        if self.add_dp:
-            mu = mu + dp
-        batch_loss = self.transfer_fn(mu + self.margin, beta=self.beta)
-        return batch_loss.sum()
-
-
-class MarginLoss(torch.nn.modules.loss._Loss):
-
-    def __init__(self,
-                 margin=0.3,
-                 size_average=None,
-                 reduce=None,
-                 reduction="mean"):
-        super().__init__(size_average, reduce, reduction)
-        self.margin = margin
-
-    def forward(self, y_pred, y_true):
-        return margin_loss(y_pred, y_true, self.margin)
-
-
-class NeuralGasEnergy(torch.nn.Module):
-
-    def __init__(self, lm, **kwargs):
-        super().__init__(**kwargs)
-        self.lm = lm
-
-    def forward(self, d):
-        order = torch.argsort(d, dim=1)
-        ranks = torch.argsort(order, dim=1)
-        cost = torch.sum(self._nghood_fn(ranks, self.lm) * d)
-
-        return cost, order
-
-    def extra_repr(self):
-        return f"lambda: {self.lm}"
-
-    @staticmethod
-    def _nghood_fn(rankings, lm):
-        return torch.exp(-rankings / lm)
-
-
-class GrowingNeuralGasEnergy(NeuralGasEnergy):
-
-    def __init__(self, topology_layer, **kwargs):
-        super().__init__(**kwargs)
-        self.topology_layer = topology_layer
-
-    @staticmethod
-    def _nghood_fn(rankings, topology):
-        winner = rankings[:, 0]
-
-        weights = torch.zeros_like(rankings, dtype=torch.float)
-        weights[torch.arange(rankings.shape[0]), winner] = 1.0
-
-        neighbours = topology.get_neighbours(winner)
-
-        weights[neighbours] = 0.1
-
-        return weights

prototorch/core/pooling.py

@@ -1,108 +0,0 @@
-"""ProtoTorch pooling"""
-
-from typing import Callable
-
-import torch
-
-
-def stratify_with(values: torch.Tensor,
-                  labels: torch.LongTensor,
-                  fn: Callable,
-                  fill_value: float = 0.0) -> (torch.Tensor):
-    """Apply an arbitrary stratification strategy on the columns on `values`.
-
-    The outputs correspond to sorted labels.
-    """
-    clabels = torch.unique(labels, dim=0, sorted=True)
-    num_classes = clabels.size()[0]
-    if values.size()[1] == num_classes:
-        # skip if stratification is trivial
-        return values
-    batch_size = values.size()[0]
-    winning_values = torch.zeros(num_classes, batch_size, device=labels.device)
-    filler = torch.full_like(values.T, fill_value=fill_value)
-    for i, cl in enumerate(clabels):
-        matcher = torch.eq(labels.unsqueeze(dim=1), cl)
-        if labels.ndim == 2:
-            # if the labels are one-hot vectors
-            matcher = torch.eq(torch.sum(matcher, dim=-1), num_classes)
-        cdists = torch.where(matcher, values.T, filler).T
-        winning_values[i] = fn(cdists)
-    if labels.ndim == 2:
-        # Transpose to return with `batch_size` first and
-        # reverse the columns to fix the ordering of the classes
-        return torch.flip(winning_values.T, dims=(1, ))
-
-    return winning_values.T  # return with `batch_size` first
-
-
-def stratified_sum_pooling(values: torch.Tensor,
-                           labels: torch.LongTensor) -> (torch.Tensor):
-    """Group-wise sum."""
-    winning_values = stratify_with(
-        values,
-        labels,
-        fn=lambda x: torch.sum(x, dim=1, keepdim=True).squeeze(),
-        fill_value=0.0)
-    return winning_values
-
-
-def stratified_min_pooling(values: torch.Tensor,
-                           labels: torch.LongTensor) -> (torch.Tensor):
-    """Group-wise minimum."""
-    winning_values = stratify_with(
-        values,
-        labels,
-        fn=lambda x: torch.min(x, dim=1, keepdim=True).values.squeeze(),
-        fill_value=float("inf"))
-    return winning_values
-
-
-def stratified_max_pooling(values: torch.Tensor,
-                           labels: torch.LongTensor) -> (torch.Tensor):
-    """Group-wise maximum."""
-    winning_values = stratify_with(
-        values,
-        labels,
-        fn=lambda x: torch.max(x, dim=1, keepdim=True).values.squeeze(),
-        fill_value=-1.0 * float("inf"))
-    return winning_values
-
-
-def stratified_prod_pooling(values: torch.Tensor,
-                            labels: torch.LongTensor) -> (torch.Tensor):
-    """Group-wise maximum."""
-    winning_values = stratify_with(
-        values,
-        labels,
-        fn=lambda x: torch.prod(x, dim=1, keepdim=True).squeeze(),
-        fill_value=1.0)
-    return winning_values
-
-
-class StratifiedSumPooling(torch.nn.Module):
-    """Thin wrapper over the `stratified_sum_pooling` function."""
-
-    def forward(self, values, labels):  # pylint: disable=no-self-use
-        return stratified_sum_pooling(values, labels)
-
-
-class StratifiedProdPooling(torch.nn.Module):
-    """Thin wrapper over the `stratified_prod_pooling` function."""
-
-    def forward(self, values, labels):  # pylint: disable=no-self-use
-        return stratified_prod_pooling(values, labels)
-
-
-class StratifiedMinPooling(torch.nn.Module):
-    """Thin wrapper over the `stratified_min_pooling` function."""
-
-    def forward(self, values, labels):  # pylint: disable=no-self-use
-        return stratified_min_pooling(values, labels)
-
-
-class StratifiedMaxPooling(torch.nn.Module):
-    """Thin wrapper over the `stratified_max_pooling` function."""
-
-    def forward(self, values, labels):  # pylint: disable=no-self-use
-        return stratified_max_pooling(values, labels)

prototorch/core/transforms.py

@@ -1,47 +0,0 @@
-"""ProtoTorch transforms"""
-
-import torch
-from torch.nn.parameter import Parameter
-
-from .initializers import (
-    AbstractLinearTransformInitializer,
-    EyeLinearTransformInitializer,
-)
-
-
-class LinearTransform(torch.nn.Module):
-
-    def __init__(
-        self,
-        in_dim: int,
-        out_dim: int,
-        initializer:
-        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
-        super().__init__()
-        self.set_weights(in_dim, out_dim, initializer)
-
-    @property
-    def weights(self):
-        return self._weights.detach().cpu()
-
-    def _register_weights(self, weights):
-        self.register_parameter("_weights", Parameter(weights))
-
-    def set_weights(
-        self,
-        in_dim: int,
-        out_dim: int,
-        initializer:
-        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
-        weights = initializer.generate(in_dim, out_dim)
-        self._register_weights(weights)
-
-    def forward(self, x):
-        return x @ self._weights
-
-    def extra_repr(self):
-        return f"weights: (shape: {tuple(self._weights.shape)})"
-
-
-# Aliases
-Omega = LinearTransform

prototorch/datasets/__init__.py

@@ -1,13 +1,11 @@
-"""ProtoTorch datasets"""
+"""ProtoTorch datasets."""
 
-from .abstract import CSVDataset, NumpyDataset
+from .abstract import NumpyDataset
-from .sklearn import (
-    Blobs,
-    Circles,
-    Iris,
-    Moons,
-    Random,
-)
 from .spiral import Spiral
 from .tecator import Tecator
-from .xor import XOR
+
+__all__ = [
+    "NumpyDataset",
+    "Spiral",
+    "Tecator",
+]

prototorch/datasets/abstract.py

@@ -1,26 +1,31 @@
-"""ProtoTorch abstract dataset classes
+"""ProtoTorch abstract dataset classes.
 
-Based on `torchvision.VisionDataset` and `torchvision.MNIST`.
+Based on `torchvision.VisionDataset` and `torchvision.MNIST`
 
 For the original code, see:
 https://github.com/pytorch/vision/blob/master/torchvision/datasets/vision.py
 https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py
-
 """
 
 import os
 
-import numpy as np
 import torch
 
 
+class NumpyDataset(torch.utils.data.TensorDataset):
+    """Create a PyTorch TensorDataset from NumPy arrays."""
+    def __init__(self, *arrays):
+        tensors = [torch.Tensor(arr) for arr in arrays]
+        super().__init__(*tensors)
+
+
 class Dataset(torch.utils.data.Dataset):
     """Abstract dataset class to be inherited."""
 
     _repr_indent = 2
 
     def __init__(self, root):
-        if isinstance(root, str):
+        if isinstance(root, torch._six.string_classes):
             root = os.path.expanduser(root)
         self.root = root
 
@@ -37,7 +42,7 @@ class ProtoDataset(Dataset):
     training_file = "training.pt"
     test_file = "test.pt"
 
-    def __init__(self, root="", train=True, download=True, verbose=True):
+    def __init__(self, root, train=True, download=True, verbose=True):
         super().__init__(root)
         self.train = train  # training set or test set
         self.verbose = verbose
@@ -89,27 +94,3 @@ class ProtoDataset(Dataset):
 
     def _download(self):
         raise NotImplementedError
-
-
-class NumpyDataset(torch.utils.data.TensorDataset):
-    """Create a PyTorch TensorDataset from NumPy arrays."""
-
-    def __init__(self, data, targets):
-        self.data = torch.Tensor(data)
-        self.targets = torch.LongTensor(targets)
-        tensors = [self.data, self.targets]
-        super().__init__(*tensors)
-
-
-class CSVDataset(NumpyDataset):
-    """Create a Dataset from a CSV file."""
-
-    def __init__(self, filepath, target_col=-1, delimiter=',', skip_header=0):
-        raw = np.genfromtxt(
-            filepath,
-            delimiter=delimiter,
-            skip_header=skip_header,
-        )
-        data = np.delete(raw, 1, target_col)
-        targets = raw[:, target_col]
-        super().__init__(data, targets)

prototorch/datasets/sklearn.py

@@ -1,165 +0,0 @@
-"""Thin wrappers for a few scikit-learn datasets.
-
-URL:
-    https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets
-
-"""
-
-from __future__ import annotations
-
-import warnings
-from typing import Sequence
-
-from sklearn.datasets import (
-    load_iris,
-    make_blobs,
-    make_circles,
-    make_classification,
-    make_moons,
-)
-
-from prototorch.datasets.abstract import NumpyDataset
-
-
-class Iris(NumpyDataset):
-    """Iris Dataset by Ronald Fisher introduced in 1936.
-
-    The dataset contains four measurements from flowers of three species of iris.
-
-    .. list-table:: Iris
-        :header-rows: 1
-
-        * - dimensions
-          - classes
-          - training size
-          - validation size
-          - test size
-        * - 4
-          - 3
-          - 150
-          - 0
-          - 0
-
-    :param dims: select a subset of dimensions
-    """
-
-    def __init__(self, dims: Sequence[int] | None = None):
-        x, y = load_iris(return_X_y=True)
-        if dims is not None:
-            x = x[:, dims]
-        super().__init__(x, y)
-
-
-class Blobs(NumpyDataset):
-    """Generate isotropic Gaussian blobs for clustering.
-
-    Read more at
-    https://scikit-learn.org/stable/datasets/sample_generators.html#sample-generators.
-
-    """
-
-    def __init__(
-        self,
-        num_samples: int = 300,
-        num_features: int = 2,
-        seed: None | int = 0,
-    ):
-        x, y = make_blobs(
-            num_samples,
-            num_features,
-            centers=None,
-            random_state=seed,
-            shuffle=False,
-        )
-        super().__init__(x, y)
-
-
-class Random(NumpyDataset):
-    """Generate a random n-class classification problem.
-
-    Read more at
-    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html.
-
-    Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
-    """
-
-    def __init__(
-        self,
-        num_samples: int = 300,
-        num_features: int = 2,
-        num_classes: int = 2,
-        num_clusters: int = 2,
-        num_informative: None | int = None,
-        separation: float = 1.0,
-        seed: None | int = 0,
-    ):
-        if not num_informative:
-            import math
-            num_informative = math.ceil(math.log2(num_classes * num_clusters))
-            if num_features < num_informative:
-                warnings.warn("Generating more features than requested.")
-                num_features = num_informative
-        x, y = make_classification(
-            num_samples,
-            num_features,
-            n_informative=num_informative,
-            n_redundant=0,
-            n_classes=num_classes,
-            n_clusters_per_class=num_clusters,
-            class_sep=separation,
-            random_state=seed,
-            shuffle=False,
-        )
-        super().__init__(x, y)
-
-
-class Circles(NumpyDataset):
-    """Make a large circle containing a smaller circle in 2D.
-
-    A simple toy dataset to visualize clustering and classification algorithms.
-
-    Read more at
-    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
-
-    """
-
-    def __init__(
-        self,
-        num_samples: int = 300,
-        noise: float = 0.3,
-        factor: float = 0.8,
-        seed: None | int = 0,
-    ):
-        x, y = make_circles(
-            num_samples,
-            noise=noise,
-            factor=factor,
-            random_state=seed,
-            shuffle=False,
-        )
-        super().__init__(x, y)
-
-
-class Moons(NumpyDataset):
-    """Make two interleaving half circles.
-
-    A simple toy dataset to visualize clustering and classification algorithms.
-
-    Read more at
-    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
-
-    """
-
-    def __init__(
-        self,
-        num_samples: int = 300,
-        noise: float = 0.3,
-        seed: None | int = 0,
-    ):
-        x, y = make_moons(
-            num_samples,
-            noise=noise,
-            random_state=seed,
-            shuffle=False,
-        )
-        super().__init__(x, y)

prototorch/datasets/spiral.py

@@ -4,23 +4,18 @@ import numpy as np
 import torch
 
 
-def make_spiral(num_samples=500, noise=0.3):
+def make_spiral(n_samples=500, noise=0.3):
-    """Generates the Spiral Dataset.
-
-    For use in Prototorch use `prototorch.datasets.Spiral` instead.
-    """
-
     def get_samples(n, delta_t):
         points = []
         for i in range(n):
-            r = i / num_samples * 5
+            r = i / n_samples * 5
             t = 1.75 * i / n * 2 * np.pi + delta_t
             x = r * np.sin(t) + np.random.rand(1) * noise
             y = r * np.cos(t) + np.random.rand(1) * noise
             points.append([x, y])
         return points
 
-    n = num_samples // 2
+    n = n_samples // 2
     positive = get_samples(n=n, delta_t=0)
     negative = get_samples(n=n, delta_t=np.pi)
     x = np.concatenate(
@@ -32,28 +27,7 @@ def make_spiral(num_samples=500, noise=0.3):
 
 
 class Spiral(torch.utils.data.TensorDataset):
-    """Spiral dataset for binary classification.
+    """Spiral dataset for binary classification."""
-
+    def __init__(self, n_samples=500, noise=0.3):
-    This datasets consists of two spirals of two different classes.
+        x, y = make_spiral(n_samples, noise)
-
-    .. list-table:: Spiral
-        :header-rows: 1
-
-        * - dimensions
-          - classes
-          - training size
-          - validation size
-          - test size
-        * - 2
-          - 2
-          - num_samples
-          - 0
-          - 0
-
-    :param num_samples: number of random samples
-    :param noise: noise added to the spirals
-    """
-
-    def __init__(self, num_samples: int = 500, noise: float = 0.3):
-        x, y = make_spiral(num_samples, noise)
         super().__init__(torch.Tensor(x), torch.LongTensor(y))

prototorch/datasets/tecator.py

@@ -36,7 +36,6 @@ Description:
     are determined by analytic chemistry.
 """
 
-import logging
 import os
 
 import numpy as np
@@ -48,23 +47,8 @@ from prototorch.datasets.abstract import ProtoDataset
 
 class Tecator(ProtoDataset):
     """
-    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__ for classification.
+    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__
-
+    for classification.
-    The dataset contains wavelength measurements of meat.
-
-    .. list-table:: Tecator
-        :header-rows: 1
-
-        * - dimensions
-          - classes
-          - training size
-          - validation size
-          - test size
-        * - 100
-          - 2
-          - 129
-          - 43
-          - 43
     """
 
     _resources = [
@@ -82,11 +66,13 @@ class Tecator(ProtoDataset):
         if self._check_exists():
             return
 
-        logging.debug("Making directories...")
+        if self.verbose:
+            print("Making directories...")
         os.makedirs(self.raw_folder, exist_ok=True)
         os.makedirs(self.processed_folder, exist_ok=True)
 
-        logging.debug("Downloading...")
+        if self.verbose:
+            print("Downloading...")
         for fileid, md5 in self._resources:
             filename = "tecator.npz"
             download_file_from_google_drive(fileid,
@@ -94,18 +80,19 @@ class Tecator(ProtoDataset):
                                             filename=filename,
                                             md5=md5)
 
-        logging.debug("Processing...")
+        if self.verbose:
+            print("Processing...")
         with np.load(os.path.join(self.raw_folder, "tecator.npz"),
                      allow_pickle=False) as f:
             x_train, y_train = f["x_train"], f["y_train"]
             x_test, y_test = f["x_test"], f["y_test"]
         training_set = [
-            torch.Tensor(x_train),
+            torch.tensor(x_train, dtype=torch.float32),
-            torch.LongTensor(y_train),
+            torch.tensor(y_train),
         ]
         test_set = [
-            torch.Tensor(x_test),
+            torch.tensor(x_test, dtype=torch.float32),
-            torch.LongTensor(y_test),
+            torch.tensor(y_test),
         ]
 
         with open(os.path.join(self.processed_folder, self.training_file),
@@ -115,4 +102,5 @@ class Tecator(ProtoDataset):
                   "wb") as f:
             torch.save(test_set, f)
 
-        logging.debug("Done!")
+        if self.verbose:
+            print("Done!")

prototorch/datasets/xor.py

@@ -1,19 +0,0 @@
-"""Exclusive-or (XOR) dataset for binary classification."""
-
-import torch
-
-
-def make_xor(num_samples=500):
-    x = torch.rand(num_samples, 2)
-    y = torch.zeros(num_samples)
-    y[torch.logical_and(x[:, 0] > 0.5, x[:, 1] < 0.5)] = 1
-    y[torch.logical_and(x[:, 1] > 0.5, x[:, 0] < 0.5)] = 1
-    return x, y
-
-
-class XOR(torch.utils.data.TensorDataset):
-    """Exclusive-or (XOR) dataset for binary classification."""
-
-    def __init__(self, num_samples: int = 500):
-        x, y = make_xor(num_samples)
-        super().__init__(x, y)

prototorch/functions/__init__.py

@@ -0,0 +1,12 @@
+"""ProtoTorch functions."""
+
+from .activations import identity, sigmoid_beta, swish_beta
+from .competitions import knnc, wtac
+
+__all__ = [
+    "identity",
+    "sigmoid_beta",
+    "swish_beta",
+    "knnc",
+    "wtac",
+]

prototorch/functions/activations.py

@@ -1,18 +1,21 @@
-"""ProtoTorch activations"""
+"""ProtoTorch activation functions."""
 
 import torch
 
 ACTIVATIONS = dict()
 
 
-def register_activation(fn):
+# def register_activation(scriptf):
+#     ACTIVATIONS[scriptf.name] = scriptf
+#     return scriptf
+def register_activation(function):
     """Add the activation function to the registry."""
-    name = fn.__name__
+    ACTIVATIONS[function.__name__] = function
-    ACTIVATIONS[name] = fn
+    return function
-    return fn
 
 
 @register_activation
+# @torch.jit.script
 def identity(x, beta=0.0):
     """Identity activation function.
 
@@ -26,6 +29,7 @@ def identity(x, beta=0.0):
 
 
 @register_activation
+# @torch.jit.script
 def sigmoid_beta(x, beta=10.0):
     r"""Sigmoid activation function with scaling.
 
@@ -40,6 +44,7 @@ def sigmoid_beta(x, beta=10.0):
 
 
 @register_activation
+# @torch.jit.script
 def swish_beta(x, beta=10.0):
     r"""Swish activation function with scaling.
 
@@ -57,10 +62,6 @@ def get_activation(funcname):
     """Deserialize the activation function."""
     if callable(funcname):
         return funcname
-    elif funcname in ACTIVATIONS:
+    if funcname in ACTIVATIONS:
         return ACTIVATIONS.get(funcname)
-    else:
+    raise NameError(f"Activation {funcname} was not found.")
-        emsg = f"Unable to find matching function for `{funcname}` " \
-            f"in `prototorch.nn.activations`. "
-        helpmsg = f"Possible values are {list(ACTIVATIONS.keys())}."
-        raise NameError(emsg + helpmsg)

prototorch/functions/competitions.py

@@ -0,0 +1,45 @@
+"""ProtoTorch competition functions."""
+
+import torch
+
+
+# @torch.jit.script
+def stratified_min(distances, labels):
+    clabels = torch.unique(labels, dim=0)
+    nclasses = clabels.size()[0]
+    if distances.size()[1] == nclasses:
+        # skip if only one prototype per class
+        return distances
+    batch_size = distances.size()[0]
+    winning_distances = torch.zeros(nclasses, batch_size)
+    inf = torch.full_like(distances.T, fill_value=float("inf"))
+    # distances_to_wpluses = torch.where(matcher, distances, inf)
+    for i, cl in enumerate(clabels):
+        # cdists = distances.T[labels == cl]
+        matcher = torch.eq(labels.unsqueeze(dim=1), cl)
+        if labels.ndim == 2:
+            # if the labels are one-hot vectors
+            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+        cdists = torch.where(matcher, distances.T, inf).T
+        winning_distances[i] = torch.min(cdists, dim=1,
+                                         keepdim=True).values.squeeze()
+    if labels.ndim == 2:
+        # Transpose to return with `batch_size` first and
+        # reverse the columns to fix the ordering of the classes
+        return torch.flip(winning_distances.T, dims=(1, ))
+
+    return winning_distances.T  # return with `batch_size` first
+
+
+# @torch.jit.script
+def wtac(distances, labels):
+    winning_indices = torch.min(distances, dim=1).indices
+    winning_labels = labels[winning_indices].squeeze()
+    return winning_labels
+
+
+# @torch.jit.script
+def knnc(distances, labels, k):
+    winning_indices = torch.topk(-distances, k=k.item(), dim=1).indices
+    winning_labels = labels[winning_indices].squeeze()
+    return winning_labels

prototorch/functions/distances.py

@@ -0,0 +1,349 @@
+"""ProtoTorch distance functions."""
+
+import numpy as np
+import torch
+
+from prototorch.functions.helper import (
+    _check_shapes,
+    _int_and_mixed_shape,
+    equal_int_shape,
+)
+
+
+def squared_euclidean_distance(x, y):
+    r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm y`.
+
+    Compute :math:`{\langle \bm x - \bm y \rangle}_2`
+
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+
+    **Alias:**
+    ``prototorch.functions.distances.sed``
+    """
+    expanded_x = x.unsqueeze(dim=1)
+    batchwise_difference = y - expanded_x
+    differences_raised = torch.pow(batchwise_difference, 2)
+    distances = torch.sum(differences_raised, axis=2)
+    return distances
+
+
+def euclidean_distance(x, y):
+    r"""Compute the Euclidean distance between :math:`x` and :math:`y`.
+
+    Compute :math:`\sqrt{{\langle \bm x - \bm y \rangle}_2}`
+
+    :param `torch.tensor` x: Input Tensor of shape :math:`X \times N`
+    :param `torch.tensor` y: Input Tensor of shape :math:`Y \times N`
+
+    :returns: Distance Tensor of shape :math:`X \times Y`
+    :rtype: `torch.tensor`
+    """
+    distances_raised = squared_euclidean_distance(x, y)
+    distances = torch.sqrt(distances_raised)
+    return distances
+
+
+def euclidean_distance_v2(x, y):
+    diff = y - x.unsqueeze(1)
+    pairwise_distances = (diff @ diff.permute((0, 2, 1))).sqrt()
+    # Passing `dim1=-2` and `dim2=-1` to `diagonal()` takes the
+    # batch diagonal. See:
+    # https://pytorch.org/docs/stable/generated/torch.diagonal.html
+    distances = torch.diagonal(pairwise_distances, dim1=-2, dim2=-1)
+    # print(f"{diff.shape=}")  # (nx, ny, ndim)
+    # print(f"{pairwise_distances.shape=}")  # (nx, ny, ny)
+    # print(f"{distances.shape=}")  # (nx, ny)
+    return distances
+
+
+def lpnorm_distance(x, y, p):
+    r"""Calculate the lp-norm between :math:`\bm x` and :math:`\bm y`.
+    Also known as Minkowski distance.
+
+    Compute :math:`{\| \bm x - \bm y \|}_p`.
+
+    Calls ``torch.cdist``
+
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+    :param p: p parameter of the lp norm
+    """
+    distances = torch.cdist(x, y, p=p)
+    return distances
+
+
+def omega_distance(x, y, omega):
+    r"""Omega distance.
+
+    Compute :math:`{\| \Omega \bm x - \Omega \bm y \|}_p`
+
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+    :param `torch.tensor` omega: Two dimensional matrix
+    """
+    projected_x = x @ omega
+    projected_y = y @ omega
+    distances = squared_euclidean_distance(projected_x, projected_y)
+    return distances
+
+
+def lomega_distance(x, y, omegas):
+    r"""Localized Omega distance.
+
+    Compute :math:`{\| \Omega_k \bm x - \Omega_k \bm y_k \|}_p`
+
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+    :param `torch.tensor` omegas: Three dimensional matrix
+    """
+    projected_x = x @ omegas
+    projected_y = torch.diagonal(y @ omegas).T
+    expanded_y = torch.unsqueeze(projected_y, dim=1)
+    batchwise_difference = expanded_y - projected_x
+    differences_squared = batchwise_difference**2
+    distances = torch.sum(differences_squared, dim=2)
+    distances = distances.permute(1, 0)
+    return distances
+
+
+def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
+    r"""Computes an euclidean distances matrix given two distinct vectors.
+    last dimension must be the vector dimension!
+    compute the distance via the identity of the dot product. This avoids the memory overhead due to the subtraction!
+
+    - ``x.shape = (number_of_x_vectors, vector_dim)``
+    - ``y.shape = (number_of_y_vectors, vector_dim)``
+
+    output: matrix of distances (number_of_x_vectors, number_of_y_vectors)
+    """
+    for tensor in [x, y]:
+        if tensor.ndim != 2:
+            raise ValueError(
+                "The tensor dimension must be two. You provide: tensor.ndim=" +
+                str(tensor.ndim) + ".")
+    if not equal_int_shape([tuple(x.shape)[1]], [tuple(y.shape)[1]]):
+        raise ValueError(
+            "The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]="
+            + str(tuple(x.shape)[1]) + " and  tuple(y.shape)(y)[1]=" +
+            str(tuple(y.shape)[1]) + ".")
+
+    y = torch.transpose(y)
+
+    diss = (torch.sum(x**2, axis=1, keepdims=True) - 2 * torch.dot(x, y) +
+            torch.sum(y**2, axis=0, keepdims=True))
+
+    if not squared:
+        if epsilon == 0:
+            diss = torch.sqrt(diss)
+        else:
+            diss = torch.sqrt(torch.max(diss, epsilon))
+
+    return diss
+
+
+def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
+    r"""Tangent distances based on the tensorflow implementation of Sascha Saralajews
+
+    For more info about Tangen distances see
+
+    DOI:10.1109/IJCNN.2016.7727534.
+
+    The subspaces is always assumed as transposed and must be orthogonal!
+    For local non sparse signals subspaces must be provided!
+
+    - shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
+    - shape(protos): proto_number x dim1 x dim2 x ... x dimN
+    - shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN)  x prod(projected_atom_shape)
+
+    subspace should be orthogonalized
+    Pytorch implementation of Sascha Saralajew's tensorflow code.
+    Translation by Christoph Raab
+    """
+    signal_shape, signal_int_shape = _int_and_mixed_shape(signals)
+    proto_shape, proto_int_shape = _int_and_mixed_shape(protos)
+    subspace_int_shape = tuple(subspaces.shape)
+
+    # check if the shapes are correct
+    _check_shapes(signal_int_shape, proto_int_shape)
+
+    atom_axes = list(range(3, len(signal_int_shape)))
+    # for sparse signals, we use the memory efficient implementation
+    if signal_int_shape[1] == 1:
+        signals = torch.reshape(signals, [-1, np.prod(signal_shape[3:])])
+
+        if len(atom_axes) > 1:
+            protos = torch.reshape(protos, [proto_shape[0], -1])
+
+        if subspaces.ndim == 2:
+            # clean solution without map if the matrix_scope is global
+            projectors = torch.eye(subspace_int_shape[-2]) - torch.dot(
+                subspaces, torch.transpose(subspaces))
+
+            projected_signals = torch.dot(signals, projectors)
+            projected_protos = torch.dot(protos, projectors)
+
+            diss = euclidean_distance_matrix(projected_signals,
+                                             projected_protos,
+                                             squared=squared,
+                                             epsilon=epsilon)
+
+            diss = torch.reshape(
+                diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
+
+            return torch.permute(diss, [0, 2, 1])
+
+        else:
+
+            # no solution without map possible --> memory efficient but slow!
+            projectors = torch.eye(subspace_int_shape[-2]) - torch.bmm(
+                subspaces,
+                subspaces)  # K.batch_dot(subspaces, subspaces, [2, 2])
+
+            projected_protos = (protos @ subspaces
+                                ).T  # K.batch_dot(projectors, protos, [1, 1]))
+
+            def projected_norm(projector):
+                return torch.sum(torch.dot(signals, projector)**2, axis=1)
+
+            diss = (torch.transpose(map(projected_norm, projectors)) -
+                    2 * torch.dot(signals, projected_protos) +
+                    torch.sum(projected_protos**2, axis=0, keepdims=True))
+
+            if not squared:
+                if epsilon == 0:
+                    diss = torch.sqrt(diss)
+                else:
+                    diss = torch.sqrt(torch.max(diss, epsilon))
+
+            diss = torch.reshape(
+                diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
+
+            return torch.permute(diss, [0, 2, 1])
+
+    else:
+        signals = signals.permute([0, 2, 1] + atom_axes)
+
+        diff = signals - protos
+
+        # global tangent space
+        if subspaces.ndim == 2:
+            # Scope Projectors
+            projectors = subspaces  #
+
+            # Scope: Tangentspace Projections
+            diff = torch.reshape(
+                diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
+            projected_diff = diff @ projectors
+            projected_diff = torch.reshape(
+                projected_diff,
+                (signal_shape[0], signal_shape[2], signal_shape[1]) +
+                signal_shape[3:],
+            )
+
+            diss = torch.norm(projected_diff, 2, dim=-1)
+            return diss.permute([0, 2, 1])
+
+        # local tangent spaces
+        else:
+            # Scope: Calculate Projectors
+            projectors = subspaces
+
+            # Scope: Tangentspace Projections
+            diff = torch.reshape(
+                diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
+            diff = diff.permute([1, 0, 2])
+            projected_diff = torch.bmm(diff, projectors)
+            projected_diff = torch.reshape(
+                projected_diff,
+                (signal_shape[1], signal_shape[0], signal_shape[2]) +
+                signal_shape[3:],
+            )
+
+            diss = torch.norm(projected_diff, 2, dim=-1)
+            return diss.permute([1, 0, 2]).squeeze(-1)
+
+
+class KernelDistance:
+    r"""Kernel Distance
+
+    Distance based on a kernel function.
+    """
+    def __init__(self, kernel_fn):
+        self.kernel_fn = kernel_fn
+
+    def __call__(self, x_batch: torch.Tensor, y_batch: torch.Tensor):
+        return self._single_call(x_batch, y_batch)
+
+    def _single_call(self, x, y):
+        remove_dims = []
+        if len(x.shape) == 1:
+            x = x.unsqueeze(0)
+            remove_dims.append(0)
+        if len(y.shape) == 1:
+            y = y.unsqueeze(0)
+            remove_dims.append(-1)
+
+        output = self.kernel_fn(x, x).diag().unsqueeze(1) - 2 * self.kernel_fn(
+            x, y) + self.kernel_fn(y, y).diag()
+
+        for dim in remove_dims:
+            output.squeeze_(dim)
+
+        return torch.sqrt(output)
+
+
+class BatchKernelDistance:
+    r"""Kernel Distance
+
+    Distance based on a kernel function.
+    """
+    def __init__(self, kernel_fn):
+        self.kernel_fn = kernel_fn
+
+    def __call__(self, x_batch: torch.Tensor, y_batch: torch.Tensor):
+        remove_dims = 0
+        # Extend Single inputs
+        if len(x_batch.shape) == 1:
+            x_batch = x_batch.unsqueeze(0)
+            remove_dims += 1
+        if len(y_batch.shape) == 1:
+            y_batch = y_batch.unsqueeze(0)
+            remove_dims += 1
+
+        # Loop over batches
+        output = torch.FloatTensor(len(x_batch), len(y_batch))
+        for i, x in enumerate(x_batch):
+            for j, y in enumerate(y_batch):
+                output[i][j] = self._single_call(x, y)
+
+        for _ in range(remove_dims):
+            output.squeeze_(0)
+
+        return output
+
+    def _single_call(self, x, y):
+        kappa_xx = self.kernel_fn(x, x)
+        kappa_xy = self.kernel_fn(x, y)
+        kappa_yy = self.kernel_fn(y, y)
+
+        squared_distance = kappa_xx - 2 * kappa_xy + kappa_yy
+
+        return torch.sqrt(squared_distance)
+
+
+class SquaredKernelDistance(KernelDistance):
+    r"""Squared Kernel Distance
+
+    Kernel distance without final squareroot.
+    """
+    def single_call(self, x, y):
+        kappa_xx = self.kernel_fn(x, x)
+        kappa_xy = self.kernel_fn(x, y)
+        kappa_yy = self.kernel_fn(y, y)
+
+        return kappa_xx - 2 * kappa_xy + kappa_yy
+
+
+# Aliases
+sed = squared_euclidean_distance

prototorch/functions/helper.py

@@ -0,0 +1,89 @@
+import torch
+
+
+def calculate_prototype_accuracy(y_pred, y_true, plabels):
+    """Computes the accuracy of a prototype based model.
+    via Winner-Takes-All rule.
+    Requirement:
+    y_pred.shape == y_true.shape
+    unique(y_pred) in plabels
+    """
+    with torch.no_grad():
+        idx = torch.argmin(y_pred, axis=1)
+        return torch.true_divide(torch.sum(y_true == plabels[idx]),
+                                 len(y_pred)) * 100
+
+
+def predict_label(y_pred, plabels):
+    r""" Predicts labels given a prediction of a prototype based model.
+    """
+    with torch.no_grad():
+        return plabels[torch.argmin(y_pred, 1)]
+
+
+def mixed_shape(inputs):
+    if not torch.is_tensor(inputs):
+        raise ValueError("Input must be a tensor.")
+    else:
+        int_shape = list(inputs.shape)
+        # sometimes int_shape returns mixed integer types
+        int_shape = [int(i) if i is not None else i for i in int_shape]
+        tensor_shape = inputs.shape
+
+        for i, s in enumerate(int_shape):
+            if s is None:
+                int_shape[i] = tensor_shape[i]
+        return tuple(int_shape)
+
+
+def equal_int_shape(shape_1, shape_2):
+    if not isinstance(shape_1,
+                      (tuple, list)) or not isinstance(shape_2, (tuple, list)):
+        raise ValueError("Input shapes must list or tuple.")
+    for shape in [shape_1, shape_2]:
+        if not all([isinstance(x, int) or x is None for x in shape]):
+            raise ValueError(
+                "Input shapes must be list or tuple of int and None values.")
+
+    if len(shape_1) != len(shape_2):
+        return False
+    else:
+        for axis, value in enumerate(shape_1):
+            if value is not None and shape_2[axis] not in {value, None}:
+                return False
+        return True
+
+
+def _check_shapes(signal_int_shape, proto_int_shape):
+    if len(signal_int_shape) < 4:
+        raise ValueError(
+            "The number of signal dimensions must be >=4. You provide: " +
+            str(len(signal_int_shape)))
+
+    if len(proto_int_shape) < 2:
+        raise ValueError(
+            "The number of proto dimensions must be >=2. You provide: " +
+            str(len(proto_int_shape)))
+
+    if not equal_int_shape(signal_int_shape[3:], proto_int_shape[1:]):
+        raise ValueError(
+            "The atom shape of signals must be equal protos. You provide: signals.shape[3:]="
+            + str(signal_int_shape[3:]) + " != protos.shape[1:]=" +
+            str(proto_int_shape[1:]))
+
+    # not a sparse signal
+    if signal_int_shape[1] != 1:
+        if not equal_int_shape(signal_int_shape[1:2], proto_int_shape[0:1]):
+            raise ValueError(
+                "If the signal is not sparse, the number of prototypes must be equal in signals and "
+                "protos. You provide: " + str(signal_int_shape[1]) + " != " +
+                str(proto_int_shape[0]))
+
+    return True
+
+
+def _int_and_mixed_shape(tensor):
+    shape = mixed_shape(tensor)
+    int_shape = tuple([i if isinstance(i, int) else None for i in shape])
+
+    return shape, int_shape

prototorch/functions/initializers.py

@@ -0,0 +1,107 @@
+"""ProtoTorch initialization functions."""
+
+from itertools import chain
+
+import torch
+
+INITIALIZERS = dict()
+
+
+def register_initializer(function):
+    """Add the initializer to the registry."""
+    INITIALIZERS[function.__name__] = function
+    return function
+
+
+def labels_from(distribution, one_hot=True):
+    """Takes a distribution tensor and returns a labels tensor."""
+    nclasses = distribution.shape[0]
+    llist = [[i] * n for i, n in zip(range(nclasses), distribution)]
+    # labels = [l for cl in llist for l in cl]  # flatten the list of lists
+    flat_llist = list(chain(*llist))  # flatten label list with itertools.chain
+    plabels = torch.tensor(flat_llist, requires_grad=False)
+    if one_hot:
+        return torch.eye(nclasses)[plabels]
+    return plabels
+
+
+@register_initializer
+def ones(x_train, y_train, prototype_distribution, one_hot=True):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.ones(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution, one_hot)
+    return protos, plabels
+
+
+@register_initializer
+def zeros(x_train, y_train, prototype_distribution, one_hot=True):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.zeros(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution, one_hot)
+    return protos, plabels
+
+
+@register_initializer
+def rand(x_train, y_train, prototype_distribution, one_hot=True):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.rand(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution, one_hot)
+    return protos, plabels
+
+
+@register_initializer
+def randn(x_train, y_train, prototype_distribution, one_hot=True):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.randn(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution, one_hot)
+    return protos, plabels
+
+
+@register_initializer
+def stratified_mean(x_train, y_train, prototype_distribution, one_hot=True):
+    nprotos = torch.sum(prototype_distribution)
+    pdim = x_train.shape[1]
+    protos = torch.empty(nprotos, pdim)
+    plabels = labels_from(prototype_distribution, one_hot)
+    for i, label in enumerate(plabels):
+        matcher = torch.eq(label.unsqueeze(dim=0), y_train)
+        if one_hot:
+            nclasses = y_train.size()[1]
+            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+        xl = x_train[matcher]
+        mean_xl = torch.mean(xl, dim=0)
+        protos[i] = mean_xl
+    plabels = labels_from(prototype_distribution, one_hot=one_hot)
+    return protos, plabels
+
+
+@register_initializer
+def stratified_random(x_train,
+                      y_train,
+                      prototype_distribution,
+                      one_hot=True,
+                      epsilon=1e-7):
+    nprotos = torch.sum(prototype_distribution)
+    pdim = x_train.shape[1]
+    protos = torch.empty(nprotos, pdim)
+    plabels = labels_from(prototype_distribution, one_hot)
+    for i, label in enumerate(plabels):
+        matcher = torch.eq(label.unsqueeze(dim=0), y_train)
+        if one_hot:
+            nclasses = y_train.size()[1]
+            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+        xl = x_train[matcher]
+        rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1)
+        random_xl = xl[rand_index]
+        protos[i] = random_xl + epsilon
+    plabels = labels_from(prototype_distribution, one_hot=one_hot)
+    return protos, plabels
+
+
+def get_initializer(funcname):
+    """Deserialize the initializer."""
+    if callable(funcname):
+        return funcname
+    if funcname in INITIALIZERS:
+        return INITIALIZERS.get(funcname)
+    raise NameError(f"Initializer {funcname} was not found.")

prototorch/functions/kernels.py

@@ -0,0 +1,28 @@
+"""
+Experimental Kernels
+"""
+
+import torch
+
+
+class ExplicitKernel:
+    def __init__(self, projection=torch.nn.Identity()):
+        self.projection = projection
+
+    def __call__(self, x, y):
+        return self.projection(x) @ self.projection(y).T
+
+
+class RadialBasisFunctionKernel:
+    def __init__(self, sigma) -> None:
+        self.s2 = sigma * sigma
+
+    def __call__(self, x, y):
+        remove_dim = False
+        if len(x.shape) > 1:
+            x = x.unsqueeze(1)
+            remove_dim = True
+        output = torch.exp(-torch.sum((x - y)**2, dim=-1) / (2 * self.s2))
+        if remove_dim:
+            output = output.squeeze(1)
+        return output

prototorch/functions/losses.py

@@ -0,0 +1,56 @@
+"""ProtoTorch loss functions."""
+
+import torch
+
+
+def _get_matcher(targets, labels):
+    """Returns a boolean tensor."""
+    matcher = torch.eq(targets.unsqueeze(dim=1), labels)
+    if labels.ndim == 2:
+        # if the labels are one-hot vectors
+        nclasses = targets.size()[1]
+        matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+    return matcher
+
+
+def _get_dp_dm(distances, targets, plabels):
+    """Returns the d+ and d- values for a batch of distances."""
+    matcher = _get_matcher(targets, plabels)
+    not_matcher = torch.bitwise_not(matcher)
+
+    inf = torch.full_like(distances, fill_value=float("inf"))
+    d_matching = torch.where(matcher, distances, inf)
+    d_unmatching = torch.where(not_matcher, distances, inf)
+    dp = torch.min(d_matching, dim=1, keepdim=True).values
+    dm = torch.min(d_unmatching, dim=1, keepdim=True).values
+    return dp, dm
+
+
+def glvq_loss(distances, target_labels, prototype_labels):
+    """GLVQ loss function with support for one-hot labels."""
+    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
+    mu = (dp - dm) / (dp + dm)
+    return mu
+
+
+def lvq1_loss(distances, target_labels, prototype_labels):
+    """LVQ1 loss function with support for one-hot labels.
+
+    See Section 4 [Sado&Yamada]
+    https://papers.nips.cc/paper/1995/file/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf
+    """
+    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
+    mu = dp
+    mu[dp > dm] = -dm[dp > dm]
+    return mu
+
+
+def lvq21_loss(distances, target_labels, prototype_labels):
+    """LVQ2.1 loss function with support for one-hot labels.
+    
+    See Section 4 [Sado&Yamada]
+    https://papers.nips.cc/paper/1995/file/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf
+    """
+    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
+    mu = dp - dm
+    return mu

prototorch/functions/normalization.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import, division, print_function
+
+import torch
+
+
+def orthogonalization(tensors):
+    r""" Orthogonalization of a given tensor via polar decomposition.
+    """
+    u, _, v = torch.svd(tensors, compute_uv=True)
+    u_shape = tuple(list(u.shape))
+    v_shape = tuple(list(v.shape))
+
+    # reshape to (num x N x M)
+    u = torch.reshape(u, (-1, u_shape[-2], u_shape[-1]))
+    v = torch.reshape(v, (-1, v_shape[-2], v_shape[-1]))
+
+    out = u @ v.permute([0, 2, 1])
+
+    out = torch.reshape(out, u_shape[:-1] + (v_shape[-2], ))
+
+    return out
+
+
+def trace_normalization(tensors):
+    r""" Trace normalization
+    """
+    epsilon = torch.tensor([1e-10], dtype=torch.float64)
+    # Scope trace_normalization
+    constant = torch.trace(tensors)
+
+    if epsilon != 0:
+        constant = torch.max(constant, epsilon)
+
+    return tensors / constant

prototorch/functions/similarities.py

@@ -1,19 +1,7 @@
-"""ProtoTorch similarities."""
+"""ProtoTorch similarity functions."""
 
 import torch
 
-from .distances import euclidean_distance
-
-
-def gaussian(x, variance=1.0):
-    return torch.exp(-(x * x) / (2 * variance))
-
-
-def euclidean_similarity(x, y, variance=1.0):
-    distances = euclidean_distance(x, y)
-    similarities = gaussian(distances, variance)
-    return similarities
-
 
 def cosine_similarity(x, y):
     """Compute the cosine similarity between :math:`x` and :math:`y`.
@@ -21,7 +9,6 @@ def cosine_similarity(x, y):
     Expected dimension of x is 2.
     Expected dimension of y is 2.
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     norm_x = x.pow(2).sum(1).sqrt()
     norm_y = y.pow(2).sum(1).sqrt()
     norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T

prototorch/modules/__init__.py

@@ -0,0 +1,7 @@
+"""ProtoTorch modules."""
+
+from .prototypes import Prototypes1D
+
+__all__ = [
+    "Prototypes1D",
+]

prototorch/modules/losses.py

@@ -0,0 +1,40 @@
+"""ProtoTorch losses."""
+
+import torch
+
+from prototorch.functions.activations import get_activation
+from prototorch.functions.losses import glvq_loss
+
+
+class GLVQLoss(torch.nn.Module):
+    def __init__(self, margin=0.0, squashing="identity", beta=10, **kwargs):
+        super().__init__(**kwargs)
+        self.margin = margin
+        self.squashing = get_activation(squashing)
+        self.beta = torch.tensor(beta)
+
+    def forward(self, outputs, targets):
+        distances, plabels = outputs
+        mu = glvq_loss(distances, targets, prototype_labels=plabels)
+        batch_loss = self.squashing(mu + self.margin, beta=self.beta)
+        return torch.sum(batch_loss, dim=0)
+
+
+class NeuralGasEnergy(torch.nn.Module):
+    def __init__(self, lm):
+        super().__init__()
+        self.lm = lm
+
+    def forward(self, d):
+        order = torch.argsort(d, dim=1)
+        ranks = torch.argsort(order, dim=1)
+        cost = torch.sum(self._nghood_fn(ranks, self.lm) * d)
+
+        return cost, order
+
+    def extra_repr(self):
+        return f"lambda: {self.lm}"
+
+    @staticmethod
+    def _nghood_fn(rankings, lm):
+        return torch.exp(-rankings / lm)

prototorch/modules/models.py

@@ -0,0 +1,194 @@
+import torch
+from torch import nn
+
+from prototorch.functions.distances import euclidean_distance_matrix, tangent_distance
+from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
+from prototorch.functions.normalization import orthogonalization
+from prototorch.modules.prototypes import Prototypes1D
+
+
+class GTLVQ(nn.Module):
+    r""" Generalized Tangent Learning Vector Quantization
+
+    Parameters
+    ----------
+    num_classes: int
+        Number of classes of the given classification problem.
+
+    subspace_data: torch.tensor of shape (n_batch,feature_dim,feature_dim)
+        Subspace data for the point approximation, required
+
+    prototype_data: torch.tensor of shape (n_init_data,feature_dim) (optional)
+        prototype data for initalization of the prototypes used in GTLVQ.
+
+    subspace_size: int (default=256,optional)
+        Subspace dimension of the Projectors. Currently only supported
+        with tagnent_projection_type=global.
+
+    tangent_projection_type: string
+        Specifies the tangent projection type
+        options:    local
+                    local_proj
+                    global
+        local: computes the tangent distances without emphasizing projected
+        data. Only distances are available
+        local_proj: computs tangent distances and returns the projected data
+        for further use. Be careful: data is repeated by number of prototypes
+        global: Number of subspaces is set to one and every prototypes
+        uses the same.
+
+    prototypes_per_class: int (default=2,optional)
+    Number of prototypes per class
+
+    feature_dim: int (default=256)
+    Dimensionality of the feature space specified as integer.
+    Prototype dimension.
+
+    Notes
+    -----
+    The GTLVQ [1] is a prototype-based classification learning model. The
+    GTLVQ uses the Tangent-Distances for a local point approximation
+    of an assumed data manifold via prototypial representations.
+
+    The GTLVQ requires subspace projectors for transforming the data
+    and prototypes into the affine subspace. Every prototype is
+    equipped with a specific subpspace and represents a point
+    approximation of the assumed manifold.
+
+    In practice prototypes and data are projected on this manifold
+    and pairwise euclidean distance computes.
+
+    References
+    ----------
+    .. [1] Saralajew, Sascha; Villmann, Thomas: Transfer learning
+    in classification based on manifolc. models and its relation
+    to tangent metric learning. In: 2017 International Joint
+    Conference on Neural Networks (IJCNN).
+    Bd. 2017-May : IEEE, 2017, S. 1756–1765
+    """
+    def __init__(
+        self,
+        num_classes,
+        subspace_data=None,
+        prototype_data=None,
+        subspace_size=256,
+        tangent_projection_type="local",
+        prototypes_per_class=2,
+        feature_dim=256,
+    ):
+        super(GTLVQ, self).__init__()
+
+        self.num_protos = num_classes * prototypes_per_class
+        self.subspace_size = feature_dim if subspace_size is None else subspace_size
+        self.feature_dim = feature_dim
+
+        if subspace_data is None:
+            raise ValueError("Init Data must be specified!")
+
+        self.tpt = tangent_projection_type
+        with torch.no_grad():
+            if self.tpt == "local" or self.tpt == "local_proj":
+                self.init_local_subspace(subspace_data)
+            elif self.tpt == "global":
+                self.init_gobal_subspace(subspace_data, subspace_size)
+            else:
+                self.subspaces = None
+
+        # Hypothesis-Margin-Classifier
+        self.cls = Prototypes1D(
+            input_dim=feature_dim,
+            prototypes_per_class=prototypes_per_class,
+            nclasses=num_classes,
+            prototype_initializer="stratified_mean",
+            data=prototype_data,
+        )
+
+    def forward(self, x):
+        # Tangent Projection
+        if self.tpt == "local_proj":
+            x_conform = (x.unsqueeze(1).repeat_interleave(self.num_protos,
+                                                          1).unsqueeze(2))
+            dis, proj_x = self.local_tangent_projection(x_conform)
+
+            proj_x = proj_x.reshape(x.shape[0] * self.num_protos,
+                                    self.feature_dim)
+            return proj_x, dis
+        elif self.tpt == "local":
+            x_conform = (x.unsqueeze(1).repeat_interleave(self.num_protos,
+                                                          1).unsqueeze(2))
+            dis = tangent_distance(x_conform, self.cls.prototypes,
+                                   self.subspaces)
+        elif self.tpt == "gloabl":
+            dis = self.global_tangent_distances(x)
+        else:
+            dis = (x @ self.cls.prototypes.T) / (
+                torch.norm(x, dim=1, keepdim=True) @ torch.norm(
+                    self.cls.prototypes, dim=1, keepdim=True).T)
+        return dis
+
+    def init_gobal_subspace(self, data, num_subspaces):
+        _, _, v = torch.svd(data)
+        subspace = (torch.eye(v.shape[0]) - (v @ v.T)).T
+        subspaces = subspace[:, :num_subspaces]
+        self.subspaces = (torch.nn.Parameter(
+            subspaces).clone().detach().requires_grad_(True))
+
+    def init_local_subspace(self, data):
+        _, _, v = torch.svd(data)
+        inital_projector = (torch.eye(v.shape[0]) - (v @ v.T)).T
+        subspaces = inital_projector.unsqueeze(0).repeat_interleave(
+            self.num_protos, 0)
+        self.subspaces = (torch.nn.Parameter(
+            subspaces).clone().detach().requires_grad_(True))
+
+    def global_tangent_distances(self, x):
+        # Tangent Projection
+        x, projected_prototypes = (
+            x @ self.subspaces,
+            self.cls.prototypes @ self.subspaces,
+        )
+        # Euclidean Distance
+        return euclidean_distance_matrix(x, projected_prototypes)
+
+    def local_tangent_projection(self, signals):
+        # Note: subspaces is always assumed as transposed and must be orthogonal!
+        # shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
+        # shape(protos): proto_number x dim1 x dim2 x ... x dimN
+        # shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN)  x prod(projected_atom_shape)
+        # subspace should be orthogonalized
+        # Origin Source Code
+        # Origin Author:
+        protos = self.cls.prototypes
+        subspaces = self.subspaces
+        signal_shape, signal_int_shape = _int_and_mixed_shape(signals)
+        _, proto_int_shape = _int_and_mixed_shape(protos)
+
+        # check if the shapes are correct
+        _check_shapes(signal_int_shape, proto_int_shape)
+
+        # Tangent Data Projections
+        projected_protos = torch.bmm(protos.unsqueeze(1), subspaces).squeeze(1)
+        data = signals.squeeze(2).permute([1, 0, 2])
+        projected_data = torch.bmm(data, subspaces)
+        projected_data = projected_data.permute([1, 0, 2]).unsqueeze(1)
+        diff = projected_data - projected_protos
+        projected_diff = torch.reshape(
+            diff, (signal_shape[1], signal_shape[0], signal_shape[2]) +
+            signal_shape[3:])
+        diss = torch.norm(projected_diff, 2, dim=-1)
+        return diss.permute([1, 0, 2]).squeeze(-1), projected_data.squeeze(1)
+
+    def get_parameters(self):
+        return {
+            "params": self.cls.prototypes,
+        }, {
+            "params": self.subspaces
+        }
+
+    def orthogonalize_subspace(self):
+        if self.subspaces is not None:
+            with torch.no_grad():
+                ortho_subpsaces = (orthogonalization(self.subspaces)
+                                   if self.tpt == "global" else
+                                   torch.nn.init.orthogonal_(self.subspaces))
+                self.subspaces.copy_(ortho_subpsaces)

prototorch/modules/prototypes.py

@@ -0,0 +1,137 @@
+"""ProtoTorch prototype modules."""
+
+import warnings
+
+import torch
+
+from prototorch.functions.initializers import get_initializer
+
+
+class _Prototypes(torch.nn.Module):
+    """Abstract prototypes class."""
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _validate_prototype_distribution(self):
+        if 0 in self.prototype_distribution:
+            warnings.warn("Are you sure about the `0` in "
+                          "`prototype_distribution`?")
+
+    def extra_repr(self):
+        return f"prototypes.shape: {tuple(self.prototypes.shape)}"
+
+    def forward(self):
+        return self.prototypes, self.prototype_labels
+
+
+class Prototypes1D(_Prototypes):
+    """Create a learnable set of one-dimensional prototypes.
+
+    TODO Complete this doc-string.
+    """
+    def __init__(
+        self,
+        prototypes_per_class=1,
+        prototype_initializer="ones",
+        prototype_distribution=None,
+        data=None,
+        dtype=torch.float32,
+        one_hot_labels=False,
+        **kwargs,
+    ):
+        warnings.warn(
+            PendingDeprecationWarning(
+                "Prototypes1D will be replaced in future versions."))
+
+        # Convert tensors to python lists before processing
+        if prototype_distribution is not None:
+            if not isinstance(prototype_distribution, list):
+                prototype_distribution = prototype_distribution.tolist()
+
+        if data is None:
+            if "input_dim" not in kwargs:
+                raise NameError("`input_dim` required if "
+                                "no `data` is provided.")
+            if prototype_distribution:
+                kwargs_nclasses = sum(prototype_distribution)
+            else:
+                if "nclasses" not in kwargs:
+                    raise NameError("`prototype_distribution` required if "
+                                    "both `data` and `nclasses` are not "
+                                    "provided.")
+                kwargs_nclasses = kwargs.pop("nclasses")
+            input_dim = kwargs.pop("input_dim")
+            if prototype_initializer in [
+                    "stratified_mean", "stratified_random"
+            ]:
+                warnings.warn(
+                    f"`prototype_initializer`: `{prototype_initializer}` "
+                    "requires `data`, but `data` is not provided. "
+                    "Using randomly generated data instead.")
+            x_train = torch.rand(kwargs_nclasses, input_dim)
+            y_train = torch.arange(kwargs_nclasses)
+            if one_hot_labels:
+                y_train = torch.eye(kwargs_nclasses)[y_train]
+            data = [x_train, y_train]
+
+        x_train, y_train = data
+        x_train = torch.as_tensor(x_train).type(dtype)
+        y_train = torch.as_tensor(y_train).type(torch.int)
+        nclasses = torch.unique(y_train, dim=-1).shape[-1]
+
+        if nclasses == 1:
+            warnings.warn("Are you sure about having one class only?")
+
+        if x_train.ndim != 2:
+            raise ValueError("`data[0].ndim != 2`.")
+
+        if y_train.ndim == 2:
+            if y_train.shape[1] == 1 and one_hot_labels:
+                raise ValueError("`one_hot_labels` is set to `True` "
+                                 "but target labels are not one-hot-encoded.")
+            if y_train.shape[1] != 1 and not one_hot_labels:
+                raise ValueError("`one_hot_labels` is set to `False` "
+                                 "but target labels in `data` "
+                                 "are one-hot-encoded.")
+        if y_train.ndim == 1 and one_hot_labels:
+            raise ValueError("`one_hot_labels` is set to `True` "
+                             "but target labels are not one-hot-encoded.")
+
+        # Verify input dimension if `input_dim` is provided
+        if "input_dim" in kwargs:
+            input_dim = kwargs.pop("input_dim")
+            if input_dim != x_train.shape[1]:
+                raise ValueError(f"Provided `input_dim`={input_dim} does "
+                                 "not match data dimension "
+                                 f"`data[0].shape[1]`={x_train.shape[1]}")
+
+        # Verify the number of classes if `nclasses` is provided
+        if "nclasses" in kwargs:
+            kwargs_nclasses = kwargs.pop("nclasses")
+            if kwargs_nclasses != nclasses:
+                raise ValueError(f"Provided `nclasses={kwargs_nclasses}` does "
+                                 "not match data labels "
+                                 "`torch.unique(data[1]).shape[0]`"
+                                 f"={nclasses}")
+
+        super().__init__(**kwargs)
+
+        if not prototype_distribution:
+            prototype_distribution = [prototypes_per_class] * nclasses
+        with torch.no_grad():
+            self.prototype_distribution = torch.tensor(prototype_distribution)
+
+        self._validate_prototype_distribution()
+
+        self.prototype_initializer = get_initializer(prototype_initializer)
+        prototypes, prototype_labels = self.prototype_initializer(
+            x_train,
+            y_train,
+            prototype_distribution=self.prototype_distribution,
+            one_hot=one_hot_labels,
+        )
+
+        # Register module parameters
+        self.prototypes = torch.nn.Parameter(prototypes)
+        self.prototype_labels = torch.nn.Parameter(
+            prototype_labels.type(dtype)).requires_grad_(False)

prototorch/nn/__init__.py

@@ -1,4 +0,0 @@
-"""ProtoTorch Neural Network Module"""
-
-from .activations import *
-from .wrappers import *

prototorch/nn/wrappers.py

@@ -1,38 +0,0 @@
-"""ProtoTorch wrappers."""
-
-import torch
-
-
-class LambdaLayer(torch.nn.Module):
-
-    def __init__(self, fn, name=None):
-        super().__init__()
-        self.fn = fn
-        self.name = name or fn.__name__  # lambda fns get <lambda>
-
-    def forward(self, *args, **kwargs):
-        return self.fn(*args, **kwargs)
-
-    def extra_repr(self):
-        return self.name
-
-
-class LossLayer(torch.nn.modules.loss._Loss):
-
-    def __init__(self,
-                 fn,
-                 name=None,
-                 size_average=None,
-                 reduce=None,
-                 reduction: str = "mean") -> None:
-        super().__init__(size_average=size_average,
-                         reduce=reduce,
-                         reduction=reduction)
-        self.fn = fn
-        self.name = name or fn.__name__  # lambda fns get <lambda>
-
-    def forward(self, *args, **kwargs):
-        return self.fn(*args, **kwargs)
-
-    def extra_repr(self):
-        return self.name

prototorch/utils/__init__.py

@@ -1,13 +0,0 @@
-"""ProtoTorch utils module"""
-
-from .colors import (
-    get_colors,
-    get_legend_handles,
-    hex_to_rgb,
-    rgb_to_hex,
-)
-from .utils import (
-    mesh2d,
-    parse_data_arg,
-    parse_distribution,
-)

prototorch/utils/celluloid.py

@@ -0,0 +1,46 @@
+"""Easy matplotlib animation. From https://github.com/jwkvam/celluloid."""
+
+from collections import defaultdict
+from typing import Dict, List
+
+from matplotlib.animation import ArtistAnimation
+from matplotlib.artist import Artist
+from matplotlib.figure import Figure
+
+__version__ = "0.2.0"
+
+
+class Camera:
+    """Make animations easier."""
+    def __init__(self, figure: Figure) -> None:
+        """Create camera from matplotlib figure."""
+        self._figure = figure
+        # need to keep track off artists for each axis
+        self._offsets: Dict[str, Dict[int, int]] = {
+            k: defaultdict(int)
+            for k in
+            ["collections", "patches", "lines", "texts", "artists", "images"]
+        }
+        self._photos: List[List[Artist]] = []
+
+    def snap(self) -> List[Artist]:
+        """Capture current state of the figure."""
+        frame_artists: List[Artist] = []
+        for i, axis in enumerate(self._figure.axes):
+            if axis.legend_ is not None:
+                axis.add_artist(axis.legend_)
+            for name in self._offsets:
+                new_artists = getattr(axis, name)[self._offsets[name][i]:]
+                frame_artists += new_artists
+                self._offsets[name][i] += len(new_artists)
+        self._photos.append(frame_artists)
+        return frame_artists
+
+    def animate(self, *args, **kwargs) -> ArtistAnimation:
+        """Animate the snapshots taken.
+        Uses matplotlib.animation.ArtistAnimation
+        Returns
+        -------
+        ArtistAnimation
+        """
+        return ArtistAnimation(self._figure, self._photos, *args, **kwargs)

prototorch/utils/colors.py

@@ -1,60 +1,78 @@
-"""ProtoTorch color utilities"""
+"""ProtoFlow color utilities."""
 
 import matplotlib.lines as mlines
-import torch
 from matplotlib import cm
-from matplotlib.colors import (
+from matplotlib.colors import Normalize, to_hex, to_rgb
-    Normalize,
-    to_hex,
-    to_rgb,
-)
 
 
-def hex_to_rgb(hex_values):
+def color_scheme(n,
-    for v in hex_values:
+                 cmap="viridis",
-        v = v.lstrip('#')
+                 form="hex",
-        lv = len(v)
+                 tikz=False,
-        c = [int(v[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]
+                 zero_indexed=False):
-        yield c
+    """Return *n* colors from the color scheme.
 
+    Arguments:
+        n (int): number of colors to return
 
-def rgb_to_hex(rgb_values):
+    Keyword Arguments:
-    for v in rgb_values:
+        cmap (str): Name of a matplotlib `colormap\
-        c = "%02x%02x%02x" % tuple(v)
+            <https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html>`_.
-        yield c
+        form (str): Colorformat (supports "hex" and "rgb").
+        tikz (bool): Output as `TikZ <https://github.com/pgf-tikz/pgf>`_
+            command.
+        zero_indexed (bool): Use zero indexing for output array.
 
-
+    Returns:
-def get_colors(vmax, vmin=0, cmap="viridis"):
+        (list): List of colors
+    """
     cmap = cm.get_cmap(cmap)
-    colornorm = Normalize(vmin=vmin, vmax=vmax)
+    colornorm = Normalize(vmin=1, vmax=n)
-    colors = dict()
+    hex_map = dict()
-    for c in range(vmin, vmax + 1):
+    rgb_map = dict()
-        colors[c] = to_hex(cmap(colornorm(c)))
+    for cl in range(1, n + 1):
-    return colors
+        if zero_indexed:
+            hex_map[cl - 1] = to_hex(cmap(colornorm(cl)))
+            rgb_map[cl - 1] = to_rgb(cmap(colornorm(cl)))
+        else:
+            hex_map[cl] = to_hex(cmap(colornorm(cl)))
+            rgb_map[cl] = to_rgb(cmap(colornorm(cl)))
+    if tikz:
+        for k, v in rgb_map.items():
+            print(f"\\definecolor{{color-{k}}}{{rgb}}{{{v[0]},{v[1]},{v[2]}}}")
+    if form == "hex":
+        return hex_map
+    elif form == "rgb":
+        return rgb_map
+    else:
+        return hex_map
 
 
-def get_legend_handles(colors, labels, marker="dots", zero_indexed=False):
+def get_legend_handles(labels, marker="dots", zero_indexed=False):
+    """Return matplotlib legend handles and colors."""
     handles = list()
-    for color, label in zip(colors.values(), labels):
+    n = len(labels)
+    colors = color_scheme(n,
+                          cmap="viridis",
+                          form="hex",
+                          zero_indexed=zero_indexed)
+    for label, color in zip(labels, colors.values()):
         if marker == "dots":
             handle = mlines.Line2D(
-                xdata=[],
+                [],
-                ydata=[],
+                [],
-                label=label,
                 color="white",
                 markerfacecolor=color,
                 marker="o",
                 markersize=10,
                 markeredgecolor="k",
+                label=label,
             )
         else:
-            handle = mlines.Line2D(
+            handle = mlines.Line2D([], [],
-                xdata=[],
+                                   color=color,
-                ydata=[],
+                                   marker="",
-                label=label,
+                                   markersize=15,
-                color=color,
+                                   label=label)
-                marker="",
+            handles.append(handle)
-                markersize=15,
+    return handles, colors
-            )
-        handles.append(handle)
-    return handles

prototorch/utils/utils.py

@@ -1,136 +1,243 @@
-"""ProtoTorch utilities"""
+"""Utilities that provide various small functionalities."""
 
-import warnings
+import os
-from typing import (
+import pickle
-    Dict,
+import sys
-    Iterable,
+from time import time
-    List,
-    Optional,
-    Union,
-)
 
+import matplotlib.pyplot as plt
 import numpy as np
-import torch
-from torch.utils.data import DataLoader, Dataset
 
 
-def generate_mesh(
+def progressbar(title, value, end, bar_width=20):
-    minima: torch.TensorType,
+    percent = float(value) / end
-    maxima: torch.TensorType,
+    arrow = "=" * int(round(percent * bar_width) - 1) + ">"
-    border: float = 1.0,
+    spaces = "." * (bar_width - len(arrow))
-    resolution: int = 100,
+    sys.stdout.write("\r{}: [{}] {}%".format(title, arrow + spaces,
-    device: Optional[torch.device] = None,
+                                             int(round(percent * 100))))
-):
+    sys.stdout.flush()
-    # Apply Border
+    if percent == 1.0:
-    ptp = maxima - minima
+        print()
-    shift = border * ptp
-    minima -= shift
-    maxima += shift
-
-    # Generate Mesh
-    minima = minima.to(device).unsqueeze(1)
-    maxima = maxima.to(device).unsqueeze(1)
-
-    factors = torch.linspace(0, 1, resolution, device=device)
-    marginals = factors * maxima + ((1 - factors) * minima)
-
-    single_dimensions = torch.meshgrid(*marginals)
-    mesh_input = torch.stack([dim.ravel() for dim in single_dimensions], dim=1)
-
-    return mesh_input, single_dimensions
 
 
-def mesh2d(x=None, border: float = 1.0, resolution: int = 100):
+def prettify_string(inputs, start="", sep=" ", end="\n"):
-    if x is not None:
+    outputs = start + " ".join(inputs.split()) + end
-        x_shift = border * np.ptp(x[:, 0])
+    return outputs
-        y_shift = border * np.ptp(x[:, 1])
-        x_min, x_max = x[:, 0].min() - x_shift, x[:, 0].max() + x_shift
-        y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
-    else:
-        x_min, x_max = -border, border
-        y_min, y_max = -border, border
-    xx, yy = np.meshgrid(np.linspace(x_min, x_max, resolution),
-                         np.linspace(y_min, y_max, resolution))
-    mesh = np.c_[xx.ravel(), yy.ravel()]
-    return mesh, xx, yy
 
 
-def distribution_from_list(list_dist: List[int],
+def pretty_print(inputs):
-                           clabels: Optional[Iterable[int]] = None):
+    print(prettify_string(inputs))
-    clabels = clabels or list(range(len(list_dist)))
-    distribution = dict(zip(clabels, list_dist))
-    return distribution
 
 
-def parse_distribution(
+def writelog(self, *logs, logdir="./logs", logfile="run.txt"):
-        user_distribution,
+    f = os.path.join(logdir, logfile)
-        clabels: Optional[Iterable[int]] = None) -> Dict[int, int]:
+    with open(f, "a+") as fh:
-    """Parse user-provided distribution.
+        for log in logs:
+            fh.write(log)
+            fh.write("\n")
 
-    Return a dictionary with integer keys that represent the class labels and
-    values that denote the number of components/prototypes with that class
-    label.
 
-    The argument `user_distribution` could be any one of a number of allowed
+def start_tensorboard(self, logdir="./logs"):
-    formats. If it is a Python list, it is assumed that there are as many
+    cmd = f"tensorboard --logdir={logdir} --port=6006"
-    entries in this list as there are classes, and the value at each index of
+    os.system(cmd)
-    this list describes the number of prototypes for that particular class. So,
-    [1, 1, 1] implies that we have three classes with one prototype per class.
-    If it is a Python tuple, a shorthand of (num_classes, prototypes_per_class)
-    is assumed. If it is a Python dictionary, the key-value pairs describe the
-    class label and the number of prototypes for that class respectively. So,
-    {0: 2, 1: 2, 2: 2} implies that we have three classes with labels {1, 2,
-    3}, each equipped with two prototypes. If however, the dictionary contains
-    the keys "num_classes" and "per_class", they are parsed to use their values
-    as one might expect.
 
+
+def make_directory(save_dir):
+    if not os.path.exists(save_dir):
+        print(f"Making directory {save_dir}.")
+        os.mkdir(save_dir)
+
+
+def make_gif(filenames, duration, output_file=None):
+    try:
+        import imageio
+    except ModuleNotFoundError as e:
+        print("Please install Protoflow with [other] extra requirements.")
+        raise (e)
+
+    images = list()
+    for filename in filenames:
+        images.append(imageio.imread(filename))
+    if not output_file:
+        output_file = f"makegif.gif"
+    if images:
+        imageio.mimwrite(output_file, images, duration=duration)
+
+
+def gif_from_dir(directory,
+                 duration,
+                 prefix="",
+                 output_file=None,
+                 verbose=True):
+    images = os.listdir(directory)
+    if verbose:
+        print(f"Making gif from {len(images)} images under {directory}.")
+    filenames = list()
+    # Sort images
+    images = sorted(
+        images,
+        key=lambda img: int(os.path.splitext(img)[0].replace(prefix, "")))
+    for image in images:
+        fname = os.path.join(directory, image)
+        filenames.append(fname)
+    if not output_file:
+        output_file = os.path.join(directory, "makegif.gif")
+    make_gif(filenames=filenames, duration=duration, output_file=output_file)
+
+
+def accuracy_score(y_true, y_pred):
+    accuracy = np.sum(y_true == y_pred)
+    normalized_acc = accuracy / float(len(y_true))
+    return normalized_acc
+
+
+def predict_and_score(clf,
+                      x_test,
+                      y_test,
+                      verbose=False,
+                      title="Test accuracy"):
+    y_pred = clf.predict(x_test)
+    accuracy = np.sum(y_test == y_pred)
+    normalized_acc = accuracy / float(len(y_test))
+    if verbose:
+        print(f"{title}: {normalized_acc * 100:06.04f}%")
+    return normalized_acc
+
+
+def remove_nan_rows(arr):
+    """Remove all rows with `nan` values in `arr`."""
+    mask = np.isnan(arr).any(axis=1)
+    return arr[~mask]
+
+
+def remove_nan_cols(arr):
+    """Remove all columns with `nan` values in `arr`."""
+    mask = np.isnan(arr).any(axis=0)
+    return arr[~mask]
+
+
+def replace_in(arr, replacement_dict, inplace=False):
+    """Replace the keys found in `arr` with the values from
+    the `replacement_dict`.
     """
-    if isinstance(user_distribution, dict):
+    if inplace:
-        if "num_classes" in user_distribution.keys():
+        new_arr = arr
-            num_classes = int(user_distribution["num_classes"])
-            per_class = int(user_distribution["per_class"])
-            return distribution_from_list([per_class] * num_classes, clabels)
-        else:
-            return user_distribution
-    elif isinstance(user_distribution, tuple):
-        assert len(user_distribution) == 2
-        num_classes, per_class = user_distribution
-        num_classes, per_class = int(num_classes), int(per_class)
-        return distribution_from_list([per_class] * num_classes, clabels)
-    elif isinstance(user_distribution, list):
-        return distribution_from_list(user_distribution, clabels)
     else:
-        msg = f"`distribution` was not understood." \
+        import copy
-            f"You have provided: {user_distribution}."
+
-        raise ValueError(msg)
+        new_arr = copy.deepcopy(arr)
+    for k, v in replacement_dict.items():
+        new_arr[arr == k] = v
+    return new_arr
 
 
-def parse_data_arg(data_arg: Union[Dataset, DataLoader, list, tuple]):
+def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
-    """Return data and target as torch tensors."""
+    """Split a classification dataset in such a way so as to
-    if isinstance(data_arg, Dataset):
+    preserve the class distribution in subsamples of the dataset.
-        if hasattr(data_arg, "__len__"):
+    """
-            ds_size = len(data_arg)  # type: ignore
+    if train + val > 1.0:
-            loader = DataLoader(data_arg, batch_size=ds_size)
+        raise ValueError("Invalid split values for train and val.")
-            data, targets = next(iter(loader))
+    Y = data[:, -1]
-        else:
+    labels = set(Y)
-            emsg = f"Dataset {data_arg} is not sized (`__len__` unimplemented)."
+    hist = dict()
-            raise TypeError(emsg)
+    for l in labels:
+        data_l = data[Y == l]
+        nl = len(data_l)
+        nl_train = int(nl * train)
+        nl_val = int(nl * val)
+        nl_test = nl - (nl_train + nl_val)
+        hist[l] = (nl_train, nl_val, nl_test)
 
-    elif isinstance(data_arg, DataLoader):
+    train_data = list()
-        data = torch.tensor([])
+    val_data = list()
-        targets = torch.tensor([])
+    test_data = list()
-        for x, y in data_arg:
+    for l, (nl_train, nl_val, nl_test) in hist.items():
-            data = torch.cat([data, x])
+        data_l = data[Y == l]
-            targets = torch.cat([targets, y])
+        if shuffle:
-    else:
+            np.random.shuffle(data_l)
-        assert len(data_arg) == 2
+        train_l = data_l[:nl_train]
-        data, targets = data_arg
+        val_l = data_l[nl_train:nl_train + nl_val]
-        if not isinstance(data, torch.Tensor):
+        test_l = data_l[nl_train + nl_val:nl_train + nl_val + nl_test]
-            wmsg = f"Converting data to {torch.Tensor}..."
+        train_data.append(train_l)
-            warnings.warn(wmsg)
+        val_data.append(val_l)
-            data = torch.Tensor(data)
+        test_data.append(test_l)
-        if not isinstance(targets, torch.LongTensor):
+
-            wmsg = f"Converting targets to {torch.LongTensor}..."
+    def _squash(data_list):
-            warnings.warn(wmsg)
+        data = np.array(data_list[0])
-            targets = torch.LongTensor(targets)
+        for item in data_list[1:]:
-    return data, targets
+            data = np.vstack((data, np.array(item)))
+        return data
+
+    train_data = _squash(train_data)
+    if val_data:
+        val_data = _squash(val_data)
+    if test_data:
+        test_data = _squash(test_data)
+    if return_xy:
+        x_train = train_data[:, :-1]
+        y_train = train_data[:, -1]
+        x_val = val_data[:, :-1]
+        y_val = val_data[:, -1]
+        x_test = test_data[:, :-1]
+        y_test = test_data[:, -1]
+        return (x_train, y_train), (x_val, y_val), (x_test, y_test)
+    return train_data, val_data, test_data
+
+
+def class_histogram(data, title="Untitled"):
+    plt.figure(title)
+    plt.clf()
+    plt.title(title)
+    dist, counts = np.unique(data[:, -1], return_counts=True)
+    plt.bar(dist, counts)
+    plt.xticks(dist)
+    print("Call matplotlib.pyplot.show() to see the plot.")
+
+
+def ntimer(n=10):
+    """Wraps a function which wraps another function to time it."""
+    if n < 1:
+        raise (Exception(f"Invalid n = {n} given."))
+
+    def timer(func):
+        """Wraps `func` with a timer and returns the wrapped `func`."""
+        def wrapper(*args, **kwargs):
+            rv = None
+            before = time()
+            for _ in range(n):
+                rv = func(*args, **kwargs)
+            after = time()
+            elapsed = after - before
+            print(f"Elapsed: {elapsed*1e3:02.02f} ms")
+            return rv
+
+        return wrapper
+
+    return timer
+
+
+def memoize(verbose=True):
+    """Wraps a function which wraps another function that memoizes."""
+    def memoizer(func):
+        """Memoize (cache) return values of `func`.
+        Wraps `func` and returns the wrapped `func` so that `func`
+        is executed when the results are not available in the cache.
+        """
+        cache = {}
+
+        def wrapper(*args, **kwargs):
+            t = (pickle.dumps(args), pickle.dumps(kwargs))
+            if t not in cache:
+                if verbose:
+                    print(f"Adding NEW rv {func.__name__}{args}{kwargs} "
+                          "to cache.")
+                cache[t] = func(*args, **kwargs)
+            else:
+                if verbose:
+                    print(f"Using OLD rv {func.__name__}{args}{kwargs} "
+                          "from cache.")
+            return cache[t]
+
+        return wrapper
+
+    return memoizer

setup.cfg

@@ -1,16 +0,0 @@
-[pylint]
-disable =
-	too-many-arguments,
-	too-few-public-methods,
-	fixme,
-
-
-[pycodestyle]
-max-line-length = 79
-
-[isort]
-multi_line_output = 3
-include_trailing_comma = True
-force_grid_wrap = 3
-use_parentheses = True
-line_length = 79

setup.py

@@ -1,12 +1,10 @@
 """
-
+  _____           _     _______             _
- ######
+ |  __ \         | |   |__   __|           | |
- #     # #####   ####  #####  ####  #####  ####  #####   ####  #    #
+ | |__) | __ ___ | |_ ___ | | ___  _ __ ___| |__
- #     # #    # #    #   #   #    #   #   #    # #    # #    # #    #
+ |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \
- ######  #    # #    #   #   #    #   #   #    # #    # #      ######
+ | |   | | | (_) | || (_) | | (_) | | | (__| | | |
- #       #####  #    #   #   #    #   #   #    # #####  #      #    #
+ |_|   |_|  \___/ \__\___/|_|\___/|_|  \___|_| |_|
- #       #   #  #    #   #   #    #   #   #    # #   #  #    # #    #
- #       #    #  ####    #    ####    #    ####  #    #  ####  #    #
 
 ProtoTorch Core Package
 """
@@ -15,43 +13,36 @@ from setuptools import find_packages, setup
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
 
-with open("README.md", encoding="utf-8") as fh:
+with open("README.md", "r") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "torch>=2.0.0",
+    "torch>=1.3.1",
-    "torchvision",
+    "torchvision>=0.5.0",
-    "numpy",
+    "numpy>=1.9.1",
-    "scikit-learn",
-    "matplotlib",
 ]
 DATASETS = [
     "requests",
     "tqdm",
 ]
-DEV = [
+DEV = ["bumpversion"]
-    "bump2version",
-    "pre-commit",
-]
 DOCS = [
     "recommonmark",
     "sphinx",
     "sphinx_rtd_theme",
     "sphinxcontrib-katex",
-    "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
+    "sklearn",
+    "matplotlib",
     "torchinfo",
 ]
-TESTS = [
+TESTS = ["codecov", "pytest"]
-    "flake8",
-    "pytest",
-]
 ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.7.6",
+    version="0.4.2",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -62,33 +53,30 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
-    python_requires=">=3.8",
     install_requires=INSTALL_REQUIRES,
     extras_require={
-        "datasets": DATASETS,
-        "dev": DEV,
         "docs": DOCS,
+        "datasets": DATASETS,
         "examples": EXAMPLES,
         "tests": TESTS,
         "all": ALL,
     },
     classifiers=[
+        "Development Status :: 2 - Pre-Alpha",
         "Environment :: Console",
-        "Natural Language :: English",
-        "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: MIT License",
+        "Natural Language :: English",
+        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Software Development :: Libraries",
         "Topic :: Software Development :: Libraries :: Python Modules",
-        "License :: OSI Approved :: MIT License",
-        "Operating System :: OS Independent",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.11",
     ],
     packages=find_packages(),
     zip_safe=False,

tests/test_core.py

@@ -1,777 +0,0 @@
-"""ProtoTorch core test suite"""
-
-import unittest
-
-import numpy as np
-import pytest
-import torch
-
-import prototorch as pt
-from prototorch.utils import parse_distribution
-
-
-# Utils
-def test_parse_distribution_dict_0():
-    distribution = {"num_classes": 1, "per_class": 0}
-    distribution = parse_distribution(distribution)
-    assert distribution == {0: 0}
-
-
-def test_parse_distribution_dict_1():
-    distribution = dict(num_classes=3, per_class=2)
-    distribution = parse_distribution(distribution)
-    assert distribution == {0: 2, 1: 2, 2: 2}
-
-
-def test_parse_distribution_dict_2():
-    distribution = {0: 1, 2: 2, -1: 3}
-    distribution = parse_distribution(distribution)
-    assert distribution == {0: 1, 2: 2, -1: 3}
-
-
-def test_parse_distribution_tuple():
-    distribution = (2, 3)
-    distribution = parse_distribution(distribution)
-    assert distribution == {0: 3, 1: 3}
-
-
-def test_parse_distribution_list():
-    distribution = [1, 1, 0, 2]
-    distribution = parse_distribution(distribution)
-    assert distribution == {0: 1, 1: 1, 2: 0, 3: 2}
-
-
-def test_parse_distribution_custom_labels():
-    distribution = [1, 1, 0, 2]
-    clabels = [1, 2, 5, 3]
-    distribution = parse_distribution(distribution, clabels)
-    assert distribution == {1: 1, 2: 1, 5: 0, 3: 2}
-
-
-# Components initializers
-def test_literal_comp_generate():
-    protos = torch.rand(4, 3, 5, 5)
-    c = pt.initializers.LiteralCompInitializer(protos)
-    components = c.generate([])
-    assert torch.allclose(components, protos)
-
-
-def test_literal_comp_generate_from_list():
-    protos = [[0, 1], [2, 3], [4, 5]]
-    c = pt.initializers.LiteralCompInitializer(protos)
-    with pytest.warns(UserWarning):
-        components = c.generate([])
-    assert torch.allclose(components, torch.Tensor(protos))
-
-
-def test_shape_aware_raises_error():
-    with pytest.raises(TypeError):
-        _ = pt.initializers.ShapeAwareCompInitializer(shape=(2, ))
-
-
-def test_data_aware_comp_generate():
-    protos = torch.rand(4, 3, 5, 5)
-    c = pt.initializers.DataAwareCompInitializer(protos)
-    components = c.generate(num_components="IgnoreMe!")
-    assert torch.allclose(components, protos)
-
-
-def test_class_aware_comp_generate():
-    protos = torch.rand(4, 2, 3, 5, 5)
-    plabels = torch.tensor([0, 0, 1, 1]).long()
-    c = pt.initializers.ClassAwareCompInitializer([protos, plabels])
-    components = c.generate(distribution=[])
-    assert torch.allclose(components, protos)
-
-
-def test_zeros_comp_generate():
-    shape = (3, 5, 5)
-    c = pt.initializers.ZerosCompInitializer(shape)
-    components = c.generate(num_components=4)
-    assert torch.allclose(components, torch.zeros(4, 3, 5, 5))
-
-
-def test_ones_comp_generate():
-    c = pt.initializers.OnesCompInitializer(2)
-    components = c.generate(num_components=3)
-    assert torch.allclose(components, torch.ones(3, 2))
-
-
-def test_fill_value_comp_generate():
-    c = pt.initializers.FillValueCompInitializer(2, 0.0)
-    components = c.generate(num_components=3)
-    assert torch.allclose(components, torch.zeros(3, 2))
-
-
-def test_uniform_comp_generate_min_max_bound():
-    c = pt.initializers.UniformCompInitializer(2, -1.0, 1.0)
-    components = c.generate(num_components=1024)
-    assert components.min() >= -1.0
-    assert components.max() <= 1.0
-
-
-def test_random_comp_generate_mean():
-    c = pt.initializers.RandomNormalCompInitializer(2, -1.0)
-    components = c.generate(num_components=1024)
-    assert torch.allclose(components.mean(),
-                          torch.tensor(-1.0),
-                          rtol=1e-05,
-                          atol=1e-01)
-
-
-def test_comp_generate_0_components():
-    c = pt.initializers.ZerosCompInitializer(2)
-    _ = c.generate(num_components=0)
-
-
-def test_stratified_mean_comp_generate():
-    # yapf: disable
-    x = torch.Tensor(
-        [[0,  -1, -2],
-         [10, 11, 12],
-         [0,   0,  0],
-         [2,   2,  2]])
-    y = torch.LongTensor([0, 0, 1, 1])
-    desired = torch.Tensor(
-        [[5.0, 5.0, 5.0],
-         [1.0, 1.0, 1.0]])
-    # yapf: enable
-    c = pt.initializers.StratifiedMeanCompInitializer(data=[x, y])
-    actual = c.generate([1, 1])
-    assert torch.allclose(actual, desired)
-
-
-def test_stratified_selection_comp_generate():
-    # yapf: disable
-    x = torch.Tensor(
-        [[0, 0, 0],
-         [1, 1, 1],
-         [0, 0, 0],
-         [1, 1, 1]])
-    y = torch.LongTensor([0, 1, 0, 1])
-    desired = torch.Tensor(
-        [[0, 0, 0],
-         [1, 1, 1]])
-    # yapf: enable
-    c = pt.initializers.StratifiedSelectionCompInitializer(data=[x, y])
-    actual = c.generate([1, 1])
-    assert torch.allclose(actual, desired)
-
-
-# Labels initializers
-def test_literal_labels_init():
-    l = pt.initializers.LiteralLabelsInitializer([0, 0, 1, 2])
-    with pytest.warns(UserWarning):
-        labels = l.generate([])
-    assert torch.allclose(labels, torch.LongTensor([0, 0, 1, 2]))
-
-
-def test_labels_init_from_list():
-    l = pt.initializers.LabelsInitializer()
-    components = l.generate(distribution=[1, 1, 1])
-    assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
-
-
-def test_labels_init_from_tuple_legal():
-    l = pt.initializers.LabelsInitializer()
-    components = l.generate(distribution=(3, 1))
-    assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
-
-
-def test_labels_init_from_tuple_illegal():
-    l = pt.initializers.LabelsInitializer()
-    with pytest.raises(AssertionError):
-        _ = l.generate(distribution=(1, 1, 1))
-
-
-def test_data_aware_labels_init():
-    data, targets = [0, 1, 2, 3], [0, 0, 1, 1]
-    ds = pt.datasets.NumpyDataset(data, targets)
-    l = pt.initializers.DataAwareLabelsInitializer(ds)
-    labels = l.generate([])
-    assert torch.allclose(labels, torch.LongTensor(targets))
-
-
-# Reasonings initializers
-def test_literal_reasonings_init():
-    r = pt.initializers.LiteralReasoningsInitializer([0, 0, 1, 2])
-    with pytest.warns(UserWarning):
-        reasonings = r.generate([])
-    assert torch.allclose(reasonings, torch.Tensor([0, 0, 1, 2]))
-
-
-def test_random_reasonings_init():
-    r = pt.initializers.RandomReasoningsInitializer(0.2, 0.8)
-    reasonings = r.generate(distribution=[0, 1])
-    assert torch.numel(reasonings) == 1 * 2 * 2
-    assert reasonings.min() >= 0.2
-    assert reasonings.max() <= 0.8
-
-
-def test_zeros_reasonings_init():
-    r = pt.initializers.ZerosReasoningsInitializer()
-    reasonings = r.generate(distribution=[0, 1])
-    assert torch.allclose(reasonings, torch.zeros(1, 2, 2))
-
-
-def test_ones_reasonings_init():
-    r = pt.initializers.ZerosReasoningsInitializer()
-    reasonings = r.generate(distribution=[1, 2, 3])
-    assert torch.allclose(reasonings, torch.zeros(6, 3, 2))
-
-
-def test_pure_positive_reasonings_init_one_per_class():
-    r = pt.initializers.PurePositiveReasoningsInitializer(
-        components_first=False)
-    reasonings = r.generate(distribution=(4, 1))
-    assert torch.allclose(reasonings[0], torch.eye(4))
-
-
-def test_pure_positive_reasonings_init_unrepresented_classes():
-    r = pt.initializers.PurePositiveReasoningsInitializer()
-    reasonings = r.generate(distribution=[9, 0, 0, 0])
-    assert reasonings.shape[0] == 9
-    assert reasonings.shape[1] == 4
-    assert reasonings.shape[2] == 2
-
-
-def test_random_reasonings_init_channels_not_first():
-    r = pt.initializers.RandomReasoningsInitializer(components_first=False)
-    reasonings = r.generate(distribution=[0, 0, 0, 1])
-    assert reasonings.shape[0] == 2
-    assert reasonings.shape[1] == 4
-    assert reasonings.shape[2] == 1
-
-
-# Transform initializers
-def test_eye_transform_init_square():
-    t = pt.initializers.EyeLinearTransformInitializer()
-    I = t.generate(3, 3)
-    assert torch.allclose(I, torch.eye(3))
-
-
-def test_eye_transform_init_narrow():
-    t = pt.initializers.EyeLinearTransformInitializer()
-    actual = t.generate(3, 2)
-    desired = torch.Tensor([[1, 0], [0, 1], [0, 0]])
-    assert torch.allclose(actual, desired)
-
-
-def test_eye_transform_init_wide():
-    t = pt.initializers.EyeLinearTransformInitializer()
-    actual = t.generate(2, 3)
-    desired = torch.Tensor([[1, 0, 0], [0, 1, 0]])
-    assert torch.allclose(actual, desired)
-
-
-# Transforms
-def test_linear_transform_default_eye_init():
-    l = pt.transforms.LinearTransform(2, 4)
-    actual = l.weights
-    desired = torch.Tensor([[1, 0, 0, 0], [0, 1, 0, 0]])
-    assert torch.allclose(actual, desired)
-
-
-def test_linear_transform_forward():
-    l = pt.transforms.LinearTransform(4, 2)
-    actual_weights = l.weights
-    desired_weights = torch.Tensor([[1, 0], [0, 1], [0, 0], [0, 0]])
-    assert torch.allclose(actual_weights, desired_weights)
-    actual_outputs = l(torch.Tensor([[1.1, 2.2, 3.3, 4.4], \
-                                     [1.1, 2.2, 3.3, 4.4], \
-                                     [5.5, 6.6, 7.7, 8.8]]))
-    desired_outputs = torch.Tensor([[1.1, 2.2], [1.1, 2.2], [5.5, 6.6]])
-    assert torch.allclose(actual_outputs, desired_outputs)
-
-
-def test_linear_transform_zeros_init():
-    l = pt.transforms.LinearTransform(
-        in_dim=2,
-        out_dim=4,
-        initializer=pt.initializers.ZerosLinearTransformInitializer(),
-    )
-    actual = l.weights
-    desired = torch.zeros(2, 4)
-    assert torch.allclose(actual, desired)
-
-
-def test_linear_transform_out_dim_first():
-    l = pt.transforms.LinearTransform(
-        in_dim=2,
-        out_dim=4,
-        initializer=pt.initializers.OLTI(out_dim_first=True),
-    )
-    assert l.weights.shape[0] == 4
-    assert l.weights.shape[1] == 2
-
-
-# Components
-def test_components_no_initializer():
-    with pytest.raises(TypeError):
-        _ = pt.components.Components(3, None)
-
-
-def test_components_no_num_components():
-    with pytest.raises(TypeError):
-        _ = pt.components.Components(initializer=pt.initializers.OCI(2))
-
-
-def test_components_none_num_components():
-    with pytest.raises(TypeError):
-        _ = pt.components.Components(None, initializer=pt.initializers.OCI(2))
-
-
-def test_components_no_args():
-    with pytest.raises(TypeError):
-        _ = pt.components.Components()
-
-
-def test_components_zeros_init():
-    c = pt.components.Components(3, pt.initializers.ZCI(2))
-    assert torch.allclose(c.components, torch.zeros(3, 2))
-
-
-def test_labeled_components_dict_init():
-    c = pt.components.LabeledComponents({0: 3}, pt.initializers.OCI(2))
-    assert torch.allclose(c.components, torch.ones(3, 2))
-    assert torch.allclose(c.labels, torch.zeros(3, dtype=torch.long))
-
-
-def test_labeled_components_list_init():
-    c = pt.components.LabeledComponents([3], pt.initializers.OCI(2))
-    assert torch.allclose(c.components, torch.ones(3, 2))
-    assert torch.allclose(c.labels, torch.zeros(3, dtype=torch.long))
-
-
-def test_labeled_components_tuple_init():
-    c = pt.components.LabeledComponents({0: 1, 1: 2}, pt.initializers.OCI(2))
-    assert torch.allclose(c.components, torch.ones(3, 2))
-    assert torch.allclose(c.labels, torch.LongTensor([0, 1, 1]))
-
-
-# Labels
-def test_standalone_labels_dict_init():
-    l = pt.components.Labels({0: 3})
-    assert torch.allclose(l.labels, torch.zeros(3, dtype=torch.long))
-
-
-def test_standalone_labels_list_init():
-    l = pt.components.Labels([3])
-    assert torch.allclose(l.labels, torch.zeros(3, dtype=torch.long))
-
-
-def test_standalone_labels_tuple_init():
-    l = pt.components.Labels({0: 1, 1: 2})
-    assert torch.allclose(l.labels, torch.LongTensor([0, 1, 1]))
-
-
-# Losses
-def test_glvq_loss_int_labels():
-    d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
-    labels = torch.tensor([0, 1])
-    targets = torch.ones(100)
-    batch_loss = pt.losses.glvq_loss(distances=d,
-                                     target_labels=targets,
-                                     prototype_labels=labels)
-    loss_value = torch.sum(batch_loss, dim=0)
-    assert loss_value == -100
-
-
-def test_glvq_loss_one_hot_labels():
-    d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
-    labels = torch.tensor([[0, 1], [1, 0]])
-    wl = torch.tensor([1, 0])
-    targets = torch.stack([wl for _ in range(100)], dim=0)
-    batch_loss = pt.losses.glvq_loss(distances=d,
-                                     target_labels=targets,
-                                     prototype_labels=labels)
-    loss_value = torch.sum(batch_loss, dim=0)
-    assert loss_value == -100
-
-
-def test_glvq_loss_one_hot_unequal():
-    dlist = [torch.ones(100), torch.zeros(100), torch.zeros(100)]
-    d = torch.stack(dlist, dim=1)
-    labels = torch.tensor([[0, 1], [1, 0], [1, 0]])
-    wl = torch.tensor([1, 0])
-    targets = torch.stack([wl for _ in range(100)], dim=0)
-    batch_loss = pt.losses.glvq_loss(distances=d,
-                                     target_labels=targets,
-                                     prototype_labels=labels)
-    loss_value = torch.sum(batch_loss, dim=0)
-    assert loss_value == -100
-
-
-# Activations
-class TestActivations(unittest.TestCase):
-
-    def setUp(self):
-        self.flist = ["identity", "sigmoid_beta", "swish_beta"]
-        self.x = torch.randn(1024, 1)
-
-    def test_registry(self):
-        self.assertIsNotNone(pt.nn.ACTIVATIONS)
-
-    def test_funcname_deserialization(self):
-        for funcname in self.flist:
-            f = pt.nn.get_activation(funcname)
-            iscallable = callable(f)
-            self.assertTrue(iscallable)
-
-    def test_callable_deserialization(self):
-
-        def dummy(x, **kwargs):
-            return x
-
-        for f in [dummy, lambda x: x]:
-            f = pt.nn.get_activation(f)
-            iscallable = callable(f)
-            self.assertTrue(iscallable)
-            self.assertEqual(1, f(1))
-
-    def test_unknown_deserialization(self):
-        for funcname in ["blubb", "foobar"]:
-            with self.assertRaises(NameError):
-                _ = pt.nn.get_activation(funcname)
-
-    def test_identity(self):
-        actual = pt.nn.identity(self.x)
-        desired = self.x
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_sigmoid_beta1(self):
-        actual = pt.nn.sigmoid_beta(self.x, beta=1.0)
-        desired = torch.sigmoid(self.x)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_swish_beta1(self):
-        actual = pt.nn.swish_beta(self.x, beta=1.0)
-        desired = self.x * torch.sigmoid(self.x)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        del self.x
-
-
-# Competitions
-class TestCompetitions(unittest.TestCase):
-
-    def setUp(self):
-        pass
-
-    def test_wtac(self):
-        d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
-        labels = torch.tensor([0, 1, 2, 3])
-        competition_layer = pt.competitions.WTAC()
-        actual = competition_layer(d, labels)
-        desired = torch.tensor([2, 0])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_wtac_unequal_dist(self):
-        d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
-        labels = torch.tensor([0, 1, 1])
-        competition_layer = pt.competitions.WTAC()
-        actual = competition_layer(d, labels)
-        desired = torch.tensor([0, 1])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_wtac_one_hot(self):
-        d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
-        labels = torch.tensor([[0, 1], [1, 0]])
-        competition_layer = pt.competitions.WTAC()
-        actual = competition_layer(d, labels)
-        desired = torch.tensor([[0, 1], [1, 0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_knnc_k1(self):
-        d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
-        labels = torch.tensor([0, 1, 2, 3])
-        competition_layer = pt.competitions.KNNC(k=1)
-        actual = competition_layer(d, labels)
-        desired = torch.tensor([2, 0])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        pass
-
-
-# Pooling
-class TestPooling(unittest.TestCase):
-
-    def setUp(self):
-        pass
-
-    def test_stratified_min(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
-        labels = torch.tensor([0, 0, 1, 2])
-        pooling_layer = pt.pooling.StratifiedMinPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_min_one_hot(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
-        labels = torch.tensor([0, 0, 1, 2])
-        labels = torch.eye(3)[labels]
-        pooling_layer = pt.pooling.StratifiedMinPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_min_trivial(self):
-        d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
-        labels = torch.tensor([0, 1, 2])
-        pooling_layer = pt.pooling.StratifiedMinPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_max(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
-        labels = torch.tensor([0, 0, 3, 2, 0])
-        pooling_layer = pt.pooling.StratifiedMaxPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_max_one_hot(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
-        labels = torch.tensor([0, 0, 2, 1, 0])
-        labels = torch.nn.functional.one_hot(labels, num_classes=3)
-        pooling_layer = pt.pooling.StratifiedMaxPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_sum(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
-        labels = torch.LongTensor([0, 0, 1, 2])
-        pooling_layer = pt.pooling.StratifiedSumPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_sum_one_hot(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
-        labels = torch.tensor([0, 0, 1, 2])
-        labels = torch.eye(3)[labels]
-        pooling_layer = pt.pooling.StratifiedSumPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_prod(self):
-        d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
-        labels = torch.tensor([0, 0, 3, 2, 0])
-        pooling_layer = pt.pooling.StratifiedProdPooling()
-        actual = pooling_layer(d, labels)
-        desired = torch.tensor([[0.0, 3.0, 2.0], [504.0, 1.0, 0.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        pass
-
-
-# Distances
-class TestDistances(unittest.TestCase):
-
-    def setUp(self):
-        self.nx, self.mx = 32, 2048
-        self.ny, self.my = 8, 2048
-        self.x = torch.randn(self.nx, self.mx)
-        self.y = torch.randn(self.ny, self.my)
-
-    def test_manhattan(self):
-        actual = pt.distances.lpnorm_distance(self.x, self.y, p=1)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=1,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def test_euclidean(self):
-        actual = pt.distances.euclidean_distance(self.x, self.y)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=2,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=3)
-        self.assertIsNone(mismatch)
-
-    def test_squared_euclidean(self):
-        actual = pt.distances.squared_euclidean_distance(self.x, self.y)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = (torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=2,
-                    keepdim=False,
-                )**2)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_p0(self):
-        actual = pt.distances.lpnorm_distance(self.x, self.y, p=0)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=0,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_p2(self):
-        actual = pt.distances.lpnorm_distance(self.x, self.y, p=2)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=2,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_p3(self):
-        actual = pt.distances.lpnorm_distance(self.x, self.y, p=3)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=3,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_pinf(self):
-        actual = pt.distances.lpnorm_distance(self.x, self.y, p=float("inf"))
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=float("inf"),
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_omega_identity(self):
-        omega = torch.eye(self.mx, self.my)
-        actual = pt.distances.omega_distance(self.x, self.y, omega=omega)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = (torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=2,
-                    keepdim=False,
-                )**2)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def test_lomega_identity(self):
-        omega = torch.eye(self.mx, self.my)
-        omegas = torch.stack([omega for _ in range(self.ny)], dim=0)
-        actual = pt.distances.lomega_distance(self.x, self.y, omegas=omegas)
-        desired = torch.empty(self.nx, self.ny)
-        for i in range(self.nx):
-            for j in range(self.ny):
-                desired[i][j] = (torch.nn.functional.pairwise_distance(
-                    self.x[i].reshape(1, -1),
-                    self.y[j].reshape(1, -1),
-                    p=2,
-                    keepdim=False,
-                )**2)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        del self.x, self.y

tests/test_datasets.py

@@ -1,186 +1,95 @@
-"""ProtoTorch datasets test suite"""
+"""ProtoTorch datasets test suite."""
 
 import os
+import shutil
 import unittest
 
-import numpy as np
 import torch
 
-import prototorch as pt
+from prototorch.datasets import abstract, tecator
-from prototorch.datasets.abstract import Dataset, ProtoDataset
 
 
 class TestAbstract(unittest.TestCase):
-
-    def setUp(self):
-        self.ds = Dataset("./artifacts")
-
     def test_getitem(self):
         with self.assertRaises(NotImplementedError):
-            _ = self.ds[0]
+            abstract.Dataset("./artifacts")[0]
 
     def test_len(self):
         with self.assertRaises(NotImplementedError):
-            _ = len(self.ds)
+            len(abstract.Dataset("./artifacts"))
-
-    def tearDown(self):
-        del self.ds
 
 
 class TestProtoDataset(unittest.TestCase):
+    def test_getitem(self):
+        with self.assertRaises(NotImplementedError):
+            abstract.ProtoDataset("./artifacts")[0]
 
     def test_download(self):
         with self.assertRaises(NotImplementedError):
-            _ = ProtoDataset("./artifacts", download=True)
+            abstract.ProtoDataset("./artifacts").download()
-
-    def test_exists(self):
-        with self.assertRaises(RuntimeError):
-            _ = ProtoDataset("./artifacts", download=False)
 
 
-class TestNumpyDataset(unittest.TestCase):
+class TestTecator(unittest.TestCase):
-
-    def test_list_init(self):
-        ds = pt.datasets.NumpyDataset([1], [1])
-        self.assertEqual(len(ds), 1)
-
-    def test_numpy_init(self):
-        data = np.random.randn(3, 2)
-        targets = np.array([0, 1, 2])
-        ds = pt.datasets.NumpyDataset(data, targets)
-        self.assertEqual(len(ds), 3)
-
-
-class TestCSVDataset(unittest.TestCase):
-
     def setUp(self):
-        data = np.random.rand(100, 4)
+        self.artifacts_dir = "./artifacts/Tecator"
-        targets = np.random.randint(2, size=(100, 1))
+        self._remove_artifacts()
-        arr = np.hstack([data, targets])
-        if not os.path.exists("./artifacts"):
-            os.mkdir("./artifacts")
-        np.savetxt("./artifacts/test.csv", arr, delimiter=",")
 
-    def test_len(self):
+    def _remove_artifacts(self):
-        ds = pt.datasets.CSVDataset("./artifacts/test.csv")
+        if os.path.exists(self.artifacts_dir):
-        self.assertEqual(len(ds), 100)
+            shutil.rmtree(self.artifacts_dir)
+
+    def test_download_false(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        self._remove_artifacts()
+        with self.assertRaises(RuntimeError):
+            _ = tecator.Tecator(rootdir, download=False)
+
+    def test_download_caching(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        _ = tecator.Tecator(rootdir, download=True, verbose=False)
+        _ = tecator.Tecator(rootdir, download=False, verbose=False)
+
+    def test_repr(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        train = tecator.Tecator(rootdir, download=True, verbose=True)
+        self.assertTrue("Split: Train" in train.__repr__())
+
+    def test_download_train(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        train = tecator.Tecator(root=rootdir,
+                                train=True,
+                                download=True,
+                                verbose=False)
+        train = tecator.Tecator(root=rootdir, download=True, verbose=False)
+        x_train, y_train = train.data, train.targets
+        self.assertEqual(x_train.shape[0], 144)
+        self.assertEqual(y_train.shape[0], 144)
+        self.assertEqual(x_train.shape[1], 100)
+
+    def test_download_test(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        x_test, y_test = test.data, test.targets
+        self.assertEqual(x_test.shape[0], 71)
+        self.assertEqual(y_test.shape[0], 71)
+        self.assertEqual(x_test.shape[1], 100)
+
+    def test_class_to_idx(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        _ = test.class_to_idx
+
+    def test_getitem(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        x, y = test[0]
+        self.assertEqual(x.shape[0], 100)
+        self.assertIsInstance(y, int)
+
+    def test_loadable_with_dataloader(self):
+        rootdir = self.artifacts_dir.rpartition("/")[0]
+        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
 
     def tearDown(self):
-        os.remove("./artifacts/test.csv")
+        pass
-
-
-class TestSpiral(unittest.TestCase):
-
-    def test_init(self):
-        ds = pt.datasets.Spiral(num_samples=10)
-        self.assertEqual(len(ds), 10)
-
-
-class TestIris(unittest.TestCase):
-
-    def setUp(self):
-        self.ds = pt.datasets.Iris()
-
-    def test_size(self):
-        self.assertEqual(len(self.ds), 150)
-
-    def test_dims(self):
-        self.assertEqual(self.ds.data.shape[1], 4)
-
-    def test_dims_selection(self):
-        ds = pt.datasets.Iris(dims=[0, 1])
-        self.assertEqual(ds.data.shape[1], 2)
-
-
-class TestBlobs(unittest.TestCase):
-
-    def test_size(self):
-        ds = pt.datasets.Blobs(num_samples=10)
-        self.assertEqual(len(ds), 10)
-
-
-class TestRandom(unittest.TestCase):
-
-    def test_size(self):
-        ds = pt.datasets.Random(num_samples=10)
-        self.assertEqual(len(ds), 10)
-
-
-class TestCircles(unittest.TestCase):
-
-    def test_size(self):
-        ds = pt.datasets.Circles(num_samples=10)
-        self.assertEqual(len(ds), 10)
-
-
-class TestMoons(unittest.TestCase):
-
-    def test_size(self):
-        ds = pt.datasets.Moons(num_samples=10)
-        self.assertEqual(len(ds), 10)
-
-
-# class TestTecator(unittest.TestCase):
-#     def setUp(self):
-#         self.artifacts_dir = "./artifacts/Tecator"
-#         self._remove_artifacts()
-
-#     def _remove_artifacts(self):
-#         if os.path.exists(self.artifacts_dir):
-#             shutil.rmtree(self.artifacts_dir)
-
-#     def test_download_false(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         self._remove_artifacts()
-#         with self.assertRaises(RuntimeError):
-#             _ = pt.datasets.Tecator(rootdir, download=False)
-
-#     def test_download_caching(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         _ = pt.datasets.Tecator(rootdir, download=True, verbose=False)
-#         _ = pt.datasets.Tecator(rootdir, download=False, verbose=False)
-
-#     def test_repr(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         train = pt.datasets.Tecator(rootdir, download=True, verbose=True)
-#         self.assertTrue("Split: Train" in train.__repr__())
-
-#     def test_download_train(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         train = pt.datasets.Tecator(root=rootdir,
-#                                     train=True,
-#                                     download=True,
-#                                     verbose=False)
-#         train = pt.datasets.Tecator(root=rootdir, download=True, verbose=False)
-#         x_train, y_train = train.data, train.targets
-#         self.assertEqual(x_train.shape[0], 144)
-#         self.assertEqual(y_train.shape[0], 144)
-#         self.assertEqual(x_train.shape[1], 100)
-
-#     def test_download_test(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-#         x_test, y_test = test.data, test.targets
-#         self.assertEqual(x_test.shape[0], 71)
-#         self.assertEqual(y_test.shape[0], 71)
-#         self.assertEqual(x_test.shape[1], 100)
-
-#     def test_class_to_idx(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-#         _ = test.class_to_idx
-
-#     def test_getitem(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-#         x, y = test[0]
-#         self.assertEqual(x.shape[0], 100)
-#         self.assertIsInstance(y, int)
-
-#     def test_loadable_with_dataloader(self):
-#         rootdir = self.artifacts_dir.rpartition("/")[0]
-#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-#         _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
-
-#     def tearDown(self):
-#         self._remove_artifacts()

tests/test_functions.py

@@ -0,0 +1,526 @@
+"""ProtoTorch functions test suite."""
+
+import unittest
+
+import numpy as np
+import torch
+
+from prototorch.functions import (
+    activations,
+    competitions,
+    distances,
+    initializers,
+    losses,
+)
+
+
+class TestActivations(unittest.TestCase):
+    def setUp(self):
+        self.flist = ["identity", "sigmoid_beta", "swish_beta"]
+        self.x = torch.randn(1024, 1)
+
+    def test_registry(self):
+        self.assertIsNotNone(activations.ACTIVATIONS)
+
+    def test_funcname_deserialization(self):
+        for funcname in self.flist:
+            f = activations.get_activation(funcname)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+
+    # def test_torch_script(self):
+    #     for funcname in self.flist:
+    #         f = activations.get_activation(funcname)
+    #         self.assertIsInstance(f, torch.jit.ScriptFunction)
+
+    def test_callable_deserialization(self):
+        def dummy(x, **kwargs):
+            return x
+
+        for f in [dummy, lambda x: x]:
+            f = activations.get_activation(f)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+            self.assertEqual(1, f(1))
+
+    def test_unknown_deserialization(self):
+        for funcname in ["blubb", "foobar"]:
+            with self.assertRaises(NameError):
+                _ = activations.get_activation(funcname)
+
+    def test_identity(self):
+        actual = activations.identity(self.x)
+        desired = self.x
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_sigmoid_beta1(self):
+        actual = activations.sigmoid_beta(self.x, beta=1.0)
+        desired = torch.sigmoid(self.x)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_swish_beta1(self):
+        actual = activations.swish_beta(self.x, beta=1.0)
+        desired = self.x * torch.sigmoid(self.x)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x
+
+
+class TestCompetitions(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_wtac(self):
+        d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
+        labels = torch.tensor([0, 1, 2, 3])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_wtac_unequal_dist(self):
+        d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
+        labels = torch.tensor([0, 1, 1])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([0, 1])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_wtac_one_hot(self):
+        d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
+        labels = torch.tensor([[0, 1], [1, 0]])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([[0, 1], [1, 0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_min(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
+        labels = torch.tensor([0, 0, 1, 2])
+        actual = competitions.stratified_min(d, labels)
+        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_min_one_hot(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
+        labels = torch.tensor([0, 0, 1, 2])
+        labels = torch.eye(3)[labels]
+        actual = competitions.stratified_min(d, labels)
+        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_min_simple(self):
+        d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
+        labels = torch.tensor([0, 1, 2])
+        actual = competitions.stratified_min(d, labels)
+        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_knnc_k1(self):
+        d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
+        labels = torch.tensor([0, 1, 2, 3])
+        actual = competitions.knnc(d, labels, k=torch.tensor([1]))
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        pass
+
+
+class TestDistances(unittest.TestCase):
+    def setUp(self):
+        self.nx, self.mx = 32, 2048
+        self.ny, self.my = 8, 2048
+        self.x = torch.randn(self.nx, self.mx)
+        self.y = torch.randn(self.ny, self.my)
+
+    def test_manhattan(self):
+        actual = distances.lpnorm_distance(self.x, self.y, p=1)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=1,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def test_euclidean(self):
+        actual = distances.euclidean_distance(self.x, self.y)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=2,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=3)
+        self.assertIsNone(mismatch)
+
+    def test_squared_euclidean(self):
+        actual = distances.squared_euclidean_distance(self.x, self.y)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = (torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=2,
+                    keepdim=False,
+                )**2)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_p0(self):
+        actual = distances.lpnorm_distance(self.x, self.y, p=0)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=0,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_p2(self):
+        actual = distances.lpnorm_distance(self.x, self.y, p=2)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=2,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_p3(self):
+        actual = distances.lpnorm_distance(self.x, self.y, p=3)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=3,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_pinf(self):
+        actual = distances.lpnorm_distance(self.x, self.y, p=float("inf"))
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=float("inf"),
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_omega_identity(self):
+        omega = torch.eye(self.mx, self.my)
+        actual = distances.omega_distance(self.x, self.y, omega=omega)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = (torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=2,
+                    keepdim=False,
+                )**2)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def test_lomega_identity(self):
+        omega = torch.eye(self.mx, self.my)
+        omegas = torch.stack([omega for _ in range(self.ny)], dim=0)
+        actual = distances.lomega_distance(self.x, self.y, omegas=omegas)
+        desired = torch.empty(self.nx, self.ny)
+        for i in range(self.nx):
+            for j in range(self.ny):
+                desired[i][j] = (torch.nn.functional.pairwise_distance(
+                    self.x[i].reshape(1, -1),
+                    self.y[j].reshape(1, -1),
+                    p=2,
+                    keepdim=False,
+                )**2)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x, self.y
+
+
+class TestInitializers(unittest.TestCase):
+    def setUp(self):
+        self.flist = [
+            "zeros",
+            "ones",
+            "rand",
+            "randn",
+            "stratified_mean",
+            "stratified_random",
+        ]
+        self.x = torch.tensor(
+            [[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
+            dtype=torch.float32)
+        self.y = torch.tensor([0, 0, 1, 1])
+        self.gen = torch.manual_seed(42)
+
+    def test_registry(self):
+        self.assertIsNotNone(initializers.INITIALIZERS)
+
+    def test_funcname_deserialization(self):
+        for funcname in self.flist:
+            f = initializers.get_initializer(funcname)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+
+    def test_callable_deserialization(self):
+        def dummy(x):
+            return x
+
+        for f in [dummy, lambda x: x]:
+            f = initializers.get_initializer(f)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+            self.assertEqual(1, f(1))
+
+    def test_unknown_deserialization(self):
+        for funcname in ["blubb", "foobar"]:
+            with self.assertRaises(NameError):
+                _ = initializers.get_initializer(funcname)
+
+    def test_zeros(self):
+        pdist = torch.tensor([1, 1])
+        actual, _ = initializers.zeros(self.x, self.y, pdist)
+        desired = torch.zeros(2, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_ones(self):
+        pdist = torch.tensor([1, 1])
+        actual, _ = initializers.ones(self.x, self.y, pdist)
+        desired = torch.ones(2, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_rand(self):
+        pdist = torch.tensor([1, 1])
+        actual, _ = initializers.rand(self.x, self.y, pdist)
+        desired = torch.rand(2, 3, generator=torch.manual_seed(42))
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_randn(self):
+        pdist = torch.tensor([1, 1])
+        actual, _ = initializers.randn(self.x, self.y, pdist)
+        desired = torch.randn(2, 3, generator=torch.manual_seed(42))
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_mean_equal1(self):
+        pdist = torch.tensor([1, 1])
+        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
+        desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_random_equal1(self):
+        pdist = torch.tensor([1, 1])
+        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
+                                                   False)
+        desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_mean_equal2(self):
+        pdist = torch.tensor([2, 2])
+        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
+        desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
+                                [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_random_equal2(self):
+        pdist = torch.tensor([2, 2])
+        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
+                                                   False)
+        desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
+                                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_mean_unequal(self):
+        pdist = torch.tensor([1, 3])
+        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
+        desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
+                                [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_random_unequal(self):
+        pdist = torch.tensor([1, 3])
+        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
+                                                   False)
+        desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
+                                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_mean_unequal_one_hot(self):
+        pdist = torch.tensor([1, 3])
+        y = torch.eye(2)[self.y]
+        desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
+                                 [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
+        actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
+        desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
+        mismatch = np.testing.assert_array_almost_equal(actual1,
+                                                        desired1,
+                                                        decimal=5)
+        mismatch = np.testing.assert_array_almost_equal(actual2,
+                                                        desired2,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_random_unequal_one_hot(self):
+        pdist = torch.tensor([1, 3])
+        y = torch.eye(2)[self.y]
+        actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
+        desired1 = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
+                                 [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
+        desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
+        mismatch = np.testing.assert_array_almost_equal(actual1,
+                                                        desired1,
+                                                        decimal=5)
+        mismatch = np.testing.assert_array_almost_equal(actual2,
+                                                        desired2,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x, self.y, self.gen
+        _ = torch.seed()
+
+
+class TestLosses(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_glvq_loss_int_labels(self):
+        d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+        labels = torch.tensor([0, 1])
+        targets = torch.ones(100)
+        batch_loss = losses.glvq_loss(distances=d,
+                                      target_labels=targets,
+                                      prototype_labels=labels)
+        loss_value = torch.sum(batch_loss, dim=0)
+        self.assertEqual(loss_value, -100)
+
+    def test_glvq_loss_one_hot_labels(self):
+        d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+        labels = torch.tensor([[0, 1], [1, 0]])
+        wl = torch.tensor([1, 0])
+        targets = torch.stack([wl for _ in range(100)], dim=0)
+        batch_loss = losses.glvq_loss(distances=d,
+                                      target_labels=targets,
+                                      prototype_labels=labels)
+        loss_value = torch.sum(batch_loss, dim=0)
+        self.assertEqual(loss_value, -100)
+
+    def test_glvq_loss_one_hot_unequal(self):
+        dlist = [torch.ones(100), torch.zeros(100), torch.zeros(100)]
+        d = torch.stack(dlist, dim=1)
+        labels = torch.tensor([[0, 1], [1, 0], [1, 0]])
+        wl = torch.tensor([1, 0])
+        targets = torch.stack([wl for _ in range(100)], dim=0)
+        batch_loss = losses.glvq_loss(distances=d,
+                                      target_labels=targets,
+                                      prototype_labels=labels)
+        loss_value = torch.sum(batch_loss, dim=0)
+        self.assertEqual(loss_value, -100)
+
+    def tearDown(self):
+        pass

tests/test_kernels.py

@@ -0,0 +1,98 @@
+"""ProtoTorch kernels test suite."""
+
+import unittest
+
+import numpy as np
+import torch
+
+from prototorch.functions.distances import KernelDistance
+from prototorch.functions.kernels import ExplicitKernel, RadialBasisFunctionKernel
+
+
+class TestExplicitKernel(unittest.TestCase):
+    def setUp(self):
+        self.single_x = torch.randn(1024)
+        self.single_y = torch.randn(1024)
+
+        self.batch_x = torch.randn(32, 1024)
+        self.batch_y = torch.randn(32, 1024)
+
+    def test_single_values(self):
+        kernel = ExplicitKernel()
+        self.assertEqual(
+            kernel(self.single_x, self.single_y).shape, torch.Size([]))
+
+    def test_single_batch(self):
+        kernel = ExplicitKernel()
+        self.assertEqual(
+            kernel(self.single_x, self.batch_y).shape, torch.Size([32]))
+
+    def test_batch_single(self):
+        kernel = ExplicitKernel()
+        self.assertEqual(
+            kernel(self.batch_x, self.single_y).shape, torch.Size([32]))
+
+    def test_batch_values(self):
+        kernel = ExplicitKernel()
+        self.assertEqual(
+            kernel(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
+
+
+class TestRadialBasisFunctionKernel(unittest.TestCase):
+    def setUp(self):
+        self.single_x = torch.randn(1024)
+        self.single_y = torch.randn(1024)
+
+        self.batch_x = torch.randn(32, 1024)
+        self.batch_y = torch.randn(32, 1024)
+
+    def test_single_values(self):
+        kernel = RadialBasisFunctionKernel(1)
+        self.assertEqual(
+            kernel(self.single_x, self.single_y).shape, torch.Size([]))
+
+    def test_single_batch(self):
+        kernel = RadialBasisFunctionKernel(1)
+        self.assertEqual(
+            kernel(self.single_x, self.batch_y).shape, torch.Size([32]))
+
+    def test_batch_single(self):
+        kernel = RadialBasisFunctionKernel(1)
+        self.assertEqual(
+            kernel(self.batch_x, self.single_y).shape, torch.Size([32]))
+
+    def test_batch_values(self):
+        kernel = RadialBasisFunctionKernel(1)
+        self.assertEqual(
+            kernel(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
+
+
+class TestKernelDistance(unittest.TestCase):
+    def setUp(self):
+        self.single_x = torch.randn(1024)
+        self.single_y = torch.randn(1024)
+
+        self.batch_x = torch.randn(32, 1024)
+        self.batch_y = torch.randn(32, 1024)
+
+        self.kernel = ExplicitKernel()
+
+    def test_single_values(self):
+        distance = KernelDistance(self.kernel)
+        self.assertEqual(
+            distance(self.single_x, self.single_y).shape, torch.Size([]))
+
+    def test_single_batch(self):
+        distance = KernelDistance(self.kernel)
+        self.assertEqual(
+            distance(self.single_x, self.batch_y).shape, torch.Size([32]))
+
+    def test_batch_single(self):
+        distance = KernelDistance(self.kernel)
+        self.assertEqual(
+            distance(self.batch_x, self.single_y).shape, torch.Size([32]))
+
+    def test_batch_values(self):
+        distance = KernelDistance(self.kernel)
+        self.assertEqual(
+            distance(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))

tests/test_modules.py

@@ -0,0 +1,298 @@
+"""ProtoTorch modules test suite."""
+
+import unittest
+
+import numpy as np
+import torch
+
+from prototorch.modules import losses, prototypes
+
+
+class TestPrototypes(unittest.TestCase):
+    def setUp(self):
+        self.x = torch.tensor(
+            [[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
+            dtype=torch.float32)
+        self.y = torch.tensor([0, 0, 1, 1])
+        self.gen = torch.manual_seed(42)
+
+    def test_prototypes1d_init_without_input_dim(self):
+        with self.assertRaises(NameError):
+            _ = prototypes.Prototypes1D(nclasses=2)
+
+    def test_prototypes1d_init_without_nclasses(self):
+        with self.assertRaises(NameError):
+            _ = prototypes.Prototypes1D(input_dim=1)
+
+    def test_prototypes1d_init_with_nclasses_1(self):
+        with self.assertWarns(UserWarning):
+            _ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
+
+    def test_prototypes1d_init_without_pdist(self):
+        p1 = prototypes.Prototypes1D(
+            input_dim=6,
+            nclasses=2,
+            prototypes_per_class=4,
+            prototype_initializer="ones",
+        )
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = torch.ones(8, 6)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_init_without_data(self):
+        pdist = [2, 2]
+        p1 = prototypes.Prototypes1D(input_dim=3,
+                                     prototype_distribution=pdist,
+                                     prototype_initializer="zeros")
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = torch.zeros(4, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_proto_init_without_data(self):
+        with self.assertWarns(UserWarning):
+            _ = prototypes.Prototypes1D(
+                input_dim=3,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=None,
+            )
+
+    def test_prototypes1d_init_torch_pdist(self):
+        pdist = torch.tensor([2, 2])
+        p1 = prototypes.Prototypes1D(input_dim=3,
+                                     prototype_distribution=pdist,
+                                     prototype_initializer="zeros")
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = torch.zeros(4, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_init_without_inputdim_with_data(self):
+        _ = prototypes.Prototypes1D(
+            nclasses=2,
+            prototypes_per_class=1,
+            prototype_initializer="stratified_mean",
+            data=[[[1.0], [0.0]], [1, 0]],
+        )
+
+    def test_prototypes1d_init_with_int_data(self):
+        _ = prototypes.Prototypes1D(
+            nclasses=2,
+            prototypes_per_class=1,
+            prototype_initializer="stratified_mean",
+            data=[[[1], [0]], [1, 0]],
+        )
+
+    def test_prototypes1d_init_one_hot_without_data(self):
+        _ = prototypes.Prototypes1D(
+            input_dim=1,
+            nclasses=2,
+            prototypes_per_class=1,
+            prototype_initializer="stratified_mean",
+            data=None,
+            one_hot_labels=True,
+        )
+
+    def test_prototypes1d_init_one_hot_labels_false(self):
+        """Test if ValueError is raised when `one_hot_labels` is set to `False`
+        but the provided `data` has one-hot encoded labels.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
+                one_hot_labels=False,
+            )
+
+    def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
+        """Test if ValueError is raised when `one_hot_labels` is set to `True`
+        but the provided `data` does not contain one-hot encoded labels.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=([[0.0], [1.0]], [0, 1]),
+                one_hot_labels=True,
+            )
+
+    def test_prototypes1d_init_one_hot_labels_true(self):
+        """Test if ValueError is raised when `one_hot_labels` is set to `True`
+        but the provided `data` contains 2D targets but
+        does not contain one-hot encoded labels.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=([[0.0], [1.0]], [[0], [1]]),
+                one_hot_labels=True,
+            )
+
+    def test_prototypes1d_init_with_int_dtype(self):
+        with self.assertRaises(RuntimeError):
+            _ = prototypes.Prototypes1D(
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=[[[1], [0]], [1, 0]],
+                dtype=torch.int32,
+            )
+
+    def test_prototypes1d_inputndim_with_data(self):
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(input_dim=1,
+                                        nclasses=1,
+                                        prototypes_per_class=1,
+                                        data=[[1.0], [1]])
+
+    def test_prototypes1d_inputdim_with_data(self):
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=2,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=[[[1.0], [0.0]], [1, 0]],
+            )
+
+    def test_prototypes1d_nclasses_with_data(self):
+        """Test ValueError raise if provided `nclasses` is not the same
+        as the one computed from the provided `data`.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=1,
+                prototypes_per_class=1,
+                prototype_initializer="stratified_mean",
+                data=[[[1.0], [2.0]], [1, 2]],
+            )
+
+    def test_prototypes1d_init_with_ppc(self):
+        p1 = prototypes.Prototypes1D(data=[self.x, self.y],
+                                     prototypes_per_class=2,
+                                     prototype_initializer="zeros")
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = torch.zeros(4, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_init_with_pdist(self):
+        p1 = prototypes.Prototypes1D(
+            data=[self.x, self.y],
+            prototype_distribution=[6, 9],
+            prototype_initializer="zeros",
+        )
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = torch.zeros(15, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_func_initializer(self):
+        def my_initializer(*args, **kwargs):
+            return torch.full((2, 99), 99.0), torch.tensor([0, 1])
+
+        p1 = prototypes.Prototypes1D(
+            input_dim=99,
+            nclasses=2,
+            prototypes_per_class=1,
+            prototype_initializer=my_initializer,
+        )
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = 99 * torch.ones(2, 99)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_forward(self):
+        p1 = prototypes.Prototypes1D(data=[self.x, self.y])
+        protos, _ = p1()
+        actual = protos.detach().numpy()
+        desired = torch.ones(2, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_prototypes1d_dist_validate(self):
+        p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
+        with self.assertWarns(UserWarning):
+            _ = p1._validate_prototype_distribution()
+
+    def test_prototypes1d_validate_extra_repr_not_empty(self):
+        p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
+        rep = p1.extra_repr()
+        self.assertNotEqual(rep, "")
+
+    def tearDown(self):
+        del self.x, self.y, self.gen
+        _ = torch.seed()
+
+
+class TestLosses(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_glvqloss_init(self):
+        _ = losses.GLVQLoss(0, "swish_beta", beta=20)
+
+    def test_glvqloss_forward_1ppc(self):
+        criterion = losses.GLVQLoss(margin=0,
+                                    squashing="sigmoid_beta",
+                                    beta=100)
+        d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+        labels = torch.tensor([0, 1])
+        targets = torch.ones(100)
+        outputs = [d, labels]
+        loss = criterion(outputs, targets)
+        loss_value = loss.item()
+        self.assertAlmostEqual(loss_value, 0.0)
+
+    def test_glvqloss_forward_2ppc(self):
+        criterion = losses.GLVQLoss(margin=0,
+                                    squashing="sigmoid_beta",
+                                    beta=100)
+        d = torch.stack([
+            torch.ones(100),
+            torch.ones(100),
+            torch.zeros(100),
+            torch.ones(100)
+        ],
+                        dim=1)
+        labels = torch.tensor([0, 0, 1, 1])
+        targets = torch.ones(100)
+        outputs = [d, labels]
+        loss = criterion(outputs, targets)
+        loss_value = loss.item()
+        self.assertAlmostEqual(loss_value, 0.0)
+
+    def tearDown(self):
+        pass

tests/test_utils.py

@@ -1,47 +0,0 @@
-"""ProtoTorch utils test suite"""
-
-import numpy as np
-import torch
-
-import prototorch as pt
-
-
-def test_mesh2d_without_input():
-    mesh, xx, yy = pt.utils.mesh2d(border=2.0, resolution=10)
-    assert mesh.shape[0] == 100
-    assert mesh.shape[1] == 2
-    assert xx.shape[0] == 10
-    assert xx.shape[1] == 10
-    assert yy.shape[0] == 10
-    assert yy.shape[1] == 10
-    assert np.min(xx) == -2.0
-    assert np.max(xx) == 2.0
-    assert np.min(yy) == -2.0
-    assert np.max(yy) == 2.0
-
-
-def test_mesh2d_with_torch_input():
-    x = 10 * torch.rand(5, 2)
-    mesh, xx, yy = pt.utils.mesh2d(x, border=0.0, resolution=100)
-    assert mesh.shape[0] == 100 * 100
-    assert mesh.shape[1] == 2
-    assert xx.shape[0] == 100
-    assert xx.shape[1] == 100
-    assert yy.shape[0] == 100
-    assert yy.shape[1] == 100
-    assert np.min(xx) == x[:, 0].min()
-    assert np.max(xx) == x[:, 0].max()
-    assert np.min(yy) == x[:, 1].min()
-    assert np.max(yy) == x[:, 1].max()
-
-
-def test_hex_to_rgb():
-    red_rgb = list(pt.utils.hex_to_rgb(["#ff0000"]))[0]
-    assert red_rgb[0] == 255
-    assert red_rgb[1] == 0
-    assert red_rgb[2] == 0
-
-
-def test_rgb_to_hex():
-    blue_hex = list(pt.utils.rgb_to_hex([(0, 0, 255)]))[0]
-    assert blue_hex.lower() == "0000ff"