Rename to Torchhd

Author: mikeheddes

README.md

@@ -24,7 +24,7 @@ Torchhd is a Python library for Hyperdimensional Computing.
 Torchhd is hosted on PyPi and Anaconda, use one of the following commands to install:
 
 ```bash
-pip install hdc
+pip install torchhd
 ```
 
 ```bash

docs/conf.py

@@ -18,7 +18,7 @@
 
 # -- Project information -----------------------------------------------------
 
-project = "hdc"
+project = "Torchhd"
 copyright = (
     "2022, Mike Heddes, Igor De Oliveira Nunes, Dheyay Desai, Pere Verges Boncompte"
 )

docs/datasets.rst

@@ -1,9 +1,9 @@
 Datasets
 ========
 
-The hdc library provides many popular built-in datasets to work with.
+The Torchhd library provides many popular built-in datasets to work with.
 
-.. currentmodule:: hdc.datasets
+.. currentmodule:: torchhd.datasets
 
 .. autosummary:: 
     :toctree: generated/

docs/embeddings.rst

@@ -1,7 +1,7 @@
 Embeddings
 ==================
 
-.. currentmodule:: hdc.embeddings
+.. currentmodule:: torchhd.embeddings
 
 .. autosummary::
     :toctree: generated/

docs/functional.rst

@@ -1,7 +1,7 @@
 Functional
 =========================
 
-.. currentmodule:: hdc.functional 
+.. currentmodule:: torchhd.functional 
 
 This module consists of the basic hypervector generation functions and operations used on hypervectors.
 

examples/emg_hand_gestures.py

@@ -12,9 +12,9 @@
 import torchmetrics
 from tqdm import tqdm
 
-from hdc import functional
-from hdc import embeddings
-from hdc.datasets import EMGHandGestures
+from torchhd import functional
+from torchhd import embeddings
+from torchhd.datasets import EMGHandGestures
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using {} device".format(device))

examples/graphhd.py

@@ -14,8 +14,8 @@
 # Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
 import torchmetrics
 
-from hdc import functional
-from hdc import embeddings
+from torchhd import functional
+from torchhd import embeddings
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using {} device".format(device))

examples/hd_hashing.py

@@ -8,7 +8,7 @@
 # Note: this example requires the mmh3 library: https://github.com/hajimes/mmh3
 import mmh3
 
-from hdc import functional
+from torchhd import functional
 
 
 class HDHashing:

examples/language_recognition.py

@@ -12,9 +12,9 @@
 import torchmetrics
 from tqdm import tqdm
 
-from hdc import functional
-from hdc import embeddings
-from hdc.datasets import EuropeanLanguages as Languages
+from torchhd import functional
+from torchhd import embeddings
+from torchhd.datasets import EuropeanLanguages as Languages
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using {} device".format(device))

examples/mnist.py

@@ -13,8 +13,8 @@
 import torchmetrics
 from tqdm import tqdm
 
-from hdc import functional
-from hdc import embeddings
+from torchhd import functional
+from torchhd import embeddings
 
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/random_projection.py

@@ -13,9 +13,9 @@
 import torchmetrics
 from tqdm import tqdm
 
-from hdc import functional
-from hdc import embeddings
-from hdc.datasets import BeijingAirQuality
+from torchhd import functional
+from torchhd import embeddings
+from torchhd.datasets import BeijingAirQuality
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using {} device".format(device))

examples/voicehd.py

@@ -11,9 +11,9 @@
 import torchmetrics
 from tqdm import tqdm
 
-from hdc import functional
-from hdc import embeddings
-from hdc.datasets.isolet import ISOLET
+from torchhd import functional
+from torchhd import embeddings
+from torchhd.datasets.isolet import ISOLET
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using {} device".format(device))