From eccea262c10ff0e2fe6a5c6dbaf13a8be2371dca Mon Sep 17 00:00:00 2001
From: quant12345 <kamil246@mail.ru>
Date: Fri, 29 Sep 2023 17:50:16 +0500
Subject: [PATCH 1/3] Replacing the generator with numpy vector operations from
lu_decomposition.
---
arithmetic_analysis/lu_decomposition.py | 8 ++++++--
1 file changed, 6 insertions(+), 2 deletions(-)
diff --git a/arithmetic_analysis/lu_decomposition.py b/arithmetic_analysis/lu_decomposition.py
index eaabce5449c5..094b20abfecc 100644
--- a/arithmetic_analysis/lu_decomposition.py
+++ b/arithmetic_analysis/lu_decomposition.py
@@ -88,15 +88,19 @@ def lower_upper_decomposition(table: np.ndarray) -> tuple[np.ndarray, np.ndarray
lower = np.zeros((rows, columns))
upper = np.zeros((rows, columns))
+
+ # in 'total', the necessary data is extracted through slices
+ # and the sum of the products is obtained.
+
for i in range(columns):
for j in range(i):
- total = sum(lower[i][k] * upper[k][j] for k in range(j))
+ total = np.sum(lower[i, :i] * upper[:i, j])
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists")
lower[i][j] = (table[i][j] - total) / upper[j][j]
lower[i][i] = 1
for j in range(i, columns):
- total = sum(lower[i][k] * upper[k][j] for k in range(j))
+ total = np.sum(lower[i, :i] * upper[:i, j])
upper[i][j] = table[i][j] - total
return lower, upper
From 4e347233906017f5b96bd53a1ac6da4bdc40ab8e Mon Sep 17 00:00:00 2001
From: quant12345 <kamil246@mail.ru>
Date: Fri, 29 Sep 2023 17:53:57 +0500
Subject: [PATCH 2/3] Revert "Replacing the generator with numpy vector
operations from lu_decomposition."
This reverts commit ad217c66165898d62b76cc89ba09c2d7049b6448.
---
arithmetic_analysis/lu_decomposition.py | 8 ++------
1 file changed, 2 insertions(+), 6 deletions(-)
diff --git a/arithmetic_analysis/lu_decomposition.py b/arithmetic_analysis/lu_decomposition.py
index 094b20abfecc..eaabce5449c5 100644
--- a/arithmetic_analysis/lu_decomposition.py
+++ b/arithmetic_analysis/lu_decomposition.py
@@ -88,19 +88,15 @@ def lower_upper_decomposition(table: np.ndarray) -> tuple[np.ndarray, np.ndarray
lower = np.zeros((rows, columns))
upper = np.zeros((rows, columns))
-
- # in 'total', the necessary data is extracted through slices
- # and the sum of the products is obtained.
-
for i in range(columns):
for j in range(i):
- total = np.sum(lower[i, :i] * upper[:i, j])
+ total = sum(lower[i][k] * upper[k][j] for k in range(j))
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists")
lower[i][j] = (table[i][j] - total) / upper[j][j]
lower[i][i] = 1
for j in range(i, columns):
- total = np.sum(lower[i, :i] * upper[:i, j])
+ total = sum(lower[i][k] * upper[k][j] for k in range(j))
upper[i][j] = table[i][j] - total
return lower, upper
From 625938df3e075b54e5937908c698a0df0cf142b8 Mon Sep 17 00:00:00 2001
From: quant12345 <kamil246@mail.ru>
Date: Tue, 10 Oct 2023 22:44:00 +0500
Subject: [PATCH 3/3] the change removes the warning:
/home/runner/work/Python/Python/machine_learning/k_means_clust.py:236:
FutureWarning: The provided callable <function sum at 0x7f20c02034c0> is
currently using SeriesGroupBy.sum. In a future version of pandas, the
provided callable will be used directly. To keep current behavior pass the
string "sum" instead. .agg(
And
/home/runner/work/Python/Python/machine_learning/k_means_clust.py:236: FutureWarning: The provided callable <function mean at 0x7f3d7db1c5e0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
.agg(
---
machine_learning/k_means_clust.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py
index d93c5addf2ee..3fe151442e2e 100644
--- a/machine_learning/k_means_clust.py
+++ b/machine_learning/k_means_clust.py
@@ -235,7 +235,7 @@ def report_generator(
] # group by cluster number
.agg(
[
- ("sum", np.sum),
+ ("sum", "sum"),
("mean_with_zeros", lambda x: np.mean(np.nan_to_num(x))),
("mean_without_zeros", lambda x: x.replace(0, np.NaN).mean()),
(
@@ -248,7 +248,7 @@ def report_generator(
)
),
),
- ("mean_with_na", np.mean),
+ ("mean_with_na", "mean"),
("min", lambda x: x.min()),
("5%", lambda x: x.quantile(0.05)),
("25%", lambda x: x.quantile(0.25)),