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Unit tests for optimization routines from minpack.py.
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from numpy.testing import *
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from numpy.testing import assert_, assert_almost_equal, assert_array_equal, \
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assert_array_almost_equal, TestCase, run_module_suite, assert_raises
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from numpy import array, float64
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from scipy import optimize
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from scipy.optimize.minpack import fsolve, leastsq, curve_fit
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class TestFSolve(TestCase):
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from scipy.optimize.minpack import leastsq, curve_fit, fixed_point
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class ReturnShape(object):
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"""This class exists to create a callable that does not have a 'func_name' attribute.
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__init__ takes the argument 'shape', which should be a tuple of ints. When an instance
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it called with a single argument 'x', it returns numpy.ones(shape).
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def __init__(self, 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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class TestFSolve(object):
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def pressure_network(self, 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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fprime=self.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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"""The callable 'func' has no 'func_name' attribute."""
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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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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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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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"""The callables 'func' and 'deriv_func' have no 'func_name' attribute."""
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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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func = lambda x: dummy_func(x, (2,))
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deriv_func = lambda x: 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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class TestLeastSq(TestCase):
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x = np.linspace(0, 10, 40)
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assert_(ier in (1,2,3,4), 'solution not found: %s'%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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"""The callable 'func' has no 'func_name' attribute."""
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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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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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assert_raises(TypeError, optimize.leastsq, dummy_func, x0, args=((1,),))
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def test_wrong_shape_Dfun_callable(self):
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"""The callables 'func' and 'deriv_func' have no 'func_name' attribute."""
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func = ReturnShape(1)
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deriv_func = ReturnShape((2,2))
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assert_raises(TypeError, optimize.leastsq, func, x0=[0,1], Dfun=deriv_func)
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def test_wrong_shape_Dfun_function(self):
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func = lambda x: dummy_func(x, (2,))
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deriv_func = lambda x: dummy_func(x, (3,3))
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assert_raises(TypeError, optimize.leastsq, func, x0=[0,1], Dfun=deriv_func)
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class TestCurveFit(TestCase):
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self.y = array([1.0, 3.2, 9.5, 13.7])
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popt, pcov = curve_fit(func, self.x, self.y)
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assert_(len(popt)==1)
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assert_(pcov.shape==(1,1))
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assert_(len(popt) == 1)
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assert_(pcov.shape == (1,1))
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assert_almost_equal(popt[0], 1.9149, decimal=4)
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assert_almost_equal(pcov[0,0], 0.0016, decimal=4)
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def func(x, a, b):
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popt, pcov = curve_fit(func, self.x, self.y)
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assert_(len(popt)==2)
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assert_(pcov.shape==(2,2))
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assert_(len(popt) == 2)
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assert_(pcov.shape == (2,2))
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assert_array_almost_equal(popt, [1.7989, 1.1642], decimal=4)
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assert_array_almost_equal(pcov, [[0.0852, -0.1260],[-0.1260, 0.1912]], decimal=4)
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class TestFixedPoint(TestCase):
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def test_scalar_trivial(self):
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"""f(x) = 2x; fixed point should be x=0"""
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x = fixed_point(func, x0)
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assert_almost_equal(x, 0.0)
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def test_scalar_basic1(self):
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"""f(x) = x**2; x0=1.05; fixed point should be x=1"""
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x = fixed_point(func, x0)
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assert_almost_equal(x, 1.0)
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def test_scalar_basic2(self):
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"""f(x) = x**0.5; x0=1.05; fixed point should be x=1"""
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x = fixed_point(func, x0)
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assert_almost_equal(x, 1.0)
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def test_array_trivial(self):
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olderr = np.seterr(all='ignore')
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x = fixed_point(func, x0)
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assert_almost_equal(x, [0.0, 0.0])
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def test_array_basic1(self):
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"""f(x) = c * x**2; fixed point should be x=1/c"""
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c = array([0.75, 1.0, 1.25])
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x0 = [1.1, 1.15, 0.9]
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olderr = np.seterr(all='ignore')
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x = fixed_point(func, x0, args=(c,))
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assert_almost_equal(x, 1.0/c)
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def test_array_basic2(self):
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"""f(x) = c * x**0.5; fixed point should be x=c**2"""
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c = array([0.75, 1.0, 1.25])
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x = fixed_point(func, x0, args=(c,))
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assert_almost_equal(x, c**2)
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if __name__ == "__main__":
added
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scipy/optimize/tests/test_minpack.py
