def __init__(self,
c_regr=OLS(),
st_regr=OLS(),
c_fit_kwargs={},
st_fit_kwargs={},
c_constraints=[ConstraintNonneg()],
st_constraints=[ConstraintNonneg()],
max_iter=50,
err_fcn=mse,
tol_increase=0.0,
tol_n_increase=10,
tol_err_change=None,
tol_n_above_min=10):
"""
Multivariate Curve Resolution - Alternating Regression
"""
self.max_iter = max_iter
self.tol_increase = tol_increase
self.tol_n_increase = tol_n_increase
self.tol_err_change = tol_err_change
self.tol_n_above_min = tol_n_above_min
self.err_fcn = err_fcn
self.err = None
self.c_constraints = c_constraints
self.st_constraints = st_constraints
self.c_regressor = self._check_regr(c_regr)
self.st_regressor = self._check_regr(st_regr)
self.c_fit_kwargs = c_fit_kwargs
self.st_fit_kwargs = st_fit_kwargs
self.C_ = None
self.ST_ = None
self.C_opt_ = None
self.ST_opt_ = None
self.n_iter_opt = None
self.n_iter = None
self.n_increase = None
self.n_above_min = None
self.exit_max_iter_reached = False
self.exit_tol_increase = False
self.exit_tol_n_increase = False
self.exit_tol_err_change = False
self.exit_tol_n_above_min = False
# Saving every C or S^T matrix at each iteration
# Could create huge memory usage
self._saveall_st = False
self._saveall_c = False
self._saved_st = []
self._saved_c = []
def test_ols_negative_x():
""" Test nnls """
A = np.array([[1,1,0],[1,0,1],[0,0,1]])
x = np.array([1,-2,3])
b = np.dot(A,x)
regr = OLS()
# b is 1D
regr.fit(A,b)
assert_allclose(x, regr.X_)
def _check_regr(self, mth):
"""
Check regressor method. If accetible strings, instantiate and return
object. If instantiated class, make sure it has a fit attribute.
"""
if isinstance(mth, str):
if mth.upper() == 'OLS':
return OLS()
elif mth.upper() == 'NNLS':
return NNLS()
else:
raise ValueError('{} is unknown. Use NNLS or OLS.'.format(mth))
elif hasattr(mth, 'fit'):
return mth
else:
raise ValueError(
'Input class {} does not have a \'fit\' method'.format(mth))
if 'ConstraintCumsumNonneg' in st_l:
st_constraints.append(ConstraintCumsumNonneg())
if 'ConstraintNorm' in st_l:
st_constraints.append(ConstraintNorm())
# print(c_constraints,st_constraints)
logger = logging.getLogger('pymcr')
logger.setLevel(logging.DEBUG)
stdout_handler = logging.StreamHandler(stream=sys.stdout)
stdout_format = logging.Formatter('%(message)s')
stdout_handler.setFormatter(stdout_format)
logger.addHandler(stdout_handler)
mcrar = McrAR(
max_iter=max_iter,
st_regr=NNLS(),
c_regr=OLS(),
c_constraints=[],
st_constraints=st_constraints)
print('calculating MCR-ALS')
mcrar.fit(D, ST=ST_guess, verbose=True)
np.savetxt('out/wavelengths.txt', x)
np.savetxt('out/C-opt.txt', mcrar.C_opt_)
if dct['smooth spectra'] == 'True':
args = (int(dct['smoothing window length']), str(dct['smoothing window']))
S = np.apply_along_axis(smooth, 1, mcrar.ST_opt_, *args).T
np.savetxt('out/S-opt.txt', S)
else:
S = mcrar.ST_opt_.T
np.savetxt('out/S-opt.txt', S)