1122 lines
41 KiB
Python
1122 lines
41 KiB
Python
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"""
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Unit tests for optimization routines from minpack.py.
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"""
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import warnings
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import pytest
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from numpy.testing import (assert_, assert_almost_equal, assert_array_equal,
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assert_array_almost_equal, assert_allclose,
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assert_warns, suppress_warnings)
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from pytest import raises as assert_raises
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import numpy as np
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from numpy import array, float64
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from multiprocessing.pool import ThreadPool
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from scipy import optimize, linalg
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from scipy.special import lambertw
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from scipy.optimize._minpack_py import leastsq, curve_fit, fixed_point
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from scipy.optimize import OptimizeWarning
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from scipy.optimize._minimize import Bounds
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class ReturnShape:
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"""This class exists to create a callable that does not have a '__name__' attribute.
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__init__ takes the argument 'shape', which should be a tuple of ints.
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When an instance is called with a single argument 'x', it returns numpy.ones(shape).
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"""
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def __init__(self, shape):
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self.shape = shape
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def __call__(self, x):
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return np.ones(self.shape)
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def dummy_func(x, shape):
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"""A function that returns an array of ones of the given shape.
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`x` is ignored.
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"""
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return np.ones(shape)
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def sequence_parallel(fs):
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with ThreadPool(len(fs)) as pool:
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return pool.map(lambda f: f(), fs)
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# Function and Jacobian for tests of solvers for systems of nonlinear
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# equations
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def pressure_network(flow_rates, Qtot, k):
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"""Evaluate non-linear equation system representing
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the pressures and flows in a system of n parallel pipes::
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f_i = P_i - P_0, for i = 1..n
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f_0 = sum(Q_i) - Qtot
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where Q_i is the flow rate in pipe i and P_i the pressure in that pipe.
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Pressure is modeled as a P=kQ**2 where k is a valve coefficient and
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Q is the flow rate.
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Parameters
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----------
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flow_rates : float
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A 1-D array of n flow rates [kg/s].
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k : float
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A 1-D array of n valve coefficients [1/kg m].
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Qtot : float
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A scalar, the total input flow rate [kg/s].
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Returns
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-------
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F : float
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A 1-D array, F[i] == f_i.
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"""
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P = k * flow_rates**2
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F = np.hstack((P[1:] - P[0], flow_rates.sum() - Qtot))
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return F
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def pressure_network_jacobian(flow_rates, Qtot, k):
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"""Return the jacobian of the equation system F(flow_rates)
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computed by `pressure_network` with respect to
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*flow_rates*. See `pressure_network` for the detailed
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description of parameters.
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Returns
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-------
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jac : float
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*n* by *n* matrix ``df_i/dQ_i`` where ``n = len(flow_rates)``
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and *f_i* and *Q_i* are described in the doc for `pressure_network`
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"""
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n = len(flow_rates)
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pdiff = np.diag(flow_rates[1:] * 2 * k[1:] - 2 * flow_rates[0] * k[0])
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jac = np.empty((n, n))
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jac[:n-1, :n-1] = pdiff * 0
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jac[:n-1, n-1] = 0
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jac[n-1, :] = np.ones(n)
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return jac
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def pressure_network_fun_and_grad(flow_rates, Qtot, k):
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return (pressure_network(flow_rates, Qtot, k),
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pressure_network_jacobian(flow_rates, Qtot, k))
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class TestFSolve:
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def test_pressure_network_no_gradient(self):
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# fsolve without gradient, equal pipes -> equal flows.
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows, info, ier, mesg = optimize.fsolve(
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pressure_network, initial_guess, args=(Qtot, k),
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full_output=True)
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assert_array_almost_equal(final_flows, np.ones(4))
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assert_(ier == 1, mesg)
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def test_pressure_network_with_gradient(self):
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# fsolve with gradient, equal pipes -> equal flows
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows = optimize.fsolve(
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pressure_network, initial_guess, args=(Qtot, k),
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fprime=pressure_network_jacobian)
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assert_array_almost_equal(final_flows, np.ones(4))
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def test_wrong_shape_func_callable(self):
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func = ReturnShape(1)
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# x0 is a list of two elements, but func will return an array with
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# length 1, so this should result in a TypeError.
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x0 = [1.5, 2.0]
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assert_raises(TypeError, optimize.fsolve, func, x0)
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def test_wrong_shape_func_function(self):
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# x0 is a list of two elements, but func will return an array with
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# length 1, so this should result in a TypeError.
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x0 = [1.5, 2.0]
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assert_raises(TypeError, optimize.fsolve, dummy_func, x0, args=((1,),))
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def test_wrong_shape_fprime_callable(self):
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func = ReturnShape(1)
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deriv_func = ReturnShape((2,2))
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assert_raises(TypeError, optimize.fsolve, func, x0=[0,1], fprime=deriv_func)
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def test_wrong_shape_fprime_function(self):
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def func(x):
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return dummy_func(x, (2,))
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def deriv_func(x):
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return dummy_func(x, (3, 3))
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assert_raises(TypeError, optimize.fsolve, func, x0=[0,1], fprime=deriv_func)
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def test_func_can_raise(self):
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def func(*args):
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raise ValueError('I raised')
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with assert_raises(ValueError, match='I raised'):
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optimize.fsolve(func, x0=[0])
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def test_Dfun_can_raise(self):
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def func(x):
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return x - np.array([10])
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def deriv_func(*args):
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raise ValueError('I raised')
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with assert_raises(ValueError, match='I raised'):
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optimize.fsolve(func, x0=[0], fprime=deriv_func)
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def test_float32(self):
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def func(x):
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return np.array([x[0] - 100, x[1] - 1000], dtype=np.float32) ** 2
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p = optimize.fsolve(func, np.array([1, 1], np.float32))
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assert_allclose(func(p), [0, 0], atol=1e-3)
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def test_reentrant_func(self):
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def func(*args):
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self.test_pressure_network_no_gradient()
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return pressure_network(*args)
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# fsolve without gradient, equal pipes -> equal flows.
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows, info, ier, mesg = optimize.fsolve(
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func, initial_guess, args=(Qtot, k),
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full_output=True)
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assert_array_almost_equal(final_flows, np.ones(4))
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assert_(ier == 1, mesg)
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def test_reentrant_Dfunc(self):
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def deriv_func(*args):
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self.test_pressure_network_with_gradient()
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return pressure_network_jacobian(*args)
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# fsolve with gradient, equal pipes -> equal flows
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows = optimize.fsolve(
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pressure_network, initial_guess, args=(Qtot, k),
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fprime=deriv_func)
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assert_array_almost_equal(final_flows, np.ones(4))
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def test_concurrent_no_gradient(self):
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v = sequence_parallel([self.test_pressure_network_no_gradient] * 10)
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assert all([result is None for result in v])
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def test_concurrent_with_gradient(self):
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v = sequence_parallel([self.test_pressure_network_with_gradient] * 10)
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assert all([result is None for result in v])
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class TestRootHybr:
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def test_pressure_network_no_gradient(self):
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# root/hybr without gradient, equal pipes -> equal flows
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows = optimize.root(pressure_network, initial_guess,
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method='hybr', args=(Qtot, k)).x
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assert_array_almost_equal(final_flows, np.ones(4))
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def test_pressure_network_with_gradient(self):
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# root/hybr with gradient, equal pipes -> equal flows
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([[2., 0., 2., 0.]])
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final_flows = optimize.root(pressure_network, initial_guess,
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args=(Qtot, k), method='hybr',
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jac=pressure_network_jacobian).x
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assert_array_almost_equal(final_flows, np.ones(4))
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def test_pressure_network_with_gradient_combined(self):
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# root/hybr with gradient and function combined, equal pipes -> equal
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# flows
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows = optimize.root(pressure_network_fun_and_grad,
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initial_guess, args=(Qtot, k),
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method='hybr', jac=True).x
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assert_array_almost_equal(final_flows, np.ones(4))
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class TestRootLM:
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def test_pressure_network_no_gradient(self):
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# root/lm without gradient, equal pipes -> equal flows
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k = np.full(4, 0.5)
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Qtot = 4
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initial_guess = array([2., 0., 2., 0.])
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final_flows = optimize.root(pressure_network, initial_guess,
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method='lm', args=(Qtot, k)).x
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assert_array_almost_equal(final_flows, np.ones(4))
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class TestNfev:
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def zero_f(self, y):
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self.nfev += 1
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return y**2-3
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@pytest.mark.parametrize('method', ['hybr', 'lm', 'broyden1',
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'broyden2', 'anderson',
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'linearmixing', 'diagbroyden',
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'excitingmixing', 'krylov',
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'df-sane'])
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def test_root_nfev(self, method):
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self.nfev = 0
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solution = optimize.root(self.zero_f, 100, method=method)
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assert solution.nfev == self.nfev
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def test_fsolve_nfev(self):
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self.nfev = 0
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x, info, ier, mesg = optimize.fsolve(self.zero_f, 100, full_output=True)
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assert info['nfev'] == self.nfev
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class TestLeastSq:
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def setup_method(self):
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x = np.linspace(0, 10, 40)
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a,b,c = 3.1, 42, -304.2
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self.x = x
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self.abc = a,b,c
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y_true = a*x**2 + b*x + c
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np.random.seed(0)
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self.y_meas = y_true + 0.01*np.random.standard_normal(y_true.shape)
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def residuals(self, p, y, x):
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a,b,c = p
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err = y-(a*x**2 + b*x + c)
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return err
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def residuals_jacobian(self, _p, _y, x):
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return -np.vstack([x**2, x, np.ones_like(x)]).T
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def test_basic(self):
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p0 = array([0,0,0])
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params_fit, ier = leastsq(self.residuals, p0,
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args=(self.y_meas, self.x))
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assert_(ier in (1,2,3,4), 'solution not found (ier=%d)' % ier)
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# low precision due to random
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assert_array_almost_equal(params_fit, self.abc, decimal=2)
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def test_basic_with_gradient(self):
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p0 = array([0,0,0])
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params_fit, ier = leastsq(self.residuals, p0,
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args=(self.y_meas, self.x),
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Dfun=self.residuals_jacobian)
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assert_(ier in (1,2,3,4), 'solution not found (ier=%d)' % ier)
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# low precision due to random
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assert_array_almost_equal(params_fit, self.abc, decimal=2)
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def test_full_output(self):
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p0 = array([[0,0,0]])
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full_output = leastsq(self.residuals, p0,
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args=(self.y_meas, self.x),
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full_output=True)
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params_fit, cov_x, infodict, mesg, ier = full_output
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assert_(ier in (1,2,3,4), f'solution not found: {mesg}')
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def test_input_untouched(self):
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p0 = array([0,0,0],dtype=float64)
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p0_copy = array(p0, copy=True)
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full_output = leastsq(self.residuals, p0,
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args=(self.y_meas, self.x),
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full_output=True)
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params_fit, cov_x, infodict, mesg, ier = full_output
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assert_(ier in (1,2,3,4), f'solution not found: {mesg}')
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assert_array_equal(p0, p0_copy)
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def test_wrong_shape_func_callable(self):
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func = ReturnShape(1)
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# x0 is a list of two elements, but func will return an array with
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# length 1, so this should result in a TypeError.
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x0 = [1.5, 2.0]
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assert_raises(TypeError, optimize.leastsq, func, x0)
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def test_wrong_shape_func_function(self):
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# x0 is a list of two elements, but func will return an array with
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# length 1, so this should result in a TypeError.
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x0 = [1.5, 2.0]
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assert_raises(TypeError, optimize.leastsq, dummy_func, x0, args=((1,),))
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||
|
|
|
||
|
|
def test_wrong_shape_Dfun_callable(self):
|
||
|
|
func = ReturnShape(1)
|
||
|
|
deriv_func = ReturnShape((2,2))
|
||
|
|
assert_raises(TypeError, optimize.leastsq, func, x0=[0,1], Dfun=deriv_func)
|
||
|
|
|
||
|
|
def test_wrong_shape_Dfun_function(self):
|
||
|
|
def func(x):
|
||
|
|
return dummy_func(x, (2,))
|
||
|
|
def deriv_func(x):
|
||
|
|
return dummy_func(x, (3, 3))
|
||
|
|
assert_raises(TypeError, optimize.leastsq, func, x0=[0,1], Dfun=deriv_func)
|
||
|
|
|
||
|
|
def test_float32(self):
|
||
|
|
# Regression test for gh-1447
|
||
|
|
def func(p,x,y):
|
||
|
|
q = p[0]*np.exp(-(x-p[1])**2/(2.0*p[2]**2))+p[3]
|
||
|
|
return q - y
|
||
|
|
|
||
|
|
x = np.array([1.475,1.429,1.409,1.419,1.455,1.519,1.472, 1.368,1.286,
|
||
|
|
1.231], dtype=np.float32)
|
||
|
|
y = np.array([0.0168,0.0193,0.0211,0.0202,0.0171,0.0151,0.0185,0.0258,
|
||
|
|
0.034,0.0396], dtype=np.float32)
|
||
|
|
p0 = np.array([1.0,1.0,1.0,1.0])
|
||
|
|
p1, success = optimize.leastsq(func, p0, args=(x,y))
|
||
|
|
|
||
|
|
assert_(success in [1,2,3,4])
|
||
|
|
assert_((func(p1,x,y)**2).sum() < 1e-4 * (func(p0,x,y)**2).sum())
|
||
|
|
|
||
|
|
def test_func_can_raise(self):
|
||
|
|
def func(*args):
|
||
|
|
raise ValueError('I raised')
|
||
|
|
|
||
|
|
with assert_raises(ValueError, match='I raised'):
|
||
|
|
optimize.leastsq(func, x0=[0])
|
||
|
|
|
||
|
|
def test_Dfun_can_raise(self):
|
||
|
|
def func(x):
|
||
|
|
return x - np.array([10])
|
||
|
|
|
||
|
|
def deriv_func(*args):
|
||
|
|
raise ValueError('I raised')
|
||
|
|
|
||
|
|
with assert_raises(ValueError, match='I raised'):
|
||
|
|
optimize.leastsq(func, x0=[0], Dfun=deriv_func)
|
||
|
|
|
||
|
|
def test_reentrant_func(self):
|
||
|
|
def func(*args):
|
||
|
|
self.test_basic()
|
||
|
|
return self.residuals(*args)
|
||
|
|
|
||
|
|
p0 = array([0,0,0])
|
||
|
|
params_fit, ier = leastsq(func, p0,
|
||
|
|
args=(self.y_meas, self.x))
|
||
|
|
assert_(ier in (1,2,3,4), 'solution not found (ier=%d)' % ier)
|
||
|
|
# low precision due to random
|
||
|
|
assert_array_almost_equal(params_fit, self.abc, decimal=2)
|
||
|
|
|
||
|
|
def test_reentrant_Dfun(self):
|
||
|
|
def deriv_func(*args):
|
||
|
|
self.test_basic()
|
||
|
|
return self.residuals_jacobian(*args)
|
||
|
|
|
||
|
|
p0 = array([0,0,0])
|
||
|
|
params_fit, ier = leastsq(self.residuals, p0,
|
||
|
|
args=(self.y_meas, self.x),
|
||
|
|
Dfun=deriv_func)
|
||
|
|
assert_(ier in (1,2,3,4), 'solution not found (ier=%d)' % ier)
|
||
|
|
# low precision due to random
|
||
|
|
assert_array_almost_equal(params_fit, self.abc, decimal=2)
|
||
|
|
|
||
|
|
def test_concurrent_no_gradient(self):
|
||
|
|
v = sequence_parallel([self.test_basic] * 10)
|
||
|
|
assert all([result is None for result in v])
|
||
|
|
|
||
|
|
def test_concurrent_with_gradient(self):
|
||
|
|
v = sequence_parallel([self.test_basic_with_gradient] * 10)
|
||
|
|
assert all([result is None for result in v])
|
||
|
|
|
||
|
|
def test_func_input_output_length_check(self):
|
||
|
|
|
||
|
|
def func(x):
|
||
|
|
return 2 * (x[0] - 3) ** 2 + 1
|
||
|
|
|
||
|
|
with assert_raises(TypeError,
|
||
|
|
match='Improper input: func input vector length N='):
|
||
|
|
optimize.leastsq(func, x0=[0, 1])
|
||
|
|
|
||
|
|
|
||
|
|
class TestCurveFit:
|
||
|
|
def setup_method(self):
|
||
|
|
self.y = array([1.0, 3.2, 9.5, 13.7])
|
||
|
|
self.x = array([1.0, 2.0, 3.0, 4.0])
|
||
|
|
|
||
|
|
def test_one_argument(self):
|
||
|
|
def func(x,a):
|
||
|
|
return x**a
|
||
|
|
popt, pcov = curve_fit(func, self.x, self.y)
|
||
|
|
assert_(len(popt) == 1)
|
||
|
|
assert_(pcov.shape == (1,1))
|
||
|
|
assert_almost_equal(popt[0], 1.9149, decimal=4)
|
||
|
|
assert_almost_equal(pcov[0,0], 0.0016, decimal=4)
|
||
|
|
|
||
|
|
# Test if we get the same with full_output. Regression test for #1415.
|
||
|
|
# Also test if check_finite can be turned off.
|
||
|
|
res = curve_fit(func, self.x, self.y,
|
||
|
|
full_output=1, check_finite=False)
|
||
|
|
(popt2, pcov2, infodict, errmsg, ier) = res
|
||
|
|
assert_array_almost_equal(popt, popt2)
|
||
|
|
|
||
|
|
def test_two_argument(self):
|
||
|
|
def func(x, a, b):
|
||
|
|
return b*x**a
|
||
|
|
popt, pcov = curve_fit(func, self.x, self.y)
|
||
|
|
assert_(len(popt) == 2)
|
||
|
|
assert_(pcov.shape == (2,2))
|
||
|
|
assert_array_almost_equal(popt, [1.7989, 1.1642], decimal=4)
|
||
|
|
assert_array_almost_equal(pcov, [[0.0852, -0.1260], [-0.1260, 0.1912]],
|
||
|
|
decimal=4)
|
||
|
|
|
||
|
|
def test_func_is_classmethod(self):
|
||
|
|
class test_self:
|
||
|
|
"""This class tests if curve_fit passes the correct number of
|
||
|
|
arguments when the model function is a class instance method.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def func(self, x, a, b):
|
||
|
|
return b * x**a
|
||
|
|
|
||
|
|
test_self_inst = test_self()
|
||
|
|
popt, pcov = curve_fit(test_self_inst.func, self.x, self.y)
|
||
|
|
assert_(pcov.shape == (2,2))
|
||
|
|
assert_array_almost_equal(popt, [1.7989, 1.1642], decimal=4)
|
||
|
|
assert_array_almost_equal(pcov, [[0.0852, -0.1260], [-0.1260, 0.1912]],
|
||
|
|
decimal=4)
|
||
|
|
|
||
|
|
def test_regression_2639(self):
|
||
|
|
# This test fails if epsfcn in leastsq is too large.
|
||
|
|
x = [574.14200000000005, 574.154, 574.16499999999996,
|
||
|
|
574.17700000000002, 574.18799999999999, 574.19899999999996,
|
||
|
|
574.21100000000001, 574.22199999999998, 574.23400000000004,
|
||
|
|
574.245]
|
||
|
|
y = [859.0, 997.0, 1699.0, 2604.0, 2013.0, 1964.0, 2435.0,
|
||
|
|
1550.0, 949.0, 841.0]
|
||
|
|
guess = [574.1861428571428, 574.2155714285715, 1302.0, 1302.0,
|
||
|
|
0.0035019999999983615, 859.0]
|
||
|
|
good = [5.74177150e+02, 5.74209188e+02, 1.74187044e+03, 1.58646166e+03,
|
||
|
|
1.0068462e-02, 8.57450661e+02]
|
||
|
|
|
||
|
|
def f_double_gauss(x, x0, x1, A0, A1, sigma, c):
|
||
|
|
return (A0*np.exp(-(x-x0)**2/(2.*sigma**2))
|
||
|
|
+ A1*np.exp(-(x-x1)**2/(2.*sigma**2)) + c)
|
||
|
|
popt, pcov = curve_fit(f_double_gauss, x, y, guess, maxfev=10000)
|
||
|
|
assert_allclose(popt, good, rtol=1e-5)
|
||
|
|
|
||
|
|
def test_pcov(self):
|
||
|
|
xdata = np.array([0, 1, 2, 3, 4, 5])
|
||
|
|
ydata = np.array([1, 1, 5, 7, 8, 12])
|
||
|
|
sigma = np.array([1, 2, 1, 2, 1, 2])
|
||
|
|
|
||
|
|
def f(x, a, b):
|
||
|
|
return a*x + b
|
||
|
|
|
||
|
|
for method in ['lm', 'trf', 'dogbox']:
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, p0=[2, 0], sigma=sigma,
|
||
|
|
method=method)
|
||
|
|
perr_scaled = np.sqrt(np.diag(pcov))
|
||
|
|
assert_allclose(perr_scaled, [0.20659803, 0.57204404], rtol=1e-3)
|
||
|
|
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, p0=[2, 0], sigma=3*sigma,
|
||
|
|
method=method)
|
||
|
|
perr_scaled = np.sqrt(np.diag(pcov))
|
||
|
|
assert_allclose(perr_scaled, [0.20659803, 0.57204404], rtol=1e-3)
|
||
|
|
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, p0=[2, 0], sigma=sigma,
|
||
|
|
absolute_sigma=True, method=method)
|
||
|
|
perr = np.sqrt(np.diag(pcov))
|
||
|
|
assert_allclose(perr, [0.30714756, 0.85045308], rtol=1e-3)
|
||
|
|
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, p0=[2, 0], sigma=3*sigma,
|
||
|
|
absolute_sigma=True, method=method)
|
||
|
|
perr = np.sqrt(np.diag(pcov))
|
||
|
|
assert_allclose(perr, [3*0.30714756, 3*0.85045308], rtol=1e-3)
|
||
|
|
|
||
|
|
# infinite variances
|
||
|
|
|
||
|
|
def f_flat(x, a, b):
|
||
|
|
return a*x
|
||
|
|
|
||
|
|
pcov_expected = np.array([np.inf]*4).reshape(2, 2)
|
||
|
|
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(OptimizeWarning,
|
||
|
|
"Covariance of the parameters could not be estimated")
|
||
|
|
popt, pcov = curve_fit(f_flat, xdata, ydata, p0=[2, 0], sigma=sigma)
|
||
|
|
popt1, pcov1 = curve_fit(f, xdata[:2], ydata[:2], p0=[2, 0])
|
||
|
|
|
||
|
|
assert_(pcov.shape == (2, 2))
|
||
|
|
assert_array_equal(pcov, pcov_expected)
|
||
|
|
|
||
|
|
assert_(pcov1.shape == (2, 2))
|
||
|
|
assert_array_equal(pcov1, pcov_expected)
|
||
|
|
|
||
|
|
def test_array_like(self):
|
||
|
|
# Test sequence input. Regression test for gh-3037.
|
||
|
|
def f_linear(x, a, b):
|
||
|
|
return a*x + b
|
||
|
|
|
||
|
|
x = [1, 2, 3, 4]
|
||
|
|
y = [3, 5, 7, 9]
|
||
|
|
assert_allclose(curve_fit(f_linear, x, y)[0], [2, 1], atol=1e-10)
|
||
|
|
|
||
|
|
def test_indeterminate_covariance(self):
|
||
|
|
# Test that a warning is returned when pcov is indeterminate
|
||
|
|
xdata = np.array([1, 2, 3, 4, 5, 6])
|
||
|
|
ydata = np.array([1, 2, 3, 4, 5.5, 6])
|
||
|
|
assert_warns(OptimizeWarning, curve_fit,
|
||
|
|
lambda x, a, b: a*x, xdata, ydata)
|
||
|
|
|
||
|
|
def test_NaN_handling(self):
|
||
|
|
# Test for correct handling of NaNs in input data: gh-3422
|
||
|
|
|
||
|
|
# create input with NaNs
|
||
|
|
xdata = np.array([1, np.nan, 3])
|
||
|
|
ydata = np.array([1, 2, 3])
|
||
|
|
|
||
|
|
assert_raises(ValueError, curve_fit,
|
||
|
|
lambda x, a, b: a*x + b, xdata, ydata)
|
||
|
|
assert_raises(ValueError, curve_fit,
|
||
|
|
lambda x, a, b: a*x + b, ydata, xdata)
|
||
|
|
|
||
|
|
assert_raises(ValueError, curve_fit, lambda x, a, b: a*x + b,
|
||
|
|
xdata, ydata, **{"check_finite": True})
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _check_nan_policy(f, xdata_with_nan, xdata_without_nan,
|
||
|
|
ydata_with_nan, ydata_without_nan, method):
|
||
|
|
kwargs = {'f': f, 'xdata': xdata_with_nan, 'ydata': ydata_with_nan,
|
||
|
|
'method': method, 'check_finite': False}
|
||
|
|
# propagate test
|
||
|
|
error_msg = ("`nan_policy='propagate'` is not supported "
|
||
|
|
"by this function.")
|
||
|
|
with assert_raises(ValueError, match=error_msg):
|
||
|
|
curve_fit(**kwargs, nan_policy="propagate", maxfev=2000)
|
||
|
|
|
||
|
|
# raise test
|
||
|
|
with assert_raises(ValueError, match="The input contains nan"):
|
||
|
|
curve_fit(**kwargs, nan_policy="raise")
|
||
|
|
|
||
|
|
# omit test
|
||
|
|
result_with_nan, _ = curve_fit(**kwargs, nan_policy="omit")
|
||
|
|
kwargs['xdata'] = xdata_without_nan
|
||
|
|
kwargs['ydata'] = ydata_without_nan
|
||
|
|
result_without_nan, _ = curve_fit(**kwargs)
|
||
|
|
assert_allclose(result_with_nan, result_without_nan)
|
||
|
|
|
||
|
|
# not valid policy test
|
||
|
|
# check for argument names in any order
|
||
|
|
error_msg = (r"nan_policy must be one of \{(?:'raise'|'omit'|None)"
|
||
|
|
r"(?:, ?(?:'raise'|'omit'|None))*\}")
|
||
|
|
with assert_raises(ValueError, match=error_msg):
|
||
|
|
curve_fit(**kwargs, nan_policy="hi")
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('method', ["lm", "trf", "dogbox"])
|
||
|
|
def test_nan_policy_1d(self, method):
|
||
|
|
def f(x, a, b):
|
||
|
|
return a*x + b
|
||
|
|
|
||
|
|
xdata_with_nan = np.array([2, 3, np.nan, 4, 4, np.nan])
|
||
|
|
ydata_with_nan = np.array([1, 2, 5, 3, np.nan, 7])
|
||
|
|
xdata_without_nan = np.array([2, 3, 4])
|
||
|
|
ydata_without_nan = np.array([1, 2, 3])
|
||
|
|
|
||
|
|
self._check_nan_policy(f, xdata_with_nan, xdata_without_nan,
|
||
|
|
ydata_with_nan, ydata_without_nan, method)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('method', ["lm", "trf", "dogbox"])
|
||
|
|
def test_nan_policy_2d(self, method):
|
||
|
|
def f(x, a, b):
|
||
|
|
x1 = x[0, :]
|
||
|
|
x2 = x[1, :]
|
||
|
|
return a*x1 + b + x2
|
||
|
|
|
||
|
|
xdata_with_nan = np.array([[2, 3, np.nan, 4, 4, np.nan, 5],
|
||
|
|
[2, 3, np.nan, np.nan, 4, np.nan, 7]])
|
||
|
|
ydata_with_nan = np.array([1, 2, 5, 3, np.nan, 7, 10])
|
||
|
|
xdata_without_nan = np.array([[2, 3, 5], [2, 3, 7]])
|
||
|
|
ydata_without_nan = np.array([1, 2, 10])
|
||
|
|
|
||
|
|
self._check_nan_policy(f, xdata_with_nan, xdata_without_nan,
|
||
|
|
ydata_with_nan, ydata_without_nan, method)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('n', [2, 3])
|
||
|
|
@pytest.mark.parametrize('method', ["lm", "trf", "dogbox"])
|
||
|
|
def test_nan_policy_2_3d(self, n, method):
|
||
|
|
def f(x, a, b):
|
||
|
|
x1 = x[..., 0, :].squeeze()
|
||
|
|
x2 = x[..., 1, :].squeeze()
|
||
|
|
return a*x1 + b + x2
|
||
|
|
|
||
|
|
xdata_with_nan = np.array([[[2, 3, np.nan, 4, 4, np.nan, 5],
|
||
|
|
[2, 3, np.nan, np.nan, 4, np.nan, 7]]])
|
||
|
|
xdata_with_nan = xdata_with_nan.squeeze() if n == 2 else xdata_with_nan
|
||
|
|
ydata_with_nan = np.array([1, 2, 5, 3, np.nan, 7, 10])
|
||
|
|
xdata_without_nan = np.array([[[2, 3, 5], [2, 3, 7]]])
|
||
|
|
ydata_without_nan = np.array([1, 2, 10])
|
||
|
|
|
||
|
|
self._check_nan_policy(f, xdata_with_nan, xdata_without_nan,
|
||
|
|
ydata_with_nan, ydata_without_nan, method)
|
||
|
|
|
||
|
|
def test_empty_inputs(self):
|
||
|
|
# Test both with and without bounds (regression test for gh-9864)
|
||
|
|
assert_raises(ValueError, curve_fit, lambda x, a: a*x, [], [])
|
||
|
|
assert_raises(ValueError, curve_fit, lambda x, a: a*x, [], [],
|
||
|
|
bounds=(1, 2))
|
||
|
|
assert_raises(ValueError, curve_fit, lambda x, a: a*x, [1], [])
|
||
|
|
assert_raises(ValueError, curve_fit, lambda x, a: a*x, [2], [],
|
||
|
|
bounds=(1, 2))
|
||
|
|
|
||
|
|
def test_function_zero_params(self):
|
||
|
|
# Fit args is zero, so "Unable to determine number of fit parameters."
|
||
|
|
assert_raises(ValueError, curve_fit, lambda x: x, [1, 2], [3, 4])
|
||
|
|
|
||
|
|
def test_None_x(self): # Added in GH10196
|
||
|
|
popt, pcov = curve_fit(lambda _, a: a * np.arange(10),
|
||
|
|
None, 2 * np.arange(10))
|
||
|
|
assert_allclose(popt, [2.])
|
||
|
|
|
||
|
|
def test_method_argument(self):
|
||
|
|
def f(x, a, b):
|
||
|
|
return a * np.exp(-b*x)
|
||
|
|
|
||
|
|
xdata = np.linspace(0, 1, 11)
|
||
|
|
ydata = f(xdata, 2., 2.)
|
||
|
|
|
||
|
|
for method in ['trf', 'dogbox', 'lm', None]:
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, method=method)
|
||
|
|
assert_allclose(popt, [2., 2.])
|
||
|
|
|
||
|
|
assert_raises(ValueError, curve_fit, f, xdata, ydata, method='unknown')
|
||
|
|
|
||
|
|
def test_full_output(self):
|
||
|
|
def f(x, a, b):
|
||
|
|
return a * np.exp(-b * x)
|
||
|
|
|
||
|
|
xdata = np.linspace(0, 1, 11)
|
||
|
|
ydata = f(xdata, 2., 2.)
|
||
|
|
|
||
|
|
for method in ['trf', 'dogbox', 'lm', None]:
|
||
|
|
popt, pcov, infodict, errmsg, ier = curve_fit(
|
||
|
|
f, xdata, ydata, method=method, full_output=True)
|
||
|
|
assert_allclose(popt, [2., 2.])
|
||
|
|
assert "nfev" in infodict
|
||
|
|
assert "fvec" in infodict
|
||
|
|
if method == 'lm' or method is None:
|
||
|
|
assert "fjac" in infodict
|
||
|
|
assert "ipvt" in infodict
|
||
|
|
assert "qtf" in infodict
|
||
|
|
assert isinstance(errmsg, str)
|
||
|
|
assert ier in (1, 2, 3, 4)
|
||
|
|
|
||
|
|
def test_bounds(self):
|
||
|
|
def f(x, a, b):
|
||
|
|
return a * np.exp(-b*x)
|
||
|
|
|
||
|
|
xdata = np.linspace(0, 1, 11)
|
||
|
|
ydata = f(xdata, 2., 2.)
|
||
|
|
|
||
|
|
# The minimum w/out bounds is at [2., 2.],
|
||
|
|
# and with bounds it's at [1.5, smth].
|
||
|
|
lb = [1., 0]
|
||
|
|
ub = [1.5, 3.]
|
||
|
|
|
||
|
|
# Test that both variants of the bounds yield the same result
|
||
|
|
bounds = (lb, ub)
|
||
|
|
bounds_class = Bounds(lb, ub)
|
||
|
|
for method in [None, 'trf', 'dogbox']:
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, bounds=bounds,
|
||
|
|
method=method)
|
||
|
|
assert_allclose(popt[0], 1.5)
|
||
|
|
|
||
|
|
popt_class, pcov_class = curve_fit(f, xdata, ydata,
|
||
|
|
bounds=bounds_class,
|
||
|
|
method=method)
|
||
|
|
assert_allclose(popt_class, popt)
|
||
|
|
|
||
|
|
# With bounds, the starting estimate is feasible.
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, method='trf',
|
||
|
|
bounds=([0., 0], [0.6, np.inf]))
|
||
|
|
assert_allclose(popt[0], 0.6)
|
||
|
|
|
||
|
|
# method='lm' doesn't support bounds.
|
||
|
|
assert_raises(ValueError, curve_fit, f, xdata, ydata, bounds=bounds,
|
||
|
|
method='lm')
|
||
|
|
|
||
|
|
def test_bounds_p0(self):
|
||
|
|
# This test is for issue #5719. The problem was that an initial guess
|
||
|
|
# was ignored when 'trf' or 'dogbox' methods were invoked.
|
||
|
|
def f(x, a):
|
||
|
|
return np.sin(x + a)
|
||
|
|
|
||
|
|
xdata = np.linspace(-2*np.pi, 2*np.pi, 40)
|
||
|
|
ydata = np.sin(xdata)
|
||
|
|
bounds = (-3 * np.pi, 3 * np.pi)
|
||
|
|
for method in ['trf', 'dogbox']:
|
||
|
|
popt_1, _ = curve_fit(f, xdata, ydata, p0=2.1*np.pi)
|
||
|
|
popt_2, _ = curve_fit(f, xdata, ydata, p0=2.1*np.pi,
|
||
|
|
bounds=bounds, method=method)
|
||
|
|
|
||
|
|
# If the initial guess is ignored, then popt_2 would be close 0.
|
||
|
|
assert_allclose(popt_1, popt_2)
|
||
|
|
|
||
|
|
def test_jac(self):
|
||
|
|
# Test that Jacobian callable is handled correctly and
|
||
|
|
# weighted if sigma is provided.
|
||
|
|
def f(x, a, b):
|
||
|
|
return a * np.exp(-b*x)
|
||
|
|
|
||
|
|
def jac(x, a, b):
|
||
|
|
e = np.exp(-b*x)
|
||
|
|
return np.vstack((e, -a * x * e)).T
|
||
|
|
|
||
|
|
xdata = np.linspace(0, 1, 11)
|
||
|
|
ydata = f(xdata, 2., 2.)
|
||
|
|
|
||
|
|
# Test numerical options for least_squares backend.
|
||
|
|
for method in ['trf', 'dogbox']:
|
||
|
|
for scheme in ['2-point', '3-point', 'cs']:
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, jac=scheme,
|
||
|
|
method=method)
|
||
|
|
assert_allclose(popt, [2, 2])
|
||
|
|
|
||
|
|
# Test the analytic option.
|
||
|
|
for method in ['lm', 'trf', 'dogbox']:
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, method=method, jac=jac)
|
||
|
|
assert_allclose(popt, [2, 2])
|
||
|
|
|
||
|
|
# Now add an outlier and provide sigma.
|
||
|
|
ydata[5] = 100
|
||
|
|
sigma = np.ones(xdata.shape[0])
|
||
|
|
sigma[5] = 200
|
||
|
|
for method in ['lm', 'trf', 'dogbox']:
|
||
|
|
popt, pcov = curve_fit(f, xdata, ydata, sigma=sigma, method=method,
|
||
|
|
jac=jac)
|
||
|
|
# Still the optimization process is influenced somehow,
|
||
|
|
# have to set rtol=1e-3.
|
||
|
|
assert_allclose(popt, [2, 2], rtol=1e-3)
|
||
|
|
|
||
|
|
def test_maxfev_and_bounds(self):
|
||
|
|
# gh-6340: with no bounds, curve_fit accepts parameter maxfev (via leastsq)
|
||
|
|
# but with bounds, the parameter is `max_nfev` (via least_squares)
|
||
|
|
x = np.arange(0, 10)
|
||
|
|
y = 2*x
|
||
|
|
popt1, _ = curve_fit(lambda x,p: p*x, x, y, bounds=(0, 3), maxfev=100)
|
||
|
|
popt2, _ = curve_fit(lambda x,p: p*x, x, y, bounds=(0, 3), max_nfev=100)
|
||
|
|
|
||
|
|
assert_allclose(popt1, 2, atol=1e-14)
|
||
|
|
assert_allclose(popt2, 2, atol=1e-14)
|
||
|
|
|
||
|
|
def test_curvefit_simplecovariance(self):
|
||
|
|
|
||
|
|
def func(x, a, b):
|
||
|
|
return a * np.exp(-b*x)
|
||
|
|
|
||
|
|
def jac(x, a, b):
|
||
|
|
e = np.exp(-b*x)
|
||
|
|
return np.vstack((e, -a * x * e)).T
|
||
|
|
|
||
|
|
np.random.seed(0)
|
||
|
|
xdata = np.linspace(0, 4, 50)
|
||
|
|
y = func(xdata, 2.5, 1.3)
|
||
|
|
ydata = y + 0.2 * np.random.normal(size=len(xdata))
|
||
|
|
|
||
|
|
sigma = np.zeros(len(xdata)) + 0.2
|
||
|
|
covar = np.diag(sigma**2)
|
||
|
|
|
||
|
|
for jac1, jac2 in [(jac, jac), (None, None)]:
|
||
|
|
for absolute_sigma in [False, True]:
|
||
|
|
popt1, pcov1 = curve_fit(func, xdata, ydata, sigma=sigma,
|
||
|
|
jac=jac1, absolute_sigma=absolute_sigma)
|
||
|
|
popt2, pcov2 = curve_fit(func, xdata, ydata, sigma=covar,
|
||
|
|
jac=jac2, absolute_sigma=absolute_sigma)
|
||
|
|
|
||
|
|
assert_allclose(popt1, popt2, atol=1e-14)
|
||
|
|
assert_allclose(pcov1, pcov2, atol=1e-14)
|
||
|
|
|
||
|
|
def test_curvefit_covariance(self):
|
||
|
|
|
||
|
|
def funcp(x, a, b):
|
||
|
|
rotn = np.array([[1./np.sqrt(2), -1./np.sqrt(2), 0],
|
||
|
|
[1./np.sqrt(2), 1./np.sqrt(2), 0],
|
||
|
|
[0, 0, 1.0]])
|
||
|
|
return rotn.dot(a * np.exp(-b*x))
|
||
|
|
|
||
|
|
def jacp(x, a, b):
|
||
|
|
rotn = np.array([[1./np.sqrt(2), -1./np.sqrt(2), 0],
|
||
|
|
[1./np.sqrt(2), 1./np.sqrt(2), 0],
|
||
|
|
[0, 0, 1.0]])
|
||
|
|
e = np.exp(-b*x)
|
||
|
|
return rotn.dot(np.vstack((e, -a * x * e)).T)
|
||
|
|
|
||
|
|
def func(x, a, b):
|
||
|
|
return a * np.exp(-b*x)
|
||
|
|
|
||
|
|
def jac(x, a, b):
|
||
|
|
e = np.exp(-b*x)
|
||
|
|
return np.vstack((e, -a * x * e)).T
|
||
|
|
|
||
|
|
np.random.seed(0)
|
||
|
|
xdata = np.arange(1, 4)
|
||
|
|
y = func(xdata, 2.5, 1.0)
|
||
|
|
ydata = y + 0.2 * np.random.normal(size=len(xdata))
|
||
|
|
sigma = np.zeros(len(xdata)) + 0.2
|
||
|
|
covar = np.diag(sigma**2)
|
||
|
|
# Get a rotation matrix, and obtain ydatap = R ydata
|
||
|
|
# Chisq = ydata^T C^{-1} ydata
|
||
|
|
# = ydata^T R^T R C^{-1} R^T R ydata
|
||
|
|
# = ydatap^T Cp^{-1} ydatap
|
||
|
|
# Cp^{-1} = R C^{-1} R^T
|
||
|
|
# Cp = R C R^T, since R^-1 = R^T
|
||
|
|
rotn = np.array([[1./np.sqrt(2), -1./np.sqrt(2), 0],
|
||
|
|
[1./np.sqrt(2), 1./np.sqrt(2), 0],
|
||
|
|
[0, 0, 1.0]])
|
||
|
|
ydatap = rotn.dot(ydata)
|
||
|
|
covarp = rotn.dot(covar).dot(rotn.T)
|
||
|
|
|
||
|
|
for jac1, jac2 in [(jac, jacp), (None, None)]:
|
||
|
|
for absolute_sigma in [False, True]:
|
||
|
|
popt1, pcov1 = curve_fit(func, xdata, ydata, sigma=sigma,
|
||
|
|
jac=jac1, absolute_sigma=absolute_sigma)
|
||
|
|
popt2, pcov2 = curve_fit(funcp, xdata, ydatap, sigma=covarp,
|
||
|
|
jac=jac2, absolute_sigma=absolute_sigma)
|
||
|
|
|
||
|
|
assert_allclose(popt1, popt2, rtol=1.2e-7, atol=1e-14)
|
||
|
|
assert_allclose(pcov1, pcov2, rtol=1.2e-7, atol=1e-14)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("absolute_sigma", [False, True])
|
||
|
|
def test_curvefit_scalar_sigma(self, absolute_sigma):
|
||
|
|
def func(x, a, b):
|
||
|
|
return a * x + b
|
||
|
|
|
||
|
|
x, y = self.x, self.y
|
||
|
|
_, pcov1 = curve_fit(func, x, y, sigma=2, absolute_sigma=absolute_sigma)
|
||
|
|
# Explicitly building the sigma 1D array
|
||
|
|
_, pcov2 = curve_fit(
|
||
|
|
func, x, y, sigma=np.full_like(y, 2), absolute_sigma=absolute_sigma
|
||
|
|
)
|
||
|
|
assert np.all(pcov1 == pcov2)
|
||
|
|
|
||
|
|
def test_dtypes(self):
|
||
|
|
# regression test for gh-9581: curve_fit fails if x and y dtypes differ
|
||
|
|
x = np.arange(-3, 5)
|
||
|
|
y = 1.5*x + 3.0 + 0.5*np.sin(x)
|
||
|
|
|
||
|
|
def func(x, a, b):
|
||
|
|
return a*x + b
|
||
|
|
|
||
|
|
for method in ['lm', 'trf', 'dogbox']:
|
||
|
|
for dtx in [np.float32, np.float64]:
|
||
|
|
for dty in [np.float32, np.float64]:
|
||
|
|
x = x.astype(dtx)
|
||
|
|
y = y.astype(dty)
|
||
|
|
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("error", OptimizeWarning)
|
||
|
|
p, cov = curve_fit(func, x, y, method=method)
|
||
|
|
|
||
|
|
assert np.isfinite(cov).all()
|
||
|
|
assert not np.allclose(p, 1) # curve_fit's initial value
|
||
|
|
|
||
|
|
def test_dtypes2(self):
|
||
|
|
# regression test for gh-7117: curve_fit fails if
|
||
|
|
# both inputs are float32
|
||
|
|
def hyperbola(x, s_1, s_2, o_x, o_y, c):
|
||
|
|
b_2 = (s_1 + s_2) / 2
|
||
|
|
b_1 = (s_2 - s_1) / 2
|
||
|
|
return o_y + b_1*(x-o_x) + b_2*np.sqrt((x-o_x)**2 + c**2/4)
|
||
|
|
|
||
|
|
min_fit = np.array([-3.0, 0.0, -2.0, -10.0, 0.0])
|
||
|
|
max_fit = np.array([0.0, 3.0, 3.0, 0.0, 10.0])
|
||
|
|
guess = np.array([-2.5/3.0, 4/3.0, 1.0, -4.0, 0.5])
|
||
|
|
|
||
|
|
params = [-2, .4, -1, -5, 9.5]
|
||
|
|
xdata = np.array([-32, -16, -8, 4, 4, 8, 16, 32])
|
||
|
|
ydata = hyperbola(xdata, *params)
|
||
|
|
|
||
|
|
# run optimization twice, with xdata being float32 and float64
|
||
|
|
popt_64, _ = curve_fit(f=hyperbola, xdata=xdata, ydata=ydata, p0=guess,
|
||
|
|
bounds=(min_fit, max_fit))
|
||
|
|
|
||
|
|
xdata = xdata.astype(np.float32)
|
||
|
|
ydata = hyperbola(xdata, *params)
|
||
|
|
|
||
|
|
popt_32, _ = curve_fit(f=hyperbola, xdata=xdata, ydata=ydata, p0=guess,
|
||
|
|
bounds=(min_fit, max_fit))
|
||
|
|
|
||
|
|
assert_allclose(popt_32, popt_64, atol=2e-5)
|
||
|
|
|
||
|
|
def test_broadcast_y(self):
|
||
|
|
xdata = np.arange(10)
|
||
|
|
target = 4.7 * xdata ** 2 + 3.5 * xdata + np.random.rand(len(xdata))
|
||
|
|
def fit_func(x, a, b):
|
||
|
|
return a * x ** 2 + b * x - target
|
||
|
|
for method in ['lm', 'trf', 'dogbox']:
|
||
|
|
popt0, pcov0 = curve_fit(fit_func,
|
||
|
|
xdata=xdata,
|
||
|
|
ydata=np.zeros_like(xdata),
|
||
|
|
method=method)
|
||
|
|
popt1, pcov1 = curve_fit(fit_func,
|
||
|
|
xdata=xdata,
|
||
|
|
ydata=0,
|
||
|
|
method=method)
|
||
|
|
assert_allclose(pcov0, pcov1)
|
||
|
|
|
||
|
|
def test_args_in_kwargs(self):
|
||
|
|
# Ensure that `args` cannot be passed as keyword argument to `curve_fit`
|
||
|
|
|
||
|
|
def func(x, a, b):
|
||
|
|
return a * x + b
|
||
|
|
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
curve_fit(func,
|
||
|
|
xdata=[1, 2, 3, 4],
|
||
|
|
ydata=[5, 9, 13, 17],
|
||
|
|
p0=[1],
|
||
|
|
args=(1,))
|
||
|
|
|
||
|
|
def test_data_point_number_validation(self):
|
||
|
|
def func(x, a, b, c, d, e):
|
||
|
|
return a * np.exp(-b * x) + c + d + e
|
||
|
|
|
||
|
|
with assert_raises(TypeError, match="The number of func parameters="):
|
||
|
|
curve_fit(func,
|
||
|
|
xdata=[1, 2, 3, 4],
|
||
|
|
ydata=[5, 9, 13, 17])
|
||
|
|
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@pytest.mark.filterwarnings('ignore::RuntimeWarning')
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def test_gh4555(self):
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|
# gh-4555 reported that covariance matrices returned by `leastsq`
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|
# can have negative diagonal elements and eigenvalues. (In fact,
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# they can also be asymmetric.) This shows up in the output of
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# `scipy.optimize.curve_fit`. Check that it has been resolved.giit
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def f(x, a, b, c, d, e):
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return a*np.log(x + 1 + b) + c*np.log(x + 1 + d) + e
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rng = np.random.default_rng(408113519974467917)
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n = 100
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x = np.arange(n)
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y = np.linspace(2, 7, n) + rng.random(n)
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p, cov = optimize.curve_fit(f, x, y, maxfev=100000)
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assert np.all(np.diag(cov) > 0)
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eigs = linalg.eigh(cov)[0] # separate line for debugging
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# some platforms see a small negative eigevenvalue
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assert np.all(eigs > -1e-2)
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assert_allclose(cov, cov.T)
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def test_gh4555b(self):
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# check that PR gh-17247 did not significantly change covariance matrix
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# for simple cases
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rng = np.random.default_rng(408113519974467917)
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def func(x, a, b, c):
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return a * np.exp(-b * x) + c
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xdata = np.linspace(0, 4, 50)
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y = func(xdata, 2.5, 1.3, 0.5)
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y_noise = 0.2 * rng.normal(size=xdata.size)
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ydata = y + y_noise
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_, res = curve_fit(func, xdata, ydata)
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# reference from commit 1d80a2f254380d2b45733258ca42eb6b55c8755b
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ref = [[+0.0158972536486215, 0.0069207183284242, -0.0007474400714749],
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[+0.0069207183284242, 0.0205057958128679, +0.0053997711275403],
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[-0.0007474400714749, 0.0053997711275403, +0.0027833930320877]]
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# Linux_Python_38_32bit_full fails with default tolerance
|
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assert_allclose(res, ref, 2e-7)
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||
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|
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|
def test_gh13670(self):
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|
# gh-13670 reported that `curve_fit` executes callables
|
||
|
|
# with the same values of the parameters at the beginning of
|
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|
# optimization. Check that this has been resolved.
|
||
|
|
|
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|
|
rng = np.random.default_rng(8250058582555444926)
|
||
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|
x = np.linspace(0, 3, 101)
|
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|
y = 2 * x + 1 + rng.normal(size=101) * 0.5
|
||
|
|
|
||
|
|
def line(x, *p):
|
||
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|
assert not np.all(line.last_p == p)
|
||
|
|
line.last_p = p
|
||
|
|
return x * p[0] + p[1]
|
||
|
|
|
||
|
|
def jac(x, *p):
|
||
|
|
assert not np.all(jac.last_p == p)
|
||
|
|
jac.last_p = p
|
||
|
|
return np.array([x, np.ones_like(x)]).T
|
||
|
|
|
||
|
|
line.last_p = None
|
||
|
|
jac.last_p = None
|
||
|
|
p0 = np.array([1.0, 5.0])
|
||
|
|
curve_fit(line, x, y, p0, method='lm', jac=jac)
|
||
|
|
|
||
|
|
|
||
|
|
class TestFixedPoint:
|
||
|
|
|
||
|
|
def test_scalar_trivial(self):
|
||
|
|
# f(x) = 2x; fixed point should be x=0
|
||
|
|
def func(x):
|
||
|
|
return 2.0*x
|
||
|
|
x0 = 1.0
|
||
|
|
x = fixed_point(func, x0)
|
||
|
|
assert_almost_equal(x, 0.0)
|
||
|
|
|
||
|
|
def test_scalar_basic1(self):
|
||
|
|
# f(x) = x**2; x0=1.05; fixed point should be x=1
|
||
|
|
def func(x):
|
||
|
|
return x**2
|
||
|
|
x0 = 1.05
|
||
|
|
x = fixed_point(func, x0)
|
||
|
|
assert_almost_equal(x, 1.0)
|
||
|
|
|
||
|
|
def test_scalar_basic2(self):
|
||
|
|
# f(x) = x**0.5; x0=1.05; fixed point should be x=1
|
||
|
|
def func(x):
|
||
|
|
return x**0.5
|
||
|
|
x0 = 1.05
|
||
|
|
x = fixed_point(func, x0)
|
||
|
|
assert_almost_equal(x, 1.0)
|
||
|
|
|
||
|
|
def test_array_trivial(self):
|
||
|
|
def func(x):
|
||
|
|
return 2.0*x
|
||
|
|
x0 = [0.3, 0.15]
|
||
|
|
with np.errstate(all='ignore'):
|
||
|
|
x = fixed_point(func, x0)
|
||
|
|
assert_almost_equal(x, [0.0, 0.0])
|
||
|
|
|
||
|
|
def test_array_basic1(self):
|
||
|
|
# f(x) = c * x**2; fixed point should be x=1/c
|
||
|
|
def func(x, c):
|
||
|
|
return c * x**2
|
||
|
|
c = array([0.75, 1.0, 1.25])
|
||
|
|
x0 = [1.1, 1.15, 0.9]
|
||
|
|
with np.errstate(all='ignore'):
|
||
|
|
x = fixed_point(func, x0, args=(c,))
|
||
|
|
assert_almost_equal(x, 1.0/c)
|
||
|
|
|
||
|
|
def test_array_basic2(self):
|
||
|
|
# f(x) = c * x**0.5; fixed point should be x=c**2
|
||
|
|
def func(x, c):
|
||
|
|
return c * x**0.5
|
||
|
|
c = array([0.75, 1.0, 1.25])
|
||
|
|
x0 = [0.8, 1.1, 1.1]
|
||
|
|
x = fixed_point(func, x0, args=(c,))
|
||
|
|
assert_almost_equal(x, c**2)
|
||
|
|
|
||
|
|
def test_lambertw(self):
|
||
|
|
# python-list/2010-December/594592.html
|
||
|
|
xxroot = fixed_point(lambda xx: np.exp(-2.0*xx)/2.0, 1.0,
|
||
|
|
args=(), xtol=1e-12, maxiter=500)
|
||
|
|
assert_allclose(xxroot, np.exp(-2.0*xxroot)/2.0)
|
||
|
|
assert_allclose(xxroot, lambertw(1)/2)
|
||
|
|
|
||
|
|
def test_no_acceleration(self):
|
||
|
|
# github issue 5460
|
||
|
|
ks = 2
|
||
|
|
kl = 6
|
||
|
|
m = 1.3
|
||
|
|
n0 = 1.001
|
||
|
|
i0 = ((m-1)/m)*(kl/ks/m)**(1/(m-1))
|
||
|
|
|
||
|
|
def func(n):
|
||
|
|
return np.log(kl/ks/n) / np.log(i0*n/(n - 1)) + 1
|
||
|
|
|
||
|
|
n = fixed_point(func, n0, method='iteration')
|
||
|
|
assert_allclose(n, m)
|