https://github.com/cair/regression-tsetlin-machine
Implementation of the Regression Tsetlin Machine
Science Score: 23.0%
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Repository
Implementation of the Regression Tsetlin Machine
Basic Info
- Host: GitHub
- Owner: cair
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://arxiv.org/abs/1905.04206
- Size: 409 KB
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- Stars: 10
- Watchers: 7
- Forks: 1
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Metadata Files
README.md
The Regression Tsetlin Machine
The inner inference mechanism of the Tsetlin Machine (https://arxiv.org/abs/1804.01508) is modified so that input patterns are transformed into a single continuous output, rather than to distinct categories.
This is achieved by:
- Using the conjunctive clauses of the Tsetlin Machine to capture arbitrarily complex patterns;
- Mapping these patterns to a continuous output through a novel voting and normalization mechanism; and
- Employing a feedback scheme that updates the Tsetlin Machine clauses to minimize the regression error.
Further details can be found in https://arxiv.org/abs/1905.04206.
Behaviour with noisy and noise-free data
Six datasets have been given in order to study the behaviour of the Regression Tsetlin Machine.
- Dataset I contains 2-bit feature input and the output is 100 times larger than the decimal value of the binary input (e.g., when the input is [1, 0], the output is 200). The training set consists of 8000 samples while testing set consists of 2000 samples, both without noise
- Dataset II contains the same data as Dataset I, except that the output of the training data is perturbed to introduce noise
- Dataset III has 3-bit input without noise
- Dataset IV has 3-bit input with noise
- Dataset V has 4-bit input without noise
- Dataset VI has 4-bit input with noise
Different datasets can be loaded by changing the following line in ArtificialDataDemo.py
df = np.loadtxt("2inNoNoise.txt").astype(dtype=np.float32)
The training error variation for each dataset with different number of clauses can be seen in the following figure.
Datasets without noise can be perfectly learned with a small number of clauses
Average Absolute Error on Training Data: 0.0
Average Absolute Error on Test Data: 0.0
Training and testing error for noisy data can be reduced by increasing the number of clauses and training rounds.
Owner
- Name: Centre for Artificial Intelligence Research (CAIR)
- Login: cair
- Kind: organization
- Email: cair-internal@uia.no
- Location: Grimstad, Norway
- Website: https://cair.uia.no/
- Repositories: 68
- Profile: https://github.com/cair
CAIR is a centre for research excellence on artificial intelligence at the University of Agder. We attack unsolved problems, seeking superintelligence.
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