def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(x, y, yerr=e,)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328), reg.predict_error(328), reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l,u=reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328), reg.predict_error(328,'SEM'),
reg.predict_error(328, 'CI')))
plt.show()
def regressor(self):
# print self.name, self.fit, self.__class__.__name__
is_mean = 'average' in self.fit.lower()
reg = self._regressor
if reg is None:
if is_mean:
reg = self._mean_regressor_factory()
else:
# print 'doing import of regressor {}'.format(self.__class__)
# st=time.time()
from pychron.core.regression.ols_regressor import PolynomialRegressor
# print 'doing import of regressor {}'.format(time.time()-st)
reg = PolynomialRegressor(tag=self.name,
xs=self.offset_xs,
ys=self.ys,
# fit=self.fit,
# filter_outliers_dict=self.filter_outliers_dict,
error_calc_type=self.error_type)
elif is_mean and not isinstance(reg, MeanRegressor):
reg = self._mean_regressor_factory()
if not is_mean:
reg.set_degree(fit_to_degree(self.fit), refresh=False)
reg.filter_outliers_dict = self.filter_outliers_dict
reg.trait_set(xs=self.offset_xs, ys=self.ys)
reg.calculate()
self._regressor = reg
return reg
def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(
x,
y,
yerr=e,
)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328),
reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(
reg.predict(328), reg.predict_error(328),
reg.predict_error(328, 'SEM'), reg.predict_error(328, 'CI')))
plt.show()
def _get_regressor(self):
# print '{} getting regerssior'.format(self.name)
# try:
if self.fit.lower() =='average':
reg = self._mean_regressor_factory()
else:
reg = PolynomialRegressor(xs=self.xs,
ys=self.ys,
degree=self.fit,
error_type=self.error_type,
filter_outliers_dict=self.filter_outliers_dict)
# except Exception, e:
# reg = PolynomialRegressor(xs=self.xs, ys=self.ys,
# degree=self.fit,
# filter_outliers_dict=self.filter_outliers_dict)
reg.calculate()
return reg
def _get_regressor(self):
if 'average' in self.fit.lower():
reg = self._mean_regressor_factory()
else:
# print 'doing import of regresor {}'.format(self.__class__)
# st=time.time()
from pychron.core.regression.ols_regressor import PolynomialRegressor
# print 'doing import of regresor {}'.format(time.time()-st)
reg = PolynomialRegressor(tag=self.name,
xs=self.offset_xs,
ys=self.ys,
# fit=self.fit,
# filter_outliers_dict=self.filter_outliers_dict,
error_calc_type=self.error_type)
reg.set_degree(self.fit, refresh=False)
reg.filter_outliers_dict = self.filter_outliers_dict
reg.calculate()
return reg
