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Add pep8-naming to pre-commit hooks and fixes incorrect naming conventions (#7062)

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* ci(pre-commit): Add pep8-naming to `pre-commit` hooks (#7038)

* refactor: Fix naming conventions (#7038)

* Update arithmetic_analysis/lu_decomposition.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* refactor(lu_decomposition): Replace `NDArray` with `ArrayLike` (#7038)

* chore: Fix naming conventions in doctests (#7038)

* fix: Temporarily disable project euler problem 104 (#7069)

* chore: Fix naming conventions in doctests (#7038)

Co-authored-by: Christian Clauss <cclauss@me.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Caeden
2022-10-12 23:54:20 +01:00
committed by GitHub
parent e2cd982b11
commit 07e991d553
140 changed files with 1552 additions and 1536 deletions
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64
machine_learning/sequential_minimum_optimization.py
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@ -80,7 +80,7 @@ class SmoSVM:
# Calculate alphas using SMO algorithm
def fit(self):
K = self._k
k = self._k
state = None
while True:
@ -106,14 +106,14 @@ class SmoSVM:
# 3: update threshold(b)
b1_new = np.float64(
-e1
- y1 * K(i1, i1) * (a1_new - a1)
- y2 * K(i2, i1) * (a2_new - a2)
- y1 * k(i1, i1) * (a1_new - a1)
- y2 * k(i2, i1) * (a2_new - a2)
+ self._b
)
b2_new = np.float64(
-e2
- y2 * K(i2, i2) * (a2_new - a2)
- y1 * K(i1, i2) * (a1_new - a1)
- y2 * k(i2, i2) * (a2_new - a2)
- y1 * k(i1, i2) * (a1_new - a1)
+ self._b
)
if 0.0 < a1_new < self._c:
@ -134,8 +134,8 @@ class SmoSVM:
if s == i1 or s == i2:
continue
self._error[s] += (
y1 * (a1_new - a1) * K(i1, s)
+ y2 * (a2_new - a2) * K(i2, s)
y1 * (a1_new - a1) * k(i1, s)
+ y2 * (a2_new - a2) * k(i2, s)
+ (self._b - b_old)
)
@ -305,56 +305,56 @@ class SmoSVM:
# Get the new alpha2 and new alpha1
def _get_new_alpha(self, i1, i2, a1, a2, e1, e2, y1, y2):
K = self._k
k = self._k
if i1 == i2:
return None, None
# calculate L and H which bound the new alpha2
s = y1 * y2
if s == -1:
L, H = max(0.0, a2 - a1), min(self._c, self._c + a2 - a1)
l, h = max(0.0, a2 - a1), min(self._c, self._c + a2 - a1)
else:
L, H = max(0.0, a2 + a1 - self._c), min(self._c, a2 + a1)
if L == H:
l, h = max(0.0, a2 + a1 - self._c), min(self._c, a2 + a1)
if l == h: # noqa: E741
return None, None
# calculate eta
k11 = K(i1, i1)
k22 = K(i2, i2)
k12 = K(i1, i2)
k11 = k(i1, i1)
k22 = k(i2, i2)
k12 = k(i1, i2)
eta = k11 + k22 - 2.0 * k12
# select the new alpha2 which could get the minimal objectives
if eta > 0.0:
a2_new_unc = a2 + (y2 * (e1 - e2)) / eta
# a2_new has a boundary
if a2_new_unc >= H:
a2_new = H
elif a2_new_unc <= L:
a2_new = L
if a2_new_unc >= h:
a2_new = h
elif a2_new_unc <= l:
a2_new = l
else:
a2_new = a2_new_unc
else:
b = self._b
l1 = a1 + s * (a2 - L)
h1 = a1 + s * (a2 - H)
l1 = a1 + s * (a2 - l)
h1 = a1 + s * (a2 - h)
# way 1
f1 = y1 * (e1 + b) - a1 * K(i1, i1) - s * a2 * K(i1, i2)
f2 = y2 * (e2 + b) - a2 * K(i2, i2) - s * a1 * K(i1, i2)
f1 = y1 * (e1 + b) - a1 * k(i1, i1) - s * a2 * k(i1, i2)
f2 = y2 * (e2 + b) - a2 * k(i2, i2) - s * a1 * k(i1, i2)
ol = (
l1 * f1
+ L * f2
+ 1 / 2 * l1**2 * K(i1, i1)
+ 1 / 2 * L**2 * K(i2, i2)
+ s * L * l1 * K(i1, i2)
+ l * f2
+ 1 / 2 * l1**2 * k(i1, i1)
+ 1 / 2 * l**2 * k(i2, i2)
+ s * l * l1 * k(i1, i2)
)
oh = (
h1 * f1
+ H * f2
+ 1 / 2 * h1**2 * K(i1, i1)
+ 1 / 2 * H**2 * K(i2, i2)
+ s * H * h1 * K(i1, i2)
+ h * f2
+ 1 / 2 * h1**2 * k(i1, i1)
+ 1 / 2 * h**2 * k(i2, i2)
+ s * h * h1 * k(i1, i2)
)
"""
# way 2
@ -362,9 +362,9 @@ class SmoSVM:
objectives
"""
if ol < (oh - self._eps):
a2_new = L
a2_new = l
elif ol > oh + self._eps:
a2_new = H
a2_new = h
else:
a2_new = a2