def trainingDataSet(longitudeRange=(-5.0, 5.0, 10),
latitudeRange=(-5.0, 5.0, 10)):
trainingDataSet = collections.defaultdict(list)
for longitude in statistics.frange(*longitudeRange):
for latidute in statistics.frange(*latitudeRange):
trainingDataSet[(longitude, latidute)] = samplesByLabels({
'x': (longitude * .1, latidute * .1, 10),
'y': (longitude * .2, latidute * .2, 10),
'z': (longitude * .4, latidute * .4, 10)
})
return trainingDataSet
def accelerometer_dataset():
calibration, modelListener = buildCalibration()
modelListener.start()
longitudes = statistics.frange(-90.0, 45.0, 25)
latitudes = statistics.frange(-180.0, 180.0, 50)
# longitudes = statistics.frange(-5.0, 5.0, 5)
# latitudes = statistics.frange(-10.0, 10.0, 5)
dataSet = calibration.exploreUnitSphere(longitudes, latitudes)
io.serialize_exploration(dataSet)
dataSet = io.deserialize_exploration()
print io.exploration_to_CSV(dataSet)
return io.float_columns_from_CSV()
def mockTrainingData(self):
xRotations, yRotations = statistics.frange(-1.5, 1.0,
5), statistics.frange(
-1.5, 1.0, 5)
targets = []
measures = []
for xRotation in xRotations:
for yRotation in yRotations:
self.exp.rotativeTableAAngle = xRotation
self.exp.rotativeTableBAngle = yRotation
gravityVector = self.exp.gravityVector_IMU_coords()
targets.append(gravityVector)
measures.append(
sp.dot(linalg.inv(self.misalignmentsAndScales),
gravityVector) + self.biases)
return targets, measures
def samplesByLabels(samplesRangeBylabel={
'x': (5.0, 15.0, 10),
'y': (0.0, 10.0, 10),
'z': (25.0, 55.0, 10)
}):
samplesByLabels = collections.defaultdict(list)
for label, samplesRange in samplesRangeBylabel.iteritems():
for sample in statistics.frange(*samplesRange):
samplesByLabels[label].append(sample)
return samplesByLabels
def findMaxPosition(self,
samplingLabelToMaximize,
dofExplorationRange=statistics.frange(-5.0, 5.0, 10),
fineTuningStepWidth=0.1):
gVectorPosition, maximum = self._exhaustiveSearch(
samplingLabelToMaximize, dofExplorationRange)
maxima = (gVectorPosition,
maximum), self._gradientAscent(samplingLabelToMaximize,
fineTuningStepWidth,
gVectorPosition, maximum)
return maxima
def setUp(self):
self.samples = statistics.frange(5.0, 15.0, 10)
self.samplesByLabels = mocks.samplesByLabels()