207 lines
7.1 KiB
Python
207 lines
7.1 KiB
Python
import numpy as np
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from numpy.linalg import lstsq
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from numpy.testing import assert_allclose, assert_equal, assert_
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from scipy.sparse import rand
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from scipy.sparse.linalg import aslinearoperator
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from scipy.optimize import lsq_linear
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A = np.array([
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[0.171, -0.057],
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[-0.049, -0.248],
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[-0.166, 0.054],
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])
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b = np.array([0.074, 1.014, -0.383])
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class BaseMixin(object):
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def setup_method(self):
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self.rnd = np.random.RandomState(0)
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def test_dense_no_bounds(self):
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, method=self.method, lsq_solver=lsq_solver)
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assert_allclose(res.x, lstsq(A, b, rcond=-1)[0])
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def test_dense_bounds(self):
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# Solutions for comparison are taken from MATLAB.
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lb = np.array([-1, -10])
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ub = np.array([1, 0])
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (lb, ub), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, lstsq(A, b, rcond=-1)[0])
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lb = np.array([0.0, -np.inf])
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (lb, np.inf), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, np.array([0.0, -4.084174437334673]),
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atol=1e-6)
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lb = np.array([-1, 0])
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (lb, np.inf), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, np.array([0.448427311733504, 0]),
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atol=1e-15)
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ub = np.array([np.inf, -5])
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (-np.inf, ub), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, np.array([-0.105560998682388, -5]))
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ub = np.array([-1, np.inf])
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (-np.inf, ub), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, np.array([-1, -4.181102129483254]))
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lb = np.array([0, -4])
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ub = np.array([1, 0])
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (lb, ub), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, np.array([0.005236663400791, -4]))
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def test_np_matrix(self):
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# gh-10711
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with np.testing.suppress_warnings() as sup:
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sup.filter(PendingDeprecationWarning)
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A = np.matrix([[20, -4, 0, 2, 3], [10, -2, 1, 0, -1]])
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k = np.array([20, 15])
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s_t = lsq_linear(A, k)
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def test_dense_rank_deficient(self):
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A = np.array([[-0.307, -0.184]])
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b = np.array([0.773])
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lb = [-0.1, -0.1]
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ub = [0.1, 0.1]
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (lb, ub), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.x, [-0.1, -0.1])
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A = np.array([
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[0.334, 0.668],
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[-0.516, -1.032],
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[0.192, 0.384],
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])
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b = np.array([-1.436, 0.135, 0.909])
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lb = [0, -1]
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ub = [1, -0.5]
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for lsq_solver in self.lsq_solvers:
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res = lsq_linear(A, b, (lb, ub), method=self.method,
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lsq_solver=lsq_solver)
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assert_allclose(res.optimality, 0, atol=1e-11)
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def test_full_result(self):
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lb = np.array([0, -4])
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ub = np.array([1, 0])
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res = lsq_linear(A, b, (lb, ub), method=self.method)
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assert_allclose(res.x, [0.005236663400791, -4])
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r = A.dot(res.x) - b
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assert_allclose(res.cost, 0.5 * np.dot(r, r))
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assert_allclose(res.fun, r)
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assert_allclose(res.optimality, 0.0, atol=1e-12)
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assert_equal(res.active_mask, [0, -1])
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assert_(res.nit < 15)
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assert_(res.status == 1 or res.status == 3)
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assert_(isinstance(res.message, str))
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assert_(res.success)
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# This is a test for issue #9982.
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def test_almost_singular(self):
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A = np.array(
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[[0.8854232310355122, 0.0365312146937765, 0.0365312146836789],
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[0.3742460132129041, 0.0130523214078376, 0.0130523214077873],
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[0.9680633871281361, 0.0319366128718639, 0.0319366128718388]])
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b = np.array(
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[0.0055029366538097, 0.0026677442422208, 0.0066612514782381])
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result = lsq_linear(A, b, method=self.method)
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assert_(result.cost < 1.1e-8)
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def test_large_rank_deficient(self):
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np.random.seed(0)
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n, m = np.sort(np.random.randint(2, 1000, size=2))
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m *= 2 # make m >> n
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A = 1.0 * np.random.randint(-99, 99, size=[m, n])
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b = 1.0 * np.random.randint(-99, 99, size=[m])
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bounds = 1.0 * np.sort(np.random.randint(-99, 99, size=(2, n)), axis=0)
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bounds[1, :] += 1.0 # ensure up > lb
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# Make the A matrix strongly rank deficient by replicating some columns
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w = np.random.choice(n, n) # Select random columns with duplicates
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A = A[:, w]
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x_bvls = lsq_linear(A, b, bounds=bounds, method='bvls').x
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x_trf = lsq_linear(A, b, bounds=bounds, method='trf').x
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cost_bvls = np.sum((A @ x_bvls - b)**2)
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cost_trf = np.sum((A @ x_trf - b)**2)
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assert_(abs(cost_bvls - cost_trf) < cost_trf*1e-10)
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def test_convergence_small_matrix(self):
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A = np.array([[49.0, 41.0, -32.0],
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[-19.0, -32.0, -8.0],
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[-13.0, 10.0, 69.0]])
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b = np.array([-41.0, -90.0, 47.0])
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bounds = np.array([[31.0, -44.0, 26.0],
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[54.0, -32.0, 28.0]])
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x_bvls = lsq_linear(A, b, bounds=bounds, method='bvls').x
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x_trf = lsq_linear(A, b, bounds=bounds, method='trf').x
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cost_bvls = np.sum((A @ x_bvls - b)**2)
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cost_trf = np.sum((A @ x_trf - b)**2)
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assert_(abs(cost_bvls - cost_trf) < cost_trf*1e-10)
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class SparseMixin(object):
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def test_sparse_and_LinearOperator(self):
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m = 5000
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n = 1000
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A = rand(m, n, random_state=0)
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b = self.rnd.randn(m)
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res = lsq_linear(A, b)
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assert_allclose(res.optimality, 0, atol=1e-6)
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A = aslinearoperator(A)
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res = lsq_linear(A, b)
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assert_allclose(res.optimality, 0, atol=1e-6)
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def test_sparse_bounds(self):
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m = 5000
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n = 1000
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A = rand(m, n, random_state=0)
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b = self.rnd.randn(m)
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lb = self.rnd.randn(n)
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ub = lb + 1
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res = lsq_linear(A, b, (lb, ub))
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assert_allclose(res.optimality, 0.0, atol=1e-6)
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res = lsq_linear(A, b, (lb, ub), lsmr_tol=1e-13)
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assert_allclose(res.optimality, 0.0, atol=1e-6)
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res = lsq_linear(A, b, (lb, ub), lsmr_tol='auto')
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assert_allclose(res.optimality, 0.0, atol=1e-6)
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class TestTRF(BaseMixin, SparseMixin):
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method = 'trf'
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lsq_solvers = ['exact', 'lsmr']
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class TestBVLS(BaseMixin):
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method = 'bvls'
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lsq_solvers = ['exact']
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