was used only for yield surface fitting
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Martin Diehl
parent
1fcbc35611
commit
d1fa2a14dc
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@ -221,263 +221,6 @@ class return_message():
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return srepr(self.message)
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def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8,
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gtol=0.0, maxfev=0, epsfcn=None, factor=100, diag=None):
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from scipy.optimize import _minpack
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"""
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Non-linear least square fitting (Levenberg-Marquardt method) with
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bounded parameters.
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the codes of transformation between int <-> ext refers to the work of
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Jonathan J. Helmus: https://github.com/jjhelmus/leastsqbound-scipy
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other codes refer to the source code of minpack.py:
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An internal parameter list is used to enforce contraints on the fitting
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parameters. The transfomation is based on that of MINUIT package.
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please see: F. James and M. Winkler. MINUIT User's Guide, 2004.
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bounds : list
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(min, max) pairs for each parameter, use None for 'min' or 'max'
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when there is no bound in that direction.
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For example: if there are two parameters needed to be fitting, then
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bounds is [(min1,max1), (min2,max2)]
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This function is based on 'leastsq' of minpack.py, the annotation of
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other parameters can be found in 'least_squares.py'.
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"""
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def _check_func(checker, argname, thefunc, x0, args, numinputs,
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output_shape=None):
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from numpy import shape
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"""The same as that of minpack.py"""
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res = np.atleast_1d(thefunc(*((x0[:numinputs],) + args)))
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if (output_shape is not None) and (shape(res) != output_shape):
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if (output_shape[0] != 1):
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if len(output_shape) > 1:
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if output_shape[1] == 1:
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return shape(res)
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msg = "%s: there is a mismatch between the input and output " \
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"shape of the '%s' argument" % (checker, argname)
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func_name = getattr(thefunc, '__name__', None)
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if func_name:
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msg += " '%s'." % func_name
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else:
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msg += "."
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raise TypeError(msg)
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if np.issubdtype(res.dtype, np.inexact):
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dt = res.dtype
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else:
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dt = dtype(float)
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return shape(res), dt
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def _int2extGrad(p_int, bounds):
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"""Calculate the gradients of transforming the internal (unconstrained) to external (constrained) parameter."""
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grad = np.empty_like(p_int)
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for i, (x, bound) in enumerate(zip(p_int, bounds)):
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lower, upper = bound
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if lower is None and upper is None: # No constraints
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grad[i] = 1.0
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elif upper is None: # only lower bound
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grad[i] = x/np.sqrt(x*x + 1.0)
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elif lower is None: # only upper bound
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grad[i] = -x/np.sqrt(x*x + 1.0)
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else: # lower and upper bounds
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grad[i] = (upper - lower)*np.cos(x)/2.0
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return grad
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def _int2extFunc(bounds):
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"""Transform internal parameters into external parameters."""
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local = [_int2extLocal(b) for b in bounds]
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def _transform_i2e(p_int):
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p_ext = np.empty_like(p_int)
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p_ext[:] = [i(j) for i, j in zip(local, p_int)]
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return p_ext
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return _transform_i2e
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def _ext2intFunc(bounds):
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"""Transform external parameters into internal parameters."""
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local = [_ext2intLocal(b) for b in bounds]
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def _transform_e2i(p_ext):
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p_int = np.empty_like(p_ext)
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p_int[:] = [i(j) for i, j in zip(local, p_ext)]
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return p_int
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return _transform_e2i
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def _int2extLocal(bound):
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"""Transform a single internal parameter to an external parameter."""
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lower, upper = bound
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if lower is None and upper is None: # no constraints
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return lambda x: x
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elif upper is None: # only lower bound
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return lambda x: lower - 1.0 + np.sqrt(x*x + 1.0)
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elif lower is None: # only upper bound
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return lambda x: upper + 1.0 - np.sqrt(x*x + 1.0)
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else:
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return lambda x: lower + ((upper - lower)/2.0)*(np.sin(x) + 1.0)
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def _ext2intLocal(bound):
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"""Transform a single external parameter to an internal parameter."""
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lower, upper = bound
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if lower is None and upper is None: # no constraints
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return lambda x: x
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elif upper is None: # only lower bound
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return lambda x: np.sqrt((x - lower + 1.0)**2 - 1.0)
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elif lower is None: # only upper bound
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return lambda x: np.sqrt((x - upper - 1.0)**2 - 1.0)
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else:
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return lambda x: np.arcsin((2.0*(x - lower)/(upper - lower)) - 1.0)
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i2e = _int2extFunc(bounds)
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e2i = _ext2intFunc(bounds)
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x0 = np.asarray(x0).flatten()
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n = len(x0)
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if len(bounds) != n:
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raise ValueError('the length of bounds is inconsistent with the number of parameters ')
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if not isinstance(args, tuple):
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args = (args,)
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shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
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m = shape[0]
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if n > m:
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raise TypeError('Improper input: N=%s must not exceed M=%s' % (n, m))
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if epsfcn is None:
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epsfcn = np.finfo(dtype).eps
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def funcWarp(x, *args):
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return func(i2e(x), *args)
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xi0 = e2i(x0)
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if Dfun is None:
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if maxfev == 0:
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maxfev = 200*(n + 1)
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retval = _minpack._lmdif(funcWarp, xi0, args, full_output, ftol, xtol,
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gtol, maxfev, epsfcn, factor, diag)
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else:
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if col_deriv:
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_check_func('leastsq', 'Dfun', Dfun, x0, args, n, (n, m))
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else:
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_check_func('leastsq', 'Dfun', Dfun, x0, args, n, (m, n))
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if maxfev == 0:
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maxfev = 100*(n + 1)
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def DfunWarp(x, *args):
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return Dfun(i2e(x), *args)
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retval = _minpack._lmder(funcWarp, DfunWarp, xi0, args, full_output, col_deriv,
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ftol, xtol, gtol, maxfev, factor, diag)
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errors = {0: ["Improper input parameters.", TypeError],
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1: ["Both actual and predicted relative reductions "
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"in the sum of squares\n are at most %f" % ftol, None],
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2: ["The relative error between two consecutive "
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"iterates is at most %f" % xtol, None],
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3: ["Both actual and predicted relative reductions in "
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"the sum of squares\n are at most %f and the "
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"relative error between two consecutive "
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"iterates is at \n most %f" % (ftol, xtol), None],
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4: ["The cosine of the angle between func(x) and any "
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"column of the\n Jacobian is at most %f in "
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"absolute value" % gtol, None],
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5: ["Number of calls to function has reached "
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"maxfev = %d." % maxfev, ValueError],
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6: ["ftol=%f is too small, no further reduction "
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"in the sum of squares\n is possible.""" % ftol,
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ValueError],
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7: ["xtol=%f is too small, no further improvement in "
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"the approximate\n solution is possible." % xtol,
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ValueError],
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8: ["gtol=%f is too small, func(x) is orthogonal to the "
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"columns of\n the Jacobian to machine "
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"precision." % gtol, ValueError],
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'unknown': ["Unknown error.", TypeError]}
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info = retval[-1] # The FORTRAN return value
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if info not in [1, 2, 3, 4] and not full_output:
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if info in [5, 6, 7, 8]:
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np.warnings.warn(errors[info][0], RuntimeWarning)
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else:
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try:
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raise errors[info][1](errors[info][0])
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except KeyError:
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raise errors['unknown'][1](errors['unknown'][0])
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mesg = errors[info][0]
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x = i2e(retval[0])
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if full_output:
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grad = _int2extGrad(retval[0], bounds)
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retval[1]['fjac'] = (retval[1]['fjac'].T / np.take(grad,
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retval[1]['ipvt'] - 1)).T
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cov_x = None
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if info in [1, 2, 3, 4]:
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from numpy.dual import inv
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from numpy.linalg import LinAlgError
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perm = np.take(np.eye(n), retval[1]['ipvt'] - 1, 0)
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r = np.triu(np.transpose(retval[1]['fjac'])[:n, :])
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R = np.dot(r, perm)
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try:
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cov_x = inv(np.dot(np.transpose(R), R))
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except LinAlgError as inverror:
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print(inverror)
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pass
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return (x, cov_x) + retval[1:-1] + (mesg, info)
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else:
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return (x, info)
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def _general_function(params, ydata, xdata, function):
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return function(xdata, *params) - ydata
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def _weighted_general_function(params, ydata, xdata, function, weights):
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return (function(xdata, *params) - ydata)*weights
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def curve_fit_bound(f, xdata, ydata, p0=None, sigma=None, bounds=None, **kw):
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"""Similar as 'curve_fit' in minpack.py."""
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if p0 is None:
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# determine number of parameters by inspecting the function
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import inspect
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args, varargs, varkw, defaults = inspect.getargspec(f)
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if len(args) < 2:
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msg = "Unable to determine number of fit parameters."
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raise ValueError(msg)
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if 'self' in args:
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p0 = [1.0] * (len(args)-2)
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else:
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p0 = [1.0] * (len(args)-1)
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if np.isscalar(p0):
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p0 = np.array([p0])
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args = (ydata, xdata, f)
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if sigma is None:
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func = _general_function
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else:
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func = _weighted_general_function
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args += (1.0/np.asarray(sigma),)
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return_full = kw.pop('full_output', False)
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res = leastsqBound(func, p0, args=args, bounds = bounds, full_output=True, **kw)
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(popt, pcov, infodict, errmsg, ier) = res
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if ier not in [1, 2, 3, 4]:
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msg = "Optimal parameters not found: " + errmsg
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raise RuntimeError(msg)
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if (len(ydata) > len(p0)) and pcov is not None:
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s_sq = (func(popt, *args)**2).sum()/(len(ydata)-len(p0))
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pcov = pcov * s_sq
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else:
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pcov = np.inf
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return (popt, pcov, infodict, errmsg, ier) if return_full else (popt, pcov)
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class ThreadPool:
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"""Pool of threads consuming tasks from a queue."""
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