XORDemo_print.py

#!/usr/bin/python

import numpy as np
import pyximport; pyximport.install(setup_args={
                              "include_dirs":np.get_include()},
                            reload_support=True)

import XOR_print

samples = 50
X = np.random.random_integers(0,1,size=(samples,2)).astype(dtype=np.int32)
Y = np.ones([samples]).astype(dtype=np.int32)

for i in range(samples):
    if X[i,0] == X[i,1]:
        Y[i] = 0
# Parameters for the Tsetlin Machine
T = 2
s = 3.9
number_of_clauses = 5
states = 100 
Th = 1


# Parameters of the pattern recognition problem
number_of_features = 2

# Training configuration
epochs = 200

# Loading of training and test data
NoOfTrainingSamples = len(X)*80//100
NoOfTestingSamples = len(X) - NoOfTrainingSamples


X_training = X[0:NoOfTrainingSamples,:] # Input features
y_training = Y[0:NoOfTrainingSamples] # Target value

X_test = X[NoOfTrainingSamples:NoOfTrainingSamples+NoOfTestingSamples,:] # Input features
y_test = Y[NoOfTrainingSamples:NoOfTrainingSamples+NoOfTestingSamples] # Target value

# This is a multiclass variant of the Tsetlin Machine, capable of distinguishing between multiple classes
tsetlin_machine = XOR_print.TsetlinMachine(number_of_clauses, number_of_features, states, s, T, Th)

# Training of the Tsetlin Machine in batch mode. The Tsetlin Machine can also be trained online
tsetlin_machine.fit(X_training, y_training, y_training.shape[0], epochs=epochs)

# Some performacne statistics

print ("Accuracy on test data (no noise):", tsetlin_machine.evaluate(X_test, y_test, y_test.shape[0]))