def test_minimize_array():
assert _round_eq(2, ds.minimize(lambda x: (x[0] - 2)**2, [0], array=True))
assert _round_eq([2, 1],
list(
ds.minimize(lambda x: (x[0] - 2)**2 + (x[1] - 1)**2,
[0, 0],
array=True)))
def test_minimize():
assert _round_eq(2, ds.minimize(lambda x: (x - 2)**2))
assert _round_eq([2, 1],
list(ds.minimize(lambda x, y: (x - 2)**2 + (y - 1)**2)))
assert _round_eq(2, ds.minimize(lambda x: (x - 2)**2, 1))
assert _round_eq([2, 1],
list(
ds.minimize(lambda x, y: (x - 2)**2 + (y - 1)**2,
[1, 1])))
def test_minimize_smooth():
assert _round_eq(2, ds.minimize(lambda x: (x - 2)**2, smooth=True))
assert _round_eq([2, 1],
list(
ds.minimize(lambda x, y: (x - 2)**2 + (y - 1)**2,
smooth=True)))
assert _round_eq(2, ds.minimize(lambda x: (x - 2)**2, 1, smooth=True))
assert _round_eq([2, 1],
list(
ds.minimize(lambda x, y: (x - 2)**2 + (y - 1)**2,
[1, 1],
smooth=True)))
def minimize(f, start=None, **vargs):
def expanded_f(*args):
return f(args)
return datascience.minimize(expanded_f,
start=start,
method="L-BFGS-B",
**vargs)
def test_minimize_array():
assert _round_eq(2, ds.minimize(lambda x: (x[0]-2)**2, [0], array=True))
assert _round_eq([2, 1], list(ds.minimize(lambda x: (x[0]-2)**2 + (x[1]-1)**2, [0, 0], array=True)))
def test_minimize_smooth():
assert _round_eq(2, ds.minimize(lambda x: (x-2)**2, smooth=True))
assert _round_eq([2, 1], list(ds.minimize(lambda x, y: (x-2)**2 + (y-1)**2, smooth=True)))
assert _round_eq(2, ds.minimize(lambda x: (x-2)**2, 1, smooth=True))
assert _round_eq([2, 1], list(ds.minimize(lambda x, y: (x-2)**2 + (y-1)**2, [1, 1], smooth=True)))
def test_minimize():
assert _round_eq(2, ds.minimize(lambda x: (x-2)**2))
assert _round_eq([2, 1], list(ds.minimize(lambda x, y: (x-2)**2 + (y-1)**2)))
assert _round_eq(2, ds.minimize(lambda x: (x-2)**2, 1))
assert _round_eq([2, 1], list(ds.minimize(lambda x, y: (x-2)**2 + (y-1)**2, [1, 1])))
def minimize(f, start=None, **vargs):
def expanded_f(*args):
return f(args)
return datascience.minimize(expanded_f, start=start, method="L-BFGS-B", **vargs)
