def testPatternTraining(self):
'test that within columns, particular cells dominate when they recognize learned temporal patterns'
htm = self.htm
#2d format, same dimensions as htm (for now)
data = [
[1,0,1,0,1,0,1,0,1,0],
[0,0,0,0,1,0,1,0,1,1],
[0,0,1,0,1,0,0,0,0,1],
[0,1,0,0,1,1,0,1,0,1],
[1,0,1,0,1,0,1,0,0,1],
[0,0,0,1,1,0,0,0,1,1],
]
htm.initialize_input(data)
flipDat = flipDataGenerator(htm)
htm.execute(data, flipDat, ticks=100)
htm.execute(data, flipDat, ticks=1)
activeCols = htm.columns_active()
#all columns should have learned particular cell patterns by now
for col in activeCols:
self.assertNotEqual(len(col.cells), len(filter(lambda cell: cell.active, col.cells)))
def testDataLoop(self):
htm = self.htm
data = [[1,0,1],[0,0,1]] #2d format, same dimensions as htm (for now)
htm.initialize_input(data)
htm.execute(data, flipDataGenerator(htm), ticks=20)
def testStability(self):
'test that repeated patterns get recognized by the same columns after stabilizing'
htm = self.htm
#2d format, same dimensions as htm (for now)
data = [[1,0,1,0,1,0,1,0,1,0],[0,0,0,0,1,0,0,0,0,1]]
htm.initialize_input(data)
flipDat = flipDataGenerator(htm)
htm.execute(data, flipDat, ticks=50)
active = htm.columns_active()
htm.execute(data, flipDat, ticks=2)
self.assertEqual(active, htm.columns_active())
htm.execute(data, flipDat, ticks=2)
self.assertEqual(active, htm.columns_active())
#show that stability is non-trivial because it changes at the next time step
htm.execute(data, flipDat)
self.assertNotEqual(active, htm.columns_active())
