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[CodeStyle][Typos][E-28] Fix typo extream #70459

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merged 1 commit into from
Dec 25, 2024
Merged

[CodeStyle][Typos][E-28] Fix typo extream #70459

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1 change: 0 additions & 1 deletion _typos.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -116,7 +116,6 @@ expaned = 'expaned'
epxand = 'epxand'
Expexted = 'Expexted'
expolitation = 'expolitation'
extream = 'extream'
faild = 'faild'
Flase = 'Flase'
featue = 'featue'
Expand Down
6 changes: 3 additions & 3 deletions test/deprecated/legacy_test/test_bfgs_deprecated.py
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Expand Up @@ -96,15 +96,15 @@ def func(x):
)

def test_inf_minima(self):
extream_point = np.array([-1, 2]).astype('float32')
extreme_point = np.array([-1, 2]).astype('float32')

def func(x):
# df = 3(x - 1.01)(x - 0.99)
# f = x^3 - 3x^2 + 3*1.01*0.99x
return (
x * x * x / 3.0
- (extream_point[0] + extream_point[1]) * x * x / 2
+ extream_point[0] * extream_point[1] * x
- (extreme_point[0] + extreme_point[1]) * x * x / 2
+ extreme_point[0] * extreme_point[1] * x
)

x0 = np.array([-1.7]).astype('float32')
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6 changes: 3 additions & 3 deletions test/deprecated/legacy_test/test_lbfgs_deprecated.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,15 +92,15 @@ def func(x):
np.testing.assert_allclose(minimum, results[2].numpy(), rtol=1e-05)

def test_inf_minima(self):
extream_point = np.array([-1, 2]).astype('float32')
extreme_point = np.array([-1, 2]).astype('float32')

def func(x):
# df = 3(x - 1.01)(x - 0.99)
# f = x^3 - 3x^2 + 3*1.01*0.99x
return (
x * x * x / 3.0
- (extream_point[0] + extream_point[1]) * x * x / 2
+ extream_point[0] * extream_point[1] * x
- (extreme_point[0] + extreme_point[1]) * x * x / 2
+ extreme_point[0] * extreme_point[1] * x
)

x0 = np.array([-1.7]).astype('float32')
Expand Down
76 changes: 38 additions & 38 deletions test/legacy_test/test_lbfgs_class.py
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Expand Up @@ -103,18 +103,18 @@ def outputs2(x):
targets = [outputs1(input), outputs2(input)]
input = paddle.to_tensor(input)

def func1(extream_point, x):
def func1(extreme_point, x):
return (
x * x * x
- 3 * x * x
+ 3 * extream_point[0] * extream_point[1] * x
+ 3 * extreme_point[0] * extreme_point[1] * x
)

def func2(extream_point, x):
return pow(x, extream_point[0]) + 5 * pow(x, extream_point[1])
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])

extream_point = np.array([-2.34, 1.45]).astype('float32')
net1 = Net(extream_point, func1)
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net1 = Net(extreme_point, func1)
# converge of old_sk.pop()
opt1 = incubate_lbfgs.LBFGS(
learning_rate=1,
Expand All @@ -127,7 +127,7 @@ def func2(extream_point, x):
parameters=net1.parameters(),
)

net2 = Net(extream_point, func2)
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = incubate_lbfgs.LBFGS(
learning_rate=1,
Expand All @@ -153,8 +153,8 @@ def func2(extream_point, x):
def test_error_incubate(self):
# test parameter is not Paddle Tensor
def error_func1():
extream_point = np.array([-1, 2]).astype('float32')
extream_point = paddle.to_tensor(extream_point)
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)
return incubate_lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
Expand All @@ -163,7 +163,7 @@ def error_func1():
tolerance_change=1e-09,
history_size=3,
line_search_fn='strong_wolfe',
parameters=extream_point,
parameters=extreme_point,
)

self.assertRaises(TypeError, error_func1)
Expand All @@ -179,11 +179,11 @@ def outputs2(x):
targets = [outputs2(input)]
input = paddle.to_tensor(input)

def func2(extream_point, x):
return pow(x, extream_point[0]) + 5 * pow(x, extream_point[1])
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])

extream_point = np.array([-2.34, 1.45]).astype('float32')
net2 = Net(extream_point, func2)
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = incubate_lbfgs.LBFGS(
learning_rate=1,
Expand Down Expand Up @@ -283,13 +283,13 @@ def func3(x, alpha, d):
def test_error3_incubate(self):
# test parameter shape size <= 0
def error_func3():
extream_point = np.array([-1, 2]).astype('float32')
extream_point = paddle.to_tensor(extream_point)
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)

def func(w, x):
return w * x

net = Net(extream_point, func)
net = Net(extreme_point, func)
net.w = paddle.create_parameter(
shape=[-1, 2],
dtype=net.w.dtype,
Expand Down Expand Up @@ -353,18 +353,18 @@ def outputs2(x):
targets = [outputs1(input), outputs2(input)]
input = paddle.to_tensor(input)

def func1(extream_point, x):
def func1(extreme_point, x):
return (
x * x * x
- 3 * x * x
+ 3 * extream_point[0] * extream_point[1] * x
+ 3 * extreme_point[0] * extreme_point[1] * x
)

def func2(extream_point, x):
return pow(x, extream_point[0]) + 5 * pow(x, extream_point[1])
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])

extream_point = np.array([-2.34, 1.45]).astype('float32')
net1 = Net(extream_point, func1)
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net1 = Net(extreme_point, func1)
# converge of old_sk.pop()
opt1 = lbfgs.LBFGS(
learning_rate=1,
Expand All @@ -377,7 +377,7 @@ def func2(extream_point, x):
parameters=net1.parameters(),
)

net2 = Net(extream_point, func2)
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = lbfgs.LBFGS(
learning_rate=1,
Expand All @@ -403,8 +403,8 @@ def func2(extream_point, x):
def test_error(self):
# test parameter is not Paddle Tensor
def error_func1():
extream_point = np.array([-1, 2]).astype('float32')
extream_point = paddle.to_tensor(extream_point)
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)
return lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
Expand All @@ -413,7 +413,7 @@ def error_func1():
tolerance_change=1e-09,
history_size=3,
line_search_fn='strong_wolfe',
parameters=extream_point,
parameters=extreme_point,
)

self.assertRaises(TypeError, error_func1)
Expand All @@ -429,11 +429,11 @@ def outputs2(x):
targets = [outputs2(input)]
input = paddle.to_tensor(input)

def func2(extream_point, x):
return pow(x, extream_point[0]) + 5 * pow(x, extream_point[1])
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])

extream_point = np.array([-2.34, 1.45]).astype('float32')
net2 = Net(extream_point, func2)
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = lbfgs.LBFGS(
learning_rate=1,
Expand Down Expand Up @@ -543,13 +543,13 @@ def func3(x, alpha, d):
def test_error3(self):
# test parameter shape size <= 0
def error_func3():
extream_point = np.array([-1, 2]).astype('float32')
extream_point = paddle.to_tensor(extream_point)
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)

def func(w, x):
return w * x

net = Net(extream_point, func)
net = Net(extreme_point, func)
net.w = paddle.create_parameter(
shape=[-1, 2],
dtype=net.w.dtype,
Expand All @@ -576,12 +576,12 @@ def error_func4():
targets = paddle.to_tensor([inputs * 2])
inputs = paddle.to_tensor(inputs)

extream_point = np.array([-1, 1]).astype('float32')
extreme_point = np.array([-1, 1]).astype('float32')

def func(extream_point, x):
return x * extream_point[0] + 5 * x * extream_point[1]
def func(extreme_point, x):
return x * extreme_point[0] + 5 * x * extreme_point[1]

net = Net(extream_point, func)
net = Net(extreme_point, func)
opt = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
Expand Down
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