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Bellman-Ford example notebook #1

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MKaczkow merged 2 commits into from
Mar 11, 2026
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Bellman-Ford example notebook #1

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Bellman-Ford example notebook
MKaczkow Mar 11, 2026
c5f4f4e
fix typo in dirname
MKaczkow Mar 11, 2026
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137 changes: 137 additions & 0 deletions bellman_ford/DEMO.ipynb
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{
"cells": [
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⚠️ Potential issue | 🟡 Minor

Typo in directory name: "belmann" → "bellman"

The directory is named belmann_ford but the algorithm is named after Richard Bellman (single 'n' after 'bell'): bellman_ford.

🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@belmann_ford/DEMO.ipynb` around lines 1 - 2, The repository uses the
misspelled directory name "belmann_ford" (and file DEMO.ipynb) but the algorithm
is Bellman–Ford; rename the directory to "bellman_ford" and update all
references to it (imports, module/package names, relative paths, notebook links,
README/documentation, tests, and CI scripts) so nothing breaks. Search for the
symbol "belmann_ford" and replace with "bellman_ford", update any Python imports
or module paths that reference that package, and ensure the notebook filename
DEMO.ipynb and any references to it are moved/updated accordingly; run tests and
CI to verify no leftover references remain.

{
"cell_type": "code",
"execution_count": null,
"id": "7f235e2e",
"metadata": {},
"outputs": [],
"source": [
"import networkx as nx\n",
"from scipy.sparse.csgraph import bellman_ford"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7d256792",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shortest distances from vertex A (0): [0, 2, 1, 3, 4]\n"
]
}
],
"source": [
"# edge (u, v, w) means an edge from u to v with weight w\n",
"edges = [\n",
" (0, 1, 3),\n",
" (0, 2, 1),\n",
" (1, 2, 7),\n",
" (1, 3, 5),\n",
" (2, 3, 2),\n",
" (3, 2, -1),\n",
" (3, 4, 1),\n",
" (4, 1, -2),\n",
"]\n",
"\n",
"# assuming A-E are indexed as 0-4\n",
"distances = [float('inf')] * 5\n",
"distances[0] = 0\n",
"\n",
"for _ in range(4): # v-1 iterations\n",
" for u, v, w in edges: # 'w' is the 'new' weight\n",
" if distances[u] < float('inf'):\n",
" distances[v] = min(distances[v], distances[u] + w)\n",
"\n",
"print(\"Shortest distances from vertex A (0):\", distances)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9afe182f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nodes: ['A', 'B', 'C', 'D', 'E']\n",
"distances from source (A): [0. 2. 1. 3. 4.]\n",
"predecessors: [-9999 4 0 2 3]\n",
" A: distance=0.0, predecessor=None\n",
" B: distance=2.0, predecessor=E\n",
" C: distance=1.0, predecessor=A\n",
" D: distance=3.0, predecessor=C\n",
" E: distance=4.0, predecessor=D\n"
]
}
],
"source": [
"# build a small directed graph with weights (same as in example)\n",
"G = nx.DiGraph()\n",
"G.add_weighted_edges_from([\n",
" ('A', 'B', 3),\n",
" ('A', 'C', 1),\n",
" ('B', 'C', 7),\n",
" ('B', 'D', 5),\n",
" ('C', 'D', 2),\n",
" ('D', 'C', -1),\n",
" ('D', 'E', 1),\n",
" ('E', 'B', -2),\n",
"])\n",
"\n",
"# convert to an adjacency/weight matrix\n",
"W = nx.to_numpy_array(G, weight='weight')\n",
"\n",
"# run scipy's bellman_ford on the weight matrix from source index 0\n",
"distances, predecessors = bellman_ford(W, indices=0, return_predecessors=True)\n",
"\n",
"nodes = list(G.nodes())\n",
"print(\"nodes:\", nodes)\n",
"print(\"distances from source (A):\", distances)\n",
"print(\"predecessors:\", predecessors)\n",
"\n",
"# interpret results in terms of node names\n",
"for i, d in enumerate(distances):\n",
" pred = predecessors[i]\n",
" pred_name = nodes[pred] if pred != -9999 else None\n",
" print(f\" {nodes[i]}: distance={d}, predecessor={pred_name}\")"
]
},
{
"cell_type": "markdown",
"id": "358750df",
"metadata": {},
"source": [
"### References\n",
"* [algorithms for cp](https://cp-algorithms.com/graph/bellman_ford.html)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv (3.11.0)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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