From eccea262c10ff0e2fe6a5c6dbaf13a8be2371dca Mon Sep 17 00:00:00 2001 From: quant12345 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 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 6dabb4d48292b9ec39a62ce27c0bc223f72d3a5e Mon Sep 17 00:00:00 2001 From: quant12345 Date: Tue, 10 Oct 2023 18:38:48 +0500 Subject: [PATCH 3/3] Removes the warning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation --- arithmetic_analysis/gaussian_elimination.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arithmetic_analysis/gaussian_elimination.py b/arithmetic_analysis/gaussian_elimination.py index 13f509a4f117..a1a35131b157 100644 --- a/arithmetic_analysis/gaussian_elimination.py +++ b/arithmetic_analysis/gaussian_elimination.py @@ -34,7 +34,7 @@ def retroactive_resolution( x: NDArray[float64] = np.zeros((rows, 1), dtype=float) for row in reversed(range(rows)): total = np.dot(coefficients[row, row + 1 :], x[row + 1 :]) - x[row, 0] = (vector[row] - total) / coefficients[row, row] + x[row, 0] = (vector[row][0] - total[0]) / coefficients[row, row] return x