def test_gaussian_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
s = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
model = TuningModel.load(s, "gaussian")
params = model.fit(data)
tol = 1e-4 # to handle rounding errors
assert allclose(params.map(lambda (_, v): v).collect()[0], array([0.36262, 0.01836]), atol=tol)
def test_circular_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
s = array([-pi / 2, -pi / 3, -pi / 4, pi / 4, pi / 3, pi / 2])
model = TuningModel.load(s, "circular")
params = model.fit(data)
tol = 1e-4 # to handle rounding errors
assert allclose(params.map(lambda (_, v): v).collect()[0], array([0.10692, 1.61944]), atol=tol)
def tuning(data, tuningmodelfile, tuningmode, regressmodelfile=None, regressmode=None):
"""Estimate parameters of a tuning curve model,
optionally preceeded by regression
:param data: RDD of data points as key value pairs
:param tuningmodelfile: model parameters for tuning (string with file location, array, or tuple)
:param: tuningmode: form of tuning ("gaussian" or "circular")
:param regressmodelfile: model parameters for regression (default=None)
:param regressmode: form of regression ("linear" or "bilinear") (default=None)
:return params: tuning curve parameters
"""
# create tuning model
tuningmodel = TuningModel.load(tuningmodelfile, tuningmode)
# get tuning curves
if regressmodelfile is not None:
# use regression results
regressmodel = RegressionModel.load(regressmodelfile, regressmode)
betas, stats, resid = regressmodel.fit(data)
params = tuningmodel.fit(betas)
else:
# use data
params = tuningmodel.fit(data)
return params
def tuning(data,
tuningmodelfile,
tuningmode,
regressmodelfile=None,
regressmode=None):
"""Estimate parameters of a tuning curve model,
optionally preceeded by regression
:param data: RDD of data points as key value pairs
:param tuningmodelfile: model parameters for tuning (string with file location, array, or tuple)
:param: tuningmode: form of tuning ("gaussian" or "circular")
:param regressmodelfile: model parameters for regression (default=None)
:param regressmode: form of regression ("linear" or "bilinear") (default=None)
:return params: tuning curve parameters
"""
# create tuning model
tuningmodel = TuningModel.load(tuningmodelfile, tuningmode)
# get tuning curves
if regressmodelfile is not None:
# use regression results
regressmodel = RegressionModel.load(regressmodelfile, regressmode)
betas, stats, resid = regressmodel.fit(data)
params = tuningmodel.fit(betas)
else:
# use data
params = tuningmodel.fit(data)
return params
def tuning(data, tuningmodelfile, tuningmode, regressmodelfile=None, regressmode=None):
"""estimate parameters of a tuning curve model,
optionally preceeded by regression
arguments:
data - RDD of data points
tuningmodelfile - model parameters (string with file location, array, or tuple)
tuningmode - form of tuning ("gaussian" or "circular")
regressmodelfile - model parameters (default=None)
regressmode - form of regression ("linear" or "bilinear") (default=None)
returns:
params - tuning curve parameters
"""
# create tuning model
tuningmodel = TuningModel.load(tuningmodelfile, tuningmode)
# get tuning curves
if regressmodelfile is not None:
# use regression results
regressmodel = RegressionModel.load(regressmodelfile, regressmode)
betas, stats, resid = regressmodel.fit(data)
params = tuningmodel.fit(betas)
else:
# use data
params = tuningmodel.fit(data)
return params
def test_gaussian_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
s = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
model = TuningModel.load(s, "gaussian")
params = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert (allclose(params.map(lambda (_, v): v).collect()[0],
array([0.36262, 0.01836]),
atol=tol))
def test_circular_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
s = array([-pi / 2, -pi / 3, -pi / 4, pi / 4, pi / 3, pi / 2])
model = TuningModel.load(s, "circular")
params = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert (allclose(params.map(lambda (_, v): v).collect()[0],
array([0.10692, 1.61944]),
atol=tol))
