import numpy
import numpy.linalg
def MyBackSubstitution(A, b):
"""
Solve the upper triangular linear system A x = b.
Parameters
----------
A : array of float
real square matrix
b : vector of float
real vector
Returns
-------
x : vector of float
solution
Notes
-----
Simplified method with limited error checking.
"""
assert(numpy.all(numpy.isreal(b))), "b must be real"
assert(numpy.all(numpy.isfinite(b))), "b must be finite"
assert(numpy.ndim(b) == 1), "b must be a vector"
n = len(b)
assert(numpy.all(numpy.isreal(A))), "A must be real"
assert(numpy.all(numpy.isfinite(A))), "A must be finite"
assert(numpy.ndim(A) == 2), "A must be a matrix"
assert(A.shape == (n, n)), "A must be a square matrix compatible with b"
x = numpy.zeros_like(b)
for i in range(n-1,-1,-1):
x[i] = b[i] / A[i, i]
for k in range(i+1,n):
x[i] -= A[i, k] * x[k] / A[i, i]
return x
def MyGaussianElimination(A, b):
"""
Solve the linear system A x = b using Gaussian Elimination without pivoting.
Parameters
----------
A : array of float
real square matrix
b : vector of float
real vector
Returns
-------
x : vector of float
solution
Notes
-----
Simplified method with limited error checking.
"""
# Error checking here
assert(numpy.all(numpy.isreal(b))), "b must be real"
assert(numpy.all(numpy.isfinite(b))), "b must be finite"
assert(numpy.ndim(b) == 1), "b must be a vector"
n = len(b)
assert(numpy.all(numpy.isreal(A))), "A must be real"
assert(numpy.all(numpy.isfinite(A))), "A must be finite"
assert(numpy.ndim(A) == 2), "A must be a matrix"
assert(A.shape == (n, n)), "A must be a square matrix compatible with b"
# Construct augmented matrix. Slightly tedious.
aug = numpy.hstack((A, numpy.reshape(b, [len(b), 1])))
# Put the augmented matrix in triangular form.
#assert(False), "Code needed here"
for i in range(n):
assert(numpy.abs(aug[i,i]) > 1e-20), "Diagonal element zero!"
for k in range(i+1,n):
pivot = aug[k,i] / aug[i,i]
aug[k,:] -= pivot * aug[i,:]
# Solve using back substitution.
x = MyBackSubstitution(aug[:, :-1], aug[:, -1])
return x
def MyGaussianEliminationWithPivoting(A, b):
"""
Solve the linear system A x = b using Gaussian Elimination with pivoting.
Parameters
----------
A : array of float
real square matrix
b : vector of float
real vector
Returns
-------
x : vector of float
solution
Notes
-----
Simplified method with limited error checking.
"""
# Error checking here
assert(numpy.all(numpy.isreal(b))), "b must be real"
assert(numpy.all(numpy.isfinite(b))), "b must be finite"
assert(numpy.ndim(b) == 1), "b must be a vector"
n = len(b)
assert(numpy.all(numpy.isreal(A))), "A must be real"
assert(numpy.all(numpy.isfinite(A))), "A must be finite"
assert(numpy.ndim(A) == 2), "A must be a matrix"
assert(A.shape == (n, n)), "A must be a square matrix compatible with b"
# Construct augmented matrix. Slightly tedious.
aug = numpy.hstack((A, numpy.reshape(b, [len(b), 1])))
# Put the augmented matrix in triangular form.
#assert(False), "Code needed here"
for i in range(n):
# Find the location of the pivot
ind = numpy.argmax(numpy.abs(aug[i:, i]))
if ind != i:
# One liner to swap the rows; think carefully!
aug[[i,ind+i],:] = aug[[ind+i, i],:]
for k in range(i+1,n):
pivot = aug[k,i] / aug[i,i]
aug[k,:] -= pivot * aug[i,:]
# Solve using back substitution.
x = MyBackSubstitution(aug[:, :-1], aug[:, -1])
return x
# What follows are testing functions to validate the code
import pytest
def test_diagonal():
A = numpy.eye(2)
b = numpy.array([1.0, 2.0])
x_my = MyGaussianElimination(A, b)
check = numpy.allclose(x_my, b)
assert check
def test_triangular():
A = numpy.array([[1.0, 2.0], [0.0, 1.0]])
b = numpy.array([4.0, 1.0])
x_my = MyGaussianElimination(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
def test_full():
A = numpy.array([[1.0, 2.0], [3.0, 4.0]])
b = numpy.array([5.0, 6.0])
x_my = MyGaussianElimination(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
def test_threebythree():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 6.0]])
b = numpy.array([4.0, 10.0, 15.0])
x_my = MyGaussianElimination(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
def test_incompatible():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 6.0]])
b = numpy.array([4.0, 10.0])
with pytest.raises(AssertionError):
MyGaussianElimination(A, b)
def test_input():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 6.0]])
b = "dog"
with pytest.raises(AssertionError):
MyGaussianElimination(A, b)
def test_singular():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 5.0]])
b = numpy.array([4.0, 10.0])
with pytest.raises(AssertionError):
MyGaussianElimination(A, b)
def test_finite():
A = numpy.array([[1.0, 1.0, 1.0], [0.0, 0.0, 2.0], [0.0, 1.0, 1.0]])
b = numpy.array([1.0, 1.0, 2.0])
with pytest.raises(AssertionError):
MyGaussianElimination(A, b)
def test_needs_pivoting():
A = numpy.array([[1.0e-20, 1.0], [1.0, 1.0]])
b = numpy.array([1.0, 2.0])
with pytest.raises(AssertionError):
MyGaussianElimination(A, b)
# Test with pivoting
def test_diagonal_pivoting():
A = numpy.eye(2)
b = numpy.array([1.0, 2.0])
x_my = MyGaussianEliminationWithPivoting(A, b)
check = numpy.allclose(x_my, b)
assert check
def test_triangular_pivoting():
A = numpy.array([[1.0, 2.0], [0.0, 1.0]])
b = numpy.array([4.0, 1.0])
x_my = MyGaussianEliminationWithPivoting(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
def test_full_pivoting():
A = numpy.array([[1.0, 2.0], [3.0, 4.0]])
b = numpy.array([5.0, 6.0])
x_my = MyGaussianEliminationWithPivoting(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
def test_threebythree_pivoting():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 6.0]])
b = numpy.array([4.0, 10.0, 15.0])
x_my = MyGaussianEliminationWithPivoting(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
def test_incompatible_pivoting():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 6.0]])
b = numpy.array([4.0, 10.0])
with pytest.raises(AssertionError):
MyGaussianEliminationWithPivoting(A, b)
def test_input_pivoting():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 6.0]])
b = "dog"
with pytest.raises(AssertionError):
MyGaussianEliminationWithPivoting(A, b)
def test_singular_pivoting():
A = numpy.array([[3.0, 0.0, 1.0], [6.0, 2.0, 4.0], [9.0, 2.0, 5.0]])
b = numpy.array([4.0, 10.0])
with pytest.raises(AssertionError):
MyGaussianEliminationWithPivoting(A, b)
def test_finite_pivoting():
A = numpy.array([[1.0, 1.0, 1.0], [0.0, 0.0, 2.0], [0.0, 1.0, 1.0]])
b = numpy.array([1.0, 1.0, 2.0])
with pytest.raises(AssertionError):
MyGaussianEliminationWithPivoting(A, b)
def test_needs_pivoting_pivoting():
A = numpy.array([[1.0e-20, 1.0], [1.0, 1.0]])
b = numpy.array([1.0, 2.0])
x_my = MyGaussianEliminationWithPivoting(A, b)
x_exact = numpy.linalg.solve(A, b)
check = numpy.allclose(x_my, x_exact)
assert check
# Run all the tests
pytest.main("-x GaussElimination.py")
尽管在尝试pytest.main("-x GaussElimination.py")时我无法对其进行有效测试，但该代码已在课堂上显示。
我收到以下错误消息：
TypeError：
args参数应该是字符串列表或元组，得到：'-x GaussElimination.py'（类型：）
这是我第一次使用pytest，但我不确定使用的参数是否正确，但这是我们在课堂上看到的，然后它起作用了。我也尝试过在线查找，但找不到简单的示例。
谢谢。
答案 0 :(得分：2)
尝试pytest.main(["-x", "GaussElimination.py"])
如果您查看以下参考链接，将会理解它为什么起作用。
参考链接：https://docs.pytest.org/en/latest/usage.html#calling-pytest-from-python-code
引用来自链接：“您可以传递选项和参数：pytest.main(["-x", "mytestdir"])”