def test_coeff_of_determination():
# make up some data and a model
data = np.arange(0,100)
model = np.arange(0,100)
# compute cod
cod = utils.coeff_of_determination(data,model)
# assert
npt.assert_equal(cod, 100)
# nan test
cod = utils.coeff_of_determination(np.zeros_like(model),model)
npt.assert_equal(cod,np.nan)
def test_coeff_of_determination():
# make up some data and a model
data = np.arange(0, 100)
model = np.arange(0, 100)
# compute cod
cod = utils.coeff_of_determination(data, model)
# assert
npt.assert_equal(cod, 100)
# nan test
cod = utils.coeff_of_determination(np.zeros_like(model), model)
npt.assert_equal(cod, np.nan)
def optimize_parameters(args):
data, ix, row = args
#if row['r2'] < r2_thr:
#return ix, return_grid_results(row)
#else:
ballpark = row.x, row.y, row.size, row.amplitude, row.baseline
r = gradient_descent_search(
data, error_function,
self.model_func.generate_prediction, ballpark, bounds,
verbose)
row['x'] = r[0][0]
row['y'] = r[0][1]
row['size'] = r[0][2]
row['amplitude'] = r[0][3]
row['baseline'] = r[0][4]
pred = self.model_func.generate_prediction(*r[0])
row['r2'] = coeff_of_determination(data, pred) / 100
row['estimation_method'] = 'Traditional method'
return ix, row
def optimize_parameters(args):
data, ix, row = args
#if row['r2'] < r2_thr:
#return ix, return_grid_results(row)
#else:
data_ = (data - data.mean()) / data.std()
ballpark = row.x, row.y, row.size
r = gradient_descent_search(data, error_function,
make_zscored_prediction,
ballpark, bounds, verbose)
row['x'] = r[0][0]
row['y'] = r[0][1]
row['size'] = r[0][2]
X = np.ones((len(data_), 2))
X[:, 1] = make_zscored_prediction(*r[0], normalize=False)
beta, residuals, _, _ = np.linalg.lstsq(X, data)
row['baseline'] = beta[0]
row['amplitude'] = beta[1]
row['r2'] = coeff_of_determination(data, X.dot(beta)) / 100
row['estimation_method'] = 'Correlation method'
return ix, row
def iterative_fit(self):
if self.gridsearch_params is None:
raise Exception('First use self.fit_grid!')
prf_params = Parallel(self.n_jobs, verbose=10)(
delayed(fit_gradient_descent)(self.model_func, data, ballpark,
self.bound_fits)
for (data, ballpark) in zip(self.data, self.gridsearch_params))
prf_params = np.vstack(prf_params)
if self.fit_model == 'gauss' or self.fit_model == 'gauss_sg':
output = np.ones((self.n_units, 6)) * nan
elif self.fit_model == 'css' or self.fit_model == 'css_sg':
output = np.ones((self.n_units, 7)) * nan
for vox in range(0, self.n_units):
data_tc = self.data[:, vox]
if self.fit_model == 'gauss' or self.fit_model == 'gauss_sg':
model_tc = self.model_func.generate_prediction(
prf_params[vox, 0], prf_params[vox, 1], prf_params[vox, 2],
prf_params[vox, 3], prf_params[vox, 4])
elif self.fit_model == 'css' or self.fit_model == 'css_sg':
model_tc = self.model_func.generate_prediction(
prf_params[vox, 0], prf_params[vox, 1], prf_params[vox, 2],
prf_params[vox, 3], prf_params[vox, 4], prf_params[vox, 5])
output[vox, :] = np.hstack([
prf_params[vox, :],
utils.coeff_of_determination(data_tc, model_tc) / 100.0
])
self.fit_output = output
return output