def testRemoveUnlikelyPredictionsLikelihoodThresholds(self):
result = CLAModel._removeUnlikelyPredictions({1: 0.1, 2: 0.001}, 0.01, 3)
self.assertDictEqual(result, {1: 0.1})
result = CLAModel._removeUnlikelyPredictions({1: 0.001, 2: 0.002}, 0.01, 3)
self.assertDictEqual(result, {2: 0.002})
result = CLAModel._removeUnlikelyPredictions({1: 0.002, 2: 0.001}, 0.01, 3)
self.assertDictEqual(result, {1: 0.002})
def testRemoveUnlikelyPredictionsMaxPredictions(self):
result = CLAModel._removeUnlikelyPredictions({1: 0.1, 2: 0.2, 3: 0.3},
0.01, 3)
self.assertDictEqual(result, {1: 0.1, 2: 0.2, 3: 0.3})
result = CLAModel._removeUnlikelyPredictions(
{1: 0.1, 2: 0.2, 3: 0.3, 4: 0.4}, 0.01, 3)
self.assertDictEqual(result, {2: 0.2, 3: 0.3, 4: 0.4})
def imagine(self, func_list):
"""
Imagines (predicts) the consequences of applying a given function to the model.
The method accepts a set of functions, to permit a number of alternatives to be
simultaneously assessed. A given function is expected to mutate the model then use it
to make a prediction. The imagination class forks the model for each function invocation,
providing a ephemeral/hypothetical model to the function.
"""
# fork the model, then apply the sets of input sequences
temp_path = tempfile.mkdtemp(prefix='nupic')
shutil.rmtree(temp_path)
self.model.save(temp_path)
results = []
for func in func_list:
model_fork = CLAModel.load(temp_path)
result = func(model_fork) # a list of predictions (one for each run)
results.append(result)
shutil.rmtree(temp_path)
return results
def testRemoveUnlikelyPredictionsSingleValues(self):
result = CLAModel._removeUnlikelyPredictions({1: 0.1}, 0.01, 3)
self.assertDictEqual(result, {1: 0.1})
result = CLAModel._removeUnlikelyPredictions({1: 0.001}, 0.01, 3)
self.assertDictEqual(result, {1: 0.001})
def testRemoveUnlikelyPredictionsEmpty(self):
result = CLAModel._removeUnlikelyPredictions({}, 0.01, 3)
self.assertDictEqual(result, {})
def testRemoveUnlikelyPredictionsSingleValues(self):
result = CLAModel._removeUnlikelyPredictions({1: 0.1}, 0.01, 3)
self.assertDictEqual(result, {1: 0.1})
result = CLAModel._removeUnlikelyPredictions({1: 0.001}, 0.01, 3)
self.assertDictEqual(result, {1: 0.001})
def testRemoveUnlikelyPredictionsEmpty(self):
result = CLAModel._removeUnlikelyPredictions({}, 0.01, 3)
self.assertDictEqual(result, {})
