本文整理匯總了Python中nupic.encoders.adaptivescalar.AdaptiveScalarEncoder.setLearning方法的典型用法代碼示例。如果您正苦於以下問題：Python AdaptiveScalarEncoder.setLearning方法的具體用法？Python AdaptiveScalarEncoder.setLearning怎麽用？Python AdaptiveScalarEncoder.setLearning使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類nupic.encoders.adaptivescalar.AdaptiveScalarEncoder的用法示例。
在下文中一共展示了AdaptiveScalarEncoder.setLearning方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: testNonPeriodicEncoderMinMaxNotSpec
# 需要導入模塊: from nupic.encoders.adaptivescalar import AdaptiveScalarEncoder [as 別名]
# 或者: from nupic.encoders.adaptivescalar.AdaptiveScalarEncoder import setLearning [as 別名]
def testNonPeriodicEncoderMinMaxNotSpec(self):
"""Non-periodic encoder, min and max not specified"""
l = AdaptiveScalarEncoder(name="scalar", n=14, w=5, minval=None,
maxval=None, periodic=False, forced=True)
def _verify(v, encoded, expV=None):
if expV is None:
expV = v
self.assertTrue(numpy.array_equal(
l.encode(v),
numpy.array(encoded, dtype=defaultDtype)))
self.assertLessEqual(
abs(l.getBucketInfo(l.getBucketIndices(v))[0].value - expV),
l.resolution/2)
def _verifyNot(v, encoded):
self.assertFalse(numpy.array_equal(
l.encode(v), numpy.array(encoded, dtype=defaultDtype)))
_verify(1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(2, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(10, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(3, [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])
_verify(-9, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-8, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-7, [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-6, [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-5, [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])
_verify(0, [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
_verify(8, [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0])
_verify(8, [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0])
_verify(10, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(11, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(12, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(13, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(14, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(15, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
#"""Test switching learning off"""
l = AdaptiveScalarEncoder(name="scalar", n=14, w=5, minval=1, maxval=10,
periodic=False, forced=True)
_verify(1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(10, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(20, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(10, [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
l.setLearning(False)
_verify(30, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], expV=20)
_verify(20, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(-10, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], expV=1)
_verify(-1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], expV=1)
l.setLearning(True)
_verify(30, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verifyNot(20, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(-10, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verifyNot(-1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])