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.

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.