def fit_lenscale(df, lenscalepool):
"""searches over the lenscales in lenscale pool for the ls that best
predicts drilling behavior given the samples in the trial for each
trial (row) in the df"""
print 'workerid: ' + df.workerid.iat[0]
X = np.linspace(0, 1, 1028)
trialFits = [get_experimentError(df, lenscale, X)
for lenscale in lenscalepool]
trialFits = np.asarray(trialFits)
xerr = trialFits[:,:,0]
mu_xerr_byLenscale = xerr.mean(axis=1)
iBestLen = mu_xerr_byLenscale.argmin()
fit_lenscale = lenscalepool[iBestLen]
return fit_lenscale
def single_exp_err(df, lenscale):
print 'workerid: ' + df.workerid.iat[0]
X = np.linspace(0, 1, 1028)
expfits = get_experimentError(df, lenscale, X)
return expfits[:, 0].mean()
